title: Metastability of synchronous and asynchronous dynamics

abstract:
  Metastability is an ubiquitous phenomenon in nature, which interests several
fields of natural sciences. Its description in the framework of thermodynamics
and statistical mechanics has been a taboo for long time since it is a genuine
non--equilibrium phenomenon. Since the publication of the first seminal paper
in which the metastable behavior of the mean field Curie--Weiss model was
approached by means of stochastic techniques, this topic has been largely
studied by the scientific community. Several papers and books have been
published in which many different spin models were studied and different
approaches were developed. In this review we focus on the comparison between
the metastable behavior of synchronous and asynchronous dynamics, namely,
stochastic processes in discrete time in which at each time either all the
spins or one single spin are updated. In particular we discuss how the two
different stochastic implementation of the very same Hamiltonian give rise to
different metastable behaviors.


title:
Data-Driven Optimal Closures for Mean-Cluster Models: Beyond the
  Classical Pair Approximation

abstract:
  This study concerns the mean-clustering approach to modelling the evolution
of lattice dynamics. Instead of tracking the state of individual lattice sites,
this approach describes the time evolution of the concentrations of different
cluster types. It leads to an infinite hierarchy of ordinary differential
equations which must be closed by truncation using a so-called closure
condition. This condition approximates the concentrations of higher-order
clusters in terms of the concentrations of lower-order ones. The pair
approximation is the most common form of closure. Here, we consider its
generalization, termed the "optimal approximation", which we calibrate using a
robust data-driven strategy. To fix attention, we focus on a recently proposed
structured lattice model for a nickel-based oxide, similar to that used as
cathode material in modern commercial Li-ion batteries. The form of the
obtained optimal approximation allows us to deduce a simple sparse closure
model. In addition to being more accurate than the classical pair
approximation, this ``sparse approximation'' is also physically interpretable
which allows us to a posteriori refine the hypotheses underlying construction
of this class of closure models. Moreover, the mean-cluster model closed with
this sparse approximation is linear and hence analytically solvable such that
its parametrization is straightforward. On the other hand, parametrization of
the mean-cluster model closed with the pair approximation is shown to lead to
an ill-posed inverse problem.


title:
CHANG-ES XXVII: A Radio/X-ray Catalogue of Compact Sources in and around
  Edge-on Galaxies

abstract:
  We present catalogues of discrete, compact radio sources in and around the
discs of 35 edge-on galaxies in the Continuum Halos in Nearby Galaxies -- an
EVLA Survey (CHANG-ES). The sources were extracted using the PyBDSF program at
both 1.6 GHz (L-band) and 6.0 GHz (C-band) from matching resolution ($\approx$
3 arcsec) data. We also present catalogues of X-ray sources from Chandra data
sets for 27 of the galaxies. The sources at the two radio frequency bands were
positionally cross-correlated with each other, and the result cross-correlated
with the X-ray sources. All catalogues are included for download with this
paper. We detect a total of 2507 sources at L-band and 1413 sources at C-band.
Seventy-five sources have been successfully cross-correlated in both radio
bands plus X-ray. Three new nuclear sources are candidates for Low Luminosity
Active Galactic Nuclei in NGC~3877, NGC~4192, and NGC~5792; the one in NGC~3877
also appears to be variable. We also find new nuclear sources in two companion
galaxies: NGC~4435 (companion to NGC~4438) and NGC~4298 (companion to
NGC~4302). We have also discovered what appears to be a foreground double-star;
each star has X-ray emission and there is radio emission at both L-band and
C-band in between them. This could be a colliding wind binary system.
Suggestions for follow-up studies are offered.


title:
An acoustic signal cavitation detection framework based on XGBoost with
  adaptive selection feature engineering

abstract:
  Valves are widely used in industrial and domestic pipeline systems. However,
during their operation, they may suffer from the occurrence of the cavitation,
which can cause loud noise, vibration and damage to the internal components of
the valve. Therefore, monitoring the flow status inside valves is significantly
beneficial to prevent the additional cost induced by cavitation. In this paper,
a novel acoustic signal cavitation detection framework--based on XGBoost with
adaptive selection feature engineering--is proposed. Firstly, a data
augmentation method with non-overlapping sliding window (NOSW) is developed to
solve small-sample problem involved in this study. Then, the each segmented
piece of time-domain acoustic signal is transformed by fast Fourier transform
(FFT) and its statistical features are extracted to be the input to the
adaptive selection feature engineering (ASFE) procedure, where the adaptive
feature aggregation and feature crosses are performed. Finally, with the
selected features the XGBoost algorithm is trained for cavitation detection and
tested on valve acoustic signal data provided by Samson AG (Frankfurt). Our
method has achieved state-of-the-art results. The prediction performance on the
binary classification (cavitation and no-cavitation) and the four-class
classification (cavitation choked flow, constant cavitation, incipient
cavitation and no-cavitation) are satisfactory and outperform the traditional
XGBoost by 4.67% and 11.11% increase of the accuracy.


title:
Towards Scalable and Robust Structured Bandits: A Meta-Learning
  Framework

abstract:
  Online learning in large-scale structured bandits is known to be challenging
due to the curse of dimensionality. In this paper, we propose a unified
meta-learning framework for a general class of structured bandit problems where
the parameter space can be factorized to item-level. The novel bandit algorithm
is general to be applied to many popular problems,scalable to the huge
parameter and action spaces, and robust to the specification of the
generalization model. At the core of this framework is a Bayesian hierarchical
model that allows information sharing among items via their features, upon
which we design a meta Thompson sampling algorithm. Three representative
examples are discussed thoroughly. Both theoretical analysis and numerical
results support the usefulness of the proposed method.


title: Kerr enhanced backaction cooling in magnetomechanics

abstract:
  Precise control over massive mechanical objects is highly desirable for
testing fundamental physics and for sensing applications. A very promising
approach is cavity optomechanics, where a mechanical oscillator is coupled to a
cavity. Usually, such mechanical oscillators are in highly excited thermal
states and require cooling to the mechanical ground state for quantum
applications, which is often accomplished by utilising optomechanical
backaction. However, this is not possible for increasingly massive oscillators,
as due to their low frequencies conventional cooling methods are less
effective. Here, we demonstrate a novel cooling scheme by using an
intrinsically nonlinear cavity together with a low frequency mechanical
oscillator. We demonstrate outperforming an identical, but linear, system by
more than one order of magnitude. While currently limited by flux noise, theory
predicts that with this approach the fundamental cooling limit of a linear
system can not only be reached, but also outperformed. These results open a new
avenue for efficient optomechanical cooling by exploiting a nonlinear cavity.


title: A Generative Model for Relation Extraction and Classification

abstract:
  Relation extraction (RE) is an important information extraction task which
provides essential information to many NLP applications such as knowledge base
population and question answering. In this paper, we present a novel generative
model for relation extraction and classification (which we call GREC), where RE
is modeled as a sequence-to-sequence generation task. We explore various
encoding representations for the source and target sequences, and design
effective schemes that enable GREC to achieve state-of-the-art performance on
three benchmark RE datasets. In addition, we introduce negative sampling and
decoding scaling techniques which provide a flexible tool to tune the precision
and recall performance of the model. Our approach can be extended to extract
all relation triples from a sentence in one pass. Although the one-pass
approach incurs certain performance loss, it is much more computationally
efficient.


title:
Metropolis Adjusted Langevin Trajectories: a robust alternative to
  Hamiltonian Monte Carlo

abstract:
  Hamiltonian Monte Carlo (HMC) is a widely used sampler, known for its
efficiency on high dimensional distributions. Yet HMC remains quite sensitive
to the choice of integration time. Randomizing the length of Hamiltonian
trajectories (RHMC) has been suggested to smooth the Auto-Correlation Functions
(ACF), ensuring robustness of tuning. We present the Langevin diffusion as an
alternative to control these ACFs by inducing randomness in Hamiltonian
trajectories through a continuous refreshment of the velocities. We connect and
compare the two processes in terms of quantitative mixing rates for the
2-Wasserstein and $\mathbb{L}_2$ distances. The Langevin diffusion is presented
as a limit of RHMC achieving the fastest mixing rate for strongly log-concave
targets. We introduce a robust alternative to HMC built upon these dynamics,
named Metropolis Adjusted Langevin Trajectories (MALT). Studying the scaling
limit of MALT, we obtain optimal tuning guidelines similar to HMC, and recover
the same scaling with respect to the dimension without additional assumptions.
We illustrate numerically the efficiency of MALT compared to HMC and RHMC.


title:
Nonreciprocal radio-frequency-to-optical conversion with an
  optoelectromechanical system

abstract:
  Nonreciprocal systems breaking time-reversal symmetry are essential tools in
modern quantum technologies enabling the suppression of unwanted reflected
signals or extraneous noise entering through detection ports. Here we propose a
scheme enabling nonreciprocal conversion between optical and radio-frequency
(rf) photons using exclusively optomechanical and electromechanical
interactions. The nonreciprocal transmission is obtained by interference of two
dissipative pathways of transmission between the two electromagnetic modes
established through two distinct intermediate mechanical modes. In our
protocol, we apply a bichromatic drive to the cavity mode and a single-tone
drive to the rf resonator, and use the relative phase between the drive tones
to obtain nonreciprocity. We analytically show how to adjust the driving
parameters for achieving nonreciprocal wavelength conversion and numerically
show that this is possible within an experimentally feasible parameter regime.


title: Definable convolution and idempotent Keisler measures II

abstract:
  We study convolution semigroups of invariant/finitely satisfiable Keisler
measures in NIP groups. We show that the ideal (Ellis) subgroups are always
trivial and describe minimal left ideals in the definably amenable case,
demonstrating that they always form a Bauer simplex. Under some assumptions, we
give an explicit construction of a minimal left ideal in the semigroup of
measures from a minimal left ideal in the corresponding semigroup of types
(this includes the case of SL$_{2}(\mathbb{R})$, which is not definably
amenable). We also show that the canonical push-forward map is a homomorphism
from definable convolution on $\mathcal{G}$ to classical convolution on the
compact group $\mathcal{G}/\mathcal{G}^{00}$, and use it to classify
$\mathcal{G}^{00}$-invariant idempotent measures.


title:
Weakly-bound clusters of atmospheric molecules: infrared spectra and
  structural calculations of (CO$_2$)$_n$-(CO)$_m$-(N$_2$)$_p$, $(n, m, p)$ =
  (2, 1, 0), (2, 0, 1), (1, 2, 0), (1, 0, 2), (1, 1, 1), (1, 3, 0), (1, 0, 3),
  (1, 2, 1), (1, 1, 2)

abstract:
  Structural calculations and high-resolution infrared spectra are reported for
trimers and tetramers containing CO$_2$ together with CO and/or N$_2$. Among
the 9 clusters studied here, only (CO$_2$)$_2$-CO was previously observed by
high-resolution spectroscopy. The spectra, which occur in the region of the
$\nu_3$ fundamental of CO$_2$ (~2350 cm$^{-1}$), were recorded using a tunable
optical parametric oscillator source to probe a pulsed supersonic slit jet
expansion. The trimers (CO$_2$)$_2$-CO and (CO$_2$)$_2$-N$_2$ have structures
in which the CO or N$_2$ is aligned along the symmetry axis of a staggered
side-by-side CO$_2$ dimer unit. The observation of two fundamental bands for
(CO$_2$)$_2$-CO and (CO$_2$)$_2$-N$_2$ shows that this CO$_2$ dimer unit is
non-planar, unlike (CO$_2$)$_2$ itself. For the trimers CO$_2$-(CO)$_2$ and
CO$_2$-(N$_2$)$_2$, the CO or N$_2$ monomers occupy equivalent positions in the
'equatorial plane' of the CO$_2$, pointing toward its C atom. To form the
tetramers CO$_2$-(CO)$_3$ and CO$_2$-(N$_2$)$_3$, a third CO or N$_2$ monomer
is then added off to the 'side' of the first two. In the mixed tetramers
CO$_2$-(CO)$_2$-N$_2$ and CO$_2$-CO-(N$_2$)$_2$, this 'side' position is taken
by N2 and not CO. In addition to the fundamental bands, combination bands are
also observed for (CO$_2$)$_2$-CO, CO$_2$-(CO)$_2$, and CO$_2$-(N$_2$)$_2$,
yielding some information about their low-frequency intermolecular vibrations.


title: Safe Exploration for Efficient Policy Evaluation and Comparison

abstract:
  High-quality data plays a central role in ensuring the accuracy of policy
evaluation. This paper initiates the study of efficient and safe data
collection for bandit policy evaluation. We formulate the problem and
investigate its several representative variants. For each variant, we analyze
its statistical properties, derive the corresponding exploration policy, and
design an efficient algorithm for computing it. Both theoretical analysis and
experiments support the usefulness of the proposed methods.


title:
A theoretical and experimental analysis of BWT variants for string
  collections

abstract:
  The extended Burrows-Wheeler-Transform (eBWT), introduced by Mantaci et al.
[Theor. Comput. Sci., 2007], is a generalization of the
Burrows-Wheeler-Transform (BWT) to multisets of strings. While the original BWT
is based on the lexicographic order, the eBWT uses the omega-order, which
differs from the lexicographic order in important ways. A number of tools are
available that compute the BWT of string collections; however, the data
structures they generate in most cases differ from the one originally defined,
as well as from each other. In this paper, we review the differences between
these BWT variants, both from a theoretical and from a practical point of view,
comparing them on several real-life datasets with different characteristics. We
find that the differences can be extensive, depending on the dataset
characteristics, and are largest on collections of many highly similar short
sequences. The widely-used parameter $r$, the number of runs of the BWT, also
shows notable variation between the different BWT variants; on our datasets, it
varied by a multiplicative factor of up to $4.2$.


title:
Lagarto I-Una plataforma hardware/software de arquitectura de
  computadoras para la academia e investigaci\'on

abstract:
  The design of Microprocessors Computer Architectures remains as a fundamental
course in Computer Science and Computer Engineering. The technology and
organization inside microprocessors have changed quite fast in the last twenty
years. That change has increased the information handled in class, difficulting
the teaching/learning process among students. Although there are tools, mainly
simulators, available to exemplify abstract concepts during the course, these
tools have not come along with the technology. The computer architecture group
of the Centro de Investigaci\'on en Computaci\'on at the IPN Mexico is working
on a project called Lagarto to create an open computing platform for research
and education to simplify the understanding of fundamental concepts involved in
computer architecture and operating systems. This paper introduces Lagarto, our
soft-core-processor micro-architecture. It has a scalar pipeline structure and
executes a full MIPS 32 R6 ISA [9] [10] and includes an MMU to support modern
Operative Systems. The complete design has been described using Verilog HDL and
is fully synthesizable in an FPGA. Additionally, this work shows different ways
to use and test the microprocessor with codes written in either assembly
language or C language. We show that the Lagarto project allows students to
incorporate during the course not only the traditional model of visualizing
theoretical knowledge in a practical exercise through simulators but also
integrate into the teaching process the RTL design to build the Microprocessor
Architecture.


title:
Orientation-Discriminative Feature Representation for Decentralized
  Pedestrian Tracking

abstract:
  This paper focuses on the problem of decentralized pedestrian tracking using
a sensor network. Traditional works on pedestrian tracking usually use a
centralized framework, which becomes less practical for robotic applications
due to limited communication bandwidth. Our paper proposes a
communication-efficient, orientation-discriminative feature representation to
characterize pedestrian appearance information, that can be shared among
sensors. Building upon that representation, our work develops a cross-sensor
track association approach to achieve decentralized tracking. Extensive
evaluations are conducted on publicly available datasets and results show that
our proposed approach leads to improved performance in multi-sensor tracking.


title: The categorical form of Fargues' conjecture for tori

abstract:
  We prove the main conjecture of arXiv:2102.13459 for integral coefficients in
the case of tori. Along the way we prove that the spectral action as
constructed in that manuscript is compatible with the action of the excursion
algebra and preserves the grading by $\pi_1(G)_Q$ on both sides. We
additionally develop a (non-solidified) version of condensed group (co)homology
and show that many constructions from classical group (co)homology extend to
that case.


title: QOC: Quantum On-Chip Training with Parameter Shift and Gradient Pruning

abstract:
  Parameterized Quantum Circuits (PQC) are drawing increasing research interest
thanks to its potential to achieve quantum advantages on near-term Noisy
Intermediate Scale Quantum (NISQ) hardware. In order to achieve scalable PQC
learning, the training process needs to be offloaded to real quantum machines
instead of using exponential-cost classical simulators. One common approach to
obtain PQC gradients is parameter shift whose cost scales linearly with the
number of qubits. We present QOC, the first experimental demonstration of
practical on-chip PQC training with parameter shift. Nevertheless, we find that
due to the significant quantum errors (noises) on real machines, gradients
obtained from naive parameter shift have low fidelity and thus degrading the
training accuracy. To this end, we further propose probabilistic gradient
pruning to firstly identify gradients with potentially large errors and then
remove them. Specifically, small gradients have larger relative errors than
large ones, thus having a higher probability to be pruned. We perform extensive
experiments with the Quantum Neural Network (QNN) benchmarks on 5
classification tasks using 5 real quantum machines. The results demonstrate
that our on-chip training achieves over 90% and 60% accuracy for 2-class and
4-class image classification tasks. The probabilistic gradient pruning brings
up to 7% PQC accuracy improvements over no pruning. Overall, we successfully
obtain similar on-chip training accuracy compared with noise-free simulation
but have much better training scalability. The QOC code is available in the
TorchQuantum library.


title:
Learning over No-Preferred and Preferred Sequence of Items for Robust
  Recommendation (Extended Abstract)

abstract:
  This paper is an extended version of [Burashnikova et al., 2021, arXiv:
2012.06910], where we proposed a theoretically supported sequential strategy
for training a large-scale Recommender System (RS) over implicit feedback,
mainly in the form of clicks. The proposed approach consists in minimizing
pairwise ranking loss over blocks of consecutive items constituted by a
sequence of non-clicked items followed by a clicked one for each user. We
present two variants of this strategy where model parameters are updated using
either the momentum method or a gradient-based approach. To prevent updating
the parameters for an abnormally high number of clicks over some targeted items
(mainly due to bots), we introduce an upper and a lower threshold on the number
of updates for each user. These thresholds are estimated over the distribution
of the number of blocks in the training set. They affect the decision of RS by
shifting the distribution of items that are shown to the users. Furthermore, we
provide a convergence analysis of both algorithms and demonstrate their
practical efficiency over six large-scale collections with respect to various
ranking measures.


title:
Extended-range order in tetrahedral amorphous semiconductors: The case
  of amorphous silicon

abstract:
  This paper reports the presence of extended-range ordering in the atomic
pair-correlation function of amorphous silicon ($a$-Si) using ultra-large
atomistic models obtained from Monte Carlo and molecular-dynamics simulations.
The extended-range order manifests itself in the form of radial oscillations,
on the length scale of 20-40 angstrom, which are examined by directly analyzing
the radial distribution of atoms in distant coordination shells and comparing
the same with those from a class of partially-ordered networks of Si atoms and
disordered configurations of crystalline silicon from an information-theoretic
point of view. The study suggests that the extended-range radial oscillations
principally originate from the propagation of radial ordering from the first
few atomic shells to a distance of up to 40 angstrom. The effect of these
oscillations on the first sharp diffraction peak (FSDP) in the structure factor
is addressed by obtaining a semi-analytical expression for the static structure
factor of $a$-Si, and calculating an estimate of the error of the intensity of
the FSDP associated with the truncation of radial information from distant
shells. The results indicate that the extended-range oscillations do not have
any noticeable effects on the position and intensity of the FSDP, which are
primarily determined by the medium-range atomic correlations of up to a length
of 20 angstrom in amorphous silicon.


title:
Fixed Point Iterations for SURE-based PSF Estimation for Image
  Deconvolution

abstract:
  Stein's unbiased risk estimator (SURE) has been shown to be an effective
metric for determining optimal parameters for many applications. The topic of
this article is focused on the use of SURE for determining parameters for blind
deconvolution. The parameters include those that define the shape of the point
spread function (PSF), as well as regularization parameters in the
deconvolution formulas. Within this context, the optimal parameters are
typically determined via a brute for search over the feasible parameter space.
When multiple parameters are involved, this parameter search is prohibitively
costly due to the curse of dimensionality. In this work, novel fixed point
iterations are proposed for optimizing these parameters, which allows for rapid
estimation of a relatively large number of parameters. We demonstrate that with
some mild tuning of the optimization parameters, these fixed point methods
typically converge to the ideal PSF parameters in relatively few iterations,
e.g. 50-100, with each iteration requiring very low computational cost.


title:
Investigating Mercury's Environment with the Two-Spacecraft BepiColombo
  Mission

abstract:
  The ESA-JAXA BepiColombo mission will provide simultaneous measurements from
two spacecraft, offering an unprecedented opportunity to investigate
magnetospheric and exospheric dynamics at Mercury as well as their interactions
with the solar wind, radiation, and interplanetary dust. Many scientific
instruments onboard the two spacecraft will be completely, or partially devoted
to study the near-space environment of Mercury as well as the complex processes
that govern it. Many issues remain unsolved even after the MESSENGER mission
that ended in 2015. The specific orbits of the two spacecraft, MPO and Mio, and
the comprehensive scientific payload allow a wider range of scientific
questions to be addressed than those that could be achieved by the individual
instruments acting alone, or by previous missions. These joint observations are
of key importance because many phenomena in Mercury's environment are highly
temporally and spatially variable. Examples of possible coordinated
observations are described in this article, analysing the required geometrical
conditions, pointing, resolutions and operation timing of different BepiColombo
instruments sensors.


title: A statistical formalism for alignment analysis

abstract:
  The detection of anisotropies with respect to a given direction in a vector
field is a common problem in astronomy. Several methods have been proposed that
rely on the distribution of the acute angles between the data and a reference
direction. Different approaches use Monte Carlo methods to quantify the
statistical significance of a signal, although often lacking an analytical
framework. Here we present two methods to detect and quantify alignment signals
and test their statistical robustness. The first method considers the deviance
of the relative fraction of vector components in the plane perpendicular to a
reference direction with respect to an isotropic distribution. We also derive
the statistical properties and stability of the resulting estimator, and
therefore does not rely on Monte Carlo simulations to assess its statistical
significance. The second method is based on a fit over the residuals of the
empirical cumulative distribution function with respect to that expected for a
uniform distribution, using a small set of harmonic orthogonal functions, which
does not rely on any binning scheme. We compare these methods with others
commonly used in the literature, using Monte Carlo simulations, finding that
the proposed statistics allow the detection of alignment signals with greater
significance.


title:
Regional-Local Adversarially Learned One-Class Classifier Anomalous
  Sound Detection in Global Long-Term Space

abstract:
  Anomalous sound detection (ASD) is one of the most significant tasks of
mechanical equipment monitoring and maintaining in complex industrial systems.
In practice, it is vital to precisely identify abnormal status of the working
mechanical system, which can further facilitate the failure troubleshooting. In
this paper, we propose a multi-pattern adversarial learning one-class
classification framework, which allows us to use both the generator and the
discriminator of an adversarial model for efficient ASD. The core idea is
learning to reconstruct the normal patterns of acoustic data through two
different patterns of auto-encoding generators, which succeeds in extending the
fundamental role of a discriminator from identifying real and fake data to
distinguishing between regional and local pattern reconstructions. Furthermore,
we present a global filter layer for long-term interactions in the frequency
domain space, which directly learns from the original data without introducing
any human priors. Extensive experiments performed on four real-world datasets
from different industrial domains (three cavitation datasets provided by SAMSON
AG, and one existing publicly) for anomaly detection show superior results, and
outperform recent state-of-the-art ASD methods.


title:
On applications of Herglotz-Nevanlinna functions in material sciences,
  II: extended applications and generalized theory

abstract:
  Part II of the review article focuses on the applications of
Herglotz-Nevanlinna functions in material sciences. It presents a diverse set
of applications with details and the role of Herglotz-Nevanlinna functions
clearly pointed out. This paper is concluded by a collection of existent
generalizations of the class of Herglotz-Nevanlinna functions that are
motivated by potential applications.


title:
On applications of Herglotz-Nevanlinna functions in material sciences,
  I: classical theory and applications of sum rules

abstract:
  This is the first part of the review article which focuses on theory and
applications of Herglotz-Nevanlinna functions in material sciences. It starts
with the definition of scalar valued Herglotz-Nevanlinna functions and explains
in detail the theorems that are pertinent to applications, followed by a short
overview of the matrix-valued and operator-valued versions of these functions
and the properties that carry over from scalar cases. The theory is
complemented by some applications from electromagnetics that are related to the
sum rules. More applications of Herglotz Nevanlinnna functions in material
sciences can be found in Part II.


title: Automated Data Augmentations for Graph Classification

abstract:
  Data augmentations are effective in improving the invariance of learning
machines. We argue that the corechallenge of data augmentations lies in
designing data transformations that preserve labels. This is
relativelystraightforward for images, but much more challenging for graphs. In
this work, we propose GraphAug, a novelautomated data augmentation method
aiming at computing label-invariant augmentations for graph
classification.Instead of using uniform transformations as in existing studies,
GraphAug uses an automated augmentationmodel to avoid compromising critical
label-related information of the graph, thereby producing
label-invariantaugmentations at most times. To ensure label-invariance, we
develop a training method based on reinforcementlearning to maximize an
estimated label-invariance probability. Comprehensive experiments show that
GraphAugoutperforms previous graph augmentation methods on various graph
classification tasks.


title: Spin alignment around Illustris-TNG voids

abstract:
  Using a new statistical approach we study the alignment signal of galactic
spins with respect to the center of voids identified in the TNG-300 simulation.
We explore this signal in different samples of galaxies, varying their distance
from the void center, mass, spin norm, local density, and velocity. We find a
strong tendency (>9 sigma) of massive, high-spin, and low radial velocity
galaxies to be aligned perpendicularly to the void-centric direction in a wide
range of distances corresponding to 0.9 to 1.4 void radii. Furthermore, we find
that in these subdense environments, local density is irrelevant in the
amplitude of spin alignment, while the largest impact is associated to the
galaxy void-centric radial velocity in the sense that those at the lowest
expansion rate are more strongly aligned perpendicularly to the center of the
void. Our results suggest that further analysis at understanding intrinsic
alignments and their relation to large scale structures may probe key for weak
lensing studies in upcoming large surveys such as Euclid and LSST.


title: Automatic Tabulation in Constraint Models

abstract:
  The performance of a constraint model can often be improved by converting a
subproblem into a single table constraint. In this paper we study heuristics
for identifying promising candidate subproblems, where converting the candidate
into a table constraint is likely to improve solver performance. We propose a
small set of heuristics to identify common cases, such as expressions that will
propagate weakly. The process of discovering promising subproblems and
tabulating them is entirely automated in the constraint modelling tool Savile
Row. Caches are implemented to avoid tabulating equivalent subproblems many
times. We give a simple algorithm to generate table constraints directly from a
constraint expression in \savilerow. We demonstrate good performance on the
benchmark problems used in earlier work on tabulation, and also for several new
problem classes. In some cases, the entirely automated process leads to orders
of magnitude improvements in solver performance.


title:
Supervising Remote Sensing Change Detection Models with 3D Surface
  Semantics

abstract:
  Remote sensing change detection, identifying changes between scenes of the
same location, is an active area of research with a broad range of
applications. Recent advances in multimodal self-supervised pretraining have
resulted in state-of-the-art methods which surpass vision models trained solely
on optical imagery. In the remote sensing field, there is a wealth of
overlapping 2D and 3D modalities which can be exploited to supervise
representation learning in vision models. In this paper we propose Contrastive
Surface-Image Pretraining (CSIP) for joint learning using optical RGB and above
ground level (AGL) map pairs. We then evaluate these pretrained models on
several building segmentation and change detection datasets to show that our
method does, in fact, extract features relevant to downstream applications
where natural and artificial surface information is relevant.


title: The Quest for a Common Model of the Intelligent Decision Maker

abstract:
  The premise of the Multi-disciplinary Conference on Reinforcement Learning
and Decision Making is that multiple disciplines share an interest in
goal-directed decision making over time. The idea of this paper is to sharpen
and deepen this premise by proposing a perspective on the decision maker that
is substantive and widely held across psychology, artificial intelligence,
economics, control theory, and neuroscience, which I call the "common model of
the intelligent agent". The common model does not include anything specific to
any organism, world, or application domain. The common model does include
aspects of the decision maker's interaction with its world (there must be input
and output, and a goal) and internal components of the decision maker (for
perception, decision-making, internal evaluation, and a world model). I
identify these aspects and components, note that they are given different names
in different disciplines but refer essentially to the same ideas, and discuss
the challenges and benefits of devising a neutral terminology that can be used
across disciplines. It is time to recognize and build on the convergence of
multiple diverse disciplines on a substantive common model of the intelligent
agent.


title: Generalized Ramanujan-Sato Series Arising from Modular Forms

abstract:
  Motivated by work of Chan, Chan, and Liu, we obtain a new general theorem
which produces Ramanujan-Sato series for $1/\pi$. We then use it to construct
explicit examples related to non-compact arithmetic triangle groups, as
classified by Takeuchi. Some of our examples are new, and some reproduce
existing examples.


title:
Observers for Differential Algebraic Equation Models of Power Networks:
  Jointly Estimating Dynamic and Algebraic States

abstract:
  Phasor measurement units ({PMUs}) have become instrumental in modern power
systems for enabling real-time, wide-area monitoring and control. Accordingly,
many studies have investigated efficient and robust dynamic state estimation
(DSE) methods in order to accurately compute the dynamic states of generation
units. Nonetheless, most of them forego the dynamic-algebraic nature of power
networks and only consider their nonlinear dynamic representations. Motivated
by the lack of DSE methods based on power network's differential-algebraic
equations (DAEs), this paper develops a novel observer-based DSE framework in
order to perform simultaneous estimation of the dynamic and algebraic states of
multi-machine power networks. Specifically, we leverage the DAE dynamics of a
power network around an operating point and combine them with a PMU-based
measurement model capable of capturing bus voltages and line currents. The
proposed $\mathcal{H}_{\infty}$ observer, which only requires detectability and
impulse observability conditions which are satisfied for various power
networks, is designed to handle various noise, unknown inputs, and input sensor
failures. The results obtained from performing extensive numerical simulations
on the IEEE $9$-bus and $39$-bus systems showcase the effectiveness of the
proposed approach for DSE purposes.


title:
Hierarchical Linear Dynamical System for Representing Notes from
  Recorded Audio

abstract:
  We seek to develop simultaneous segmentation and classification of notes from
audio recordings in presence of outliers. The selected architecture for
modeling time series is hierarchical linear dynamical system (HLDS). We propose
a novel method for its parameter setting. HLDS can potentially be employed in
two ways: 1) simultaneous segmentation and clustering for exploring data, i.e.
finding unknown notes, 2) simultaneous segmentation and classification of audio
recording for finding the notes of interest in the presence of outliers. We
adapted HLDS for the second purpose since it is an easier task and still a
challenging problem, e.g. in the field of bioacoustics. Each test clip has the
same notes (but different instances) as of the training clip and also contain
outlier notes. At test, it is automatically decided to which class of interest
a note belongs to if any. Two applications of this work are to the fields of
bioacoustics for detection of animal sounds in audio field recordings and also
to musicology. Experiments have been conducted for segmentation and
classification of both avian and musical notes from recorded audio.


title: On two conjectures concerning the ternary digits of powers of two

abstract:
  Erd\H{o}s conjectured that 1, 4, and 256 are the only powers of two whose
ternary representations consist solely of 0s and 1s. Sloane conjectured that,
except for $\{2^0,2^1,2^2,2^3,2^4,2^{15}\}$, every other power of two has at
least one 0 in its ternary representation. In this paper, numerical results are
given in strong support of these conjectures. In particular, we verify both
conjectures for all $2^n$ with $n \leq 2 \cdot 3^{45} \approx 5.9 \times
10^{21}$. Our approach makes use of a simple recursive construction of numbers
$2^n$ having prescribed patterns in their trailing ternary digits.


title: Controllable Natural Language Generation with Contrastive Prefixes

abstract:
  To guide the generation of large pretrained language models (LM), previous
work has focused on directly fine-tuning the language model or utilizing an
attribute discriminator. In this work, we propose a novel lightweight framework
for controllable GPT2 generation, which utilizes a set of small
attribute-specific vectors, called prefixes, to steer natural language
generation. Different from prefix-tuning, where each prefix is trained
independently, we take the relationship among prefixes into consideration and
train multiple prefixes simultaneously. We propose a novel supervised method
and also an unsupervised method to train the prefixes for single-aspect control
while the combination of these two methods can achieve multi-aspect control.
Experimental results on both single-aspect and multi-aspect control show that
our methods can guide generation towards the desired attributes while keeping
high linguistic quality.


title:
Dual function spin-wave logic gates based on electric field control
  magnetic anisotropy boundary

abstract:
  Spin waves (SWs) have been considered a promising candidate for encoding
information with lower power consumption. Here, we propose the dual function SW
logic gates based on the electric field controlling the SW propagation in the
Fe film of Fe/BaTiO3 heterostructure with the motion of magnetic anisotropy
boundary (MAB). We show micromagnetic simulations to validate the AND-OR and
NAND-NOR logic gates. Our research may find a path for simplifying integrated
logic circuits using such dual function SW logic gates.


title:
Dual bounds for the positive definite functions approach to mutually
  unbiased bases

abstract:
  A long-standing open problem asks if there can exist 7 mutually unbiased
bases (MUBs) in $\mathbb{C}^6$, or, more generally, $d + 1$ MUBs in
$\mathbb{C}^d$ for any $d$ that is not a prime power. The recent work of
Kolountzakis, Matolcsi, and Weiner (2016) proposed an application of the method
of positive definite functions (a relative of Delsarte's method in coding
theory and Lov\'{a}sz's semidefinite programming relaxation of the independent
set problem) as a means of answering this question in the negative. Namely,
they ask whether there exists a polynomial of a unitary matrix input satisfying
various properties which, through the method of positive definite functions,
would show the non-existence of 7 MUBs in $\mathbb{C}^6$. Using a convex
duality argument, we prove that such a polynomial of degree at most 6 cannot
exist. We also propose a general dual certificate which we conjecture to
certify that this method can never show that there exist strictly fewer than $d
+ 1$ MUBs in $\mathbb{C}^d$.


title: Perceptions of the State of D&I and D&I Initiative in the ASF

abstract:
  Open Source Software (OSS) Foundations and projects are investing in creating
Diversity and Inclusion (D&I) initiatives. However, little is known about
contributors' perceptions about the usefulness and success of such initiatives.
We aim to close this gap by investigating how contributors perceive the state
of D&I in their community. In collaboration with the Apache Software Foundation
(ASF), we surveyed 600+ OSS contributors and conducted 11 follow-up interviews.
We used mixed methods to analyze our data-quantitative analysis of Likert-scale
questions and qualitative analysis of open-ended survey question and the
interviews to understand contributors' perceptions and critiques of the D&I
initiative and how to improve it. Our results indicate that the ASF
contributors felt that the state of D&I was still lacking, especially regarding
gender, seniority, and English proficiency. Regarding the D&I initiative, some
participants felt that the effort was unnecessary, while others agreed with the
effort but critiqued its implementation. These findings show that D&I
initiatives in OSS communities are a good start, but there is room for
improvements. Our results can inspire the creation of new and the refinement of
current initiatives.


title:
On a discrete scheme for the Mullins-Sekerka flow and its fine
  properties

abstract:
  The Mullins-Sekerka problem is numerically solved in $\mathbb{R}^2$ with the
aid of the charge simulation method. This is an expansion of a numerical scheme
by which Sakakibara and Yazaki computed the Hele-Shaw flow. We investigate a
sufficient condition for the number of collocation points to ensure that the
length of generated approximate polygonal curves exactly decrease step by step.
Moreover, changing fundamental solutions of the charge simulation method, we
are successful to establish a numerical scheme which can be used to treat the
Mullins-Sekerka problem with the right contact angle condition.


title:
De Haas-van Alphen oscillations near the Lifshitz transition from two
  electron pockets to one electron pocket in the two-dimensional Dirac fermion
  systems

abstract:
  We theoretically study the de Haas-van Alphen (dHvA) oscillations in the
system with changing the topology of the Fermi surface (the Lifshitz
transition) by electron dopings. We employ the two-dimensional tight binding
model for $\alpha$-(BEDT-TTF)$_2$I$_3$ under pressure which has two Dirac
points in the first Brillouin zone. When this system is slightly doped, there
exists two closed Fermi surfaces with the same area and the dHvA oscillations
become saw-tooth pattern or inversed saw-tooth pattern for both cases of fixed
electron filling ($\nu$) or fixed chemical potential ($\mu$) with respect to
the magnetic field, respectively. By increasing dopings, the system approaches
the Lifshitz transition, where two closed Fermi surfaces are close each other.
Then, we find that the pattern of the dHvA oscillations changes. A jump of the
magnetization appears at the center of the fundamental period and its magnitude
increases in the case of the fixed electron filling, while a jump is separated
into a pair of jumps and its separation becomes large in the case of the fixed
chemical potential. This is due to the lifting of double degeneracy in the
Landau levels. Since this lifting is seen in the two-dimensional Dirac fermion
system with two Dirac points, the obtained results in this study can be applied
to not only $\alpha$-(BEDT-TTF)$_2$I$_3$ but also other materials with closely
located Dirac points such as graphene under the uniaxial strain, in black
phosphorus, twisted bilayer graphene, and so on.


title:
Next-Best-View Prediction for Active Stereo Cameras and Highly
  Reflective Objects

abstract:
  Depth acquisition with the active stereo camera is a challenging task for
highly reflective objects. When setup permits, multi-view fusion can provide
increased levels of depth completion. However, due to the slow acquisition
speed of high-end active stereo cameras, collecting a large number of
viewpoints for a single scene is generally not practical. In this work, we
propose a next-best-view framework to strategically select camera viewpoints
for completing depth data on reflective objects. In particular, we explicitly
model the specular reflection of reflective surfaces based on the Phong
reflection model and a photometric response function. Given the object CAD
model and grayscale image, we employ an RGB-based pose estimator to obtain
current pose predictions from the existing data, which is used to form
predicted surface normal and depth hypotheses, and allows us to then assess the
information gain from a subsequent frame for any candidate viewpoint. Using
this formulation, we implement an active perception pipeline which is evaluated
on a challenging real-world dataset. The evaluation results demonstrate that
our active depth acquisition method outperforms two strong baselines for both
depth completion and object pose estimation performance.


title:
Supersolid-like square- and honeycomb-lattice crystallization of
  droplets in a dipolar condensate

abstract:
  We demonstrate a supersolid-like spatially-periodic square- and
honeycomb-lattice crystallization of droplets, in addition to the
commonly-studied triangular-lattice crystallization, in a
cylindrically-symmetric quasi-two-dimensional trapped dipolar condensate, using
a beyond-mean-field model including a quantum-fluctuation Lee-Huang-Yang-type
interaction. These three types of crystallization of droplets may appear for
the same atomic interactions and the same trap frequencies. The energy $E$ of
all three crystallization as a function of number $N$ of atoms satisfy the
universal scaling relation $E\sim N^{0.4}$ indicating that all three
arrangements of the droplets should be energetically probable processes of
phenomenological interest. The state of square-lattice crystallization may have
the central site occupied or unoccupied, corresponding to a parity-symmetric or
parity-antisymmetric state, respectively. The state of square-lattice
crystallization with the occupied central site and the state of
triangular-lattice crystallization, for a fixed $N$, constitute two
quasi-degenerate ground states while the other states are low-lying excited
states. This makes the square-lattice crystallization with the occupied central
site an ideal candidate for future experimental observation.


title:
Lattice of Integer Flows and the Poset of Strongly Connected
  Orientations for Regular Matroids

abstract:
  A 2010 result of Amini provides a way to extract information about the
structure of the graph from the geometry of the Voronoi polytope of the lattice
of integer flows (which determines the graph up to two-isomorphism).
Specifically, Amini shows that the face poset of the Voronoi polytope is
isomorphic to the poset of strongly connected orientations of subgraphs. This
answers a question raised by Caporaso and Viviani, and Amini also proves a dual
result for integer cuts. In this paper we generalise Amini's result to regular
matroids; in this context the theorem for integer cuts becomes a direct
consequence of the theorem for integer flows, by making duality explicit as
matroid duality.


title:
An Exact Consistent Tangent Stiffness Matrix for a Second Gradient Model
  for Porous Plastic Solids: Derivation and Assessment

abstract:
  It is well known that the use of a consistent tangent stiffness matrix is
critical to obtain quadratic convergence of the global Newton iterations in the
finite element simulations of problems involving elasto-plastic deformation of
metals, especially for large scale metallic structure problems. In this paper
we derive an exact consistent stiffness matrix for a porous material model, the
GLPD model developed by Gologanu, Leblond, Perrin, and Devaux for ductile
fracture for porous metals based on generalized continuum mechanics
assumptions. Full expressions for the derivatives of the Cauchy stress tensor
and the generalized moments stress tensor the model involved are provided. The
effectiveness and robustness of the proposed tangent stiffness moduli are
assessed by applying the formulation in the finite element simulations of
ductile fracture problems. Comparisons between the performance our stiffness
matrix and the standard ones are also provided.


title:
Layered Opposite Rashba Spin-Orbit Coupling in Bilayer Graphene: Loss of
  Spin Chirality, Symmetry Breaking and Topological Transition

abstract:
  Layered opposite Rashba spin-orbit coupling (LO-RSOC) means that the RSOC has
the same magnitude but the opposite sign on two coupled spatially separated
layers. We show that the LO-RSOC, allowed by inversion symmetry in bilayer
graphene, results in the loss of spin chirality in the momentum space, in
contrast to uniform RSOC. This chiral loss leads to the difficulty of deciding
whether the LO-RSOC (on the order of 10 meV) exists or not due to the
imperceptible band variation. We propose to identify the LO-RSOC by gating to
break inversion symmetry or by magnetic field to break time-reversal symmetry.
Remarkably, we observe the trivial-nontrivial transition of band topology when
the system deviates from the LO Rashba state. Ab inito calculations suggest
bilayer graphene encapsulated by two monolayers of Au is a LO Rashba system.


title: Deep Learning the Functional Renormalization Group

abstract:
  We perform a data-driven dimensionality reduction of the scale-dependent
4-point vertex function characterizing the functional Renormalization Group
(fRG) flow for the widely studied two-dimensional $t - t'$ Hubbard model on the
square lattice. We demonstrate that a deep learning architecture based on a
Neural Ordinary Differential Equation solver in a low-dimensional latent space
efficiently learns the fRG dynamics that delineates the various magnetic and
$d$-wave superconducting regimes of the Hubbard model. We further present a
Dynamic Mode Decomposition analysis that confirms that a small number of modes
are indeed sufficient to capture the fRG dynamics. Our work demonstrates the
possibility of using artificial intelligence to extract compact representations
of the 4-point vertex functions for correlated electrons, a goal of utmost
importance for the success of cutting-edge quantum field theoretical methods
for tackling the many-electron problem.


title: Ribbon dioperads and modular ribbon properads

abstract:
  We define and study the notions of ribbon dioperads and modular ribbon
properads. We give a Lie algebra structure on the colimit total object and the
limit total object of a ribbon dioperad, and we give a norm map between them.
We give a cobar construction for dg ribbon co-dioperads. We also prove similar
results for modular ribbon properads. These results are applied to the study of
higher Hochschild cochain complexes and pre-Calabi-Yau categories.


title:
Texture Characterization of Histopathologic Images Using Ecological
  Diversity Measures and Discrete Wavelet Transform

abstract:
  Breast cancer is a health problem that affects mainly the female population.
An early detection increases the chances of effective treatment, improving the
prognosis of the disease. In this regard, computational tools have been
proposed to assist the specialist in interpreting the breast digital image
exam, providing features for detecting and diagnosing tumors and cancerous
cells. Nonetheless, detecting tumors with a high sensitivity rate and reducing
the false positives rate is still challenging. Texture descriptors have been
quite popular in medical image analysis, particularly in histopathologic images
(HI), due to the variability of both the texture found in such images and the
tissue appearance due to irregularity in the staining process. Such variability
may exist depending on differences in staining protocol such as fixation,
inconsistency in the staining condition, and reagents, either between
laboratories or in the same laboratory. Textural feature extraction for
quantifying HI information in a discriminant way is challenging given the
distribution of intrinsic properties of such images forms a non-deterministic
complex system. This paper proposes a method for characterizing texture across
HIs with a considerable success rate. By employing ecological diversity
measures and discrete wavelet transform, it is possible to quantify the
intrinsic properties of such images with promising accuracy on two HI datasets
compared with state-of-the-art methods.


title: Collisional-model quantum trajectories for entangled qubit environments

abstract:
  We study the dynamics of quantum systems interacting with a stream of
entangled qubits. Under fairly general conditions, we present a detailed
framework describing the conditional dynamical maps for the system, called
quantum trajectories, when the qubits are measured. Depending on the
measurement basis, these quantum trajectories can be jump-type or
diffusive-type, and they can exhibit features not present with quantum optical
and single-qubit trajectories. As an example, we consider the case of two
remote atoms, where jump-type quantum trajectories herald the birth and death
of entanglement.


title: On the spectral radius of uniform weighted hypergraph

abstract:
  Let $\mathbb{Q}_{k,n}$ be the set of the connected $k$-uniform weighted
hypergraphs with $n$ vertices, where $k,n\geq 3$. For a hypergraph $G\in
\mathbb{Q}_{k,n}$, let $\mathcal{A}(G)$, $\mathcal{L} (G)$ and $\mathcal{Q}
(G)$ be its adjacency tensor, Laplacian tensor and signless Laplacian tensor,
respectively. The spectral radii of $\mathcal{A}(G)$ and $\mathcal{Q} (G)$ are
investigated. Some basic properties of the $H$-eigenvalue, the
$H^{+}$-eigenvalue and the $H^{++}$-eigenvalue of $\mathcal{A}(G)$,
$\mathcal{L} (G)$ and $\mathcal{Q} (G)$ are presented. Several lower and upper
bounds of the $H$-eigenvalue, the $H^{+}$-eigenvalue and the
$H^{++}$-eigenvalue for $\mathcal{A}(G)$, $\mathcal{L} (G)$ and $\mathcal{Q}
(G)$ are established. The largest $H^{+}$-eigenvalue of $\mathcal{L} (G)$ and
the smallest $H^{+}$-eigenvalue of $\mathcal{Q} (G)$ are characterized. A
relationship among the $H$-eigenvalues of $\mathcal{L} (G)$, $\mathcal{Q} (G)$
and $\mathcal{A} (G)$ is also given.


title: On $1$d quadratic Klein-Gordon equations with a potential and symmetries

abstract:
  This paper is a continuation of a previous work Germain-Pusateri (2020) by
the first two authors. We focus on $1$ dimensional quadratic Klein-Gordon
equations with a potential, under some assumptions that are less general than
Germain-Pusateri (2020), but allow us to present some simplifications in the
proof of global existence with decay for small solutions. In particular, we can
propagate a stronger control on a basic $L^2$-weighted type norm while
providing some shorter and less technical proofs for some of the arguments.


title: OCR Improves Machine Translation for Low-Resource Languages

abstract:
  We aim to investigate the performance of current OCR systems on low resource
languages and low resource scripts. We introduce and make publicly available a
novel benchmark, OCR4MT, consisting of real and synthetic data, enriched with
noise, for 60 low-resource languages in low resource scripts. We evaluate
state-of-the-art OCR systems on our benchmark and analyse most common errors.
We show that OCR monolingual data is a valuable resource that can increase
performance of Machine Translation models, when used in backtranslation. We
then perform an ablation study to investigate how OCR errors impact Machine
Translation performance and determine what is the minimum level of OCR quality
needed for the monolingual data to be useful for Machine Translation.


title:
A Dual Neighborhood Hypergraph Neural Network for Change Detection in
  VHR Remote Sensing Images

abstract:
  The very high spatial resolution (VHR) remote sensing images have been an
extremely valuable source for monitoring changes occurred on the earth surface.
However, precisely detecting relevant changes in VHR images still remains a
challenge, due to the complexity of the relationships among ground objects. To
address this limitation, a dual neighborhood hypergraph neural network is
proposed in this article, which combines the multiscale superpixel segmentation
and hypergraph convolution to model and exploit the complex relationships.
First, the bi-temporal image pairs are segmented under two scales and fed to a
pre-trained U-net to obtain node features by treating each object under the
fine scale as a node. The dual neighborhood is then defined using the
father-child and adjacent relationships of the segmented objects to construct
the hypergraph, which permits models to represent the higher-order structured
information far more complex than just pairwise relationships. The hypergraph
convolutions are conducted on the constructed hypergraph to propagate the label
information from a small amount of labeled nodes to the other unlabeled ones by
the node-edge-node transform. Moreover, to alleviate the problem of imbalanced
sample, the focal loss function is adopted to train the hypergraph neural
network. The experimental results on optical, SAR and heterogeneous optical/SAR
data sets demonstrate that the proposed method comprises better effectiveness
and robustness compared to many state-of-the-art methods.


title:
An analysis of indifference curves and areas from a human nutrition
  perspective

abstract:
  Through documentary research and interviews with nutrition experts, we found
that all nutrients have two thresholds, the Recommended Daily Allowance (RDA)
and the Tolerable Upper Intake Level (UL). Intake less than the RDA or more
than the UL negatively affects health. Intake quantities of nutrients within
these limits covers 100% of the objective physiological needs without negative
repercussions. These characteristics, and others, are common knowledge among
nutrition experts; however, these are not adequately reflected in the
microeconomics models that study these needs. We conclude that the generalized
presence of these thresholds determines the existence of significant
indifference areas that should be added to the microeconomics models of the
indifference curves, thus improving the modelling of reality.


title: Learning the Beauty in Songs: Neural Singing Voice Beautifier

abstract:
  We are interested in a novel task, singing voice beautifying (SVB). Given the
singing voice of an amateur singer, SVB aims to improve the intonation and
vocal tone of the voice, while keeping the content and vocal timbre. Current
automatic pitch correction techniques are immature, and most of them are
restricted to intonation but ignore the overall aesthetic quality. Hence, we
introduce Neural Singing Voice Beautifier (NSVB), the first generative model to
solve the SVB task, which adopts a conditional variational autoencoder as the
backbone and learns the latent representations of vocal tone. In NSVB, we
propose a novel time-warping approach for pitch correction: Shape-Aware Dynamic
Time Warping (SADTW), which ameliorates the robustness of existing time-warping
approaches, to synchronize the amateur recording with the template pitch curve.
Furthermore, we propose a latent-mapping algorithm in the latent space to
convert the amateur vocal tone to the professional one. To achieve this, we
also propose a new dataset containing parallel singing recordings of both
amateur and professional versions. Extensive experiments on both Chinese and
English songs demonstrate the effectiveness of our methods in terms of both
objective and subjective metrics. Audio samples are available
at~\url{https://neuralsvb.github.io}. Codes:
\url{https://github.com/MoonInTheRiver/NeuralSVB}.


title:
The maximal spectral radius of the uniform unicyclic hypergraph with
  perfect matchings

abstract:
  Let $\mathcal{U}(n,k)$ and $\Gamma(n,k)$ be the set of the $k$-uniform linear
and nonlinear unicyclic hypergraphs having perfect matchings with $n$ vertices
respectively, where $n\geq k(k-1)$ and $k\geq 3$. By using some techniques of
transformations and constructing the incidence matrices for the hypergraphs
considered, we get the hypergraphs with the maximal spectral radii among three
kinds of hypergraphs, namely $\mathcal{U}(n,k)$ with $n= 2k(k-1)$ and $n\geq
9k(k-1)$, $\Gamma(n,k)$ with $n\geq k(k-1)$, and $\mathcal{U}(n,k)\cup
\Gamma(n,k)$ with $n\geq 2k(k-1)$, where $k\geq 3$.


title: On the walk matrix of the Dynkin graph $D_n$

abstract:
  Let $W(D_n)$ denote the walk matrix of the Dynkin graph $D_n$, a tree
obtained from the path of order $n-1$ by adding a pendant edge at the second
vertex. We prove that $\text{rank}\,W(D_n)=n-2$ if $4\mid n$ and
$\text{rank}\,W(D_n)=n-1$ otherwise. Furthermore, we prove that the Smith
normal form of $W(D_n)$ is
$$\text{diag}[\underbrace{1,1,\ldots,1}_{\lceil\frac{n}{2}\rceil},\underbrace{2,2,\ldots,2}_{\lfloor\frac{n}{2}\rfloor-1},0]$$
when $4\nmid n$. This confirms a recent conjecture in [W.Wang, F.Liu, W.Wang,
Generalized spectral characterizations of almost controllable graphs, European
J. Combin., 96(2021):103348].


title:
Uniqueness in inverse diffraction grating problems with infinitely many
  plane waves at a fixed frequency

abstract:
  This paper is concerned with the inverse diffraction problems by a periodic
curve with Dirichlet boundary condition in two dimensions. It is proved that
the periodic curve can be uniquely determined by the near-field measurement
data corresponding to infinitely many incident plane waves with distinct
directions at a fixed frequency. Our proof is based on Schiffer's idea which
consists of two ingredients: i) the total fields for incident plane waves with
distinct directions are linearly independent, and ii) there exist only finitely
many linearly independent Dirichlet eigenfunctions in a bounded domain or in a
closed waveguide under additional assumptions on the waveguide boundary. Based
on the Rayleigh expansion, we prove that the phased near-field data can be
uniquely determined by the phaseless near-field data in a bounded domain, with
the exception of a finite set of incident angles. Such a phase retrieval result
leads to a new uniqueness result for the inverse grating diffraction problem
with phaseless near-field data at a fixed frequency. Since the incident
direction determines the quasi-periodicity of the boundary value problem, our
inverse issues are different from the existing results of [Htttlich & Kirsch,
Inverse Problems 13 (1997): 351-361] where fixed-direction plane waves at
multiple frequencies were considered.


title:
Polynomial algebras of superintegrable systems separating in Cartesian
  coordinates from higher order ladder operators

abstract:
  We introduce the general polynomial algebras characterizing a class of higher
order superintegrable systems that separate in Cartesian coordinates. The
construction relies on underlying polynomial Heisenberg algebras and their
defining higher order ladder operators. One feature of these algebras is that
they preserve by construction some aspects of the structure of the
$\mathfrak{gl}(n)$ Lie algebra. Among the classes of Hamiltonians arising in
this framework are various deformations of harmonic oscillator and singular
oscillator related to exceptional orthogonal polynomials and even Painlev\'e
and higher order Painlev\'e analogs. As an explicit example, we investigate a
new three-dimensional superintegrable system related to Hermite exceptional
orthogonal polynomials of type III. Among the main results is the determination
of the degeneracies of the model in terms of the finite-dimensional irreducible
representations of the polynomial algebra.


title:
The Cell Physiome: What do we need in a computational physiology
  framework for predicting single cell biology?

abstract:
  Modern biology and biomedicine are undergoing a big-data explosion needing
advanced computational algorithms to extract mechanistic insights on the
physiological state of living cells. We present the motivation for the Cell
Physiome: a framework and approach for creating, sharing, and using
biophysics-based computational models of single cell physiology. Using examples
in calcium signaling, bioenergetics, and endosomal trafficking, we highlight
the need for spatially detailed, biophysics-based computational models to
uncover new mechanisms underlying cell biology. We review progress and
challenges to date towards creating cell physiome models. We then introduce
bond graphs as an efficient way to create cell physiome models that integrate
chemical, mechanical, electromagnetic, and thermal processes while maintaining
mass and energy balance. Bond graphs enhance modularization and re-usability of
computational models of cells at scale. We conclude with a look forward into
steps that will help fully realize this exciting new field of mechanistic
biomedical data science.

categories: q-bio.CB physics.bio-ph q-bio.QM q-bio.SC


title: Onset of color transparency in holographic light-front QCD

abstract:
  The color transparency (CT) of a hadron, propagating with reduced absorption
in a nucleus, is a fundamental property of QCD, reflecting its internal
structure and effective size when it is produced at high transverse momentum
$Q$. CT has been confirmed in many experiments, such as semi-exclusive hard
electroproduction, $e A \to e' \pi X$ for mesons produced at $Q^2 > 3 ~ {\rm
GeV}^2$. However, a recent JLab measurement for a proton electroproduced in
carbon $e C \to e' p X$ fails to observe CT at $Q^2$ up to 14.2 GeV$^2$. In
this article we determine the onset of CT by comparing the $Q^2$ dependence of
the hadronic cross sections for the initial formation of a small color-singlet
configuration using the generalized parton distributions from holographic
light-front QCD. We find a critical dependence on the hadron's twist $\tau$,
the number of its constituents, for the onset of CT, with no significant
effects from the nuclear medium. This effect can explain the absence of proton
CT in the present kinematic range of the JLab experiment. The proton is
predicted to have a "two-stage" color transparency with the onset of CT
differing for the spin-conserving (twist-3) Dirac form factor with a later
onset for the spin-flip Pauli (twist-4) form factor.


title:
Parallel algorithm for pattern matching problems under substring
  consistent equivalence relations

abstract:
  Given a text and a pattern over an alphabet, the pattern matching problem
searches for all occurrences of the pattern in the text. An equivalence
relation $\approx$ is called a substring consistent equivalence relation
(SCER), if for two strings $X$ and $Y$, $X \approx Y$ implies $|X| = |Y|$ and
$X[i:j] \approx Y[i:j]$ for all $1 \le i \le j \le |X|$. In this paper, we
propose an efficient parallel algorithm for pattern matching under any SCER
using the"duel-and-sweep" paradigm. For a pattern of length $m$ and a text of
length $n$, our algorithm runs in $O(\xi_m^\mathrm{t} \log^3 m)$ time and
$O(\xi_m^\mathrm{w} \cdot n \log^2 m)$ work, with $O(\tau_n^\mathrm{t} +
\xi_m^\mathrm{t} \log^2 m)$ time and $O(\tau_n^\mathrm{w} + \xi_m^\mathrm{w}
\cdot m \log^2 m)$ work preprocessing on the Priority Concurrent Read
Concurrent Write Parallel Random-Access Machines (P-CRCW PRAM).


title:
A Computer Vision-assisted Approach to Automated Real-Time Road
  Infrastructure Management

abstract:
  Accurate automated detection of road pavement distresses is critical for the
timely identification and repair of potentially accident-inducing road hazards
such as potholes and other surface-level asphalt cracks. Deployment of such a
system would be further advantageous in low-resource environments where lack of
government funding for infrastructure maintenance typically entails heightened
risks of potentially fatal vehicular road accidents as a result of inadequate
and infrequent manual inspection of road systems for road hazards. To remedy
this, a recent research initiative organized by the Institute of Electrical and
Electronics Engineers ("IEEE") as part of their 2020 Global Road Damage
Detection ("GRDC") Challenge published in May 2020 a novel 21,041 annotated
image dataset of various road distresses calling upon academic and other
researchers to submit innovative deep learning-based solutions to these road
hazard detection problems. Making use of this dataset, we propose a supervised
object detection approach leveraging You Only Look Once ("YOLO") and the Faster
R-CNN frameworks to detect and classify road distresses in real-time via a
vehicle dashboard-mounted smartphone camera, producing 0.68 F1-score
experimental results ranking in the top 5 of 121 teams that entered this
challenge as of December 2021.


title:
Motion by mean curvature from Glauber-Kawasaki dynamics with speed
  change

abstract:
  We derive a continuum mean-curvature flow as a certain hydrodynamic scaling
limit of Glauber-Kawasaki dynamics with speed change. The Kawasaki part
describes the movement of particles through particle interactions. It is
speeded up in a diffusive space-time scaling. The Glauber part governs the
creation and annihilation of particles. The Glauber part is set to favor two
levels of particle density. It is also speeded up in time, but at a lesser rate
than the Kawasaki part. Under this scaling, a mean-curvature interface flow
emerges, with a homogenized `surface tension-mobility' parameter reflecting
microscopic rates. The interface separates the two levels of particle density.
  Similar hydrodynamic limits have been derived in two recent papers; one where
the Kawasaki part describes simple nearest neighbor interactions, and one where
the Kawasaki part is replaced by a zero-range process. We extend the main
results of these two papers beyond nearest-neighbor interactions. The main
novelty of our proof is the derivation of a `Boltzmann-Gibbs' principle which
covers a class of local particle interactions.


title:
The high-Re far wake of a slender body and the effects of density
  stratification

abstract:
  The high-Reynolds-number axisymmetric wake of a slender body with a turbulent
boundary layer is investigated using a hybrid simulation. The wake generator is
a 6:1 prolate spheroid and the Reynolds number based on the diameter $D$ is $Re
= 10^5$. The transition of the wake to a state of complete self-similarity is
investigated by looking for the first time into the far-field of a slender-body
wake. Unlike bluff-body wakes, here the flow is not dominated by vortex
shedding in the near wake. Instead, the recirculation region is very small, the
near wake is quasi-parallel and is characterized by the presence of broadband
turbulence. The simulations show that these particularities alter the decay
rate of the wake significantly and challenge the notion of universality -- the
idea that far from the body all turbulent wakes decay at the same rate. The
influence of stratification in the wake is also studied at Froude numbers
$\Fro=U/ND=2$ and $10$. The results show that the onset of buoyancy effects
depends on the wake generator and that the far wake retains information of the
initial conditions.


title: ICASSP 2022 Deep Noise Suppression Challenge

abstract:
  The Deep Noise Suppression (DNS) challenge is designed to foster innovation
in the area of noise suppression to achieve superior perceptual speech quality.
This is the 4th DNS challenge, with the previous editions held at INTERSPEECH
2020, ICASSP 2021, and INTERSPEECH 2021. We open-source datasets and test sets
for researchers to train their deep noise suppression models, as well as a
subjective evaluation framework based on ITU-T P.835 to rate and rank-order the
challenge entries. We provide access to DNSMOS P.835 and word accuracy (WAcc)
APIs to challenge participants to help with iterative model improvements. In
this challenge, we introduced the following changes: (i) Included mobile device
scenarios in the blind test set; (ii) Included a personalized noise suppression
track with baseline; (iii) Added WAcc as an objective metric; (iv) Included
DNSMOS P.835; (v) Made the training datasets and test sets fullband (48 kHz).
We use an average of WAcc and subjective scores P.835 SIG, BAK, and OVRL to get
the final score for ranking the DNS models. We believe that as a research
community, we still have a long way to go in achieving excellent speech quality
in challenging noisy real-world scenarios.


title: Modal analysis of bluff body wakes

abstract:
  Spectral proper orthogonal decomposition (SPOD) analyses are performed for
unstratified and stratified wakes of a circular disk at Reynolds number,
$U_\infty D/\nu = 50,000$, where $U_\infty$ is the freestream velocity and $D$
is the diameter of the circular disk. At $\Rey = 50,000$, three diameter-based
Froude numbers, $\Fro = U_\infty/ND = \infty$, $2$ and $10$ are analyzed. We
find that in the unstratified configuration, two modes: (i) the vortex shedding
(VS) mode ($m=1, \Str = 0.135$) and (ii) the double helix (DH) mode ($m=2, \Str
\rightarrow 0$) are the most dominant features. At far-downstream distances, it
is the DH mode which is the most dominant one while at near and intermediate
locations, VS mode dominates the energetics. Reconstruction of TKE and Reynolds
shear stresses are also explored at near and far wake locations. In the
stratified cases ($\Fro = 2$ and $10$), the vortex shedding mode (at $\Str
\approx 0.13-0.14$) is the single most dominant feature in the wake. At
intermediate to late streamwise locations ($x/D$), we establish through
pressure wave flux reconstruction that the VS mode is responsible for the
internal gravity wave (IGW) generation. A comparison between the SPOD
decomposition analyses between $\Rey =5000$ and $\Rey = 50,000$ is also
performed at $\Fro =2$. Preliminary results show that the low-$\Rey$ wake is
also dominated by the VS mode but exhibits more coherence, namely higher
contribution of leading SPOD modes, than the high-$\Rey$ wake.


abstract:
  The ICASSP 2022 Acoustic Echo Cancellation Challenge is intended to stimulate
research in acoustic echo cancellation (AEC), which is an important area of
speech enhancement and still a top issue in audio communication. This is the
third AEC challenge and it is enhanced by including mobile scenarios, adding
speech recognition rate in the challenge goal metrics, and making the default
sample rate 48 kHz. In this challenge, we open source two large datasets to
train AEC models under both single talk and double talk scenarios. These
datasets consist of recordings from more than 10,000 real audio devices and
human speakers in real environments, as well as a synthetic dataset. We also
open source an online subjective test framework and provide an online objective
metric service for researchers to quickly test their results. The winners of
this challenge are selected based on the average Mean Opinion Score achieved
across all different single talk and double talk scenarios, and the speech
recognition word acceptance rate.


title: A Simple Discretization Scheme for Gain Matrix Conditioning

abstract:
  In industrial model predictive controllers (MPCs), models generated from
regression-based system identification methods typically contain small or even
physically non-existent degrees of freedom. Control issues can arise when the
steady-state optimizer uses these small degrees of freedom to calculate targets
for plant operation due to matrix ill-conditioning. Mathematical techniques
like Relative Gain Array (RGA) and Singular Value Decomposition (SVD) are
helpful for analyzing controller gain interactions and identifying conditioning
issues, which can be corrected relatively easily in small models. However,
these techniques are difficult and tedious to apply for larger, more complex
models. This paper describes a novel, non-iterative, RGA-based, binning
technique for discretizing the gain matrix and quickly solving 2$\times$2
conditioning issues for any model size, while guaranteeing gain adjustments
below a certain threshold. Higher order interactions are also discussed.


title:
Twisted convolution quantum information channels, one-parameter
  semigroups and their generators

abstract:
  Using the tool of quantum characteristic functions of n-mode states in the
boson Fock space {\Gamma}(C_n) we construct a semigroup of quantum information
channels. This leads to a special class of one-parameter semigroups of such
channels. These semigroups are concrete but their generators have unbounded
operator coefficients. These one-parameter semigroups are also quantum
dynamical semigroups and the form of the generators involve additional features
which do not appear in the standard GKSL form. A heuristic discussion of the
form of these generators is included. In the wake of this analysis many open
problems arise naturally.


title: Past, Present and Future of Hadoop: A Survey

abstract:
  In this paper, a technology for massive data storage and computing named
Hadoop is surveyed. Hadoop consists of heterogeneous computing devices like
regular PCs abstracting away the details of parallel processing and developers
can just concentrate on their computational problem. A Hadoop cluster is made
of two parts: HDFs and Mapreduce. Hadoop cluster uses HDFS for data management.
HDFS provides storage for input and output data in MapReduce jobs and is
designed with abilities like high-fault tolerance, high-distribution capacity,
and high throughput. It is also suitable for storing Terabyte data on clusters
and it runs on flexible hardware like commodity devices.


title:
Multimodal distribution of transient time of predator extinction in a
  three-species food chain

abstract:
  Studies of transient dynamics captures the time history of any dramatic
changes in the dynamics of a system. The transient dynamics is investigated
here in a classic ecological model of a bistable tri-trophic food chain. All
the species in the food chain either coexist or undergoes a partial extinction
with the loss of predator after a transient time that depends upon the initial
density of species populations. The distribution of transient time of
extinction of the predator in response to initial states shows interesting
pattern of inhomogeneity and anisotropy in the basin of predator-fee state.
Precisely, the distribution shows a multimodal character when the initial
points are near the basin boundary, and unimodal at locations far from the
border. The distribution is also anisotropic in the sense that the number of
modes depends on the direction from the basin location. We define two new
metrics, viz., homogeneity index and local isotropic index to characterize the
distinctive features of the distribution. The inhomogeneity of distribution
reduces significantly with resource enrichment, however, a similar change in
anisotropy is relatively low. We try to explain the origin of such multimodal
distributions from the dynamical system perspectives and present its ecological
implications.


title:
Efficient Attribute Unlearning: Towards Selective Removal of Input
  Attributes from Feature Representations

abstract:
  Recently, the enactment of privacy regulations has promoted the rise of the
machine unlearning paradigm. Existing studies of machine unlearning mainly
focus on sample-wise unlearning, such that a learnt model will not expose
user's privacy at the sample level. Yet we argue that such ability of selective
removal should also be presented at the attribute level, especially for the
attributes irrelevant to the main task, e.g., whether a person recognized in a
face recognition system wears glasses or the age range of that person. Through
a comprehensive literature review, it is found that existing studies on
attribute-related problems like fairness and de-biasing learning cannot address
the above concerns properly. To bridge this gap, we propose a paradigm of
selectively removing input attributes from feature representations which we
name `attribute unlearning'. In this paradigm, certain attributes will be
accurately captured and detached from the learned feature representations at
the stage of training, according to their mutual information. The particular
attributes will be progressively eliminated along with the training procedure
towards convergence, while the rest of attributes related to the main task are
preserved for achieving competitive model performance. Considering the
computational complexity during the training process, we not only give a
theoretically approximate training method, but also propose an acceleration
scheme to speed up the training process. We validate our method by spanning
several datasets and models and demonstrate that our design can preserve model
fidelity and reach prevailing unlearning efficacy with high efficiency. The
proposed unlearning paradigm builds a foundation for future machine unlearning
system and will become an essential component of the latest privacy-related
legislation.


title:
Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question
  Answering

abstract:
  Recent works on knowledge base question answering (KBQA) retrieve subgraphs
for easier reasoning. A desired subgraph is crucial as a small one may exclude
the answer but a large one might introduce more noises. However, the existing
retrieval is either heuristic or interwoven with the reasoning, causing
reasoning on the partial subgraphs, which increases the reasoning bias when the
intermediate supervision is missing. This paper proposes a trainable subgraph
retriever (SR) decoupled from the subsequent reasoning process, which enables a
plug-and-play framework to enhance any subgraph-oriented KBQA model. Extensive
experiments demonstrate SR achieves significantly better retrieval and QA
performance than existing retrieval methods. Via weakly supervised pre-training
as well as the end-to-end fine-tuning, SRl achieves new state-of-the-art
performance when combined with NSM, a subgraph-oriented reasoner, for
embedding-based KBQA methods.


title:
Feasibility of Material Decomposition-based Photon-counting CT
  Thermometry

abstract:
  Over the past decades, thermal ablation procedures such as high intensity
focused ultrasound (HIFU) have been developed vaporize cancerous tissues in a
focal area. Thermal ablation has the potential to non-invasively eliminate
tumors and treat other medical conditions with high efficacy and better patient
outcomes. However, it is necessary for treatment to be coupled with real time
temperature monitoring in order to deliver sufficient thermal dosage to the
target while sparing surrounding, healthy tissues. To this end, computed
tomography (CT) has been explored to offer fine spatial resolution at a rapid
speed. However, current CT thermometry techniques are sensitive to
heterogeneous tissue properties, imaging noise, and artifacts. To address this
challenge, this paper utilizes the emerging photon-counting CT technology,
performs tomographic thermometry based on material decomposition, and obtains
excellent results in realistic numerical simulation. Specifically, three
algorithms were designed to decouple material composition and thermal expansion
from spectral CT reconstruction and compared in a comparative study. The best
algorithm, referred to as the one-step algorithm, uniquely finds both material
decomposition and the associated temperature at the same time in a closed form
solution, which is robust to changes in tissue composition and generates
temperature predictions with centigrade accuracy under realistic CT image noise
levels.


title: Approximation Algorithms for Flexible Graph Connectivity

abstract:
  We present approximation algorithms for several network design problems in
the model of Flexible Graph Connectivity (Adjiashvili, Hommelsheim and
M\"uhlenthaler, "Flexible Graph Connectivity", Math. Program. pp. 1-33 (2021),
and IPCO 2020: pp. 13-26).
  Let $k\geq 1$, $p\geq 1$ and $q\geq 0$ be integers. In an instance of the
$(p,q)$-Flexible Graph Connectivity problem, denoted $(p,q)$-FGC, we have an
undirected connected graph $G = (V,E)$, a partition of $E$ into a set of safe
edges $S$ and a set of unsafe edges $U$, and nonnegative costs $c: E\to\Re$ on
the edges. A subset $F \subseteq E$ of edges is feasible for the $(p,q)$-FGC
problem if for any subset $F'$ of unsafe edges with $|F'|\leq q$, the subgraph
$(V, F \setminus F')$ is $p$-edge connected. The algorithmic goal is to find a
feasible solution $F$ that minimizes $c(F) = \sum_{e \in F} c_e$. We present a
simple $2$-approximation algorithm for the $(1,1)$-FGC problem via a reduction
to the minimum-cost rooted $2$-arborescence problem. This improves on the
$2.527$-approximation algorithm of Adjiashvili et al. Our $2$-approximation
algorithm for the $(1,1)$-FGC problem extends to a $(k+1)$-approximation
algorithm for the $(1,k)$-FGC problem. We present a $4$-approximation algorithm
for the $(p,1)$-FGC problem, and an $O(q\log|V|)$-approximation algorithm for
the $(p,q)$-FGC problem. Finally, we improve on the result of Adjiashvili et
al. for the unweighted $(1,1)$-FGC problem by presenting a
$16/11$-approximation algorithm.
  The $(p,q)$-FGC problem is related to the well-known Capacitated
$k$-Connected Subgraph problem (denoted Cap-k-ECSS) that arises in the area of
Capacitated Network Design. We give a $\min(k,2 u_{max})$-approximation
algorithm for the Cap-k-ECSS problem, where $u_{max}$ denotes the maximum
capacity of an edge.


title:
The buckling load of cylindrical shells under axial compression depends
  on the cross-section curvature

abstract:
  It is known that the famous theoretical formula by Koiter for the critical
buckling load of circular cylindrical shells under axial compression does not
coincide with the experimental data. Namely, while Koiter's formula predicts
linear dependence of the buckling load $\lambda(h)$ of the shell thickness $h$
($h>0$ is a small paramater), one observes the dependence $\lambda(h)\sim
h^{3/2}$ in experiments; i.e., the shell buckles at much smaller loads for
small thickness. This theoretical prediction failure is believed to be caused
by the so-called sensitivity to imperfections phenomenon (both, shape and
load). Grabovsky and the first author have rigorously proven in [\textit{J.
Nonl. Sci.,} Vol. 26, Iss. 1, pp. 83--119, Feb. 2016], that in the problem of
circular cylindrical shells buckling under axial compression, a small load
twist leads to the buckling load scaling $\lambda(h)\sim h^{5/4},$ while shape
imperfections are likely to result in the scaling $\lambda(h)\sim h^{3/2}.$ In
this work we prove, that in fact the buckling load $\lambda(h)$ of cylindrical
(not necessarily circular) shells under vertical compression depends on the
curvature of the cross section curve. When the cross section is a convex curve
with uniformly positive curvature, then $\lambda(h)\sim h,$ and when the the
cross section curve has positive curvature except at finitely many points, then
$C_1h^{8/5}\leq \lambda(h)\leq C_2h^{3/2}$ for $h$ small thickness $h>0.$


title: Vortex formation in collisions of Bose-Einstein condensates

abstract:
  The vortex is a topological excitation in a superfluid in presence of local
orbital angular momentum. The stationary properties of a single vortex as well
as the formation processes of vortices and vortex-antivortex pairs are
investigated by solving the Gross-Pitaevskii Equation. In a rotating
environment, the primary vortex with winding number one is stable against
perturbation, which has a minimal energy and fixed OAM, but the vortices with
larger winding numbers are unstable and will decay into primary vortices
through a redistribution of the energy and vorticity. The injection of OAM can
also be realized in non-central collisions of self-interacting condensates,
part of the OAM of the initial state will induce the formation of vortices
through concentration of energy and vorticity density around topological
defects. Different from a hydrodynamical description, the interference of the
wave function plays an important role in the transport of energy and vorticity,
reflecting the quantum nature of the vortex formation process. The study of the
vortex formation may shed light on the nature of particle spin and spin-orbit
couplings in strong interaction matter produced in heavy-ion collisions.


title: An Explicit Watson--Ichino Formula with CM Newforms

abstract:
  In this paper, we extend the work of Humphries and Khan [HK20] to establish
an explicit version of Watson--Ichino formula for $L(1/2,f\otimes\mathrm{ad}
g)$, where $f$ is a Hecke--Maass form and $g$ is a CM newform.


title: Volume growth estimates of gradient Ricci solitons

abstract:
  In this paper, we survey the volume growth estimates for shrinking, steady,
and expanding gradient Ricci solitons. Together with the known results, we also
prove some new volume growth estimates for expanding gradient Ricci solitons.


title:
How to Debug Inclusivity Bugs? A Debugging Process with Information
  Architecture

abstract:
  Although some previous research has found ways to find inclusivity bugs
(biases in software that introduce inequities), little attention has been paid
to how to go about fixing such bugs. Without a process to move from finding to
fixing, acting upon such findings is an ad-hoc activity, at the mercy of the
skills of each individual developer. To address this gap, we created
Why/Where/Fix, a systematic inclusivity debugging process whose inclusivity
fault localization harnesses Information Architecture(IA) -- the way
user-facing information is organized, structured and labeled. We then conducted
a multi-stage qualitative empirical evaluation of the effectiveness of
Why/Where/Fix, using an Open Source Software (OSS) project's infrastructure as
our setting. In our study, the OSS project team used the Why/Where/Fix process
to find inclusivity bugs, localize the IA faults behind them, and then fix the
IA to remove the inclusivity bugs they had found. Our results showed that using
Why/Where/Fix reduced the number of inclusivity bugs that OSS newcomer
participants experienced by 90%.


title:
DXM-TransFuse U-net: Dual Cross-Modal Transformer Fusion U-net for
  Automated Nerve Identification

abstract:
  Accurate nerve identification is critical during surgical procedures for
preventing any damages to nerve tissues. Nerve injuries can lead to long-term
detrimental effects for patients as well as financial overburdens. In this
study, we develop a deep-learning network framework using the U-Net
architecture with a Transformer block based fusion module at the bottleneck to
identify nerve tissues from a multi-modal optical imaging system. By leveraging
and extracting the feature maps of each modality independently and using each
modalities information for cross-modal interactions, we aim to provide a
solution that would further increase the effectiveness of the imaging systems
for enabling the noninvasive intraoperative nerve identification.


title: Private Location Sharing for Decentralized Routing services

abstract:
  Data-driven methodologies offer many exciting upsides, but they also
introduce new challenges, particularly in the realm of user privacy.
Specifically, the way data is collected can pose privacy risks to end users. In
many routing services, a single entity (e.g., the routing service provider)
collects and manages user trajectory data. When it comes to user privacy, these
systems have a central point of failure since users have to trust that this
entity will not sell or use their data to infer sensitive private information.
Unfortunately, in practice many advertising companies offer to buy such data
for the sake of targeted advertisements.
  With this as motivation, we study the problem of using location data for
routing services in a privacy-preserving way. Rather than having users report
their location to a central operator, we present a protocol in which users
participate in a decentralized and privacy-preserving computation to estimate
travel times for the roads in the network in a way that no individuals'
location is ever observed by any other party. The protocol uses the Laplace
mechanism in conjunction with secure multi-party computation to ensure that it
is cryptogrpahically secure and that its output is differentially private.
  A natural question is if privacy necessitates degradation in accuracy or
system performance. We show that if a road has sufficiently high capacity, then
the travel time estimated by our protocol is provably close to the ground truth
travel time. We validate the protocol through numerical experiments which show
that using the protocol as a routing service provides privacy guarantees with
minimal overhead to user travel time.


title: Heroes in oriented complete multipartite graphs

abstract:
  The dichromatic number of a digraph is the minimum size of a partition of its
vertices into acyclic induced subgraphs. Given a class of digraphs $\mathcal
C$, a digraph $H$ is a hero in $\mc C$ if $H$-free digraphs of $\mathcal C$
have bounded dichromatic number. In a seminal paper, Berger at al. give a
simple characterization of all heroes in tournaments. In this paper, we give a
simple proof that heroes in quasi-transitive oriented graphs are the same as
heroes in tournaments. We also prove that it is not the case in the class of
oriented multipartite graphs, disproving a conjecture of Aboulker, Charbit and
Naserasr. We also give a full characterisation of heroes in oriented complete
multipartite graphs up to the status of a single tournament on $6$ vertices.


title:
The Unfairness of Active Users and Popularity Bias in Point-of-Interest
  Recommendation

abstract:
  Point-of-Interest (POI) recommender systems provide personalized
recommendations to users and help businesses attract potential customers.
Despite their success, recent studies suggest that highly data-driven
recommendations could be impacted by data biases, resulting in unfair outcomes
for different stakeholders, mainly consumers (users) and providers (items).
Most existing fairness-related research works in recommender systems treat user
fairness and item fairness issues individually, disregarding that RS work in a
two-sided marketplace. This paper studies the interplay between (i) the
unfairness of active users, (ii) the unfairness of popular items, and (iii) the
accuracy (personalization) of recommendation as three angles of our study
triangle. We group users into advantaged and disadvantaged levels to measure
user fairness based on their activity level. For item fairness, we divide items
into short-head, mid-tail, and long-tail groups and study the exposure of these
item groups into the top-k recommendation list of users. Experimental
validation of eight different recommendation models commonly used for POI
recommendation (e.g., contextual, CF) on two publicly available POI
recommendation datasets, Gowalla and Yelp, indicate that most well-performing
models suffer seriously from the unfairness of popularity bias (provider
unfairness). Furthermore, our study shows that most recommendation models
cannot satisfy both consumer and producer fairness, indicating a trade-off
between these variables possibly due to natural biases in data. We choose the
POI recommendation as our test scenario; however, the insights should be
trivially extendable on other domains.


title: Modified Starobinsky inflation by the $R\ln\left( \square\right) R$ term

abstract:
  In the context of effective theories of gravity, a minimalist bottom-up
approach which takes into account $1$-loop quantum corrections leads to
modifications in the Einstein-Hilbert action through the inclusion of four
extra terms: $R^{2}$, $C_{\kappa\rho\alpha\beta}C^{\kappa\rho\alpha\beta}$,
$R\ln\left( \square\right) R$ and $C_{\kappa\rho\alpha\beta}\ln\left(
\square\right) C^{\kappa\rho\alpha\beta}$. The first two terms are necessary to
guarantee the renormalizability of the gravitational theory, and the last two
terms (nonlocal terms) arise from the integration of massless/light matter
fields. This work aims to analyze how one of the nonlocal terms, namely
$R\ln\left( \square\right) R$, affects the Starobinsky inflation. We consider
the nonlocal term as a small correction to the $R^{2}$ term, and we demonstrate
that the model behaves like a local model in this context. In addition, we show
that the approximate model in the Einstein frame is described by a canonical
scalar field minimally coupled to general relativity. Finally, we study the
inflationary regime of this model and constrain its free parameters through
observations of CMB anisotropies.