title: Reflexive tactics for algebra, revisited

abstract:
  Computational reflection allows us to turn verified decision procedures into
efficient automated reasoning tools in proof assistants. The typical
applications of such methodology include mathematical structures that have
decidable theory fragments, e.g., equational theories of commutative rings and
lattices. However, such existing tools are known not to cooperate with packed
classes, a methodology to define mathematical structures in dependent type
theory, that allows for the sharing of vocabulary across the inheritance
hierarchy. Additionally, such tools do not support homomorphisms whose domain
and codomain types may differ. This paper demonstrates how to implement
reflexive tactics that support packed classes and homomorphisms. As
applications of our methodology, we adapt the ring and field tactics of Coq to
the commutative ring and field structures of the Mathematical Components
library, and apply the resulting tactics to the formal proof of the
irrationality of $\zeta(3)$ by Chyzak, Mahboubi, and Sibut-Pinote, to bring
more proof automation.


title:
Geometric decomposition of entropy production into excess, housekeeping
  and coupling parts

abstract:
  For a generic overdamped Langevin dynamics driven out of equilibrium by both
time-dependent and nonconservative forces, the entropy production rate can be
decomposed into two positive terms, termed excess and housekeeping entropy.
However, this decomposition is not unique: There are two distinct
decompositions, one due to Hatano and Sasa, the other one due to Maes and
Neto\v{c}ny. Here, we establish the connection between these two decompositions
and provide a simple, geometric interpretation. We show that this leads to a
decomposition of the entropy production rate into three positive terms, which
we call excess, housekeeping and coupling part, respectively. The coupling part
characterizes the interplay between the time-dependent and nonconservative
forces. We also derive thermodynamic uncertainty relations for the excess and
housekeeping entropy in both the Hatano-Sasa and Maes-Neto\v{c}ny-decomposition
and show that all quantities obey integral fluctuation theorems. We illustrate
the decomposition into three terms using a solvable example of a dragged
particle in a nonconservative force field.


title: Imitation Learning by State-Only Distribution Matching

abstract:
  Imitation Learning from observation describes policy learning in a similar
way to human learning. An agent's policy is trained by observing an expert
performing a task. While many state-only imitation learning approaches are
based on adversarial imitation learning, one main drawback is that adversarial
training is often unstable and lacks a reliable convergence estimator. If the
true environment reward is unknown and cannot be used to select the
best-performing model, this can result in bad real-world policy performance. We
propose a non-adversarial learning-from-observations approach, together with an
interpretable convergence and performance metric.
  Our training objective minimizes the Kulback-Leibler divergence (KLD) between
the policy and expert state transition trajectories which can be optimized in a
non-adversarial fashion. Such methods demonstrate improved robustness when
learned density models guide the optimization. We further improve the sample
efficiency by rewriting the KLD minimization as the Soft Actor Critic objective
based on a modified reward using additional density models that estimate the
environment's forward and backward dynamics. Finally, we evaluate the
effectiveness of our approach on well-known continuous control environments and
show state-of-the-art performance while having a reliable performance estimator
compared to several recent learning-from-observation methods.


title:
Who to Watch Next: Two-side Interactive Networks for Live Broadcast
  Recommendation

abstract:
  With the prevalence of live broadcast business nowadays, a new type of
recommendation service, called live broadcast recommendation, is widely used in
many mobile e-commerce Apps. Different from classical item recommendation, live
broadcast recommendation is to automatically recommend user anchors instead of
items considering the interactions among triple-objects (i.e., users, anchors,
items) rather than binary interactions between users and items. Existing
methods based on binary objects, ranging from early matrix factorization to
recently emerged deep learning, obtain objects' embeddings by mapping from
pre-existing features. Directly applying these techniques would lead to limited
performance, as they are failing to encode collaborative signals among
triple-objects. In this paper, we propose a novel TWo-side Interactive NetworkS
(TWINS) for live broadcast recommendation. In order to fully use both static
and dynamic information on user and anchor sides, we combine a product-based
neural network with a recurrent neural network to learn the embedding of each
object. In addition, instead of directly measuring the similarity, TWINS
effectively injects the collaborative effects into the embedding process in an
explicit manner by modeling interactive patterns between the user's browsing
history and the anchor's broadcast history in both item and anchor aspects.
Furthermore, we design a novel co-retrieval technique to select key items among
massive historic records efficiently. Offline experiments on real large-scale
data show the superior performance of the proposed TWINS, compared to
representative methods; and further results of online experiments on Diantao
App show that TWINS gains average performance improvement of around 8% on ACTR
metric, 3% on UCTR metric, 3.5% on UCVR metric.


title: The null bootstrap

abstract:
  A stable physical system has an energy spectrum that is bounded from below.
For quantum systems, the dangerous states of unboundedly low energies should
decouple and become null. We propose the principle of nullness and apply it to
the bootstrap study of Hermitian and non-Hermitian anharmonic oscillators.

categories: hep-th cond-mat.stat-mech cond-mat.str-el hep-lat quant-ph


title:
Effect of solvent polarization on electric double layer of a charged
  soft surface in an electrolyte solution

abstract:
  We study the electric double layer near a charged soft surface by using a
mean-field approach including non-uniform size effect and solvent polarization.
Based on a free energy model, electrostatic potential and number densities of
water and ions are obtained numerically by solving coupled differential
equations including the Poisson's equation. We find that the solvent
polarization significantly affects electrostatic potential, ion number
densities and permittivity in an electrolyte solution near a charged soft
surface. We prove that the consideration of the solvent polarization increases
the magnitude of Donnan potential. We also demonstrate that within the fixed
charge layer the number densities of counterions and coions are slightly
smaller than when the solvent polarization is not considered. An increase in
the ionic size enhances the influence of solvent polarization on the
electrostatic properties within the fixed charged layer of charged soft
surface. In the region of high surface-charge-density, the permittivity and the
number density of water dipoles considerably decrease due to water
polarization.


title:
Searching for extra Higgs bosons via $pp\to H,A\to \tau\mu, \tau\tau $
  at the Large Hadron Collider

abstract:
  We follow the heavy Higgs searches at the Large Hadron Collider (LHC) for $H
\to \tau \mu$ by CMS, and $H\to\tau\tau$ by ATLAS and CMS, to study the
prospects of discovering the extra Higgs states in $pp \to H,A \to \tau \mu,
\tau\tau$ with $e\mu + {E}^{\rm miss}_T$ and $j_{\tau} \mu + {E}^{\rm miss}_T $
final states, where $j_{\tau} = \pi \, , \rho\, , a_1$ and ${E}^{miss}_T$ is
missing transverse energy. In a general two Higgs doublet model framework
without $Z_2$ symmetry, extra Yukawa couplings $\rho_{\tau\tau}$ and
$\rho_{\tau\mu}$ can drive $H,A \to \tau\tau$ and $\tau\mu$ channels at hadron
colliders, following gluon-gluon fusion production with extra $\rho_{tt}$
couplings. The light Higgs boson $h(125)$ is found to resemble closely the
Standard Model Higgs boson. In the alignment limit of $\cos\gamma \to 0$ for
$h$--$H$ mixing, flavor changing neutral Higgs couplings such as $h \to
\tau\mu$ are naturally suppressed, but the couplings of the heavier $H$ is
optimized by $\sin \gamma \to 1$. We define various signal regions for $H,A \to
\tau\mu$ and $\tau \tau$, and evaluate physics backgrounds from dominant
processes with realistic acceptance cuts and tagging efficiencies. Two
different studies are presented: we first perform a parton level study without
any hadronization and with minimal detector smearing; we then include
hadronization using PYTHIA 8.2 and fast detector simulation using DELPHES to
give event level simulation. Results for $\sqrt{s}= 13$ TeV appear promising,
which we extend further to $\sqrt{s} = 14$ TeV for the High Luminosity LHC.


title: Scenario-Assisted Deep Reinforcement Learning

abstract:
  Deep reinforcement learning has proven remarkably useful in training agents
from unstructured data. However, the opacity of the produced agents makes it
difficult to ensure that they adhere to various requirements posed by human
engineers. In this work-in-progress report, we propose a technique for
enhancing the reinforcement learning training process (specifically, its reward
calculation), in a way that allows human engineers to directly contribute their
expert knowledge, making the agent under training more likely to comply with
various relevant constraints. Moreover, our proposed approach allows
formulating these constraints using advanced model engineering techniques, such
as scenario-based modeling. This mix of black-box learning-based tools with
classical modeling approaches could produce systems that are effective and
efficient, but are also more transparent and maintainable. We evaluated our
technique using a case-study from the domain of internet congestion control,
obtaining promising results.


title:
Electron-Electron Interaction and Weak Antilocalization Effect in a
  Transition Metal Dichalcogenide Superconductor

abstract:
  In disordered transition-metal dichalcogenide (TMD) superconductor, both the
strong spin-orbit coupling (SOC) and disorder show remarkable effects on
superconductivity. However, the features of SOC and disorder were rarely
detected directly. Here we report the quantum transport behaviors arising from
the interplay of SOC and disorder in the TMD superconductor 1T-NbSeTe. Before
entering the superconducting state, the single crystal at low temperature shows
a resistivity upturn, which is T1/2 dependent and insensitive to the applied
magnetic fields. The magnetoresistance (MR) at low temperatures shows a H1/2
dependence at high magnetic fields. The characteristics are in good agreement
with the electron-electron interaction (EEI) in a disordered conductor. In
addition, the upturn changes and MR at low magnetic fields suggest the
contribution of weak antilocalization (WAL) effect arising from the strong SOC
in the material. Moreover, the quantitative analyses of the transport features
in different samples imply anomalous disorder-enhanced superconductivity that
needs to be further understood. The results reveal the disorder enhanced EEI
and the strong SOC induced WAL effect in 1T-NbSeTe, which illustrate the
resistivity minimum in the widely studied doped superconductors. The work also
provides insights into the disorder effect on the superconductivity.


title: Semiparametric Bayesian Estimation of Dynamic Discrete Choice Models

abstract:
  We propose a tractable semiparametric estimation method for dynamic discrete
choice models. The distribution of additive utility shocks is modeled by
location-scale mixtures of extreme value distributions with varying numbers of
mixture components. Our approach exploits the analytical tractability of
extreme value distributions and the flexibility of the location-scale mixtures.
We implement the Bayesian approach to inference using Hamiltonian Monte Carlo
and an approximately optimal reversible jump algorithm from Norets (2021). For
binary dynamic choice model, our approach delivers estimation results that are
consistent with the previous literature. We also apply the proposed method to
multinomial choice models, for which previous literature does not provide
tractable estimation methods in general settings without distributional
assumptions on the utility shocks. We develop theoretical results on
approximations by location-scale mixtures in an appropriate distance and
posterior concentration of the set identified utility parameters and the
distribution of shocks in the model.


title:
Efficient Construction of Reversible Transducers from Regular Transducer
  Expressions

abstract:
  The class of regular transformations has several equivalent characterizations
such as functional MSO transductions, deterministic two-way transducers,
streaming string transducers, as well as regular transducer expressions (RTE).
  For algorithmic applications, it is very common and useful to transform a
specification, here, an RTE, to a machine, here, a transducer. In this paper,
we give an efficient construction of a two-way reversible transducer (2RFT)
equivalent to a given RTE. 2RFTs are a well behaved class of transducers which
are deterministic and co-deterministic (hence allows evaluation in linear time
\wrt the input word), and where composition has only polynomial complexity.
  We show that, for full RTE, the constructed 2RFT has size doubly exponential
in the size of the expression, while, if the RTE does not use Hadamard product
or chained-star, the constructed
  2RFT has size exponential in the size of the RTE.


title:
Electron Beam Weibel Instability in the Collisionless Shock with Low
  Mach Number

abstract:
  The electron beam Weibel instability in the electrostatic collisionless shock
is studied via particle in cell simulation. When the non-relativistic incoming
plasmas collide with cold dilute plasmas, an electrostatic shock forms near the
interface. Following that, a filamentary out-of-plane magnetic field is formed
as a result of the Weibel instability. It is demonstrated that the anisotropy
of incoming hot electrons is insufficient to trigger the Weibel instability.
And the Weibel instability is excited by cold electrons in dilute plasmas.
After being accelerated to relativistic velocities by the shock electric field
into the dense plasmas, electrons in the dilute plasma have considerable
anisotropy and can trigger the Weibel instability


title: A Network Approach to Atomic Spectra

abstract:
  Network science provides a universal framework for modeling complex systems,
contrasting the reductionist approach generally adopted in physics. In a
prototypical study, we utilize network models created from spectroscopic data
of atoms to predict microscopic properties of the underlying physical system.
For simple atoms such as helium, an a posteriori inspection of spectroscopic
network communities reveals the emergence of quantum numbers and symmetries.
For more complex atoms such as thorium, finer network hierarchies suggest
additional microscopic symmetries or configurations. Link prediction yields a
quantitative ranking of yet unknown atomic transitions, offering opportunities
to discover new spectral lines in a well-controlled manner. Our work promotes a
genuine bi-directional exchange of methodology between network science and
physics, and presents new perspectives for the study of atomic spectra.


title:
Two-dimensional ferroelectricity induced by octahedral rotation
  distortion in perovskite oxides

abstract:
  Two-dimensional (2D) ferroelectricity has attracted extensive attention since
its discovery in the monolayers of van der Waals materials. Here we show that
2D ferroelectricity induced by octahedral rotation distortion is widely present
in the perovskite bilayer system through first-principles calculations. The
perovskite tolerance factor plays a crucial role in the lattice dynamics and
ground-state structure of the perovskite monolayers and bilayers, thus
providing an important indicator for screening this hybrid improper
ferroelectricity. Generally, the ferroelectric switching via an orthorhombic
twin state has the lowest energy barrier. Epitaxial strain can effectively tune
the ferroelectric polarization and ferroelectric switching by changing the
amplitude of octahedral rotation and tilt distortion. The increasing
compressive strain causes a polar to nonpolar phase transition by suppressing
the tilt distortion. The cooperative effect of octahedral distortion at the
interface with the substrate can reduce the energy barrier of the reversing
rotation mode and can even change the lowest-energy ferroelectric switching
path.


title: Multistage Postional Games

abstract:
  We initiate the study of a new variant of the Maker-Breaker positional game,
which we call multistage game. Given a hypergraph
$\mathcal{H}=(\mathcal{X},\mathcal{F})$ and a bias $b \ge 1$, the $(1:b)$
multistage Maker-Breaker game on $\mathcal{H}$ is played in several stages as
follows. Each stage is played as a usual $(1:b)$ Maker-Breaker game, until all
the elements of the board get claimed by one of the players, with the first
stage being played on $\mathcal{H}$. In every subsequent stage, the game is
played on the board reduced to the elements that Maker claimed in the previous
stage, and with the winning sets reduced to those fully contained in the new
board. The game proceeds until no winning sets remain, and the goal of Maker is
to prolong the duration of the game for as many stages as possible. In this
paper we estimate the maximum duration of the $(1:b)$ multistage Maker-Breaker
game, for biases $b$ subpolynomial in $n$, for some standard graph games played
on the edge set of $K_n$: the connectivity game, the Hamilton cycle game, the
non-$k$-colorability game, the pancyclicity game and the $H$-game. While the
first three games exhibit a probabilistic intuition, it turns out that the last
two games fail to do so.


title:
Securing Smart Grids Through an Incentive Mechanism for Blockchain-Based
  Data Sharing

abstract:
  Smart grids leverage the data collected from smart meters to make important
operational decisions. However, they are vulnerable to False Data Injection
(FDI) attacks in which an attacker manipulates meter data to disrupt the grid
operations. Existing works on FDI are based on a simple threat model in which a
single grid operator has access to all the data, and only some meters can be
compromised.
  Our goal is to secure smart grids against FDI under a realistic threat model.
To this end, we present a threat model in which there are multiple operators,
each with a partial view of the grid, and each can be fully compromised. An
effective defense against FDI in this setting is to share data between the
operators. However, the main challenge here is to incentivize data sharing. We
address this by proposing an incentive mechanism that rewards operators for
uploading data, but penalizes them if the data is missing or anomalous. We
derive formal conditions under which our incentive mechanism is provably secure
against operators who withhold or distort measurement data for profit. We then
implement the data sharing solution on a private blockchain, introducing
several optimizations that overcome the inherent performance limitations of the
blockchain. Finally, we conduct an experimental evaluation that demonstrates
that our implementation has practical performance.


title:
First-principles theory of extending the spin qubit coherence time in
  hexagonal boron nitride

abstract:
  Negatively charged boron vacancies (VB-) in hexagonal boron nitride (h-BN)
are a rapidly developing qubit platform in two-dimensional materials for
solid-state quantum applications. However, their spin coherence time (T2) is
very short, limited to a few microseconds owing to the inherently dense nuclear
spin bath of the h-BN host. As the coherence time is one of the most
fundamental properties of spin qubits, the short T2 time of VB- could
significantly limit its potential as a promising spin qubit candidate. In this
study, we theoretically proposed two materials engineering methods, which can
substantially extend the T2 time of the VB- spin by four times more than its
intrinsic T2. We performed quantum many-body computations by combining density
functional theory and cluster correlation expansion and showed that replacing
all the boron atoms in h-BN with the 10B isotope leads to the coherence
enhancement of the VB- spin by a factor of three. In addition, the T2 time of
the VB- can be enhanced by a factor of 1.3 by inducing a curvature around VB-.
Herein, we elucidate that the curvature-induced inhomogeneous strain creates
spatially varying quadrupole nuclear interactions, which effectively suppress
the nuclear spin flip-flop dynamics in the bath. Importantly, we find that the
combination of isotopic enrichment and strain engineering can maximize the VB-
T2, yielding 207.2 and 161.9 {\mu}s for single- and multi-layer h-10BN,
respectively. Furthermore, our results can be applied to any spin qubit in
h-BN, strengthening their potential as material platforms to realize
high-precision quantum sensors, quantum spin registers, and atomically thin
quantum magnets.


title: Gradient Methods Provably Converge to Non-Robust Networks

abstract:
  Despite a great deal of research, it is still unclear why neural networks are
so susceptible to adversarial examples. In this work, we identify natural
settings where depth-$2$ ReLU networks trained with gradient flow are provably
non-robust (susceptible to small adversarial $\ell_2$-perturbations), even when
robust networks that classify the training dataset correctly exist. Perhaps
surprisingly, we show that the well-known implicit bias towards margin
maximization induces bias towards non-robust networks, by proving that every
network which satisfies the KKT conditions of the max-margin problem is
non-robust.


title:
MBCT: Tree-Based Feature-Aware Binning for Individual Uncertainty
  Calibration

abstract:
  Most machine learning classifiers only concern classification accuracy, while
certain applications (such as medical diagnosis, meteorological forecasting,
and computation advertising) require the model to predict the true probability,
known as a calibrated estimate. In previous work, researchers have developed
several calibration methods to post-process the outputs of a predictor to
obtain calibrated values, such as binning and scaling methods. Compared with
scaling, binning methods are shown to have distribution-free theoretical
guarantees, which motivates us to prefer binning methods for calibration.
However, we notice that existing binning methods have several drawbacks: (a)
the binning scheme only considers the original prediction values, thus limiting
the calibration performance; and (b) the binning approach is non-individual,
mapping multiple samples in a bin to the same value, and thus is not suitable
for order-sensitive applications. In this paper, we propose a feature-aware
binning framework, called Multiple Boosting Calibration Trees (MBCT), along
with a multi-view calibration loss to tackle the above issues. Our MBCT
optimizes the binning scheme by the tree structures of features, and adopts a
linear function in a tree node to achieve individual calibration. Our MBCT is
non-monotonic, and has the potential to improve order accuracy, due to its
learnable binning scheme and the individual calibration. We conduct
comprehensive experiments on three datasets in different fields. Results show
that our method outperforms all competing models in terms of both calibration
error and order accuracy. We also conduct simulation experiments, justifying
that the proposed multi-view calibration loss is a better metric in modeling
calibration error.


title: Cartesian Tree Subsequence Matching

abstract:
  Park et al. [TCS 2020] observed that the similarity between two (numerical)
strings can be captured by the Cartesian trees: The Cartesian tree of a string
is a binary tree recursively constructed by picking up the smallest value of
the string as the root of the tree. Two strings of equal length are said to
Cartesian-tree match if their Cartesian trees are isomorphic. Park et al. [TCS
2020] introduced the following Cartesian tree substring matching (CTMStr)
problem: Given a text string $T$ of length $n$ and a pattern string of length
$m$, find every consecutive substring $S = T[i..j]$ of a text string $T$ such
that $S$ and $P$ Cartesian-tree match. They showed how to solve this problem in
$\tilde{O}(n+m)$ time. In this paper, we introduce the Cartesian tree
subsequence matching (CTMSeq) problem, that asks to find every minimal
substring $S = T[i..j]$ of $T$ such that $S$ contains a subsequence $S'$ which
Cartesian-tree matches $P$. We prove that the CTMSeq problem can be solved
efficiently, in $O(m n p(n))$ time, where $p(n)$ denotes the update/query time
for dynamic predecessor queries. By using a suitable dynamic predecessor data
structure, we obtain $O(mn \log \log n)$-time and $O(n \log m)$-space solution
for CTMSeq. This contrasts CTMSeq with closely related order-preserving
subsequence matching (OPMSeq) which was shown to be NP-hard by Bose et al. [IPL
1998].


title: pNLP-Mixer: an Efficient all-MLP Architecture for Language

abstract:
  Large pre-trained language models drastically changed the natural language
processing(NLP) landscape. Nowadays, they represent the go-to framework to
tackle diverse NLP tasks, even with a limited number of annotations. However,
using those models in production, either in the cloud or at the edge, remains a
challenge due to the memory footprint and/or inference costs. As an
alternative, recent work on efficient NLP has shown that small weight-efficient
models can reach competitive performance at a fraction of the costs. Here, we
introduce pNLP-Mixer, an embbedding-free model based on the MLP-Mixer
architecture that achieves high weight-efficiency thanks to a novel
linguistically informed projection layer. We evaluate our model on two
multi-lingual semantic parsing datasets, MTOP and multiATIS. On MTOP our
pNLP-Mixer almost matches the performance of mBERT, which has 38 times more
parameters, and outperforms the state-of-the-art of tiny models (pQRNN) with 3
times fewer parameters. On a long-sequence classification task (Hyperpartisan)
our pNLP-Mixer without pretraining outperforms RoBERTa, which has 100 times
more parameters, demonstrating the potential of this architecture.


title:
Accumulation and alignment of elongated gyrotactic swimmers in
  turbulence

abstract:
  We study the dynamics of gyrotactic swimmers in turbulence, whose orientation
is governed by gravitational torque and local fluid velocity gradient. The
gyrotaxis strength is measured by the ratio of the Kolmogorov time scale to the
reorientation time scale due to gravity, and a large value of this ratio means
the gyrotaxis is strong. By means of direct numerical simulations, we
investigate the effects of swimming velocity and gyrotactic stability on
spatial accumulation and alignment. Three-dimensional Vorono{\"\i} analysis is
used to study the spatial distribution and time evolution of the particle
concentration. We study spatial distribution by examing the overall
preferential sampling and where clusters and voids (subsets of particles that
have small and large Vorono{\"\i} volumes respectively) form. Compared with the
ensemble particles, the preferential sampling of clusters and voids is found to
be more pronounced. The clustering of fast swimmers lasts much longer than
slower swimmers when the gyrotaxis is strong and intermediate, but an opposite
trend emerges when the gyrotaxis is weak. In addition, we study the
preferential alignment with the Lagrangian stretching direction, with which
passive slender rods have been known to align. We show that the Lagrangian
alignment is reduced by the swimming velocity when the gyrotaxis is weak, while
the Lagrangian alignment is enhanced for the regime in which gyrotaxis is
strong.


title: On models of algebraic group actions

abstract:
  We show that every action of a smooth algebraic group on a variety admits a
normal projective model. Along the way, we present new proofs of some basic
results on algebraic transformation groups, including Weil's regularization
theorem.


title:
Magnetic helicity and energy of emerging solar active regions and their
  erruptivity

abstract:
  Aims. We investigate the role of the accumulation of both magnetic helicity
and magnetic energy in the generation of coronal mass ejections (CMEs) from
emerging solar active regions (ARs). Methods. Using vector magnetic field data
obtained by the Helioseismic and Magnetic Imager on board the Solar Dynamics
Observatory, we calculate the magnetic helicity and magnetic energy injection
rates as well as the resulting accumulated budgets in 52 emerging ARs from the
start time of magnetic flux emergence until they reach heliographic longitude
of 45$^{\circ}$ West (W45). Results. Seven of the ARs produced CMEs while 45
did not. In a statistical sense, the eruptive ARs accumulate larger budgets of
both magnetic helicity and energy than the noneruptive ones over intervals that
start from flux emergence start time and end (i) at the end of flux emergence
phase, and (ii) when the AR produces its first CME or crosses W45, whichever
happens first. We found magnetic helicity and energy thresholds of $9 \times
10^{41}$ Mx$^2$ and $2 \times 10^{32}$ erg, respectively, which, if crossed,
ARs are likely to erupt. The segregation, in terms of accumulated magnetic
helicity and energy budgets, of the eruptive ARs from the noneruptive ones is
violated in one case when an AR erupts early in its emergence phase and in six
cases with noneruptive ARs exhibiting large magnetic helicity and energy
budgets. Decay index calculations may indicate that these ARs did not erupt
because the overlying magnetic field provided stronger or more extended
confinement than in eruptive ARs. Conclusions. Our results indicate that
emerging ARs tend to produce CMEs when they accumulate significant budgets of
both magnetic helicity and energy. Any study of their eruptive potential should
place magnetic helicity on equal footing with magnetic energy.


title:
Nuclear Spectroscopy with Heavy Ion Nucleon Knockout and (p,2p)
  Reactions

abstract:
  Knockout reactions with heavy ion targets in inverse kinematics, as well as
"quasi-free" (p,2p) and (p,pn) reactions are useful tools for nuclear
spectroscopy. We report calculations on \textit{ab-initio} many-body
wavefunctions based on the no-core shell model to study the nucleon removal
reactions in light nuclei, including beryllium, carbon, and oxygen isotopic
chains, and explore the importance of using an \textit{ab-initio} method. Our
study helps clarifying how the extraction of spectroscopic factors from the
experiments depend on the details of the many-body wavefunctions being probed.
We show that recent advances with the ab-initio method can provide more
insights on the spectroscopy information extracted from experiments.


title:
Mass diffusion in multilayer systems: an electrical analogue modelling
  approach

abstract:
  We develop a lumped parameter model to describe and predict the mass release
of (absorption from) an arbitrary shaped body of any dimension in a large
environment. Through the one-to-one analogy between diffusion-dominated mass
transfer systems and electrical circuits we provide exact solutions in terms of
averaged concentrations and mass released. An estimate of the release and
characteristic time is also given. The proposed electrical analogue approach
allows the definition of a time constant and lets an easy extension to a
multi-layer and multi-phase cases. The simulation results are compared with
those obtained from the solution of the corresponding analytical and numerical
solutions, showing a good agreement.


title: Geometric phases of light: insights from fibre bundle theory

abstract:
  Geometric phases are ubiquitous in physics; they act as memories of the
transformation of a physical system. In optics, the most prominent examples are
the Pancharatnam-Berry phase and the spin-redirection phase. Additional
geometric phases of light have recently been discovered, following
technological advances in phase and polarization structuring. Fibre bundle
theory shows how a state transformation leads to a geometric phase. Despite its
fundamental importance, this framework has been overlooked in optics. In this
colloquium, we interpret geometric phases of light in terms of fibre bundles.
We show that fibre bundle theory does not only sheds light on the origin of
geometric phases of light, but also lays the foundations for the exploration of
high dimensional state spaces, with implications for topological photonics and
quantum communications.

categories: physics.optics math-ph math.MP physics.app-ph


title: Generalized Strategic Classification and the Case of Aligned Incentives

abstract:
  Predicative machine learning models are frequently being used by companies,
institutes and organizations to make choices about humans. Strategic
classification studies learning in settings where self-interested users can
strategically modify their features to obtain favorable predictive outcomes. A
key working assumption, however, is that 'favorable' always means 'positive';
this may be appropriate in some applications (e.g., loan approval, university
admissions and hiring), but reduces to a fairly narrow view what user interests
can be. In this work we argue for a broader perspective on what accounts for
strategic user behavior, and propose and study a flexible model of generalized
strategic classification. Our generalized model subsumes most current models,
but includes other novel settings; among these, we identify and target one
intriguing sub-class of problems in which the interests of users and the system
are aligned. For this cooperative setting, we provide an in-depth analysis, and
propose a practical learning approach that is effective and efficient. We
compare our approach to existing learning methods and show its statistical and
optimization benefits. Returning to our fully generalized model, we show how
our results and approach can extend to the most general case. We conclude with
a set of experiments that empirically demonstrate the utility of our approach.


title:
House Price Valuation Model Based on Geographically Neural Network
  Weighted Regression: The Case Study of Shenzhen, China

abstract:
  Confronted with the spatial heterogeneity of real estate market, some
traditional research utilized Geographically Weighted Regression (GWR) to
estimate the house price. However, its kernel function is non-linear, elusive,
and complex to opt bandwidth, the predictive power could also be improved.
Consequently, a novel technique, Geographical Neural Network Weighted
Regression (GNNWR), has been applied to improve the accuracy of real estate
appraisal with the help of neural networks. Based on Shenzhen house price
dataset, this work conspicuously captures the weight distribution of different
variants at Shenzhen real estate market, which GWR is difficult to materialize.
Moreover, we focus on the performance of GNNWR, verify its robustness and
superiority, refine the experiment process with 10-fold cross-validation,
extend its application area from natural to socioeconomic geospatial data. It's
a practical and trenchant way to assess house price, and we demonstrate the
effectiveness of GNNWR on a complex socioeconomic dataset.


title:
Cost-effective Framework for Gradual Domain Adaptation with
  Multifidelity

abstract:
  In domain adaptation, when there is a large distance between the source and
target domains, the prediction performance will degrade. Gradual domain
adaptation is one of the solutions to such an issue, assuming that we have
access to intermediate domains, which shift gradually from the source to target
domains. In previous works, it was assumed that the number of samples in the
intermediate domains is sufficiently large; hence, self-training was possible
without the need for labeled data. If access to an intermediate domain is
restricted, self-training will fail. Practically, the cost of samples in
intermediate domains will vary, and it is natural to consider that the closer
an intermediate domain is to the target domain, the higher the cost of
obtaining samples from the intermediate domain is. To solve the trade-off
between cost and accuracy, we propose a framework that combines multifidelity
and active domain adaptation. The effectiveness of the proposed method is
evaluated by experiments with both artificial and real-world datasets. Codes
are available at https://github.com/ssgw320/gdamf.


title: How-To compute EPRL spin foam amplitudes

abstract:
  Spin foam theory is a concrete framework for quantum gravity where numerical
calculations of transition amplitudes are possible. Recently, the field became
very active, but the entry barrier is steep, mainly because of its unusual
language and notions scattered around the literature. This paper is a
pedagogical guide to spin foam transition amplitude calculations. We show how
to write an EPRL-FK transition amplitude, from the definition of the 2-complex
to its numerical implementation using \texttt{sl2cfoam-next}. We guide the
reader using an explicit example balancing mathematical rigor with a practical
approach. We discuss the advantages and disadvantages of this approach and
provide a novel look at a recently proposed approximation scheme.


title:
Molecular-scale Integration of Multi-modal Sensing and Neuromorphic
  Computing with Organic Electrochemical Transistors

abstract:
  Abstract: Bionic learning with fused sensing, memory and processing functions
outperforms artificial neural networks running on silicon chips in terms of
efficiency and footprint. However, digital hardware implementation of bionic
learning suffers from device heterogeneity in sensors and processing cores,
which incurs large hardware, energy and time overheads. Here, we present a
universal solution to simultaneously perform multi-modal sensing, memory and
processing using organic electrochemical transistors with designed architecture
and tailored channel morphology, selective ion injection into the
crystalline/amorphous regions. The resultant device work as either a volatile
receptor that shows multi-modal sensing, or a non-volatile synapse that
features record-high 10-bit analog states, low switching stochasticity and good
retention without the integration of any extra devices. Homogeneous integration
of such devices enables bionic learning functions such as conditioned reflex
and real-time cardiac disease diagnose via reservoir computing, illustrating
the promise for future smart edge health informatics.


title:
Some integral inequalities on weighted Riemannian manifolds with
  boundary

abstract:
  In this paper, we continue to study some applications with respect to a
Reilly type integral formula associated with the $\phi$-Laplacian. Some
inequalities of Brascamp-Lieb type and Colesanti type are provided.


title:
PKS 1424+240: yet another masquerading BL Lac object as a possible
  IceCube neutrino source

abstract:
  We show that the blazar PKS 1424+240, which has been recently associated by
IceCube with a neutrino excess at the $3.3\,\sigma$ level together with three
other sources, is similar to the first plausible non-stellar neutrino source,
TXS 0506+056, in being also a masquerading BL Lac object, i.e., intrinsically a
flat-spectrum radio quasar with hidden broad lines and a standard accretion
disk. We point out that these two sources share other properties, including
spectral energy distribution, high powers, parsec scale properties, and
possibly radio morphology. We speculate that the relatively rare combination of
proton-loaded jets, possibly typical of high-excitation sources, and efficient
particle acceleration processes, related to their relatively high synchrotron
peak frequencies, might favour neutrino production in these two sources. GB6
J1542+6129, which has also recently appeared twice in a list of IceCube
associations, seems also to belong to this rare blazar sub-class, which
includes at most ~ 20 Fermi-4LAC blazars.


title: Can Gravitational Waves posterior constrain the mass of a WIMP?

abstract:
  In this work, we explore the likelihood of neutron star admixed with dark
matter (DM) as a secondary compact component of GW190814 merger event and
constrain the mass of WIMP, for the first time with the help of universality
relation, and GW170817 posteriors. We have considered a relatively light
neutralino, a component of weakly interacting massive particles, as a dark
matter candidate which interacts with the baryonic matter of the neutron star
through the exchange of Higgs boson. The baryonic part of the
equations-of-state of NS is considered within the relativistic mean-field model
with the IOPB-I, G3, and NL3 parameter sets. In efforts to constrain the WIMP
mass, we have presumed the DM inside a neutron star core and analyzed its
effects on the $M-R$ curves, and $C-\Lambda$ universality relations at
different DM masses. With the help of obtained deviation of $C-\Lambda$ curves
from the fitted curve when no DM is present, and consequently, the relative
shift of MR curves, we constrain the mass of WIMP $M_{DM} \sim 20$ GeV,
presuming stiffer equation of state- NL3- to be sacred. The neutron star,
described by NL3 EOS, admixed with DM of particle mass $\sim 20$ GeV satisfies
the mass and radius range of secondary compact object of GW190814 event, and
thus it could be a possible candidate.


title: AIVC: Artificial Intelligence based Video Codec

abstract:
  This paper introduces AIVC, an end-to-end neural video codec. It is based on
two conditional autoencoders MNet and CNet, for motion compensation and coding.
AIVC learns to compress videos using any coding configurations through a single
end-to-end rate-distortion optimization. Furthermore, it offers performance
competitive with the recent video coder HEVC under several established test
conditions. A comprehensive ablation study is performed to evaluate the
benefits of the different modules composing AIVC. The implementation is made
available at https://orange-opensource.github.io/AIVC/.


title:
A Pre-Transformation Method to Increase the Minimum Distance of
  Polar-Like Codes

abstract:
  Reed Muller (RM) codes are known for their good minimum distance. One can use
their structure to construct polar-like codes with good distance properties by
choosing the information set as the rows of the polarization matrix with the
highest Hamming weight, instead of the most reliable synthetic channels.
However, the information length options of RM codes are quite limited due to
their specific structure. In this work, we present sufficient conditions to
increase the information length by at least one bit for some underlying RM
codes and in order to obtain pre-transformed polar-like codes with the same
minimum distance than lower rate codes.The proofs give a constructive method to
choose the row triples to be merged together to increase the information length
of the code and they follow from partitioning the row indices of the polar
encoding matrix with respect to the recursive structure imposed by the binary
representation of row indices. Moreover, our findings are combined with the
method presented in [2] to further reduce the number of minimum weight
codewords. Numerical results show that the designed codes perform close to the
meta-converse bound at short blocklengths and better than the
polarization-adjusted-convolutional polar codes with the same parameters.


title:
A Reinforcement Learning Approach to Domain-Knowledge Inclusion Using
  Grammar Guided Symbolic Regression

abstract:
  In recent years, symbolic regression has been of wide interest to provide an
interpretable symbolic representation of potentially large data relationships.
Initially circled to genetic algorithms, symbolic regression methods now
include a variety of Deep Learning based alternatives. However, these methods
still do not generalize well to real-world data, mainly because they hardly
include domain knowledge nor consider physical relationships between variables
such as known equations and units. Regarding these issues, we propose a
Reinforcement-Based Grammar-Guided Symbolic Regression (RBG2-SR) method that
constrains the representational space with domain-knowledge using context-free
grammar as reinforcement action space. We detail a Partially-Observable Markov
Decision Process (POMDP) modeling of the problem and benchmark our approach
against state-of-the-art methods. We also analyze the POMDP state definition
and propose a physical equation search use case on which we compare our
approach to grammar-based and non-grammarbased symbolic regression methods. The
experiment results show that our method is competitive against other
state-of-the-art methods on the benchmarks and offers the best error-complexity
trade-off, highlighting the interest of using a grammar-based method in a
real-world scenario.


title: The Galactic high mass X-ray binary population with Fermi -LAT

abstract:
  We search for gamma-ray emission from 114 Galactic high mass X-ray binaries,
including 4 well studied catalogued sources, in 12.5 years of Fermi-LAT data in
conjunction with the 10-year point source catalogue. Where a gamma-ray excess
appears to be spatially coincident with an X-ray binary, further investigation
is performed to ascertain whether this excess is the product of physical
processes within the binary system itself. We identify gamma-ray excesses
coincident with 20 high mass X-ray binaries where there is little or no prior
evidence for gamma-ray emission. However, we find that many of these are false
positives caused by source confusion or the gamma-ray background. Nonetheless,
tentative but promising indicators of gamma-ray emission are identified for
several new systems, notably including 1A 0535+262, RX J2030.5+4751 and SAX
J1324.4-6200.


title:
A new perspective on classification: optimally allocating limited
  resources to uncertain tasks

abstract:
  A central problem in business concerns the optimal allocation of limited
resources to a set of available tasks, where the payoff of these tasks is
inherently uncertain. In credit card fraud detection, for instance, a bank can
only assign a small subset of transactions to their fraud investigations team.
Typically, such problems are solved using a classification framework, where the
focus is on predicting task outcomes given a set of characteristics. Resources
are then allocated to the tasks that are predicted to be the most likely to
succeed. However, we argue that using classification to address task
uncertainty is inherently suboptimal as it does not take into account the
available capacity. Therefore, we first frame the problem as a type of
assignment problem. Then, we present a novel solution using learning to rank by
directly optimizing the assignment's expected profit given limited, stochastic
capacity. This is achieved by optimizing a specific instance of the net
discounted cumulative gain, a commonly used class of metrics in learning to
rank. Empirically, we demonstrate that our new method achieves higher expected
profit and expected precision compared to a classification approach for a wide
variety of application areas and data sets. This illustrates the benefit of an
integrated approach and of explicitly considering the available resources when
learning a predictive model.


title:
Simultaneous Transmit Diversity and Passive Beamforming with Large-Scale
  Intelligent Reflecting Surface

abstract:
  Intelligent reflecting surface (IRS) has emerged as a cost-effective solution
to enhance wireless communication performance via passive signal reflection.
Existing works on IRS have mainly focused on investigating IRS's passive
beamforming/reflection design to boost the communication rate for users
assuming that their channel state information (CSI) is fully or partially
known. However, how to exploit IRS to improve the wireless transmission
reliability without any CSI, which is typical in high-mobility/delay-sensitive
communication scenarios, remains largely open. In this paper, we study a new
IRS-aided communication system with the IRS integrated to its aided access
point (AP) to achieve both functions of transmit diversity and passive
beamforming simultaneously. Specifically, we first show an interesting result
that the IRS's passive beamforming gain in any direction is invariant to the
common phase-shift applied to all of its reflecting elements. Accordingly, we
design the common phase-shift of IRS elements to achieve transmit diversity at
the AP side without the need of any CSI of the users. In addition, we propose a
practical method for the users to estimate the CSI at the receiver side for
information decoding. Meanwhile, we show that the conventional passive
beamforming gain of IRS can be retained for the other users with their CSI
known at the AP. Furthermore, we derive the asymptotic performance of both
IRS-aided transmit diversity and passive beamforming in closed-form, by
considering the large-scale IRS with an infinite number of elements. Numerical
results validate our analysis and show the performance gains of the proposed
IRS-aided simultaneous transmit diversity and passive beamforming scheme over
other benchmark schemes.


title: Hypergraph characterization of split matroids

abstract:
  We provide a combinatorial study of split matroids, a class that was
motivated by the study of matroid polytopes from a tropical geometry point of
view. A nice feature of split matroids is that they generalize paving matroids,
while being closed under duality and taking minors. Furthermore, these matroids
proved to be useful in giving exact asymptotic bounds for the dimension of the
Dressian, and also implied new results on the rays of the tropical
Grassmannians.
  In the present paper, we introduce the notion of elementary split matroids, a
subclass of split matroids that contains all connected split matroids. We give
a hypergraph characterization of elementary split matroids in terms of
independent sets, and show that the proposed class is closed not only under
duality and taking minors but also truncation. We further show that, in
contrast to split matroids, the proposed class can be characterized by a single
forbidden minor. As an application, we provide a complete list of binary split
matroids.


title:
Rotation periods and shape asphericity in asteroid families based on
  TESS S1-S13 observations

abstract:
  Here we present the analysis of the distribution of rotation periods and
light curve amplitudes based on 2859 family asteroids in 16 Main Belt families
based on 9912 TESS asteroid light curves in the TSSYS-DR1 asteroid light curve
database. We found that the distribution of the light curve properties follow a
family-specific character in some asteroid families, including the Hungaria,
Maria, Juno, Eos, Eucharis, and Alauda families. While in other large families,
these distributions are in general very similar to each other. We confirm that
older families tend to contain a larger fraction of more spheroidal,
low-amplitude asteroids. We found that rotation period distributions are
different in the cores and outskirts of the Flora and Maria families, while the
Vesta, Eos, and Eunomia families lack this feature. We also confirm that very
fast spinning asteroids are close to spherical (or spinning top shapes), and
minor planets rotating slower than ~11 hour are also more spherical than
asteroids in the 4--8 hour period range and this group is expected to contain
the most elongated bodies.


title:
Glyme-based electrolytes: suitable solutions for next-generation lithium
  batteries

abstract:
  The concept of green in a battery involves the chemical nature of electrodes
and electrolytes as well as the economic sustainability of the cell. Although
these aspects are typically discussed separately, they are deeply
interconnected: indeed, a new electrolyte can allow the use of different
cathodes with higher energy, lower cost or more pronounced environmental
compatibility. We focus on alternative class of electrolyte solutions for
lithium batteries formed by dissolving LiX salts in glyme solvents, i.e.,
organic ethers with the molecular formula CH3O[CH2CH2O]nCH3 differing by chain
length. The advantages of these electrolytes are illustrated in terms of
flammability, stability, toxicity, environmental compatibility, cell
performances and economic impact. A particular light is shed on the stability
of these systems, particularly in the polymer state, and in various
environments including oxygen, sulfur and high-energy lithium metal. The most
relevant studies on the chemical-physical features, the characteristic
structures, the favorable properties, and the electrochemical behavior of the
glyme-based solutions are discussed, and the most recent technological
achievements in terms of cell design and battery performance are described. The
use of glyme-based electrolytes in high-energy cells arranged by coupling the
lithium-metal anode with conventional insertion cathodes as well as in
alternative and new batteries exploiting the Li-S and Li-O2 conversion
processes are described in detail. The paragraphs reveal bonuses, including
safety, low cost and sustainability, that can be achieved by employing the
glyme-based electrolytes with respect to the commercially available ones, in
particular taking into account future and alternative applications. Particular
relevance is given by the glymes with long chain that reveal a remarkable
stability, high safety and very low toxicity.


title: Metal-insulator transition in a boundary three chain model

abstract:
  We study the boundary physics of bulk insulators by considering three coupled
Hubbard chains in a linear confining potential. In the Hartree-Fock
approximation, the ground state at and slightly off the particle-hole symmetric
point remains insulating even at large slopes of the confining potential. By
contrast, accounting for quantum fluctuations and correlations through a
combination of bosonization and RG methods, we find that there is always a
gapless dipole mode, but it does not contribute to a finite compressibility.
Moreover, when increasing the slope of the confining potential at half filling,
we find a quantum phase transition between an insulating and a metallic state,
indicating the formation of a soft edge state of non-topological origin. Away
from half filling, a similar transition takes place.


title:
Temporal Logic Guided Motion Primitives for Complex Manipulation Tasks
  with User Preferences

abstract:
  Dynamic movement primitives (DMPs) are a flexible trajectory learning scheme
widely used in motion generation of robotic systems. However, existing
DMP-based methods mainly focus on simple go-to-goal tasks. Motivated to handle
tasks beyond point-to-point motion planning, this work presents temporal logic
guided optimization of motion primitives, namely PIBB-TL algorithm, for complex
manipulation tasks with user preferences. In particular, weighted truncated
linear temporal logic (wTLTL) is incorporated in the PIBB-TL algorithm, which
not only enables the encoding of complex tasks that involve a sequence of
logically organized action plans with user preferences, but also provides a
convenient and efficient means to design the cost function. The black-box
optimization is then adapted to identify optimal shape parameters of DMPs to
enable motion planning of robotic systems. The effectiveness of the PIBB-TL
algorithm is demonstrated via simulation and experime


title:
Improving short-term bike sharing demand forecast through an irregular
  convolutional neural network

abstract:
  As an important task for the management of bike sharing systems, accurate
forecast of travel demand could facilitate dispatch and relocation of bicycles
to improve user satisfaction. In recent years, many deep learning algorithms
have been introduced to improve bicycle usage forecast. A typical practice is
to integrate convolutional (CNN) and recurrent neural network (RNN) to capture
spatial-temporal dependency in historical travel demand. For typical CNN, the
convolution operation is conducted through a kernel that moves across a
"matrix-format" city to extract features over spatially adjacent urban areas.
This practice assumes that areas close to each other could provide useful
information that improves prediction accuracy. However, bicycle usage in
neighboring areas might not always be similar, given spatial variations in
built environment characteristics and travel behavior that affect cycling
activities. Yet, areas that are far apart can be relatively more similar in
temporal usage patterns. To utilize the hidden linkage among these distant
urban areas, the study proposes an irregular convolutional Long-Short Term
Memory model (IrConv+LSTM) to improve short-term bike sharing demand forecast.
The model modifies traditional CNN with irregular convolutional architecture to
extract dependency among "semantic neighbors". The proposed model is evaluated
with a set of benchmark models in five study sites, which include one dockless
bike sharing system in Singapore, and four station-based systems in Chicago,
Washington, D.C., New York, and London. We find that IrConv+LSTM outperforms
other benchmark models in the five cities. The model also achieves superior
performance in areas with varying levels of bicycle usage and during peak
periods. The findings suggest that "thinking beyond spatial neighbors" can
further improve short-term travel demand prediction of urban bike sharing
systems.


title: Constant Approximating Parameterized $k$-SetCover is W[2]-hard

abstract:
  In this paper, we prove that it is W[2]-hard to approximate $k$-SetCover
within any constant ratio. Our proof is built upon the recently developed
threshold graph composition technique. We propose a strong notion of threshold
graph and use a new composition method to prove this result. Our technique
could also be applied to rule out polynomial time $o\left(\frac{\log n}{\log
\log n}\right)$ ratio approximation algorithms for the non-parameterized
$k$-SetCover problem, assuming W[1]$\ne$FPT.
  We highlight that our proof does not depend on the well-known PCP theorem,
and only involves simple combinatorial objects. Furthermore, our reduction
results in a $k$-SetCover instance with $k$ as small as $O\left(\log^2 n\cdot
\log \log n\right)$.


title:
A Measurement-Based Robust Non-Gaussian Process Emulator Applied to
  Data-Driven Stochastic Power Flow

abstract:
  In this paper, we propose a robust non-Gaussian process emulator based on the
Schweppe-type generalized maximum likelihood estimator, which is trained on
metered time series of voltage phasors and power injections to perform
stochastic power flow. Power system data are often corrupted with outliers
caused by fault conditions, power outages, and extreme weather, to name a few.
The proposed emulator bounds the influence of the outliers using weights
calculated based on projection statistics, which are robust distances of the
data points associated with the rows vectors of the factor space. Specifically,
the developed estimator is robust to vertical outliers and bad leverage points
while retaining good leverage points in the measurements of the training
dataset. The proposed method is demonstrated on an unbalanced radial IEEE
33-Bus system heavily integrated with renewable energy sources.


title: Quantum Limits on product manifolds

abstract:
  We establish some properties of quantum limits on a product manifold, proving
for instance that, under appropriate assumptions, the quantum limits on the
product of manifolds are absolutely continuous if the quantum limits on each
manifolds are absolutely continuous. On a product of Riemannian manifolds
satisfying the minimal multiplicity property, we prove that a periodic geodesic
can never be charged by a quantum limit.


title:
Entanglement Hamiltonian during a domain wall melting in the free Fermi
  chain

abstract:
  We study the unitary time evolution of the entanglement Hamiltonian of a free
Fermi lattice gas in one dimension initially prepared in a domain wall
configuration. To this aim, we exploit the recent development of quantum
fluctuating hydrodynamics. Our findings for the entanglement Hamiltonian are
based on the effective field theory description of the domain wall melting and
are expected to exactly describe the Euler scaling limit of the lattice gas.
However, such field theoretical results can be recovered from high-precision
numerical lattice calculations only when summing appropriately over all the
hoppings up to distant sites.


title:
Sample thickness measurements by phase-sensitive terahertz upconversion
  detection

abstract:
  Nonlinear frequency conversion provides an elegant method to detect photons
in a spectral range which differs from the pump wavelength, making it highly
attractive for photons with inherently low energy. Aside from the intensity of
the light, represented by the number of photons, their phase provides important
information and enables a plethora of applications. We present a
phase-sensitive measurement method in the terahertz spectral range by only
detecting visible light. Using the optical interference of frequency-converted
photons and leftover pump photons of the involved ultrashort pulses, fast
determination of layer-thicknesses is demonstrated. The new method enables
phase-resolved detection of terahertz pulses using standard sCMOS equipment
while achieving sample measurement times of less than one second with a
precision error of less than 0.6%.


title: Leveraging Experience in Lifelong Multi-Agent Pathfinding

abstract:
  In Lifelong Multi-Agent Path Finding (L-MAPF) a team of agents performs a
stream of tasks consisting of multiple locations to be visited by the agents on
a shared graph while avoiding collisions with one another. L-MAPF is typically
tackled by partitioning it into multiple consecutive, and hence similar,
"one-shot" MAPF queries, as in the Rolling-Horizon Collision Resolution (RHCR)
algorithm. Therefore, a solution to one query informs the next query, which
leads to similarity with respect to the agents' start and goal positions, and
how collisions need to be resolved from one query to the next. Thus, experience
from solving one MAPF query can potentially be used to speedup solving the next
one. Despite this intuition, current L-MAPF planners solve consecutive MAPF
queries from scratch. In this paper, we introduce a new RHCR-inspired approach
called exRHCR, which exploits experience in its constituent MAPF queries. In
particular, exRHCR employs a new extension of Priority-Based Search (PBS), a
state-of-the-art MAPF solver. Our extension, called exPBS, allows to warm-start
the search with the priorities between agents used by PBS in the previous MAPF
instances. We demonstrate empirically that exRHCR solves L-MAPF instances up to
39% faster than RHCR, and has the potential to increase system throughput for
given task streams by increasing the number of agents a planner can cope with
for a given time budget.


title: \'Etale Brauer-Manin obstruction for Weil restrictions

abstract:
  The \'etale Brauer\textendash Manin obstruction is equivalent to each other
among Weil restrictions of an arbitrarily given quasi-projective algebraic
variety defined over a number field.


title:
An estimation of non-valence contributions to form factors of
  heavy-light mesons

abstract:
  We study the influence of non-valence quark-pair contributions in weak
transition form factors of heavy-light mesons. Form factors are first
calculated for spacelike momentum transfers in a reference frame where such
contributions are suppressed. Analytic continuation to the timelike region and
a comparison with the direct decay calculation, done with pure valence degrees
of freedom, provides an estimate of the role that quark-pair contributions may
play. We use the point form of relativistic quantum mechanics, which is
particularly useful when treating heavy-light systems.


title:
Empirical Risk Minimization with Relative Entropy Regularization:
  Optimality and Sensitivity Analysis

abstract:
  The optimality and sensitivity of the empirical risk minimization problem
with relative entropy regularization (ERM-RER) are investigated for the case in
which the reference is a sigma-finite measure instead of a probability measure.
This generalization allows for a larger degree of flexibility in the
incorporation of prior knowledge over the set of models. In this setting, the
interplay of the regularization parameter, the reference measure, the risk
function, and the empirical risk induced by the solution of the ERM-RER problem
is characterized. This characterization yields necessary and sufficient
conditions for the existence of a regularization parameter that achieves an
arbitrarily small empirical risk with arbitrarily high probability. The
sensitivity of the expected empirical risk to deviations from the solution of
the ERM-RER problem is studied. The sensitivity is then used to provide upper
and lower bounds on the expected empirical risk. Moreover, it is shown that the
expectation of the sensitivity is upper bounded, up to a constant factor, by
the square root of the lautum information between the models and the datasets.


title: Contactless Rheology of Soft Gels over a Broad Frequency Range

abstract:
  We report contactless measurements of the viscoelastic rheological properties
of soft gels. The experiments are performed using a colloidal-probe Atomic
Force Microscope (AFM) in a liquid environment and in dynamic mode. The
mechanical response is measured as a function of the liquid gap thickness for
different oscillation frequencies. Our measurements reveal an
elastohydrodynamic (EHD) coupling between the flow induced by the probe
oscillation and the viscoelastic deformation of the gels. The data are
quantitatively described by a viscoelastic lubrication model. The
frequency-dependent storage and loss moduli of the polydimethylsiloxane (PDMS)
gels are extracted from fits of the data to the model and are in good agreement
with the Chasset--Thirion law. Our results demonstrate that contactless
colloidal-probe methods are powerful tools that can be used for probing soft
interfaces finely over a wide range of frequencies.


title:
Mean field description of aging linear response in athermal amorphous
  solids

abstract:
  We study the linear response to strain in a mean field elastoplastic model
for athermal amorphous solids, incorporating the power-law mechanical noise
spectrum arising from plastic events. In the "jammed" regime of the model,
where the plastic activity exhibits a non-trivial slow relaxation referred to
as aging, we find that the stress relaxes incompletely to an age-dependent
plateau, on a timescale which grows with material age. We determine the scaling
behaviour of this aging linear response analytically, finding that key scaling
exponents are universal and independent of the noise exponent $\mu$. For
$\mu>1$, we find simple aging, where the stress relaxation timescale scales
linearly with the age $t_{\rm w}$ of the material. At $\mu=1$, which
corresponds to interactions mediated by the physical elastic propagator, we
find instead a $t_{\rm w}^{1/2}$ scaling arising from the stretched exponential
decay of the plastic activity. We compare these predictions with measurements
of the linear response in computer simulations of a model jammed system of
repulsive soft athermal particles, during its slow dissipative relaxation
towards mechanical equilibrium, and find good agreement with the theory.


title: Hypergeometric ${}_4F_3(1)$ with integral parameter differences

abstract:
  In this paper we continue investigation of the hypergeometric function
${}_4F_3(1)$ as the function of its seven parameters. We deduce several
reduction formulas for this function under additional conditions that one of
the top parameters exceeds one of the bottom parameters by a positive integer
or reversely one of the bottom parameters exceeds one of the top parameters by
a positive integer or both. We show that all such cases reduce to the case of
the unit parameter difference. The latter case, in turn, can be expressed in
terms of certain linear combination of two series involving the logarithmic
derivative of the gamma function.


title:
Quantum Multicritical Behavior for Coupled Optical Cavities with Driven
  Laser Fields

abstract:
  We propose and study an extended Dicke model describing the interaction
between a quantized light field and a two-level atomic ensemble in optical
cavities coupled by a Zeeman (microwave) field. The system allows the
occurrence of multicritical points for the superradiant quantum phase
transition. We determine the number and position of the critical points and
show that they can be effectively manipulated through the tuning of system
parameters. When the Rabi frequency of the light field is modulated
periodically in time, the quantum critical points can evolve into a Lifshitz
point. The magnetic orders of the three phases around the Lifshitz point,
represented by the atomic pseudo-spins, are similar to those of an axial
next-nearest-neighboring Ising model. Both the radiance strength and the
texture of atomic pseudo-spins can be used to characterize the quantum critical
behaviors of the system.


title:
Dual field structure-preserving discretization of port-Hamiltonian
  systems using finite element exterior calculus

abstract:
  In this paper we propose a novel approach to discretize linear
port-Hamiltonian systems while preserving the underlying structure. We present
a finite element exterior calculus formulation that is able to mimetically
represent conservation laws and cope with mixed open boundary conditions using
a single computational mesh. The possibility of including open boundary
conditions allows for modular composition of complex multi-physical systems
whereas the exterior calculus formulation provides a coordinate-free treatment.
Our approach relies on a dual-field representation of the physical system that
is redundant at the continuous level but eliminates the need of mimicking the
Hodge star operator at the discrete level. By considering the Stokes-Dirac
structure representing the system together with its adjoint, which embeds the
metric information directly in the codifferential, the need for an explicit
discrete Hodge star is avoided altogether. By imposing the boundary conditions
in a strong manner, the power balance characterizing the Stokes-Dirac structure
is then retrieved at the discrete level via symplectic Runge-Kutta integrators
based on Gauss-Legendre collocation points. Numerical experiments validate the
convergence of the method and the conservation properties in terms of energy
balance both for the wave and Maxwell equations in a three dimensional domain.
For the latter example, the magnetic and electric fields preserve their
divergence free nature at the discrete level.


title:
Evolution of sheath and leading edge structures of interplanetary
  coronal mass ejections in the inner heliosphere based on Helios and Parker
  Solar Probe observations

abstract:
  Context: We investigate the evolution of the sheath and leading edge (LE)
structure of interplanetary coronal mass ejections (ICMEs) as function of
distance in the inner heliosphere. Results are related both to the magnetic
ejecta (ME) and to the ambient solar wind (SW). Aims: From a sample of 40
well-observed Helios 1/2 events, we derive the average density separately for
sheath, LE, and ME. The results are placed into comparison with the upstream SW
to investigate at which distance the sheath is formed. Methods: We use plasma
and magnetic field measurements from Helios 1/2 data in the distance range
0.3-1 au from the ICME list by Bothmer and Schwenn (1998). For comparison, we
add a sample of four ICMEs observed with PSP in 2019-2021 covering 0.32-0.62
au. Results. At the distance of ~13 Rs, the CME sheath becomes denser than the
ambient SW density. At ~38 Rs the sheath structure density starts to dominate
over the density within the ME. The ME density falls below the ambient SW
density at ~230 Rs. Besides the well-known expansion of the ME, the sheath size
shows a weak positive correlation with distance, while the LE does not expand.
We find a moderate anti-correlation between sheath density and local SW plasma
speed upstream of the ICME shock. An empirical relation is derived connecting
the ambient SW speed with sheath and LE density. Conclusions: The average
starting distance for actual sheath formation is found to be located at ~13 Rs.
The ME expansion changes strongly at ~38 Rs, leading to a density dominance of
the sheath structure. The LE can be understood as a structure isolated from the
ambient SW flow. The results allow for better interpretation of ICME evolution
and possibly mass increase due to sheath enlargement. The empirical results
between sheath and LE density and ambient SW speed can be used for more
detailed modeling of ICME evolution in the inner heliosphere.


title: Model Architecture Adaption for Bayesian Neural Networks

abstract:
  Bayesian Neural Networks (BNNs) offer a mathematically grounded framework to
quantify the uncertainty of model predictions but come with a prohibitive
computation cost for both training and inference. In this work, we show a novel
network architecture search (NAS) that optimizes BNNs for both accuracy and
uncertainty while having a reduced inference latency. Different from canonical
NAS that optimizes solely for in-distribution likelihood, the proposed scheme
searches for the uncertainty performance using both in- and out-of-distribution
data. Our method is able to search for the correct placement of Bayesian
layer(s) in a network. In our experiments, the searched models show comparable
uncertainty quantification ability and accuracy compared to the
state-of-the-art (deep ensemble). In addition, the searched models use only a
fraction of the runtime compared to many popular BNN baselines, reducing the
inference runtime cost by $2.98 \times$ and $2.92 \times$ respectively on the
CIFAR10 dataset when compared to MCDropout and deep ensemble.


title:
Binaural Audio Rendering in the Spherical Harmonic Domain: A Summary of
  the Mathematics and its Pitfalls

abstract:
  The present document reviews the mathematics behind binaural rendering of
sound fields that are available as spherical harmonic expansion coefficients.
This process is also known as binaural ambisonic decoding. We highlight that
the details entail some amount peculiarity so that one has to be well aware of
the precise definitions that are chosen for some of the involved quantities to
obtain a consistent formulation.


title: Exploring interpolating momentum schemes

abstract:
  We compute the renormalisation factors of the quark mass and wave function
using IMOM (Interpolating MOMenta) schemes. The framework is the
Rome-Southampton non-renormalisation method, but the momentum transfer in the
quark bilinears is not restricted to zero or to the symmetric point. We study
the scale dependence, infrared contamination and lattice artefacts for
different values of this momentum transfer and for two different kinds of
projectors. For the numerical simulations, we use data generated by the
RBC-UKQCD collaborations, with $N_f = 2+1$ flavours of Domain-Wall fermions,
and inverse lattice spacing of $1.79 $ and $2.38$ GeV.


title: Measurement of the H to ZZ branching fraction at 350 GeV and 3 TeV CLIC

abstract:
  In this paper we investigate the prospects for measuring the branching
fraction of the Standard Model Higgs boson decay into a pair of $Z$ bosons at
the future Compact Linear Collider (CLIC) at 350 GeV and 3 TeV centre-of-mass
energies. Studies are performed using a detailed simulation of the detector for
CLIC, taking into consideration all relevant physics and beam-induced
background processes. It is shown that the product of the Higgs production
cross-section and the branching fraction BR(${H\rightarrow\thinspace ZZ^\ast}$)
can be measured with a relative statistical uncertainty of 20% (3.0%) at a
centre-of-mass energy of 350 GeV (3 TeV) using semileptonic final states,
assuming an integrated luminosity of 1 ab$^{-1}$ (5 ab$^{-1}$).


title:
A Priori Error Estimates of a Discontinuous Galerkin Finite Element
  Method for the Kelvin-Voigt Viscoelastic Fluid Motion Equations

abstract:
  This paper applies a discontinuous Galerkin finite element method to the
Kelvin-Voigt viscoelastic fluid motion equations when the forcing function is
in $L^\infty({\bf L}^2)$-space. Optimal a priori error estimates in
$L^\infty({\bf L}^2)$-norm for the velocity and in $L^\infty(L^2)$-norm for the
pressure approximations for the semi-discrete discontinuous Galerkin method are
derived here. The main ingredients for establishing the error estimates are the
standard elliptic duality argument and a modified version of the Sobolev-Stokes
operator defined on appropriate broken Sobolev spaces. Further, under the
smallness assumption on the data, it has been proved that these estimates are
valid uniformly in time. Then, a first-order accurate backward Euler method is
employed to discretize the semi-discrete discontinuous Galerkin Kelvin-Voigt
formulation completely. The fully discrete optimal error estimates for the
velocity and pressure are established. Finally, using the numerical
experiments, theoretical results are verified. It is worth highlighting here
that the error results in this article for the discontinuous Galerkin method
applied to the Kelvin-Voigt model using finite element analysis are the first
attempt in this direction.


title:
A hypothesis-driven method based on machine learning for neuroimaging
  data analysis

abstract:
  There remains an open question about the usefulness and the interpretation of
Machine learning (MLE) approaches for discrimination of spatial patterns of
brain images between samples or activation states. In the last few decades,
these approaches have limited their operation to feature extraction and linear
classification tasks for between-group inference. In this context, statistical
inference is assessed by randomly permuting image labels or by the use of
random effect models that consider between-subject variability. These
multivariate MLE-based statistical pipelines, whilst potentially more effective
for detecting activations than hypotheses-driven methods, have lost their
mathematical elegance, ease of interpretation, and spatial localization of the
ubiquitous General linear Model (GLM). Recently, the estimation of the
conventional GLM has been demonstrated to be connected to an univariate
classification task when the design matrix is expressed as a binary indicator
matrix. In this paper we explore the complete connection between the univariate
GLM and MLE \emph{regressions}. To this purpose we derive a refined statistical
test with the GLM based on the parameters obtained by a linear Support Vector
Regression (SVR) in the \emph{inverse} problem (SVR-iGLM). Subsequently, random
field theory (RFT) is employed for assessing statistical significance following
a conventional GLM benchmark. Experimental results demonstrate how parameter
estimations derived from each model (mainly GLM and SVR) result in different
experimental design estimates that are significantly related to the predefined
functional task. Moreover, using real data from a multisite initiative the
proposed MLE-based inference demonstrates statistical power and the control of
false positives, outperforming the regular GLM.


title: Large time behavior for a nonlocal nonlinear gradient flow

abstract:
  We study the large time behavior of the nonlinear and nonlocal equation $$
v_t+(-\Delta_p)^sv=f \, , $$ where $p\in (1,2)\cup (2,\infty)$, $s\in (0,1)$
and $$ (-\Delta_p)^s v\, (x,t)=2 \,\text{pv}
\int_{\mathbb{R}^n}\frac{|v(x,t)-v(x+y,t)|^{p-2}(v(x,t)-v(x+y,t))}{|y|^{n+sp}}\,
dy. $$ This equation arises as a gradient flow in fractional Sobolev spaces. We
obtain sharp decay estimates as $t\to\infty$. The proofs are based on an
iteration method in the spirit of J. Moser previously used by P. Juutinen and
P. Lindqvist.


title:
Zero Bias Power Detector Circuits based on MoS$_2$ Field Effect
  Transistors on Wafer-Scale Flexible Substrates

abstract:
  We demonstrate the design, fabrication, and characterization of wafer-scale,
zero-bias power detectors based on two-dimensional MoS$_2$ field effect
transistors (FETs). The MoS$_2$ FETs are fabricated using a wafer-scale process
on 8 $\mu$m thick polyimide film, which in principle serves as flexible
substrate. The performances of two CVD-MoS$_2$ sheets, grown with different
processes and showing different thicknesses, are analyzed and compared from the
single device fabrication and characterization steps to the circuit level. The
power detector prototypes exploit the nonlinearity of the transistors above the
cut-off frequency of the devices. The proposed detectors are designed employing
a transistor model based on measurement results. The fabricated circuits
operate in Ku-band between 12 and 18 GHz, with a demonstrated voltage
responsivity of 45 V/W at 18 GHz in the case of monolayer MoS2 and 104 V/W at
16 GHz in the case of multilayer MoS$_2$, both achieved without applied DC
bias. They are the best performing power detectors fabricated on flexible
substrate reported to date. The measured dynamic range exceeds 30 dB
outperforming other semiconductor technologies like silicon complementary metal
oxide semiconductor (CMOS) circuits and GaAs Schottky diodes.


title: $\hbar$-Riemann-Hilbert correspondence

abstract:
  We formulate and prove a Riemann-Hilbert correspondence between
$\hbar$-differential equations and sheaf quantizations, which can be considered
as a correspondence between two kinds of quantizations (deformation and sheaf
quantization) of holomorphic cotangent bundles. The latter category is expected
to be equivalent to a version of Fukaya category, which is a "quantization" of
Lagrangian intersection theory. The ideas of the constructions are based on
asymptotic/WKB analysis, which is related to geometric quantization.


title:
Representations of affine Lie superalgebras and their quantization in
  type A

abstract:
  We construct a new family of irreducible modules over any basic classical
affine Kac-Moody Lie superalgebra which are induced from modules over the
Heisenberg subalgebra. We also obtain irreducible deformations of these modules
for the quantum affine superalgebra of type A.


title:
Breakdown of spin-to-helicity locking at the nanoscale in topological
  photonic crystal edge states

abstract:
  We measure the local near-field spin in topological edge state waveguides
that emulate the quantum spin Hall effect. We reveal a highly structured spin
density distribution that is not linked to a unique pseudospin value. From
experimental near-field real-space maps and numerical calculations, we confirm
that this local structure is essential in understanding the properties of
optical edge states and light-matter interactions. The global spin is reduced
by a factor of 30 in the near field and, for certain frequencies, flipped
compared to the pseudospin measured in the far-field. We experimentally reveal
the influence of higher-order Bloch harmonics in spin inhomogeneity, leading to
a breakdown in the coupling between local helicity and global spin.


title:
Active Brownian motion with memory delay induced by a viscoelastic
  medium

abstract:
  By now active Brownian motion is a well-established model to describe the
motion of mesoscopic self-propelled particles in a Newtonian fluid. On the
basis of the generalized Langevin equation, we present an analytic framework
for active Brownian motion with memory delay assuming time-dependent friction
kernels for both translational and orientational degrees of freedom to account
for the time-delayed response of a viscoelastic medium. Analytical results are
obtained for the orientational correlation function, mean displacement and
mean-square displacement which we evaluate in particular for a Maxwell fluid
characterized by a kernel which decays exponentially in time. Further, we
identify a memory induced delay between the effective self-propulsion force and
the particle orientation which we quantify in terms of a special dynamical
correlation function. In principle our predictions can be verified for an
active colloidal particle in various viscoelastic environments such as a
polymer solution.


title:
Variational Stochastic Parameterisations and their Applications to
  Primitive Equation Models

abstract:
  We present a numerical investigation into the stochastic parameterisations of
the Primitive Equations (PE) using the Stochastic Advection by Lie Transport
(SALT) and Stochastic Forcing by Lie Transport (SFLT) frameworks. These
frameworks were chosen due to their structure-preserving introduction of
stochasticity, which decomposes the transport velocity and fluid momentum into
their drift and stochastic parts, respectively. In this paper, we develop a new
calibration methodology to implement the momentum decomposition of SFLT and
compare with the Lagrangian path methodology implemented for SALT. The
resulting stochastic Primitive Equations are then integrated numerically using
a modification of the FESOM2 code. For certain choices of the stochastic
parameters, we show that SALT causes an increase in the eddy kinetic energy
field and an improvement in the spatial spectrum. SFLT also shows improvements
in these areas, though to a lesser extent. SALT does, however, have the
drawback of an excessive downwards diffusion of temperature.


title:
Time-Frequency Mask Aware Bi-directional LSTM: A Deep Learning Approach
  for Underwater Acoustic Signal Separation

abstract:
  The underwater acoustic signals separation is a key technique for the
underwater communications. The existing methods are mostly model-based, and
could not accurately characterise the practical underwater acoustic
communication environment. They are only suitable for binary signal separation,
but cannot handle multivariate signal separation. On the other hand, the
recurrent neural network (RNN) shows powerful capability in extracting the
features of the temporal sequences. Inspired by this, in this paper, we present
a data-driven approach for underwater acoustic signals separation using deep
learning technology. We use the Bi-directional Long Short-Term Memory (Bi-LSTM)
to explore the features of Time-Frequency (T-F) mask, and propose a T-F mask
aware Bi-LSTM for signal separation. Taking advantage of the sparseness of the
T-F image, the designed Bi-LSTM network is able to extract the discriminative
features for separation, which further improves the separation performance. In
particular, this method breaks through the limitations of the existing methods,
not only achieves good results in multivariate separation, but also effectively
separates signals when mixed with 40dB Gaussian noise signals. The experimental
results show that this method can achieve a $97\%$ guarantee ratio (PSR), and
the average similarity coefficient of the multivariate signal separation is
stable above 0.8 under high noise conditions.


title: Rigorous mathematical optimization of synthetic hepatic vascular trees

abstract:
  In this paper, we introduce a new framework for generating synthetic vascular
trees, based on rigorous model-based mathematical optimization. Our main
contribution is the reformulation of finding the optimal global tree geometry
into a nonlinear optimization problem (NLP). This rigorous mathematical
formulation accommodates efficient solution algorithms such as the interior
point method and allows us to easily change boundary conditions and constraints
applied to the tree. Moreover, it creates trifurcations in addition to
bifurcations. A second contribution is the addition of an optimization stage
for the tree topology. Here, we combine constrained constructive optimization
(CCO) with a heuristic approach to search among possible tree topologies. We
combine the NLP formulation and the topology optimization into a single
algorithmic approach. Finally, we attempt the validation of our new model-based
optimization framework using a detailed corrosion cast of a human liver, which
allows a quantitative comparison of the synthetic tree structure to the tree
structure determined experimentally down to the fifth generation. The results
show that our new framework is capable of generating asymmetric synthetic trees
that match the available physiological corrosion cast data better than trees
generated by the standard CCO approach.


title:
Magnetic field-temperature phase diagrams for multiple-$Q$ magnetic
  orderings: Exact steepest decent approach to long-range interacting spin
  systems

abstract:
  Multiple-$Q$ magnetic orderings represent magnetic textures composed of
superpositions of multiple spin density waves or spin spirals, as represented
by skyrmion crystals and hedgehog lattices. Such magnetic orderings have been
observed in various magnetic materials in recent years, and attracted enormous
attention, especially from the viewpoint of topology and emergent
electromagnetic fields originating from noncoplanar magnetic structures.
Although they often exhibit successive phase transitions among different
multiple-$Q$ states while changing temperature and an external magnetic field,
it is not straightforward to elucidate the phase diagrams, mainly due to the
lack of concise theoretical tools as well as appropriate microscopic models.
Here, we provide a theoretical framework for a class of effective spin models
with long-range magnetic interactions mediated by conduction electrons in
magnetic metals. Our framework is based on the steepest decent method with a
set of self-consistent equations that leads to exact solutions in the
thermodynamic limit, and has many advantages over existing methods such as
biased variational calculations and numerical Monte Carlo simulations. Applying
the framework to the models with instabilities toward triple- and hextuple-$Q$
magnetic orderings, we find that interesting reentrant phase transitions where
the multiple-$Q$ phases appear only at finite temperature and/or nonzero
magnetic field. Furthermore, we show that the multiple-$Q$ states can be
topologically-nontrivial stacked skyrmion crystals or hedgehog lattices, which
exhibit large net spin scalar chirality associated with nonzero skyrmion
number. The results demonstrate that our framework could be a versatile tool
for studying magnetic and topological phase transitions and related quantum
phenomena in actual magnetic metals hosting multiple-$Q$ magnetic orderings.


title:
Relaxation and a non-local, resistivity boundary layer in
  superconductors

abstract:
  Superconductors like other solids cannot relax instantaneously from thermally
excited (disturbed) states to thermodynamic equilibrium. In this paper,
relaxation of a multi-filamentary and of a thin film superconductor from
thermal excitations is simulated. Absorption of radiation or, under conductor
movement, release and transformation of mechanical tension to thermal energy
are examples. The paper applies numerical simulations of superconductor energy
states, as many-particle systems, under basic thermodynamic and standard,
multi-component heat transfer principles (solid conduction plus radiation in
thin films). A recently described microscopic stability model and application
of a traditional, continuum cell model allows to explain curvature of the
resistance vs. temperature excursion below critical temperature, TCrit, and
suggests an alternative to standard explanation of increased electrical
conductivity at temperature exceeding TCrit. A non-local, resistivity boundary
layer (a temperature uncertainty) is observed near critical temperature within
which the resistivity curve smoothly approaches, from the superconducting
state, the normal conduction resistivity.
  Keywords Superconductor; phase transition; relaxation; critical current
density; critical temperature; thermal fluctuations; boundary layers


title:
Radiative corrections to elastic muon-proton scattering at low momentum
  transfers

abstract:
  We systematically calculate the radiative corrections of order $\alpha/\pi$
to elastic muon-proton scattering at low momentum transfers. These include
vacuum polarization, photon-loop form factors of the muon and the proton,
two-photon exchange corrections and soft photon radiation. In particular, we
discuss these corrections for the kinematics of the upcomimg AMBER experiment
with a $100\,$GeV muon beam energy. It is found that for the ratio to the Born
cross section, only the minor terms from the photon-loop form factors of the
proton and two-photon exchange depend on the proton structure predetermined by
the strong interactions. Since a prominent role among the radiative corrections
is played by soft photon radiation, the calculation of the bremsstrahlung
process $\mu p \to\mu p \gamma$ should be extended beyond the soft photon
approximation and tailored to the specific experimental conditions.


title:
Leptonic scalar portal: the origin of muon $g-2$ anomaly and dark
  matter?

abstract:
  We present a model explaining both the 4.2 $\sigma$ muon $g-2$ anomaly and
the relic density of dark matter (DM) in which DM interacts with the Standard
Model (SM) via a scalar portal boson $\varphi$ carrying both dark and SM
leptonic numbers, and mediating a nondiagonal interaction between the electron
and muon that allows $e \leftrightarrow \mu$ transitions. The $\varphi$ could
be produced in high-energy electron scattering off a target nuclei in the
reaction $e Z \to \mu Z \varphi$ followed by the prompt invisible decay
$\varphi\to$ DM particles and searched for in events with large missing energy
accompanied by a single outgoing muon in the final state. Intriguingly, a few
events with a similar signature have been observed in a data sample of $\simeq
3\times 10^{11}$ electrons on target collected during 2016-2018 for the search
for light dark matter in the NA64 experiment at the CERN SPS (PRL {\bf 123},
121801 (2019)). Attributing so far these events to background allows us to set
first constraints on the $\varphi$ mass and couplings while leaving at the same
time the decisively probing the origin of these events and a large fraction of
the remaining parameter space to a near exiting future with the upgraded NA64
detector or other planned experiments.


title: A.I. and Data-Driven Mobility at Volkswagen Financial Services AG

abstract:
  Machine learning is being widely adapted in industrial applications owing to
the capabilities of commercially available hardware and rapidly advancing
research. Volkswagen Financial Services (VWFS), as a market leader in vehicle
leasing services, aims to leverage existing proprietary data and the latest
research to enhance existing and derive new business processes. The
collaboration between Information Systems and Machine Learning Lab (ISMLL) and
VWFS serves to realize this goal. In this paper, we propose methods in the
fields of recommender systems, object detection, and forecasting that enable
data-driven decisions for the vehicle life-cycle at VWFS.


title: Dynamics of magnetic flux tubes in accretion disks of Herbig Ae/Be stars

abstract:
  The dynamics of magnetic flux tubes (MFTs) in the accretion disk of typical
Herbig Ae/Be star with fossil large-scale magnetic field is modeled taking into
account the buoyant and drag forces, radiative heat exchange with the
surrounding gas, and the magnetic field of the disk. The structure of the disk
is simulated using our magnetohydrodynamic (MHD) model, taking into account the
heating of the surface layers of the disk with the stellar radiation. The
simulations show that MFTs periodically rise from the innermost region of the
disk with speeds up to $10-12$ km s$^{-1}$. MFTs experience decaying magnetic
oscillations under the action of the external magnetic field near the disk's
surface. The oscillation period increases with distance from the star and
initial plasma beta of the MFT, ranging from several hours at $r=0.012$ au up
to several months at $r=1$ au. The oscillations are characterized by pulsations
of the MFT's characteristics including its temperature. We argue that the
oscillations can produce observed IR-variability of Herbig Ae/Be stars, which
would be more intense than in the case of T Tauri stars, since the disks of
Herbig Ae/Be stars are hotter, denser and have stronger magnetic field.


title:
Amorphous-silicon visible-light detector integrated in silicon nitride
  waveguides

abstract:
  Visible light integrated photonics is emerging as a promising technology for
the realization of optical devices for applications in sensing, quantum
information and communications, imaging and displays. Among the existing
photonic platforms, high-index contrast silicon nitride ($Si_{3}N_{4}$)
waveguides offer broadband transparency in the visible spectral range and a
high scale of integration. As far as the complexity of photonic integrated
circuits (PICs) increases, on-chip detectors are required to monitor their
working point for reconfiguration and stabilization operations. In this work we
present a compact in-line power monitor integrated in $Si_{3}N_{4}$ waveguides
that operates in the red-light wavelength range (660 nm). The proposed device
exploits the photoconductivity of a hydrogenated amorphous silicon (a-Si:H)
film employed as a coating layer of the optical waveguide. Experimental results
show a responsivity of 30 mA/W, a sensitivity of -45 dBm and a sub-${\mu}$s
time response. These features enable the use of the proposed photoconductor for
high-sensitivity monitoring and control of visible-light $Si_{3}N_{4}$ PICs.