title: On the transcendence of a series related to Sturmian words

abstract:
  Let $b$ be an algebraic number with $|b|>1$ and $\mathcal{H}$ a finite set of
algebraic numbers. We study the transcendence of numbers of the form
$\sum_{n=0}^\infty \frac{a_n}{b^n}$ where $a_n \in \mathcal{H}$ for all
$n\in\mathbb{N}$. We assume that the sequence $(a_n)_{n=0}^\infty$ is generated
by coding the orbit of a point under an irrational rotation of the unit circle.
In particular, this assumption holds whenever the sequence is Sturmian. Our
main result shows that all numbers of the above form are transcendental. We
moreover give sufficient conditions for a finite set of such numbers to be
linearly independent over~$\bar{\mathbb{Q}}$.


title: Differentiable Time-Frequency Scattering in Kymatio

abstract:
  Joint time-frequency scattering (JTFS) is a convolutional operator in the
time-frequency domain which extracts spectrotemporal modulations at various
rates and scales. It offers an idealized model of spectrotemporal receptive
fields (STRF) in the primary auditory cortex, and thus may serve as a
biological plausible surrogate for human perceptual judgments at the scale of
isolated audio events. Yet, prior implementations of JTFS and STRF have
remained outside of the standard toolkit of perceptual similarity measures and
evaluation methods for audio generation. We trace this issue down to three
limitations: differentiability, speed, and flexibility. In this paper, we
present an implementation of time-frequency scattering in Kymatio, an
open-source Python package for scattering transforms. Unlike prior
implementations, Kymatio accommodates NumPy and PyTorch as backends and is thus
portable on both CPU and GPU. We demonstrate the usefulness of JTFS in Kymatio
via three applications: unsupervised manifold learning of spectrotemporal
modulations, supervised classification of musical instruments, and texture
resynthesis of bioacoustic sounds.


title:
Lane-Free Crossing of CAVs through Intersections as a Minimum-Time
  Optimal Control Problem

abstract:
  Unlike conventional cars, connected and autonomous vehicles (CAVs) can cross
intersections in a lane-free order and utilise the whole area of intersections.
This paper presents a minimum-time optimal control problem to centrally control
the CAVs to simultaneously cross an intersection in the shortest possible time.
Dual problem theory is employed to convexify the constraints of CAVs to avoid
collision with each other and with road boundaries. The developed formulation
is smooth and solvable by gradient-based algorithms. Simulation results show
that the proposed strategy reduces the crossing time of intersections by an
average of 52% and 54% as compared to, respectively, the state-of-the-art
reservation-based and lane-free methods. Furthermore, the crossing time by the
proposed strategy is fixed to a constant value for an intersection regardless
of the number of CAVs.


title:
Unsupervised domain adaptation and super resolution on drone images for
  autonomous dry herbage biomass estimation

abstract:
  Herbage mass yield and composition estimation is an important tool for dairy
farmers to ensure an adequate supply of high quality herbage for grazing and
subsequently milk production. By accurately estimating herbage mass and
composition, targeted nitrogen fertiliser application strategies can be
deployed to improve localised regions in a herbage field, effectively reducing
the negative impacts of over-fertilization on biodiversity and the environment.
In this context, deep learning algorithms offer a tempting alternative to the
usual means of sward composition estimation, which involves the destructive
process of cutting a sample from the herbage field and sorting by hand all
plant species in the herbage. The process is labour intensive and time
consuming and so not utilised by farmers. Deep learning has been successfully
applied in this context on images collected by high-resolution cameras on the
ground. Moving the deep learning solution to drone imaging, however, has the
potential to further improve the herbage mass yield and composition estimation
task by extending the ground-level estimation to the large surfaces occupied by
fields/paddocks. Drone images come at the cost of lower resolution views of the
fields taken from a high altitude and requires further herbage ground-truth
collection from the large surfaces covered by drone images. This paper proposes
to transfer knowledge learned on ground-level images to raw drone images in an
unsupervised manner. To do so, we use unpaired image style translation to
enhance the resolution of drone images by a factor of eight and modify them to
appear closer to their ground-level counterparts. We then ...
~\url{www.github.com/PaulAlbert31/Clover_SSL}.


title:
A Multidimensional Artistic Approach to Enhance Understanding of Julia
  Sets through Computer Programming

abstract:
  This article proposes an artistic approach to increase and enrich the
understanding of Julia Sets. This approach includes the mathematical, the
playful, the artistic and the computational dimensions. It is argued that these
four dimensions are not disjointed or dissociated despite general rejection by
traditional academic communities and art critics communities. Also, some
significant collections of Computational Art or Computer-Generated Mathematical
Art are mentioned. Four artistic creations based on Julia Sets are presented as
examples using the CFDG language. Finally, an informal application of the
approach was carried out and artistic production of students are presented and
discussed.


title:
Iterative Complexity and Its Applications of Linearized Generalized
  Alternating Direction Method of Multipliers with Multi-block Case

abstract:
  We consider a multi-block separable convex optimization problem with the
linear constraints, where the objective function is the sum of m individual
convex functions without overlapping variables. The linearized version of the
generalized alternating direction method of multipliers (L-GADMM) is
particularly efficient for the two-block separable convex programming problem
and its convergence was proved when two blocks of variables are alternatively
updated. However,the convergence and some practical applications of the
extension (m>=3) of the L-GADMM is still in its infancy. In this paper, we
extend this algorithm to the general case where the objective function consists
of the sum of m-block convex functions. Theoretically, we prove global
convergence of the new method and establish the worst-case convergence rate in
the ergodic and nonergodic senses for the proposed algorithm. The efficiency of
the new method is further demonstrated through numerical results on the
calibration of the correlation matrices.


title:
Iterative Hard Thresholding with Adaptive Regularization: Sparser
  Solutions Without Sacrificing Runtime

abstract:
  We propose a simple modification to the iterative hard thresholding (IHT)
algorithm, which recovers asymptotically sparser solutions as a function of the
condition number. When aiming to minimize a convex function $f(x)$ with
condition number $\kappa$ subject to $x$ being an $s$-sparse vector, the
standard IHT guarantee is a solution with relaxed sparsity $O(s\kappa^2)$,
while our proposed algorithm, regularized IHT, returns a solution with sparsity
$O(s\kappa)$. Our algorithm significantly improves over ARHT which also finds a
solution of sparsity $O(s\kappa)$, as it does not require re-optimization in
each iteration (and so is much faster), is deterministic, and does not require
knowledge of the optimal solution value $f(x^*)$ or the optimal sparsity level
$s$. Our main technical tool is an adaptive regularization framework, in which
the algorithm progressively learns the weights of an $\ell_2$ regularization
term that will allow convergence to sparser solutions. We also apply this
framework to low rank optimization, where we achieve a similar improvement of
the best known condition number dependence from $\kappa^2$ to $\kappa$.

categories: math.OC cs.DS cs.IT cs.LG math.IT stat.ML


title:
Evaluations of some series of the type
  $\sum_{k=0}^\infty(ak+b)x^k/\binom{mk}{nk}$

abstract:
  In this paper, via the beta function we evaluate some series of the type
$\sum_{k=0}^\infty(ak+b)x^k/\binom{mk}{nk}$. For example, we prove that
$$\sum_{k=0}^\infty\frac{10k-1}{\binom{4k}{2k}}=\frac{4\sqrt 3}{27}\pi,$$ and
give a general series for $\log(1-x)$ with $-3<x<1$, which converges much
faster than the Taylor series of $\log(1-x)$. We also establish the following
efficient formula for computing $\log n$ with $1<n\le 85/4$: \begin{align*}
&\sum_{k=0}^\infty\frac{(2(n^2+6n+1)^2(n^2-10n+1)k+P(n))(n-1)^{4k}}
{(-n)^k(n+1)^{2k}\binom{4k}{2k}}\\ \ \ &=6n(n+1)(n-1)^3\log
n-32n(n+1)^2(n^2-4n+1), \end{align*} where
$$P(n):=n^6-58n^5+159n^4+52n^3+159n^2-58n+1.$$


title: What is the optimal schedule for the UEFA Champions League groups?

abstract:
  In a sports competition, a team might lose a powerful incentive to exert full
effort if its final rank does not depend on the outcome of the matches still to
be played. Therefore, the organiser should reduce the probability of such a
situation to the extent possible. Our paper provides a classification scheme to
identify these weakly (where one team is indifferent) or strongly (where both
teams are indifferent) stakeless games. A statistical model is estimated to
simulate the UEFA Champions League groups and compare the candidate schedules
used in the 2021/22 season according to the competitiveness of the matches
played in the last round(s). The option followed in four of the eight groups is
found to be optimal under a wide set of parameters. Minimising the number of
strongly stakeless matches is verified to be a likely goal in the computer draw
of the fixture that remains hidden from the public.


title:
A Note on Sun's Conjectures on Ap\'ery-like Sums Involving Lucas
  Sequences and Harmonic Numbers

abstract:
  In this paper, we will prove Zhi-Wei Sun's four conjectural identities on
Ap\'{e}ry-like sums involving Lucas sequences and harmonic numbers by using a
few results of Davydychev--Kalmykov.


title:
Emergence of negative viscosities and colored noise under current-driven
  Ehrenfest molecular dynamics

abstract:
  Molecules in molecular junctions are subject to current-induced forces that
can break chemical bonds, induce reactions, destabilize molecular geometry, and
halt the operation of the junction. Theories behind current-driven molecular
dynamics simulations rely on a perturbative time-scale separation within the
system with subsequent use of nonequilibrium Green's functions (NEGF) to
compute conservative, non-conservative, and fluctuating forces exerted by
electrons on nuclear degrees of freedom. We analyze the effectiveness of this
approximation, paying particular attention to the phenomenon of negative
viscosities. The perturbative approximation is directly compared to the
nonequilibrium Ehrenfest approach. We introduce a novel time-stepping approach
to calculate the forces present in the Ehrenfest method via exact integration
of the equations of motion for the nonequilibrium Green's functions, which does
not necessitate a time-scale separation within the system and provides an exact
description for the corresponding classical dynamics. We observe that negative
viscosities are not artifacts of a perturbative treatment but also emerge in
Ehrenfest dynamics. However, the effects of negative viscosity have the
possibility of being overwhelmed by the predominantly positive dissipation due
to the higher-order forces unaccounted for by the perturbative approach.
Additionally, we assess the validity of the white-noise approximation for the
fluctuating forces, finding that it is only justifiable in the presence of a
clear time-scale separation. Finally, we demonstrate the method for molecular
junction models consisting of one and two classical degrees of freedom.


title: Communication Bounds for Convolutional Neural Networks

abstract:
  Convolutional neural networks (CNNs) are important in a wide variety of
machine learning tasks and applications, so optimizing their performance is
essential. Moving words of data between levels of a memory hierarchy or between
processors on a network is much more expensive than the cost of arithmetic, so
minimizing communication is critical to optimizing performance. In this paper,
we present new lower bounds on data movement for mixed precision convolutions
in both single-processor and parallel distributed memory models, as well as
algorithms that outperform current implementations such as Im2Col. We obtain
performance figures using GEMMINI, a machine learning accelerator, where our
tiling provides improvements between 13% and 150% over a vendor supplied
algorithm.


title: Non-intrusive reduced-order modeling using convolutional autoencoders

abstract:
  The use of reduced-order models (ROMs) in physics-based modeling and
simulation almost always involves the use of linear reduced basis (RB) methods
such as the proper orthogonal decomposition (POD). For some nonlinear problems,
linear RB methods perform poorly, failing to provide an efficient subspace for
the solution space. The use of nonlinear manifolds for ROMs has gained traction
in recent years, showing increased performance for certain nonlinear problems
over linear methods. Deep learning has been popular to this end through the use
of autoencoders for providing a nonlinear trial manifold for the solution
space. In this work, we present a non-intrusive ROM framework for steady-state
parameterized partial differential equations (PDEs) that uses convolutional
autoencoders (CAEs) to provide a nonlinear solution manifold and is augmented
by Gaussian process regression (GPR) to approximate the expansion coefficients
of the reduced model. When applied to a numerical example involving the steady
incompressible Navier-Stokes equations solving a lid-driven cavity problem, it
is shown that the proposed ROM offers greater performance in prediction of
full-order states when compared to a popular method employing POD and GPR over
a number of ROM dimensions.


title:
Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic
  Environments

abstract:
  This paper studies the problem of expected loss minimization given a data
distribution that is dependent on the decision-maker's action and evolves
dynamically in time according to a geometric decay process. Novel algorithms
for both the information setting in which the decision-maker has a first order
gradient oracle and the setting in which they have simply a loss function
oracle are introduced. The algorithms operate on the same underlying principle:
the decision-maker repeatedly deploys a fixed decision over the length of an
epoch, thereby allowing the dynamically changing environment to sufficiently
mix before updating the decision. The iteration complexity in each of the
settings is shown to match existing rates for first and zero order stochastic
gradient methods up to logarithmic factors. The algorithms are evaluated on a
"semi-synthetic" example using real world data from the SFpark dynamic pricing
pilot study; it is shown that the announced prices result in an improvement for
the institution's objective (target occupancy), while achieving an overall
reduction in parking rates.


title: Graded Almost Prime Ideals over Non-Commutative Graded Rings

abstract:
  The purpose of this article is to define and examine graded almost prime
ideals over a non-commutative graded ring, and consider some cases where all
graded right ideals of a non-commutative graded ring are graded almost prime.


title:
Feature-based intermittent demand forecast combinations: bias, accuracy
  and inventory implications

abstract:
  Intermittent demand forecasting is a ubiquitous and challenging problem in
operations and supply chain management. There has been a growing focus on
developing forecasting approaches for intermittent demand from academic and
practical perspectives in recent years. However, limited attention has been
given to forecast combination methods, which have been proved to achieve
competitive performance in forecasting fast-moving time series. The current
study aims to examine the empirical outcomes of some existing forecast
combination methods, and propose a generalized feature-based framework for
intermittent demand forecasting. We conduct a simulation study to perform a
large-scale comparison of a series of combination methods based on an
intermittent demand classification scheme. Further, a real data set is used to
investigate the forecasting performance and offer insights with regards the
inventory performance of the proposed framework by considering some
complementary error measures. The proposed framework leads to a significant
improvement in forecast accuracy and offers the potential of flexibility and
interpretability in inventory control.


title: Inverse Occam's razor

abstract:
  Scientists have long preferred the simplest possible explanation of their
data. More re-cently, a worrying trend to favor complex interpretations has
taken hold because they are perceived as more impactful.


title: Information Theory and Point Processes

abstract:
  This paper addresses theoretically correct vs. incorrect ways to apply
information theory to point processes.


title:
A Novel Multi-Task Learning Empowered Codebook Design for Downlink SCMA
  Networks

abstract:
  Sparse code multiple access (SCMA) is a promising code-domain non-orthogonal
multiple access (NOMA) scheme for the enabling of massive machine-type
communication. In SCMA, the design of good sparse codebooks and efficient
multiuser decoding have attracted tremendous research attention in the past few
years. This paper aims to leverage deep learning to jointly design the downlink
SCMA encoder and decoder with the aid of autoencoder. We introduce a novel
end-to-end learning based SCMA (E2E-SCMA) design framework, under which
improved sparse codebooks and low-complexity decoder are obtained. Compared to
conventional SCMA schemes, our numerical results show that the proposed
E2E-SCMA leads to significant improvements in terms of error rate and
computational complexity.


title: Autocorrelation Invariance Property of Chaos for Wireless Communication

abstract:
  A new feature of the chaotic signal generated by chaotic shape-forming filter
(CSF) is uncovered in this work. We find that, the autocorrelation function
(ACF) of the transmitting signal generated by CSF keeps the same as that of the
base function of CSF, no matter what information is encoded. We derive the
analytical equation to describe the relation between the ACF of the received
signal and the wireless channel parameters using the ACF of the transmitted
signal as prior knowledge revealed by the finding in this work. This new
property can be utilized together with different wireless communication systems
to improve the system performance. Specially, to demonstrate the improvement,
channel state information (CSI) is identified using the chaotic baseband
wireless communication as a paradigm. Two significant benefits by using the new
property are 1) the CSI can be identified without the probe information known
to the receiver as done in the conventional wireless communication systems,
which improves the bandwidth efficiency, especially in the time-varying
channel; 2) the correlation operation is insensitive to the channel noise,
which improves the identification accuracy as compared to the commonly used
methods.


title: Retrospective Observation of Ball Lightning

abstract:
  Ball lightning is a very rarely observed phenomenon that makes it extremely
difficult to characterize scientifically. Eye-witness accounts of ball
lightning that describe many features in common have been variously recorded
for centuries. This note is an account of my observation of ball lightning
almost five decades ago. Although it is mostly a compendium of recalled
impressions (no photos or videos), it so happens that certain corroborating
information has since become available on the Internet and supports the
conclusion that I did indeed observe ball lightning. This note also helps to
explain why obtaining hard evidence is even more difficult than is generally
assumed. Paradoxically, the inordinate delay between the original observation
and this documentation may actually have enhanced its scientific merit.


title:
A Dual Generalized Long Memory Modelling for Forecasting Electricity
  Spot Price: Neural Network and Wavelet Estimate

abstract:
  In this paper, dual generalized long memory modelling has been proposed to
predict the electricity spot price. First, we focus on modelling the
conditional mean of the series so we adopt a generalized fractional k-factor
Gegenbauer process ( k-factor GARMA). Secondly, the residual from the k-factor
GARMA model has been used as a proxy for the conditional variance; these
residuals were predicted using two different approaches. In the first approach,
a local linear wavelet neural network model (LLWNN) has developed to predict
the conditional variance using two different learning algorithms, so we
estimate the hybrid k- factor GARMA-LLWNN based backpropagation (BP) algorithm
and based particle swarm optimization (PSO) algorithm. In the second approach,
the Gegenbauer generalized autoregressive conditional heteroscedasticity
process (G-GARCH) has been adopted, and the parameters of the k-factor GARMAG-
GARCH model have been estimated using the wavelet methodology based on the
discrete wavelet packet transform (DWPT) approach. To illustrate the usefulness
of our methodology, we carry out an empirical application using the hourly
returns of electricity prices from the Nord Pool market. The empirical results
have shown that the k-factor GARMA-G-GARCH model has the best prediction
accuracy in terms of forecasting criteria, and find that this is more
appropriate for forecasts.


title:
Effective reflected entropy and entanglement negativity for general 2D
  eternal black holes

abstract:
  Both reflected entropy and entanglement negativity provide valid measures of
entanglement between subsystems of a mixed state. For general 2D eternal black
holes coupled with CFT matters in large $c$ limit, we perform the replica-trick
computation and find that both effective Renyi reflected entropy and effective
entanglement negativity can be expressed in terms of the combination of
modified backreacting cosmic branes in $\mathrm{AdS}_3$ bulk. We then develop a
holographic scheme to calculate effective reflected entropy and entanglement
negativity for general 2D eternal black holes coupled with CFT matters in large
$c$ limit. Using the scheme, we check the consistency condition of the island
formulae for entanglement negativity and reflected entropy. We find that the
combinations of modified backreacting cosmic branes in $\mathrm{AdS}_3$ bulk
from the two island proposals of entanglement negativity exactly match with
each other. Finally, we study the saturation of the reflected entropy
inequality.


title:
Squeezed States Generation using Cryogenic InP HEMT Transistor
  Nonlinearity

abstract:
  This study focuses on generating and manipulating squeezed states with two
external oscillators coupled by an InP HEMT transistor operating at cryogenic
temperatures. First, the small-signal nonlinear model of the transistor at high
frequency at 5 K is analyzed using quantum theory, and the related Lagrangian
is theoretically derived. Subsequently, the total quantum Hamiltonian of the
system is derived using Legendre transformation. The Hamiltonian of the system
includes linear and nonlinear terms, by which the effects on the time evolution
of the states are studied. The main result shows that the squeezed state can be
generated owing to the nonlinearity of the transistor, and more importantly, it
can be manipulated by some specific terms introduced in the nonlinear
Hamiltonian. In fact, the nonlinearity of the transistors induces some effects
such as capacitance, inductance, and second-order transconductance, by which
the properties of the external oscillators are changed. These changes may lead
to squeezing or manipulation of the parameters related to squeezing in the
oscillators. In addition, it is theoretically derived that the circuit can
generate two-mode squeezing. Finally, second-order correlation (photon counting
statistics) is studied as a complementary task, and the results demonstrate
that the designed circuit exhibits antibunching, where the quadrature operator
shows squeezing behavior.


title:
StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in
  Texts

abstract:
  Inferring spatial relations in natural language is a crucial ability an
intelligent system should possess. The bAbI dataset tries to capture tasks
relevant to this domain (task 17 and 19). However, these tasks have several
limitations. Most importantly, they are limited to fixed expressions, they are
limited in the number of reasoning steps required to solve them, and they fail
to test the robustness of models to input that contains irrelevant or redundant
information. In this paper, we present a new Question-Answering dataset called
StepGame for robust multi-hop spatial reasoning in texts. Our experiments
demonstrate that state-of-the-art models on the bAbI dataset struggle on the
StepGame dataset. Moreover, we propose a Tensor-Product based Memory-Augmented
Neural Network (TP-MANN) specialized for spatial reasoning tasks. Experimental
results on both datasets show that our model outperforms all the baselines with
superior generalization and robustness performance.


title:
Experimental Visualization of Dispersion Characteristics of Backward
  Volume Spin Wave Modes

abstract:
  Basing on the measurement of spatial spectra (spectra of wavenumbers), the
dispersion characteristics of the first three modes of backward volume spin
wave, propagating along the direction of a constant uniform magnetic field in a
tangentially magnetized ferrite film, were visualized firstly. The study was
carried out by microwave probing of spin waves with subsequent use of spatial
Fourier analysis of the complex wave amplitude for a series of frequencies. It
was found that every m-th mode of the backward volume spins wave can be split
into n satellite modes due to the existence of layers with similar magnetic
parameters in ferrite film. It was found that satellites of the first mode of
this wave are excited most effectively, while satellites of the third mode -
least effectively, and the effectiveness of satellites excitation decreases as
the number n increases. It is found that the theoretical dispersion
dependencies of the first three modes of the wave coincide well with the
experimental dispersion dependencies of the satellite mode that are excited
most effectively.


title: VCDM and Cuscuton

abstract:
  We investigate two Type-IIa Minimally Modified Gravity theories, namely VCDM
and Cuscuton theories. We confirm that all acceptable Cuscuton solutions are
always solutions for VCDM theory. However, the inverse does not hold. We find
that VCDM allows for the existence of exact General Relativity (GR) solutions
with or without the presence of matter fields and a cosmological constant. We
determine the conditions of existence for such GR-VCDM solutions in terms of
the trace of the extrinsic curvature and on the fields which define the VCDM
theory. On the other hand, for the Cuscuton theory, we find that the same set
of exact GR solutions (such as Schwarzschild and Kerr spacetimes) is not
compatible with timelike configurations of the Cuscuton field and therefore
cannot be considered as acceptable solutions. Nonetheless, in Cuscuton theory,
there could exist solutions which are not the same but close enough to GR
solutions. We also show the conditions to determine intrinsic-VCDM solutions,
i.e. solutions which differ from GR and do not belong to the Cuscuton model. We
finally show that in cosmology a mapping between VCDM and the Cuscuton is
possible, for a generic form of the VCDM potential. In particular, we find that
for a quadratic potential in VCDM theory, this mapping is well defined giving
an effective redefinition of the Planck mass for the cosmological background
solutions of both theories.


title:
Ill-posedness for the stationary Navier-Stokes equations in critical
  Besov spaces

abstract:
  This paper presents some progress toward an open question which proposed by
Tsurumi (Arch. Ration. Mech. Anal. 234:2, 2019): whether or not the stationary
Navier-Stokes equations in $\R^d$ is well-posed from $\dot{B}_{p, q}^{-2}$ to
$\mathbb{P} \dot{B}_{p, q}^{0}$ with $p=d$ and $1 \leq q < 2$. In this paper,
we demonstrate that for the case $1\leq q<2$ the 4D stationary Navier-Stokes
equations is ill-posed from $\dot{B}_{4, q}^{-2}(\R^4)$ to $\mathbb{P}
\dot{B}_{4, q}^{0}(\R^4)$ by showing that a sequence of external forces is
constructed to show discontinuity of the solution map at zero. Indeed in such
case of $q$, there exists a sequence of external forces which converges to zero
in $\dot{B}_{4, q}^{-2}$ and yields a sequence of solutions which does not
converge to zero in $\dot{B}_{4, q}^{0}$.


title:
Engineering an Interstellar Communications Network by Deploying Relay
  Probes

abstract:
  We develop a model for an interstellar communication network that is composed
of relay nodes that transmit diffraction-limited beams of photons. We provide a
multi-dimensional rationale for such a network of communication in lieu of
interstellar beacons. We derive a theoretical expression for the bit rate of
communication based on fundamental physics, constrained by the energy available
for photons and the diffraction of the beam that dilutes the information by the
inverse square law. We find that meter-scale probes are severely limited in
their bit rate, under 1 Gbps, over distances of a light year. However, that bit
rate is proportional to the 4th power of the size of the optics that transmit
and receive the photons, and inversely proportional to the square of the
distance between them, thus favoring large optics and short separations between
nodes. The optimized architecture of interstellar communication consists of a
network of nodes separated by sub-light-year distances and strung out between
neighboring stars.

categories: physics.pop-ph astro-ph.EP astro-ph.GA astro-ph.IM astro-ph.SR


title: All-loop ABJM amplitudes from projected, bipartite amplituhedron

abstract:
  We define a new geometry obtained from the all-loop amplituhedron in ${\cal
N}=4$ SYM, by projecting its external and loop momenta to three dimensions.
Focusing on the simplest four-point case, we provide strong evidence that the
canonical form of this "projected amplituhedron" gives all-loop integrand of
ABJM four-point amplitude! In addition to various all-loop cuts manifested by
the geometry, we present explicitly new results for the integrand up to five
loops, which are much simpler than ${\cal N}=4$ SYM results. Such all-loop
simplifications become most transparent for projected negative geometries,
where projected loop variables live in AdS$_3$ space and mutual negativity
conditions lead to time orderings; amazingly we find that only bipartite
graphs, where each node is either a source or sink in time ordering, survive
the projection. Our results suggest an unexpected relation between four-point
amplitudes in these two theories.


title: Hidden Quantum Memory: Is Memory There When Somebody Looks?

abstract:
  In classical physics, memoryless processes and Markovian statistics are one
and the same. This is not true for quantum processes, first and foremost due to
the fact that quantum measurements are invasive. Independently of measurement
invasiveness, here we derive a novel distinction between classical and quantum
processes, namely the possibility of hidden quantum memory. While Markovian
statistics of classical processes can always be reproduced by a memoryless
dynamics, our main result establishes that this is not the case in quantum
mechanics: We first provide an example of quantum non-Markovianity that depends
on whether or not a previous measurement is performed -- a phenomenon that is
impossible for memoryless processes; we then strengthen this result by
demonstrating statistics that are Markovian independent of how they are probed,
but are are nonetheless still incompatible with memoryless quantum dynamics.
Thus, we establish the existence of Markovian statistics that fundamentally
require quantum memory for their creation.


title: Continuity of the drift in groups acting on strongly hyperbolic spaces

abstract:
  The Avalanche principle, in its original setting, together with large
deviations yields a systematic way of proving the continuity of the Lyapunov
exponent. In this text we present a geometric version of the Avalanche
Principle in the context of hyperbolic spaces, which will extend the usage of
these techniques to study the drift in such spaces. This continuity criteria
applies not only to the drift but also to the limit point of the process
itself. We apply this abstract result to derive continuity of the drift for
Markov processes in strongly hyperbolic spaces.


title: A Characterization of Draft Rules

abstract:
  This paper considers the problem of allocating bundles of heterogeneous and
indivisible objects to agents, when monetary transfers are not allowed and
agents reveal only ordinal preferences over objects, e.g., allocating players'
contract rights to teams in professional sporting leagues. Preferences over
objects are extended to incomplete preferences over bundles using pairwise
dominance. We provide a simple characterization of the class of draft rules:
they are the only allocation rules satisfying $\textit{efficiency}$,
$\textit{respectfulness of the priority}$, $\textit{envy-freeness up to one
object}$ and $\textit{resource-monotonicity}$. We also prove two impossibility
theorems: (i) $\textit{non-wastefulness}$, $\textit{respectfulness of the
priority}$ and $\textit{envy-freeness up to one object}$ are incompatible with
$\textit{weak strategy-proofness}$; (ii) $\textit{efficiency}$ and
$\textit{envy-freeness up to one object}$ are incompatible with $\textit{weak
strategy-proofness}$. If agents may declare some objects unacceptable, then
draft rules are characterized by $\textit{efficiency}$, $\textit{respectfulness
of the priority}$, $\textit{envy-freeness up to one object}$,
$\textit{resource-monotonicity}$ together with a mild invariance property
called $\textit{truncation-invariance}$.


title:
Sharp bounds on the $A_{\alpha}$-index of graphs in terms of the
  independence number

abstract:
  Given a graph $G$, the adjacency matrix and degree diagonal matrix of $G$ are
denoted by $A(G)$ and $D(G)$, respectively. In 2017, Nikiforov \cite{0007}
proposed the $A_{\alpha}$-matrix: $A_{\alpha}(G)=\alpha D(G)+(1-\alpha)A(G),$
where $\alpha\in [0, 1]$. The largest eigenvalue of this novel matrix is called
the $A_\alpha$-index of $G$. In this paper, we characterize the graphs with
minimum $A_\alpha$-index among $n$-vertex graphs with independence number $i$
for $\alpha\in[0,1)$, where
$i=1,\lfloor\frac{n}{2}\rfloor,\lceil\frac{n}{2}\rceil,{\lfloor\frac{n}{2}\rfloor+1},n-3,n-2,n-1,$
whereas for $i=2$ we consider the same problem for $\alpha\in
[0,\frac{3}{4}{]}.$ Furthermore, we determine the unique graph (resp. tree) on
$n$ vertices with given independence number having the maximum $A_\alpha$-index
with $\alpha\in[0,1)$, whereas for the $n$-vertex bipartite graphs with given
independence number, we characterize the unique graph having the maximum
$A_\alpha$-index with $\alpha\in[\frac{1}{2},1).$


title:
Minimality conditions equivalent to the finitude of Fermat and Mersenne
  primes

abstract:
  It is still open whether there exist infinitely many Fermat primes or
infinitely many composite Fermat numbers. The same question concerning the
Mersenne numbers is also unsolved. In [5], Megrelishvili and the author
characterized the Fermat primes in terms of the topological minimality of some
special linear groups. In this short paper, we prove the following main result.
  (1) Let $\mathcal F_\pi$ and $\mathcal F_c $ be the set of Fermat primes and
the set of composite Fermat numbers, respectively. For $\mathcal{A}\in
\{\mathcal F_\pi, \mathcal F_c\}$ it holds that $\mathcal{A}$ is finite if and
only if $\prod_{F_n\in \mathcal{A}}\operatorname{SL}(F_n-1, \Bbb{Q}(i))$ is
minimal, where $\Bbb{Q}(i)$ is the Gaussian rational field.
  (2) Let $\mathcal M_\pi$ and $\mathcal M_c $ be the set of Mersenne primes
and the set of composite Mersenne numbers, respectively. For $\mathcal B\in\{
\mathcal M_\pi, \mathcal M_c\}$ it holds that $\mathcal B$ is finite if and
only if $\prod_{M_p\in \mathcal{B}}\operatorname{SL}(M_p+1, \Bbb{Q}(i))$ is
minimal.


title:
Fixed-optics four-dimensional emittance measurement at the Spallation
  Neutron Source

abstract:
  A hadron beam with a uniform charge density, elliptical transverse profile,
and small four-dimensional (4D) emittance could mitigate space charge effects
in circular accelerators and improve collider performance. A phase space
painting method to generate such a distribution is being tested in the
Spallation Neutron Source (SNS) accumulator ring. A critical component of these
efforts is to measure the 4D emittance of the beam. Reconstruction of the 4D
emittance from wire-scanner data is a well-known technique. In this paper, we
discuss the implementation of a variant of the multi-optics method using the
four available wire-scanners near the SNS target, as well as the modification
of the wire-scanner region to utilize the fixed-optics method, a method that is
preferred due to its speed but can potentially lead to unacceptable bias and
uncertainty in the reconstructed emittances. We then demonstrate the usefulness
of the fixed-optics method by reconstructing the 4D emittance as a function of
time during accumulation in the SNS ring.


title:
UMass PCL at SemEval-2022 Task 4: Pre-trained Language Model Ensembles
  for Detecting Patronizing and Condescending Language

abstract:
  Patronizing and condescending language (PCL) is everywhere, but rarely is the
focus on its use by media towards vulnerable communities. Accurately detecting
PCL of this form is a difficult task due to limited labeled data and how subtle
it can be. In this paper, we describe our system for detecting such language
which was submitted to SemEval 2022 Task 4: Patronizing and Condescending
Language Detection. Our approach uses an ensemble of pre-trained language
models, data augmentation, and optimizing the threshold for detection.
Experimental results on the evaluation dataset released by the competition
hosts show that our work is reliably able to detect PCL, achieving an F1 score
of 55.47% on the binary classification task and a macro F1 score of 36.25% on
the fine-grained, multi-label detection task.


title:
Electron quantum optics with beam splitters and waveguides in Dirac
  Matter

abstract:
  An electron behaves as both a particle and a wave. On account of this it can
be controlled in a similar way to a photon and electronic devices can be
designed in analogy to those based on light when there is minimal excitation of
the underlying Fermi sea. Here splitting of the electron wavefunction is
explored for systems supporting Dirac type physics, with a focus on graphene
but being equally applicable to electronic states in topological insulators,
liquid helium, and other systems described relativistically. Electron
beam-splitters and superfocusers are analysed along with propagation through
nanoribbons, demonstrating that the waveform, system geometry, and energies all
need to balance to maximise the probability density and hence lifetime of the
flying electron. These findings form the basis for novel quantum electron
optics.


title:
A Convergence Analysis of Nesterov's Accelerated Gradient Method in
  Training Deep Linear Neural Networks

abstract:
  Momentum methods, including heavy-ball~(HB) and Nesterov's accelerated
gradient~(NAG), are widely used in training neural networks for their fast
convergence. However, there is a lack of theoretical guarantees for their
convergence and acceleration since the optimization landscape of the neural
network is non-convex. Nowadays, some works make progress towards understanding
the convergence of momentum methods in an over-parameterized regime, where the
number of the parameters exceeds that of the training instances. Nonetheless,
current results mainly focus on the two-layer neural network, which are far
from explaining the remarkable success of the momentum methods in training deep
neural networks. Motivated by this, we investigate the convergence of NAG with
constant learning rate and momentum parameter in training two architectures of
deep linear networks: deep fully-connected linear neural networks and deep
linear ResNets. Based on the over-parameterization regime, we first analyze the
residual dynamics induced by the training trajectory of NAG for a deep
fully-connected linear neural network under the random Gaussian initialization.
Our results show that NAG can converge to the global minimum at a $(1 -
\mathcal{O}(1/\sqrt{\kappa}))^t$ rate, where $t$ is the iteration number and
$\kappa > 1$ is a constant depending on the condition number of the feature
matrix. Compared to the $(1 - \mathcal{O}(1/{\kappa}))^t$ rate of GD, NAG
achieves an acceleration over GD. To the best of our knowledge, this is the
first theoretical guarantee for the convergence of NAG to the global minimum in
training deep neural networks. Furthermore, we extend our analysis to deep
linear ResNets and derive a similar convergence result.


title:
Heavy Rain Face Image Restoration: Integrating Physical Degradation
  Model and Facial Component Guided Adversarial Learning

abstract:
  With the recent increase in intelligent CCTVs for visual surveillance, a new
image degradation that integrates resolution conversion and synthetic rain
models is required. For example, in heavy rain, face images captured by CCTV
from a distance have significant deterioration in both visibility and
resolution. Unlike traditional image degradation models (IDM), such as rain
removal and superresolution, this study addresses a new IDM referred to as a
scale-aware heavy rain model and proposes a method for restoring
high-resolution face images (HR-FIs) from low-resolution heavy rain face images
(LRHR-FI). To this end, a 2-stage network is presented. The first stage
generates low-resolution face images (LR-FIs), from which heavy rain has been
removed from the LRHR-FIs to improve visibility. To realize this, an
interpretable IDM-based network is constructed to predict physical parameters,
such as rain streaks, transmission maps, and atmospheric light. In addition,
the image reconstruction loss is evaluated to enhance the estimates of the
physical parameters. For the second stage, which aims to reconstruct the HR-FIs
from the LR-FIs outputted in the first stage, facial component guided
adversarial learning (FCGAL) is applied to boost facial structure expressions.
To focus on informative facial features and reinforce the authenticity of
facial components, such as the eyes and nose, a face-parsing-guided generator
and facial local discriminators are designed for FCGAL. The experimental
results verify that the proposed approach based on physical-based network
design and FCGAL can remove heavy rain and increase the resolution and
visibility simultaneously. Moreover, the proposed heavy-rain face image
restoration outperforms state-of-the-art models of heavy rain removal,
image-to-image translation, and superresolution.


title: Saliency in Augmented Reality

abstract:
  With the rapid development of multimedia technology, Augmented Reality (AR)
has become a promising next-generation mobile platform. The primary theory
underlying AR is human visual confusion, which allows users to perceive the
real-world scenes and augmented contents (virtual-world scenes) simultaneously
by superimposing them together. To achieve good Quality of Experience (QoE), it
is important to understand the interaction between two scenarios, and
harmoniously display AR contents. However, studies on how this superimposition
will influence the human visual attention are lacking. Therefore, in this
paper, we mainly analyze the interaction effect between background (BG) scenes
and AR contents, and study the saliency prediction problem in AR. Specifically,
we first construct a Saliency in AR Dataset (SARD), which contains 450 BG
images, 450 AR images, as well as 1350 superimposed images generated by
superimposing BG and AR images in pair with three mixing levels. A large-scale
eye-tracking experiment among 60 subjects is conducted to collect eye movement
data. To better predict the saliency in AR, we propose a vector quantized
saliency prediction method and generalize it for AR saliency prediction. For
comparison, three benchmark methods are proposed and evaluated together with
our proposed method on our SARD. Experimental results demonstrate the
superiority of our proposed method on both of the common saliency prediction
problem and the AR saliency prediction problem over benchmark methods. Our data
collection methodology, dataset, benchmark methods, and proposed saliency
models will be publicly available to facilitate future research.


title:
Tracking monocular camera pose and deformation for SLAM inside the human
  body

abstract:
  Monocular SLAM in deformable scenes will open the way to multiple medical
applications like computer-assisted navigation in endoscopy, automatic drug
delivery or autonomous robotic surgery. In this paper we propose a novel method
to simultaneously track the camera pose and the 3D scene deformation, without
any assumption about environment topology or shape. The method uses an
illumination-invariant photometric method to track image features and estimates
camera motion and deformation combining reprojection error with spatial and
temporal regularization of deformations. Our results in simulated colonoscopies
show the method's accuracy and robustness in complex scenes under increasing
levels of deformation. Our qualitative results in human colonoscopies from
Endomapper dataset show that the method is able to successfully cope with the
challenges of real endoscopies: deformations, low texture and strong
illumination changes. We also compare with previous tracking methods in simpler
scenarios from Hamlyn dataset where we obtain competitive performance, without
needing any topological assumption.


title:
Average estimates and sign change of Fourier coefficients of cusp forms
  at integers represented by binary quadratic form of fixed discriminant

abstract:
  In this article, we establish an average behaviour of the normalised Fourier
coefficients of the Hecke eigenforms supported at the integers represented by
any primitive integral positive definite binary quadratic form of fixed
discriminant $D < 0$ when the class number $h(D) = 1$.
  We also obtain a quantitative result for the number of sign changes of the
sequence of the normalised Fourier coefficients $\lambda_{f}(n)$ of the Hecke
eigenforms $f$ where $n$ is represented by any primitive integral positive
definite binary quadratic form of fixed discriminant $D < 0$ when the class
number $h(D) = 1$ in the interval $(x,2x]$, for sufficiently large $x$.


title:
Application of Transfer Learning and Ensemble Learning in Image-level
  Classification for Breast Histopathology

abstract:
  Background: Breast cancer has the highest prevalence in women globally. The
classification and diagnosis of breast cancer and its histopathological images
have always been a hot spot of clinical concern. In Computer-Aided Diagnosis
(CAD), traditional classification models mostly use a single network to extract
features, which has significant limitations. On the other hand, many networks
are trained and optimized on patient-level datasets, ignoring the application
of lower-level data labels.
  Method: This paper proposes a deep ensemble model based on image-level labels
for the binary classification of benign and malignant lesions of breast
histopathological images. First, the BreakHis dataset is randomly divided into
a training, validation and test set. Then, data augmentation techniques are
used to balance the number of benign and malignant samples. Thirdly,
considering the performance of transfer learning and the complementarity
between each network, VGG-16, Xception, Resnet-50, DenseNet-201 are selected as
the base classifiers.
  Result: In the ensemble network model with accuracy as the weight, the
image-level binary classification achieves an accuracy of $98.90\%$. In order
to verify the capabilities of our method, the latest Transformer and Multilayer
Perception (MLP) models have been experimentally compared on the same dataset.
Our model wins with a $5\%-20\%$ advantage, emphasizing the ensemble model's
far-reaching significance in classification tasks.
  Conclusion: This research focuses on improving the model's classification
performance with an ensemble algorithm. Transfer learning plays an essential
role in small datasets, improving training speed and accuracy. Our model has
outperformed many existing approaches in accuracy, providing a method for the
field of auxiliary medical diagnosis.


title: Optimal Coding Theorems in Time-Bounded Kolmogorov Complexity

abstract:
  The classical coding theorem in Kolmogorov complexity states that if an
$n$-bit string $x$ is sampled with probability $\delta$ by an algorithm with
prefix-free domain then K$(x) \leq \log(1/\delta) + O(1)$. In a recent work, Lu
and Oliveira [LO21] established an unconditional time-bounded version of this
result, by showing that if $x$ can be efficiently sampled with probability
$\delta$ then rKt$(x) = O(\log(1/\delta)) + O(\log n)$, where rKt denotes the
randomized analogue of Levin's Kt complexity. Unfortunately, this result is
often insufficient when transferring applications of the classical coding
theorem to the time-bounded setting, as it achieves a $O(\log(1/\delta))$ bound
instead of the information-theoretic optimal $\log(1/\delta)$.
  We show a coding theorem for rKt with a factor of $2$. As in previous work,
our coding theorem is efficient in the sense that it provides a polynomial-time
probabilistic algorithm that, when given $x$, the code of the sampler, and
$\delta$, it outputs, with probability $\ge 0.99$, a probabilistic
representation of $x$ that certifies this rKt complexity bound.
  Assuming the security of cryptographic pseudorandom generators, we show that
no efficient coding theorem can achieve a bound of the form rKt$(x) \leq (2 -
o(1)) \cdot \log(1/\delta) +$ poly$(\log n)$. Under a weaker assumption, we
exhibit a gap between efficient coding theorems and existential coding theorems
with near-optimal parameters.
  We consider pK$^t$ complexity [GKLO22], a variant of rKt where the randomness
is public and the time bound is fixed. We observe the existence of an optimal
coding theorem for pK$^t$, and employ this result to establish an unconditional
version of a theorem of Antunes and Fortnow [AF09] which characterizes the
worst-case running times of languages that are in average polynomial-time over
all P-samplable distributions.


title: An anisotropic summability and mixed sequences

abstract:
  In this paper we define and study a vector-valued sequence space, called the
space of anisotropic $(s,q,r)$-summable sequences, that generalizes the
classical space of $(s; q)$-mixed sequences (or mixed $(s; q)$-summable
sequences). Furthermore, we define two classes of linear operators involving
this new space and one of them generalizes the class of $(s; q) $-mixed linear
operators due A. Pietsch. Some characterizations, inclusion results and a
Pietsch domination-type theorem are presented for these classes. It is worth
mentioning that some of these results are new even in the particular cases of
mixed summable sequences and mixed summing operators.


title:
Joule-Thomson expansion of d-dimensional charged AdS black holes with
  cloud of strings and quintessence

abstract:
  Herein, we focus on the study of Joule-Thomson expansion corresponding to a
d-dimensional charged AdS black hole with cloud of strings and quintessence.
Then its relevant solution and some thermodynamic properties are investigated.
Specifically, we evaluate its Joule-Thomson expansion from four important
aspects, including the Joule-Thomson coefficient, inversion curve, isenthalpic
curve, and ratio $\frac{T_{i}^{min}}{T_{c}}$. After analysis, different
dimensions with strings of cloud and quintessence parameters have different
effects on the Joule-Thomson coefficient (the same situation are found for the
inversion curve, isenthalpic curve, and ratio $\frac{T_{i}^{min}}{T_{c}}$).


title:
A Multi-stage Stochastic Programming Approach for Pre-positioning of
  Relief Supplies

abstract:
  Pre-positioning of relief supplies is an important aspect of disaster
operations management that aims at decreasing the response time by advancing
procurement and storage of needed supplies. In this paper we consider commodity
life-time period with the related costs in keeping the commodity and removing
it from the storage when it is close to expiration (i.e. holding and removal
cost). We develop a multi-stage stochastic programming model for
pre-positioning of relief supplies, which provides relief agencies insight on
how to have dynamically control inventories due to uncertain demands when
disasters (e.g., hurricane or earthquake) occur. We also present a case study
based on mainland United States to illustrate the proposed model as well as
provide managerial insights to relief agencies.


title:
Imprint of primordial gravitational wave with extremely low frequency on
  gravitational lens system

abstract:
  Primordial gravitational waves with extremely low frequency are expected to
origin from inflation in the early Universe. The detection of such kind of
gravitational waves is of great significance to verify the inflationary theory
and determine the energy scale of inflation. Due to the foreground
contamination from dust in our Milky Way galaxy, the traditional method using
B-mode polarization faces challenges. In this work, we propose an alternative
way of detection by investigating the effect of primordial gravitational wave
with extremely low frequency on a gravitational lens system with a non-aligned
source-deflector-observer configuration. The results show that, with a series
of chosen parameters, gravitational lens system with perturbation from
extremely low frequency primordial gravitational wave could induce time delay
which could deviate from the time delay deduced from theoretical model as much
as about sixty percent, meaning that gravitational lens system with a
non-aligned configuration could serve as a potential long-base-line detector of
extremely low frequency primordial gravitational wave.


title:
An alternative approach for distributed parameter estimation under
  Gaussian settings

abstract:
  This paper takes a different approach for the distributed linear parameter
estimation over a multi-agent network. The parameter vector is considered to be
stochastic with a Gaussian distribution. The sensor measurements at each agent
are linear and corrupted with additive white Gaussian noise. Under such
settings, this paper presents a novel distributed estimation algorithm that
fuses the the concepts of consensus and innovations by incorporating the
consensus terms (of neighboring estimates) into the innovation terms. Under the
assumption of distributed parameter observability, introduced in this paper, we
design the optimal gain matrices such that the distributed estimates are
consistent and achieves fast convergence.

categories: eess.SY cs.IT cs.LG cs.SY math.IT math.OC


title:
1-point functions for symmetrized Heisenberg and symmetrized lattice
  vertex operator algebras

abstract:
  We obtain explicit formulas for the $1$-point functions of all states in the
symmetrized Heisenberg algebra $M^+$ and symmetrized lattice VOAs $V_L^+$. For
this we employ a new $\mathbf Z_2$-twisted variant of so-called Zhu recursion.


title: Backward Reachability Analysis for Neural Feedback Loops

abstract:
  The increasing prevalence of neural networks (NNs) in safety-critical
applications calls for methods to certify their behavior and guarantee safety.
This paper presents a backward reachability approach for safety verification of
neural feedback loops (NFLs), i.e., closed-loop systems with NN control
policies. While recent works have focused on forward reachability as a strategy
for safety certification of NFLs, backward reachability offers advantages over
the forward strategy, particularly in obstacle avoidance scenarios. Prior works
have developed techniques for backward reachability analysis for systems
without NNs, but the presence of NNs in the feedback loop presents a unique set
of problems due to the nonlinearities in their activation functions and because
NN models are generally not invertible. To overcome these challenges, we use
existing forward NN analysis tools to find affine bounds on the control inputs
and solve a series of linear programs (LPs) to efficiently find an
approximation of the backprojection (BP) set, i.e., the set of states for which
the NN control policy will drive the system to a given target set. We present
an algorithm to iteratively find BP set estimates over a given time horizon and
demonstrate the ability to reduce conservativeness in the BP set estimates by
up to 88% with low additional computational cost. We use numerical results from
a double integrator model to verify the efficacy of these algorithms and
demonstrate the ability to certify safety for a linearized ground robot model
in a collision avoidance scenario where forward reachability fails.


title:
Mixed integer programming and adaptive problem solver learned by
  landscape analysis for clinical laboratory scheduling

abstract:
  This paper attempts to derive a mathematical formulation for real-practice
clinical laboratory scheduling, and to present an adaptive problem solver by
leveraging landscape structures. After formulating scheduling of medical tests
as a distributed scheduling problem in heterogeneous, flexible job shop
environment, we establish a mixed integer programming model to minimize mean
test turnaround time. Preliminary landscape analysis sustains that these
clinics-orientated scheduling instances are difficult to solve. The search
difficulty motivates the design of an adaptive problem solver to reduce
repetitive algorithm-tuning work, but with a guaranteed convergence. Yet, under
a search strategy, relatedness from exploitation competence to landscape
topology is not transparent. Under strategies that impose different-magnitude
perturbations, we investigate changes in landscape structure and find that
disturbance amplitude, local-global optima connectivity, landscape's ruggedness
and plateau size fairly predict strategies' efficacy. Medium-size instances of
100 tasks are easier under smaller-perturbation strategies that lead to
smoother landscapes with smaller plateaus. For large-size instances of 200-500
tasks, extant strategies at hand, having either larger or smaller
perturbations, face more rugged landscapes with larger plateaus that impede
search. Our hypothesis that medium perturbations may generate smoother
landscapes with smaller plateaus drives our design of this new strategy and its
verification by experiments. Composite neighborhoods managed by meta-Lamarckian
learning show beyond average performance, implying reliability when prior
knowledge of landscape is unknown.


title:
Electron-phonon interaction contribution to the total energy of group IV
  semiconductor polytypes

abstract:
  In density functional theory (DFT) based total energy studies, the van der
Waals (vdW) dispersion and zero-point vibrational energy (ZPVE) correction
terms are included to obtain energy differences between polymorphs. We
introduce a new correction term, due to electron-phonon interactions (EPI). We
calculate EPI corrections to the total energy for cubic and hexagonal polytypes
of Carbon, Silicon and Silicon Carbide. The EPI corrections alter the energy
differences between polytypes in C, Si, and SiC; especially in C and SiC where
the EPI strength is significant. The EPI correction term is more sensitive to
crystal structure than the vdW and ZPVE terms in SiC polytypes making it more
important in determining energy differences. Including EPI corrections
establishes that, in ab initio studies, the cubic SiC-3C is metastable and
hexagonal SiC-4H is the stable polytype. For these studies, we extend Allen's
formalism and this enables, for the first time, inclusion of EPI corrections as
separate terms in the free energy expression. This opens the way to study their
role on all thermodynamic properties through the Quasi-Harmonic Approximation.


title: A high-resolution canopy height model of the Earth

abstract:
  The worldwide variation in vegetation height is fundamental to the global
carbon cycle and central to the functioning of ecosystems and their
biodiversity. Geospatially explicit and, ideally, highly resolved information
is required to manage terrestrial ecosystems, mitigate climate change, and
prevent biodiversity loss. Here, we present the first global, wall-to-wall
canopy height map at 10 m ground sampling distance for the year 2020. No single
data source meets these requirements: dedicated space missions like GEDI
deliver sparse height data, with unprecedented coverage, whereas optical
satellite images like Sentinel-2 offer dense observations globally, but cannot
directly measure vertical structures. By fusing GEDI with Sentinel-2, we have
developed a probabilistic deep learning model to retrieve canopy height from
Sentinel-2 images anywhere on Earth, and to quantify the uncertainty in these
estimates. The presented approach reduces the saturation effect commonly
encountered when estimating canopy height from satellite images, allowing to
resolve tall canopies with likely high carbon stocks. According to our map,
only 5% of the global landmass is covered by trees taller than 30 m. Such data
play an important role for conservation, e.g., we find that only 34% of these
tall canopies are located within protected areas. Our model enables consistent,
uncertainty-informed worldwide mapping and supports an ongoing monitoring to
detect change and inform decision making. The approach can serve ongoing
efforts in forest conservation, and has the potential to foster advances in
climate, carbon, and biodiversity modelling.


title:
Experimental twin-field quantum key distribution with flawed and
  correlated sources

abstract:
  The security of quantum key distribution (QKD) is severely threatened by
discrepancies between realistic devices and theoretical assumptions. Recently,
a significant framework called the reference technique was proposed to provide
security against arbitrary source flaws, including pulse correlations. Here, we
propose an efficient four-phase twin-field QKD using laser pulses adopting the
reference technique for security against all possible source imperfections. We
present a characterization of source flaws and connect them to experimental
data, together with a finite-key analysis. In addition, we demonstrate the
feasibility of our protocol through a proof-of-principle experimental
implementation and demonstrate a secure key rate of 1.63 kbps with a 20 dB
channel loss. Compared with previous QKD protocols with imperfect devices, our
work considerably improves both the secure key rate and the transmission
distance, and shows application potential in the practical deployment of secure
QKD with device imperfections.


title: Hierarchical Optimal Transport for Comparing Histopathology Datasets

abstract:
  Scarcity of labeled histopathology data limits the applicability of deep
learning methods to under-profiled cancer types and labels. Transfer learning
allows researchers to overcome the limitations of small datasets by
pre-training machine learning models on larger datasets similar to the small
target dataset. However, similarity between datasets is often determined
heuristically. In this paper, we propose a principled notion of distance
between histopathology datasets based on a hierarchical generalization of
optimal transport distances. Our method does not require any training, is
agnostic to model type, and preserves much of the hierarchical structure in
histopathology datasets imposed by tiling. We apply our method to H&E stained
slides from The Cancer Genome Atlas from six different cancer types. We show
that our method outperforms a baseline distance in a cancer-type prediction
task. Our results also show that our optimal transport distance predicts
difficulty of transferability in a tumor vs.normal prediction setting.


title:
GL-CLeF: A Global-Local Contrastive Learning Framework for Cross-lingual
  Spoken Language Understanding

abstract:
  Due to high data demands of current methods, attention to zero-shot
cross-lingual spoken language understanding (SLU) has grown, as such approaches
greatly reduce human annotation effort. However, existing models solely rely on
shared parameters, which can only perform implicit alignment across languages.
We present Global--Local Contrastive Learning Framework (GL-CLeF) to address
this shortcoming. Specifically, we employ contrastive learning, leveraging
bilingual dictionaries to construct multilingual views of the same utterance,
then encourage their representations to be more similar than negative example
pairs, which achieves to explicitly aligned representations of similar
sentences across languages. In addition, a key step in GL-CLeF is a proposed
Local and Global component, which achieves a fine-grained cross-lingual
transfer (i.e., sentence-level Local intent transfer, token-level Local slot
transfer, and semantic-level Global transfer across intent and slot).
Experiments on MultiATIS++ show that GL-CLeF achieves the best performance and
successfully pulls representations of similar sentences across languages
closer.


title:
MP2: A Momentum Contrast Approach for Recommendation with Pointwise and
  Pairwise Learning

abstract:
  Binary pointwise labels (aka implicit feedback) are heavily leveraged by deep
learning based recommendation algorithms nowadays. In this paper we discuss the
limited expressiveness of these labels may fail to accommodate varying degrees
of user preference, and thus lead to conflicts during model training, which we
call annotation bias. To solve this issue, we find the soft-labeling property
of pairwise labels could be utilized to alleviate the bias of pointwise labels.
To this end, we propose a momentum contrast framework (MP2) that combines
pointwise and pairwise learning for recommendation. MP2 has a three-tower
network structure: one user network and two item networks. The two item
networks are used for computing pointwise and pairwise loss respectively. To
alleviate the influence of the annotation bias, we perform a momentum update to
ensure a consistent item representation. Extensive experiments on real-world
datasets demonstrate the superiority of our method against state-of-the-art
recommendation algorithms.


title:
Automatic Encoding and Repair of Reactive High-Level Tasks with Learned
  Abstract Representations

abstract:
  We present a framework that, given a set of skills a robot can perform,
abstracts sensor data into symbols that we use to automatically encode the
robot's capabilities in Linear Temporal Logic. We specify reactive high-level
tasks based on these capabilities, for which a strategy is automatically
synthesized and executed on the robot, if the task is feasible. If a task is
not feasible given the robot's capabilities, we present two methods, one
enumeration-based and one synthesis-based, for automatically suggesting
additional skills for the robot or modifications to existing skills that would
make the task feasible. We demonstrate our framework on a Baxter robot
manipulating blocks on a table, a Baxter robot manipulating plates on a table,
and a Kinova arm manipulating vials, with multiple sensor modalities, including
raw images.


title: Scaling properties of elastic proton-proton scattering at LHC energies

abstract:
  The TOTEM Collaboration has measured the differential cross section of
elastic proton-proton scattering $\mathrm{d}\sigma/\mathrm{d}|t|$ at $\sqrt{s}
= 2.76$, 7, 8, and 13 TeV. It is observed that all
$\mathrm{d}\sigma/\mathrm{d}|t|$ data points vary in the same way as a function
of the center-of-mass energy in the so-called "dip" and "bump" regions. These
features hint at possible universal properties of elastic scattering. Based on
these empirical observations, and taking inspiration from saturation models, we
propose a simple scaling law for proton-proton elastic scattering at LHC
energies.
  We find that the $\mathrm{d}\sigma/\mathrm{d}|t|$ at LHC energies fall onto a
universal curve when they are mapped to the scaling variables $
\mathrm{d}\sigma/\mathrm{d}|t| \times (s/\text{TeV}^2)^{-0.305} $ versus
$(s/\text{TeV}^2)^{0.065} (|t|/\text{GeV}^2)^{0.72}$. Some implications of this
scaling law in the impact parameter picture of the scattering amplitudes are
explored.


title: A Comprehensive Survey on Data-Efficient GANs in Image Generation

abstract:
  Generative Adversarial Networks (GANs) have achieved remarkable achievements
in image synthesis. These successes of GANs rely on large scale datasets,
requiring too much cost. With limited training data, how to stable the training
process of GANs and generate realistic images have attracted more attention.
The challenges of Data-Efficient GANs (DE-GANs) mainly arise from three
aspects: (i) Mismatch Between Training and Target Distributions, (ii)
Overfitting of the Discriminator, and (iii) Imbalance Between Latent and Data
Spaces. Although many augmentation and pre-training strategies have been
proposed to alleviate these issues, there lacks a systematic survey to
summarize the properties, challenges, and solutions of DE-GANs. In this paper,
we revisit and define DE-GANs from the perspective of distribution
optimization. We conclude and analyze the challenges of DE-GANs. Meanwhile, we
propose a taxonomy, which classifies the existing methods into three
categories: Data Selection, GANs Optimization, and Knowledge Sharing. Last but
not the least, we attempt to highlight the current problems and the future
directions.


title:
A new method for direct measurement of isotopologue ratios in
  protoplanetary disks: a case study of the $^{12}$CO/$^{13}$CO ratio in the TW
  Hya disk

abstract:
  Planetary systems are thought to be born in protoplanetary disks. Isotope
ratios are a powerful tool for investigating the material origin and evolution
from molecular clouds to planetary systems via protoplanetary disks. However,
it is challenging to measure the isotope (isotopologue) ratios, especially in
protoplanetary disks, because the emission lines of major species are
saturated. We developed a new method to overcome these challenges by using
optically thin line wings induced by thermal broadening. As a first application
of the method, we analyzed two carbon monoxide isotopologue lines, $^{12}$CO
$3-2$ and $^{13}$CO $3-2$, from archival observations of a protoplanetary disk
around TW Hya with the Atacama Large Millimeter/sub-millimeter Array. The
$^{12}$CO/$^{13}$CO ratio was estimated to be ${ 20\pm5}$ at disk radii of ${
70-110}$ au, which is significantly smaller than the value observed in the
local interstellar medium, $\sim69$. It implies that an isotope exchange
reaction occurs in a low-temperature environment with $\rm C/O>1$ . In
contrast, it is suggested that $^{12}$CO/$^{13}$CO is higher than $\sim{ 84}$
in the outer disk ($r > { 130}$ au), which can be explained by the difference
in the binding energy of the isotopologues on dust grains and the CO gas
depletion processes. Our results imply that the gas-phase $^{12}$CO/$^{13}$CO
can vary by a factor of ${ > 4}$ even inside a protoplanetary disk, and
therefore, can be used to trace material evolution in disks.


title: Practical KMP/BM Style Pattern-Matching on Indeterminate Strings

abstract:
  In this paper we describe two simple, fast, space-efficient algorithms for
finding all matches of an indeterminate pattern $p = p[1..m]$ in an
indeterminate string $x = x[1..n]$, where both $p$ and $x$ are defined on a
"small" ordered alphabet $\Sigma$ $-$ say, $\sigma = |\Sigma| \le 9$. Both
algorithms depend on a preprocessing phase that replaces $\Sigma$ by an integer
alphabet $\Sigma_I$ of size $\sigma_I = \sigma$ which (reversibly, in time
linear in string length) maps both $x$ and $p$ into equivalent regular strings
$y$ and $q$, respectively, on $\Sigma_I$, whose maximum (indeterminate) letter
can be expressed in a 32-bit word (for $\sigma \le 4$, thus for DNA sequences,
an 8-bit representation suffices). We first describe an efficient version KMP
Indet of the venerable Knuth-Morris-Pratt algorithm to find all occurrences of
$q$ in $y$ (that is, of $p$ in $x$), but, whenever necessary, using the prefix
array, rather than the border array, to control shifts of the transformed
pattern $q$ along the transformed string $y$. We go on to describe a similar
efficient version BM Indet of the Boyer- Moore algorithm that turns out to
execute significantly faster than KMP Indet over a wide range of test cases. A
noteworthy feature is that both algorithms require very little additional
space: $\Theta(m)$ words. We conjecture that a similar approach may yield
practical and efficient indeterminate equivalents to other well-known
pattern-matching algorithms, in particular the several variants of Boyer-Moore.


title:
BSRT: Improving Burst Super-Resolution with Swin Transformer and
  Flow-Guided Deformable Alignment

abstract:
  This work addresses the Burst Super-Resolution (BurstSR) task using a new
architecture, which requires restoring a high-quality image from a sequence of
noisy, misaligned, and low-resolution RAW bursts. To overcome the challenges in
BurstSR, we propose a Burst Super-Resolution Transformer (BSRT), which can
significantly improve the capability of extracting inter-frame information and
reconstruction. To achieve this goal, we propose a Pyramid Flow-Guided
Deformable Convolution Network (Pyramid FG-DCN) and incorporate Swin
Transformer Blocks and Groups as our main backbone. More specifically, we
combine optical flows and deformable convolutions, hence our BSRT can handle
misalignment and aggregate the potential texture information in multi-frames
more efficiently. In addition, our Transformer-based structure can capture
long-range dependency to further improve the performance. The evaluation on
both synthetic and real-world tracks demonstrates that our approach achieves a
new state-of-the-art in BurstSR task. Further, our BSRT wins the championship
in the NTIRE2022 Burst Super-Resolution Challenge.


title:
Linear-quadratic stochastic Volterra controls I: Causal feedback
  strategies

abstract:
  In this paper, we formulate and investigate the notion of causal feedback
strategies arising in linear-quadratic control problems for stochastic Volterra
integral equations (SVIEs) with singular and non-convolution-type coefficients.
We show that there exists a unique solution, which we call the causal feedback
solution, to the closed-loop system of a controlled SVIE associated with a
causal feedback strategy. Furthermore, introducing two novel equations named a
Type-II extended backward stochastic Volterra integral equation and a
Lyapunov--Volterra equation, we prove a duality principle and a representation
formula for a quadratic functional of controlled SVIEs in the framework of
causal feedback strategies.


title:
Time Series Clustering for Grouping Products Based on Price and Sales
  Patterns

abstract:
  Developing technology and changing lifestyles have made online grocery
delivery applications an indispensable part of urban life. Since the beginning
of the COVID-19 pandemic, the demand for such applications has dramatically
increased, creating new competitors that disrupt the market. An increasing
level of competition might prompt companies to frequently restructure their
marketing and product pricing strategies. Therefore, identifying the change
patterns in product prices and sales volumes would provide a competitive
advantage for the companies in the marketplace. In this paper, we investigate
alternative clustering methodologies to group the products based on the price
patterns and sales volumes. We propose a novel distance metric that takes into
account how product prices and sales move together rather than calculating the
distance using numerical values. We compare our approach with traditional
clustering algorithms, which typically rely on generic distance metrics such as
Euclidean distance, and image clustering approaches that aim to group data by
capturing its visual patterns. We evaluate the performances of different
clustering algorithms using our custom evaluation metric as well as Calinski
Harabasz and Davies Bouldin indices, which are commonly used internal validity
metrics. We conduct our numerical study using a propriety price dataset from an
online food and grocery delivery company, and the publicly available Favorita
sales dataset. We find that our proposed clustering approach and image
clustering both perform well for finding the products with similar price and
sales patterns within large datasets.


title: Active Learning with Weak Labels for Gaussian Processes

abstract:
  Annotating data for supervised learning can be costly. When the annotation
budget is limited, active learning can be used to select and annotate those
observations that are likely to give the most gain in model performance. We
propose an active learning algorithm that, in addition to selecting which
observation to annotate, selects the precision of the annotation that is
acquired. Assuming that annotations with low precision are cheaper to obtain,
this allows the model to explore a larger part of the input space, with the
same annotation costs. We build our acquisition function on the previously
proposed BALD objective for Gaussian Processes, and empirically demonstrate the
gains of being able to adjust the annotation precision in the active learning
loop.


title:
Simultaneous confidence intervals for the interpretation of primary and
  secondary effects in factorial designs without a pre-test on interaction

abstract:
  The analysis of low dimensional factorial designs with possible interactions
is a relevant issue. Instead of the common pre-tests for interaction, a
simultaneous inference procedure of the primary factor at the respective level
of the secondary factor as well as pooled over all its levels is proposed.


title: Philosophical and epistemological views of the notion of space

abstract:
  From antiquity the conceptual perception of space changed painfully and at a
relatively slow pace. It went through mythological descriptions, religious
beliefs, metaphysical worldviews and cosmological models with a mechanistic
structure, until it reached the Newtonian perception of the infinite and
homogeneous three-dimensional continuum, which dominates to this day as a
"common perception" even in education. This perception of space, which today
seems self-evident, is the culmination of a long historical development, which
we will try to outline. Our aim is to briefly present the conceptual course of
models related to natural space from antiquity to the 20th century, focusing on
the theories and models that can be mathematized. At the same time we try to
highlight one of the most important starting points of geometric concepts,
which is the need for mathematical modeling of physical space, as in modern
education there is no such view.


title:
Type III seesaw under $A_4$ modular symmetry with leptogenesis and muon
  $g-2$

abstract:
  We make an attempt to study neutrino phenomenology in the framework of
type-III seesaw by considering $A_4$ modular symmetry in the super-symmetric
context. In addition, we have included local $U(1)_{B-L}$ symmetry which
eventually helps us to avoid certain unwanted terms in the superpotential.
Hitherto, the seesaw being type-III, it involves the fermion triplet
superfields $\Sigma$, along with which, we have included a singlet weighton
field $(\rho)$. In here, modular symmetry plays a crucial role by avoiding the
usage of excess flavon (weighton) fields. Also, the Yukawa couplings acquire
modular forms which are expressed in terms of Dedekind eta function
$\eta(\tau)$. However, for numerical analysis we use $q$ expansion expressions
of these couplings. Therefore, the model discussed here is triumphant enough to
accommodate the observed neutrino oscillation data. Additionally, it also
successfully explains leptogenesis and sheds some light on the current results
of muon ($g-2$).


title:
Migrating Face Swap to Mobile Devices: A lightweight Framework and A
  Supervised Training Solution

abstract:
  Existing face swap methods rely heavily on large-scale networks for adequate
capacity to generate visually plausible results, which inhibits its
applications on resource-constraint platforms. In this work, we propose
MobileFSGAN, a novel lightweight GAN for face swap that can run on mobile
devices with much fewer parameters while achieving competitive performance. A
lightweight encoder-decoder structure is designed especially for image
synthesis tasks, which is only 10.2MB and can run on mobile devices at a
real-time speed. To tackle the unstability of training such a small network, we
construct the FSTriplets dataset utilizing facial attribute editing techniques.
FSTriplets provides source-target-result training triplets, yielding
pixel-level labels thus for the first time making the training process
supervised. We also designed multi-scale gradient losses for efficient
back-propagation, resulting in faster and better convergence. Experimental
results show that our model reaches comparable performance towards
state-of-the-art methods, while significantly reducing the number of network
parameters. Codes and the dataset have been released.


title:
Research on the accumulation effect model of technological innovation in
  textile industry based on chaos theory

abstract:
  Technological innovation is one of the most important variables in the
evolution of the textile industry system. As the innovation process changes, so
does the degree of technological diffusion and the state of competitive
equilibrium in the textile industry system, and this leads to fluctuations in
the economic growth of the industry system. The fluctuations resulting from the
role of innovation are complex, irregular and imperfectly cyclical. The study
of the chaos model of the accumulation of innovation in the evolution of the
textile industry can help to provide theoretical guidance for technological
innovation in the textile industry, and can help to provide suggestions for the
interaction between the government and the textile enterprises themselves. It
is found that reasonable government regulation parameters contribute to the
accelerated accumulation of innovation in the textile industry.


title:
itdr: An R package of Integral Transformation Methods to Estimate the
  SDR Subspaces in Regression

abstract:
  Sufficient dimension reduction (SDR) is a successful tool in regression
models. It is a feasible method to solve and analyze the nonlinear nature of
the regression problems. This paper introduces the itdr R package that provides
several functions based on integral transformation methods to estimate the SDR
subspaces in a comprehensive and user-friendly manner. In particular, the itdr
package includes the Fourier method (FM) and the convolution method (CM) of
estimating the SDR subspaces such as the central mean subspace (CMS) and the
central subspace (CS). In addition, the itdr package facilitates the recovery
of the CMS and the CS by using the iterative Hessian transformation (IHT)
method and the Fourier transformation approach for inverse dimension reduction
method (invFM), respectively. Moreover, the use of the package is illustrated
by three datasets. Furthermore, this is the first package that implements
integral transformation methods to estimate SDR subspaces. Hence, the itdr
package may provide a huge contribution to research in the SDR field.


title: $n$-gon centers and central lines

abstract:
  In this paper we provide a review of the concept of center of a $n$-gon,
generalizing the original idea given by C. Kimberling for triangles. We also
generalize the concept of central line for $n$-gons for $n\geq 3$ and establish
its basic properties.


title:
One-Loop Hybrid Renormalization Matching Kernels for Quasi-Parton
  Distributions

abstract:
  Large momentum effective theory allows extraction of hadron parton
distribution functions in lattice QCD by matching them to quark bilinear matrix
elements of hadrons with large momenta. We calculate the matching kernels for
the unpolarized, helicity, and transversity isovector parton distribution
functions and skewless generalized parton distributions of all hadrons in the
hybrid-RI/MOM scheme. This renormalization scheme uses RI/MOM when the Wilson
line length is less then $z_s$, otherwise a mass subtraction scheme is used. By
design, the non-hybrid scheme is recovered as $z_s \to \infty$. In the opposite
limit, $z \to 0$, the self renormalization scheme is obtained. When the
parameters $p_z^R=0$ and $\mu^R z_s \ll 1$, the hybrid-RI/MOM scheme coincides
with the hybrid-ratio scheme times the charge of the PDF. We also discuss the
subtlety related to the commutativity of Fourier transform and $\epsilon$
expansion in the $\bar{\text{MS}}$ scheme.


title: Pointwise inner automorphisms of almost periodic factors

abstract:
  We prove that a large class of nonamenable almost periodic type ${\rm III_1}$
factors $M$, including all McDuff factors that tensorially absorb $R_\infty$
and all free Araki-Woods factors, satisfy Haagerup-Stormer's conjecture (1988):
any pointwise inner automorphism of $M$ is the composition of an inner and a
modular automorphism.


title:
Extracting Targeted Training Data from ASR Models, and How to Mitigate
  It

abstract:
  Recent work has designed methods to demonstrate that model updates in ASR
training can leak potentially sensitive attributes of the utterances used in
computing the updates. In this work, we design the first method to demonstrate
information leakage about training data from trained ASR models. We design
Noise Masking, a fill-in-the-blank style method for extracting targeted parts
of training data from trained ASR models. We demonstrate the success of Noise
Masking by using it in four settings for extracting names from the LibriSpeech
dataset used for training a SOTA Conformer model. In particular, we show that
we are able to extract the correct names from masked training utterances with
11.8% accuracy, while the model outputs some name from the train set 55.2% of
the time. Further, we show that even in a setting that uses synthetic audio and
partial transcripts from the test set, our method achieves 2.5% correct name
accuracy (47.7% any name success rate). Lastly, we design Word Dropout, a data
augmentation method that we show when used in training along with MTR, provides
comparable utility as the baseline, along with significantly mitigating
extraction via Noise Masking across the four evaluated settings.


title:
A 3-Queue Polling System with Join the Shortest -- Serve the Longest
  Policy

abstract:
  In 1987, J.W. Cohen analyzed the so-called Serve the Longest Queue (SLQ)
queueing system, where a single server attends two non-symmetric $M/G/1$-type
queues, exercising a non-preemptive priority switching policy. Cohen further
analyzed in 1998 a non-symmetric 2-queue Markovian system, where newly arriving
customers follow the Join the Shortest Queue (JSQ) discipline. The current
paper generalizes and extends Cohen's works by studying a combined JSQ-SLQ
model, and by broadening the scope of analysis to a non-symmetric 3-queue
system, where arriving customers follow the JSQ strategy and a single server
exercises the preemptive priority SLQ discipline. The system states'
multi-dimensional probability distribution function is derived while applying a
non-conventional representation of the underlying process's state-space. The
analysis combines both Probability Generating Functions and Matrix Geometric
methodologies. It is shown that the joint JSQ-SLQ operating policy achieves
extremely well the goal of balancing between queue sizes. This is emphasized
when calculating the Gini Index associated with the differences between mean
queue sizes: the value of the coefficient is close to zero. Extensive numerical
results are presented.


title:
Using Statistical Emulation and Knowledge of Grain-Surface Diffusion for
  Bayesian Inference of Reaction Rate Parameters: An Application to a Glycine
  Network

abstract:
  There exists much uncertainty surrounding interstellar grain-surface
chemistry. One of the major reaction mechanisms is grain-surface diffusion for
which the the binding energy parameter for each species needs to be known.
However, these values vary significantly across the literature which can lead
to debate as to whether or not a particular reaction takes place via diffusion.
In this work we employ Bayesian inference to use available ice abundances to
estimate the reaction rates of the reactions in a chemical network that
produces glycine. Using this we estimate the binding energy of a variety of
important species in the network, by assuming that the reactions take place via
diffusion. We use our understanding of the diffusion mechanism to reduce the
dimensionality of the inference problem from 49 to 14, by demonstrating that
reactions can be separated into classes. This dimensionality reduction makes
the problem computationally feasible. A neural network statistical emulator is
used to also help accelerate the Bayesian inference process substantially.
  The binding energies of most of the diffusive species of interest are found
to match some of the disparate literature values, with the exceptions of atomic
and diatomic hydrogen. The discrepancies with these two species are related to
limitations of the physical and chemical model. However, the use of a dummy
reaction of the form H + X -> HX is found to somewhat reduce the discrepancy
with the binding energy of atomic hydrogen. Using the inferred binding energies
in the full gas-grain version of UCLCHEM results in almost all the molecular
abundances being recovered.


title: Automated Test Generation for REST APIs: No Time to Rest Yet

abstract:
  Modern web services routinely provide REST APIs for clients to access their
functionality. These APIs present unique challenges and opportunities for
automated testing, driving the recent development of many techniques and tools
that generate test cases for API endpoints using various strategies.
Understanding how these techniques compare to one another is difficult, as they
have been evaluated on different benchmarks and using different metrics. To
fill this gap, we performed an empirical study aimed to understand the
landscape in automated testing of REST APIs and guide future research in this
area. We first identified, through a systematic selection process, a set of 10
state-of-the-art REST API testing tools that included tools developed by both
researchers and practitioners. We then applied these tools to a benchmark of 20
real-world open-source RESTful services and analyzed their performance in terms
of code coverage achieved and unique failures triggered. This analysis allowed
us to identify strengths, weaknesses, and limitations of the tools considered
and of their underlying strategies, as well as implications of our findings for
future research in this area.


title: Quantum many-body systems in thermal equilibrium

abstract:
  The thermal or equilibrium ensemble is one of the most ubiquitous states of
matter. For models comprised of many locally interacting quantum particles, it
describes a wide range of physical situations, relevant to condensed matter
physics, high energy physics, quantum chemistry and quantum computing, among
others. We give a pedagogical overview of some of the most important universal
features about the physics and complexity of these states, which have the
locality of the Hamiltonian at its core. We focus on mathematically rigorous
statements, many of them inspired by ideas and tools from quantum information
theory. These include bounds on their correlations, the form of the subsystems,
various statistical properties, and the performance of classical and quantum
algorithms. We also include a summary of a few of the most important technical
tools, as well as some self-contained proofs.


title:
Dynamical systems defined on simplicial complexes: symmetries,
  conjugacies, and invariant subspaces

abstract:
  We consider the general model for dynamical systems defined on a simplicial
complex. We describe the conjugacy classes of these systems and show how
symmetries in a given simplicial complex manifest in the dynamics defined
thereon, especially with regard to invariant subspaces in the dynamics.


title: Introducing sheaves over commutative semicartesian quantales

abstract:
  We extend the classic definition of sheaves on locales introducing an
original notion of sheaves on semicartesian quantales. We show that the
resulting category and the category of sheaves on locales share similar
categorical properties, and discuss the difficulties in concluding whether our
sheaves on quantales form a Grothendieck topos. We also prove a base change
theorem, which may be useful not only to study the relation between sheaves on
locales and sheaves on quantales, but also may be applied in the presence of an
isomorphism of commutative and unital rings.


title:
MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for
  Video Summarization

abstract:
  Video summarization intends to produce a concise video summary by effectively
capturing and combining the most informative parts of the whole content.
Existing approaches for video summarization regard the task as a frame-wise
keyframe selection problem and generally construct the frame-wise
representation by combining the long-range temporal dependency with the
unimodal or bimodal information. However, the optimal video summaries need to
reflect the most valuable keyframe with its own information, and one with
semantic power of the whole content. Thus, it is critical to construct a more
powerful and robust frame-wise representation and predict the frame-level
importance score in a fair and comprehensive manner. To tackle the above
issues, we propose a multimodal hierarchical shot-aware convolutional network,
denoted as MHSCNet, to enhance the frame-wise representation via combining the
comprehensive available multimodal information. Specifically, we design a
hierarchical ShotConv network to incorporate the adaptive shot-aware
frame-level representation by considering the short-range and long-range
temporal dependency. Based on the learned shot-aware representations, MHSCNet
can predict the frame-level importance score in the local and global view of
the video. Extensive experiments on two standard video summarization datasets
demonstrate that our proposed method consistently outperforms state-of-the-art
baselines. Source code will be made publicly available.


title:
Stability Analysis and Stabilization of Continuous-Time Linear Systems
  with Distributed Delays

abstract:
  This book is an extension of my Ph.D. thesis which is devoted to the methods
for the stability (dissipativity) analysis and stabilization of linear systems
with non-trivial distributed delays based on the application of the Liapunov-
Krasovski\u{i} functional (LKF) approach.


title: Unveiling User Behavior on Summit Login Nodes as a User

abstract:
  We observe and analyze usage of the login nodes of the leadership class
Summit supercomputer from the perspective of an ordinary user -- not a system
administrator -- by periodically sampling user activities (job queues, running
processes, etc.) for two full years (2020-2021). Our findings unveil key usage
patterns that evidence misuse of the system, including gaming the policies,
impairing I/O performance, and using login nodes as a sole computing resource.
Our analysis highlights observed patterns for the execution of complex
computations (workflows), which are key for processing large-scale
applications.


title: Hydrogen-like Schr\"odinger Operators at Low Energies

abstract:
  Consider a Schr\"odinger operator on an asymptotically Euclidean manifold $X$
of dimension at least two, and suppose that the potential is of attractive
Coulomb-like type. Using Vasy's second 2nd-microlocal approach, "the Lagrangian
approach," we analyze -- uniformly, all the way down to $E=0$ -- the output of
the limiting resolvent $R(E\pm i 0) = \lim_{\epsilon \to 0^+} R(E\pm i
\epsilon)$. The Coulomb potential causes the output of the low-energy resolvent
to possess oscillatory asymptotics which differ substantially from the sorts of
asymptotics observed in the short-range case by Guillarmou, Hassell, Sikora,
and (more recently) Hintz and Vasy. Specifically, the compound asymptotics at
low energy and large spatial scales are more delicate, and the resolvent output
is smooth all the way down to $E=0$. In fact, we will construct a
compactification of $(0,1]_E\times X$ on which the resolvent output is given by
a specified (and relatively complicated) function that oscillates as
$r\to\infty$ times something smooth. As a corollary, we get complete and
compatible asymptotic expansions for solutions to the scattering problem as
functions of both position and energy, with a transitional regime.

categories: math.AP math-ph math.CA math.DG math.MP math.SP