title:
Hydrogen Atom in Electric and Magnetic Fields: Dynamical Symmetries,
  Superintegrable and Integrable Systems, Exact Solutions

abstract:
  The Hamiltonian of a pure hydrogen atom possesses the SO(4) symmetry group
generated by the integrals of motion: the angular momentum and the Runge-Lenz
vector. The pure hydrogen atom is a supersymmetric and superintegrable system,
since the Hamilton-Jacobi and the Schr\"odinger equations are separable in
several different coordinate systems and has an exact analytical solution. The
Schr\"odinger equation for a hydrogen atom in a uniform electric field (Stark
effect) is separable in parabolic coordinates. The system has two conserved
quantities: z-projections of the generalized Runge-Lenz vector and of the
angular momentum. The problem is integrable and has the symmetry group
SO(2)xSO(2). The ion of the hydrogen molecule (problem of two Coulomb centers)
has similar symmetry group SO(2)xSO(2) generated by two conserved z-projections
of the generalized Runge-Lenz and of the angular momentum on the internuclear
axis. The corresponding Schr\"odinger equation is separable in the elliptical
coordinates. For the hydrogen atom in a uniform magnetic field, the respective
Schr\"odinger equation is not separable. The problem is non-separable and
non-integrable and is considered as a representative example of quantum chaos
that cannot be solved by any analytical method. Nevertheless, an exact
analytical solution describing the quantum states of a hydrogen atom in a
uniform magnetic field can be obtained as a convergent power series in two
variables, the radius and the sine of the polar angle. The energy levels and
wave functions for the ground and excited states can be calculated exactly,
with any desired accuracy, for an arbitrary strength of the magnetic field.
Therefore, the problem can be considered superintegrable, although it does not
possess supersymmetry and additional integrals of motion.


title:
On the Atomic Structure of Two-Dimensional Materials with Janus
  Structures

abstract:
  The discrepancy between the bright theoretical projections for
two-dimensional (2D) Janus structures and the lack of experimental realisation
of these structures motivated us to study the effect of structural disorder on
the stability of MoSSe, SnSSe, PtSSe, In2SSe and GaInSe2. The calculation
results demonstrate that the difference between metal-sulfur and metal-selenium
bonds makes Janus structures frustrated and less energetically favourable than
less ordered allotropes of the same compounds. This result explains the
difficulties encountered in experimental fabrication of these materials. In the
bulk, there is an additional contribution to the total energy from
dipole-dipole interactions between layers with a Janus structure that can
overcome the energetic cost of structural frustration in layers for compounds
with sufficiently large dipole moments. However, the entropic contribution to
the free energy decreases the favourability of the ordered Janus structure. The
calculation results are used to make recommendations to enable the discovery
and synthesis of 2D materials with Janus structures.


title: Theoretical study of laser intensity noise effect on CW-STED microscopy

abstract:
  Spatial resolution of stimulated emission depletion (STED) microscopy varies
with sample labeling techniques and microscope components, e.g., lasers,
lenses, and photo-detectors. Fluctuations in the intensity of the depletion
laser decrease achievable resolution in STED microscopy; the stronger the
fluctuations, the higher the average intensity needed to achieve a given
resolution. This phenomenon is encountered in every STED measurement. However,
a theoretical framework that evaluates the effect of intensity fluctuations on
spatial resolution is lacking. This article presents an analytical formulation
based on a stochastic model that characterizes the impact of the laser
fluctuations and correlation time on the depletion efficiency in the continuous
wave (CW) STED microscopy. We compared analytical results with simulations
using a wide range of intensity noise conditions and found a high degree of
agreement. The stochastic model used considers a colored noise distribution for
the laser intensity fluctuations. Simple analytical expressions were obtained
in the limit of small and large fluctuations correlation time. These
expressions fitted very well the available experimental data. Finally, this
work offers a starting point to model other laser noise effects in various
microscopy implementations.


title:
Magnetization reversal and domain structures in perpendicular synthetic
  antiferromagnets prepared on rigid and flexible substrates

abstract:
  Ferromagnetic (FM) layers separated by nonmagnetic metallic spacer layers can
exhibit Ruderman Kittel Kasuya Yosida (RKKY) coupling which may lead to a
stable synthetic antiferromagnetic (SAF) phase. In this article we study
magnetization reversal in [Co/Pt] layers by varying the number of bilayer
stacks (Pt/Co) as well as thickness of Ir space layer tIr on rigid Si(100) and
flexible polyimide substrates. The samples with tIr = 1.0 nm shows a FM
coupling whereas samples with tIr = 1.5 nm shows an AFM coupling between the FM
layers. At tIr = 2.0 nm, it shows a bow-tie shaped hysteresis loop indicating a
canting of magnetization at the reversal. Higher anisotropy energy as compared
to the interlayer exchange coupling (IEC) energy is an indication of the
smaller relative angle between the magnetization of lower and upper FM layers.
We have also demonstrated the strain induced modification of IEC as well as
magnetization reversal phenomena. The IEC shows a slight decrease upon
application of compressive strain and increase upon application of tensile
strain which indicates the potential of SAFs in flexible spintronics.


title:
PyWolf: A PyOpenCL implementation for simulating the propagation of
  partially coherent light

abstract:
  We present PyWolf, an open-source software capable of performing numerical
simulations of partially coherent light propagation from two-dimensional light
sources. PyWolf computes the evolution of a user-defined cross-spectral density
function in the Fresnel and far field approximations, which enables the
retrieval of second-order optical quantities of interest such as the spectral
degree of coherence and spectral density for a given frequency. The open-source
tool kit PyOpenCL is used to increase the computation speed. We present
examples of propagation of different source models and optical systems to
validate our implementation. Performance results for the computation speed when
using parallel computation through PyOpenCL is shown. Source models and
propagation systems can be easily added to PyWolf, which has a graphical user
interface built with PyQt5. This software can be of great utility for partially
coherent light simulation problems that are difficult to treat analytically.


title: aaeCAPTCHA: The Design and Implementation of Audio Adversarial CAPTCHA

abstract:
  CAPTCHAs are designed to prevent malicious bot programs from abusing
websites. Most online service providers deploy audio CAPTCHAs as an alternative
to text and image CAPTCHAs for visually impaired users. However, prior research
investigating the security of audio CAPTCHAs found them highly vulnerable to
automated attacks using Automatic Speech Recognition (ASR) systems. To improve
the robustness of audio CAPTCHAs against automated abuses, we present the
design and implementation of an audio adversarial CAPTCHA (aaeCAPTCHA) system
in this paper. The aaeCAPTCHA system exploits audio adversarial examples as
CAPTCHAs to prevent the ASR systems from automatically solving them.
Furthermore, we conducted a rigorous security evaluation of our new audio
CAPTCHA design against five state-of-the-art DNN-based ASR systems and three
commercial Speech-to-Text (STT) services. Our experimental evaluations
demonstrate that aaeCAPTCHA is highly secure against these speech recognition
technologies, even when the attacker has complete knowledge of the current
attacks against audio adversarial examples. We also conducted a usability
evaluation of the proof-of-concept implementation of the aaeCAPTCHA scheme. Our
results show that it achieves high robustness at a moderate usability cost
compared to normal audio CAPTCHAs. Finally, our extensive analysis highlights
that aaeCAPTCHA can significantly enhance the security and robustness of
traditional audio CAPTCHA systems while maintaining similar usability.


title: A short proof of the existence of master test plans

abstract:
  The aim of this brief note is to provide a quick and elementary proof of the
following known fact: on a metric measure space whose Sobolev space is
separable, there exists a test plan that is sufficient to identify the minimal
weak upper gradient of every Sobolev function.


title:
Distributed Sparse Identification for Stochastic Dynamic Systems under
  Cooperative Non-Persistent Excitation Condition

abstract:
  This paper considers the distributed sparse identification problem over
wireless sensor networks such that all sensors cooperatively estimate the
unknown sparse parameter vector of stochastic dynamic systems by using the
local information from neighbors. A distributed sparse least squares algorithm
is proposed by minimizing a local information criterion formulated as a linear
combination of accumulative local estimation error and L_1-regularization term.
The upper bounds of the estimation error and the regret of the adaptive
predictor of the proposed algorithm are presented. Furthermore, by designing a
suitable adaptive weighting coefficient based on the local observation data,
the set convergence of zero elements with a finite number of observations is
obtained under a cooperative non-persistent excitation condition. It is shown
that the proposed distributed algorithm can work well in a cooperative way even
though none of the individual sensors can fulfill the estimation task. Our
theoretical results are obtained without relying on the independency
assumptions of regression signals that have been commonly used in the existing
literature. Thus, our results are expected to be applied to stochastic feedback
systems. Finally, the numerical simulations are provided to demonstrate the
effectiveness of our theoretical results.


title:
Theoretical modelling of arch-shaped carbon nanotube resonators
  exhibiting Euler-Bernoulli snap-through bi-stability

abstract:
  In this work, we present a detailed static and dynamic analysis of a recently
reported electrically actuated buckled carbon nanotube (CNT) resonator, based
on the Euler-Bernoulli beam theory. The system behavior is analyzed using the
Galerkin reduced order model. We show that a simple single-modal analysis can
already predict snap-through bi-stability in a buckled CNT resonator. However,
we prove that the experimental data, in which the snap-through buckling occurs
at a finite frequency, cannot be explained without taking into account
out-of-plane motion. The buckled CNTs are the first type of buckled beams to
exhibit out-of-plane static motion, resulting in a unique three-dimensional
snap-through transition, never before predicted. In addition, we show the
criteria under which these devices can also exhibit latching phenomena, meaning
that they can maintain their buckle configuration when no force is applied,
making these devices appealing for mechanical memory applications.


title:
Degenerate fourth order parabolic equations with Neumann boundary
  conditions

abstract:
  We study the generation property for a fourth order operator in divergence or
in non divergence form with suitable Neumann boundary conditions. As a
consequence we obtain the well posedness for the parabolic equations governed
by these operators. The novelty of this paper is that the operators depend on a
function $a: [0,1] \rightarrow \mathcal R_+$ that degenerates somewhere in the
interval.


title:
MaxDropoutV2: An Improved Method to Drop out Neurons in Convolutional
  Neural Networks

abstract:
  In the last decade, exponential data growth supplied the machine
learning-based algorithms' capacity and enabled their usage in daily life
activities. Additionally, such an improvement is partially explained due to the
advent of deep learning techniques, i.e., stacks of simple architectures that
end up in more complex models. Although both factors produce outstanding
results, they also pose drawbacks regarding the learning process since training
complex models denotes an expensive task and results are prone to overfit the
training data. A supervised regularization technique called MaxDropout was
recently proposed to tackle the latter, providing several improvements
concerning traditional regularization approaches. In this paper, we present its
improved version called MaxDropoutV2. Results considering two public datasets
show that the model performs faster than the standard version and, in most
cases, provides more accurate results.


title:
Weighted Mean and Median graph Filters with Attenuation Factor for
  Sensor Network

abstract:
  This paper proposes a weighted attenuation k-hop graph, which depicts the
spatial neighbor nodes with their hops from the central node. Based on this
k-kop graph, we further propose a node selecting graph, which selects temporal
neighbor nodes of multiple instances of the central node. With this node
selecting graph, we propose a graph mean filter. In addition, we also apply the
proposed node selecting graph to the median filter. Finally, the experimental
results show that the proposed mean filter performs better than the original
median filter in the signal denoising polluted by white noise and the median
filter using node selecting graph also has better performance than the original
median filter.


title: Distinctive Onset of Electron Correlation in Molecular Tautomers

abstract:
  We investigate the attosecond response of the electronic cloud of a molecular
system to an outer-valence ionization. The time needed for the remaining
electrons to respond to a sudden perturbation in the electronic structure of
the molecule is a measure of the degree of electron correlation. Using the ab
initio multielectron wave-packet propagation method, we show that this response
time can be sensitive to the molecular structure and the symmetry of the
ionized molecular orbital. Our analysis revealed differences in the temporal
signatures of ultrafast charge migration processes in the context of tautomeric
hydrogen in multinuclear systems, and the relevance of distinctive onset of
electron correlation within the overall molecular complexity.


title:
Convergence of the Distributed SG Algorithm Under Cooperative Excitation
  Condition

abstract:
  In this paper, a distributed stochastic gradient (SG) algorithm is proposed
where the estimators are aimed to collectively estimate an unknown
time-invariant parameter from a set of noisy measurements obtained by
distributed sensors. The proposed distributed SG algorithm combines the
consensus strategy of the estimation of neighbors with the diffusion of
regression vectors. A cooperative excitation condition is introduced, under
which the convergence of the distributed SG algorithm can be obtained without
relying on the independency and stationarity assumptions of regression vectors
which are commonly used in existing literature. Furthermore, the convergence
rate of the algorithm can be established. Finally, we show that all sensors can
cooperate to fulfill the estimation task even though any individual sensor can
not by a simulation example.


title:
Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph
  Representation Learning

abstract:
  Complex heterogeneous dynamic networks like knowledge graphs are powerful
constructs that can be used in modeling data provenance from computer systems.
From a security perspective, these attributed graphs enable causality analysis
and tracing for analyzing a myriad of cyberattacks. However, there is a paucity
in systematic development of pipelines that transform system executions and
provenance into usable graph representations for machine learning tasks. This
lack of instrumentation severely inhibits scientific advancement in provenance
graph machine learning by hindering reproducibility and limiting the
availability of data that are critical for techniques like graph neural
networks. To fulfill this need, we present Flurry, an end-to-end data pipeline
which simulates cyberattacks, captures provenance data from these attacks at
multiple system and application layers, converts audit logs from these attacks
into data provenance graphs, and incorporates this data with a framework for
training deep neural models that supports preconfigured or custom-designed
models for analysis in real-world resilient systems. We showcase this pipeline
by processing data from multiple system attacks and performing anomaly
detection via graph classification using current benchmark graph
representational learning frameworks. Flurry provides a fast, customizable,
extensible, and transparent solution for providing this much needed data to
cybersecurity professionals.


title: The Impact of Differential Privacy on Group Disparity Mitigation

abstract:
  The performance cost of differential privacy has, for some applications, been
shown to be higher for minority groups; fairness, conversely, has been shown to
disproportionally compromise the privacy of members of such groups. Most work
in this area has been restricted to computer vision and risk assessment. In
this paper, we evaluate the impact of differential privacy on fairness across
four tasks, focusing on how attempts to mitigate privacy violations and
between-group performance differences interact: Does privacy inhibit attempts
to ensure fairness? To this end, we train $(\varepsilon,\delta)$-differentially
private models with empirical risk minimization and group distributionally
robust training objectives. Consistent with previous findings, we find that
differential privacy increases between-group performance differences in the
baseline setting; but more interestingly, differential privacy reduces
between-group performance differences in the robust setting. We explain this by
reinterpreting differential privacy as regularization.


title:
A simplified voltage-conductance kinetic model for interacting neurons
  and its asymptotic limit

abstract:
  The voltage-conductance kinetic model for the collective behavior of neurons
has been studied by scientists and mathematicians for two decades, but the
rigorous analysis of its solution structure has been only partially obtained in
spite of plenty of numerical evidence in various scenarios. In this work, we
consider a simplified voltage-conductance model in which the velocity field in
the voltage variable is in a separable form. The long time behavior of the
simplified model is fully investigated leading to the following dichotomy:
either the density function converges to the global equilibrium, or the firing
rate diverges as time goes to infinity. Besides, the fast conductance
asymptotic limit is justified and analyzed, where the solution to the limit
model either blows up in finite time, or globally exists leading to time
periodic solutions. An important implication of these results is that the
non-separable velocity field, or physically the leaky mechanism, is a key
element for the emergence of periodic solutions in the original model based on
the available numerical evidence.


title:
The massless thermal field and the thermal fermion bosonization in two
  dimensions

abstract:
  We come back to the issue of bosonization of Fermions in two spacetime
dimension and give a new costruction in the thermal equilibrium case. Left and
right moving particles can coexist at equilibrium two different temperatures. A
crucial role in our construction is played by translation invariant fields
which are linked to the infrared behaviour of the correlation functions. As an
application of our construction we provide a full operator solution of the
massless Thirring model with two temperatures for the left and right fields.


title: Bounds on Power and Common Message Fractions for RSMA with Imperfect SIC

abstract:
  Rate-Splitting multiple access (RSMA) has emerged as a key enabler to improve
the performance of the beyond fifth-generation (5G) cellular networks. The
existing RSMA literature has typically considered the sum-rate of the users to
evaluate the performance of RSMA. However, it has been shown in the existing
works that maximizing sum-rate can result in asymmetric user performance. It
can result in a significant enhancement of one user rate at the cost of the
other RSMA user. Further, imperfections can reduce the performance of
successive interference cancellation (SIC)-based RSMA. Therefore, in this
paper, we consider the imperfection in successive interference cancellation
(SIC) and derive suitable bounds on fractions of the power of common and
private messages and the fraction of common message intended for each user in
an RSMA pair such that their individual RSMA rates are greater than their
respective OMA rates. Through simulations, we validate the derived bounds and
show that the individual RSMA rates of users can be greater than their
respective OMA rates by selecting the parameters within the derived bounds.


title:
Global existence and large time behavior of weak solutions to the
  two-phase flow

abstract:
  In this paper, we consider a two-phase flow model consisting of the
compressible Navier-Stokes systems with degenerate viscosity coupled with the
compressible Navier-Stokes systems with constant viscosities via a drag force,
which can be derived from Chapman-Enskog expansion for the compressible
Navier-Stokes-Vlasov-Fokker-Planck system. For general initial data, we
establish the global existence of weak solutions with finite energy to the
initial value problem in the three-dimensional periodic domain, and prove the
convergence of global weak solutions to its equilibrium state as the time tends
to infinity.


title: On Rotation Gains Within and Beyond Perceptual Limitations for Seated VR

abstract:
  Head tracking in head-mounted displays (HMDs) enables users to explore a
360-degree virtual scene with free head movements. However, for seated use of
HMDs such as users sitting on a chair or a couch, physically turning around
360-degree is not possible. Redirection techniques decouple tracked physical
motion and virtual motion, allowing users to explore virtual environments with
more flexibility. In seated situations with only head movements available, the
difference of stimulus might cause the detection thresholds of rotation gains
to differ from that of redirected walking. Therefore we present an experiment
with a two-alternative forced-choice (2AFC) design to compare the thresholds
for seated and standing situations. Results indicate that users are unable to
discriminate rotation gains between 0.89 and 1.28, a smaller range compared to
the standing condition. We further treated head amplification as an interaction
technique and found that a gain of 2.5, though not a hard threshold, was near
the largest gain that users consider applicable. Overall, our work aims to
better understand human perception of rotation gains in seated VR and the
results provide guidance for future design choices of its applications.


title: MetaFormer: A Unified Meta Framework for Fine-Grained Recognition

abstract:
  Fine-Grained Visual Classification(FGVC) is the task that requires
recognizing the objects belonging to multiple subordinate categories of a
super-category. Recent state-of-the-art methods usually design sophisticated
learning pipelines to tackle this task. However, visual information alone is
often not sufficient to accurately differentiate between fine-grained visual
categories. Nowadays, the meta-information (e.g., spatio-temporal prior,
attribute, and text description) usually appears along with the images. This
inspires us to ask the question: Is it possible to use a unified and simple
framework to utilize various meta-information to assist in fine-grained
identification? To answer this problem, we explore a unified and strong
meta-framework(MetaFormer) for fine-grained visual classification. In practice,
MetaFormer provides a simple yet effective approach to address the joint
learning of vision and various meta-information. Moreover, MetaFormer also
provides a strong baseline for FGVC without bells and whistles. Extensive
experiments demonstrate that MetaFormer can effectively use various
meta-information to improve the performance of fine-grained recognition. In a
fair comparison, MetaFormer can outperform the current SotA approaches with
only vision information on the iNaturalist2017 and iNaturalist2018 datasets.
Adding meta-information, MetaFormer can exceed the current SotA approaches by
5.9% and 5.3%, respectively. Moreover, MetaFormer can achieve 92.3% and 92.7%
on CUB-200-2011 and NABirds, which significantly outperforms the SotA
approaches. The source code and pre-trained models are released
athttps://github.com/dqshuai/MetaFormer.


title:
Efficient Distinction between Quantum Direct and Common Causes and its
  Experimental Verification

abstract:
  Identifying the causal structures between two statistically correlated events
has been widely investigated in many fields of science. While some of the
well-studied classical methods are carefully generalized to quantum version of
causal inference for certain cases, an effective and efficient way to detect
the more general quantum causal structures is still lacking. Here, we introduce
a quantity named `Causal Determinant' to efficiently identify the quantum
causal structures between two quantum systems and experimentally verify the
validity of the method. According to the causal determinant, the quantum direct
cause imposed by an arbitrary unitary operator can be perfectly discriminated
with the quantum common cause, in which the two quantum systems share a joint
quantum state. In addition, the causal determinant has the capability to
discriminate between more general causal structures and predict the range of
their parameters. The ability to detect more general quantum causal structures
of our method can shed new light on the field of quantum causal inference.


title: Feeding What You Need by Understanding What You Learned

abstract:
  Machine Reading Comprehension (MRC) reveals the ability to understand a given
text passage and answer questions based on it. Existing research works in MRC
rely heavily on large-size models and corpus to improve the performance
evaluated by metrics such as Exact Match ($EM$) and $F_1$. However, such a
paradigm lacks sufficient interpretation to model capability and can not
efficiently train a model with a large corpus. In this paper, we argue that a
deep understanding of model capabilities and data properties can help us feed a
model with appropriate training data based on its learning status.
Specifically, we design an MRC capability assessment framework that assesses
model capabilities in an explainable and multi-dimensional manner. Based on it,
we further uncover and disentangle the connections between various data
properties and model performance. Finally, to verify the effectiveness of the
proposed MRC capability assessment framework, we incorporate it into a
curriculum learning pipeline and devise a Capability Boundary Breakthrough
Curriculum (CBBC) strategy, which performs a model capability-based training to
maximize the data value and improve training efficiency. Extensive experiments
demonstrate that our approach significantly improves performance, achieving up
to an 11.22% / 8.71% improvement of $EM$ / $F_1$ on MRC tasks.


title: Selecting Sub-tables for Data Exploration

abstract:
  We present a framework for creating small, informative sub-tables of large
data tables to facilitate the first step of data science: data exploration.
Given a large data table table T, the goal is to create a sub-table of small,
fixed dimensions, by selecting a subset of T's rows and projecting them over a
subset of T's columns. The question is: which rows and columns should be
selected to yield an informative sub-table?
  We formalize the notion of "informativeness" based on two complementary
metrics: cell coverage, which measures how well the sub-table captures
prominent association rules in T, and diversity. Since computing optimal
sub-tables using these metrics is shown to be infeasible, we give an efficient
algorithm which indirectly accounts for association rules using table
embedding. The resulting framework can be used for visualizing the complete
sub-table, as well as for displaying the results of queries over the sub-table,
enabling the user to quickly understand the results and determine subsequent
queries. Experimental results show that we can efficiently compute high-quality
sub-tables as measured by our metrics, as well as by feedback from
user-studies.


title: Multi-level images around Kerr-Newman black holes

abstract:
  We study the lensing effects of Kerr-Newman (KN) black holes and the
multi-level images of luminous sources around the black holes. We obtain the
analytic forms of the lens equations in KN spacetime and derive approximately
their limiting forms for photon emissions near the bounded photon orbits. After
deriving the expressions of three key parameters $\gamma$, $\delta$ and $\tau$,
we investigate the primary, the secondary and the higher-order images of
spherical sources and equatorial disk-like sources. In particular, we find that
the charge of KN black hole has a significant effect on the higher-order
images.


title: Data Science vs Putin: How much does each of us pay for Putin's war?

abstract:
  Putin's Ukraine war has caused gas prices to skyrocket. Because of Europe's
dependence on Russian gas supplies, we all pay significantly more for heating,
involuntarily helping to fund Russia's war against Ukraine. Based on an
analysis of real-time gas price data, we present a calculation that estimates
every household's financial contribution for heating paid to Russian gas
suppliers daily at current prices - six euros per household and day. We show
ways everyone can save energy and help reduce the dependency on Russian gas
supply.


title: The M/G/1 retrial queue with event-dependent arrivals

abstract:
  We introduce a novel single-server queue with general retrial times and
event-dependent arrivals. This is a versatile model for the study of service
systems, in which the server needs a non-negligible time to retrieve waiting
customers upon a service completion, while future arrivals depend on the last
realized event. Such a model is motivated by the customers' behaviour in
service systems where they decide to join based on the last realized event. We
investigate the necessary and sufficient stability condition and derive the
stationary distribution both at service completion epochs, and at an arbitrary
epoch using the supplementary variable technique. We also study the asymptotic
behaviour under high rate of retrials. Performance measures are explicitly
derived and extensive numerical examples are performed to investigate the
impact of event-dependency. Moreover, constrained optimisation problems are
formulated and solved with ultimate goal to investigate the admission control
problem.


title: Consequences of neutron decay inside neutron stars

abstract:
  The hypothesis that neutrons might decay into dark matter is explored using
neutron stars as a testing ground. It is found that in order to obtain stars
with masses at the upper end of those observed, the dark matter must experience
a relatively strong self-interaction. Conservation of baryon number and energy
then require that the star must undergo some heating, with a decrease in
radius, leading to an increase in speed of rotation over a period of days.


title: The Performance of Random Template Banks

abstract:
  When searching for new gravitational-wave or electromagnetic sources, the $n$
signal parameters (masses, sky location, frequencies,...) are unknown. In
practice, one hunts for signals at a discrete set of points in parameter space,
called a template bank. These may be constructed systematically as a lattice,
or alternatively, by placing templates at randomly selected points in parameter
space. Here, we calculate the fraction of signals lost by an $n$-dimensional
random template bank (compared to a very finely spaced bank). This fraction is
compared to the corresponding loss fraction for the best possible lattice-based
template banks containing the same number of grid points. For dimensions $n<4$
the lattice-based template banks significantly outperform the random ones.
However, remarkably, for dimensions $n>8$, the difference is negligible. In
high dimensions, random template banks outperform the best known lattices.


title: Azimuthal angular correlation as a new boosted top jet substructure

abstract:
  When a top quark is highly boosted, the $W$ boson from its decay has a
substantial linear polarization that results in a $\cos 2\phi$ azimuthal
angular correlation among the top decay products. We show that this correlation
can be measured for hadronically decayed boosted tops, and its magnitude
provides a way to measure the longitudinal polarization of top quark, which is
an important probe of new physics that couples to top sector.


title:
A randomized singular value decomposition for third-order oriented
  tensors

abstract:
  The oriented singular value decomposition (O-SVD) proposed in [Numer. Linear
Algebra Appl., 27(2020), e2290] provides a hybrid approach to the t-product
based third-order tensor singular value decomposition with the transform matrix
being a factor matrix of the higher order singular value decomposition.
Continuing along this vein, this paper gives a truncated O-SVD. Motivated by
the success of probabilistic algorithms, we develop a randomized version of the
O-SVD and present its detailed error analysis. The new algorithm has advantages
in efficiency while keeping good accuracy compared with the current tensor
decompositions. Our claims are supported by numerical experiments on several
oriented tensors from real applications.


title:
DrawingInStyles: Portrait Image Generation and Editing with Spatially
  Conditioned StyleGAN

abstract:
  The research topic of sketch-to-portrait generation has witnessed a boost of
progress with deep learning techniques. The recently proposed StyleGAN
architectures achieve state-of-the-art generation ability but the original
StyleGAN is not friendly for sketch-based creation due to its unconditional
generation nature. To address this issue, we propose a direct conditioning
strategy to better preserve the spatial information under the StyleGAN
framework. Specifically, we introduce Spatially Conditioned StyleGAN
(SC-StyleGAN for short), which explicitly injects spatial constraints to the
original StyleGAN generation process. We explore two input modalities, sketches
and semantic maps, which together allow users to express desired generation
results more precisely and easily. Based on SC-StyleGAN, we present
DrawingInStyles, a novel drawing interface for non-professional users to easily
produce high-quality, photo-realistic face images with precise control, either
from scratch or editing existing ones. Qualitative and quantitative evaluations
show the superior generation ability of our method to existing and alternative
solutions. The usability and expressiveness of our system are confirmed by a
user study.


title: Online List Labeling: Breaking the $\log^2n$ Barrier

abstract:
  The online list labeling problem is an algorithmic primitive with a large
literature of upper bounds, lower bounds, and applications. The goal is to
store a dynamically-changing set of $n$ items in an array of $m$ slots, while
maintaining the invariant that the items appear in sorted order, and while
minimizing the relabeling cost, defined to be the number of items that are
moved per insertion/deletion.
  For the linear regime, where $m = (1 + \Theta(1)) n$, an upper bound of
$O(\log^2 n)$ on the relabeling cost has been known since 1981. A lower bound
of $\Omega(\log^2 n)$ is known for deterministic algorithms and for so-called
smooth algorithms, but the best general lower bound remains $\Omega(\log n)$.
The central open question in the field is whether $O(\log^2 n)$ is optimal for
all algorithms.
  In this paper, we give a randomized data structure that achieves an expected
relabeling cost of $O(\log^{3/2} n)$ per operation. More generally, if $m = (1
+ \varepsilon) n$ for $\varepsilon = O(1)$, the expected relabeling cost
becomes $O(\varepsilon^{-1} \log^{3/2} n)$.
  Our solution is history independent, meaning that the state of the data
structure is independent of the order in which items are inserted/deleted. For
history-independent data structures, we also prove a matching lower bound: for
all $\epsilon$ between $1 / n^{1/3}$ and some sufficiently small positive
constant, the optimal expected cost for history-independent list-labeling
solutions is $\Theta(\varepsilon^{-1}\log^{3/2} n)$.


title:
Bridging the Gap Between Learning in Discrete and Continuous
  Environments for Vision-and-Language Navigation

abstract:
  Most existing works in vision-and-language navigation (VLN) focus on either
discrete or continuous environments, training agents that cannot generalize
across the two. The fundamental difference between the two setups is that
discrete navigation assumes prior knowledge of the connectivity graph of the
environment, so that the agent can effectively transfer the problem of
navigation with low-level controls to jumping from node to node with high-level
actions by grounding to an image of a navigable direction. To bridge the
discrete-to-continuous gap, we propose a predictor to generate a set of
candidate waypoints during navigation, so that agents designed with high-level
actions can be transferred to and trained in continuous environments. We refine
the connectivity graph of Matterport3D to fit the continuous
Habitat-Matterport3D, and train the waypoints predictor with the refined graphs
to produce accessible waypoints at each time step. Moreover, we demonstrate
that the predicted waypoints can be augmented during training to diversify the
views and paths, and therefore enhance agent's generalization ability. Through
extensive experiments we show that agents navigating in continuous environments
with predicted waypoints perform significantly better than agents using
low-level actions, which reduces the absolute discrete-to-continuous gap by
11.76% Success Weighted by Path Length (SPL) for the Cross-Modal Matching Agent
and 18.24% SPL for the Recurrent VLN-BERT. Our agents, trained with a simple
imitation learning objective, outperform previous methods by a large margin,
achieving new state-of-the-art results on the testing environments of the
R2R-CE and the RxR-CE datasets.


title: Flare echos from relaxation shocks in perturbed relativistic jets

abstract:
  One of the main scenarios to account for the multi-wavelength flux
variability observed in relativistic jets of AGNs is based on diffusive shock
acceleration of a population of relativistic electrons on internal shocks of
various origins. Any complete AGN emission scenario has to be able to explain
the wide range of observed variability time scales between the radio and
gamma-ray band. In addition constraints are also provided by very-long-baseline
interferometry (VLBI), which shows a large variety of moving and standing
emission zones with distinct behaviors. We aim to characterize the evolution of
stationary and moving emission zones in the jet and to study their
multi-wavelength signatures through emission maps and light curves. We focus
our study on flare events that occur during strong interactions between moving
ejecta and stationary recollimation shocks. We simulate relativistic jets with
the magneto-hydrodynamic code MPI-AMRVAC and inject non-thermal particle
distributions of electrons in shock regions. We follow the propagation of a
moving shock and its interactions with a structure of standing re-collimation
shocks in the jet. Synchrotron emission and radiative transfer are calculated
in the post-processing code RIPTIDE for given observation angles and
frequencies. We demonstrate the appearance of trailing components behind the
leading moving shock. The latter destabilizes the jet, causing the emergence of
oscillating standing shocks and relaxation shocks. Emissions from these regions
can dominate the overall flux or lead to flare echos in the light curve.
Another observational marker of relaxation shocks appears in time-distance
plots of bright VLBI components of the jet. Our scenario provides a plausible
explanation for radio VLBI observations of the radio-galaxy 3C 111 where
trailing components have been observed during a radio outburst event in 1997.


title: Improved bound for improper colorings of graphs with no odd clique minor

abstract:
  Strengthening Hadwiger's conjecture, Gerards and Seymour conjectured in 1995
that every graph with no odd $K_t$-minor is properly $(t-1)$-colorable, this is
known as the Odd Hadwiger's conjecture. We prove a relaxation of the above
conjecture, namely we show that every graph with no odd $K_t$-minor admits a
vertex $(2t-2)$-coloring such that all monochromatic components have size at
most $\lceil \frac{1}{2}(t-2) \rceil$. The bound on the number of colors is
optimal up to a factor of $2$, improves previous bounds for the same problem by
Kawarabayashi (2008), Kang and Oum (2019), Liu and Wood (2021), and strengthens
a result by van den Heuvel and Wood (2018), who showed that the above
conclusion holds under the more restrictive assumption that the graph is
$K_t$-minor free. In addition, the bound on the component-size in our result is
much smaller than those of previous results, in which the dependency on $t$ was
non-explicit. Our short proof combines the method by van den Heuvel and Wood
for $K_t$-minor free graphs with some additional ideas, which make the
extension to odd $K_t$-minor free graphs possible.


title:
Towards Robust Part-aware Instance Segmentation for Industrial Bin
  Picking

abstract:
  Industrial bin picking is a challenging task that requires accurate and
robust segmentation of individual object instances. Particularly, industrial
objects can have irregular shapes, that is, thin and concave, whereas in
bin-picking scenarios, objects are often closely packed with strong occlusion.
To address these challenges, we formulate a novel part-aware instance
segmentation pipeline. The key idea is to decompose industrial objects into
correlated approximate convex parts and enhance the object-level segmentation
with part-level segmentation. We design a part-aware network to predict part
masks and part-to-part offsets, followed by a part aggregation module to
assemble the recognized parts into instances. To guide the network learning, we
also propose an automatic label decoupling scheme to generate ground-truth
part-level labels from instance-level labels. Finally, we contribute the first
instance segmentation dataset, which contains a variety of industrial objects
that are thin and have non-trivial shapes. Extensive experimental results on
various industrial objects demonstrate that our method can achieve the best
segmentation results compared with the state-of-the-art approaches.


title: Mediated interactions between ions in quantum degenerate gases

abstract:
  We explore the interaction between two trapped ions mediated by a surrounding
quantum degenerate Bose or Fermi gas. Using perturbation theory valid for weak
atom-ion interaction, we show analytically that the interaction mediated by a
Bose gas has a power-law behaviour for large distances whereas it has a Yukawa
form for intermediate distances. For a Fermi gas, the mediated interaction is
given by a power-law for large density and by a Ruderman-Kittel-Kasuya-Yosida
form for low density. For strong atom-ion interactions, we use a diagrammatic
theory to demonstrate that the mediated interaction can be a significant
addition to the bare Coulomb interaction between the ions, when an atom-ion
bound state is close to threshold. Finally, we show that the induced
interaction leads to substantial and observable shifts in the ion phonon
frequencies.


title:
Dimits transition in three-dimensional ion-temperature-gradient
  turbulence

abstract:
  We extend our previous work on the 2D Dimits transition in ion-scale
turbulence (Ivanov et al. 2020) to include variations along the magnetic field.
We consider a three-field fluid model for the perturbations of electrostatic
potential, ion temperature, and ion parallel flow in a
constant-magnetic-curvature geometry without magnetic shear. It is derived in
the cold-ion, long-wavelength asymptotic limit of the gyrokinetic theory. Just
as in the 2D model, a low-transport (Dimits) regime exists and is found to be
dominated by a quasi-static staircase-like arrangement of strong zonal flows
and zonal temperature. This zonal staircase is formed and maintained by a
negative turbulent viscosity for the zonal flows. Unlike the 2D model, the 3D
one does not suffer from an unphysical blow up beyond the Dimits threshold
where the staircase becomes nonlinearly unstable. Instead, a well-defined
finite-amplitude saturated state is established. This qualitative difference
between 2D and 3D is due to the appearance of small-scale `parasitic' modes
that exist only if we allow perturbations to vary along the magnetic field
lines. These modes extract energy from the large-scale perturbations and
provide an effective enhancement of large-scale thermal diffusion, thus aiding
the energy transfer from large injection scales to small dissipative ones. We
show that in our model, the parasitic modes always favour a
zonal-flow-dominated state. In fact, a Dimits state with a zonal staircase is
achieved regardless of the strength of the linear drive provided the system is
sufficiently extended along the magnetic field and sufficient parallel
resolution is provided.


title:
Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing
  Performance

abstract:
  Generative adversarial networks (GANs) have received an upsurging interest
since being proposed due to the high quality of the generated data. While
achieving increasingly impressive results, the resource demands associated with
the large model size hinders the usage of GANs in resource-limited scenarios.
For inference, the existing model compression techniques can reduce the model
complexity with comparable performance. However, the training efficiency of
GANs has less been explored due to the fragile training process of GANs. In
this paper, we, for the first time, explore the possibility of directly
training sparse GAN from scratch without involving any dense or pre-training
steps. Even more unconventionally, our proposed method enables directly
training sparse unbalanced GANs with an extremely sparse generator from
scratch. Instead of training full GANs, we start with sparse GANs and
dynamically explore the parameter space spanned over the generator throughout
training. Such a sparse-to-sparse training procedure enhances the capacity of
the highly sparse generator progressively while sticking to a fixed small
parameter budget with appealing training and inference efficiency gains.
Extensive experiments with modern GAN architectures validate the effectiveness
of our method. Our sparsified GANs, trained from scratch in one single run, are
able to outperform the ones learned by expensive iterative pruning and
re-training. Perhaps most importantly, we find instead of inheriting parameters
from expensive pre-trained GANs, directly training sparse GANs from scratch can
be a much more efficient solution. For example, only training with a 80% sparse
generator and a 70% sparse discriminator, our method can achieve even better
performance than the dense BigGAN.


title:
Remiod: Reference-based Controlled Multiple Imputation of Longitudinal
  Binary and Ordinal Outcomes with non-ignorable missingness

abstract:
  Missing data on response variables are common in clinical studies.
Corresponding to the uncertainty of missing mechanism, theoretical frameworks
on controlled imputation have been developed. In practice, it is recommended to
conduct a statistically valid analysis under the primary assumptions on missing
data, followed by sensitivity analysis under alternative assumptions to assess
the robustness of results. Due to the availability of software, controlled
multiple imputation (MI) procedures, including delta-based and reference-based
approaches, have become popular for analyzing continuous variables under
missing-not-at-random assumptions. Similar tools, however, still limit
application of these methods to categorical data. In this paper, we introduce
the R package \textbf{remiod}, which utilizes the Bayesian framework to perform
imputation in regression models on binary and ordinal outcomes. Following
outlining theoretical backgrounds, usage and features of \textbf{remiod} are
described and illustrated using examples.


title: Rib Suppression in Digital Chest Tomosynthesis

abstract:
  Digital chest tomosynthesis (DCT) is a technique to produce sectional 3D
images of a human chest for pulmonary disease screening, with 2D X-ray
projections taken within an extremely limited range of angles. However, under
the limited angle scenario, DCT contains strong artifacts caused by the
presence of ribs, jamming the imaging quality of the lung area. Recently, great
progress has been achieved for rib suppression in a single X-ray image, to
reveal a clearer lung texture. We firstly extend the rib suppression problem to
the 3D case at the software level. We propose a $\textbf{T}$omosynthesis
$\textbf{RI}$b Su$\textbf{P}$pression and $\textbf{L}$ung
$\textbf{E}$nhancement $\textbf{Net}$work (TRIPLE-Net) to model the 3D rib
component and provide a rib-free DCT. TRIPLE-Net takes the advantages from both
2D and 3D domains, which model the ribs in DCT with the exact FBP procedure and
3D depth information, respectively. The experiments on simulated datasets and
clinical data have shown the effectiveness of TRIPLE-Net to preserve lung
details as well as improve the imaging quality of pulmonary diseases. Finally,
an expert user study confirms our findings.


title: The Physics and Metaphysics of Pure Shape Dynamics

abstract:
  The goal of this essay is twofold. First, it provides a quick look at the
foundations of modern relational mechanics by tracing its development from
Julian Barbour and Bruno Bertotti's original ideas until present-day's pure
shape dynamics. Secondly, it discusses the most appropriate metaphysics for
pure shape dynamics, showing that relationalism is more of a nuanced thesis
rather than an elusive one. The chapter ends with a brief assessment of the
prospects of pure shape dynamics in light of quantum physics.


title: Algebraic theory of phase retrieval

abstract:
  The purpose of this article is to discuss recent advances in the growing
field of phase retrieval, and to publicize open problems that we believe will
be of interest to mathematicians in general, and algebraists in particular.

categories: eess.SP cs.IT math-ph math.AG math.IT math.MP


title:
On sheaf cohomology for supergroups arising from simple classical Lie
  superalgebras

abstract:
  In this paper the authors study the behavior of the sheaf cohomology functors
$R^{\bullet}\text{ind}_{B}^{G}(-)$ where $G$ is an algebraic group scheme
corresponding to a simple classical Lie superalgebra and $B$ is a BBW parabolic
subgroup as defined by D. Grantcharov, N. Grantcharov, Nakano and Wu. We
provide a systematic treatment that allows us to study the behavior of these
cohomology groups $R^{\bullet}\text{ind}_{B}^{G}L_{\mathfrak f}(\lambda)$ where
$L_{\mathfrak f}(\lambda)$ is an irreducible representation for the detecting
subalgebra ${\mathfrak f}$. In particular, we prove an analog of Kempf's
vanishing theorem and the Bott-Borel-Weil theorem for large weights.


title: Boosting human decision-making with AI-generated decision aids

abstract:
  Human decision-making is plagued by many systematic errors. Many of these
errors can be avoided by providing decision aids that guide decision-makers to
attend to the important information and integrate it according to a rational
decision strategy. Designing such decision aids is a tedious manual process.
Advances in cognitive science might make it possible to automate this process
in the future. We recently introduced machine learning methods for discovering
optimal strategies for human decision-making automatically and an automatic
method for explaining those strategies to people. Decision aids constructed by
this method were able to improve human decision-making. However, following the
descriptions generated by this method is very tedious. We hypothesized that
this problem can be overcome by conveying the automatically discovered decision
strategy as a series of natural language instructions for how to reach a
decision. Experiment 1 showed that people do indeed understand such procedural
instructions more easily than the decision aids generated by our previous
method. Encouraged by this finding, we developed an algorithm for translating
the output of our previous method into procedural instructions. We applied the
improved method to automatically generate decision aids for a naturalistic
planning task (i.e., planning a road trip) and a naturalistic decision task
(i.e., choosing a mortgage). Experiment 2 showed that these automatically
generated decision-aids significantly improved people's performance in planning
a road trip and choosing a mortgage. These findings suggest that AI-powered
boosting has potential for improving human decision-making in the real world.


title: Cosine Model Watermarking Against Ensemble Distillation

abstract:
  Many model watermarking methods have been developed to prevent valuable
deployed commercial models from being stealthily stolen by model distillations.
However, watermarks produced by most existing model watermarking methods can be
easily evaded by ensemble distillation, because averaging the outputs of
multiple ensembled models can significantly reduce or even erase the
watermarks. In this paper, we focus on tackling the challenging task of
defending against ensemble distillation. We propose a novel watermarking
technique named CosWM to achieve outstanding model watermarking performance
against ensemble distillation. CosWM is not only elegant in design, but also
comes with desirable theoretical guarantees. Our extensive experiments on
public data sets demonstrate the excellent performance of CosWM and its
advantages over the state-of-the-art baselines.


title:
A Modular Approach to the Embodiment of Hand Motions from Human
  Demonstrations

abstract:
  Manipulating objects with robotic hands is a complicated task. Not only the
pose of the robot's end effector, but also the fingers of the hand need to be
controlled and coordinated. Using human demonstrations of movements is an
intuitive and data-efficient way of guiding the robot's behavior. We propose a
modular framework with an automatic embodiment mapping to transfer human hand
motions to robotic systems and use motion capture to record human motion. We
evaluate our approach on eight challenging tasks, in which a robotic arm with a
mounted robotic hand needs to grasp and manipulate deformable objects or small,
fragile material.


title: Atomic Size Pearls being Dark Matter giving Electron Signal

abstract:
  We seek to explain both the seeming observation of dark matter by the
seasonal variation of the DAMA-LIBRA data and the observation of "electron
recoil" events at Xenon1T in which the liquid Xe scintillator was excited by
electrons - in excess to the expected background - by the same dark matter
model. In our model the dark matter consists of bubbles of a new type of vacuum
containing ordinary atomic matter, say diamond, under high pressure ensured by
the surface tension of the separation domain wall. We also seek to explain the
self interactions of dark matter suggested by astronomical studies of dwarf
galaxies and the central structure of galaxy clusters. The interaction with
matter in the shielding is responsible for slowing the dark matter down to a
low terminal velocity, so underground detectors have insufficient energy for
detection. Further we explain the "mysterious" X-ray line of 3.5 keV, even the
3.5 keV X-ray radiation from the Tycho supernova remnant. The DAMA-LIBRA and
Xenon1T experiments see decaying dark matter pearls. Both these underground
experiments see events with about 3.5 keV energy, just the energy of the X-ray
line. We fit numerically the cross section over mass ratio for the self
interaction of the dark matter as observed in the study of dwarf galaxies and
find the size of the pearls to be rather close to the smallest possible in our
model. However DAMA cause some problem to get heavier pearls. Also the total
energy of the dark matter pearls stopped in the shield is reasonably matching.
Accepting the different phases of the vacuum the model could be realized inside
the Standard Model, our whole scheme could be realized inside the Standard
Model.


title: The Completeness of 2D Rubik's Shapes

abstract:
  The Rubik's cube was invented in 1974 by Erno Rubik, who had no idea of the
incredible popularity and mathematical fascinations his toy would bring.
Through the years of study on the mathematical properties of the cube, the
Rubik's Cube group was introduced to represent all possible moves one could
perform on the cube. In this paper, we define a planar analogue to the Rubik's
cube, which we dub the Rubik's Square, and prove that the Rubik's square is
complete in the sense that given any two configurations there is a sequence of
moves which changes one to the other. The Rubik's cube does not have this
property. We then abstract the concept of the Rubik's Square to a Rubik's Shape
and analyse the completeness in this more general setting.


title: Varying fine-structure constant cosmography

abstract:
  Cosmography is a phenomenological and relatively model-independent approach
to cosmology, where physical quantities are expanded as a Taylor series in the
cosmological redshift, or in related variables. Here we apply this methodology
to constrain possible cosmological variations of the fine-structure constant,
$\alpha$. Two peculiarities of this case are the existence of high-redshift
data, and the fact that one term in the series is directly and tightly
constraint by local laboratory tests with atomic clocks. We use this atomic
clock data, together with direct model-independent high-resolution
astrophysical spectroscopy measurements of $\alpha$ up to redshift $z\sim7$ and
additional model-dependent constraints on $\alpha$ from the cosmic microwave
background and big bang nucleosynthesis, to place stringent (parts per million
level) constraints on the first two terms in the $\alpha$ cosmographic series.


title: Laplace and Dirac Operators on Graphs

abstract:
  Discrete versions of the Laplace and Dirac operators haven been studied in
the context of combinatorial models of statistical mechanics and quantum field
theory. In this paper we introduce several variations of the Laplace and Dirac
operators on graphs, and we investigate graph-theoretic versions of the
Schr\"odinger and Dirac equation. We provide a combinatorial interpretation for
solutions of the equations and we prove gluing identities for the Dirac
operator on lattice graphs, as well as for graph Clifford algebras.


title:
Entanglement and correlations in fast collective neutrino flavor
  oscillations

abstract:
  Collective neutrino oscillations play a crucial role in transporting lepton
flavor in astrophysical settings like supernovae and neutron star binary merger
remnants, which are characterized by large neutrino densities. In these
settings, simulations in the mean-field approximation show that
neutrino-neutrino interactions can overtake vacuum oscillations and give rise
to fast collective flavor evolution on time-scales $t\propto\mu^{-1}$, with
$\mu$ proportional to the local neutrino density. In this work, we study the
full out-of-equilibrium flavor dynamics in simple multi-angle geometries
displaying fast oscillations in the mean field linear stability analysis.
Focusing on simple initial conditions, we analyze the production of pair
correlations and entanglement in the complete many-body-dynamics as a function
of the number $N$ of neutrinos in the system, for up to thousands of neutrinos.
Similarly to simpler geometries with only two neutrino beams, we identify three
regimes: stable configurations with vanishing flavor oscillations, marginally
unstable configurations with evolution occurring on long time scales
$\tau\approx\mu^{-1}\sqrt{N}$, and unstable configurations showing flavor
evolution on short time scales $\tau\approx\mu^{-1}\log(N)$. We present
evidence that these fast collective modes are generated by the same dynamical
phase transition which leads to the slow bipolar oscillations, establishing a
connection between these two phenomena and explaining the difference in their
time scales. We conclude by discussing a semi-classical approximation which
reproduces the entanglement entropy at short to medium time scales and can be
potentially useful in situations with more complicated geometries where
classical simulation methods starts to become inefficient.


title:
Deterministic Identification for Molecular Communications over the
  Poisson Channel

abstract:
  Various applications of molecular communications (MC) are event-triggered,
and, as a consequence, the prevalent Shannon capacity may not be the right
measure for performance assessment. Thus, in this paper, we motivate and
establish the identification capacity as an alternative metric. In particular,
we study deterministic identification (DI) for the discrete-time Poisson
channel (DTPC), subject to an average and a peak power constraint, which serves
as a model for MC systems employing molecule counting receivers. It is
established that the codebook size for this channel scales as $2^{(n\log n)R}$,
where $n$ and $R$ are the codeword length and coding rate, respectively. Lower
and upper bounds on the DI capacity of the DTPC are developed. The obtained
large capacity of the DI channel sheds light on the performance of natural DI
systems such as natural olfaction, which are known for their extremely large
chemical discriminatory power in biology. Furthermore, numerical simulations
for the empirical miss-identification and false identification error rates are
provided for finite length codes. This allows us to quantify the scale of error
reduction in terms of the codeword length.


title:
The effect of convection on the position of the free liquid surface
  under zero and terrestrial gravity

abstract:
  Theresults of numerical modelinghas based on the solution of Navier-Stokes
equations for convection oftwo-layer system air-water are presented.The
influence of thermocapillary and gravitational convection on the deformation
and time averaged position of the interface of air-water system under sudden
lateral heating of a square-section cavity are shown.


title: Stratification and segregation under laminar convection

abstract:
  The paper is devoted to the study of the formation of stratification in an
incompressible fluid due to convective laminar flows in horizontal layers
heated from the side. Medium and intensive modes of stationary laminar thermal,
concentrational and thermo-concentrational (in particular thermohaline)
convection are considered, in which nonlinear flow features are manifested,
which can radically change the flow structure and the characteristics of heat
and mass transfer. The solutions of the problems of laminar problems of
convection show the features of the formation of layered structures, vertical
temperature and concentration stratification depending on the determining
dimensionless parameters. The metastable instability of the direction of the
stratification vector (the location of the free surface) in weightlessness in
the presence of capillary convection was shown.


title:
Heat and mass transfer during crystal growing by the Czochralski method
  with a submerged vibrator

abstract:
  It was shown that the vibrations can intensify of heat-mass transfer and
mixing of the melt, and also to reduce dynamic and temperature boundary layers
and to increase a temperature gradient on melt/crystal interface, that can to
increase rate of crystal growth by the Czochralski method with a submerged
vibrator. It is can be very impotent for manufacturing and quality of crystals.


title: Constructing Artificial Traffic Fluids by Designing Cruise Controllers

abstract:
  In this paper, we apply a Control Lyapunov Function methodology to design two
families of cruise controllers for the two-dimensional movement of autonomous
vehicles on lane-free roads using the bicycle kinematic model. The control
Lyapunov functions are based on measures of the energy of the system with the
kinetic energy expressed in ways similar to Newtonian or relativistic
mechanics. The derived feedback laws (cruise controllers) are decentralized, as
each vehicle determines its control input based on its own speed and on the
relative speeds and distances from adjacent vehicles and from the boundary of
the road. Moreover, the corresponding macroscopic models are derived, obtaining
fluid-like models that consist of a conservation equation and a momentum
equation with pressure and viscous terms. Finally, we show that, by selecting
appropriately the parameters of the feedback laws, we can determine the
physical properties of the "traffic fluid", i.e. we get free hand to create an
artificial fluid that approximates the emerging traffic flow.


title: A monotonicity version of a concavity theorem of Lieb

abstract:
  We give a simple proof of a strengthened version of a theorem of Lieb that
played a key role in the proof of strong subadditivity of the quantum entropy.


title: A Robust Spectral Algorithm for Overcomplete Tensor Decomposition

abstract:
  We give a spectral algorithm for decomposing overcomplete order-4 tensors, so
long as their components satisfy an algebraic non-degeneracy condition that
holds for nearly all (all but an algebraic set of measure $0$) tensors over
$(\mathbb{R}^d)^{\otimes 4}$ with rank $n \le d^2$. Our algorithm is robust to
adversarial perturbations of bounded spectral norm.
  Our algorithm is inspired by one which uses the sum-of-squares semidefinite
programming hierarchy (Ma, Shi, and Steurer STOC'16, arXiv:1610.01980), and we
achieve comparable robustness and overcompleteness guarantees under similar
algebraic assumptions. However, our algorithm avoids semidefinite programming
and may be implemented as a series of basic linear-algebraic operations. We
consequently obtain a much faster running time than semidefinite programming
methods: our algorithm runs in time $\tilde O(n^2d^3) \le \tilde O(d^7)$, which
is subquadratic in the input size $d^4$ (where we have suppressed factors
related to the condition number of the input tensor).


title:
Deep Q-Learning Based Resource Allocation in Interference Systems With
  Outage Constraint

abstract:
  This correspondence considers the resource allocation problem in wireless
interference channel (IC) under link outage constraints. Since the optimization
problem is non-convex in nature, existing approaches to find the optimal power
allocation are computationally intensive and thus practically infeasible.
Recently, deep reinforcement learning has shown promising outcome in solving
non-convex optimization problems with reduced complexity. In this
correspondence, we utilize a deep Q-learning (DQL) approach which interacts
with the wireless environment and learns the optimal power allocation of a
wireless IC while maximizing overall sum-rate of the system and maintaining
reliability requirement of each link. We have used two separate deep Q-networks
to remove the inherent instability in learning process. Simulation results
demonstrate that the proposed DQL approach outperforms existing geometric
programming based solution.


title:
Adversarial Dual-Student with Differentiable Spatial Warping for
  Semi-Supervised Semantic Segmentation

abstract:
  A common challenge posed to robust semantic segmentation is the expensive
data annotation cost. Existing semi-supervised solutions show great potential
toward solving this problem. Their key idea is constructing consistency
regularization with unsupervised data augmentation from unlabeled data for
model training. The perturbations for unlabeled data enable the consistency
training loss, which benefits semi-supervised semantic segmentation. However,
these perturbations destroy image context and introduce unnatural boundaries,
which is harmful for semantic segmentation. Besides, the widely adopted
semi-supervised learning framework, i.e. mean-teacher, suffers performance
limitation since the student model finally converges to the teacher model. In
this paper, first of all, we propose a context friendly differentiable
geometric warping to conduct unsupervised data augmentation; secondly, a novel
adversarial dual-student framework is proposed to improve the Mean-Teacher from
the following two aspects: (1) dual student models are learnt independently
except for a stabilization constraint to encourage exploiting model
diversities; (2) adversarial training scheme is applied to both students and
the discriminators are resorted to distinguish reliable pseudo-label of
unlabeled data for self-training. Effectiveness is validated via extensive
experiments on PASCAL VOC2012 and Citescapes. Our solution significantly
improves the performance and state-of-the-art results are achieved on both
datasets. Remarkably, compared with fully supervision, our solution achieves
comparable mIoU of 73.4% using only 12.5% annotated data on PASCAL VOC2012.


title: New satellites of figure-eight orbit computed with high precision

abstract:
  In this paper we use a Modified Newton's method based on the Continuous
analog of Newton's method and high precision arithmetic for a search of new
satellites of the famous figure-eight orbit. By making a purposeful search for
such satellites, we found over 300 new satellites, including 7 new stable
choreographies.

categories: math.NA astro-ph.EP cs.NA nlin.CD physics.comp-ph


title:
Machine Learning Applications in Lung Cancer Diagnosis, Treatment and
  Prognosis

abstract:
  The recent development of imaging and sequencing technologies enables
systematic advances in the clinical study of lung cancer. Meanwhile, the human
mind is limited in effectively handling and fully utilizing the accumulation of
such enormous amounts of data. Machine learning-based approaches play a
critical role in integrating and analyzing these large and complex datasets,
which have extensively characterized lung cancer through the use of different
perspectives from these accrued data. In this article, we provide an overview
of machine learning-based approaches that strengthen the varying aspects of
lung cancer diagnosis and therapy, including early detection, auxiliary
diagnosis, prognosis prediction and immunotherapy practice. Moreover, we
highlight the challenges and opportunities for future applications of machine
learning in lung cancer.


title: Strict Feasibility and Degeneracy in Linear Programming

abstract:
  Currently, the simplex method and the interior point method are indisputably
the most popular algorithms for solving linear programs. Unlike general conic
programs, linear programs with a finite optimal value do not require strict
feasibility in order to establish strong duality. Hence strict feasibility is
often less emphasized. In this note we discuss that the lack of strict
feasibility necessarily causes difficulties in both simplex and interior point
methods. In particular, the lack of strict feasibility implies that every basic
feasible solution is degenerate. We achieve this using facial reduction and
simple means of linear algebra. Furthermore, we emphasize that facial reduction
involves two steps where the first guarantees strict feasibility, and the
second recovers full-row rankness of the constraint matrix.


title:
Distributed Optimal Power Flow for VSC-MTDC Meshed AC/DC Grids Using
  ALADIN

abstract:
  The increasing application of voltage source converter (VSC) high voltage
direct current (VSC-HVDC) technology in power grids has raised the importance
of incorporating DC grids and converters into the existing transmission
network. This poses significant challenges in dealing with the resulting
optimal power flow (OPF) problem. In this paper, a recently proposed nonconvex
distributed optimization algorithm -- Augmented Lagrangian based Alternating
Direction Inexact Newton method (ALADIN), is tailored to solve the nonconvex
AC/DC OPF problem for emerging voltage source converter (VSC) based
multiterminal high voltage direct current (VSC-MTDC) meshed AC/DC hybrid
systems. The proposed scheme decomposes this AC/DC hybrid OPF problem and
handles it in a fully distributed way. Compared to the existing state-of-art
Alternating Direction Method of Multipliers(ADMM), which is in general, not
applicable for nonconvex problems, ALADIN has a theoretical convergence
guarantee. Applying these two approaches to (VSC-MTDC) coupled with an IEEE
benchmark AC power system illustrates that the tailored ALADIN outperforms ADMM
in convergence speed and numerical robustness.


title:
ClueGraphSum: Let Key Clues Guide the Cross-Lingual Abstractive
  Summarization

abstract:
  Cross-Lingual Summarization (CLS) is the task to generate a summary in one
language for an article in a different language. Previous studies on CLS mainly
take pipeline methods or train the end-to-end model using the translated
parallel data. However, the quality of generated cross-lingual summaries needs
more further efforts to improve, and the model performance has never been
evaluated on the hand-written CLS dataset. Therefore, we first propose a
clue-guided cross-lingual abstractive summarization method to improve the
quality of cross-lingual summaries, and then construct a novel hand-written CLS
dataset for evaluation. Specifically, we extract keywords, named entities, etc.
of the input article as key clues for summarization and then design a
clue-guided algorithm to transform an article into a graph with less noisy
sentences. One Graph encoder is built to learn sentence semantics and article
structures and one Clue encoder is built to encode and translate key clues,
ensuring the information of important parts are reserved in the generated
summary. These two encoders are connected by one decoder to directly learn
cross-lingual semantics. Experimental results show that our method has stronger
robustness for longer inputs and substantially improves the performance over
the strong baseline, achieving an improvement of 8.55 ROUGE-1
(English-to-Chinese summarization) and 2.13 MoverScore (Chinese-to-English
summarization) scores over the existing SOTA.


title:
pylspack: Parallel algorithms and data structures for sketching, column
  subset selection, regression and leverage scores

abstract:
  We present parallel algorithms and data structures for three fundamental
operations in Numerical Linear Algebra: (i) Gaussian and CountSketch random
projections and their combination, (ii) computation of the Gram matrix and
(iii) computation of the squared row norms of the product of two matrices, with
a special focus on ``tall-and-skinny'' matrices, which arise in many
applications. We provide a detailed analysis of the ubiquitous CountSketch
transform and its combination with Gaussian random projections, accounting for
memory requirements, computational complexity and workload balancing. {We also
demonstrate how these results can be applied to} column subset selection, least
squares regression and leverage scores computation. These tools have been
implemented in {\tt pylspack}, a publicly available Python
package,\footnote{\hyperlink{https://github.com/IBM/pylspack}{https://github.com/IBM/pylspack}}
whose core is written in C++ and parallelized with OpenMP, and which is
compatible with standard matrix data structures of SciPy and NumPy. Extensive
numerical experiments indicate that the proposed algorithms scale well and
significantly outperform existing libraries for tall-and-skinny matrices.


title:
Accelerated carrier invoice factoring using predictive freight transport
  events

abstract:
  Invoice factoring is an invoice financing process where business
organizations sell their invoices to banks or financial institutions at a
discount to gain faster access to the invoice amount. In global trade, ocean
and land carriers exercise invoice factoring to gain quick access to the money
they get paid for the shipment of consignments by shippers. Shippers typically
clear the invoice payment within 60-90 days of goods getting delivered. In
order to get early access to capital, carriers initiate invoice factoring after
the completion of goods delivery to the shipper. In this work, we provide an
approach to enable accelerated carrier invoice factoring even before the
delivery of goods. Carrier invoice value for a given shipment depends on the
actual values of shipment tracking events. We predict the carrier invoice value
at different freight transport milestone events as the goods transportation
progresses from supplier to shipper using smart contracts on a blockchain
network that is operated by the global trade logistics participants. The
prediction at a given stage is based on past shipment tracking events and
predicted future shipment tracking events. Accurate prediction of invoice value
for ongoing shipment enables the carrier organization to initiate invoice
factoring on the trade finance network before the completion of goods delivery
to the shipper. Further, based on the past accuracy of prediction models, the
financial institutions may choose to release the invoice amount in installments
at different freight transportation milestone events.


title: Rifts in Rafts

abstract:
  The failure of materials is often considered as a quasi-static process in
classical fracture mechanics. No dynamics can be allowed because the speed of
sound is much greater than any other velocity. In this paper, we introduce a
macroscopic system where the dynamics of particle relaxation and rift formation
can be resolved: a particle aggregate is stretched uniaxially by the expansion
of the air-fluid interface on which it floats. The failure morphology of these
rafts change continuously with pulling velocity. This can be understood by the
competition between two velocities: the speed of particle relaxation towards
the nearest low energy configuration (i.e., re-aggregation), which is
determined by viscous and capillary forces, and the velocity difference between
neighboring particles, which is caused by the expanding underlying fluid. This
crossover behavior selects the cluster length scales, i.e., the distance
between adjacent rifts, and is consistent with the the experimental failure
patterns.


title: Object-centric Process Predictive Analytics

abstract:
  Object-centric processes (a.k.a. Artifact-centric processes) are
implementations of a paradigm where an instance of one process is not executed
in isolation but interacts with other instances of the same or other processes.
Interactions take place through bridging events where instances exchange data.
Object-centric processes are recently gaining popularity in academia and
industry, because their nature is observed in many application scenarios. This
poses significant challenges in predictive analytics due to the complex
intricacy of the process instances that relate to each other via many-to-many
associations. Existing research is unable to directly exploit the benefits of
these interactions, thus limiting the prediction quality. This paper proposes
an approach to incorporate the information about the object interactions into
the predictive models. The approach is assessed on real-life object-centric
process event data, using different KPIs. The results are compared with a naive
approach that overlooks the object interactions, thus illustrating the benefits
of their use on the prediction quality.


title: On discrepancy, intrinsic Diophantine approximation, and spectral gaps

abstract:
  In the present paper we establish bounds for the size of the spectral gap for
actions of algebraic groups on certain homogeneous spaces. Our approach is
based on estimating operator norms of suitable averaging operators, and we
develop techniques for establishing both upper and lower bounds for such norms.
We shall show that this analytic problem is closely related to the arithmetic
problem of establishing bounds on the discrepancy of distribution for rational
points on algebraic group varieties. As an application, we show how to
establish an effective bound for property $\tau$ of congruence subgroups of
arithmetic lattices in algebraic groups which are forms of $SL(2)$, using
estimates in intrinsic Diophantine approximation which follow from
Heath-Brown's analysis of rational points on 3-dimensional quadratic surfaces.


title: Localized scalar structures around static black holes

abstract:
  In this work we address a way to capture scalar field solutions on static
spacetimes by using BPS formalism and relaxing the general covariance
condition. We focus on configurations where the background geometry describes
topological black holes and present both analytical and numerical solutions, in
addition to discussing the use of conserved charges associated to such field
configurations. The obtained solutions are radially stable and the zero-mode
arising from the stability equation can be written analytically.


title:
A highly efficient tensor network algorithm for multi-asset Fourier
  options pricing

abstract:
  Risk assessment and in particular derivatives pricing is one of the core
areas in computational finance and accounts for a sizeable fraction of the
global computing resources of the financial industry. We outline a
quantum-inspired algorithm for multi-asset options pricing. The algorithm is
based on tensor networks, which have allowed for major conceptual and numerical
breakthroughs in quantum many body physics and quantum computation. In the
proof-of-concept example explored, the tensor network approach yields several
orders of magnitude speedup over vanilla Monte Carlo simulations. We take this
as good evidence that the use of tensor network methods holds great promise for
alleviating the computation burden of risk evaluation in the financial and
other industries, thus potentially lowering the carbon footprint these
simulations incur today.


title:
Stability Constrained Characterization of the 23 Myr-old V1298 Tau
  System: Do Young Planets Form in Mean Motion Resonance Chains?

abstract:
  A leading theoretical expectation for the final stages of planet formation is
that disk migration should naturally drive orbits into chains of mean motion
resonances (MMRs). In order to explain the dearth of MMR chains observed at Gyr
ages ($<1\%$), this picture requires such configurations to destabilize and
scramble period ratios following disk dispersal. Strikingly, the only two known
stars with three or more planets younger than $\lesssim 100$ Myrs, HR 8799 and
V1298 Tau, have been suggested to be in such MMR chains, given the orbits'
near-integer period ratios. We incorporate recent transit and radial velocity
observations of the V1298 Tau system, and investigate constraints on the
system's orbital architecture imposed by requiring dynamical stability on
timescales much shorter than the system's age. We show that the recent
radial-velocity mass measurement of V1298 Tau $b$ places it within a factor of
two of the instability limit, and that this allows us to set significantly
lower limits on the eccentricity ($e_b \leq 0.17$ at $99.7\%$ confidence).
Additionally, we rule out a resonant chain configuration for V1298 Tau at
$\gtrsim 99\%$ confidence. Thus, if the $\sim 23$ Myr-old V1298 Tau system did
form as a resonant chain, it must have undergone instability and rearrangement
shortly after disk dispersal. We expect that similar stability constrained
characterization of future young multi-planet systems will be valuable in
informing planet formation models.


title:
Converging approximations of attractors via almost Lyapunov functions
  and semidefinite programming

abstract:
  In this paper we combine the approaches from previous work and and atricle by
Jones and Peet for approximating global attractors. In the first mentioned one,
the global attractors is arbitrarily well approximated by sets that are not
necessarily positively invariant. On the contrary, the second method provides
supersets of the global attractor which are positively invariant but not
necessarily converging. In this paper we marry both approaches by combining
their techniques and get converging outer approximations of the global
attractor consisting of positively invariant sets. Because both underlying
methods are based on convex optimization via sum-of-squares techniques the same
is true for our proposed method. The method is easy to use and numerical
examples illustrate the procedure.


title: Off-Policy Evaluation in Embedded Spaces

abstract:
  Off-policy evaluation methods are important in recommendation systems and
search engines, whereby data collected under an old logging policy is used to
predict the performance of a new target policy. However, in practice most
systems are not observed to recommend most of the possible actions, which is an
issue since existing methods require that the probability of the target policy
recommending an item can only be non-zero when the probability of the logging
policy is non-zero (known as absolute continuity). To circumvent this issue, we
explore the use of action embeddings. By representing contexts and actions in
an embedding space, we are able to share information to extrapolate behaviors
for actions and contexts previously unseen.


title: Unveiling and Manipulating Hidden Symmetries in Graphene Nanoribbons

abstract:
  Armchair graphene nanoribbons are a highly promising class of semiconductors
for all-carbon nanocircuitry. Here, we present a new perspective on their
electronic structure from simple model Hamiltonians. We focus on a specific set
of nanoribbons of width $n = 3p+2$, where $n$ is the number of carbon atoms
across the nanoribbon axis and $p$ is a positive integer. We demonstrate that
the band-gap opening in these nanoribbons originates from the breaking of a
previously unidentified hidden symmetry by long-ranged hopping of
$\pi$-electrons. This symmetry can be restored or manipulated through in-plane
lattice deformations, which result in continuous band-gap tuning, emergence of
Dirac points at the Fermi level, and topological quantum phase transitions. Our
work establishes an original interpretation of the semiconducting character of
armchair graphene nanoribbons and offers guidelines for rationally designing
their electronic structure.


title:
28 GHz Phased Array-Based Self-Interference Measurements for Millimeter
  Wave Full-Duplex

abstract:
  We present measurements of the 28 GHz self-interference channel for
full-duplex sectorized multi-panel millimeter wave (mmWave) systems, such as
integrated access and backhaul. We measure the isolation between the input of a
transmitting phased array panel and the output of a co-located receiving phased
array panel, each of which is electronically steered across a number of
directions in azimuth and elevation. In total, nearly 6.5 million measurements
were taken in an anechoic chamber to densely inspect the directional nature of
the coupling between 256-element phased arrays. We observe that highly
directional mmWave beams do not necessarily offer widespread high isolation
between transmitting and receiving arrays. Rather, our measurements indicate
that steering the transmitter or receiver away from the other tends to offer
higher isolation but even slight steering changes can lead to drastic
variations in isolation. These measurements can be useful references when
developing mmWave full-duplex solutions and can motivate a variety of future
topics including beam/user selection and beamforming codebook design.


title:
Virtual Reality Digital Twin and Environment for Troubleshooting
  Lunar-based Infrastructure Assembly Failures

abstract:
  Humans and robots will need to collaborate in order to create a sustainable
human lunar presence by the end of the 2020s. This includes cases in which a
human will be required to teleoperate an autonomous rover that has encountered
an instrument assembly failure. To aid teleoperators in the troubleshooting
process, we propose a virtual reality digital twin placed in a simulated
environment. Here, the operator can virtually interact with a digital version
of the rover and mechanical arm that uses the same controls and kinematic
model. The user can also adopt the egocentric (a first person view through
using stereoscopic passthrough) and exocentric (a third person view where the
operator can virtually walk around the environment and rover as if they were on
site) view. We also discuss our metrics for evaluating the differences between
our digital and physical robot, as well as the experimental concept based on
real and applicable missions, and future work that would compare our platform
to traditional troubleshooting methods.


title:
Temperature Distributions in the Protosatellite Disks of Uranus,
  Jupiter, Neptune and Saturn and Common Structure in Their Satellite Systems

abstract:
  We observe similar structures in the orbital radii of satellite and ring
systems of Uranus, Jupiter and Neptune. This stimulates an investigation that
the evolution of these systems and Saturn's system is initiated by a common
mechanism that involves the interaction of radiation with their subnebulae
(protosatellite disks). A model is presented that is characterized by resonance
created through stimulated radiative molecular association (SRMA) reactions. In
this model thermal energy is extracted from a protoplanetary disk at specific
distances from the protoplanet wherever there is a match between the local
thermal energy of the disk and the energy of photons impinging on the disk.
Radiation is supplied by a portion of the hydrogen molecule's spectrum for the
Uranian, Jovian and Neptunian disks and a portion of the hydrogen atom's
spectrum for the Saturnian disk. Findings shed light on the early evolution and
structure of satellite systems including the complicated ring system of Saturn.
They also link satellite orbital radii size distributions to shapes of
temperature distributions (TD's) in protosatellite disks. Theoretically
determined TD's in the protosatellite disks of Uranus (Mousis 2004) and Saturn
(Mousis Gautier and Bockelee-Moran, 2002) are essential to the present
investigation.


title:
Partially polaron-transformed quantum master equation for exciton and
  charge transport dynamics

abstract:
  Polaron-transformed quantum master equation (PQME) offers a unified framework
to describe the dynamics of quantum systems in both limits of weak and strong
couplings to environmental degrees of freedom. Thus, PQME serves as an
efficient method to describe charge and exciton transfer/transport dynamics for
a broad range of parameters in condensed or complex environments. However, in
some cases, the polaron transformation (PT) being employed in the formulation
invokes an over-relaxation of slow modes and results in premature suppression
of important coherence terms. This work develops a formal framework to address
this issue by employing a partial PT that has smaller weights for low frequency
bath modes. It is shown here that a closed form expression of a 2nd order
time-local PQME including all the inhomogeneous terms can be derived for a
general form of partial PT, although more complicated than that for the full
PT. All the expressions needed for numerical calculation are derived in detail.
Applications to a model with two system states demonstrate the feasibility and
utility of the present approach.

categories: quant-ph cond-mat.mes-hall physics.bio-ph physics.chem-ph physics.comp-ph


title: Explicit expressions for \vSapovalov elements in Type A

abstract:
  We give explicit expressions for \vSapovalov elements in Type A Lie algebras
and superalgebras. Explicit expressions were already given in arXiv:1710.10528
Section 9, using non-commutative determinants, and in fact our first main
results, Theorems 2.3 and 2.6 can be viewed as complete expansions of these
determinants. But we give new proofs, which seem easier because they avoid
induction and cofactor expansion. We also describe \vSapovalov elements for
fgl(m,n)with respect to an arbitrary Borel subalgebra in Theorem 3.7, and
interpret \vSapovalov elements in Type A as determinants of Hessenberg matrices
in Theorems 4.5 and 4.9.The exact form of the explicit expressions depends on
an ordering on the set of positive roots, and Hessenberg matrices are useful in
changing the ordering. Having explicit expressions for \vSapovalov elements
allows us to give easy proofs of several results in representation theory, see
Section 6 and Subsection 4.6.


title: Competitive Exclusion in an Artificial Foraging Exosystem

abstract:
  Artificial ecosystems provide an addition experimental tool to support field
work and theoretical developments in competitive exclusion research. This
experimental approach provides precise control over the environmental and
species' parameters and produces detailed trajectories of populations and their
characteristics. An agent based model simulating two foraging species of
different intrinsic growth rates competing for the same replenishing resource
in a finite space with stable and seasonal environments is presented. Zones of
potential coexistence are identified within the environment and species
parameter space. Results of these experiments are discussed in the context of
modern coexistence theory. An example of commensalism is identified.
Constraints on possible origination scenarios are identified.


title: Double Choco is NP-complete

abstract:
  In the Nikoli pencil-and-paper game Double Choco, a puzzle consists of an m
$\times$ n grid of cells of white or gray color, separated by dotted lines
where each cell possibly contains an integer. The goal is to partition the grid
into blocks by drawing solid lines over the dotted lines, where every block
must contain a pair of areas of white and gray cells having the same form (size
and shape). An integer indicates the number of cells of that color in the
block. A block can contain any number of cells with the integer. We prove this
puzzle NP-complete, establishing a Nikoli gap of 2 years.


title:
Safely: Safe Stochastic Motion Planning Under Constrained Sensing via
  Duality

abstract:
  Consider a robot operating in an uncertain environment with stochastic,
dynamic obstacles. Despite the clear benefits for trajectory optimization, it
is often hard to keep track of each obstacle at every time step due to sensing
and hardware limitations. We introduce the Safely motion planner, a
receding-horizon control framework, that simultaneously synthesizes both a
trajectory for the robot to follow as well as a sensor selection strategy that
prescribes trajectory-relevant obstacles to measure at each time step while
respecting the sensing constraints of the robot. We perform the motion planning
using sequential quadratic programming, and prescribe obstacles to sense based
on the duality information associated with the convex subproblems. We guarantee
safety by ensuring that the probability of the robot colliding with any of the
obstacles is below a prescribed threshold at every time step of the planned
robot trajectory. We demonstrate the efficacy of the Safely motion planner
through software and hardware experiments.


title: Asymptotic symmetries and soft charges of fractons

abstract:
  The asymptotic structure of gauge theories describing fracton interactions is
analyzed. Two sets of asymptotic conditions are proposed. Both encompass all
known solutions, lead to finite charges and resolve the problem of the
divergent energy coming from the monopole contribution. While the first set
leads to the expected fracton symmetry algebra, including a dipole charge, the
second set provides a soft infinite-dimensional extension of it. These soft
charges provide evidence of a rich infrared structure for fracton-like theories
and provide one corner of a possible fracton infrared triangle.

categories: hep-th cond-mat.stat-mech cond-mat.str-el gr-qc


title:
Fuzzy Forests For Feature Selection in High-Dimensional Survey Data: An
  Application to the 2020 U.S. Presidential Election

abstract:
  An increasingly common methodological issue in the field of social science is
high-dimensional and highly correlated datasets that are unamenable to the
traditional deductive framework of study. Analysis of candidate choice in the
2020 Presidential Election is one area in which this issue presents itself: in
order to test the many theories explaining the outcome of the election, it is
necessary to use data such as the 2020 Cooperative Election Study Common
Content, with hundreds of highly correlated features. We present the Fuzzy
Forests algorithm, a variant of the popular Random Forests ensemble method, as
an efficient way to reduce the feature space in such cases with minimal bias,
while also maintaining predictive performance on par with common algorithms
like Random Forests and logit. Using Fuzzy Forests, we isolate the top
correlates of candidate choice and find that partisan polarization was the
strongest factor driving the 2020 presidential election.


title:
Workflow for investigating thermodynamic, structural and energy
  properties of condensed polymer systems from Molecular Dynamics

abstract:
  Soft matter materials and polymers are widely used in the controlled delivery
of drugs. Simulation and modeling provide insight at the atomic scale enabling
a level of control unavailable to experiments. We present a workflow protocol
for modeling, simulating, and analyzing structural and thermodynamic response
properties of poly-lactic-co-glycolic acid (PLGA), a well-studied and FDA
approved material. We concatenate a battery of molecular dynamics,
computational chemistry, highly parallel scripting, and analysis tools for
generating properties of bulk polymers in the condensed phase. We provide the
workflow leading to the glass transition temperature, enthalpy, density,
isobaric heat capacity, thermal expansion coefficient, isothermal
compressibility, bulk modulus, sonic velocity, cohesive energy, and solubility
parameters. Calculated properties agree very well with experiments, when
available. This methodology has been extended to a variety of polymer types and
environments.