title: An analogue of Ingham's theorem on the Heisenberg group

abstract:
  We prove an exact analogue of Ingham's uncertainty principle for the group
Fourier transform on the Heisenberg group. This is accomplished by explicitly
constructing compactly supported functions on the Heisenberg group whose
operator-valued Fourier transforms have suitable Ingham type decay and proving
an analogue of Chernoff's theorem for the family of special Hermite operators.


title: Path-Specific Objectives for Safer Agent Incentives

abstract:
  We present a general framework for training safe agents whose naive
incentives are unsafe. As an example, manipulative or deceptive behaviour can
improve rewards but should be avoided. Most approaches fail here: agents
maximize expected return by any means necessary. We formally describe settings
with 'delicate' parts of the state which should not be used as a means to an
end. We then train agents to maximize the causal effect of actions on the
expected return which is not mediated by the delicate parts of state, using
Causal Influence Diagram analysis. The resulting agents have no incentive to
control the delicate state. We further show how our framework unifies and
generalizes existing proposals.


title: Standing on the Shoulders of Giant Frozen Language Models

abstract:
  Huge pretrained language models (LMs) have demonstrated surprisingly good
zero-shot capabilities on a wide variety of tasks. This gives rise to the
appealing vision of a single, versatile model with a wide range of
functionalities across disparate applications. However, current leading
techniques for leveraging a "frozen" LM -- i.e., leaving its weights untouched
-- still often underperform fine-tuning approaches which modify these weights
in a task-dependent way. Those, in turn, suffer forgetfulness and compromise
versatility, suggesting a tradeoff between performance and versatility. The
main message of this paper is that current frozen-model techniques such as
prompt tuning are only the tip of the iceberg, and more powerful methods for
leveraging frozen LMs can do just as well as fine tuning in challenging domains
without sacrificing the underlying model's versatility. To demonstrate this, we
introduce three novel methods for leveraging frozen models: input-dependent
prompt tuning, frozen readers, and recursive LMs, each of which vastly improves
on current frozen-model approaches. Indeed, some of our methods even outperform
fine-tuning approaches in domains currently dominated by the latter. The
computational cost of each method is higher than that of existing frozen model
methods, but still negligible relative to a single pass through a huge frozen
LM. Each of these methods constitutes a meaningful contribution in its own
right, but by presenting these contributions together we aim to convince the
reader of a broader message that goes beyond the details of any given method:
that frozen models have untapped potential and that fine-tuning is often
unnecessary.


title:
Cross-Speaker Emotion Transfer for Low-Resource Text-to-Speech Using
  Non-Parallel Voice Conversion with Pitch-Shift Data Augmentation

abstract:
  Data augmentation via voice conversion (VC) has been successfully applied to
low-resource expressive text-to-speech (TTS) when only neutral data for the
target speaker are available. Although the quality of VC is crucial for this
approach, it is challenging to learn a stable VC model because the amount of
data is limited in low-resource scenarios, and highly expressive speech has
large acoustic variety. To address this issue, we propose a novel data
augmentation method that combines pitch-shifting and VC techniques. Because
pitch-shift data augmentation enables the coverage of a variety of pitch
dynamics, it greatly stabilizes training for both VC and TTS models, even when
only 1,000 utterances of the target speaker's neutral data are available.
Subjective test results showed that a FastSpeech 2-based emotional TTS system
with the proposed method improved naturalness and emotional similarity compared
with conventional methods.


title:
Disjoint odd circuits in a bridgeless cubic graph can be quelled by a
  single perfect matching

abstract:
  Let $G$ be a bridgeless cubic graph. The Berge-Fulkerson Conjecture (1970s)
states that $G$ admits a list of six perfect matchings such that each edge of
$G$ belongs to exactly two of these perfect matchings. If answered in the
affirmative, two other recent conjectures would also be true: the Fan-Raspaud
Conjecture (1994), which states that $G$ admits three perfect matchings such
that every edge of $G$ belongs to at most two of them; and a conjecture by
Mazzuoccolo (2013), which states that $G$ admits two perfect matchings whose
deletion yields a bipartite subgraph of $G$. It can be shown that given an
arbitrary perfect matching of $G$, it is not always possible to extend it to a
list of three or six perfect matchings satisfying the statements of the
Fan-Raspaud and the Berge-Fulkerson conjectures, respectively. In this paper,
we show that given any $1^+$-factor $F$ (a spanning subgraph of $G$ such that
its vertices have degree at least 1) and an arbitrary edge $e$ of $G$, there
always exists a perfect matching $M$ of $G$ containing $e$ such that
$G\setminus (F\cup M)$ is bipartite. Our result implies Mazzuoccolo's
conjecture, but not only. It also implies that given any collection of disjoint
odd circuits in $G$, there exists a perfect matching of $G$ containing at least
one edge of each circuit in this collection.


title:
Scalable Sensitivity and Uncertainty Analysis for Causal-Effect
  Estimates of Continuous-Valued Interventions

abstract:
  Estimating the effects of continuous-valued interventions from observational
data is critically important in fields such as climate science, healthcare, and
economics. Recent work focuses on designing neural-network architectures and
regularization functions to allow for scalable estimation of average and
individual-level dose response curves from high-dimensional, large-sample data.
Such methodologies assume ignorability (all confounding variables are observed)
and positivity (all levels of treatment can be observed for every unit
described by a given covariate value), which are especially challenged in the
continuous treatment regime. Developing scalable sensitivity and uncertainty
analyses that allow us to understand the ignorance induced in our estimates
when these assumptions are relaxed receives less attention. Here, we develop a
continuous treatment-effect marginal sensitivity model (CMSM) and derive bounds
that agree with both the observed data and a researcher-defined level of hidden
confounding. We introduce a scalable algorithm to derive the bounds and
uncertainty-aware deep models to efficiently estimate these bounds for
high-dimensional, large-sample observational data. We validate our methods
using both synthetic and real-world experiments. For the latter, we work in
concert with climate scientists interested in evaluating the climatological
impacts of human emissions on cloud properties using satellite observations
from the past 15 years: a finite-data problem known to be complicated by the
presence of a multitude of unobserved confounders.


title:
Insights into nanoparticle shape transformation by energetic ions using
  atomistic simulations

abstract:
  The shape of metal nanoparticles embedded in dielectric matrices influences
the optical properties of the composite material. Swift heavy ion irradiation
can induce a controllable shape transformation in gold and other metals
embedded in amorphous silicon dioxide, where the particles elongate along the
direction of the ion beam. The details of this transformation are not fully
understood, but it is presumably related to nanometer-scale phase transitions
induced by individual ion impacts. The phenomenon has been reproduced using
atomistic simulations, although the time scale limitations and the lack of
accurate interatomic models within the metal-silica interface lead unavoidably
to severe simplifications. We improve the realism in the simulations with an
accurate model for surface adhesion between gold and silica and by simulating
the processes in the matrix between impacts. The simulations with correct
adhesion show that the nanoparticles can grow in aspect ratio in the molten
state even after silicon dioxide solidifies. Moreover, we demonstrate the
active role of the matrix: without explicitly modeling processes in the matrix
between impacts, the elongation is limited and does not reach significant
aspect ratios seen in experiments. These results significantly improve the
theoretical understanding of processes developing in embedded nanoparticles
under swift heavy ion irradiation. The knowledge brings forward the ion beam
technology as a precise tool for shaping of embedded nanostructures for various
optical applications.


title: Understanding the Domain Gap in LiDAR Object Detection Networks

abstract:
  In order to make autonomous driving a reality, artificial neural networks
have to work reliably in the open-world. However, the open-world is vast and
continuously changing, so it is not technically feasible to collect and
annotate training datasets which accurately represent this domain. Therefore,
there are always domain gaps between training datasets and the open-world which
must be understood. In this work, we investigate the domain gaps between
high-resolution and low-resolution LiDAR sensors in object detection networks.
Using a unique dataset, which enables us to study sensor resolution domain gaps
independent of other effects, we show two distinct domain gaps - an inference
domain gap and a training domain gap. The inference domain gap is characterised
by a strong dependence on the number of LiDAR points per object, while the
training gap shows no such dependence. These fndings show that different
approaches are required to close these inference and training domain gaps.


title: The Case for Space Environmentalism

abstract:
  The shell bound by the Karman line at a height of 80 to 100km above the
Earth's surface, and Geosynchronous Orbit, at 36,000km, is defined as the
orbital space surrounding the Earth. It is within this region, and especially
in Low Earth Orbit (LEO), where environmental issues are becoming urgent
because of the rapid growth of the anthropogenic space object population,
including satellite "mega-constellations". In this Perspective, we summarise
the case that the orbital space around the Earth should be considered an
additional ecosystem, and so subject to the same care and concerns and the same
broad regulations as, for example, the oceans and the atmosphere. We rely on
the orbital space environment by looking through it as well as by working
within it. Hence, we should consider damage to professional astronomy, public
stargazing and the cultural importance of the sky, as well as the
sustainability of commercial, civic and military activity in space. Damage to
the orbital space environment has problematic features in common with other
types of environmental issue. First, the observed and predicted damage is
incremental and complex, with many contributors. Second, whether or not space
is formally and legally seen as a global commons, the growing commercial
exploitation of what may appear a "free" resource is in fact externalising the
true costs.


title: A Structured Survey of Quantum Computing for the Financial Industry

abstract:
  Quantum computers can solve specific problems that are not feasible on
"classical" hardware. Harvesting the speed-up provided by quantum computers
therefore has the potential to change any industry which uses computation,
including finance. First quantum applications for the financial industry
involving optimization, simulation, and machine learning problems have already
been proposed and applied to use cases such as portfolio management, risk
management, and pricing derivatives. This survey reviews platforms, algorithms,
methodologies, and use cases of quantum computing for various applications in
finance in a structured way. It is aimed at people working in the financial
industry and serves to gain an overview of the current development and
capabilities and understand the potential of quantum computing in the financial
industry.


title: Is Neuron Coverage Needed to Make Person Detection More Robust?

abstract:
  The growing use of deep neural networks (DNNs) in safety- and
security-critical areas like autonomous driving raises the need for their
systematic testing. Coverage-guided testing (CGT) is an approach that applies
mutation or fuzzing according to a predefined coverage metric to find inputs
that cause misbehavior. With the introduction of a neuron coverage metric, CGT
has also recently been applied to DNNs. In this work, we apply CGT to the task
of person detection in crowded scenes. The proposed pipeline uses YOLOv3 for
person detection and includes finding DNN bugs via sampling and mutation, and
subsequent DNN retraining on the updated training set. To be a bug, we require
a mutated image to cause a significant performance drop compared to a clean
input. In accordance with the CGT, we also consider an additional requirement
of increased coverage in the bug definition. In order to explore several types
of robustness, our approach includes natural image transformations,
corruptions, and adversarial examples generated with the Daedalus attack. The
proposed framework has uncovered several thousand cases of incorrect DNN
behavior. The relative change in mAP performance of the retrained models
reached on average between 26.21\% and 64.24\% for different robustness types.
However, we have found no evidence that the investigated coverage metrics can
be advantageously used to improve robustness.


title: A Learned Index for Exact Similarity Search in Metric Spaces

abstract:
  Indexing is an effective way to support efficient query processing in large
databases. Recently the concept of learned index has been explored actively to
replace or supplement traditional index structures with machine learning models
to reduce storage and search costs. However, accurate and efficient similarity
query processing in high-dimensional metric spaces remains to be an open
challenge. In this paper, a novel indexing approach called LIMS is proposed to
use data clustering and pivot-based data transformation techniques to build
learned indexes for efficient similarity query processing in metric spaces. The
underlying data is partitioned into clusters such that each cluster follows a
relatively uniform data distribution. Data redistribution is achieved by
utilizing a small number of pivots for each cluster. Similar data are mapped
into compact regions and the mapped values are totally ordinal. Machine
learning models are developed to approximate the position of each data record
on the disk. Efficient algorithms are designed for processing range queries and
nearest neighbor queries based on LIMS, and for index maintenance with dynamic
updates. Extensive experiments on real-world and synthetic datasets demonstrate
the superiority of LIMS compared with traditional indexes and state-of-the-art
learned indexes.


title: The Future Circular Collider (FCC) at CERN

abstract:
  With the LHC about to start its last data-taking period before being upgraded
to the High-Luminosity LHC, it is time for the international high energy
physics community to define the future of collider particle physics. The
European Strategy for Particle Physics highlights an electron-positron Higgs
boson factory as the main priority and as a first step towards a very
high-energy future hadron collider.
  A staged Future Circular Collider (FCC), consisting of a luminosity-frontier
highest-energy electron-positron collider (FCC-ee) followed by an
energy-frontier hadron collider (FCC-hh), promises the most far-reaching
physics program for the post-LHC era. FCC-ee is a precision instrument to study
the Z, W, Higgs and top particles, and offers unprecedented sensitivity to
signs of new physics. Most of the FCC-ee infrastructure can later be reused for
the subsequent hadron collider, FCC-hh.
  The FCC-hh provides proton-proton collisions at a centre-of-mass energy of
100 TeV and can directly produce new particles with masses of up to several
tens of TeV. This collider will also measure the Higgs self-coupling and
explore the dynamics of electroweak symmetry breaking. Thermal dark matter
candidates will be either discovered or conclusively ruled out by FCC-hh.
  Heavy-ion collisions and ep collisions (FCC-eh) further contribute to the
breadth of the overall FCC program. The integrated FCC infrastructure will
serve the particle physics community through the end of the 21st century.
  This presentation summarizes the feasibility of such a plan, possible
implementation and conceptual designs of FCC-ee and FCC-hh, as well as physics
potential.


title:
Stability, Linear Convergence, and Robustness of the Wang-Elia Algorithm
  for Distributed Consensus Optimization

abstract:
  We revisit an algorithm for distributed consensus optimization proposed in
2010 by J. Wang and N. Elia. By means of a Lyapunov-based analysis, we prove
input-to-state stability of the algorithm relative to a closed invariant set
composed of optimal equilibria and with respect to perturbations affecting the
algorithm's dynamics. In the absence of perturbations, this result implies
linear convergence of the local estimates and Lyapunov stability of the optimal
steady state. Moreover, we unveil fundamental connections with the well-known
Gradient Tracking and with distributed integral control. Overall, our results
suggest that a control theoretic approach can have a considerable impact on
(distributed) optimization, especially when robustness is considered.


title:
Beyond the density operator and Tr(\rho A): Exploiting the higher-order
  statistics of random-coefficient pure states for quantum information
  processing

abstract:
  Two types of states are widely used in quantum mechanics, namely
(deterministic-coefficient) pure states and statistical mixtures. A density
operator can be associated with each of them. We here address a third type of
states, that we previously introduced in a more restricted framework. These
states generalize pure ones by replacing each of their deterministic ket
coefficients by a random variable. We therefore call them Random-Coefficient
Pure States, or RCPS. We analyze their properties and their relationships with
both types of usual states. We show that RCPS contain much richer information
than the density operator and mean of observables that we associate with them.
This occurs because the latter operator only exploits the second-order
statistics of the random state coefficients, whereas their higher-order
statistics contain additional information. That information can be accessed in
practice with the multiple-preparation procedure that we propose for RCPS, by
using second-order and higher-order statistics of associated random
probabilities of measurement outcomes. Exploiting these higher-order statistics
opens the way to a very general approach for performing advanced quantum
information processing tasks. We illustrate the relevance of this approach with
a generic example, dealing with the estimation of parameters of a quantum
process and thus related to quantum process tomography. This parameter
estimation is performed in the non-blind (i.e. supervised) or blind (i.e.
unsupervised) mode. We show that this problem cannot be solved by using only
the density operator \rho of an RCPS and the associated mean value Tr(\rho A)
of the operator A that corresponds to the considered physical quantity. We
succeed in solving this problem by exploiting a fourth-order statistical
parameter of state coefficients, in addition to second-order statistics.
Numerical tests validate this result.


title:
One-dimensional viscoelastic von K\'{a}rm\'{a}n theories derived from
  nonlinear thin-walled beams

abstract:
  We derive an effective one-dimensional limit from a three-dimensional
Kelvin-Voigt model for viscoelastic thin-walled beams, in which the elastic and
the viscous stress tensor comply with a frame-indifference principle. The
limiting system of equations comprises stretching, bending, and twisting both
in the elastic and the viscous stress. It coincides with the model already
identified via [Friedrich-Kru\v{z}\'ik '20] and [Friedrich-Machill '22] by a
successive dimension reduction, first from 3D to a 2D theory for von
K\'{a}rm\'{a}n plates and then from 2D to a 1D theory for ribbons. In the
present paper, we complement the previous analysis by showing that the limit
can also be obtained by sending the height and width of the beam to zero
simultaneously. Our arguments rely on the static $\Gamma$-convergence in
[Freddi-Mora-Paroni '13], on the abstract theory of metric gradient flows, and
on evolutionary $\Gamma$-convergence by Sandier and Serfaty.


title: Characterizations of classes of countable Boolean inverse monoids

abstract:
  We characterize those countable Boolean inverse monoids that can be written
as increasing unions of finite Boolean inverse monoids (respectively, finite
fundamental Boolean inverse monoids, respectively finite simple Boolean inverse
monoids). Our monoids are therefore countably infinite versions of finite
Boolean inverse monoids, finite direct products of finite symmetric inverse
monoids and of finite symmetric inverse monoids, respectively. We use
MV-algebras to characterize those Boolean inverse monoids that can be written
as increasing unions of finite symmetric inverse monoids.


title:
Measuring the anomalous quartic gauge couplings in the $W^+W^-\to
  W^+W^-$ process at muon collider using artificial neural networks

abstract:
  The muon collider provides a unique opportunity to study the vector boson
scattering processes and dimension-8 operators contributing to anomalous
quartic gauge couplings. Because of the cleaner final state, it is easier to
decode subprocess and certain operator couplings at a muon collider. We attempt
to identify the anomalous $WWWW$ coupling in $WW\to WW$ scattering in this
paper. The vector boson scattering process corresponding to the anomalous
$WWWW$ coupling is $\mu^+\mu^-\to \nu\nu\bar{\nu}\bar{\nu}\ell^+\ell^-$, with
four (anti-)neutrinos in the final state, which pose difficulties for
phenomenological studies. In this paper, the machine learning method is used to
tackle this problem. We find that, the artificial neural network can be used to
extract the $W^+W^-\to W^+W^-$ contribution, and is useful to reconstruct the
center of mass energy of the subprocess which is important in the study of the
Standard Model effective field theory. The sensitivities and the expected
constraints on the dimension-8 operators at the muon collider with
$\sqrt{s}=30$ TeV are presented. The artificial neural networks exhibit great
potential in the phenomenological study of processes with multiple neutrinos in
the final state.


title:
Projective normality and basepoint-freeness thresholds of general
  polarized abelian varieties

abstract:
  For a polarized abelian variety $(X,L)$, Z. Jiang and G. Pareschi introduce
an invariant $\beta(X,L)$, called the basepoint-freeness threshold. Using this
invariant, we show that a general polarized abelian variety $(X,L)$ of
dimension $g$ is projectively normal if $\chi(L) \geq 2^{2g-1}$ and the type of
$L$ is not $(2,4,\dots,4)$. This bound is sharp since it is known that any
polarized abelian variety of type $(2,4,\dots,4)$ is not projectively normal.
We also give an application of $\beta(X,L)$ to the Infinitesimal Torelli
Theorem for $Y \in |L|$.


title:
The flux ratio of the [N II]$\lambda\lambda$ 6548, 6583 \AA\ lines in
  sample of Active Galactic Nuclei Type 2

abstract:
  In spectra of the Active Galactic Nuclei (AGNs), the [N II] 6548, 6583 A
lines are commonly fitted using the fixed intensity ratio of these two lines
(R[N II]=I$_{6583}$/I$_{6548}$). However, the used values for fixed intensity
ratio are slightly different through literature. There are several theoretical
calculations of the transition probabilities which can be used for the line
ratio estimation, but there are no experimental measurements of this ratio,
since the [N II] lines are extremely weak in laboratory plasma. Therefore, the
intensity ratio of [N II] lines can be measured only in the spectra of
astrophysical objects. However, precise and systematic measurements have not be
done so far, because of difficulties in measurement of the [N II] ratio in
various spectra (overlapping with H$\alpha$, weak intensity of [N II],
influence of the continuum noise and outflow contribution, etc.). Here we
present the measurements of the flux ratio of the [N II]$\lambda\lambda$ 6548,
6583 A emission lines for a sample of 250 Type 2 AGNs spectra taken form Sloan
Digital Sky Survey (SDSS) data base. The spectra are chosen to have high
signal-to-noise ratio and to [N II] and H$\alpha$ lines do not overlap. The
obtained mean flux ratio from measurements is 3.049 $\pm$ 0.021. Our result is
in agreement with theoretical result obtained by taking into account the
relativistic corrections to the magnetic dipole operator.


title: DropMessage: Unifying Random Dropping for Graph Neural Networks

abstract:
  Graph Neural Networks (GNNs) are powerful tools for graph representation
learning. Despite their rapid development, GNNs also faces some challenges,
such as over-fitting, over-smoothing, and non-robustness. Previous works
indicate that these problems can be alleviated by random dropping methods,
which integrate noises into models by randomly masking parts of the input.
However, some open-ended problems of random dropping on GNNs remain to solve.
First, it is challenging to find a universal method that are suitable for all
cases considering the divergence of different datasets and models. Second,
random noises introduced to GNNs cause the incomplete coverage of parameters
and unstable training process. In this paper, we propose a novel random
dropping method called DropMessage, which performs dropping operations directly
on the message matrix and can be applied to any message-passing GNNs.
Furthermore, we elaborate the superiority of DropMessage: it stabilizes the
training process by reducing sample variance; it keeps information diversity
from the perspective of information theory, which makes it a theoretical upper
bound of other methods. Also, we unify existing random dropping methods into
our framework and analyze their effects on GNNs. To evaluate our proposed
method, we conduct experiments that aims for multiple tasks on five public
datasets and two industrial datasets with various backbone models. The
experimental results show that DropMessage has both advantages of effectiveness
and generalization.


title: On the location of chromatic zeros of series-parallel graphs

abstract:
  In this paper we consider the zeros of the chromatic polynomial of
series-parallel graphs. Complementing a result of Sokal, showing density
outside the disk $\{z\in\mathbb{C} \mid |z-1| \leq 1\}$, we show density of
these zeros in the half plane $\Re(q)>3/2$ and we show there exists an open
region $U$ containing the interval $(0,32/27)$ such that $U\setminus\{1\}$ does
not contain zeros of the chromatic polynomial of series-parallel graphs.
  We also disprove a conjecture of Sokal by showing that for each large enough
integer $\Delta$ there exists a series-parallel graph for which all vertices
but one have degree at most $\Delta$ and whose chromatic polynomial has a zero
with real part exceeding $\Delta$.


title: A New Dataset and Transformer for Stereoscopic Video Super-Resolution

abstract:
  Stereo video super-resolution (SVSR) aims to enhance the spatial resolution
of the low-resolution video by reconstructing the high-resolution video. The
key challenges in SVSR are preserving the stereo-consistency and
temporal-consistency, without which viewers may experience 3D fatigue. There
are several notable works on stereoscopic image super-resolution, but there is
little research on stereo video super-resolution. In this paper, we propose a
novel Transformer-based model for SVSR, namely Trans-SVSR. Trans-SVSR comprises
two key novel components: a spatio-temporal convolutional self-attention layer
and an optical flow-based feed-forward layer that discovers the correlation
across different video frames and aligns the features. The parallax attention
mechanism (PAM) that uses the cross-view information to consider the
significant disparities is used to fuse the stereo views. Due to the lack of a
benchmark dataset suitable for the SVSR task, we collected a new stereoscopic
video dataset, SVSR-Set, containing 71 full high-definition (HD) stereo videos
captured using a professional stereo camera. Extensive experiments on the
collected dataset, along with two other datasets, demonstrate that the
Trans-SVSR can achieve competitive performance compared to the state-of-the-art
methods. Project code and additional results are available at
https://github.com/H-deep/Trans-SVSR/


title: Adapting Stable Matchings to Forced and Forbidden Pairs

abstract:
  We introduce the problem of adapting a stable matching to forced and
forbidden pairs. Specifically, given a stable matching $M_1$, a set $Q$ of
forced pairs, and a set $P$ of forbidden pairs, we want to find a stable
matching that includes all pairs from $Q$, no pair from $P$, and that is as
close as possible to $M_1$. We study this problem in four classical stable
matching settings: Stable Roommates (with Ties) and Stable Marriage (with
Ties). As our main contribution, we develop an algorithmic technique to
"propagate" changes through a stable matching. This technique is at the core of
our polynomial-time algorithm for adapting Stable Roommates matchings to forced
pairs. In contrast to this, we show that the same problem for forbidden pairs
is NP-hard. However, our propagation technique allows for a fixed-parameter
tractable algorithm with respect to the number of forbidden pairs when both
forced and forbidden pairs are present. Moreover, we establish strong
intractability results when preferences contain ties.


title:
Large Interferometer For Exoplanets (LIFE): V. Diagnostic potential of a
  mid-infrared space-interferometer for studying Earth analogs

abstract:
  An important future goal in exoplanetology is to detect and characterize
potentially habitable planets. Using nulling interferometry, LIFE will allow us
to constrain the radius and effective temperature of (terrestrial) exoplanets,
as well as provide unique information about their atmospheric structure and
composition. We explore the potential of LIFE in characterizing emission
spectra of Earth at various stages of its evolution. We perform Bayesian
retrievals on simulated spectra of 8 different scenarios, which correspond to
cloud-free and cloudy spectra of four different epochs of the evolution of the
Earth. Assuming a distance of 10 pc and a Sun-like host star, we simulate
observations obtained with LIFE using its simulator LIFEsim, considering all
major astrophysical noise sources. With the nominal spectral resolution (R=50)
and signal-to-noise ratio (assumed to be S/N=10 at 11.2 $\mu$m), we can
identify the main spectral features of all the analyzed scenarios (most notably
CO$_2$, H$_2$O, O$_3$, CH$_4$). This allows us to distinguish between inhabited
and lifeless scenarios. Results suggest that particularly O$_3$ and CH$_4$
yield an improved abundance estimate by doubling the S/N from 10 to 20. We
conclude that the baseline requirements for R and S/N are sufficient for LIFE
to detect O$_3$ and CH$_4$ in the atmosphere of an Earth-like planet with an
abundance of O$_2$ of around 2% in volume mixing ratio. This information is
relevant in terms of the LIFE mission planning. We also conclude that
cloud-free retrievals of cloudy planets can be used to characterize the
atmospheric composition of terrestrial habitable planets, but not the thermal
structure of the atmosphere. From the inter-model comparison performed, we
deduce that differences in the opacity tables (caused by e.g. a different line
wing treatment) may be an important source of systematic errors.


title:
Measurement of the motional heating of a levitated nanoparticle by
  thermal light

abstract:
  We report on measurements of photon induced heating of silica nanospheres
levitated in vacuum by a thermal light source formed by a superluminescent
diode. Heating of the nanospheres motion along the three trap axes was measured
as a function of gas pressure and for two particle sizes. Heating rates were
also compared with the much lower reheating of the same sphere when levitated
by a laser. We find the measured trap heating rates are dominated by the much
larger heating rates expected from the recoil of thermal photons.


title:
Magnetically regulated collapse in the B335 protostar? II. Observational
  constraints on gas ionization and magnetic field coupling

abstract:
  Non-ideal magnetohydrodynamic effects that rule the coupling of the magnetic
field to the circumstellar gas during the low-mass star formation process
depend heavily on the local physical conditions, such as the ionization
fraction of the gas. The purpose of this work is to observationally
characterize the level of ionization of the circumstellar gas at small envelope
radii and investigate its relation to the efficiency of the coupling between
the star-forming gas and the magnetic field in the Class 0 protostar B335. We
have obtained molecular line emission maps of B335 with ALMA, which we use to
measure the deuteration fraction of the gas, its ionization fraction, and the
cosmic-ray ionization rate, at envelope radii $\lesssim$1000 au. We find large
fractions of ionized gas, $\chi_{e} \simeq 1-8 \times 10^{-6}$. Our
observations also reveal an enhanced ionization that increases at small
envelope radii, reaching values up to $\zeta_{CR} \simeq 10^{-14}$~s$^{-1}$ at
a few hundred au from the central protostellar object. We show that this
extreme ionization rate can be attributed to the presence of cosmic rays
accelerated close to the protostar. We report the first resolved map of the
cosmic-ray ionization rate at scales $\lesssim 1000$~au in a solar-type Class 0
protostar, finding remarkably high values. Our observations suggest that local
acceleration of cosmic rays, and not the penetration of interstellar Galactic
cosmic rays, may be responsible for the gas ionization in the inner envelope,
potentially down to disk forming scales. If confirmed, our findings imply that
protostellar disk properties may also be determined by local processes setting
the coupling between the gas and the magnetic field, and not only by the amount
of angular momentum available at large envelope scales and the magnetic field
strength in protostellar cores.


title: Temperature uncertainty relation in non-equilibrium thermodynamics

abstract:
  Temperature uncertainty of a quantum system in canonical ensemble is
inversely determined by its energy fluctuation, which is known as the
temperature-energy uncertainty relation. No such uncertainty relation was
discovered for a non-equilibrium open quantum system. In this article, we
derive a universal temperature uncertainty relation for general non-equilibrium
processes. We find that it is the fluctuation of heat, which is defined as the
change in bath energy, determines the temperature uncertainty in
non-equilibrium case. Specifically, the heat is divided into trajectory heat
and backaction heat, which are associated with the system's trajectory of
evolution and the backaction of partial measurement on system, respectively.
Based on this decomposition, we reveal that both correlations between system
and bath's process function and state function are the resources for enhancing
temperature precision. Our findings are conductive to design ultrahigh
sensitive quantum thermometer.


title:
Mass spectra of $\Xi_{cc}$, $\Xi_{bc}$, $\Omega_{cc}$, and $\Omega_{bc}$
  baryons in Regge Phenomenology

abstract:
  In this article, we study the mass spectra of baryons containing two heavy
quarks; charm-charm ($cc$) and bottom-charm ($bc$) with a light quark ($u,d,s$)
within the framework of Regge phenomenology. With the assumption of linear
Regge trajectories we have derived the relations between slope ratios,
intercepts, and baryon masses. Using these relations, the ground state masses
of $\Xi_{cc}$, $\Xi_{bc}$, $\Omega_{cc}$, and $\Omega_{bc}$ baryons are
obtained. The values of Regge slopes and Regge intercepts are extracted for
these baryons to estimate the excited state masses in both the ($J,M^{2}$) and
($n,M^{2}$) planes. Our obtained results are compared with the experimental
observations where available and other theoretical predictions, which could be
a valuable addition to the interpretations of experimentally unknown heavy
baryon spectra.


title: Robustness of Machine Learning Models Beyond Adversarial Attacks

abstract:
  Correctly quantifying the robustness of machine learning models is a central
aspect in judging their suitability for specific tasks, and thus, ultimately,
for generating trust in the models. We show that the widely used concept of
adversarial robustness and closely related metrics based on counterfactuals are
not necessarily valid metrics for determining the robustness of ML models
against perturbations that occur "naturally", outside specific adversarial
attack scenarios. Additionally, we argue that generic robustness metrics in
principle are insufficient for determining real-world-robustness. Instead we
propose a flexible approach that models possible perturbations in input data
individually for each application. This is then combined with a probabilistic
approach that computes the likelihood that a real-world perturbation will
change a prediction, thus giving quantitative information of the robustness of
the trained machine learning model. The method does not require access to the
internals of the classifier and thus in principle works for any black-box
model. It is, however, based on Monte-Carlo sampling and thus only suited for
input spaces with small dimensions. We illustrate our approach on two dataset,
as well as on analytically solvable cases. Finally, we discuss ideas on how
real-world robustness could be computed or estimated in high-dimensional input
spaces.


title: Backfilling Cohorts in Phase I Dose-Escalation Studies

abstract:
  The use of `backfilling', assigning additional patients to doses deemed safe,
in phase I dose-escalation studies has been used in practice to collect
additional information on the safety profile, pharmacokinetics and activity of
a drug. These additional patients help ensure that the Maximum Tolerated Dose
(MTD) is reliably estimated and give additional information in order to
determine the recommended phase II dose (RP2D). In this paper, we study the
effect of employing backfilling in a phase I trial on the estimation of the MTD
and the duration of the study. We consider the situation where only one cycle
of follow-up is used for escalation as well as the case where there may be
delayed onset toxicities. We find that, over a range of scenarios, there is an
increase in the proportion of correct selections and a notable reduction in the
trial duration at the cost of more patients required in the study.


title: Solutions to aliasing in time-resolved flow data

abstract:
  Avoiding aliasing in time-resolved flow data obtained through high fidelity
simulations while keeping the computational and storage costs at acceptable
levels is often a challenge. Well-established solutions such as increasing the
sampling rate or low-pass filtering to reduce aliasing can be prohibitively
expensive for large data sets. This paper provides a set of alternative
strategies for identifying and mitigating aliasing that are applicable even to
large data sets. We show how time-derivative data, which can be obtained
directly from the governing equations, can be used to detect aliasing and to
turn the ill-posed problem of removing aliasing from data into a well-posed
problem, yielding a prediction of the true spectrum. Similarly, we show how
spatial filtering can be used to remove aliasing for convective systems. We
also propose strategies to avoid aliasing when generating a database, including
a method tailored for computing nonlinear forcing terms that arise within the
resolvent framework. These methods are demonstrated using large-eddy simulation
(LES) data for a subsonic turbulent jet and a non-linear Ginzburg-Landau model.


title: On Distribution Shift in Learning-based Bug Detectors

abstract:
  Deep learning has recently achieved initial success in program analysis tasks
such as bug detection. Lacking real bugs, most existing works construct
training and test data by injecting synthetic bugs into correct programs.
Despite achieving high test accuracy (e.g. >90%), the resulting bug detectors
are found to be surprisingly unusable in practice, i.e., <10% precision when
used to scan real software repositories. In this work, we argue that this
massive performance difference is caused by distribution shift, i.e., a
fundamental mismatch between the real bug distribution and the synthetic bug
distribution used to train and evaluate the detectors. To address this key
challenge, we propose to train a bug detector in two phases, first on a
synthetic bug distribution to adapt the model to the bug detection domain, and
then on a real bug distribution to drive the model towards the real
distribution. During these two phases, we leverage a multi-task hierarchy,
focal loss, and contrastive learning to further boost performance. We evaluate
our approach extensively on three widely studied bug types, for which we
construct new datasets carefully designed to capture the real bug distribution.
The results demonstrate that our approach is practically effective and
successfully mitigates the distribution shift: our learned detectors are highly
performant on both our constructed test set and the latest version of open
source repositories.


title:
SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence
  Embedding

abstract:
  This paper presents the shared task on Multilingual Idiomaticity Detection
and Sentence Embedding, which consists of two subtasks: (a) a binary
classification one aimed at identifying whether a sentence contains an
idiomatic expression, and (b) a task based on semantic text similarity which
requires the model to adequately represent potentially idiomatic expressions in
context. Each subtask includes different settings regarding the amount of
training data. Besides the task description, this paper introduces the datasets
in English, Portuguese, and Galician and their annotation procedure, the
evaluation metrics, and a summary of the participant systems and their results.
The task had close to 100 registered participants organised into twenty five
teams making over 650 and 150 submissions in the practice and evaluation phases
respectively.


title:
Bunching instability and asymptotic properties in epitaxial growth with
  elasticity effects: continuum model

abstract:
  We study the continuum epitaxial model for elastic interacting atomic steps
on vicinal surfaces proposed by Xiang and E (Xiang, SIAM J. Appl. Math.
63:241-258, 2002; Xiang and E, Phys. Rev. B 69:035409, 2004). The non-local
term and the singularity complicate the analysis of its PDE. In this paper, we
first generalize this model to the Lennard-Jones (m,n) interaction between
steps. Based on several important formulations of the non-local energy, we
prove the existence, symmetry, unimodality, and regularity of the energy
minimizer in the periodic setting. In particular, the symmetry and unimodality
of the minimizer implies that it has a bunching profile. Furthermore, we derive
the minimum energy scaling law for the original continnum model. All results
are consistent with the corresponding results proved for discrete models by Luo
et al. (Luo et al., Multiscale Model. Simul. 14:737 - 771, 2016).


title:
Magnetic phase transition induced ferroelectric polarization in BaFeF4
  with room temperature weak ferromagnetism

abstract:
  BaMF4 (M=Fe, Co, Ni and Mn) family are typical multiferroic materials, having
antiferromagnetism at around liquid nitrogen temperature. In this work,
polycrystalline BaFeF4 has been prepared by solid state reaction. The slight
deficiency of Fe leads to the coexistence of valence states of +2 and +3,
facilitating the electrons to hop between the neighboring Fe2+ and Fe3+ ions
through the middle F- ion, leading to the strong double exchange interaction
with weak ferromagnetism above room temperature. A bifurcation at about 170 K
between the zero-field-cooled and field-cooled temperature dependent
magnetization curves indicates the onset of 2-dimensional antiferromagnetism,
which is completed at about 125 K with the sudden drop of magnetization.
Despite the fact of type-I multiferroic, its magnetoelectricity can be
evidenced by the pyroelectric current, which shows a peak starting at about 170
K and finishing at about 125 K. The saturated ferroelectric polarization change
of around 34 {\mu}C/m2 is observed, which is switchable by the reversed poling
electric field and decreases to about 30 {\mu}C/m2 under a magnetic field of 90
kOe. This magnetoelectricity can be qualitatively reproduced by
first-principles calculations. Our results represent substantial progress to
search for high-temperature multiferroics in ferroelectric fluorides.


title:
Time Window Frechet and Metric-Based Edit Distance for Passively
  Collected Trajectories

abstract:
  The advances of modern localization techniques and the wide spread of mobile
devices have provided us great opportunities to collect and mine human mobility
trajectories. In this work, we focus on passively collected trajectories, which
are sequences of time-stamped locations that mobile entities visit. To analyse
such trajectories, a crucial part is a measure of similarity between two
trajectories. We propose the time-window Frechet distance, which enforces the
maximum temporal separation between points of two trajectories that can be
paired in the calculation of the Frechet distance, and the metric-based edit
distance which incorporates the underlying metric in the computation of the
insertion and deletion costs. Using these measures, we can cluster trajectories
to infer group motion patterns. We look at the $k$-gather problem which
requires each cluster to have at least $k$ trajectories. We prove that k-gather
remains NP-hard under edit distance, metric-based edit distance and Jaccard
distance. Finally, we improve over previous results on discrete Frechet
distance and show that there is no strongly sub-quadratic time with
approximation factor less than $1.61$ in two dimensional setting unless SETH
fails.


title:
A special self-similar solution and existence of global solutions for a
  reaction-diffusion equation with Hardy potential

abstract:
  Existence and uniqueness of a specific self-similar solution is established
for the following reaction-diffusion equation with Hardy singular potential $$
\partial_tu=\Delta u^m+|x|^{-2}u^p, \qquad (x,t)\in \real^N\times(0,\infty), $$
in the range of exponents $1\leq p<m$ and dimension $N\geq3$. The self-similar
solution is unbounded at $x=0$ and has a logarithmic vertical asymptote, but it
remains bounded at any $x\neq0$ and $t\in(0,\infty)$ and it is a weak solution
in $L^1$ sense, which moreover satisfies $u(t)\in L^p(\real^N)$ for any $t>0$
and $p\in[1,\infty)$. As an application of this self-similar solution, it is
shown that there exists at least a weak solution to the Cauchy problem
associated to the previous equation for any bounded, nonnegative and compactly
supported initial condition $u_0$, contrasting with previous results in
literature for the critical limit $p=m$.


title: Generative Compression for Face Video: A Hybrid Scheme

abstract:
  As the latest video coding standard, versatile video coding (VVC) has shown
its ability in retaining pixel quality. To excavate more compression potential
for video conference scenarios under ultra-low bitrate, this paper proposes a
bitrate adjustable hybrid compression scheme for face video. This hybrid scheme
combines the pixel-level precise recovery capability of traditional coding with
the generation capability of deep learning based on abridged information, where
Pixel wise Bi-Prediction, Low-Bitrate-FOM and Lossless Keypoint Encoder
collaborate to achieve PSNR up to 36.23 dB at a low bitrate of 1.47 KB/s.
Without introducing any additional bitrate, our method has a clear advantage
over VVC under a completely fair comparative experiment, which proves the
effectiveness of our proposed scheme. Moreover, our scheme can adapt to any
existing encoder / configuration to deal with different encoding requirements,
and the bitrate can be dynamically adjusted according to the network condition.


title:
Optoelectronic properties of the CuI, AgI and Janus Cu2BrI, and Ag2BrI
  monolayers by many-body perturbation theory

abstract:
  In an outstanding experimental advance in the field of two-dimensional
nanomaterials, cuprous iodide (CuI) and silver iodide (AgI) monolayers have
been grown via a novel graphene encapsulation synthesis approach
[Adv.Mater.2022, 34, 2106922]. Inspired by this accomplishment, we conduct
first-principles calculations to investigate the elastic, phononic thermal
transport, electronic, and optical properties of the native CuI and AgI and
Janus Cu2BrI and Ag2BrI monolayers. Electronic and excitonic optical properties
are elaborately studied using the many-body perturbation theory on the basis of
GW approximation. Our results indicate that these novel systems are stable but
with soft elastic modulus and ultralow lattice thermal conductivity. It is also
shown that the studied monolayers are wide-gap semiconductors with exciton
binding energies close to 1 eV. The effects of mechanical straining and
electric field on the resulting electronic and optical properties are also
analyzed. The presented first-principles results provide a deep understanding
of the stability, phononic transport, and tunable optoelectronic properties of
the native CuI and AgI and Janus Cu2BrI and Ag2BrI monolayers, which can serve
as a guide for the oncoming studies.


title:
Detectability of core level crossing and electronic topological
  transformations: the case of Osmium

abstract:
  Osmium, the least compressible metal, has recently been observed to undergo
abrupt changes in the c/a ratio at extreme pressures. These are claimed to
provide evidence for two unusual electronic behaviours: a crossing of the
semicore 4f and 5p levels, and an electronic topological transition. We
demonstrate that these two electronic phenomena are readily reproduced and
understood in density functional theory, but that neither perturb the trend in
c/a ratio against pressure. Hence the observed anomalies in c/a must have
another cause. Osmium is also notable for its high yield stress: the c/a
anomalies lie well within the differential strains which osmium can support. We
propose that observed c/a changes can arise from mechanical yield of
crystallites with strong preferred orientation under high deviatoric stress in
the experimental data.


title:
Consent on the Fly: Developing Ethical Verbal Consent for Voice
  Assistants

abstract:
  Determining how voice assistants should broker consent to share data with
third party software has proven to be a complex problem. Devices often require
users to switch to companion smartphone apps in order to navigate permissions
menus for their otherwise hands-free voice assistant. More in line with
smartphone app stores, Alexa now offers "voice-forward consent", allowing users
to grant skills access to personal data mid-conversation using speech.
  While more usable and convenient than opening a companion app, asking for
consent 'on the fly' can undermine several concepts core to the informed
consent process. The intangible nature of voice interfaces further blurs the
boundary between parts of an interaction controlled by third-party developers
from the underlying platforms. We outline a research agenda towards usable and
effective voice-based consent to address the problems with brokering consent
verbally, including our own work drawing on the GDPR and work on consent in
Ubicomp.


title: Steering self-organisation through confinement

abstract:
  Self-organisation is the spontaneous emergence of spatio-temporal structures
and patterns from the interaction of smaller individual units. Examples are
found across many scales in very different systems and scientific disciplines,
from physics, materials science and robotics to biology, geophysics and
astronomy. Recent research has highlighted how self-organisation can be both
mediated and controlled by confinement. Confinement occurs through interactions
with boundaries, and can function as either a catalyst or inhibitor of
self-organisation. It can then become a means to actively steer the emergence
or suppression of collective phenomena in space and time. Here, to provide a
common framework for future research, we examine the role of confinement in
self-organisation and identify overarching scientific challenges across
disciplines that need to be addressed to harness its full scientific and
technological potential. This framework will not only accelerate the generation
of a common deeper understanding of self-organisation but also trigger the
development of innovative strategies to steer it through confinement, with
impact, e.g., on the design of smarter materials, tissue engineering for
biomedicine and crowd management.

categories: nlin.AO cond-mat.soft physics.bio-ph physics.soc-ph


title: Implicit Shape Completion via Adversarial Shape Priors

abstract:
  We present a novel neural implicit shape method for partial point cloud
completion. To that end, we combine a conditional Deep-SDF architecture with
learned, adversarial shape priors. More specifically, our network converts
partial inputs into a global latent code and then recovers the full geometry
via an implicit, signed distance generator. Additionally, we train a PointNet++
discriminator that impels the generator to produce plausible, globally
consistent reconstructions. In that way, we effectively decouple the challenges
of predicting shapes that are both realistic, i.e. imitate the training set's
pose distribution, and accurate in the sense that they replicate the partial
input observations. In our experiments, we demonstrate state-of-the-art
performance for completing partial shapes, considering both man-made objects
(e.g. airplanes, chairs, ...) and deformable shape categories (human bodies).
Finally, we show that our adversarial training approach leads to visually
plausible reconstructions that are highly consistent in recovering missing
parts of a given object.


title: Scalable measures of magic for quantum computers

abstract:
  Magic characterizes the degree of non-stabilizerness of quantum states. It is
a crucial resource for quantum computing and a necessary condition for quantum
advantage. However, quantifying magic beyond a few qubits has been a major
challenge. Here, we introduce Bell magic to efficiently measure magic for any
number of qubits. Our method can be easily implemented in experiments together
with a cost-free error mitigation scheme. We experimentally demonstrate the
transition of classically simulable stabilizer states into intractable quantum
states on the IonQ quantum computer. For applications, Bell magic distinguishes
stabilizer and magical states with a low measurement cost. Further, variational
quantum algorithms can maximize the magic of quantum states via the shift-rule.
Our results pave the way to benchmark the non-classical power of quantum
computers, quantum simulators and quantum many-body systems.


title:
Reply to Comments on "Universal and Non-Universal Correction Terms of
  Bose Gases in Dilute Region: A Quantum Monte Carlo Study"

abstract:
  We reply to the comments on our previous paper J. Phys. Soc. Jpn. 91, 024001
(2022)


title:
Resilient robot teams: a review integrating decentralised control,
  change-detection, and learning

abstract:
  Purpose of review: This paper reviews opportunities and challenges for
decentralised control, change-detection, and learning in the context of
resilient robot teams.
  Recent findings: Exogenous fault detection methods can provide a generic
detection or a specific diagnosis with a recovery solution. Robot teams can
perform active and distributed sensing for detecting changes in the
environment, including identifying and tracking dynamic anomalies, as well as
collaboratively mapping dynamic environments. Resilient methods for
decentralised control have been developed in learning
perception-action-communication loops, multi-agent reinforcement learning,
embodied evolution, offline evolution with online adaptation, explicit task
allocation, and stigmergy in swarm robotics.
  Summary: Remaining challenges for resilient robot teams are integrating
change-detection and trial-and-error learning methods, obtaining reliable
performance evaluations under constrained evaluation time, improving the safety
of resilient robot teams, theoretical results demonstrating rapid adaptation to
given environmental perturbations, and designing realistic and compelling case
studies.


title:
Bakry-\'Emery curvature sharpness and curvature flow in finite weighted
  graphs. I. Theory

abstract:
  In this sequence of two papers, we introduce a curvature flow on (mixed)
weighted graphs which is based on the Bakry-\'Emery calculus. The flow is
described via a time-continuous evolution through the weighting schemes. By
adapting this flow to preserve the Markovian property, its limits turn out to
be curvature sharp. Our aim is to present the flow in the most general case of
not necessarily reversible random walks allowing laziness, including vanishing
transition probabilities along some edges ("degenerate" edges). This approach
requires to extend all concepts (in particular, the Bakry-\'Emery curvature
related notions) to this general case and it leads to a distinction between the
underlying topology (a mixed combinatorial graph) and the weighting scheme
(given by transition rates). We present various results about curvature sharp
vertices and weighted graphs as well as some fundamental properties of this new
curvature flow. This paper is accompanied by a second paper discussing the
curvature flow implementation in Python for practical use. In this second paper
we present examples and exhibit further properties of the flow.


title: Emergent tri-criticality in magnetic metamaterials

abstract:
  Metallic discs engineered on the 100 nm scale have an internal magnetic
texture which varies from a fully magnetized state to a vortex state with zero
moment. The interplay between this internal structure and the inter-disc
interactions is studied in magnetic metamaterials made of square arrays of the
magnetic discs. The texture is modeled by a mesospin of varying length with
O(2) symmetry and the inter-disc interaction by a nearest neighbour coupling
between mesospins. The thermodynamic properties of the model are studied
numerically and an ordering transition is found which varies from
Kosterlitz-Thouless to first order via an apparent tri-critical point. The
effective critical exponent characterising the finite size magnetization
evolves from the value for the 2D-XY model to less than half this value at the
tri-critical point. The consequences for future experiments both in and out of
equilibrium are discussed.


title:
Sensitivity analysis of chaotic systems using a frequency-domain
  shadowing approach

abstract:
  We present a frequency-domain method for computing the sensitivities of
time-averaged quantities of chaotic systems with respect to input parameters.
Such sensitivities cannot be computed by conventional adjoint analysis tools,
because the presence of positive Lyapunov exponents leads to exponential growth
of the adjoint variables. The proposed method is based on the least-square
shadowing (LSS) approach [1], that formulates the evaluation of sensitivities
as an optimisation problem, thereby avoiding the exponential growth of the
solution. However, all existing formulations of LSS (and its variants) are in
the time domain and the computational cost scales with the number of positive
Lyapunov exponents. In the present paper, we reformulate the LSS method in the
Fourier space using harmonic balancing. The new method is tested on the
Kuramoto-Sivashinski system and the results match with those obtained using the
standard time-domain formulation. Although the cost of the direct solution is
independent of the number of positive Lyapunov exponents, storage and computing
requirements grow rapidly with the size of the system. To mitigate these
requirements, we propose a resolvent-based iterative approach that needs much
less storage. Application to the Kuramoto-Sivashinski system gave accurate
results with very low computational cost. The method is applicable to large
systems and paves the way for application of the resolvent-based shadowing
approach to turbulent flows. Further work is needed to assess its performance
and scalability.

categories: nlin.CD cs.NA math.AP math.DS math.NA math.OC


title:
Modified models of radiation pressure instability in application to 10,
  10$^5$, and 10$^7$ $M_{\odot}$ accreting black holes

abstract:
  Some of the accreting black holes exhibit much stronger variability patterns
than the usual stochastic variations. Radiation pressure instability is one of
the proposed mechanisms which could account for this effect. We aim to model
luminosity changes for objects with black hole mass of 10, 10$^5$, and 10$^7$
solar masses, using the time-dependent evolution of an accretion disk unstable
due to the dominant radiation pressure. We use a 1-dimensional, vertically
integrated time-dependent numerical scheme which models simultaneous evolution
of the disk and corona, coupled by the vertical mass exchange. We also discuss
the possibility of presence of an inner optically thin flow, namely the
Advection-Dominated Accretion Flow (ADAF). We found that the outburst character
strongly depends on the magnetic field and the outer radius of the disk if this
radius is smaller (due to TDE phenomenon) than the size of the instability zone
in a stationary disk with infinite radius. For microquasars, the dependence on
the magnetic field is monotonic, and the period decreases with the field
strength. For larger black hole masses, the dependence is non-monotonic, and
initial rise of the period is later replaced with the relatively rapid decrease
as the magnetic field continues to rise. Still stronger magnetic field
stabilizes the disk. Our computations confirm that the radiation pressure
instability model can account for heartbeat states in microquasars. Rapid
variability detected in IMBH in the form of Quasi-Periodic Ejection can be
consistent with the model but only if combined with TDE phenomenon. Yearly
repeating variability in Changing Look AGN also requires, in our model, small
outer radius either due to the recent TDE or due to the presence of the gap in
the disk related to the presence of a secondary black hole.


title:
Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection
  via Negative Deterministic Information

abstract:
  Weakly supervised object detection (WSOD) is a challenging task, in which
image-level labels (e.g., categories of the instances in the whole image) are
used to train an object detector. Many existing methods follow the standard
multiple instance learning (MIL) paradigm and have achieved promising
performance. However, the lack of deterministic information leads to part
domination and missing instances. To address these issues, this paper focuses
on identifying and fully exploiting the deterministic information in WSOD. We
discover that negative instances (i.e. absolutely wrong instances), ignored in
most of the previous studies, normally contain valuable deterministic
information. Based on this observation, we here propose a negative
deterministic information (NDI) based method for improving WSOD, namely
NDI-WSOD. Specifically, our method consists of two stages: NDI collecting and
exploiting. In the collecting stage, we design several processes to identify
and distill the NDI from negative instances online. In the exploiting stage, we
utilize the extracted NDI to construct a novel negative contrastive learning
mechanism and a negative guided instance selection strategy for dealing with
the issues of part domination and missing instances, respectively. Experimental
results on several public benchmarks including VOC 2007, VOC 2012 and MS COCO
show that our method achieves satisfactory performance.


title: Strings from linear recurrences and permutations: a Gray code

abstract:
  Each positive increasing integer sequence $\{a_n\}_{n\geq 0}$ can serve as a
numeration system to represent each non-negative integer by means of suitable
coefficient strings. We analyse the case of $k$-generalized Fibonacci sequences
leading to the binary strings avoiding $1^k$. We prove a bijection between the
set %$F_n^{(k)}$ of strings of length $n$ and the set of permutations of
$S_{n+1}(321,312,23\ldots(k+1)1)$. Finally, basing on a known Gray code for
those strings, we define a Gray code for $S_{n+1}(321,312,23\ldots(k+1)1)$,
where two consecutive permutations differ by an adjacent transposition.


title:
Multi-UAV trajectory planning for 3D visual inspection of complex
  structures

abstract:
  This paper presents a new trajectory planning algorithm for 3D autonomous UAV
volume coverage and visual inspection. The algorithm is an extension of a
state-of-the-art Heat Equation Driven Area Coverage (HEDAC) multi-agent area
coverage algorithm for 3D domains. With a given target exploration density
field, the algorithm designs a potential field and directs UAVs to the regions
of higher potential, i.e., higher values of remaining density. Collisions
between the agents and agents with domain boundaries are prevented by
implementing the distance field and correcting the agent's directional vector
when the distance threshold is reached. A unit cube test case is considered to
evaluate this trajectory planning strategy for volume coverage. For visual
inspection applications, the algorithm is supplemented with camera direction
control. A field containing the nearest distance from any point in the domain
to the structure surface is designed. The gradient of this field is calculated
to obtain the camera orientation throughout the trajectory. Three different
test cases of varying complexities are considered to validate the proposed
method for visual inspection. The simplest scenario is a synthetic portal-like
structure inspected using three UAVs. The other two inspection scenarios are
based on realistic structures where UAVs are commonly utilized: a wind turbine
and a bridge. When deployed to a wind turbine inspection, two simulated UAVs
traversing smooth spiral trajectories have successfully explored the entire
turbine structure while cameras are directed to the curved surfaces of the
turbine's blades. In the bridge test case an efficacious visual inspection of a
complex structure is demonstrated by employing a single UAV and five UAVs. The
proposed methodology is successful, flexible and applicable in real-world UAV
inspection tasks.


title:
Large-amplitude steady gravity water waves with general vorticity and
  critical layers

abstract:
  We consider two-dimensional steady periodic gravity waves on water of finite
depth with a prescribed but arbitrary vorticity distribution. The water surface
is allowed to be overhanging and no assumptions regarding the absence of
stagnation points and critical layers are made. Using conformal mappings and a
new Babenko-type reformulation of Bernoulli's equation, we uncover an
equivalent formulation as "identity plus compact", which is amenable to
Rabinowitz' global bifurcation theorem. This allows us to construct a global
connected set of solutions, bifurcating from laminar flows with a flat surface.
Moreover, a nodal analysis is carried out for these solutions under a
monotonicity assumption on the vorticity function.


title: Detecting Topology Attacks against Graph Neural Networks

abstract:
  Graph neural networks (GNNs) have been widely used in many real applications,
and recent studies have revealed their vulnerabilities against topology
attacks. To address this issue, existing efforts have mainly been dedicated to
improving the robustness of GNNs, while little attention has been paid to the
detection of such attacks. In this work, we study the victim node detection
problem under topology attacks against GNNs. Our approach is built upon the key
observation rooted in the intrinsic message passing nature of GNNs. That is,
the neighborhood of a victim node tends to have two competing group forces,
pushing the node classification results towards the original label and the
targeted label, respectively. Based on this observation, we propose to detect
victim nodes by deliberately designing an effective measurement of the
neighborhood variance for each node. Extensive experimental results on four
real-world datasets and five existing topology attacks show the effectiveness
and efficiency of the proposed detection approach.


title: New confidence interval methods for Shannon index

abstract:
  Several factors affect the structure of communities, including biological,
physical and chemical phenomena, impacting the quantification of biodiversity,
measured by diversity indexes such as Shannon's entropy. Then, once a point
estimate is obtained, confidence intervals methods such as the bootstrap ones
are often used. These methods, however, can have different performances, which
many authors have revealed in the last decade. Furthermore, problems such as
the asymmetry of the distribution of estimates and the possibility of Shannon's
diversity index estimator bias can lead to incorrect recommendations to the
research community. Thus, we propose two methods and compare them with seven
others using their performances to face these problems. The first idea uses the
credible interval (CI) method to build a bootstrap confidence interval. The
second one starts by correcting the bias and then uses an asymptotic approach.
We considered 27 community structures representing scenarios with high
dominance, high codominance or moderate dominance, the number of species equal
to 4, 20 or 80 and 10, 50 or 500 individuals to compare their performances.
Then, we generated 1000 samples, built 95% confidence intervals, and calculated
the percentage of times they included the community diversity index (coverage
percentage) for each community structure. Our results showed the feasibility of
both proposed methods to estimate Shannon's diversity. The simulation study
revealed the bootstrap-t technique had the best performance, i.e., best
coverage percentage, compared with the other methods. Finally, we illustrate
the methodology by applying it to an original aphid and parasitoid species
dataset. We recommend the bootstrap-t when the community structure analysed is
similar to the simulated ones. Also, the methods provided high performance for
the high dominance scenarios.


title:
Estimates for $k$-dimensional spherical summations of arithmetic
  functions of the GCD and LCM

abstract:
  Let $k\ge 2$ be a fixed integer. We consider sums of type
$\sum_{n_1^2+\cdots+ n_k^2\le x} F(n_1,\ldots,n_k)$, taken over the
$k$-dimensional spherical region $\{(n_1,\ldots,n_k)\in {\Bbb Z}^k:
n_1^2+\cdots+ n_k^2\le x\}$, where $F:{\Bbb Z}^k\to {\Bbb C}$ is a given
function. In particular, we deduce asymptotic formulas with remainder terms for
the spherical summations $\sum_{n_1^2+\cdots+ n_k^2\le x} f((n_1,\ldots,n_k))$
and $\sum_{n_1^2+\cdots+ n_k^2\le x} f([n_1,\ldots,n_k])$, involving the GCD
and LCM of the integers $n_1,\ldots,n_k$, where $f:{\Bbb N}\to {\Bbb C}$
belongs to certain classes of functions.


title:
Revealing giant exciton fine-structure splitting in 2D perovskites using
  van der Waals passivation

abstract:
  The study of two-dimensional (2D) van der Waals materials has been an active
field of research in the development of new optoelectronics and photonic
applications over the last decade. Organic-inorganic layered perovskites are
currently some of the most promising 2D van der Waals materials, due to their
exceptional optical brightness and enhanced excitonic effects. However, low
crystal quality and spectral diffusion usually broaden the exciton linewidth,
obscuring the fine structure of the exciton in conventional photoluminescence
experiments. Here, we propose a mechanical approach for reducing the effect of
spectral diffusion by means of hBN-capping on layered perovskites with
different thicknesses, revealing the exciton fine structure. We used a
stochastic model to link the reduction of the spectral linewidth with the
population of active charge fluctuation centres present in the organic spacer
taking part in the dynamical Stark shift. Active fluctuation centres are
reduced by a factor of 3.7 to 7.1 when we include hBN-capping according to our
direct spectral measurements. This rate is in good agreement with the analysis
of the overlap between the squared perovskite lattice and the hexagonal hBN
lattice. Van der Waals forces between both lattices cause the partial clamping
of the perovskite organic spacer molecules, and hence, the amplitude of the
dynamical Stark shift characteristic of the spectral diffusion effect is
reduced. Our work provides an easy and low-cost solution to the problem of
accessing important fine-structure excitonic state information, along with an
explanation of the important carrier dynamics present in the organic spacer
that affect the quality of the optical emission.


title: Fedder type criteria for quasi-$F$-splitting

abstract:
  Yobuko recently introduced the notion of quasi-$F$-splitting and $F$-split
heights, which generalize and quantify the notion of Frobenius-splitting, and
proved that $F$-split heights coincide with Artin-Mazur heights for Calabi-Yau
varieties. In this paper, we prove Fedder type criteria of quasi-$F$-splittings
of complete intersections, and in particular obtain an easy formula to compute
Artin-Mazur heights of Calabi-Yau hypersurfaces. Moreover, as applications, we
give explicit examples of quartic K3 surfaces over $\mathbb{F}_{3}$ realizing
all the possible Artin-Mazur heights, we provide explicit computations of
$F$-split heights for all the rational double points and bielliptic surfaces,
and introduce interesting phenomena concerned with inversion of adjunction,
fiber products, Fano varieties, and general fibers of fibrations.


title: Le rang des tissus de Nakai

abstract:
  According to Alain H\'enaut, a planar 4-web is called Nakai's web if the
cross-ratio of the tangents to the four foliations at each point is constant
and if it has no hexagonal 3-subweb. We prove that Nakai's webs have rank 0 or
1. We give examples with rank 1 and present a universal way to build such
examples.


title: Public awareness and attitudes towards search engine optimization

abstract:
  This research focuses on what users know about search engine optimization
(SEO) and how well they can identify results that have potentially been
influenced by SEO. We conducted an online survey with a sample representative
of the German online population (N = 2,012). We found that 43% of users assume
a better ranking can be achieved without paying money to Google. This is in
stark contrast to the possibility of influence through paid advertisements,
which 79% of internet users are aware of. However, only 29.2% know how ads
differ from organic results. The term "search engine optimization" is known to
8.9% of users but 14.5% can correctly name at least one SEO tactic. Success in
labelling results that can be influenced through SEO varies by search engine
result page (SERP) complexity and devices: participants achieved higher success
rates on SERPs with simple structures than on the more complex SERPs. SEO
results were identified better on the small screen than on the large screen.
59.2% assumed that SEO has a (very) strong impact on rankings. SEO is more
often perceived as positive (75.2%) than negative (68.4%). The insights from
this study have implications for search engine providers, regulators, and
information literacy.


title: Tunable frequency-bin multi-mode squeezed states of light

abstract:
  Squeezed states are a versatile class of quantum states with applications
ranging from quantum computing to high-precision detection. We propose a method
for generating tunable squeezed states of light with multiple modes encoded in
frequency bins. Our method uses custom-engineered spontaneous parametric
downconversion pumped by a pulse-shaped pump field. The multi-mode squeezed
states are generated in a single pass and can be tuned in real time by
adjusting the properties of the pump field. Exploring new quantum states of
light, encoded in new degrees of freedom, can be a fruitful path toward
discovering new quantum applications.


title:
Identifying and Characterizing Active Citizens who Refute Misinformation
  in Social Media

abstract:
  The phenomenon of misinformation spreading in social media has developed a
new form of active citizens who focus on tackling the problem by refuting posts
that might contain misinformation. Automatically identifying and characterizing
the behavior of such active citizens in social media is an important task in
computational social science for complementing studies in misinformation
analysis. In this paper, we study this task across different social media
platforms (i.e., Twitter and Weibo) and languages (i.e., English and Chinese)
for the first time. To this end, (1) we develop and make publicly available a
new dataset of Weibo users mapped into one of the two categories (i.e.,
misinformation posters or active citizens); (2) we evaluate a battery of
supervised models on our new Weibo dataset and an existing Twitter dataset
which we repurpose for the task; and (3) we present an extensive analysis of
the differences in language use between the two user categories.


title: The Scott conjecture for large Coulomb systems: a review

abstract:
  We review some older and more recent results concerning the energy and
particle distribution in ground states of heavy Coulomb systems. The reviewed
results are asymptotic in nature: they describe properties of many-particle
systems in the limit of a large number of particles. Particular emphasis is put
on models that take relativistic kinematics into account. While
non-relativistic models are typically rather well understood, this is generally
not the case for relativistic ones and leads to a variety of open questions.


title:
Viko 2.0: A Hierarchical Gecko-inspired Adhesive Gripper with
  Visuotactile Sensor

abstract:
  Robotic grippers with visuotactile sensors have access to rich tactile
information for grasping tasks but encounter difficulty in partially
encompassing large objects with sufficient grip force. While hierarchical
gecko-inspired adhesives are a potential technique for bridging performance
gaps, they require a large contact area for efficient usage. In this work, we
present a new version of an adaptive gecko gripper called Viko 2.0 that
effectively combines the advantage of adhesives and visuotactile sensors.
Compared with a non-hierarchical structure, a hierarchical structure with a
multimaterial design achieves approximately a 1.5 times increase in normal
adhesion and double in contact area. The integrated visuotactile sensor
captures a deformation image of the hierarchical structure and provides a
real-time measurement of contact area, shear force, and incipient slip
detection at 24 Hz. The gripper is implemented on a robotic arm to demonstrate
an adaptive grasping pose based on contact area, and grasps objects with a wide
range of geometries and textures.


title:
A two-level machine learning framework for predictive maintenance:
  comparison of learning formulations

abstract:
  Predicting incoming failures and scheduling maintenance based on sensors
information in industrial machines is increasingly important to avoid downtime
and machine failure. Different machine learning formulations can be used to
solve the predictive maintenance problem. However, many of the approaches
studied in the literature are not directly applicable to real-life scenarios.
Indeed, many of those approaches usually either rely on labelled machine
malfunctions in the case of classification and fault detection, or rely on
finding a monotonic health indicator on which a prediction can be made in the
case of regression and remaining useful life estimation, which is not always
feasible. Moreover, the decision-making part of the problem is not always
studied in conjunction with the prediction phase. This paper aims to design and
compare different formulations for predictive maintenance in a two-level
framework and design metrics that quantify both the failure detection
performance as well as the timing of the maintenance decision. The first level
is responsible for building a health indicator by aggregating features using a
learning algorithm. The second level consists of a decision-making system that
can trigger an alarm based on this health indicator. Three degrees of
refinements are compared in the first level of the framework, from simple
threshold-based univariate predictive technique to supervised learning methods
based on the remaining time before failure. We choose to use the Support Vector
Machine (SVM) and its variations as the common algorithm used in all the
formulations. We apply and compare the different strategies on a real-world
rotating machine case study and observe that while a simple model can already
perform well, more sophisticated refinements enhance the predictions for
well-chosen parameters.


title:
On the number of ergodic physical/SRB measures of singular-hyperbolic
  attracting sets

abstract:
  It is known that sectional-hyperbolic attracting sets, for a $C^2$ flow on a
finite dimensional compact manifold, have at most finitely many ergodic
physical invariant probability measures. We prove an upper bound for the number
of distinct ergodic physical measures supported on a connected
singular-hyperbolic attracting set for a $3$-flow. This bound depends only on
the number of Lorenz-like equilibria contained in the attracting set. Examples
of singular-hyperbolic attracting sets are provided showing that the bound is
sharp.


title:
Forgetting Prevention for Cross-regional Fraud Detection with
  Heterogeneous Trade Graph

abstract:
  With the booming growth of e-commerce, detecting financial fraud has become
an urgent task to avoid transaction risks. Despite the successful application
of graph neural networks in fraud detection, the existing solutions are only
suitable for a narrow scope due to the limitation of data collection.
Especially when expanding a business into new territory, e.g., new cities or
new countries, developing a totally new model will bring the cost issue and
result in forgetting previous knowledge. Besides, most existing GNNs-based
solutions concentrate on either homogeneous graphs or decomposing heterogeneous
interactions into several homogeneous connections for convenience. To this end,
this study proposes a novel solution based on heterogeneous trade graphs,
namely HTG-CFD, to prevent knowledge forgetting of cross-regional fraud
detection. In particular, the heterogeneous trade graph (HTG) is constructed
from initial transaction records to explore the complex semantics among
different types of entities and relationships. Extensive experiments
demonstrate that the proposed HTG-CFD not only promotes the performance in
cross-regional scenarios but also significantly contributes to the
single-regional fraud detection. The code of the paper can be accessed at
https://github.com/YujieLi42/HTG-CFD.


title: OTExtSum: Extractive Text Summarisation with Optimal Transport

abstract:
  Extractive text summarisation aims to select salient sentences from a
document to form a short yet informative summary. While learning-based methods
have achieved promising results, they have several limitations, such as
dependence on expensive training and lack of interpretability. Therefore, in
this paper, we propose a novel non-learning-based method by for the first time
formulating text summarisation as an Optimal Transport (OT) problem, namely
Optimal Transport Extractive Summariser (OTExtSum). Optimal sentence extraction
is conceptualised as obtaining an optimal summary that minimises the
transportation cost to a given document regarding their semantic distributions.
Such a cost is defined by the Wasserstein distance and used to measure the
summary's semantic coverage of the original document. Comprehensive experiments
on four challenging and widely used datasets - MultiNews, PubMed, BillSum, and
CNN/DM demonstrate that our proposed method outperforms the state-of-the-art
non-learning-based methods and several recent learning-based methods in terms
of the ROUGE metric.


title: Stable bound orbits around static Einstein-Gauss-Bonnet black holes

abstract:
  As is well-known, asymptotically flat, static and spherically symmetric black
holes do not admit stable bound orbits of massive/massless particles outside
the horizon in higher-dimensional Einstein gravity. However, for massive
particles, this dramatically changes in higher curvature theories. We clarify
the parameter range such that there exist stable bound orbits in
$d$-dimensional Einstein-Gauss-Bonnet theories for $6\leq d\leq 9$. In
particular, we show the existence of the lower bound of the Gauss-Bonnet
coupling constant below which stable bound orbits cease to exist. Moreover, we
also find that for AdS-black holes in the theories, there can exist two stable
circular orbits outside the horizon.


title:
A novel two-party semiquantum key distribution protocol based on
  GHZ-like states

abstract:
  In this paper, we propose a novel two-party semiquantum key distribution
(SQKD) protocol by only employing one kind of GHZ-like state. The proposed SQKD
protocol can create a private key shared between one quantum party with
unlimited quantum abilities and one classical party with limited quantum
abilities without the existence of a third party. The proposed SQKD protocol
doesn't need the Hadamard gate or quantum entanglement swapping. Detailed
security analysis turns out that the proposed SQKD protocol can resist various
famous attacks from an outside eavesdropper, such as the Trojan horse attacks,
the entangle-measure attack, the double CNOT attacks, the measure-resend attack
and the intercept-resend attack.


title:
Topologically isotopic and smoothly inequivalent 2-spheres in simply
  connected 4-manifolds whose complement has a prescribed fundamental group

abstract:
  We describe a procedure to construct infinite sets of pairwise smoothly
inequivalent 2-spheres in simply connected 4-manifolds, which are topologically
isotopic and whose complement has a prescribed fundamental group that satisfies
some conditions. This class of groups include finite cyclic groups and the
binary icosahedral group. These are the first known examples of knotting
phenomena in 4-manifolds with such properties. Examples of locally flat
embedded 2-spheres in non-smoothable 4-manifolds are also given.


title:
Learn from Unpaired Data for Image Restoration: A Variational Bayes
  Approach

abstract:
  Collecting paired training data is difficult in practice, but the unpaired
samples broadly exist. Current approaches aim at generating synthesized
training data from the unpaired samples by exploring the relationship between
the corrupted and clean data. This work proposes LUD-VAE, a deep generative
method to learn the joint probability density function from data sampled from
marginal distributions. Our approach is based on a carefully designed
probabilistic graphical model in which the clean and corrupted data domains are
conditionally independent. Using variational inference, we maximize the
evidence lower bound (ELBO) to estimate the joint probability density function.
Furthermore, we show that the ELBO is computable without paired samples under
the inference invariant assumption. This property provides the mathematical
rationale of our approach in the unpaired setting. Finally, we apply our method
to real-world image denoising and super-resolution tasks and train the models
using the synthetic data generated by the LUD-VAE. Experimental results
validate the advantages of our method over other learnable approaches.


title: Frequently hypercyclic random vectors

abstract:
  We show that, under suitable conditions, an operator acting like a shift on
some sequence space has a frequently hypercyclic random vector whose
distribution is strongly mixing for the operator. This result will be applied
to chaotic weighted shifts. We also apply it to every operator satisfying the
Frequent Hypercyclicity Criterion, recovering a result of Murillo and Peris.


title: Emerging Fermi liquids from regulated Quantum Electron Stars

abstract:
  We construct a fully quantum zero-temperature electron star in a soft-wall
regulated anti-de-Sitter Einstein-Maxwell-Dirac theory that is
thermodynamically stable compared to the Reissner-Nordstr\"{o}m black hole. The
soft wall only acts on the effective mass of the fermionic degrees of freedom,
and allows for a controlled fully backreacted solution. The star is
holographically dual to an RG flow where a gapped Fermi liquid starts to emerge
from a UV CFT, but decouples again once the effective energy scale becomes
lower than the the gap of the fermionic degrees of freedom. The RG flow then
returns to a non-trivial strongly coupled relativistic fixed point with a
holographic dual. Our regulated quantum electron star is thus the fermionic
analogue of the Horowitz-Roberts-Gubser-Rocha AdS-to-AdS domain wall solution
for the holographic superconductor.


title:
Medium modification of the nucleon mechanical properties: Abel
  tomography case

abstract:
  We investigate how the gravitational form factors of the nucleon undergo
changes in nuclear matter, emphasizing the Abel transformation from the
three-dimensional (3D) Breit frame to the two-dimensional (2D) light-front
frame. Since the gravitational form factors reveal the mechanical structure of
the nucleon, we examine also the medium modifications of the energy-momentum,
pressure, and shear-force distributions. We scrutinize the stabilities of the
nucleon in nuclear matter. For this purpose, we employ the in-medium modified
SU(2) Skyrme model to study these mechanical quantities of the nucleon, since
it provides a simple but clear framework. In this in-medium modified SU(2)
Skyrme model, the modification of pionic properties is performed by using
low-energy pion-nucleus scattering data and the saturation properties of
nuclear matter near the normal nuclear matter density, $\rho_0=0.5m_\pi^3$. The
results reveal how the nucleon swells in nuclear matter as the mass
distribution of the nucleon is broadened in medium. We also show that the mean
square radii corresponding to the mass and angular momentum distributions
increase in nuclear medium. This feature is kept both in 3D and 2D cases. We
visualize how the strong force fields inside the nucleon in the 2D plane are
distributed and illustrate how these forces undergo change in nuclear matter.


title: The ReBB model and its $H(x)$ scaling version at 8 TeV

abstract:
  The Real Extended Bialas-Bzdak (ReBB) model is shown here to describe, in the
0.37 $\le -t \le$ 1.2 GeV$^2$ region, the proton-proton elastic differential
cross section data published by the TOTEM Collaboration at LHC at $\sqrt{s} =$
8 TeV colliding energy. In this kinematic range, corresponding to the
diffractive minimum-maximum region, a model-dependent Odderon signal higher
than 18 $\sigma$ is obtained by comparing the ReBB model prediction for the
$p\bar{p}$ elastic differential cross section to this TOTEM measured $pp$
elastic differential cross section data at 8 TeV. However, when combining this
signal with the Odderon signals from the ReBB model in the 0.37 $\leq -t \leq$
1.2 GeV$^2$ four-momentum-transfer range at $\sqrt{s} = $ 1.96, 2.76 and 7 TeV,
it turns out that the combined significance is dominated not by the new 8 TeV
but by that of earlier 7 TeV TOTEM data, that carry an even larger Odderon
effect. Thus, in any practical terms, within the framework of the ReBB model,
the Odderon signal in the limited 0.37 $\leq -t \leq$ 1.2 GeV$^2$ and 1.96
$\leq \sqrt{s} \leq$ 8 TeV kinematic region is not a probability, but a
certainty. We show also that the $H(x)$ version of the ReBB model works
reasonably well at 8 TeV in the 0.37 $\le -t \le$ 0.97 GeV$^2$ region.


title: R2-Trans:Fine-Grained Visual Categorization with Redundancy Reduction

abstract:
  Fine-grained visual categorization (FGVC) aims to discriminate similar
subcategories, whose main challenge is the large intraclass diversities and
subtle inter-class differences. Existing FGVC methods usually select
discriminant regions found by a trained model, which is prone to neglect other
potential discriminant information. On the other hand, the massive interactions
between the sequence of image patches in ViT make the resulting class-token
contain lots of redundant information, which may also impacts FGVC performance.
In this paper, we present a novel approach for FGVC, which can simultaneously
make use of partial yet sufficient discriminative information in environmental
cues and also compress the redundant information in class-token with respect to
the target. Specifically, our model calculates the ratio of high-weight regions
in a batch, adaptively adjusts the masking threshold and achieves moderate
extraction of background information in the input space. Moreover, we also use
the Information Bottleneck~(IB) approach to guide our network to learn a
minimum sufficient representations in the feature space. Experimental results
on three widely-used benchmark datasets verify that our approach can achieve
outperforming performance than other state-of-the-art approaches and baseline
models.


title:
Factorization of Ising correlations C(M,N) for $ \nu= \, -k$ and M+N
  odd, $M \le N$, $T < T_c$ and their lambda extensions

abstract:
  We study the factorizations of Ising low-temperature correlations C(M,N) for
$\nu=-k$ and M+N odd, $M \le N$, for both the cases $M\neq 0$ where there are
two factors, and $M=0$ where there are four factors. We find that the two
factors for $ M \neq 0$ satisfy the same non-linear differential equation and,
similarly, for M=0 the four factors each satisfy Okamoto sigma-form of
Painlev\'e VI equations with the same Okamoto parameters. Using a Landen
transformation we show, for $M\neq 0$, that the previous non-linear
differential equation can actually be reduced to an Okamoto sigma-form of
Painlev\'e VI equation. For both the two and four factor case, we find that
there is a one parameter family of boundary conditions on the Okamoto
sigma-form of Painlev\'e VI equations which generalizes the factorization of
the correlations C(M,N) to an additive decomposition of the corresponding
sigma's solutions of the Okamoto sigma-form of Painlev\'e VI equation which we
call lambda extensions. At a special value of the parameter, the
lambda-extensions of the factors of C(M,N) reduce to homogeneous polynomials in
the complete elliptic functions of the first and second kind. We also
generalize some Tracy-Widom (Painlev\'e V) relations between the sum and
difference of sigma's to this Painlev\'e VI framework.


title: Content negotiation on the Web: State of the art

abstract:
  The openness and accessibility of the Web has contributed greatly to its
worldwide adoption. Uniform Resource Identifiers (URIs) are used for resource
identification on the Web. A resource on the Web can be described in many ways,
which makes it difficult for a user to find an adequate representation. This
situation has motivated fruitful research on content negotiation to satisfy
user requirements efficiently and effectively. We focus on the important topic
of content negotiation, and our goal is to present the first comprehensive
state of the art. Our contributions include (1) identifying the characteristics
of content negotiation scenarios (styles, dimensions, and means of conveying
constraints), (2) comparing and classifying existing contributions, (3)
identifying use cases that the current state of content negotiation struggles
to address, (4) suggesting research directions for future work. The results of
the state of the art show that the problem of content negotiation is relevant
and far from being solved.


title: Contour Generation with Realistic Inter-observer Variation

abstract:
  Contours are used in radiotherapy treatment planning to identify regions to
be irradiated with high dose and regions to be spared. Therefore, any
contouring uncertainty influences the whole treatment. Even though this is the
biggest remaining source of uncertainty when daily IGRT or adaptation is used,
it has not been accounted for quantitatively in treatment planning. Using
probabilistic planning allows to directly account for contouring uncertainties
in plan optimisation. The first step is to create an algorithm that can
generate many realistic contours with variation matching actual inter-observer
variation.
  We propose a methodology to generate random contours, based on measured
spatial inter-observer variation, IOV, and a single parameter that controls its
geometrical dependency: alpha, the width of the 3D Gaussian used as point
spread function (PSF). We used a level set formulation of the median shape,
with the level set function defined as the signed distance transform. To create
a new contour, we added the median level set and a noise map which was weighted
with the IOV map and then convolved with the PSF. Thresholding the level set
function reconstructs the newly generated contour.
  We used data from 18 patients from the golden atlas, consisting of five
prostate delineations on T2-w MRI scans. To evaluate the similarity between the
contours, we calculated the maximum distance to agreement to the median shape
(maxDTA), and the minimum dose of the contours using an ideal dose
distribution. We used the two-sample Kolmogorov-Smirnov test to compare the
distributions for maxDTA and minDose between the generated and manually
delineated contours.
  Only alpha=0.75cm produced maxDTA and minDose distributions that were not
significantly different from the manually delineated structures. Accounting for
the PSF is essential to correctly simulate inter-observer variation.