title:
Bias in multivariable Mendelian randomization studies due to measurement
  error on exposures

abstract:
  Multivariable Mendelian randomization estimates the causal effect of multiple
exposures on an outcome, typically using summary statistics of genetic variant
associations. However, exposures of interest in Mendelian randomization
applications will often be measured with error. The summary statistics will
therefore not be of the genetic associations with the exposure, but with the
exposure measured with error. Classical measurement error will not bias genetic
association estimates but will increase their standard errors. With a single
exposure, this will result in bias toward the null in a two sample framework.
However, this will not necessarily be the case with multiple correlated
exposures. In this paper, we examine how the direction and size of bias, as
well as coverage, power and type I error rates in multivariable Mendelian
randomization studies are affected by measurement error on exposures. We show
how measurement error can be accounted for in a maximum likelihood framework.
We consider two applied examples. In the first, we show that measurement error
leads to the effect of body mass index on coronary heart disease risk to be
overestimated, and that of waist-to-hip ratio to be underestimated. In the
second, we show that the proportion of the effect of education on coronary
heart disease risk which is mediated by body mass index, smoking and blood
pressure may be underestimated if measurement error is not taken into account.


title:
MPAF: Model Poisoning Attacks to Federated Learning based on Fake
  Clients

abstract:
  Existing model poisoning attacks to federated learning assume that an
attacker has access to a large fraction of compromised genuine clients.
However, such assumption is not realistic in production federated learning
systems that involve millions of clients. In this work, we propose the first
Model Poisoning Attack based on Fake clients called MPAF. Specifically, we
assume the attacker injects fake clients to a federated learning system and
sends carefully crafted fake local model updates to the cloud server during
training, such that the learnt global model has low accuracy for many
indiscriminate test inputs. Towards this goal, our attack drags the global
model towards an attacker-chosen base model that has low accuracy.
Specifically, in each round of federated learning, the fake clients craft fake
local model updates that point to the base model and scale them up to amplify
their impact before sending them to the cloud server. Our experiments show that
MPAF can significantly decrease the test accuracy of the global model, even if
classical defenses and norm clipping are adopted, highlighting the need for
more advanced defenses.


title: Measuring Fairness of Text Classifiers via Prediction Sensitivity

abstract:
  With the rapid growth in language processing applications, fairness has
emerged as an important consideration in data-driven solutions. Although
various fairness definitions have been explored in the recent literature, there
is lack of consensus on which metrics most accurately reflect the fairness of a
system. In this work, we propose a new formulation : ACCUMULATED PREDICTION
SENSITIVITY, which measures fairness in machine learning models based on the
model's prediction sensitivity to perturbations in input features. The metric
attempts to quantify the extent to which a single prediction depends on a
protected attribute, where the protected attribute encodes the membership
status of an individual in a protected group. We show that the metric can be
theoretically linked with a specific notion of group fairness (statistical
parity) and individual fairness. It also correlates well with humans'
perception of fairness. We conduct experiments on two text classification
datasets : JIGSAW TOXICITY, and BIAS IN BIOS, and evaluate the correlations
between metrics and manual annotations on whether the model produced a fair
outcome. We observe that the proposed fairness metric based on prediction
sensitivity is statistically significantly more correlated with human
annotation than the existing counterfactual fairness metric.


title: Additive decompositions of cubes in finite fields

abstract:
  Let $p\equiv1\pmod3$ be a prime . We study several topics on additive
decompositions concerning the set $C_p$ of all non-zero cubes in the finite
field of $p$ elements. For example, we show that when $p>184291$ , the set
$C_p$ has no decomposition of the form $C_p=A+B+C$ with $|A|,|B|,|C|\ge2$.


title: How the Spirals in the Milky Way's ISM form

abstract:
  We construct a model for the Milky Way where the interstellar medium (ISM) is
equipped with self-consistent dynamics. In simulations a spiral structure
emerges from this model that is almost identical with the one in the Milky
Way's ISM. Further, the Jeans instability offers an explanation for the
observed velocity dispersion of atomic hydrogen in the ISM; this instability
vanishes from our model if we choose a velocity dispersion just above the
observed one. Surprisingly, our model gets along completely without dark
matter. The 'missing mass' distributes uniformly over the baryonic components
making it possible to explain the occurring mass gap.


title:
How to construct Gorenstein projective modules relative to complete
  duality pairs over Morita rings

abstract:
  Let $\Delta =\left(\begin{smallmatrix} A & {_AN_B}\\ {_BM_A} & B
\\\end{smallmatrix}\right)$ be a Morita ring with
$M\otimes_{A}N=0=N\otimes_{B}M$.We first study how to construct (complete)
duality pairs of $\Delta$-modules using (complete) duality pairs of $A$-modules
and $B$-modules, generalizing the result of Mao (Comm. Algebra, 2020, 12:
5296--5310) about the duality pairs over a triangular matrix ring. Moreover, we
construct Gorenstein projective modules relative to complete duality pairs of
$\Delta$-modules. Finally, we give an application to Ding projective modules.


title: Know your sensORs -- A Modality Study For Surgical Action Classification

abstract:
  The surgical operating room (OR) presents many opportunities for automation
and optimization. Videos from various sources in the OR are becoming
increasingly available. The medical community seeks to leverage this wealth of
data to develop automated methods to advance interventional care, lower costs,
and improve overall patient outcomes. Existing datasets from OR room cameras
are thus far limited in size or modalities acquired, leaving it unclear which
sensor modalities are best suited for tasks such as recognizing surgical action
from videos. This study demonstrates that surgical action recognition
performance can vary depending on the image modalities used. We perform a
methodical analysis on several commonly available sensor modalities, presenting
two fusion approaches that improve classification performance. The analyses are
carried out on a set of multi-view RGB-D video recordings of 18 laparoscopic
procedures.


title:
Interpenetration of fractal clusters drives elasticity in colloidal gels
  formed upon flow cessation

abstract:
  Colloidal gels are out of equilibrium soft solids composed of attractive
Brownian particles that form a space-spanning network at low volume fractions.
The elastic properties of these systems result from the network microstructure,
which is very sensitive to shear history. Here, we take advantage of such
sensitivity to tune the viscoelastic properties of a colloidal gel made of
carbon black nanoparticles. Starting from a fluidized state under an applied
shear rate $\dot \gamma_0$, we use an abrupt flow cessation to trigger a
liquid-to-solid transition. We observe that the resulting gel is all the more
elastic when the shear rate $\dot \gamma_0$ is low and that the viscoelastic
spectra can be mapped on a master curve. Moreover, coupling rheometry to small
angle X-ray scattering allows us to show that the gel microstructure is
composed of a percolated network of fractal clusters that interpenetrate each
other. Experiments on gels prepared with various shear histories reveal that
cluster interpenetration increases with decreasing values of the shear rate
$\dot \gamma_0$ applied before flow cessation. These observations strongly
suggest that cluster interpenetration drives the gel elasticity, which we
confirm using a structural model. Our results, which are in stark contrast with
previous literature, where gel elasticity was either linked to cluster
connectivity or to bending modes, highlight a novel local parameter controlling
the macroscopic viscoelastic properties of colloidal gels.


title:
The hadronic running of the electromagnetic coupling and the electroweak
  mixing angle from lattice QCD

abstract:
  We compute the hadronic running of the electromagnetic and weak couplings in
lattice QCD with $N_{\mathrm{f}}=2+1$ flavors of $\mathcal{O}(a)$ improved
Wilson fermions. Using two different discretizations of the vector current, we
compute the quark-connected and -disconnected contributions to the hadronic
vacuum polarization (HVP) functions $\bar{\Pi}^{\gamma\gamma}$ and
$\bar{\Pi}^{\gamma Z}$ for Euclidean squared momenta $Q^2\leq
7\,\mathrm{GeV}^2$. Gauge field ensembles at four values of the lattice spacing
and several values of the pion mass, including its physical value, are used to
extrapolate the results to the physical point. The ability to perform an exact
flavor decomposition allows us to present the most precise determination to
date of the $\mathrm{SU}(3)$-flavor-suppressed HVP function $\bar{\Pi}^{08}$
that enters the running of $\sin^2\theta_{\mathrm{W}}$. Our results for
$\bar{\Pi}^{\gamma\gamma}$, $\bar{\Pi}^{\gamma Z}$ and $\bar{\Pi}^{08}$ are
presented in terms of rational functions for continuous values of $Q^2$ below
$7 \,\mathrm{GeV}^2$. We observe a tension of up to $3.5$ standard deviation
between our lattice results for $\Delta\alpha^{(5)}_{\mathrm{had}}(-Q^2)$ and
estimates based on the $R$-ratio for space-like momenta in the range
$3$--$7\,\mathrm{GeV}^2$. The tension is, however, strongly diminished when
translating our result to the $Z$ pole, by employing the Euclidean split
technique and perturbative QCD, which yields
$\Delta\alpha^{(5)}_{\mathrm{had}}(M_Z^2)=0.027\,73(15)$ and agrees with
results based on the $R$-ratio within the quoted uncertainties.


title: Volterra square-root process: Stationarity and regularity of the law

abstract:
  The Volterra square-root process on $\mathbb{R}_+^m$ is an affine Volterra
process with continuous sample paths. Under a suitable integrability condition
on the resolvent of the second kind associated with the Volterra convolution
kernel, we establish the existence of limiting distributions. In contrast to
the classical square-root diffusion process, here the limiting distributions
may depend on the initial state of the process. Our result shows that the
non-uniqueness of limiting distributions is closely related to the
integrability of the Volterra convolution kernel. Using an extension of the
exponential-affine transformation formula we also give the construction of
stationary processes associated with the limiting distributions. Finally, we
prove that the time-marginals as well as the limiting distributions, when
restricted to the interior of the state space $\mathbb{R}_+^m$, are absolutely
continuous with respect to the Lebesgue measure and their densities belong to a
weighted Besov space of type $B_{1,\infty}^{\lambda}$.


title: Dynamic Programming Through the Lens of Semismooth Newton-Type Methods

abstract:
  Policy iteration and value iteration are at the core of many (approximate)
dynamic programming methods. For Markov Decision Processes with finite state
and action spaces, we show that they are instances of semismooth Newton-type
methods to solve the Bellman equation. In particular, we prove that policy
iteration is equivalent to the exact semismooth Newton method and enjoys local
quadratic convergence rate. This finding is corroborated by extensive numerical
evidence in the fields of control and operations research, which confirms that
policy iteration generally requires few iterations to achieve convergence even
when the number of policies is vast. We then show that value iteration is an
instance of the fixed-point iteration method. In this spirit, we develop a
novel locally accelerated version of value iteration with global convergence
guarantees and negligible extra computational costs.


title: Decoupled Knowledge Distillation

abstract:
  State-of-the-art distillation methods are mainly based on distilling deep
features from intermediate layers, while the significance of logit distillation
is greatly overlooked. To provide a novel viewpoint to study logit
distillation, we reformulate the classical KD loss into two parts, i.e., target
class knowledge distillation (TCKD) and non-target class knowledge distillation
(NCKD). We empirically investigate and prove the effects of the two parts: TCKD
transfers knowledge concerning the "difficulty" of training samples, while NCKD
is the prominent reason why logit distillation works. More importantly, we
reveal that the classical KD loss is a coupled formulation, which (1)
suppresses the effectiveness of NCKD and (2) limits the flexibility to balance
these two parts. To address these issues, we present Decoupled Knowledge
Distillation (DKD), enabling TCKD and NCKD to play their roles more efficiently
and flexibly. Compared with complex feature-based methods, our DKD achieves
comparable or even better results and has better training efficiency on
CIFAR-100, ImageNet, and MS-COCO datasets for image classification and object
detection tasks. This paper proves the great potential of logit distillation,
and we hope it will be helpful for future research. The code is available at
https://github.com/megvii-research/mdistiller.


title:
GPU-Accelerated Parallel Gene-pool Optimal Mixing in a Gray-Box
  Optimization Setting

abstract:
  In a Gray-Box Optimization (GBO) setting that allows for partial evaluations,
the fitness of an individual can be updated efficiently after a subset of its
variables has been modified. This enables more efficient evolutionary
optimization with the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA)
due to its key strength: Gene-pool Optimal Mixing (GOM). For each solution, GOM
performs variation for many (small) sets of variables. To improve efficiency
even further, parallel computing can be leveraged. For EAs, typically, this
comprises population-wise parallelization. However, unless population sizes are
large, this offers limited gains. For large GBO problems, parallelizing
GOM-based variation holds greater speed-up potential, regardless of population
size. However, this potential cannot be directly exploited because of
dependencies between variables. We show how graph coloring can be used to group
sets of variables that can undergo variation in parallel without violating
dependencies. We test the performance of a CUDA implementation of parallel GOM
on a Graphics Processing Unit (GPU) for the Max-Cut problem, a well-known
problem for which the dependency structure can be controlled. We find that, for
sufficiently large graphs with limited connectivity, finding high-quality
solutions can be achieved up to 100 times faster, showcasing the great
potential of our approach.


title:
Large amplitude periodic outbursts and long period variables in the VVV
  VIRAC2-$\beta$ database

abstract:
  The VISTA Variables in the Via Lactea (VVV) survey obtained near-infrared
photometry toward the Galactic bulge and the southern disc plane for a decade
(2010 - 2019). We designed a modified Lomb-Scargle method to search for
large-amplitude ($\Delta$Ks > 1.5 mag) mid to long-term periodic variables (P >
10 d) in the 2nd version of VVV Infrared Astrometric Catalogue
(VIRAC2-$\beta$). In total, 1520 periodic sources were discovered, including 59
candidate periodic outbursting young stellar objects (YSOs), based on the
unique morphology of the phase-folded light curves, proximity to Galactic HII
regions and mid-infrared colours. Five sources are spectroscopically confirmed
as accreting YSOs. Both fast-rise/slow-decay and slow-rise/fast-decay periodic
outbursts were found, but fast-rise/slow-decay outbursts predominate at the
highest amplitudes. The multi-wavelength colour variations are consistent with
a variable mass accretion process, as opposed to variable extinction. The
cycles are likely to be caused by dynamical perturbations from stellar or
planetary companions within the circumstellar disc. An additional search for
periodic variability amongst YSO candidates in published Spitzer-based
catalogues yielded a further 71 candidate periodic accretors, mostly with lower
amplitudes. These resemble cases of pulsed accretion but with unusually long
periods and greater regularity. The majority of other long-period variables are
pulsating dusty Miras with smooth and symmetric light curves. We find that some
Miras have redder $W3 - W4$ colours than previously thought, most likely due to
their surface chemical compositions.


title:
Fast and efficient demultiplexing of single photons from a quantum dot
  with resonantly enhanced electro-optic modulators

abstract:
  We report on a multi-photon source based on active demultiplexing of single
photons emitted from a resonantly excited GaAs quantum dot. Active
temporal-to-spatial mode demultipexing is implemented via resonantly enhanced
free-space electro-optic modulators, making it possible to route individual
photons at high switching rates of 38 MHz. We demonstrate routing into four
spatial modes with a high end-to-end efficiency of 79% and measure a
four-photon coincidence rate of 0.17 Hz mostly limited by the single-photon
source brightness and not by the efficiency of the demultiplexer itself. We use
the demultiplexer to characterize the pairwise indistinguishability of
consecutively emitted photons from the quantum dot with variable delay time.


title:
Generalized radius problems for quotient functions having fixed second
  coefficient

abstract:
  In this manuscript, we deal with three classes of quotient functions having
fixed second coefficient described on open unit disk. The radius of strongly
starlikeness, lemniscate starlikeness, lune starlikeness, parabolic
starlikeness, sine starlikeness, exponential starlikeness and several other
radius estimates for such classes are examined. Relevant relations of obtained
radius estimates with the existing estimates are also discussed.


title: Search for Neutrinoless $\beta^+EC$ Decay of $^{120}$Te with CUORE

abstract:
  CUORE is a large scale cryogenic experiment searching for neutrinoless double
beta decay ($0\nu\beta\beta$) in $^{130}$Te. The CUORE detector is made of
natural tellurium, providing the possibility of rare event searches on isotopes
other than $^{130}$Te. In this work we describe a search for neutrinoless
positron emitting electron capture ($0\nu\beta^+EC$) decay in $^{120}$Te with a
total TeO$_2$ exposure of 355.7 kg $\cdot$ yr, corresponding to 0.2405 kg
$\cdot$ yr of $^{120}$Te. Albeit $0 \nu \beta^-\beta^-$ with two final state
electrons represents the most promising channel, the emission of a positron and
two 511-keV $\gamma$s make $0\nu\beta^+EC$ decay signature extremely clear. To
fully exploit the potential offered by the detector modularity we include
events with different topology and perform a simultaneous fit of five selected
signal signatures. Using blinded data we extract a median exclusion sensitivity
of $3.4 \cdot 10^{22}$ yr at 90% Credibility Interval (C.I.). After unblinding
we find no evidence of $0\nu\beta^+EC$ signal and set a 90% C.I. Bayesian lower
limit of $2.9 \cdot 10^{22}$ yr on $^{120}$Te half-life. This result improves
by an order of magnitude the existing limit from the combined analysis of
CUORE-0 and Cuoricino.


title: A Feasibility Study of Answer-Agnostic Question Generation for Education

abstract:
  We conduct a feasibility study into the applicability of answer-agnostic
question generation models to textbook passages. We show that a significant
portion of errors in such systems arise from asking irrelevant or
uninterpretable questions and that such errors can be ameliorated by providing
summarized input. We find that giving these models human-written summaries
instead of the original text results in a significant increase in acceptability
of generated questions (33% $\rightarrow$ 83%) as determined by expert
annotators. We also find that, in the absence of human-written summaries,
automatic summarization can serve as a good middle ground.


title:
Core overshoot constrained by the absence of a solar convective core and
  some solar-like stars

abstract:
  Convective-core overshoot mixing is a significant uncertainty in stellar
evolution. Because numerical simulations and turbulent convection models
predict exponentially decreasing radial rms turbulent velocity, a popular
treatment of the overshoot mixing is to apply a diffusion process with
exponentially decreasing diffusion coefficient. It is important to investigate
the parameters of the diffusion coefficient because they determine the
efficiency of the mixing in the overshoot region. In this paper, we have
investigated the effects of the core overshoot mixing on the properties of the
core in solar models and have constrained the parameters of the overshoot model
by using helioseismic inferences and the observation of the solar 8B neutrino
flux. For solar-mass stars, the core overshoot mixing helps to prolong the
lifetime of the convective core developed at the ZAMS. If the strength of the
mixing is sufficiently high, the convective core in a solar model could survive
till the present solar age, leading to large deviations of the sound-speed and
density profiles comparing with the helioseismic inferences. The 8B neutrino
flux also favours a radiative solar core. Those provide a constraint on the
parameters of the exponential diffusion model of the convective overshoot
mixing. A limited asteroseismic investigation of 13 Kepler low-mass stars with
1.0 < M < 1.5 shows a mass-dependent range of the overshoot parameter. The
overshoot mixing processes for different elements are analyzed in detail. It is
found that the exponential diffusion overshoot model leads to different
effective overshoot mixing lengths for elements with different nuclear
equilibrium timescale.


title:
Experimental proof of moisture clog through neutron tomography in a
  porous medium under truly one-directional drying

abstract:
  Structural failure of concrete buildings on fire and complete destruction of
the monolithic refractory lining during their drying stage are dangerous
examples of the effect of explosive spalling on partially saturated porous
media. Several observations in both cases indicated the presence of moisture
accumulation ahead of the drying front, which are in tune with the most common
theories on the explosive spalling of concrete. Previous studies have shown
evidence of the existence of this phenomenon, however, they were biased by
artifacts and experimental limitations (such as the beam hardening effect and
changes in the microstructure of the material due to the presence of pressure
and temperature sensors). In the current work, rapid neutron tomography was
used to investigate the in-operando drying behavior of a high-alumina
refractory castable, proposing a novel experimental layout aimed at a truly
one-dimensional drying front. This setup provided more realistic boundary
conditions, such as the behavior of a larger wall heated from one of its sides,
while also preventing some nonphysical artifacts (notably beam hardening). By
eliminating these aspects, a direct proof that moisture accumulates ahead of
the drying front was obtained. This work also lays the basis for further
studies focusing on the response sensitivity analysis to boundary conditions
and other parameters (e.g., heating rates and properties of the sample related
to the moisture clog formation), as well as useful data for the validation and
characterization stages of numerical models of partially saturated porous
media.


title: Learning video retrieval models with relevance-aware online mining

abstract:
  Due to the amount of videos and related captions uploaded every hour, deep
learning-based solutions for cross-modal video retrieval are attracting more
and more attention. A typical approach consists in learning a joint text-video
embedding space, where the similarity of a video and its associated caption is
maximized, whereas a lower similarity is enforced with all the other captions,
called negatives. This approach assumes that only the video and caption pairs
in the dataset are valid, but different captions - positives - may also
describe its visual contents, hence some of them may be wrongly penalized. To
address this shortcoming, we propose the Relevance-Aware Negatives and
Positives mining (RANP) which, based on the semantics of the negatives,
improves their selection while also increasing the similarity of other valid
positives. We explore the influence of these techniques on two video-text
datasets: EPIC-Kitchens-100 and MSR-VTT. By using the proposed techniques, we
achieve considerable improvements in terms of nDCG and mAP, leading to
state-of-the-art results, e.g. +5.3% nDCG and +3.0% mAP on EPIC-Kitchens-100.
We share code and pretrained models at
\url{https://github.com/aranciokov/ranp}.


title: Client-Wise Targeted Backdoor in Federated Learning

abstract:
  Federated Learning (FL) emerges from the privacy concerns traditional machine
learning raised. FL trains decentralized models by averaging them without
compromising clients' datasets. Ongoing research has found that FL is also
prone to security and privacy violations. Recent studies established that FL
leaks information by exploiting inference attacks, reconstructing a data piece
used during training, or extracting information. Additionally, poisoning
attacks and backdoors corrupt FL security by inserting poisoned data into
clients' datasets or directly modifying the model, degrading every client's
model performance.
  Our proposal utilizes these attacks in combination for performing a
client-wise targeted backdoor, where a single victim client is backdoored while
the rest remains unaffected. Our results establish the viability of the
presented attack, achieving a 100% attack success rate downgrading the target
label accuracy up to 0%. Our code will be publicly available after acceptance.


title:
Constructing number field isomorphisms from *-isomorphisms of certain
  crossed product C*-algebras

abstract:
  We prove that the class of crossed product C*-algebras associated with the
action of the multiplicative group of a number field on its ring of finite
adeles is rigid in the following explicit sense: Given any *-isomorphism
between two such C*-algebras, we construct an isomorphism between the
underlying number fields. As an application, we prove an analogue of the
Neukirch--Uchida theorem using topological full groups, which gives a new class
of discrete groups associated with number fields whose abstract isomorphism
class completely characterises the number field.


title:
Reconsidering the relation between "matter wave interference" and
  "wave-particle duality"

abstract:
  Interference of more and more massive objects provides a spectacular
confirmation of quantum theory. It is usually regarded as support for
"wave-particle duality" and in an extension of this duality even as support for
"complementarity". We first give an outline of the historical development of
these notions. Already here it becomes evident that they are hard to define
rigorously, i.e. have mainly a heuristic function. Then we discuss recent
interference experiments of large and complex molecules which seem to support
this heuristic function of "duality". However, we show that in these
experiments the diffraction of a {\em delocalized} center-of-mass wave function
depends on the interaction of the {\em localized} structure of the molecule
with the diffraction element. Thus, the molecules display "dual features" at
the same time, which contradicts the usual understanding of wave-particle
duality. We conclude that the notion of "wave-particle duality" deserves no
place in modern quantum physics.


title: The origin of Weyl gauging in metric-affine theories

abstract:
  In the first part, we discuss the interplay between local scale invariance
and metric-affine degrees of freedom from few distinct points of view. We
argue, rather generally, that the gauging of Weyl symmetry is a natural
byproduct of requiring that scale invariance is a symmetry of a gravitational
theory that is based on a metric and on an independent affine structure degrees
of freedom. In the second part, we compute the N\"other identities associated
with all the gauge symmetries, including Weyl, Lorentz and diffeomorphisms
invariances, for general actions with matter degrees of freedom, exploiting a
gauge covariant generalization of the Lie derivative. We find two equivalent
ways to approach the problem, based on how we regard the spin-connection
degrees of freedom, either as an independent object or as the sum of two Weyl
invariant terms. The latter approach, which rests upon the use of a new
connection, denoted $\hat{\nabla}$, is particularly convenient and constitutes
one of our main results.


title:
Crosslinks increase the elastic modulus and fracture toughness of
  gelatin hydrogels

abstract:
  Hydrogels have the ability to undergo large deformations and yet fail like
brittle materials. The development of biocompatible hydrogels with high
strength and toughness is an ongoing challenge in many applications. We
crosslinked bovine gelatin using glutaraldehyde (control) and methylglyoxal
(MGO) and assessed changes in their fracture toughness. Swelling experiments
show ~710% retention of water in MGO hydrogels as compared to ~450% in control
specimens. We used FTIR to identify the presence of chemical groups that may be
involved in the crosslinking of gelatin gels. Scanning electron micrographs of
lyophilized MGO hydrogels show large pores with plate-like intact walls that
help retain water as compared to control specimens. Monotonic compression tests
demonstrate nonlinear stress-strain behaviors for both hydrogel groups. MGO
samples had 96% higher moduli as compared to control hydrogels that had moduli
of 4.77+-0.73 kPa (n=4). A first order Ogden model fit the stress-strain data
well as compared to Mooney-Rivlin and neo-Hookean models. We used cavitation
rheology to quantify the maximum pressure for bubble failure in the hydrogels
using blunt needles with inner radii of 75, 150, 230, and 320 {\mu}m
respectively. Pressures inside the bubbles increased linearly with time and
dropped sharply following a critical value. Bubbles in MGO gels were small and
penny-shaped as compared to large spherical bubbles in control samples. We used
the critical pressures to quantify the fracture energies of the hydrogels. MGO
treatment increased the fracture energy by 187% from 13.09 J/m2 for control
gels. Finally, we discuss the challenges in using the Ogden and Mooney-Rivlin
models to compute the failure energy for gelatin hydrogels.


title:
Turning Stocks into Memes: A Dataset for Understanding How Social
  Communities Can Drive Wall Street

abstract:
  Who actually expresses an intent to buy GameStop shares on Reddit? What
convinces people to buy stocks? Are people convinced to support a coordinated
plan to adversely impact Wall Street investors? Existing literature on
understanding intent has mainly relied on surveys and self reporting; however
there are limitations to these methodologies. Hence, in this paper, we develop
an annotated dataset of communications centered on the GameStop phenomenon to
analyze the subscriber intentions behaviors within the r/WallStreetBets
community to buy (or not buy) stocks. Likewise, we curate a dataset to better
understand how intent interacts with a user's general support towards the
coordinated actions of the community for GameStop. Overall, our dataset can
provide insight to social scientists on the persuasive power to buy into social
movements online by adopting common language and narrative. WARNING: This paper
contains offensive language that commonly appears on Reddit's r/WallStreetBets
subreddit.


title:
A new flow model for a thin viscous fluid film between two moving
  surfaces

abstract:
  We propose a two-dimensional flow model of a viscous fluid between two close
moving surfaces. We show that its asymptotic behavior, when the distance
between the two surfaces tends to zero, is the same as that of the the
Navier-Stokes equations. The solutions of the new model and Navier-Stokes
equations converge to the same limit problem, that depends on the boundary
conditions. If slip velocity boundary conditions are imposed on the upper and
lower bound surfaces, the limit is solution of a lubrication model, but if the
tractions and friction forces are known on both bound surfaces, the limit is
solution of a shallow water model. The model proposed has been obtained to be a
valuable tool for computing viscous fluid flow between two close moving
surfaces, without the need to decide a priori whether the flow is typical of a
lubrication problem or it is of shallow water type, and without the enormous
computational effort that would be required to solve the Navier-Stokes
equations in such a thin domain.


title: Improved estimation for energy dissipation in biochemical oscillations

abstract:
  Biochemical oscillations, regulating the timing of life processes, need
consume energy to achieve good performance on crucial functions, such as high
accuracy of phase period and high sensitivity to external signals. However, it
is a great challenge to precisely estimate the energy dissipation in such
systems. Here, based on the stochastic normal form theory (SNFT), we calculate
the Pearson correlation coefficient between the oscillatory amplitude and
phase, and a trade-off relation between transport efficiency and phase
sensitivity can then be derived, which serves as a tighter form than the
estimator resulting from the conventional thermodynamic uncertainty relation
(TUR). Our findings demonstrate that a more precise energy dissipation
estimation can be obtained by enhancing the sensitivity of the biochemical
oscillations. Moreover, the internal noise and amplitude power effects have
also been discovered.


title:
New Generalized Informatics Framework for Development of Large Scale
  Virtual Battery Material Databases

abstract:
  In this paper, we introduce an approach for the prediction of capacity for
over 100,000 spinel compounds relevant for battery materials, from which we
propose the 20 most promising candidate materials. In the design of batteries,
selecting the proper material is difficult because there are so many metrics to
consider, including capacity which is a fundamental engineering property. Using
reported experimental data as our starting point, we demonstrate how we can
build a dataset that provides a guide for the selection of battery materials.
Although we focus on capacity of Li based spinel structures for electrode
materials relevant for usage in batteries, the methodology developed and
demonstrated here can be adapted to other properties, structures, and site
occupancies. Further, theoretical capacity is often used as a guideline for
material design of battery materials. In this paper, we show how this is
insufficient for representing experimental measurements, while our methodology
closes this gap and provides an accurate computational representation of
experimental data.


title:
Architectures and Synchronization Techniques for Distributed Satellite
  Systems: A Survey

abstract:
  Cohesive Distributed Satellite Systems (CDSS) is a key enabling technology
for the future of remote sensing and communication missions. However, they have
to meet strict synchronization requirements before their use is generalized.
When clock or local oscillator signals are generated locally at each of the
distributed nodes, achieving exact synchronization in absolute phase,
frequency, and time is a complex problem. In addition, satellite systems have
significant resource constraints, especially for small satellites, which are
envisioned to be part of the future CDSS. Thus, the development of precise,
robust, and resource-efficient synchronization techniques is essential for the
advancement of future CDSS. In this context, this survey aims to summarize and
categorize the most relevant results on synchronization techniques for DSS.
First, some important architecture and system concepts are defined. Then, the
synchronization methods reported in the literature are reviewed and
categorized. This article also provides an extensive list of applications and
examples of synchronization techniques for DSS in addition to the most
significant advances in other operations closely related to synchronization,
such as inter-satellite ranging and relative position. The survey also provides
a discussion on emerging data-driven synchronization techniques based on ML.
Finally, a compilation of current research activities and potential research
topics is proposed, identifying problems and open challenges that can be useful
for researchers in the field.


title:
High-Throughput Computation of Li-based Battery Material Databases:
  Chemistry-Processing-Property Relationships

abstract:
  In this paper, we incorporate processing into the data driven property
modeling of Li-based spinel battery materials. When considering spinel
compounds of the form LiMe2O4 with Me as a metal or metals, there are 125,000
possible combinations assuming a maximum of three metallic elements. In this
work, we focus on capacity for our predicted property, and increase the number
of possible combinations to two million by incorporating processing into the
modeling. Due to the non-linear relationship between processing and property,
as well as ensuring proper sensitivity, a new approach which tracks the change
with changing processing is introduced. This work provides an invaluable tool
for guiding the next generation of battery experiments, providing a significant
narrowing of the infinite design space.


title: A structural description of extended ${\mathbb Z}_{2n}$-Schottky groups

abstract:
  Real points of Schottky space ${\mathcal S}_{g}$ are in correspondence with
extended Kleinian groups $K$ containing, as a normal subgroup, a Schottky group
$\Gamma$ of rank $g$ such that $K/\Gamma \cong {\mathbb Z}_{2n}$ for a suitable
integer $n \geq 1$. These kind of groups are called extended ${\mathbb
Z}_{2n}$-Schottky groups of rank $g$.
  In this paper, we provide a structural decomposition theorem, in terms of
Klein-Maskit's combination theorems, of these kind of groups.


title:
One-step weighting to generalize and transport treatment effect
  estimates to a target population

abstract:
  Weighting methods are often used to generalize and transport estimates of
causal effects from a study sample to a target population. Traditional methods
construct the weights by separately modeling the treatment assignment and the
study selection probabilities and then multiplying functions (e.g., inverses)
of the estimated probabilities. These estimated multiplicative weights may not
produce adequate covariate balance and can be highly variable, resulting in
biased and/or unstable estimators, particularly when there is limited covariate
overlap across populations or treatment groups. To address these limitations,
we propose a weighting approach for both randomized and observational studies
that weights each treatment group directly in `one go' towards the target
population. We present a general framework for generalization and
transportation by characterizing the study and target populations in terms of
generic probability distributions. Under this framework, we justify this
one-step weighting approach. By construction, this approach directly balances
covariates relative to the target population and produces weights that are
stable. Moreover, in some settings, this approach does not require
individual-level data from the target population. We connect this approach to
inverse probability and inverse odds weighting. We show that the one-step
weighting estimator for the target average treatment effect is consistent,
asymptotically Normal, doubly-robust, and semiparametrically efficient. We
demonstrate the performance of this approach using a simulation study and a
randomized case study on the effects of physician racial diversity on
preventive healthcare utilization among Black men in California.


title:
On the evolution and impact of Architectural Smells -- An industrial
  case study

abstract:
  Architectural smells (AS) are notorious for their long-term impact on the
Maintainability and Evolvability of software systems. The majority of research
work has investigated this topic by mining software repositories of open source
Java systems, making it hard to generalise and apply them to an industrial
context and other programming languages. To address this research gap, we
conducted an embedded multiple-case case study, in collaboration with a large
industry partner, to study how AS evolve in industrial embedded systems. We
detect and track AS in 9 C/C++ projects with over 30 releases for each project
that span over two years of development, with over 20 millions lines of code in
the last release only. In addition to these quantitative results, we also
interview 12 among the developers and architects working on these projects,
collecting over six hours of qualitative data about the usefulness of AS
analysis and the issues they experienced while maintaining and evolving
artefacts affected by AS. Our quantitative findings show how individual smell
instances evolve over time, how long they typically survive within the system,
how they overlap with instances of other smell types, and finally what the
introduction order of smell types is when they overlap. Our qualitative
findings, instead, provide insights on the effects of AS on the long-term
maintainability and evolvability of the system, supported by several excerpts
from our interviews. Practitioners also mention what parts of the AS analysis
actually provide actionable insights that they can use to plan refactoring
activities.


title:
Nonlinear quantum spectroscopy with Parity-Time symmetric integrated
  circuits

abstract:
  We propose a novel quantum nonlinear interferometer design that incorporates
a passive PT symmetric coupler sandwiched between two nonlinear sections where
signal-idler photon pairs are generated. The PT-symmetry enables efficient
coupling of the longer-wavelength idler photons and facilitates the sensing of
losses in the second waveguide exposed to analyte under investigation, whose
absorption can be inferred by measuring only the signal intensity at a shorter
wavelength where efficient detectors are readily available. Remarkably, we
identify a new phenomenon of sharp signal intensity fringe shift at critical
idler loss values, which is distinct from the previously studied PT-symmetry
breaking. We discuss how such unconventional properties arising from quantum
interference can provide a route to enhancing the sensing of analytes and
facilitate broadband spectroscopy applications in integrated photonic
platforms.


title: Certain subclasses of Analytic Functions with Fixed Second Coefficient

abstract:
  This paper aims to pursue some classes of normalized analytic functions $f$
with fixed second coefficient defined on open unit disk, such that
${(1+z)^2f(z)}/{z}$ and ${(1+z)f(z)}/{z}$ are functions having positive real
part. The radius of strongly starlikeness, the radius of lemniscate
starlikeness, the radius of parabolic starlikeness and other starlikeness
estimates are calculated for such functions. As well relevant connections of
computed radii estimates with the existing one are also shown.


title:
Engineering the speedup of quantum tunneling in Josephson systems via
  dissipation

abstract:
  We theoretically investigate the escape rate occurring via quantum tunneling
in a system affected by tailored dissipation. Specifically, we study the
environmental assisted quantum tunneling of the superconducting phase in a
current-biased Josephson junction. We consider Ohmic resistors inducing
dissipation both in the phase and in the charge of the quantum circuit. We find
that the charge dissipation leads to an enhancement of the quantum escape rate.
This effect appears already in the low Ohmic regime and also occurs in the
presence of phase dissipation that favors localization. Inserting realistic
circuit parameters, we address the question of its experimental observability
and discuss suitable parameter spaces for the observation of the enhanced rate.


title: Invariance of Brownian motion associated with exponential functionals

abstract:
  It is well known that Brownian motion enjoys several distributional
invariances such as the scaling property and the time reversal. In this paper,
we prove another invariance of Brownian motion that is compatible with the time
reversal. The invariance, which seems to be new to our best knowledge, is
described in terms of an anticipative path transformation involving exponential
functionals as anticipating factors. Some related results are also provided.


title:
Versatile Multi-MW Proton Facility with Synchrotron Upgrade of Fermilab
  Proton Complex

abstract:
  DUNE/LBNF constitutes an international multi-decadal physics program for
leading-edge neutrino science and proton decay studies [1] and is expected to
serve as the flagship particle experiment based at Fermilab.


title: Analysis of a Cesium lattice optical clock

abstract:
  We propose and analyze a Cesium lattice optical clock (CLOC) which has the
potential for high performance and simple operation in a compact form factor
using a forbidden optical transition in Cs atoms at 685 nm. Cs atoms are
trapped in a 3D optical lattice using a magic trap wavelength of
$\lambda_{\textrm m}=803~\textrm nm$. To reduce sensitivity to magnetic fields
the atoms are probed on two cycling transitions with equal magnitude, but
opposite magnetic shifts. Operation of the clock requires only three diode
lasers at 685, 803, 852 nm and is simplified compared to other
higher-performance optical clocks that rely on alkaline earth atoms or single
trapped ions. Analysis shows a quantum noise limited stability of $8.4\times
10^{-16}/\sqrt\tau$ and the potential for reaching 1 ns timing uncertainty at 1
month with realistic system parameters.


title: High dimensional change-point detection: a complete graph approach

abstract:
  The aim of online change-point detection is for a accurate, timely discovery
of structural breaks. As data dimension outgrows the number of data in
observation, online detection becomes challenging. Existing methods typically
test only the change of mean, which omit the practical aspect of change of
variance. We propose a complete graph-based, change-point detection algorithm
to detect change of mean and variance from low to high-dimensional online data
with a variable scanning window. Inspired by complete graph structure, we
introduce graph-spanning ratios to map high-dimensional data into metrics, and
then test statistically if a change of mean or change of variance occurs.
Theoretical study shows that our approach has the desirable pivotal property
and is powerful with prescribed error probabilities. We demonstrate that this
framework outperforms other methods in terms of detection power. Our approach
has high detection power with small and multiple scanning window, which allows
timely detection of change-point in the online setting. Finally, we applied the
method to financial data to detect change-points in S&P 500 stocks.


title:
Potential Nanoscale Sources of Decoherence in Niobium based Transmon
  Qubit Architectures

abstract:
  Superconducting thin films of niobium have been extensively employed in
transmon qubit architectures. Although these architectures have demonstrated
remarkable improvements in recent years, further improvements in performance
through materials engineering will aid in large-scale deployment. Here, we use
information retrieved from electron microscopy and analysis to conduct a
detailed assessment of potential decoherence sources in transmon qubit test
devices. In the niobium thin film, we observe the presence of localized strain
at interfaces, which may amplify interactions between two-level systems and
impose limits on T$_1$ and T$_2$ relaxation times. Additionally, we observe the
presence of a surface oxide with varying stoichiometry and bond distances,
which can generate a broad two-level system noise spectrum. Finally, a
similarly disordered and rough interface is observed between Nb and the Si
substrate. We propose that this interface can also degrade the overall
superconducting properties.


title: Inelastic rotations and plastic turbulence

abstract:
  Plastic deformations in crystals often produce textures taking the form of
apparently randomly oriented patches of the unstressed lattice. We use a
mesoscopic Landau model of crystal plasticity to show that the implied large
rigid rotations can originate from inelastic slip at the microscale. Our
numerical experiments suggest that the process of the formation of such patchy
textures is inherently unstable and involves quasi-turbulent motions with
power-law distributed spatial correlations.

categories: cond-mat.mes-hall cond-mat.mtrl-sci nlin.PS physics.comp-ph


title: More Limiting Distributions for Eigenvalues of Wigner Matrices

abstract:
  The Tracy-Widom distributions are among the most famous laws in probability
theory, partly due to their connection with Wigner matrices. In particular, for
$A=\frac{1}{\sqrt{n}}(a_{ij})_{1 \leq i,j \leq n} \in \mathbb{R}^{n \times n}$
symmetric with $(a_{ij})_{1 \leq i \leq j \leq n}$ i.i.d. standard normal, the
fluctuations of its largest eigenvalue $\lambda_1(A)$ are asymptotically
described by a real-valued Tracy-Widom distribution $TW_1:$
$n^{2/3}(\lambda_1(A)-2) \Rightarrow TW_1.$ As it often happens, Gaussianity
can be relaxed, and this results holds when $\mathbb{E}[a_{11}]=0,
\mathbb{E}[a^2_{11}]=1,$ and the tail of $a_{11}$ decays sufficiently fast:
$\lim_{x \to \infty}{x^4\mathbb{P}(|a_{11}|>x)}=0,$ whereas when the law of
$a_{11}$ is regularly varying with index $\alpha \in (0,4),$
$c_a(n)n^{1/2-2/\alpha}\lambda_1(A)$ converges to a Fr\'echet distribution for
$c_a:(0,\infty) \to (0,\infty)$ slowly varying and depending solely on the law
of $a_{11}.$ This paper considers a family of edge cases, $\lim_{x \to
\infty}{x^4\mathbb{P}(|a_{11}|>x)}=c \in (0,\infty),$ and unveils a new type of
limiting behavior for $\lambda_1(A):$ a continuous function of a Fr\'echet
distribution in which $2,$ the almost sure limit of $\lambda_1(A)$ in the
light-tailed case, plays a pivotal role:
  $f(x)=\begin{cases}
  2, & 0<x<1 \newline
  x+\frac{1}{x}, & x \geq 1
  \end{cases}.$


title: DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation

abstract:
  This paper proposes a simple baseline framework for video-based 2D/3D human
pose estimation that can achieve 10 times efficiency improvement over existing
works without any performance degradation, named DeciWatch. Unlike current
solutions that estimate each frame in a video, DeciWatch introduces a simple
yet effective sample-denoise-recover framework that only watches sparsely
sampled frames, taking advantage of the continuity of human motions and the
lightweight pose representation. Specifically, DeciWatch uniformly samples less
than 10% video frames for detailed estimation, denoises the estimated 2D/3D
poses with an efficient Transformer architecture, and then accurately recovers
the rest of the frames using another Transformer-based network. Comprehensive
experimental results on three video-based human pose estimation and body mesh
recovery tasks with four datasets validate the efficiency and effectiveness of
DeciWatch.


title: Procesi's Conjecture on the Formanek-Weingarten Function is False

abstract:
  In this paper, we disprove a recent monotonicity conjecture of C. Procesi on
the generating function for monotone walks on the symmetric group, an object
which is equivalent to the Weingarten function of the unitary group.


title: Multiscale Sensor Fusion and Continuous Control with Neural CDEs

abstract:
  Though robot learning is often formulated in terms of discrete-time Markov
decision processes (MDPs), physical robots require near-continuous multiscale
feedback control. Machines operate on multiple asynchronous sensing modalities,
each with different frequencies, e.g., video frames at 30Hz, proprioceptive
state at 100Hz, force-torque data at 500Hz, etc. While the classic approach is
to batch observations into fixed-time windows then pass them through
feed-forward encoders (e.g., with deep networks), we show that there exists a
more elegant approach -- one that treats policy learning as modeling latent
state dynamics in continuous-time. Specifically, we present 'InFuser', a
unified architecture that trains continuous time-policies with Neural
Controlled Differential Equations (CDEs). InFuser evolves a single latent state
representation over time by (In)tegrating and (Fus)ing multi-sensory
observations (arriving at different frequencies), and inferring actions in
continuous-time. This enables policies that can react to multi-frequency multi
sensory feedback for truly end-to-end visuomotor control, without discrete-time
assumptions. Behavior cloning experiments demonstrate that InFuser learns
robust policies for dynamic tasks (e.g., swinging a ball into a cup) notably
outperforming several baselines in settings where observations from one sensing
modality can arrive at much sparser intervals than others.


title:
Quantum graphs: different perspectives, homomorphisms and quantum
  automorphisms

abstract:
  We undertake a study of the notion of a quantum graph over arbitrary
finite-dimensional $C^*$-algebras $B$ equipped with arbitrary faithful states.
Quantum graphs are realised principally as either certain operators on
$L^2(B)$, the quantum adjacency matrices, or as certain operator bimodules over
$B'$. We present a simple, purely algebraic approach to proving equivalence
between these settings, thus recovering existing results in the tracial state
setting. For non-tracial states, our approach naturally suggests a
generalisation of the operator bimodule definition, which takes account of
(some aspect of) the modular automorphism group of the state. Furthermore, we
show that each such "non-tracial" quantum graphs corresponds to a "tracial"
quantum graph which satisfies an extra symmetry condition. We study
homomorphisms (or CP-morphisms) of quantum graphs arising from UCP maps, and
the closely related examples of quantum graphs constructed from UCP maps. We
show that these constructions satisfy automatic bimodule properties. We study
quantum automorphisms of quantum graphs, give a definition of what it means for
a compact quantum group to act on an operator bimodule, and prove an
equivalence between this definition, and the usual notion defined using a
quantum adjacency matrix. We strive to give a relatively self-contained,
elementary, account, in the hope this will be of use to other researchers in
the field.


title: Relational Self-Supervised Learning

abstract:
  Self-supervised Learning (SSL) including the mainstream contrastive learning
has achieved great success in learning visual representations without data
annotations. However, most methods mainly focus on the instance level
information (\ie, the different augmented images of the same instance should
have the same feature or cluster into the same class), but there is a lack of
attention on the relationships between different instances. In this paper, we
introduce a novel SSL paradigm, which we term as relational self-supervised
learning (ReSSL) framework that learns representations by modeling the
relationship between different instances. Specifically, our proposed method
employs sharpened distribution of pairwise similarities among different
instances as \textit{relation} metric, which is thus utilized to match the
feature embeddings of different augmentations. To boost the performance, we
argue that weak augmentations matter to represent a more reliable relation, and
leverage momentum strategy for practical efficiency. The designed asymmetric
predictor head and an InfoNCE warm-up strategy enhance the robustness to
hyper-parameters and benefit the resulting performance. Experimental results
show that our proposed ReSSL substantially outperforms the state-of-the-art
methods across different network architectures, including various lightweight
networks (\eg, EfficientNet and MobileNet).


title: Is Fermi liquid topologically protected?

abstract:
  The book by Volovik [1] contains the argument, which can be considered as the
topological proof of the Luttinger theorem. The Green function of the ideal
Fermi gas has a pole in the (E,|p|) plane (where E and p are energy and
momentum). This pole is considered to be analogous to a vortex in liquid
helium. Since a vortex is topologically stable against restricted
perturbations, one can include interaction adiabatically (as a succession of
small perturbations), and observe transformation of the Fermi gas pole to the
Fermi liquid pole. In this argument, the topological stability arises already
on the level of the ideal Fermi gas, which is in conflict with its Cooper
instability. We discuss the origin of this controversy.

categories: cond-mat.mes-hall cond-mat.dis-nn cond-mat.quant-gas cond-mat.stat-mech cond-mat.str-el cond-mat.supr-con


title:
Autostabilisation de la phase acoustique dans un miroir Brillouin
  invers{\'e} en pr{\'e}sence d'une forte dispersion acoustique

abstract:
  We consider a counter-intuitive regime of stimulated Brillouin scattering in
which the pump wave absorbs the phase defects of the Stokes wave, yielding a
nonlinear self-stabilisation of the acoustic wave and a narrowing of the
acoustic spectrum


title:
Omitting Types Theorem in hybrid-dynamic first-order logic with rigid
  symbols

abstract:
  In the the present contribution, we prove an Omitting Types Theorem (OTT) for
an arbitrary fragment of hybriddynamic first-order logic with rigid symbols
(i.e. symbols with fixed interpretations across worlds) closed under negation
and retrieve. The logical framework can be regarded as a parameter and it is
instantiated by some well-known hybrid and/or dynamic logics from the
literature. We develop a forcing technique and then we study a forcing property
based on local satisfiability, which lead to a refined proof of the OTT. For
uncountable signatures, the result requires compactness, while for countable
signatures, compactness is not necessary. We apply the OTT to obtain upwards
and downwards L\"owenheim-Skolem theorems for our logic, as well as a
completeness theorem for its constructor-based variant. The main result of this
paper can easily be recast in the institutional model theory framework, giving
it a higher level of generality.


title: Axiomatization via translation: Hiz's warning for predicate logic

abstract:
  The problems of logical translation of axiomatizations and the choice of
primitive operators have surfaced several times over the years. An early issue
was raised by H. Hi{\. z} in the 1950s on the incompleteness of translated
calculi. Further pertinent work, some of it touched on here, was done in the
1970s by W. Frank and S. Shapiro, as well as by others in subsequent decades.
As we shall see, overlooking such possibilities has led to incorrect claims of
completeness being made (e.g. by J. L. Bell and A. B. Slomson as well as J. N.
Crossley) for axiomatizations of classical predicate logic obtained by
translation from axiomatizations suited to differently chosen logical
primitives. In this note we begin by discussing some problematic aspects of an
early article by W. Frank on the difficulties of obtaining completeness
theorems for translated calculi. Shapiro had established the incompleteness of
Crossley's axiomatization by exhibiting a propositional tautology that was not
provable. In contrast, to deal with Bell and Slomson's system which is complete
for propositional tautologies, we go on to show that taking a formal system for
classical predicate calculus with the primitive $ \exists$, setting $\forall x
\phi(x) \stackrel{\text{def}}{=}\neg \exists x \neg \phi(x)$, and writing down
a set of axioms and rules complete for the calculus with $\forall $ instead of
$ \exists$ as primitive, does not guarantee completeness of the resulting
system. In particular, instances of the valid schema $\exists x \phi (x)
\rightarrow \exists x \neg \neg\phi (x)$ are not provable, which is analogous
to what occurs in modal logic with $\Box$ and $\Diamond$.


title: Maximality of logic without identity

abstract:
  Lindstr\"om theorem obviously fails as a characterization of
$\mathcal{L}_{\omega \omega}^{-} $, first-order logic without identity. In this
note we provide a fix: we show that $\mathcal{L}_{\omega \omega}^{-} $ is
\emph{maximal} among abstract logics satisfying a weak form of the isomorphism
property (suitable for identity-free languages and studied in \cite{Casa}), the
L\"owenheim--Skolem property, and compactness. Furthermore, we show that
compactness can be replaced by being recursively enumerable for validity under
certain conditions. In the proofs we use a form of strong upwards
L\"owenheim--Skolem theorem not available in the framework with identity.


title:
Improved short-segment detection statistic for continuous gravitational
  waves

abstract:
  Continuous gravitational waves represent one of the long-sought types of
signals that have yet to be detected. Due to their small amplitude, long
observational datasets (months-years) have to be analyzed together, thereby
vastly increasing the computational cost of these searches. All-sky searches
face the most severe computational obstacles, especially searches for sources
in unknown binary systems, which need to break the data into very short
segments in order to be computationally feasible. In this paper, we present a
new detection statistic that improves sensitivity by up to 19% compared to the
standard $\mathcal{F}$-statistic for segments shorter than a few hours.


title: Time series analysis and modelling of the freezing of gait phenomenon

abstract:
  Freezing of Gait (FOG) is one of the most debilitating symptoms of
Parkinson's Disease and is associated with falls and loss of independence. The
patho-physiological mechanisms underpinning FOG are currently poorly
understood. In this paper we combine time series analysis and mathematical
modelling to study the FOG phenomenon's dynamics. We focus on the transition
from stepping in place into freezing and treat this phenomenon in the context
of an escape from an oscillatory attractor into an equilibrium attractor state.
We extract a discrete-time discrete-space Markov chain from experimental data
and divide its state space into communicating classes to identify the
transition into freezing. This allows us to develop a methodology for
computationally estimating the time to freezing as well as the phase along the
oscillatory (stepping) cycle of a patient experiencing Freezing Episodes (FE).
The developed methodology is general and could be applied to any time series
featuring transitions between different dynamic regimes including time series
data from forward walking in people with FOG.


title:
Attacking deep networks with surrogate-based adversarial black-box
  methods is easy

abstract:
  A recent line of work on black-box adversarial attacks has revived the use of
transfer from surrogate models by integrating it into query-based search.
However, we find that existing approaches of this type underperform their
potential, and can be overly complicated besides. Here, we provide a short and
simple algorithm which achieves state-of-the-art results through a search which
uses the surrogate network's class-score gradients, with no need for other
priors or heuristics. The guiding assumption of the algorithm is that the
studied networks are in a fundamental sense learning similar functions, and
that a transfer attack from one to the other should thus be fairly "easy". This
assumption is validated by the extremely low query counts and failure rates
achieved: e.g. an untargeted attack on a VGG-16 ImageNet network using a
ResNet-152 as the surrogate yields a median query count of 6 at a success rate
of 99.9%. Code is available at https://github.com/fiveai/GFCS.


title:
Differential Phase-Shift QKD in a 2:16-Split Lit PON with 19
  Carrier-Grade Channels

abstract:
  We investigate the practical network integration of differential phase shift
quantum key distribution following a cost-optimized deployment scheme where
complexity is off-loaded to a centralized location. User terminal equipment for
quantum state preparation at 1 GHz symbol rate is kept technologically lean
through use of a directly-modulated laser as optical encoder. Integration in a
passive optical network infrastructure is experimentally studied for legacy and
modern optical access standards. We analyze the implications that result from
Raman scattering arising from different spectral allocations of the classical
channels in the O-, S-, C- and L-band, and prove that the quantum channel can
co-exist with up to 19 classical channels of a fully-loaded modern access
standard. Secure-key generation at a rate of 5.1 times 10e-7 bits per pulse at
a quantum bit error ratio of 3.28 percent is obtained over a 13.5 km reach, 2
to 16 split passive network configuration. The high power difference of 93.8 dB
between launched classical and quantum signals in the lit access network leads
to a low penalty of 0.52 percent in terms of error ratio.


title:
Linear slices of hyperbolic polynomials and positivity of symmetric
  polynomial functions

abstract:
  A real univariate polynomial of degree $n$ is called hyperbolic if all of its
$n$ roots are on the real line. We study families of hyperbolic polynomials
defined by $k$ linear conditions on the coefficients. We show that the
polynomials corresponding to local extreme points of such families have at most
$k$ distinct roots. Furthermore, we find that generically the convex hull of
such a family is a polyhedron. Building on these results, we give consequences
of our results to the study of symmetric real varieties and symmetric
semi-algebraic sets. Here, we show that sets defined by symmetric polynomials
which can be expressed sparsely in terms of elementary symmetric polynomials
can be sampled on points with few distinct coordinates. This in turn allows for
algorithmic simplifications, for example to verify that such polynomials are
non-negative or that a semi-algebraic set defined by such polynomials is empty.


title:
An Error-State Model Predictive Control on Connected Matrix Lie Groups
  for Legged Robot Control

abstract:
  This paper reports on a new error-state Model Predictive Control (MPC)
approach on connected matrix Lie groups for robot control. The linearized
tracking error dynamics and the linearized equations of motion are derived in
the Lie algebra. Moreover, given an initial condition, the linearized tracking
error dynamics and equations of motion are globally valid and evolve
independently of the system trajectory. By exploiting the symmetry of the
problem, the proposed approach shows faster convergence of rotation and
position simultaneously than the state-of-the-art geometric variational MPC
based on variational-based linearization. Numerical simulation on tracking
control of a fully-actuated 3D rigid body dynamics confirms the benefits of the
proposed approach compared to the baselines. Furthermore, the proposed MPC is
also verified in pose control and locomotion experiments on a quadrupedal robot
MIT Mini Cheetah.


title: Input Influence Matrix Design for MIMO Discrete-Time Ultra-Local Model

abstract:
  Ultra-Local Models (ULM) have been applied to perform model-free control of
nonlinear systems with unknown or partially known dynamics. Unfortunately,
extending these methods to MIMO systems requires designing a dense input
influence matrix which is challenging. This paper presents guidelines for
designing an input influence matrix for discrete-time, control-affine MIMO
systems using an ULM-based controller. This paper analyzes the case that uses
ULM and a model-free control scheme: the H\"older-continuous Finite-Time Stable
(FTS) control. By comparing the ULM with the actual system dynamics, the paper
describes how to extract the input-dependent part from the lumped ULM dynamics
and finds that the tracking and state estimation error are coupled. The
stability of the ULM-FTS error dynamics is affected by the eigenvalues of the
difference (defined by matrix multiplication) between the actual and designed
input influence matrix. Finally, the paper shows that a wide range of input
influence matrix designs can keep the ULM-FTS error dynamics (at least locally)
asymptotically stable. A numerical simulation is included to verify the result.
The analysis can also be extended to other ULM-based controllers.


title: Anomalous energy flux in critical $L^p$-based spaces

abstract:
  We construct a three-dimensional vector field that exhibits positive energy
flux at every Littlewood-Paley shell and has the best possible regularity in
$L^p$-based spaces, $p \le 3$; in particular, it belongs to $H^{(\frac56)^-}$.


title: Tangles and Hierarchical Clustering

abstract:
  We establish a connection between tangles, a concept from structural graph
theory that plays a central role in Robertson and Seymour's graph minor
project, and hierarchical clustering. Tangles cannot only be defined for
graphs, but in fact for arbitrary connectivity functions, which are functions
defined on the subsets of some finite universe. In typical clustering
applications these universes consist of points in some metric space.
Connectivity functions are usually required to be submodular. It is our first
contribution to show that the central duality theorem connecting tangles with
hierarchical decompositions (so-called branch decompositions) also holds if
submodularity is replaced by a different property that we call
maximum-submodular. We then define a connectivity function on finite data sets
in an arbitrary metric space and prove that its tangles are in one-to-one
correspondence with the clusters obtained by applying the well-known single
linkage clustering algorithms to the same data set. Lastly we generalize this
correspondence for any hierarchical clustering. We show that the data structure
that represents hierarchical clustering results, called dendograms, are
equivalent to maximum-submodular connectivity functions and their tangles. The
idea of viewing tangles as clusters has first been proposed by Diestel and
Whittle in 2016 as an approach to image segmentation. To the best of our
knowledge, our result is the first that establishes a precise technical
connection between tangles and clusters.


title: Radical support for multigraded ideals

abstract:
  Can one tell if an ideal is radical just by looking at the degrees of the
generators? In general, this is hopeless. However, there are special
collections of degrees in multigraded polynomial rings, with the property that
any multigraded ideal generated by elements of those degrees is radical. We
call such a collection of degrees a radical support. In this paper, we give a
combinatorial characterization of radical supports. Our characterization is in
terms of properties of cycles in an associated labelled graph. We also show
that the notion of radical support is closely related to that of
Cartwright-Sturmfels ideals. In fact, any ideal generated by multigraded
generators whose multidegrees form a radical support is a Cartwright-Sturmfels
ideal. Conversely, a collection of degrees such that any multigraded ideal
generated by elements of those degrees is Cartwright-Sturmfels is a radical
support.


title: A Note on the Outliers Theorem

abstract:
  An outlier is a datapoint set apart from a sample population. The outliers
theorem in algorithmic information theory states given a computable sampling
method, outliers have to appear in samples. In this paper we present a plain
complexity variant to the outliers theorem. This variant achieves better error
bounds than the original prefix-free Kolmogorov complexity version. We extend
the outlier theorem to dynamical systems. Dynamical systems are guaranteed to
hit ever larger outlier states with diminishing measure.


title: Learning Where To Look -- Generative NAS is Surprisingly Efficient

abstract:
  The efficient, automated search for well-performing neural architectures
(NAS) has drawn increasing attention in the recent past. Thereby, the
predominant research objective is to reduce the necessity of costly evaluations
of neural architectures while efficiently exploring large search spaces. To
this aim, surrogate models embed architectures in a latent space and predict
their performance, while generative models for neural architectures enable
optimization-based search within the latent space the generator draws from.
Both, surrogate and generative models, have the aim of facilitating
query-efficient search in a well-structured latent space. In this paper, we
further improve the trade-off between query-efficiency and promising
architecture generation by leveraging advantages from both, efficient surrogate
models and generative design. To this end, we propose a generative model,
paired with a surrogate predictor, that iteratively learns to generate samples
from increasingly promising latent subspaces. This approach leads to very
effective and efficient architecture search, while keeping the query amount
low. In addition, our approach allows in a straightforward manner to jointly
optimize for multiple objectives such as accuracy and hardware latency. We show
the benefit of this approach not only w.r.t. the optimization of architectures
for highest classification accuracy but also in the context of hardware
constraints and outperform state-of-the art methods on several NAS benchmarks
for single and multiple objectives. We also achieve state-of-the-art
performance on ImageNet.


title:
Recovery of piecewise smooth density and Lam\'e parameters from
  high-frequency exterior Cauchy data

abstract:
  We consider an isotropic elastic medium occupying a bounded domain D whose
density and Lam\'e parameters are piecewise smooth. In the elastic wave initial
value inverse problem, we are given the solution operator for the elastic wave
equation, but only outside the domain D and only for initial data supported
outside D, and we study the recovery of the density and Lam\'e parameters. For
known density, results have recently been obtained using the scattering control
method to recover wave speeds. Here, we extend this result to include the
recovery of the density in addition to the Lam\'e parameters under certain
geometric conditions using techniques from microlocal analysis and a connection
to local tensor tomography.


title:
REBCO -- a silver bullet for our next high-field magnet and collider
  budget?

abstract:
  High-field superconducting magnets with a dipole field of 16 T and above
enable future energy-frontier circular particle colliders. Although we believe
these magnets can be built, none exists today. They can also be a showstopper
for future high-energy machines due to a prohibitively high price tag based on
the current conductor and magnet fabrication cost. The high-temperature
superconducting REBCO coated conductor can address both the technical and cost
issues, a silver bullet to lay both monsters to rest. The challenges and
unknowns, however, can be too arduous to make the silver bullet. We lay out a
potential road forward and suggest key action items. As a contribution from the
accelerator community, we attempt to clarify for our theorist and experimenter
colleagues a few aspects about the future high-field superconducting magnets.
We hope to stimulate an effective plan for the 2023 P5 process that can lead to
a cost-effective high-field magnet technology for future colliders and the
exciting physics they can steward.


title:
Hardware Approximate Techniques for Deep Neural Network Accelerators: A
  Survey

abstract:
  Deep Neural Networks (DNNs) are very popular because of their high
performance in various cognitive tasks in Machine Learning (ML). Recent
advancements in DNNs have brought beyond human accuracy in many tasks, but at
the cost of high computational complexity. To enable efficient execution of DNN
inference, more and more research works, therefore, exploit the inherent error
resilience of DNNs and employ Approximate Computing (AC) principles to address
the elevated energy demands of DNN accelerators. This article provides a
comprehensive survey and analysis of hardware approximation techniques for DNN
accelerators. First, we analyze the state of the art and by identifying
approximation families, we cluster the respective works with respect to the
approximation type. Next, we analyze the complexity of the performed
evaluations (with respect to the dataset and DNN size) to assess the
efficiency, the potential, and limitations of approximate DNN accelerators.
Moreover, a broad discussion is provided, regarding error metrics that are more
suitable for designing approximate units for DNN accelerators as well as
accuracy recovery approaches that are tailored to DNN inference. Finally, we
present how Approximate Computing for DNN accelerators can go beyond energy
efficiency and address reliability and security issues, as well.


title:
The Second Catalog of Interplanetary Network Localizations of Konus
  Short Duration Gamma-Ray Bursts

abstract:
  We present the catalog of Interplanetary Network (IPN) localizations for 199
short-duration gamma-ray bursts (sGRBs) detected by the Konus-Wind (KW)
experiment between 2011 January 1 and 2021 August 31, which extends the initial
sample of IPN localized KW sGRBs (arXiv:1301.3740) to 495 events. We present
the most comprehensive IPN localization data on these events, including
probability sky maps in HEALPix format.


title:
What Do Adversarially trained Neural Networks Focus: A Fourier
  Domain-based Study

abstract:
  Although many fields have witnessed the superior performance brought about by
deep learning, the robustness of neural networks remains an open issue.
Specifically, a small adversarial perturbation on the input may cause the model
to produce a completely different output. Such poor robustness implies many
potential hazards, especially in security-critical applications, e.g.,
autonomous driving and mobile robotics. This work studies what information the
adversarially trained model focuses on. Empirically, we notice that the
differences between the clean and adversarial data are mainly distributed in
the low-frequency region. We then find that an adversarially-trained model is
more robust than its naturally-trained counterpart due to the reason that the
former pays more attention to learning the dominant information in
low-frequency components. In addition, we consider two common ways to improve
model robustness, namely, by data augmentation and by using stronger network
architectures, and understand these techniques from a frequency-domain
perspective. We are hopeful this work can shed light on the design of more
robust neural networks.


title:
Echoes of the gravitational decoupling: scalar perturbations and
  quasinormal modes of hairy black holes

abstract:
  The behavior of hairy black hole solutions, obtained by the gravitational
decoupling (GD) method, is investigated under scalar perturbations. The
quasinormal mode frequencies of such solutions are regulated by GD hair. The
numerically generated wave solutions are derived for a range of values for the
GD hairy black hole parameters, with higher-frequency modes very sensitive to
them. The results are confronted with the corresponding ones for the
Schwarzschild solution, whose deviations from it demonstrate a unique physical
identification of GD hairy black holes. The method here presented comprises the
first steps towards the obtainment of the observable signature of GD hairy
black holes at ground-based detectors, emitted from coalescing binary systems
of GD hairy black hole mergers in the ringdown phase.


title:
Vibrational properties of graphdiynes as 2D carbon materials beyond
  graphene

abstract:
  Two-dimensional (2D) hybrid sp-sp2 carbon systems are an appealing subject
for science and technology. For these materials, topology and structure
significantly affect electronic and vibrational properties. We investigate here
by periodic density-functional theory (DFT) calculations we here investigate
the Raman and IR spectra of 2D carbon crystals belonging to the family of
graphdiynes (GDYs) and having different structures and topologies. By joining
DFT calculations with a symmetry analysis, we assign the IR and Raman modes in
the spectra of all the investigated systems. On this basis we discuss how the
modulation of the Raman and IR active bands depends on the different
interactions between sp and sp2 domains. The symmetry-based classification
allows identifying the marker bands sensitive to the different peculiar
topologies. These results show the effectiveness of vibrational spectroscopy
for the characterization of new nanostructures, deepening the knowledge on the
subtle interactions that take place in these 2D materials.


title: Cactus Doodles

abstract:
  Cactus doodles are combinatorial/geometric objects that are related to cactus
groups in the same way as knots are related to braids. We define them in terms
of local moves on plane curves, show that they can be obtained from elements of
the cactus group by a "closing" procedure and establish some of their basic
properties.


title: Does (TaSe4)2I really harbor an axionic charge density wave?

abstract:
  A recent experimental work has reported an excess of the non-linear
conductivity in the charge density wave (CDW) sliding mode of the quasi
one-dimensional compound (TaSe4)2I, when a magnetic field is applied
co-linearly to the electric field [Gooth et al., Nature 575, 315 (2019)]. This
result has opened a conceptual approach, where the CDW gap in (TaSe4)2I is
opened between Weyl fermions of opposite chirality with the assumption that
this compound is a Weyl semi-metal in its undistorted high temperature phase.
We report measurements in the sliding state of (TaSe4)2I performed in similar
conditions. We have found no increase in the magnetoconductivity. In our
attempts for understanding this unsettling discrepancy, we stress the specific
nature of the Peierls transition in (TaSe4)2I and the strong electron-phonon
coupling present in this compound. Given the lack of further evidence, we think
that it is premature to assert that (TaSe4)2I is an axionic insulator.


title:
Quasistatic evolution in magnetoelasticity under subcritical coercivity
  assumptions

abstract:
  We study a variational model of magnetoelasticity both in the static and in
the quasistatic setting. The model features a mixed Eulerian-Lagrangian
formulation, as magnetizations are defined on the deformed configuration in the
actual space. The magnetic saturation constraint is formulated in the reference
configuration and involves the Jacobian determinant of deformations. These
belong to the class of possibility discontinuous deformations excluding
cavitation introduced by Barchiesi, Henao and Mora-Corral. We establish a
compactness result which, in particular, yields the convergence of the
compositions of magnetizations with deformations. In the static setting, this
enables us to prove the existence of minimizers by means of classical lower
semicontinuity methods. Our compactness result also allows us to address the
analysis in the quasistatic setting, where we examine rate-independent
evolutions driven by applied loads and boundary conditions. In this case, we
prove the existence of energetic solutions.


title: Multi-Stage Prompting for Knowledgeable Dialogue Generation

abstract:
  Existing knowledge-grounded dialogue systems typically use finetuned versions
of a pretrained language model (LM) and large-scale knowledge bases. These
models typically fail to generalize on topics outside of the knowledge base,
and require maintaining separate potentially large checkpoints each time
finetuning is needed. In this paper, we aim to address these limitations by
leveraging the inherent knowledge stored in the pretrained LM as well as its
powerful generation ability. We propose a multi-stage prompting approach to
generate knowledgeable responses from a single pretrained LM. We first prompt
the LM to generate knowledge based on the dialogue context. Then, we further
prompt it to generate responses based on the dialogue context and the
previously generated knowledge. Results show that our knowledge generator
outperforms the state-of-the-art retrieval-based model by 5.8% when combining
knowledge relevance and correctness. In addition, our multi-stage prompting
outperforms the finetuning-based dialogue model in terms of response
knowledgeability and engagement by up to 10% and 5%, respectively. Furthermore,
we scale our model up to 530 billion parameters and show that larger LMs
improve the generation correctness score by up to 10%, and response relevance,
knowledgeability and engagement by up to 10%. Our code is available at:
https://github.com/NVIDIA/Megatron-LM.


title:
CLUE-AI: A Convolutional Three-stream Anomaly Identification Framework
  for Robot Manipulation

abstract:
  Robot safety has been a prominent research topic in recent years since robots
are more involved in daily tasks. It is crucial to devise the required safety
mechanisms to enable service robots to be aware of and react to anomalies
(i.e., unexpected deviations from intended outcomes) that arise during the
execution of these tasks. Detection and identification of these anomalies is an
essential step towards fulfilling these requirements. Although several
architectures are proposed for anomaly detection, identification is not yet
thoroughly investigated. This task is challenging since indicators may appear
long before anomalies are detected. In this paper, we propose a ConvoLUtional
threE-stream Anomaly Identification (CLUE-AI) framework to address this
problem. The framework fuses visual, auditory and proprioceptive data streams
to identify everyday object manipulation anomalies. A stream of 2D images
gathered through an RGB-D camera placed on the head of the robot is processed
within a self-attention enabled visual stage to capture visual anomaly
indicators. The auditory modality provided by the microphone placed on the
robot's lower torso is processed within a designed convolutional neural network
(CNN) in the auditory stage. Last, the force applied by the gripper and the
gripper state are processed within a CNN to obtain proprioceptive features.
These outputs are then combined with a late fusion scheme. Our novel
three-stream framework design is analyzed on everyday object manipulation tasks
with a Baxter humanoid robot in a semi-structured setting. The results indicate
that the framework achieves an f-score of 94% outperforming the other baselines
in classifying anomalies that arise during runtime.


title:
Existence of solutions to relativistic non-Abelian Chern-Simons-Higgs
  vortex equations on graphs

abstract:
  Let $G=(V,E)$ be a connected finite graph. We study the relativistic
non-Abelian Chern-Simons-Higgs vortex equations on the graph $G$. We establish
an existence result to the relativistic non-Abelian Chern-Simons-Higgs vortex
equations.


title: Accessibility in High Energy Physics: Lessons from the Snowmass Process

abstract:
  Accessibility to participation in the high energy physics community can be
impeded by many barriers. These barriers must be acknowledged and addressed to
make access more equitable in the future. An accessibility survey, the Snowmass
Summer Study attendance survey, and an improved accessibility survey were sent
to the Snowmass2021 community. This paper will summarize and present the
barriers that prevent people from participating in the Snowmass2021
process,recommendations for the various barriers, and discussions of resources
and funding needed to enact these recommendations, based on the results of all
three surveys, along with personal experiences of the community members.


title:
On estimating the structure factor of a point process, with applications
  to hyperuniformity

abstract:
  Hyperuniformity is the study of stationary point processes with a sub-Poisson
variance of the number of points in a large window. In other words, counting
the points of a hyperuniform point process that fall in a given large region
yields a small-variance Monte Carlo estimator of the volume of that region.
Hyperuniform point processes have received a lot of attention in statistical
physics, both for the investigation of natural organized structures and the
synthesis of materials. Unfortunately, rigorously proving that a point process
is hyperuniform is usually difficult. A common practice in statistical physics
and chemistry is to use a few point process samples to estimate a spectral
measure called the structure factor. Plotting the estimated structure factor,
and evaluating its decay around zero, provides a graphical diagnostic of
hyperuniformity. Different applied fields use however different estimators, and
important algorithmic choices proceed from each field's lore. This paper is a
systematic survey and derivation of known or otherwise natural estimators of
the structure factor. In an effort to make investigations of the structure
factor and hyperuniformity systematic and reproducible, we further provide a
Python toolbox strucutre_factor, containing all the estimators and tools that
we discuss.

categories: stat.ME cond-mat.stat-mech math.PR stat.CO


title: Common Coil Dipole for High Field Magnet Design and R&D

abstract:
  The common coil geometry provides an alternate design to the conventional
cosine theta dipoles. It allows a wider range of conductor and magnet
technologies. It also facilitates a low-cost, rapid-turn-around design and R&D
program. Recent studies carried out as a part of the US Magnet Development
Program revealed that at high fields (20 T with 15% operating margin or more),
the common coil design also uses significantly less conductor (particularly
much less HTS), as compared to that in the other designs.


title:
Diffuse flux of ultra-high energy photons from cosmic-ray interactions
  in the disk of the Galaxy and implications for the search for decaying
  super-heavy dark matter

abstract:
  An estimate of the expected photon flux above $10^{17}~$eV from the
interactions of ultra-high energy cosmic rays with the matter in the Galactic
disk is presented. Uncertainties arising from the distribution of the gas in
the disk, the absolute level of the cosmic ray flux, and the composition of the
cosmic rays are taken into account. Within these uncertainties, the integrated
photon flux above $10^{17}~$eV is, averaged out over Galactic latitude less
than $5^\circ$, between $\simeq
3.2{\times}10^{-2}~$km$^{-2}~$yr$^{-1}~$sr$^{-1}$ and $\simeq
8.7{\times}10^{-2}~$km$^{-2}~$yr$^{-1}~$sr$^{-1}$. The all-sky average value
amounts to $\simeq 1.1{\times}10^{-2}~$km$^{-2}~$yr$^{-1}~$sr$^{-1}$ above
$10^{17}~$eV and decreases roughly as $E^{-2}$, making this diffuse flux the
dominant one from cosmic-ray interactions for energy thresholds between
$10^{17}~$eV and $10^{18}$~eV. Compared to the current sensitivities of
detection techniques, a gain between two and three orders of magnitude in
exposure is required for a detection below $\simeq 10^{18}$~eV. The
implications for searches for photon fluxes from the Galactic center that would
be indicative of the decay of super-heavy dark matter particles are discussed,
as the photon flux presented in this study can be considered as a floor below
which other signals would be overwhelmed.


title: Flux Qubit Based on Hybrid Ferromagnetic-Superconducting Device

abstract:
  We propose a realization of flux qubit based on the hybrid
ferromagnetic-superconducting device where the flux bias is induced purely by
vector potential of the vanishing magnetic field. We support our conclusions
with theoretical analysis and self-consistent three-dimensional simulations for
material specific parameters.


title:
Behaviour in social media for floods and heat waves in disaster response
  via Artificial Intelligence

abstract:
  This paper analyses social media data in multiple disaster-related
collections of floods and heat waves in the UK. The proposed method uses
machine learning classifiers based on deep bidirectional neural networks
trained on benchmark datasets of disaster responses and extreme events. The
resulting models are applied to perform sentiment and qualitative analysis of
inferred topics in text data. We further analyse a set of behavioural
indicators and match them with climate variables via decoding synoptical
records to analyse thermal comfort. We highlight the advantages of aligning
behavioural indicators along with climate variables to provide with additional
valuable information to be considered especially in different phases of a
disaster and applicable to extreme weather periods. The positiveness of
messages is around 8% for disaster, 1% for disaster and medical response, 7%
for disaster and humanitarian related messages. This shows the reliability of
such data for our case studies. We show the transferability of this approach to
be applied to any social media data collection.


title:
Structural, elastic and optoelectronic properties of inorganic cubic
  FrBX3 (B = Ge, Sn; X = Cl, Br, I) perovskite: the density functional theory
  approach

abstract:
  Inorganic metal-halide cubic perovskite semiconductors have become more
popular in industrial applications of photovoltaic and optoelectronic devices.
Among various perovskites, lead-free materials are currently most explored due
to their non-toxic effect on the environment. In this study, the structural,
electronic, optical, and mechanical properties of lead-free cubic perovskite
materials FrBX3 (B = Ge, Sn; X = Cl, Br, I) are investigated through
first-principles density-functional theory (DFT) calculations. These materials
are found to exhibit semiconducting behavior with direct bandgap energy and
mechanical phase stability. The observed variation in the bandgap is explained
based on the substitutions of cations and anions sitting over B and X-sites of
the FrBX3 compounds. The high absorption coefficient, low reflectivity, and
high optical conductivity make these materials suitable for photovoltaic and
other optoelectronic device applications. It is observed that the material
containing Ge (germanium) in the B-site has higher optical absorption and
conductivity than Sn containing materials. A systematic analysis of the
electronic, optical, and mechanical properties suggests that among all the
perovskite materials, FrGeI3 would be a potential candidate for optoelectronic
applications. The radioactive element Fr-containing perovskite FrGeI3 may have
applications in nuclear medicine and diagnosis such as X-ray imaging
technology.


title:
Nuclear Magnetic Resonance for Arbitrary Spin Values in the Rotating
  Wave Approximation

abstract:
  In order to probe the transitions of a nuclear spin $s$ from one of its
substate quantum numbers $m$ to another substate $m'$, the experimenter applies
a magnetic field ${\bm B}_0$ in some particular direction, such along $\hat{\bm
z}$, and then applies an weaker field ${\bm B}_1(t)$ that is oscillatory in
time with the angular frequency $\omega$, and is normally perpendicular to
${\bm B}_0$, such as ${\bm B}_1(t)=B_1\hat{\bm x}\cos(\omega t)$. In the
rotating wave approximation, ${\bm B}_1(t)=B_1[\hat{\bm x}\cos(\omega
t)+\hat{\bm y}\sin(\omega t)]$. Although this problem is solved for spin
$\frac{1}{2}$ in every quantum mechanics textbook, for the general spin $s$
case, its general solution has been published only for the overall probability
of a transition between the states, but the time dependence of the probability
of finding the nucleus in each of the substates has not previously been
published. Here we present an elementary method to solve this problem exactly,
and present figures for the time dependencies of the various substates states
for a variety of initial substate probabilities for a variety of $s$ values. We
found a new result: unlike the $s=\frac{1}{2}$ case, for which if the initial
probability of finding the particle in one of the substates was 1, and the time
dependence of the probabilities of each of the substates oscillates between 0
and 1, for higher spin values, the time dependencies of the probabilities
finding the particle in each of its substates, which periodic, is considerably
more complicated.


title:
Ultrafast spin-charge conversion at SnBi$_2$Te$_4$/Co topological
  insulator interfaces probed by terahertz emission spectroscopy

abstract:
  Spin-to-charge conversion (SCC) involving topological surface states (TSS) is
one of the most promising routes for highly efficient spintronic devices for
terahertz (THz) emission. Here, the THz generation generally occurs mainly via
SCC consisting in efficient dynamical spin injection into spin-locked TSS. In
this work, we demonstrate sizable THz emission from a nanometric thick
topological insultator (TI)/ferromagnetic junction - SnBi$_2$Te$_4$/Co -
specifically designed to avoid bulk band crossing with the TSS at the Fermi
level, unlike its parent material Bi$_2$Te$_3$. THz emission time domain
spectroscopy (TDS) is used to indicate the TSS contribution to the SCC by
investigating the TI thickness and angular dependence of the THz emission. This
work illustrates THz emission TDS as a powerful tool alongside angular resolved
photoemission spectroscopy (ARPES) methods to investigate the interfacial
spintronic properties of TI/ferromagnet bilayers.