title: The maximum size of $k$-connected graphs with given order and diameter

abstract:
  In 1968, Ore determined the maximum size of $k$-connected graphs with given
order and diameter. In this note, we give a new short proof.


title:
Color-flavor reflection in the continuum limit of two-dimensional
  lattice gauge theories with scalar fields

abstract:
  We address the interplay between local and global symmetries in determining
the continuum limit of two-dimensional lattice scalar theories characterized by
$SO(N_c)$ gauge symmetry and non-Abelian $O(N_f)$ global invariance. We argue
that, when a quartic interaction is present, the continuum limit of these model
corresponds in some cases to the gauged non-linear $\sigma$ model field theory
associated with the real Grassmannian manifold $SO(N_f)/(SO(N_c)\times
SO(N_f-N_c)$), which is characterized by the invariance under the color-flavor
reflection $N_c\leftrightarrow N_f-N_c$. Monte Carlo simulations and
Finite-Size Scaling analyses, performed for $N_f=7$ and several values of
$N_c$, confirm the emergence of the color-flavor reflection symmetry in the
scaling limit, and support the identification of the continuum limit.


title: Coherent Photogalvanic Effect for Second-Order Nonlinear Photonics

abstract:
  The coherent photogalvanic effect leads to the generation of a current under
the absorption interference of coherent beams and allows for the inscription of
space-charge gratings leading to an effective second-order susceptibility
($\chi^{(2)}$). The inscribed grating automatically results in
quasi-phase-matching between the interfering beams. Theoretical and
experimental studies have been carried out, mostly focusing on the degenerate
case of second-harmonic generation, showing significant conversion efficiency
enhancements. However, the link between the theory and experiment was not fully
established such that general guidelines and achievable conversion efficiency
for a given material platform are still unclear. In this work, we theoretically
analyze the phenomenological model of coherent photogalvanic effect in optical
waveguides. Our model predicts the existence of non-degenerate sum-frequency
generation quasi-phase-matching gratings, which is confirmed experimentally for
the first time. In addition, we rigorously formulate the time dynamics of the
space-charge grating inscription in coherent photogalvanic process. Based on
developed theoretical equations for the time dynamics of the space-charge
grating formation, we extract the material parameters governing the process for
our experimental platform, stoichiometric silicon nitride. The results obtained
provides a basis to compare the performances and potentials of different
platforms. This work not only supplements the theory of coherent photogalvanic
effect, but also enables us to identify critical parameters and limiting
factors for the inscription of $\chi^{(2)}$ gratings.


title: Numerical shape optimization among convex sets

abstract:
  This article proposes a new discrete framework for approximating solutions to
shape optimization problems under convexity constraints. The numerical method,
based on the support function or the gauge function, is guaranteed to generate
discrete convex shapes and is easily implementable using standard optimization
software. The framework can handle various objective functions ranging from
geometric quantities to functionals depending on partial differential
equations. Width or diameter constraints are handled using the support
function. Functionals depending on a convex body and its polar body can be
handled using a unified framework.


title: COP: Control & Observability-aware Planning

abstract:
  In this research, we aim to answer the question: How to combine Closed-Loop
State and Input Sensitivity-based with Observability-aware trajectory planning?
These possibly opposite optimization objectives can be used to improve
trajectory control tracking and, at the same time, estimation performance. Our
proposed novel Control & Observability-aware Planning (COP) framework is the
first that uses these possibly opposing objectives in a Single-Objective
Optimization Problem (SOOP) based on the Augmented Weighted Tchebycheff method
to perform the balancing of them and generation of B\'ezier curve-based
trajectories. Statistically relevant simulations for a 3D quadrotor unmanned
aerial vehicle (UAV) case study produce results that support our claims and
show the negative correlation between both objectives. We were able to reduce
the positional mean integral error norm as well as the estimation uncertainty
with the same trajectory to comparable levels of the trajectories optimized
with individual objectives.


title:
A faster exact method for solving the robust multi-mode
  resource-constrained project scheduling problem

abstract:
  This paper presents a mixed-integer linear programming formulation for the
multi-mode resource-constrained project scheduling problem with uncertain
activity durations. We consider a two-stage robust optimisation approach and
find solutions that minimise the worst-case project makespan, whilst assuming
that activity durations lie in a budgeted uncertainty set. Computational
experiments show that this easy-to-implement formulation is many times faster
than the current state-of-the-art solution approach for this problem, whilst
solving over 40% more instances to optimality over the same benchmarking set.


title:
Quantum Hydrodynamics of Fractonic Superfluids with Lineon Condensate:
  from Navier-Stokes-like Equations to Landau-like Criterion

abstract:
  Fractonic superfluids are exotic states of matter with spontaneously broken
higher-rank $U(1)$ symmetry. The latter is associated with conserved quantities
that include not only particle number (i.e. charge) but also higher moments,
such as dipoles, quadrupoles, and angular moments. Due to the presence of such
conserved quantities, the mobility of particles is restricted either completely
or partially. In this work, we systematically study hydrodynamical properties
of fractonic superfluids, especially focusing on the fractonic superfluids with
conserved angular moments. The constituent bosons are called "lineons" with
$d$-components in $d$-dimensional space. From Euler-Lagrange equation, we
derive the continuity equation and Navier-Stokes-like equations, in which the
angular moment conservation introduces extra terms. Furthermore, we discuss the
current configurations that are related to the defects. Like the conventional
superfluid, we study the critical values of velocity fields and density
currents, which gives rise to a Landau-like criterion. At the end of this work,
several future directions are discussed.


title: Neural Theorem Provers Delineating Search Area Using RNN

abstract:
  Although traditional symbolic reasoning methods are highly interpretable,
their application in knowledge graphs link prediction has been limited due to
their computational inefficiency. A new RNNNTP method is proposed in this
paper, using a generalized EM-based approach to continuously improve the
computational efficiency of Neural Theorem Provers(NTPs). The RNNNTP is divided
into relation generator and predictor. The relation generator is trained
effectively and interpretably, so that the whole model can be carried out
according to the development of the training, and the computational efficiency
is also greatly improved. In all four data-sets, this method shows competitive
performance on the link prediction task relative to traditional methods as well
as one of the current strong competitive methods.


title: Trotter-Kato Approximations of Impulsive Neutral SPDEs in Hilbert Spaces

abstract:
  This paper studies a class of impulsive neutral stochastic partial
differential equations in real Hilbert spaces. The main goal here is to
consider the Trotter-Kato approximations of mild solutions of such equations in
the $p$th-mean ($p\geq2$). As an application, a classical limit theorem on the
dependence of such equations on a parameter is obtained. The novelty of this
paper is that the combination of this approximating system and such equations
has not been considered before.


title: Fluid flow at interfaces driven by thermal gradients

abstract:
  Thermal forces drive several nonequilibrium phenomena able to set a fluid in
motion without pressure gradients. Although the most celebrated effect is
thermophoresis, also known as Ludwig-Soret effect, probably the simplest
example where thermal forces are at play is thermo-osmosis: The motion of a
{\it confined} fluid exclusively due to the presence of a temperature gradient.
We present a concise but complete derivation of the microscopic theory of
thermo-osmosis based on linear response theory. This approach is applied to a
simple fluid confined in a slab geometry, mimicking the flow through a pore in
a membrane separating two fluid reservoirs at different temperatures. We
consider both the case of an open channel, where the fluid can flow freely, and
that of a closed channel, where mass transport is inhibited and a pressure drop
sets in at the boundaries. Quantitative results require the evaluation of
generalized transport coefficients, but a preliminary check on a specific
prediction of the theory has been successfully performed via nonequilibrium
molecular dynamics simulations.


title: Generation of Twisted Gamma-Rays via Two-Photon Transition

abstract:
  We present a new and efficient method of generating twisted gamma rays
utilizing highly accelerated helium-like ions; they are excited to a chosen
state by irradiating two optical lasers and emit a photon with orbital angular
momentum in the deexcitation process. We study its emission rate together with
other properties such as background and photo-ionization processes.


title:
Identifying the root cause of cable network problems with machine
  learning

abstract:
  Good quality network connectivity is ever more important. For hybrid fiber
coaxial (HFC) networks, searching for upstream high noise in the past was
cumbersome and time-consuming. Even with machine learning due to the
heterogeneity of the network and its topological structure, the task remains
challenging. We present the automation of a simple business rule (largest
change of a specific value) and compare its performance with state-of-the-art
machine-learning methods and conclude that the precision@1 can be improved by
2.3 times. As it is best when a fault does not occur in the first place, we
secondly evaluate multiple approaches to forecast network faults, which would
allow performing predictive maintenance on the network.


title:
Electric Dipole Moments and Static Dipole Polarizabilities of
  Alkali--Alkaline-Earth Molecules: Non-relativistic versus relativistic
  coupled-cluster theory analyses

abstract:
  We analyze the electric dipole moments (PDMs) and static electric dipole
polarizabilities of the alkali--alkaline-earth (Alk-AlkE) dimers by employing
finite-field coupled-cluster methods, both in the frameworks of
non-relativistic and four-component spinfree relativistic theory. In order to
carry out comparative analyses rigorously, we consider those Alk-AlkE molecules
made out of the lightest to the medium-heavy constituent atoms (Alk: Li to Rb
and AlkE: Be through Sr). We present behaviour of electron correlation effects
as well as relativistic effects with the size of the molecules. Uncertainties
to the above quantities of the investigated Alk-AlkE molecules are inferred by
analyzing our results from different form of Hamiltonian, basis set, and
perturbative parameter in a few representative molecules. We have also provided
empirical relations by connecting average polarizabilities of the Alk-AlkE
molecules with their PDMs, and atomic numbers and polarizabilities of the
corresponding Alk and AlKE atoms, which can be used to roughly estimate the
average polarizabilities of other heavier Alk-AlkE molecules. We finally give
our recommended results, and compare them with the literature values.


title: Loschmidt amplitude spectrum in dynamical quantum phase transitions

abstract:
  Dynamical quantum phase transitions (DQPTs) are criticalities in the time
evolution of quantum systems and their existence has been theoretically
predicted and experimentally observed. However, how the system behaves in the
vicinity of DQPT and its connection to physical observables remains an open
question. In this work, we introduce the concept of the Loschmidt amplitude
spectrum (LAS), which extends the Loscmidt amplitude - the detector of the
transition - by considering the overlap of the initial state to all the
eigenstates of the prequench Hamiltonian. By analysing the LAS in the
integrable transverse-field Ising model, we find that the system undergoes a
population redistribution in the momentum space across DQPT. In the
quasiparticle picture, all the lower-half k modes are excited when the system
is at DQPT. The LAS is also applicable to study the dynamics of non-integrable
models where we have investigated the Ising model with next-nearest-neighbour
interactions as an example. The time evolution of the system's magnetization is
found to be connected to the products of the LAS and there exists a
simultaneous overlap of the time-evolved state to pairs of eigenstates of the
prequnech Hamiltonian that possess spin configurations of negative
magnetization. Our findings provide a better understanding of the
characteristics of the out-of-equilibrium system around DQPT.


title:
A note on the eigenvalues, singular values, and eigenvectors of Toeplitz
  and Hankel matrices

abstract:
  In a series of recent papers the spectral behavior of the matrix sequence
$\{Y_nT_n(f)\}$ is studied in the sense of the spectral distribution, where
$Y_n$ is the main antidiagonal (or flip matrix) and $T_n(f)$ is the Toeplitz
matrix generated by the function $f$, with $f$ being Lebesgue integrable and
with real Fourier coefficients. This kind of study is also motivated by
computational purposes for the solution of the related large linear systems
using the (preconditioned) MINRES algorithm. Here we complement the spectral
study with more results holding both asymptotically and for a fixed dimension
$n$, and with regard to eigenvalues and singular values: the final target is
the design of ad hoc procedures for the computation of the related spectra via
matrix-less algorithms, with a cost being linear in the number of computed
eigenvalues. We emphasize that the challenge of the case of non-monotone
generating functions is considered in this note, for which the previous
matrix-less algorithms fail. Numerical experiments are reported and commented,
with the aim of showing in a visual way the theoretical analysis.


title:
Supervised segmentation of NO2 plumes from individual ships using
  TROPOMI satellite data

abstract:
  Starting from 2021, the International Maritime Organization significantly
tightened the $\text{NO}_\text{x}$ emission requirements for ships entering the
Baltic and North Sea waters. Since all methods currently used for the ships'
compliance monitoring are costly and require proximity to the ship, the
performance of a global and continuous monitoring of the emission standards'
fulfillment has been impossible up to now. A promising approach is the use of
remote sensing with the recently launched TROPOMI/S5P satellite. Due to its
unprecedentedly high spatial resolution, it allows for the visual distinction
of $\text{NO}_\text{2}$ plumes of individual ships. To successfully deploy a
compliance monitoring system that is based on TROPOMI data, an automated
procedure for the attribution of $\text{NO}_\text{2}$ to individual ships has
to be developed. However, due to the extremely low signal-to-noise ratio,
interference with the signal from other - often stronger - sources, and the
absence of ground truth, the task is very challenging.
  In this study, we present an automated method for segmentation of plumes
produced by individual ships using TROPOMI satellite data - a first step
towards the automated procedure for global ship compliance monitoring. We
develop a multivariate plume segmentation method based on various ships',
wind's and spatial properties. For this, we propose to automatically define a
region of interest - a ship sector that we normalize with respect to scale and
orientation. We create a dataset, where each pixel has a label for belonging to
the respective ship plume or not. We train five linear and nonlinear
classifiers. The results show a significant improvement over the
threshold-based baselines. Moreover, the aggregated $\text{NO}_\text{2}$ levels
of the segmented plumes show high correlation with the theoretically derived
measure of ship's emission potential.


title: AT2020hur: A Possible Optical Counterpart of FRB 180916B

abstract:
  The physical origin of fast radio bursts (FRBs) remains unclear. Finding
multiwavelength counterparts of FRBs can be a breakthrough in understanding
their nature. In this work, we perform a systematic search for astronomical
transients (ATs) whose positions are consistent with FRBs. We find an
unclassified optical transient AT2020hur ($\alpha=01^{\mathrm{h}}
58^{\mathrm{m}} 00.750^{\mathrm{s}} \pm 1$ arcsec, $\delta=65^{\circ}
43^{\prime} 00.30^{\prime \prime} \pm 1$ arcsec) that is spatially coincident
with the repeating FRB 180916B ($\alpha=01^{\mathrm{h}} 58^{\mathrm{m}}
00.7502^{\mathrm{s}} \pm 2.3$ mas, $\delta=65^{\circ} 43^{\prime}
00.3152^{\prime \prime} \pm 2.3$ mas). The chance possibility for the
AT2020hur-FRB 180916B association is about 0.04%, which corresponds to a
significance of $3.5\sigma$. We develop a giant flare afterglow model to fit
AT2020hur. Although the giant flare afterglow model can interpret the
observations of AT2020hur, the derived kinetic energy of such a GF is at least
three orders of magnitude larger than that of the typical GF, and there is a
lot of fine tuning and coincidences required for this model. Another possible
explanation is that AT2020hur might consist of two or more optical flares
originating from the FRB source, e.g. fast optical bursts produced by the
inverse Compton scattering of FRB emission. Besides, AT2020hur is located in
one of the activity windows of FRB 180916B, which is an independent support for
the association. This coincidence may be due to the reason that the optical
counterpart is subject to the same periodic modulation as FRB 180916B, as
implied by the prompt FRB counterparts. Future simultaneous observations of
FRBs and their optical counterparts may help reveal their physical origin.


title: The many facets of academic mobility and its impact on scholars' career

abstract:
  International mobility in academia can enhance the human and social capital
of researchers and consequently their scientific outcome. However, there is
still a very limited understanding of the different mobility patterns among
scholars with various socio-demographic characteristics. The aim of this study
is twofold. First, we investigate to what extent individual factors associate
with the mobility of researchers. Second, we explore the relationship between
mobility and scientific activity and impact. For this purpose, we used a
bibliometric approach to track the mobility of authors. To compare the
scientific outcomes of researchers, we considered the number of publications
and received citations as indicators, as well as the number of unique
co-authors in all their publications. We also analysed the co-authorship
network of researchers and compared centrality measures of mobile and
non-mobile researchers. Results show that researchers from North America and
Sub-Saharan Africa, particularly female ones, have the lowest, respectively,
highest tendency towards international mobility. Having international
co-authors increases the probability of international movement. Our findings
uncover gender inequality in international mobility across scientific fields
and countries. Across genders, researchers in the Physical sciences have the
most and in the Social sciences the least rate of mobility. We observed more
mobility for Social scientists at the advanced career stage, while researchers
in other fields prefer to move at earlier career stages. Also, we found a
positive correlation between mobility and scientific outcomes, but no apparent
difference between females and males. Comparing the centrality of mobile and
non-mobile researchers in the co-authorship networks reveals a higher social
capital advantage for mobile researchers.


title: On modeling ICME cross-sections as static MHD columns

abstract:
  Solar coronal mass ejections are well known to expand as they propagate
through the heliosphere. Despite this, their cross-sections are usually modeled
as static plasma columns within the magnetohydrodynamics (MHD) framework. We
test the validity of this approach using in-situ plasma data from 151 magnetic
clouds (MCs) observed by the WIND spacecraft and 45 observed by the Helios
spacecrafts. We find that the most probable cross-section expansion speeds for
the WIND events are only $\approx 0.06$ times the Alfv\'en speed inside the MCs
while the most probable cross-section expansion speeds for the Helios events is
$\approx 0.03$. MC cross-sections can thus be considered to be nearly static
over an Alfv\'en crossing timescale. Using estimates of electrical conductivity
arising from Coulomb collisions, we find that the Lundquist number inside MCs
is high ($\approx 10^{13}$), suggesting that the MHD description is well
justified. The Joule heating rates using our conductivity estimates are several
orders of magnitude lower than the requirement for plasma heating inside MCs
near the Earth. While the (low) heating rates we compute are consistent with
the MHD description, the discrepancy with the heating requirement points to
possible departures from MHD and the need for a better understanding of plasma
heating in MCs.


title: Modelling Non-Smooth Signals with Complex Spectral Structure

abstract:
  The Gaussian Process Convolution Model (GPCM; Tobar et al., 2015a) is a model
for signals with complex spectral structure. A significant limitation of the
GPCM is that it assumes a rapidly decaying spectrum: it can only model smooth
signals. Moreover, inference in the GPCM currently requires (1) a mean-field
assumption, resulting in poorly calibrated uncertainties, and (2) a tedious
variational optimisation of large covariance matrices. We redesign the GPCM
model to induce a richer distribution over the spectrum with relaxed
assumptions about smoothness: the Causal Gaussian Process Convolution Model
(CGPCM) introduces a causality assumption into the GPCM, and the Rough Gaussian
Process Convolution Model (RGPCM) can be interpreted as a Bayesian
nonparametric generalisation of the fractional Ornstein-Uhlenbeck process. We
also propose a more effective variational inference scheme, going beyond the
mean-field assumption: we design a Gibbs sampler which directly samples from
the optimal variational solution, circumventing any variational optimisation
entirely. The proposed variations of the GPCM are validated in experiments on
synthetic and real-world data, showing promising results.


title:
Mapping topology of skyrmions and fractional quantum Hall droplets to
  nuclear EFT for ultra-dense baryonic matter

abstract:
  We describe the mapping at high density of topological structure of baryonic
matter to a nuclear effective field theory that implements hidden symmetries
emergent from strong nuclear correlations. The theory so constructed is found
to be consistent with no conflicts with the presently available observations in
both normal nuclear matter and compact-star matter. The hidden symmetries
involved are "local flavor symmetry" of the vector mesons identified to be
(Seiberg-)dual to the gluons of QCD and hidden "quantum scale symmetry" with an
IR fixed point with a "genuine dilaton (GD)" characterized by non-vanishing
pion and dilaton decay constants. Both the skyrmion topology for $N_f \geq 2$
baryons and the fractional quantum Hall (FQH) droplet topology for $N_f=1$
baryons are unified in the "homogeneous/hidden" Wess-Zumino term in the hidden
local symmetry (HLS) Lagrangian. The possible indispensable role of the FQH
droplets in going beyond the density regime of compact stars approaching
scale-chiral restoration is explored by moving toward the limit where both the
dilaton and the pion go massless.


title: A new protonated molecule discovered in TMC-1: HCCNCH+

abstract:
  In recent years we have seen an important increase in the number of
protonated molecules detected in cold dense clouds. Here we report the
detection in TMC-1 of HCCNCH+, the protonated form of HCCNC, which is a
metastable isomer of HC3N. This is the first protonated form of a metastable
isomer detected in a cold dense cloud. The detection was based on observations
carried out with the Yebes 40m and IRAM 30m telescopes, which revealed four
harmonically related lines. We derive a rotational constant B = 4664.431891 +/-
0.000692 MHz and a centrifugal distortion constant D = 519.14 +/- 4.14 Hz. From
a high-level ab initio screening of potential carriers we confidently assign
the series of lines to the ion HCCNCH+. We derive a column density of (3.0 +/-
0.5)e10 cm-2 for HCCNCH+, which results in a HCCNCH+/HCCNC abundance ratio of
0.010 +/- 0.002. This value is well reproduced by a state-of-the-art chemical
model, which however is subject to important uncertainties regarding the
chemistry of HCCNCH+. The observational and theoretical status of protonated
molecules in cold dense clouds indicate that there exists a global trend in
which protonated-to-neutral abundance ratios MH+/M increase with increasing
proton affinity of the neutral M, although if one restricts to species M with
high proton affinities (>700 kJ/mol), MH+/M ratios fall in the range 0.001-0.1,
with no apparent correlation with proton affinity. We suggest various
protonated molecules that are good candidates for detection in cold dense
clouds in the near future.


title:
Cross-View-Prediction: Exploring Contrastive Feature for Hyperspectral
  Image Classification

abstract:
  This paper presents a self-supervised feature learning method for
hyperspectral image classification. Our method tries to construct two different
views of the raw hyperspectral image through a cross-representation learning
method. And then to learn semantically consistent representation over the
created views by contrastive learning method. Specifically, four
cross-channel-prediction based augmentation methods are naturally designed to
utilize the high dimension characteristic of hyperspectral data for the view
construction. And the better representative features are learned by maximizing
mutual information and minimizing conditional entropy across different views
from our contrastive network. This 'Cross-View-Predicton' style is
straightforward and gets the state-of-the-art performance of unsupervised
classification with a simple SVM classifier.


title:
The Dzhumadildaev brackets: a hidden supersymmetry of commutators and
  the Amitsur-Levitzki--type identities

abstract:
  The Amitsur--Levitzki identity for matrices was generalized in several
directions: by Kostant for simple finite-dimensional Lie algebras, by Kirillov
(later joined by Kontsevich, Molev, Ovsienko, and Udalova) for simple vectorial
Lie algebras with polynomial coefficients, and by Gie, Pinczon, and Ushirobira
for the orthosymplectic Lie superalgebra $\mathfrak{osp}(1|n)$. Dzhumadildaev
switched the focus of attention in these results by considering the algebra
formed by antisymmetrizors and discovered a hidden supersymmetry of
commutators. We overview these results and their possible generalizations (open
problems).


title: Filament collapse: A two phase process

abstract:
  Using numerical simulations, we investigate the gravitational evolution of
filamentary molecular cloud structures and their condensation into dense
protostellar cores. One possible process is the so called 'edge effect', the
pile-up of matter at the end of the filament due to self-gravity. This effect
is predicted by theory but only rarely observed. To get a better understanding
of the underlying processes we used a simple analytic approach to describe the
collapse and the corresponding collapse time. We identify a model of two
distinct phases: The first phase is free fall dominated, due to the
self-gravity of the filament. In the second phase, after the turning point, the
collapse is balanced by the ram pressure, produced by the inside material of
the filament, which leads to a constant collapse velocity. This approach
reproduces the established collapse time of uniform density filaments and
agrees well with our hydrodynamic simulations. In addition, we investigate the
influence of different radial density profiles on the collapse. We find that
the deviations compared to the uniform filament are less than 10%. Therefore,
the analytic collapse model of the uniform density filament is an excellent
general approach.


title: MTLDesc: Looking Wider to Describe Better

abstract:
  Limited by the locality of convolutional neural networks, most existing local
features description methods only learn local descriptors with local
information and lack awareness of global and surrounding spatial context. In
this work, we focus on making local descriptors "look wider to describe better"
by learning local Descriptors with More Than just Local information (MTLDesc).
Specifically, we resort to context augmentation and spatial attention
mechanisms to make our MTLDesc obtain non-local awareness. First, Adaptive
Global Context Augmented Module and Diverse Local Context Augmented Module are
proposed to construct robust local descriptors with context information from
global to local. Second, Consistent Attention Weighted Triplet Loss is designed
to integrate spatial attention awareness into both optimization and matching
stages of local descriptors learning. Third, Local Features Detection with
Feature Pyramid is given to obtain more stable and accurate keypoints
localization. With the above innovations, the performance of our MTLDesc
significantly surpasses the prior state-of-the-art local descriptors on
HPatches, Aachen Day-Night localization and InLoc indoor localization
benchmarks.


title: Rethinking Minimal Sufficient Representation in Contrastive Learning

abstract:
  Contrastive learning between different views of the data achieves outstanding
success in the field of self-supervised representation learning and the learned
representations are useful in broad downstream tasks. Since all supervision
information for one view comes from the other view, contrastive learning
approximately obtains the minimal sufficient representation which contains the
shared information and eliminates the non-shared information between views.
Considering the diversity of the downstream tasks, it cannot be guaranteed that
all task-relevant information is shared between views. Therefore, we assume the
non-shared task-relevant information cannot be ignored and theoretically prove
that the minimal sufficient representation in contrastive learning is not
sufficient for the downstream tasks, which causes performance degradation. This
reveals a new problem that the contrastive learning models have the risk of
over-fitting to the shared information between views. To alleviate this
problem, we propose to increase the mutual information between the
representation and input as regularization to approximately introduce more
task-relevant information, since we cannot utilize any downstream task
information during training. Extensive experiments verify the rationality of
our analysis and the effectiveness of our method. It significantly improves the
performance of several classic contrastive learning models in downstream tasks.
Our code is available at https://github.com/Haoqing-Wang/InfoCL.


title:
The quantum Hamilton Jacobi equation and the link between classical and
  quantum mechanics

abstract:
  We study how the classical Hamilton's principal and characteristic functions
are generated from the solutions of the quantum Hamilton-Jacobi equation. While
in the classically forbidden regions these quantum quantities directly tend to
the classical ones, this is not the case in the allowed regions. There, the
limit is reached only if the quantum fluctuations are eliminated by means of
coarse-graining averages. Analogously, the classical Hamilton-Jacobi scheme
bringing to the motion's equations arises from a similar formal quantum
procedure.


title:
Spiking Neural Network Integrated Circuits: A Review of Trends and
  Future Directions

abstract:
  In this paper, we reviewed Spiking neural network (SNN) integrated circuit
designs and analyzed the trends among mixed-signal cores, fully digital cores
and large-scale, multi-core designs. Recently reported SNN integrated circuits
are compared under three broad categories: (a) Large-scale multi-core designs
that have dedicated NOC for spike routing, (b) digital single-core designs and
(c) mixed-signal single-core designs. Finally, we finish the paper with some
directions for future progress.


title: On pseudoeffective cones of projective bundles and volume function

abstract:
  In this article, we compute the pseudoeffective cones of various projective
bundles $\mathbb{P}_X(E)$ over higher dimensional varieties $X$ under some
assumptions on $X$ as well as on the vector bundle $E$. We also compute the
volume function on fibre product of two projective bundles
$\mathbb{P}(F)\times_C\mathbb{P}(E)$ over a smooth irreducible complex
projective curve $C$.


title:
Active control of particle position by boundary slip in inertial
  microfluidics

abstract:
  Inertial microfluidic is able to focus and separate particles in
microchannels based on the characteristic geometry and intrinsic hydrodynamic
effect. Yet, the vertical position of suspended particles in the microchannel
cannot be manipulated in real time. In this study, we utilize the boundary slip
effect to regulate the parabolic velocity distribution of fluid in the
microchannel and present a scheme to active control the vertical position of
particles in inertial microfluidics. The flow field of a microchannel with a
unilateral slip boundary is equivalent to that of the microchannel widened by
the relevant slip length, and the particle equilibrium positions in the two
microchannels are consistent consequently. Then, we simulate the lateral
migrations of three kinds of typical particles, namely circle, ellipse, and
rectangle in the microchannel. Unlike the smooth trajectories of circular
particles, the motions of the elliptical and rectangular particles are
accompanied by regular fluctuations and non-uniform rotations due to their
non-circular geometries. The results demonstrate that the unilateral slip
boundary can effectively control the vertical equilibrium position of
particles. Thus, the present scheme enables to active manipulate the particles
positions in vertical direction and can promote more accurate focusing,
separating, and transport in inertial microfluidics.


title: Higher derivative extension of the functional renormalization group

abstract:
  We study higher derivative extension of the functional renormalization group
(FRG). We consider FRG equations for a scalar field that consist of terms with
higher functional derivatives of the effective action and arbitrary cutoff
functions. We show that the epsilon expansion around the Wilson-Fisher fixed
point is indeed reproduced by the local potential approximation of the FRG
equations.


title:
The upper critical field in the BiCh2-based superconductors
  CeOBiS1.7Se0.3 and PrO0.5F0.5BiS2-xSex (x = 0, 0.3)

abstract:
  We report the upper critical field (Bc2) in the BiCh2-based superconductors
CeOBiS1.7Se0.3 and PrO0.5F0.5BiS2-xSex (x = 0, 0.3). Single crystals of
CeOBiS1.7Se0.3 and PrO0.5F0.5BiS2-xSex (x = 0, 0.3) were grown using the flux
method. Single-crystal structural analysis revealed that the crystal structure
at room temperature is tetragonal (P4/nmm). Through electrical resistivity and
magnetization measurements, bulk superconductivity was observed in all samples.
For CeOBiS1.7Se0.3, the in-plane Bc2 is smaller than the conventional orbital
limit and Pauli limit, suggesting that ferromagnetic ordering, which has been
observed in a related Ce-containing BiCh2-based compound, affects Bc2 and
superconductivity. In contrast, high in-plane Bc2 was observed for
PrO0.5F0.5BiS2-xSex (x = 0, 0.3). We propose that the in-plane Bc2 is enhanced
by antisymmetric spin-orbit coupling, which arises from the lack of local
inversion symmetry.


title:
Performance of the KAGRA detector during the first joint observation
  with GEO 600 (O3GK)

abstract:
  KAGRA, the kilometer-scale underground gravitational-wave detector, is
located at Kamioka, Japan. In April 2020, an astrophysics observation was
performed at the KAGRA detector in combination with the GEO 600 detector; this
observation operation is called O3GK. The optical configuration in O3GK is
based on a power recycled Fabry-P\'{e}rot Michelson interferometer; all the
mirrors were set at room temperature. The duty factor of the operation was
approximately 53%, and the strain sensitivity was
$3\times10^{-22}~/\sqrt{\rm{Hz}}$ at 250 Hz. In addition, the
binary-neutron-star (BNS) inspiral range was approximately 0.6 Mpc. The
contributions of various noise sources to the sensitivity of O3GK were
investigated to understand how the observation range could be improved; this
study is called a "noise budget". According to our noise budget, the measured
sensitivity could be approximated by adding up the effect of each noise. The
sensitivity was dominated by noise from the sensors used for local controls of
the vibration isolation systems, acoustic noise, shot noise, and laser
frequency noise. Further, other noise sources that did not limit the
sensitivity were investigated. This paper provides a detailed account of the
KAGRA detector in O3GK including interferometer configuration, status, and
noise budget. In addition, strategies for future sensitivity improvements such
as hardware upgrades, are discussed.


title:
Extended Finite Elements for 3D-1D coupled problems via a
  PDE-constrained optimization approach

abstract:
  In this work we propose the application of the eXtended Finite Element Method
(XFEM) to the simulation of coupled elliptic problems on 3D and 1D domains,
arising from the application of geometrical reduction models to fully three
dimensional problems with cylindrical, or nearly cylindrical, inclusions with
small radius. In this context, the use of non conforming meshes is widely
adopted, even if, due to the presence of singular source terms for the 3D
problems, mesh adaptation near the embedded 1D domains may be necessary to
improve solution accuracy and to recover optimal convergence rates. The XFEM
are used here as an alternative to mesh adaptation. The choice of a suitable
enrichment function is presented and an effective quadrature strategy is
proposed. Numerical tests show the effectiveness of the approach.


title:
Excitable reaction-diffusion waves of curvature-inducing proteins on
  deformable membrane tubes

abstract:
  Living cells employ excitable reaction-diffusion waves for internal cellular
functions, in which curvature-inducing proteins are often involved. However,
the role of their mechanochemical coupling is not well understood. Here, we
report the membrane deformation induced by the excitable reaction-diffusion
waves of curvature-inducing proteins and the alternation in the waves due to
the deformation, using a coarse-grained simulation of tubular membranes with a
modified FitzHugh--Nagumo model. Protein-propagating waves deform tubular
membranes into meandering forms, and large deformations induce budding and
erase waves. The wave speed and shape are determined by a combination of
membrane deformation and spatial distribution of the curvature-inducing
protein. Waves are also undulated in the azimuthal direction depending on the
condition. Rotationally symmetric waves locally deform the tubes into a
symmetric shape but maintain a straight shape on average. Alternatively, the
wave bends the tube up and down such that the tube deforms into meandering
forms when the waves are not rotationally symmetric. Our simulation method can
be applied to other chemical reaction models and used to investigate various
biomembrane phenomena.


title: On the Excitation and Radiative Decay Rates of Plasmonic Nanoantennas

abstract:
  Plasmonic nanoantennas have the ability to confine and enhance incident
electromagnetic fields into very sub-wavelength volumes, while at the same time
efficiently radiating energy to the far-field. These properties have allowed
plasmonic nanoantennas to be extensively used for exciting quantum
emitters-such as molecules and quantum dots-and also for the extraction of
photons from them for measurements in the far-field. Due to electromagnetic
reciprocity, it is expected that plasmonic nanoantennas radiate energy as
efficiently as an external source can couple energy to them. In this paper, we
adopt a multipole expansion (Mie theory) and numerical simulations to show that
although reciprocity holds, certain plasmonic antennas radiate energy much more
efficiently than one can couple energy into them. This work paves the way
towards designing plasmonic antennas with specific properties for applications
where the near-to-far-field relationship is of high significance, such as:
surface-enhanced Raman spectroscopy, strong coupling at room temperature, and
the engineering of quantum states in nanoplasmonic devices.


title:
Comparative structural evolution under pressure of powder and single
  crystals of the layered antiferromagnet FePS$_3$

abstract:
  The layered antiferromagnet FePS$_3$ has been shown to undergo a structural
transition under pressure linked to an insulator-metal transition, with two
incompatible models previously proposed for the highest-pressure structure. We
present a study of the high-pressure crystal structures of FePS$_3$ using both
single-crystal and powder x-ray diffraction. We show that the highest pressure
transition involves a collapse of the inter-planar spacing of this material,
along with an increase in symmetry from a monoclinic to a trigonal structure,
to the exclusion of other models. The extent of this volume collapse is shown
to be sensitive to the presence of a helium pressure medium in the sample
environment, indicating that consideration of such experimental factors is
important for understanding high-pressure behaviours in this material.


title: A $p$-adic Descartes solver: the Strassman solver

abstract:
  Solving polynomials is a fundamental computational problem in mathematics. In
the real setting, we can use Descartes' rule of signs to efficiently isolate
the real roots of a square-free real polynomial. In this paper, we translate
this method into the $p$-adic worlds. We show how the $p$-adic analog of
Descartes' rule of signs, Strassman's theorem, leads to an algorithm to isolate
the roots of a square-free $p$-adic polynomial. Moreover, we show that this
algorithm runs in $\mathcal{O}(d^2\log^3d)$-time for a random $p$-adic
polynomial of degree $d$. To perform this analysis, we introduce the
condition-based complexity framework from real/complex numerical algebraic
geometry into $p$-adic numerical algebraic geometry.

categories: math.NT cs.CC cs.NA cs.SC math.NA math.PR


title:
Bragg diffraction-induced imperfections of the signal in retroreflective
  atom interferometers

abstract:
  We present a detailed study of the effects of imperfect atom-optical
manipulation in Bragg-based light-pulse atom interferometers. Off-resonant
higher-order diffraction leads to population loss, spurious interferometer
paths, and diffraction phases. In a path-dependent formalism, we study
numerically various effects and analyze the interference signal caused by an
external phase or gravity. We compare single and double Bragg diffraction in
retroreflective setups. In double Bragg diffraction, phase imperfections lead
to a beating due to three-path interference. Some effects of diffraction phases
can be avoided by adding the population of the outer exit ports of double
diffraction.


title:
A review of Generative Adversarial Networks for Electronic Health
  Records: applications, evaluation measures and data sources

abstract:
  Electronic Health Records (EHRs) are a valuable asset to facilitate clinical
research and point of care applications; however, many challenges such as data
privacy concerns impede its optimal utilization. Generative Adversarial
Networks (GANs) show great promise in generating synthetic EHR data by learning
underlying data distributions while achieving excellent performance and
addressing these challenges. This work aims to review the major developments in
various applications of GANs for EHRs and provides an overview of the proposed
methodologies. For this purpose, we combine perspectives from healthcare
applications and machine learning techniques in terms of source datasets and
the fidelity and privacy evaluation of the generated synthetic datasets. We
also compile a list of the metrics and datasets used by the reviewed works,
which can be utilized as benchmarks for future research in the field. We
conclude by discussing challenges in GANs for EHRs development and proposing
recommended practices. We hope that this work motivates novel research
development directions in the intersection of healthcare and machine learning.


title: Entropic optimal planning for path-dependent mean field games

abstract:
  In the context of mean field games, with possible control of the diffusion
coefficient, we consider a path-dependent version of the planning problem
introduced by P.L. Lions: given a pair of marginal distributions $(\mu_0,
\mu_1)$, find a specification of the game problem starting from the initial
distribution $\mu_0$, and inducing the target distribution $\mu_1$ at the mean
field game equilibrium. Our main result reduces the path-dependent planning
problem into an embedding problem, that is, constructing a McKean-Vlasov
dynamics with given marginals $(\mu_0,\mu_1)$. Some sufficient conditions on
$(\mu_0,\mu_1)$ are provided to guarantee the existence of solutions. We also
characterize, up to integrability, the minimum entropy solution of the planning
problem. In particular, as uniqueness does not hold anymore in our
path-dependent setting, one can naturally introduce an optimal planning problem
which would be reduced to an optimal transport problem along with controlled
McKean-Vlasov dynamics.


title:
The subgroup of the Goeritz group of the Heegaard splitting induced by
  an openbook decomposition consisting of elements preserving the binding

abstract:
  When a 3-manifold admits an openbook decomposition, we get a Heegaard
splitting by thickening a page. This splitting surface has a special multi
curves coming from the binding. In this paper, we consider the subgroup of the
Goeritz group of this Heegaard splitting, which is the mapping class group of
the 3-manifold preserving the given Heegaard splitting, consisting of elements
preserving the binding. This subgroup turned out to be the quotient of the
subgroup of the orientation preserving mapping class group consisting of
elements commuting with the monodromy by the subgroup generated by the Dehn
twists along the boundary curves. We also get a criterion for the existence of
an element of the Goeritz group which fixes the binding as a set and reverses
the orientation. At last, we give some example of computation of a Goeritz
group.


title:
The molecular chemistry of Type Ibc supernovae, and diagnostic potential
  with the James Webb Space Telescope

abstract:
  A currently unsolved question in supernova research is the origin of
stripped-envelope supernovae (SESNe). Such SNe lack spectral signatures of
hydrogen (Type Ib), or hydrogen and helium (Type Ic), indicating that the outer
stellar layers have been stripped during their evolution. The mechanism for
this is not well understood, and to disentangle the different scenarios
determination of nucleosynthesis yields from observed spectra can be attempted.
However, the interpretation of observations depends on the adopted spectral
models. A previously missing ingredient in these is the inclusion of molecular
effects, which can be significant. We aim to investigate how the molecular
chemistry in stripped-envelope supernovae affects physical conditions and
optical spectra and produces ro-vibrational emission in the mid-infrared (MIR).
We also aim to assess the diagnostic potential of observations of such MIR
emission with JWST. We couple a chemical kinetic network including carbon,
oxygen, silicon, and sulfur-bearing molecules into the NLTE spectral synthesis
code SUMO. We let four species - CO, SiO, SiS and SO - participate in the NLTE
cooling of the gas to achieve self-consistency between the molecule formation
and the temperature. We apply the new framework to model the spectrum of a Type
Ic supernova in the 100-600d time range. Molecules are predicted to form in
SESN ejecta in significant quantities (typical mass $10^{-3}$ $M_\odot$)
throughout the 100-600d interval. The impact on the temperature and optical
emission depends on the density of the oxygen zones and varies with epoch. For
example, the [O I] 6300, 6364 feature can be quenched by molecules from 200 to
450d depending on density. The MIR predictions show strong emission in the
fundamental bands of CO, SiO, and SiS, and in the CO and SiO overtones.


title: Swap, Shift and Trim to Edge Collapse a Filtration

abstract:
  Boissonnat and Pritam introduced an algorithm to reduce a filtration of flag
(or clique) complexes, which can in particular speed up the computation of its
persistent homology. They used so-called edge collapse to reduce the input flag
filtration and their reduction method required only the 1-skeleton of the
filtration. In this paper we revisit the usage of edge collapse for efficient
computation of persistence homology. We first give a simple and intuitive
explanation of the principles underlying that algorithm. This in turn allows us
to propose various extensions including a zigzag filtration simplification
algorithm. We finally show some experiments to better understand how it
behaves.


title:
Designing broadband pulsed dynamic nuclear polarization sequences in
  static solids

abstract:
  Dynamic nuclear polarization (DNP) is an NMR hyperpolarization technique that
mediates polarization transfer from highly polarized unpaired electrons to
NMR-active nuclei via microwave (mw) irradiation. The ability to generate
arbitrarily shaped mw pulses using arbitrary waveform generators opens up the
opportunity to remarkably improve the robustness and versatility of DNP, in
many ways resembling the early stages of pulsed NMR. We present here novel
design principles based on single-spin vector effective Hamiltonian theory to
develop new broadband DNP pulse sequences, namely an adiabatic XiX-DNP
experiment and a broadband amplitude modulated signal enhanced (BASE)
experiment. We demonstrate that the adiabatic BASE pulse sequence may achieve a
DNP $^{1}$H enhancement factor of $\sim$ 360, a record that outperforms all
previously known pulsed DNP sequences at $\sim$ 0.35 T and 80 K in static
solids. The bandwidth of the BASE-DNP experiments is about 3 times the $^{1}$H
Larmor frequency ($\sim$50 MHz).


title:
A Review of Augmented Reality Apps for an AR-Based STEM Education
  Framework

abstract:
  Within the past two decades, Augmented Reality (AR) applications have
received increased attention. Augmented Reality is now widely used in the
education sector at level K to 12. AR is expected to be generally adopted in
two to three years in higher education and four to five years in K to 12.
Applying AR technology in the education sector especially in STEM subjects, can
result in having a smart campus. In adopting a SMART Campus strategy, education
practitioners must address many intrinsic issues in science, technology,
engineering, and mathematics (STEM) research. For example, in physics, there
are expensive or insufficient laboratory systems, system faults, and difficulty
simulating other experimental circumstances. In technology, many schools do not
have enough computers. In engineering, there are only a few instructors who are
knowledgeable in computer aided design (CAD). In mathematics, few teachers
incorporate technology into their lessons often because they believe it is
still better to teach through the traditional methods. Hence, In this paper we
discuss how AR is being used now in different learning areas in STEM to open
new doors to researchers and teachers as they transition their schools into
SMART campuses with the use of AR apps. Aligned with this, a suggested
framework for school administrators and policymakers is proposed based on a
review of the positive benefits of different AR apps.


title: Nanomechanical spectroscopy of 2D materials

abstract:
  We introduce a nanomechanical platform for fast and sensitive measurements of
the spectrally-resolved optical dielectric function of 2D materials. At the
heart of our approach is a suspended 2D material integrated into a
nanomechanical resonator illuminated by a wavelength-tunable laser source. From
the heating-related frequency shift of the resonator as well as its optical
reflection measured as a function of photon energy, we obtain the real and
imaginary parts of the dielectric function. Our measurements are unaffected by
substrate-related screening and do not require any assumptions on the underling
optical constants. This fast ($\tau_{rise}$ $\sim$ 135 ns), sensitive
(noise-equivalent power = 90 $\frac{pW}{\sqrt{Hz}}$ ), and broadband (1.2 $-$
3.1 eV, extendable to UV-THz) method provides an attractive alternative to
spectroscopic or ellipsometric characterisation techniques.

categories: cond-mat.mes-hall cond-mat.mtrl-sci physics.app-ph physics.optics


title:
Extracting associations and meanings of objects depicted in artworks
  through bi-modal deep networks

abstract:
  We present a novel bi-modal system based on deep networks to address the
problem of learning associations and simple meanings of objects depicted in
"authored" images, such as fine art paintings and drawings. Our overall system
processes both the images and associated texts in order to learn associations
between images of individual objects, their identities and the abstract
meanings they signify. Unlike past deep nets that describe depicted objects and
infer predicates, our system identifies meaning-bearing objects ("signifiers")
and their associations ("signifieds") as well as basic overall meanings for
target artworks. Our system had precision of 48% and recall of 78% with an F1
metric of 0.6 on a curated set of Dutch vanitas paintings, a genre celebrated
for its concentration on conveying a meaning of great import at the time of
their execution. We developed and tested our system on fine art paintings but
our general methods can be applied to other authored images.


title:
Learning from Attacks: Attacking Variational Autoencoder for Improving
  Image Classification

abstract:
  Adversarial attacks are often considered as threats to the robustness of Deep
Neural Networks (DNNs). Various defending techniques have been developed to
mitigate the potential negative impact of adversarial attacks against task
predictions. This work analyzes adversarial attacks from a different
perspective. Namely, adversarial examples contain implicit information that is
useful to the predictions i.e., image classification, and treat the adversarial
attacks against DNNs for data self-expression as extracted abstract
representations that are capable of facilitating specific learning tasks. We
propose an algorithmic framework that leverages the advantages of the DNNs for
data self-expression and task-specific predictions, to improve image
classification. The framework jointly learns a DNN for attacking Variational
Autoencoder (VAE) networks and a DNN for classification, coined as Attacking
VAE for Improve Classification (AVIC). The experiment results show that AVIC
can achieve higher accuracy on standard datasets compared to the training with
clean examples and the traditional adversarial training.


title:
Trustable Mobile Crowd Sourcing for Acquiring Information from a Flooded
  Smart Area

abstract:
  Flood is a natural phenomenon that causes severe environmental damage and
destruction in smart cities. After a flood, topographic, geological, and living
conditions change. As a result, the previous information regarding the
environment is no more valid. Rescue and relief organizations that intend to
help the affected people need to obtain new and accurate information about the
conditions of the flooded environment. Acquiring this required information in
the shortest time is a challenge for realizing smart cities. Due to the
advances in the Internet of Things technology and the prevalence of smartphones
with several sensors and functionalities, it is possible to obtain the required
information by leveraging the Crowdsourcing model. In this paper, the
information required from a flooded area is classified into four categories:
victim, Facility and Livelihood, medical, and transfer. Next, a crowdsourcing
scheme for acquiring information is proposed, including malicious user
detection to ensure the accuracy of information received. Finally, simulation
results indicate that the proposed scheme correctly detects malicious users and
ensures the quality of obtained information.


title:
SuperCone: Unified User Segmentation over Heterogeneous Experts via
  Concept Meta-learning

abstract:
  We study the problem of user segmentation: given a set of users and one or
more predefined groups or segments, assign users to their corresponding
segments. As an example, for a segment indicating particular interest in a
certain area of sports or entertainment, the task will be to predict whether
each single user will belong to the segment. However, there may exist numerous
long tail prediction tasks that suffer from data availability and may be of
heterogeneous nature, which make it hard to capture using single off the shelf
model architectures. In this work, we present SuperCone, our unified
predicative segments system that addresses the above challenges. It builds on
top of a flat concept representation that summarizes each user's heterogeneous
digital footprints, and uniformly models each of the prediction task using an
approach called "super learning ", that is, combining prediction models with
diverse architectures or learning method that are not compatible with each
other. Following this, we provide an end to end approach that learns to
flexibly attend to best suited heterogeneous experts adaptively, while at the
same time incorporating deep representations of the input concepts that
augments the above experts. Experiments show that SuperCone significantly
outperform state-of-the-art recommendation and ranking algorithms on a wide
range of predicative segment tasks and public structured data learning
benchmarks.


title:
Digital Twin in Practice: Emergent Insights from an ethnographic-action
  research study

abstract:
  Based on an ethnographic action research study for a Digital Twin (DT)
deployment on an automated highway maintenance project, this paper reports some
of the stumbling blocks that practitioners face while deploying a DT in
practice. At the outset, the scope of the case study was broadly defined in
terms of digitalization, and software development and deployment, which later
pivoted towards the concept of Digital Twin during the collective reflection
sessions between the project participants. Through an iterative learning cycle
via discussions among the various project stakeholders, the case study led to
uncovering the roadblocks in practice faced by the Architecture, Engineering,
and Construction (AEC) practitioners. This research finds that the
practitioners are facing difficulty in: (1) Creating a shared understanding due
to the lack of consensus on the Digital Twin concept, (2) Adapting and
investing in Digital Twin due to inability to exhaustively evaluate and select
the appropriate capabilities in a Digital Twin, and (3) Allocation of resources
for Digital Twin development due to the inability to assess the impact of DT on
the organizational conditions and processes.


title:
Model Positionality and Computational Reflexivity: Promoting Reflexivity
  in Data Science

abstract:
  Data science and machine learning provide indispensable techniques for
understanding phenomena at scale, but the discretionary choices made when doing
this work are often not recognized. Drawing from qualitative research
practices, we describe how the concepts of positionality and reflexivity can be
adapted to provide a framework for understanding, discussing, and disclosing
the discretionary choices and subjectivity inherent to data science work. We
first introduce the concepts of model positionality and computational
reflexivity that can help data scientists to reflect on and communicate the
social and cultural context of a model's development and use, the data
annotators and their annotations, and the data scientists themselves. We then
describe the unique challenges of adapting these concepts for data science work
and offer annotator fingerprinting and position mining as promising solutions.
Finally, we demonstrate these techniques in a case study of the development of
classifiers for toxic commenting in online communities.


title:
Thermal circuits assembling and state-space extraction for modelling
  heat transfer in buildings

abstract:
  State-space representation is essential in the theory of dynamic systems.
This paper introduces a methodology for obtaining state-space representation
from the thermal models of elementary components of a building by the
conjunction of two methods: 1) assembling of thermal circuits and 2)
state-space extraction from thermal circuit. These methods are fully
illustrated on a very simple model and tested on a real house of about 100 m 2
on which detailed measurements were achieved for 40 days at a time step of 10
min. The errors obtained between the measurements and the simulation results
are in the order of $\pm$1 {\textdegree}C for a single zone and $\pm$2
{\textdegree}C for seven thermal zones. Besides simulation, parameter
identification and control, the methods for assembling thermal circuits and
extraction of state-space representation may be useful in Building Information
Modelling (BIM).


title:
Compressing CNN Kernels for Videos Using Tucker Decompositions: Towards
  Lightweight CNN Applications

abstract:
  Convolutional Neural Networks (CNN) are the state-of-the-art in the field of
visual computing. However, a major problem with CNNs is the large number of
floating point operations (FLOPs) required to perform convolutions for large
inputs. When considering the application of CNNs to video data, convolutional
filters become even more complex due to the extra temporal dimension. This
leads to problems when respective applications are to be deployed on mobile
devices, such as smart phones, tablets, micro-controllers or similar,
indicating less computational power.
  Kim et al. (2016) proposed using a Tucker-decomposition to compress the
convolutional kernel of a pre-trained network for images in order to reduce the
complexity of the network, i.e. the number of FLOPs. In this paper, we
generalize the aforementioned method for application to videos (and other 3D
signals) and evaluate the proposed method on a modified version of the THETIS
data set, which contains videos of individuals performing tennis shots. We show
that the compressed network reaches comparable accuracy, while indicating a
memory compression by a factor of 51. However, the actual computational
speed-up (factor 1.4) does not meet our theoretically derived expectation
(factor 6).


title: Active Learning by Feature Mixing

abstract:
  The promise of active learning (AL) is to reduce labelling costs by selecting
the most valuable examples to annotate from a pool of unlabelled data.
Identifying these examples is especially challenging with high-dimensional data
(e.g. images, videos) and in low-data regimes. In this paper, we propose a
novel method for batch AL called ALFA-Mix. We identify unlabelled instances
with sufficiently-distinct features by seeking inconsistencies in predictions
resulting from interventions on their representations. We construct
interpolations between representations of labelled and unlabelled instances
then examine the predicted labels. We show that inconsistencies in these
predictions help discovering features that the model is unable to recognise in
the unlabelled instances. We derive an efficient implementation based on a
closed-form solution to the optimal interpolation causing changes in
predictions. Our method outperforms all recent AL approaches in 30 different
settings on 12 benchmarks of images, videos, and non-visual data. The
improvements are especially significant in low-data regimes and on self-trained
vision transformers, where ALFA-Mix outperforms the state-of-the-art in 59% and
43% of the experiments respectively.


title:
Teleconnection patterns of different El Ni\~no types revealed by climate
  network curvature

abstract:
  The diversity of El Ni\~no events is commonly described by two distinct
flavors, the Eastern Pacific (EP) and Central Pacific (CP) types. While the
remote impacts, i.e. teleconnections, of EP and CP events have been studied for
different regions individually, a global picture of their teleconnection
patterns is still lacking. Here, we use Forman-Ricci curvature applied on
climate networks constructed from 2-meter air temperature data to distinguish
regional links from teleconnections. Our results confirm that teleconnection
patterns are strongly influenced by the El Ni\~no type. EP events have
primarily tropical teleconnections whereas CP events involve
tropical-extratropical connections, particularly in the Pacific. Moreover, the
central Pacific region does not have many teleconnections, even during CP
events. It is mainly the eastern Pacific that mediates the remote influences
for both El Ni\~no types.


title: Microwave and Acoustic Absorption Metamaterials

abstract:
  Wave absorption metamaterials have been an enduring topic over the past two
decades, propelled not only by novel scientific advances, but also by their
extensive application potential. In this review, we aim to provide some general
insights into the absorption mechanism common to both microwave and acoustic
systems. By establishing a universal model for resonance-based metamaterials,
we present the theoretical conditions for broadband impedance matching and
introduce the fundamental causal limit as an evaluation tool for absorption
performance. Under this integrated framework, we survey the recent advances on
metamaterials absorption in both microwave and acoustic systems, with the focus
on those that pushed the overall performance close to the causal limit. We take
note of some new, emerging metastructures that can circumvent the constraint
imposed by causal limit, thereby opening a new avenue to low-frequency
absorption. This review concludes by discussing the existing challenges with
possible solutions and the broad horizon for future developments.


title:
Nonequilibrium Transport Characteristics of substances in a Rough
  Potential Field

abstract:
  A Langevin equation is proposed to describe the transport of overdamped
Brownian particles in a periodic rough potential and driven by an unbiased
periodic force. The equation can be transformed into the Fokker-Planck equation
by using the Kramers-Moyal expansions. The time-dependent solution of
Fokker-Planck equation demonstrates different modes of the probability flow.
These modes include creeping in a single direction, direction reversal and
oscillating in both directions in the coordinate space. By varying the
roughness and noise intensity, the flow can transform between the modes. The
correlation between the noise and space indicates that the noise can maintain
the oscillation of the modes and prolong the transient time to the steady state
at which the flow tends to be zero.


title: Bose-Einstein condensation: what, how and beyond

abstract:
  The piling up of a macroscopic fraction of noninteracting bosons in the
lowest energy state of a system at very low temperatures is known as
Bose-Einstein condensation. It took nearly 70 years to observe the condensate
after their theoretical prediction. A brief history of the relevant
developments, essentials of the basic theory, physics of the steps involved in
producing the condensate in a gas of alkali atoms together with the pertinent
theory, and some important features of the research work carried out in the
last about 25 years have been dealt with. An effort has been made to present
the material in a manner that it can be easily followed by undergraduate
students as well as non-specialists and may even be used for classroom
teaching.


title: Ensemble plasticity and network adaptability in SNNs

abstract:
  Artificial Spiking Neural Networks (ASNNs) promise greater information
processing efficiency because of discrete event-based (i.e., spike)
computation. Several Machine Learning (ML) applications use biologically
inspired plasticity mechanisms as unsupervised learning techniques to increase
the robustness of ASNNs while preserving efficiency. Spike Time Dependent
Plasticity (STDP) and Intrinsic Plasticity (IP) (i.e., dynamic spiking
threshold adaptation) are two such mechanisms that have been combined to form
an ensemble learning method. However, it is not clear how this ensemble
learning should be regulated based on spiking activity. Moreover, previous
studies have attempted threshold based synaptic pruning following STDP, to
increase inference efficiency at the cost of performance in ASNNs. However,
this type of structural adaptation, that employs individual weight mechanisms,
does not consider spiking activity for pruning which is a better representation
of input stimuli. We envisaged that plasticity-based spike-regulation and
spike-based pruning will result in ASSNs that perform better in low resource
situations. In this paper, a novel ensemble learning method based on entropy
and network activation is introduced, which is amalgamated with a spike-rate
neuron pruning technique, operated exclusively using spiking activity. Two
electroencephalography (EEG) datasets are used as the input for classification
experiments with a three-layer feed forward ASNN trained using one-pass
learning. During the learning process, we observed neurons assembling into a
hierarchy of clusters based on spiking rate. It was discovered that pruning
lower spike-rate neuron clusters resulted in increased generalization or a
predictable decline in performance.


title: Soft-margin classification of object manifolds

abstract:
  A neural population responding to multiple appearances of a single object
defines a manifold in the neural response space. The ability to classify such
manifolds is of interest, as object recognition and other computational tasks
require a response that is insensitive to variability within a manifold. Linear
classification of object manifolds was previously studied for max-margin
classifiers. Soft-margin classifiers are a larger class of algorithms and
provide an additional regularization parameter used in applications to optimize
performance outside the training set by balancing between making fewer training
errors and learning more robust classifiers. Here we develop a mean-field
theory describing the behavior of soft-margin classifiers applied to object
manifolds. Analyzing manifolds with increasing complexity, from points through
spheres to general manifolds, a mean-field theory describes the expected value
of the linear classifier's norm, as well as the distribution of fields and
slack variables. By analyzing the robustness of the learned classification to
noise, we can predict the probability of classification errors and their
dependence on regularization, demonstrating a finite optimal choice. The theory
describes a previously unknown phase transition, corresponding to the
disappearance of a non-trivial solution, thus providing a soft version of the
well-known classification capacity of max-margin classifiers.

categories: cond-mat.dis-nn cond-mat.stat-mech cs.NE q-bio.NC stat.ML


title: Symplectic Reduction of Classical Mechanics on Shape Space

abstract:
  One of the foremost goals of research in physics is to find the most basic
and universal theories that describe our universe. Many theories assume the
presence of an absolute space and time in which the physical objects are
located and physical processes take place. However, it is more fundamental to
understand time as relative to the motion of another object, e.g. the number of
swings of a pendulum, and the position of an object primarily as relative to
other objects. The goals of this paper is to explain, how using the principle
of relationalism (to be introduced below), classical mechanics can be
formulated on a most elementary space, which is freed from absolute entities:
shape space. On shape space only the relative orientation and length of
subsystems are taken into account. In order to find out how the shape of a
classical system evolves in time, the method of "symplectic reduction of
Hamiltonian systems" is extended to include scale transformations, and in this
way the reduction of a classical system with respect to the full similarity
group is achieved. A necessary requirement for the validity of the principle of
relationalism is that changing the length scale of a system, all parameters of
the theory that depend on the length, get changed accordingly. In particular,
the principle of relationalism requires a proper transformation of the coupling
constants of the interaction potentials in Classical Physics. This leads
consequently to a transformation in Planck's measuring units, which enables us
to derive a metric on shape space in a unique way. Later in this paper, we will
explain the derivation of the reduced Hamiltonian and symplectic form on shape
space.

categories: physics.hist-ph math-ph math.MP physics.class-ph


title:
Hybrid Active-Passive Reconfigurable Intelligent Surface-Assisted
  Multi-User MISO Systems

abstract:
  We consider a multi-user multiple-input single-output (MISO) communications
system which is assisted by a hybrid active-passive reconfigurable intelligent
surface (RIS). Unlike conventional passive RISs, hybrid RIS is equipped with a
few active elements with the ability to reflect and amplify incident signals to
significantly improve the system performance. Towards a fairness-oriented
design, we maximize the minimum rate among all users through jointly optimizing
the transmit beamforming vectors and RIS reflecting/amplifying coefficients.
Combining tools from block coordinate ascent and successive convex
approximation, the challenging nonconvex problem is efficiently solved by a
low-complexity iterative algorithm. The numerical results show that a hybrid
RIS with 4 active elements out of a total of 50 elements with a power budget of
-1 dBm offers an improvement of up to 80% to the considered system, while that
achieved by a fully passive RIS is only 27%.


title:
Thermal Shear Waves induced in Mesoscopic Liquids at Low Frequency
  Mechanical Deformation

abstract:
  We show that a viscous liquid confined between two low thermal conductive
surfaces (Al2O3) emit a thermal response upon applying a low frequency (Hz)
shear excitation. Hot and cold thermal waves are observed in situ at
atmospheric pressure and room temperature, in polypropylene glycol layers of
various thicknesses ranging from 100 microns up to 340 microns, upon applying a
mechanical oscillatory shear strain. The observed thermal effects, synchronous
with the mechanical excitation, are inconsistent with a homogeneous viscous
flow. It indicates that mesoscopic liquids are able to (partly) convert
mechanical shear energy in non-equilibrium thermodynamic states. This effect
called thermoelasticity is well known in solid materials. The observation of a
thermal coupling to the mechanical shear deformation reinforces the assumption
of elastically correlated liquid molecules. The amplitude of the thermoelastic
waves increases linearly by increasing the shear strain amplitude up to a
transition to a non-linear thermal behavior, similar in strain behavior to an
elastic to plastic regime. The thermo-elastic effects are not detectable via
stress measurements and thus appear as a complementary liquid dynamic
characterization.


title: The landscape of polymer quantum cosmology

abstract:
  We show that the quantization ambiguities of loop quantum cosmology, when
considered in wider generality, can be used to produce discretionary dynamical
behavior. There is an infinite dimensional space of ambiguities which parallels
the infinite list of higher curvature corrections in perturbative quantum
gravity. There is however an ensemble of qualitative consequences which are
generic in the sense that they are independent of these ambiguities. Among
these, one has well defined fundamental dynamics across the big bang, and the
existence of extra microscopic quantum degrees of freedom that might be
relevant in discussions about unitarity in quantum gravity. We show that (in
addition to the well known bouncing solutions of the effective equations) there
are other generic type of solutions for sufficiently soft initial conditions in
the matter sector (tunneling solutions) where the scale factor goes through
zero and the spacetime orientation is inverted. We also show that generically,
a contracting semiclassical universe branches off at the big bang into a
quantum superposition of universes with different quantum numbers. Despite
their lack of quantitative predictive power these models offer a fertile
playground for the discussion of qualitative and conceptual issues in quantum
gravity.


title:
A Microring as a Reservoir Computing Node: Memory/Nonlinear Tasks and
  Effect of Input Non-ideality

abstract:
  The nonlinear response of an optical microresonator is used in a time
multiplexed reservoir computing neural network. Within a virtual node approach
combined with an offline training through ridge regression, we solved linear
and nonlinear logic operations. We analyzed the nonlinearity of the
microresonator as a memory between bits and/or as a neural activation function.
This is made possible by controlling both the distance between bits subject to
the logical operation and the number of bits supplied to the ridge regression.
We show that the optical microresonator exhibits up to two bits of memory in
linear tasks and that it allows solving nonlinear tasks providing both memory
and nonlinearity. Finally, we demonstrate that the virtual node approach always
requires a comparison of the reservoir's performance with the results obtained
by applying the same training process on the input signal.


title: Bi-accessible and bipresentable 2-categories

abstract:
  We develop a 2-dimensional version of accessibility and presentability
compatible with the formalism of flat pseudofunctors. First we give
prerequisites on the different notions of 2-dimensional colimits, filteredness
and cofinality; in particular we show that \emph{$\sigma$-filteredness} and
\emph{bifilteredness} are actually equivalent in practice for our purposes.
Then, we define bi-accessible and bipresentable 2-categories in terms of
\emph{bicompact} objects and \emph{bifiltered} bicolimits. We then characterize
them as categories of \emph{flat pseudofunctors}. We also prove a bi-accessible
right bi-adjoint functor theorem and deduce a 2-dimensional Gabriel-Ulmer
duality relating small \emph{bilex} 2-categories and finitely bipresentable
2-categories. Finally, we show that 2-categories of pseudo-algebras of finitary
2-monads on $\Cat$ are finitely bipresentable, which in particular captures the
case of $\Lex$, the 2-category of small lex categories. Invoking the technology
of \emph{lex-colimits}, we prove further that several 2-categories arising in
categorical logic (\textbf{Reg, Ex, Coh, Ext, Adh, Pretop}) are also finitely
bipresentable.


title: Convergence of frame series

abstract:
  If $\{x_n\}_{n \in \mathbb{N}}$ is a frame for a Hilbert space $H,$ then
there exists a canonical dual frame $\{\tilde{x_n}\}_{n \in \mathbb{N}}$ such
that for every $x \in H$ we have $x = \sum \langle x, \tilde{x_n} \rangle \,
x_n,$ with unconditional convergence of this series. However, if the frame is
not a Riesz basis, then there exist alternative duals $\{y_n\}_{n \in
\mathbb{N}}$ and synthesis-pseudo duals $\{z_n\}_{n \in \mathbb{N}}$ such that
$x = \sum \langle x, y_n \rangle \, x_n,$ and $x = \sum \langle x, x_n \rangle
\, z_n,$ for every $x.$ We characterize the frames for which the frame series
($x = \sum \langle x, y_n \rangle \, x_n,$) converges unconditionally for every
$x$ for every alternative dual, and similarly for synthesis-pseudo duals. In
particular, we prove that if $\{x_n\}_{n \in \mathbb{N}}$ does not contain
infinitely many zeros then the frame series converge unconditionally for every
alternative dual (or synthesis-pseudo dual) if and only if $\{x_n\}_{n \in
\mathbb{N}}$ is a near-Riesz basis. We also prove that all alternative duals
and synthesis-pseudo duals have the same excess as their associated frame.


title: LHCb future dark-sector sensitivity projections for Snowmass 2021

abstract:
  We provide future LHCb dark-sector sensitivity projections for use in the
Snowmass reports. These include updated projections for dark photons and the
Higgs portal, along with new projections for axion-like particles that couple
predominantly to gluons.


title:
Snowmass2021 Cosmic Frontier White Paper: Cosmological Simulations for
  Dark Matter Physics

abstract:
  Over the past several decades, unexpected astronomical discoveries have been
fueling a new wave of particle model building and are inspiring the next
generation of ever-more-sophisticated simulations to reveal the nature of Dark
Matter (DM). This coincides with the advent of new observing facilities coming
online, including JWST, the Rubin Observatory, the Nancy Grace Roman Space
Telescope, and CMB-S4. The time is now to build a novel simulation program to
interpret observations so that we can identify novel signatures of DM
microphysics across a large dynamic range of length scales and cosmic time.
This white paper identifies the key elements that are needed for such a
simulation program. We identify areas of growth on both the particle theory
side as well as the simulation algorithm and implementation side, so that we
can robustly simulate the cosmic evolution of DM for well-motivated models. We
recommend that simulations include a fully calibrated and well-tested treatment
of baryonic physics, and that outputs should connect with observations in the
space of observables. We identify the tools and methods currently available to
make predictions and the path forward for building more of these tools. A
strong cosmic DM simulation program is key to translating cosmological
observations to robust constraints on DM fundamental physics, and provides a
connection to lab-based probes of DM physics.


title: New Constraints on Exotic Spin-Velocity-Dependent Interactions

abstract:
  Experimental searches for new, "fifth" forces are attracting a lot of
attention because they allow to test theoretical extensions to the standard
model. Here, we report a new experimental search for possible fifth forces,
specifically spin-and-velocity dependent forces, by using a K-Rb-$^{21}$Ne
co-magnetometer and a tungsten ring featuring a high nucleon density. Taking
advantage of the high sensitivity of the co-magnetometer, the pseudomagnetic
field from the fifth force is measured to be $<7$\,aT. This sets new limits on
coupling constants for the neutron-nucleon and proton-nucleon interactions in
the range of $\ge 0.1$ m. The coupling constant limits are established to be
$|g_V^n|<6.6\times 10^{-11}$ and $|g_V^p|<3.0\times 10^{-10}$, which are more
than one order of magnitude tighter than astronomical and cosmological limits
on the coupling between the new gauge boson such as Z$'$ and standard model
particles.


title:
Real-Robot Deep Reinforcement Learning: Improving Trajectory Tracking of
  Flexible-Joint Manipulator with Reference Correction

abstract:
  Flexible-joint manipulators are governed by complex nonlinear dynamics,
defining a challenging control problem. In this work, we propose an approach to
learn an outer-loop joint trajectory tracking controller with deep
reinforcement learning. The controller represented by a stochastic policy is
learned in under two hours directly on the real robot. This is achieved through
bounded reference correction actions and use of a model-free off-policy
learning method. In addition, an informed policy initialization is proposed,
where the agent is pre-trained in a learned simulation. We test our approach on
the 7 DOF manipulator of a Baxter robot. We demonstrate that the proposed
method is capable of consistent learning across multiple runs when applied
directly on the real robot. Our method yields a policy which significantly
improves the trajectory tracking accuracy in comparison to the vendor-provided
controller, generalizing to an unseen payload.


title:
On approximation properties related to unconditionally p-compact
  operators and Sinha-Karn p-compact operators

abstract:
  We establish new results on the $\mathcal I$-approximation property for the
Banach operator ideal $\mathcal I=\mathcal{K}_{up}$ of the unconditionally
$p$-compact operators in the case of $1\le p<2$. As a consequence of our
results, we provide a negative answer for the case $p=1$ of a problem posed by
J.M. Kim (2017). Namely, the $\mathcal K_{u1}$-approximation property implies
neither the $\mathcal{SK}_1$-approximation property nor the (classical)
approximation property; and the $\mathcal{SK}_1$-approximation property implies
neither the $\mathcal{K}_{u1}$-approximation property nor the approximation
property. Here $\mathcal{SK}_p$ denotes the $p$-compact operators of Sinha and
Karn for $p\ge 1$. We also show for all $2<p,q<\infty$ that there is a closed
subspace $X\subset\ell^q$ that fails the $\mathcal{SK}_r$-approximation
property for all $r\ge p$.


title: Statistical Mechanical Interpretation of 2D Pauli Crystals

abstract:
  A manifestation of the Pauli Exclusion Principle is observed when fermions
are trapped in the ground state of a 2D harmonic oscillator trap at very low
temperatures. This non-interaction of fermions results in the formation of
Pauli crystals. This work introduces a statistical mechanical interpretation of
the principles that led to the observation of Pauli crystals by calculating the
energy, radius and other parameters of the non-interacting arrangement of
ultra-cold fermions. This model approaches the problem from two different
directions, namely fermion degeneracy and harmonic oscillator treatment.
Unifying the two different approaches gives a more comprehensive and robust
description of the various parameters of Pauli crystals.

categories: cond-mat.stat-mech cond-mat.quant-gas hep-th quant-ph


title:
Energy Efficiency Maximization of Simultaneous Transmission and
  Reflection RIS Assisted Full-Duplex Communications

abstract:
  This work studies the effectiveness of a novel simultaneous transmission and
reflection reconfigurable intelligent surface (STAR-RIS) aided Full-Duplex (FD)
communication system. We aim to maximize the energy efficiency by jointly
optimizing the transmit power and passive beamforming at the STAR-RIS. We
propose an efficient algorithm to optimize them iteratively under the
alternating optimization framework. The successive convex approximation (SCA)
and Dinkelbach's method are used to solve the power optimization subproblem.
The penalty-based method is used to design passive beamforming at the STAR-RIS.
Numerical results verify the convergence and effectiveness of the proposed
algorithm, and further reveal the benifits of the combining of the STAR-RIS and
FD communication compared to benchmarks.


title:
A novel constraint tightening approach for robust data-driven predictive
  control

abstract:
  In this paper, we present a data-driven model predictive control (MPC) scheme
that is capable of stabilizing unknown linear time-invariant systems under the
influence of process disturbances. To this end, Willems' lemma is used to
predict the future behavior of the system. This allows the entire scheme to be
set up using only a priori measured data and knowledge of an upper bound on the
system order. First, we develop a state-feedback MPC scheme, based on
input-state data, which guarantees closed-loop practical exponential stability
and recursive feasibility as well as closed-loop constraint satisfaction. The
scheme is extended by a suitable constraint tightening, which can also be
constructed using only data. In order to control a priori unstable systems, the
presented scheme contains a pre-stabilizing controller and an associated input
constraint tightening. We first present the proposed data-driven MPC scheme for
the case of full state measurements, and also provide extensions for obtaining
similar closed-loop guarantees in case of output feedback. The presented scheme
is applied to a numerical example.


title: Stau study at the ILC and its implication for the muon g-2 anomaly

abstract:
  Once all the sleptons as well as the Bino are observed at the ILC, the Bino
contribution to the muon anomalous magnetic dipole moment (muon $g-2$) in
supersymmetric (SUSY) models can be reconstructed. Motivated by the recently
confirmed muon $g-2$ anomaly, we examine the reconstruction accuracy at the ILC
with $\sqrt{s}$ = 500 GeV. For this purpose, measurements of stau parameters
are important. We quantitatively study the determination of the mass and mixing
parameters of the staus at the ILC. Furthermore, we discuss the implication of
the stau study to the reconstruction of the SUSY contribution to muon $g-2$. At
the benchmark point of our choice, it is found that the uncertainty can be
$\sim 8\%$ with the stau study at the ILC.


title: Self-Promoted Supervision for Few-Shot Transformer

abstract:
  The few-shot learning ability of vision transformers (ViTs) is rarely
investigated though heavily desired. In this work, we empirically find that
with the same few-shot learning frameworks, e.g., Meta-Baseline, replacing the
widely used CNN feature extractor with a ViT model often severely impairs
few-shot classification performance. Moreover, our empirical study shows that
in the absence of inductive bias, ViTs often learn the dependencies among input
tokens slowly under few-shot learning regime where only a few labeled training
data are available, which largely contributes to the above performance
degradation. To alleviate this issue, for the first time, we propose a simple
yet effective few-shot training framework for ViTs, namely Self-promoted
sUpervisioN (SUN). Specifically, besides the conventional global supervision
for global semantic learning, SUN further pretrains the ViT on the few-shot
learning dataset and then uses it to generate individual location-specific
supervision for guiding each patch token. This location-specific supervision
tells the ViT which patch tokens are similar or dissimilar and thus accelerates
token dependency learning. Moreover, it models the local semantics in each
patch token to improve the object grounding and recognition capability which
helps learn generalizable patterns. To improve the quality of location-specific
supervision, we further propose two techniques:~1) background patch filtration
to filtrate background patches out and assign them into an extra background
class; and 2) spatial-consistent augmentation to introduce sufficient diversity
for data augmentation while keeping the accuracy of the generated local
supervisions. Experimental results show that SUN using ViTs significantly
surpasses other few-shot learning frameworks with ViTs and is the first one
that achieves higher performance than those CNN state-of-the-arts.


title:
Revisiting stress-corrosion cracking and hydrogen embrittlement in
  7xxx-Al alloys at the near-atomic-scale

abstract:
  Hydrogen embrittlement (HE) affects all major high-strength structural
materials and as such is a major impediment to lightweighting e.g. vehicles and
help reduce carbon-emissions and reach net-zero. The high-strength 7xxx series
aluminium alloys can fulfil the need for light, high strength materials, and
are already extensively used in aerospace for weight reduction purposes.
However, depending on the thermomechanical and loading state, these alloys can
be sensitive to stress-corrosion cracking (SCC) through anodic dissolution and
hydrogen embrittlement. Here, we study at the near-atomic-scale the intra- and
inter-granular microstructure ahead and in the wake of a propagating SCC crack.
Moving away from model cases not strictly relevant to application, we performed
an industry-standard test on an engineering Al-7XXX alloy. H is found
segregated to planar arrays of dislocations and to grain boundaries that we can
associate to the combined effects of hydrogen-enhanced localized plasticity
(HELP) and hydrogen-enhanced decohesion (HEDE) mechanisms. We report on a
Mg-rich H-rich amorphous oxide on the corroded crack surface and evidence of
Mg-related diffusional processes leading to dissolution of the strengthening
eta-phase precipitates ahead of the crack. We show ingress of up to 1 at% O,
i.e. well above the solubility limit of O in Al, near the oxide-metal
interface, while no increased level of H is found in the matrix. We provide an
array of discussion points relative to the interplay of structural defects,
transport of solutes, thereby changing the resistance against crack
propagation, which have been overlooked across the SCC literature and prevent
accurate service life predictions.


title:
Theories and Experiments for Testable Baryogenesis Mechanisms: A
  Snowmass White Paper

abstract:
  The baryon asymmetry of the Universe is one of the central motivations to
expect physics beyond the Standard Model. In this Snowmass white paper, we
review the challenges and opportunities in testing some of the central
paradigms that predict physics at scales low enough to expect new experimental
data in the next decade. Focusing on theoretical ideas and some of their
experimental implications, in particular, we discuss neutron-antineutron
transformations, flavor observables, next generation colliders, future neutron
facilities, gravitational waves, searches for permanent electric dipole
moments, $0\nu \beta \beta $ decay and some future large underground
experiments as methods to test post-sphaleron baryogenesis, electroweak
baryogenesis, mesogenesis and low scale leptogenesis. Finally, we comment on
the cases where high scale physics can be probed through some of these same
mechanisms.


title:
MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic
  Environments

abstract:
  This work addresses a gap in semantic scene completion (SSC) data by creating
a novel outdoor data set with accurate and complete dynamic scenes. Our data
set is formed from randomly sampled views of the world at each time step, which
supervises generalizability to complete scenes without occlusions or traces. We
create SSC baselines from state-of-the-art open source networks and construct a
benchmark real-time dense local semantic mapping algorithm, MotionSC, by
leveraging recent 3D deep learning architectures to enhance SSC with temporal
information. Our network shows that the proposed data set can quantify and
supervise accurate scene completion in the presence of dynamic objects, which
can lead to the development of improved dynamic mapping algorithms. All
software is available at https://github.com/UMich-CURLY/3DMapping.


title: On the Skolem Problem for Reversible Sequences

abstract:
  Given an integer linear recurrence sequence $\langle X_n \rangle_n$, the
Skolem Problem asks to determine whether there is a natural number $n$ such
that $X_n = 0$. In a recent preprint, Lipton, Luca, Nieuwveld, Ouaknine,
Purser, and Worrell prove that the Skolem Problem is decidable for a class of
reversible sequences of order at most seven. Herein, we give an alternative
proof of the result. The novelty of our approach arises from our use of results
concerning the polynomial relations between Galois conjugates.


title:
On $(t_2,t_3)-$Zakharov-Shabat equations of generalized
  Kadomtsev-Petviashvili hierarchies

abstract:
  We review the integration of the KP hierarchy in several non-standard
contexts. Specifically, we consider KP in the following associative
differential algebras: an algebra equipped with a nilpotent derivation; an
algebra of functions equipped with a derivation that generalizes the gradient
operator; an algebra of quaternion-valued functions; a differential Lie
algebra; an algebra of polynomials equipped with the Pincherle differential; a
Moyal algebra. In all these cases we can formulate and solve the Cauchy problem
of the KP hierarchy. Also, in each of these cases we derive different
Zakharov-Shabat $(t_2,t_3)$-equations -- that is, different
Kadomtsev-Petviashvili equations -- and we prove existence of solutions arising
from solutions to the corresponding KP hierarchy.