title:
Magnetic properties of the noncentrosymmetric tetragonal antiferromagnet
  EuPtSi$_{3}$

abstract:
  We report a comprehensive study of single crystals of the noncentrosymmetric
rare-earth compound EuPtSi$_{3}$ grown by the optical floating-zone technique.
Measurements of the magnetization, ac susceptibility, and specific heat
consistently establish antiferromagnetic order of localized Eu$^{2+}$ moments
below the N\'{e}el temperature $T_{\mathrm{N}} = 17~\mathrm{K}$, followed by a
second magnetic transition at $T_{\mathrm{N1}} = 16~\mathrm{K}$. For a magnetic
field along the easy $[001]$ axis, the magnetic phase diagram is composed of
these two phases. For fields applied in the magnetically hard basal plane, two
additional phases emerge under magnetic field, where the in-plane anisotropy is
weak with $[100]$ being the hardest axis. At the phase transitions, the
magnetic properties exhibit hysteresis and discrepancies between differential
and ac susceptibility, suggesting slow reorientation processes of mesoscale
magnetic textures. Consistently, powder and single-crystal neutron diffraction
in zero field identify magnetic textures that are modulated on a length scale
of the order of $100~\textrm{\r{A}}$, most likely in the form of N\'{e}el-type
antiferromagnetic cycloids.


title:
MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation
  with Limited Source Labels

abstract:
  The success of deep convolutional neural networks (DCNNs) benefits from high
volumes of annotated data. However, annotating medical images is laborious,
expensive, and requires human expertise, which induces the label scarcity
problem. Especially when encountering the domain shift, the problem becomes
more serious. Although deep unsupervised domain adaptation (UDA) can leverage
well-established source domain annotations and abundant target domain data to
facilitate cross-modality image segmentation and also mitigate the label
paucity problem on the target domain, the conventional UDA methods suffer from
severe performance degradation when source domain annotations are scarce. In
this paper, we explore a challenging UDA setting - limited source domain
annotations. We aim to investigate how to efficiently leverage unlabeled data
from the source and target domains with limited source annotations for
cross-modality image segmentation. To achieve this, we propose a new
label-efficient UDA framework, termed MT-UDA, in which the student model
trained with limited source labels learns from unlabeled data of both domains
by two teacher models respectively in a semi-supervised manner. More
specifically, the student model not only distills the intra-domain semantic
knowledge by encouraging prediction consistency but also exploits the
inter-domain anatomical information by enforcing structural consistency.
Consequently, the student model can effectively integrate the underlying
knowledge beneath available data resources to mitigate the impact of source
label scarcity and yield improved cross-modality segmentation performance. We
evaluate our method on MM-WHS 2017 dataset and demonstrate that our approach
outperforms the state-of-the-art methods by a large margin under the
source-label scarcity scenario.


title: Assessing Network Representations for Identifying Interdisciplinarity

abstract:
  Many studies have sought to identify interdisciplinary research as a function
of the diversity of disciplines identified in an article's references or
citations. However, given the constant evolution of the scientific landscape,
disciplinary boundaries are shifting and blurring, making it increasingly
difficult to describe research within a strict taxonomy. In this work, we
explore the potential for graph learning methods to learn embedded
representations for research papers that encode their 'interdisciplinarity' in
a citation network. This facilitates the identification of interdisciplinary
research without the use of disciplinary categories. We evaluate these
representations and their ability to identify interdisciplinary research,
according to their utility in interdisciplinary citation prediction. We find
that those representations which preserve structural equivalence in the
citation graph are best able to predict distant, interdisciplinary interactions
in the network, according to multiple definitions of citation distance.


title:
Reducing overestimating and underestimating volatility via the augmented
  blending-ARCH model

abstract:
  SVR-GARCH model tends to "backward eavesdrop" when forecasting the financial
time series volatility in which case it tends to simply produce the prediction
by deviating the previous volatility. Though the SVR-GARCH model has achieved
good performance in terms of various performance measurements, trading
opportunities, peak or trough behaviors in the time series are all hampered by
underestimating or overestimating the volatility. We propose a blending ARCH
(BARCH) and an augmented BARCH (aBARCH) model to overcome this kind of problem
and make the prediction towards better peak or trough behaviors. The method is
illustrated using real data sets including SH300 and S&P500. The empirical
results obtained suggest that the augmented and blending models improve the
volatility forecasting ability.


title:
Neural Network and Order Flow, Technical Analysis: Predicting short-term
  direction of futures contract

abstract:
  Predictions of short-term directional movement of the futures contract can be
challenging as its pricing is often based on multiple complex dynamic
conditions. This work presents a method for predicting the short-term
directional movement of an underlying futures contract. We engineered a set of
features from technical analysis, order flow, and order-book data. Then,
Tabnet, a deep learning neural network, is trained using these features. We
train our model on the Silver Futures Contract listed on Shanghai Futures
Exchange and achieve an accuracy of 0.601 on predicting the directional change
during the selected period.


title:
Discrete Boltzmann multi-scale modeling of non-equilibrium multiphase
  flows

abstract:
  Numerical solving of the Boltzmann equation is a very demanding task.
Fortunately, in most instances, such exploration can be restricted to a
low-dimensional linear manifold of phase-space, spanned by a suitable subset of
low order kinetic moments of the Boltzmann distribution. The identification of
the most suitable manifold is problem-specific. In this paper, we formulate and
validate a multi-scale discrete Boltzmann model (DBM) based on density
functional kinetic theory for thermal multiphase flow systems, ranging from
continuum to transition flow regime. Our procedure builds upon two main
pillars: (i) the determination of the fewest kinetic moment relations, which
are required by the description of significant thermo-hydrodynamic
non-equilibrium (THNE) effects beyond the realm of continuum fluid mechanics,
and (ii) the construction of appropriate discrete equilibrium distribution
function recovering all the desired kinetic moments. It is found that the
incorporation of appropriate additional higher-order thermodynamic kinetic
moments considerably extends the DBM's capability of handling larger values of
the liquid-vapor density ratio, curbing spurious currents, and ensuring mass,
momentum, and energy conservation. Importantly, it is shown that the above
capability strictly depends on the specific kinetic moments included in the
non-equilibrium multi-scale procedure. Compared with the DBM at the
Navier-Stokes level [Gan et al., Soft Matter 11, 5336 (2015)], the proposed
model retrieves kinetic moments beyond the third-order super-Burnett level and
is accurate for weak, moderate, and strong THEN cases even when the local
Knudsen number exceeds $1/3$.


title:
Activation-Based Sampling for Pixel- to Image-Level Aggregation in
  Weakly-Supervised Segmentation

abstract:
  Classification networks can be used to localize and segment objects in images
by means of class activation maps (CAMs). However, without pixel-level
annotations, they are known to (1) mainly focus on discriminative regions, and
(2) to produce diffuse CAMs without well-defined prediction contours. In this
work, we approach both problems with two contributions for improving CAM
learning. First, we incorporate importance sampling based on the class-wise
probability mass function induced by the CAMs to produce stochastic image-level
class predictions. This results in CAMs which activate over a larger extent of
the objects. Second, we formulate a feature similarity loss term which aims to
match the prediction contours with edges in the image. As a third contribution,
we conduct experiments on the PASCAL VOC and MS-COCO benchmark datasets to
demonstrate that these modifications significantly increase the performance in
terms of contour accuracy, while being comparable to current state-of-the-art
methods in terms of region similarity.


title: An Exploratory Study of Stock Price Movements from Earnings Calls

abstract:
  Financial market analysis has focused primarily on extracting signals from
accounting, stock price, and other numerical hard data reported in P&L
statements or earnings per share reports. Yet, it is well-known that the
decision-makers routinely use soft text-based documents that interpret the hard
data they narrate. Recent advances in computational methods for analyzing
unstructured and soft text-based data at scale offer possibilities for
understanding financial market behavior that could improve investments and
market equity. A critical and ubiquitous form of soft data are earnings calls.
Earnings calls are periodic (often quarterly) statements usually by CEOs who
attempt to influence investors' expectations of a company's past and future
performance. Here, we study the statistical relationship between earnings
calls, company sales, stock performance, and analysts' recommendations. Our
study covers a decade of observations with approximately 100,000 transcripts of
earnings calls from 6,300 public companies from January 2010 to December 2019.
In this study, we report three novel findings. First, the buy, sell and hold
recommendations from professional analysts made prior to the earnings have low
correlation with stock price movements after the earnings call. Second, using
our graph neural network based method that processes the semantic features of
earnings calls, we reliably and accurately predict stock price movements in
five major areas of the economy. Third, the semantic features of transcripts
are more predictive of stock price movements than sales and earnings per share,
i.e., traditional hard data in most of the cases.


title:
Intralayer and interlayer synchronization in multiplex network with
  higher-order interactions

abstract:
  Recent developments in complex systems have witnessed that many real-world
scenarios, successfully represented as networks are not always restricted to
binary interactions but often include higher-order interactions among the
nodes. These beyond pairwise interactions are preferably modeled by
hypergraphs, where hyperedges represent higher-order interactions between a set
of nodes. In this work, we consider a multiplex network where the intralayer
connections are represented by hypergraphs, called multiplex hypergraph. The
hypergraph is constructed by mapping the maximal cliques of a scale-free
network to hyperedges of suitable sizes. We investigate the intralayer and
interlayer synchronization of such multiplex structures. Our study unveils that
the intralayer synchronization appreciably enhances when the higher-order
structure is taken into consideration inspite of only pairwise connections. We
derive the necessary condition for stable synchronization states by the master
stability function approach, which perfectly agrees with the numerical results.
We also explore the robustness of interlayer synchronization and find that for
the multiplex structures with many-body interaction, the interlayer
synchronization is more persistent than multiplex networks with pairwise
interaction.


title: Ergodic theory of multi-layer interacting particle systems

abstract:
  We consider a class of multi-layer interacting particle systems and
characterize the set of ergodic measures with finite moments. The main
technical tool is duality combined with successful coupling.


title:
Implementation of synthetic fast-ion loss detector and imaging heavy ion
  beam probe diagnostics in the 3D hybrid kinetic-MHD code MEGA

abstract:
  A synthetic Fast-Ion Loss Detector (FILD) and an imaging Heavy Ion Beam Probe
(i-HIBP) have been implemented in the 3D hybrid kinetic-magnetohydrodynamic
code MEGA. First synthetic measurements from these two diagnostics have been
obtained for neutral beam injection (NBI) driven Alfv\'en Eigenmode (AE)
simulated with MEGA. The synthetic fast-ion losses show a strong correlation
with the AE amplitude. This correlation is observed in the phase-space,
represented in coordinates toroidal canonical momentum and energy. Fast-ion
losses and the energy exchange diagrams of the confined population are
connected with lines of constant E' , a linear combination of E and P{\phi} .
First i-HIBP synthetic signals also have been computed for the simulated AE,
showing displacements in the strikeline of the order of around 1 mm, above the
expected resolution in the i-HIBP scintillator of approximately 100 {\mu}m.


title: Two-sample nonparametric test for proportional reversed hazards

abstract:
  Several works have been undertaken in the context of proportional reversed
hazard rate (PRHR) since last few decades. But any specific statistical
methodology for the PRHR hypothesis is absent in the literature. In this paper,
a two-sample nonparametric test based on two independent samples is proposed
for verifying the PRHR assumption. Based on a consistent U-statistic three
statistical methodologies have been developed exploiting U-statistics theory,
jackknife empirical likelihood and adjusted jackknife empirical likelihood
method. A simulation study has been performed to assess the merit of the
proposed test procedures. Finally, the test is applied to a data in the context
of brain injury-related biomarkers and a data related to Ducheme muscular
dystrophy.


title: Multi-agent Searching System for Medical Information

abstract:
  In the paper is proposed a model of multi-agent security system for searching
a medical information in Internet. The advantages when using mobile agent are
described, so that to perform searching in Internet. Nowadays, multi-agent
systems found their application into distribution of decisions. For modeling
the proposed multi-agent medical system is used JADE. Finally, the results when
using mobile agent are generated that could reflect performance when working
with BIG DATA. The proposed system is having also relatively high precision
96%.


title:
Which programming languages do hackers use? A survey at the German Chaos
  Computer Club

abstract:
  There are numerous articles about the programming languages most commonly
used by hackers. Among them, however, there are hardly any scientific studies.
One reason might be that hackers mainly operate anonymously and are difficult
to reach. This paper aims to shed light on this interesting and relevant
research question. In order to find answers, we conducted a survey among the
members of the German Chaos Computer Club. As one of the world's largest
organisations for information security and hacking, the club provides a good
basis for our study. We examine the question of which programming languages are
used by hackers as well as the importance of the programming language for their
work. The paper offers first insights into the topic and can provide a starting
point for further research.


title: A Lower-bound for Variable-length Source Coding in LQG Feedback Control

abstract:
  In this letter, we consider a Linear Quadratic Gaussian (LQG) control system
where feedback occurs over a noiseless binary channel and derive lower bounds
on the minimum communication cost (quantified via the channel bitrate) required
to attain a given control performance. We assume that at every time step an
encoder can convey a packet containing a variable number of bits over the
channel to a decoder at the controller. Our system model provides for the
possibility that the encoder and decoder have shared randomness, as is the case
in systems using dithered quantizers. We define two extremal prefix-free
requirements that may be imposed on the message packets; such constraints are
useful in that they allow the decoder, and potentially other agents to uniquely
identify the end of a transmission in an online fashion. We then derive a lower
bound on the rate of prefix-free coding in terms of directed information; in
particular we show that a previously known bound still holds in the case with
shared randomness. We also provide a generalization of the bound that applies
if prefix-free requirements are relaxed. We conclude with a rate-distortion
formulation.

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


title: The VoicePrivacy 2022 Challenge Evaluation Plan

abstract:
  For new participants - Executive summary: (1) The task is to develop a voice
anonymization system for speech data which conceals the speaker's voice
identity while protecting linguistic content, paralinguistic attributes,
intelligibility and naturalness. (2) Training, development and evaluation
datasets are provided in addition to 3 different baseline anonymization
systems, evaluation scripts, and metrics. Participants apply their developed
anonymization systems, run evaluation scripts and submit objective evaluation
results and anonymized speech data to the organizers. (3) Results will be
presented at a workshop held in conjunction with INTERSPEECH 2022 to which all
participants are invited to present their challenge systems and to submit
additional workshop papers.
  For readers familiar with the VoicePrivacy Challenge - Changes w.r.t. 2020:
(1) A stronger, semi-informed attack model in the form of an automatic speaker
verification (ASV) system trained on anonymized (per-utterance) speech data.
(2) Complementary metrics comprising the equal error rate (EER) as a privacy
metric, the word error rate (WER) as a primary utility metric, and the pitch
correlation and gain of voice distinctiveness as secondary utility metrics. (3)
A new ranking policy based upon a set of minimum target privacy requirements.


title:
3D Adapted Random Forest Vision (3DARFV) for Untangling
  Heterogeneous-Fabric Exceeding Deep Learning Semantic Segmentation Efficiency
  at the Utmost Accuracy

abstract:
  Planetary exploration depends heavily on 3D image data to characterize the
static and dynamic properties of the rock and environment. Analyzing 3D images
requires many computations, causing efficiency to suffer lengthy processing
time alongside large energy consumption. High-Performance Computing (HPC)
provides apparent efficiency at the expense of energy consumption. However, for
remote explorations, the conveyed surveillance and the robotized sensing need
faster data analysis with ultimate accuracy to make real-time decisions. In
such environments, access to HPC and energy is limited. Therefore, we realize
that reducing the number of computations to optimal and maintaining the desired
accuracy leads to higher efficiency. This paper demonstrates the semantic
segmentation capability of a probabilistic decision tree algorithm, 3D Adapted
Random Forest Vision (3DARFV), exceeding deep learning algorithm efficiency at
the utmost accuracy.

categories: cs.CV astro-ph.IM cs.LG eess.IV physics.geo-ph


title: On Factors with Prescribed Degrees in Bipartite Graphs

abstract:
  We establish a new criterion for a bigraph to have a subgraph with prescribed
degree conditions. We show that the bigraph $G[X,Y]$ has a spanning subgraph
$F$ such that $g(x)\leq deg_F(x) \leq f(x)$ for $x\in X$ and $deg_F(y) \leq
f(y)$ for $y\in Y$ if and only if $\sum\nolimits_{b\in B} f(b)\geq
\sum\nolimits_{a\in A} \max \big\{0, g(a) - deg_{G-B}(a)\big\}$ for $A\subseteq
X, B\subseteq Y$. Using Folkman-Fulkerson's Theorem, Cymer and Kano found a
different criterion for the existence of such a subgraph (Graphs Combin. 32
(2016), 2315--2322). Our proof is self-contained and relies on alternating path
technique. As an application, we prove the following extension of Hall's
theorem. A bigraph $G[X,Y]$ in which each edge has multiplcity at least $m$ has
a subgraph $F$ with $g(x)\leq deg_F(x)\leq f(x)\leq deg(x)$ for $x\in X$,
$deg_F(y)\leq m$ for $y\in Y$ if and only if $\sum_{y\in N_G(S)}f(y)\geq
\sum_{x\in S}g(x)$ for $S\subseteq X$.


title:
Data Analysis using Riemannian Geometry and Applications to Chemical
  Engineering

abstract:
  We explore the use of tools from Riemannian geometry for the analysis of
symmetric positive definite matrices (SPD). An SPD matrix is a versatile data
representation that is commonly used in chemical engineering (e.g.,
covariance/correlation/Hessian matrices and images) and powerful techniques are
available for its analysis (e.g., principal component analysis). A key
observation that motivates this work is that SPD matrices live on a Riemannian
manifold and that implementing techniques that exploit this basic property can
yield significant benefits in data-centric tasks such classification and
dimensionality reduction. We demonstrate this via a couple of case studies that
conduct anomaly detection in the context of process monitoring and image
analysis.


title: Planning Landscape Analysis for Self-Adaptive Systems

abstract:
  To assure performance on the fly, planning is arguably one of the most
important steps for self-adaptive systems (SASs), especially when they are
highly configurable with a daunting number of adaptation options. However,
there has been little understanding of the planning landscape or ways by which
it can be analyzed. This inevitably creates barriers to the design of better
and tailored planners for SASs. In this paper, we showcase how the planning
landscapes of SASs can be quantified and reasoned, particularly with respect to
the different environments. By studying four diverse real-world SASs and 14
environments, we found that (1) the SAS planning landscapes often provide
strong guidance to the planner, but their ruggedness and multi-modality can be
the major obstacle; (2) the extents of guidance and number of global/local
optima are sensitive to the changing environment, but not the ruggedness of the
surface; (3) the local optima are often closer to the global optimum than other
random points; and (4) there are considerable (and useful) overlaps on the
global/local optima between landscapes under different environments. We then
discuss the potential implications to the future work of planner designs for
SASs.


title: Improved Lieb-Oxford bound on the indirect and exchange energies

abstract:
  The Lieb-Oxford inequality provides a lower bound on the Coulomb energy of a
classical system of $N$ identical charges only in terms of their one-particle
density. We prove here a new estimate on the best constant in this inequality.
Numerical evaluation provides the value 1.58, which is a significant
improvement to the previously known value 1.64. The best constant has recently
been shown to be larger than 1.44. In a second part, we prove that the constant
can be reduced to 1.25 when the inequality is restricted to Hartree-Fock
states. This is the first proof that the exchange term is always much lower
than the full indirect Coulomb energy.

categories: math-ph cond-mat.stat-mech math.MP physics.chem-ph


title:
Separating the Wheat from the Chaff: Bayesian Regularization in Dynamic
  Social Networks

abstract:
  In recent years there has been an increasing interest in the use of
relational event models for dynamic social network analysis. The basis of these
models is the concept of an "event", defined as a triplet of time, sender, and
receiver of some social interaction. The key question that relational event
models aim to answer is what drives social interactions among actors.
Researchers often consider a very large number of predictors in their studies
(including exogenous variables, endogenous network effects, and various
interaction effects). The problem is however that employing an excessive number
of effects may lead to model overfitting and inflated Type-I error rates.
Consequently, the fitted model can easily become overly complex and the implied
social interaction behavior becomes difficult to interpret. A potential
solution to this problem is to apply Bayesian regularization using shrinkage
priors. In this paper, we propose Bayesian regularization methods for
relational event models using four different priors: a flat prior model with no
shrinkage effect, a ridge estimator with a normal prior, a Bayesian lasso with
a Laplace prior, and a horseshoe estimator with a numerically constructed prior
that has an asymptote at zero. We develop and use these models for both an
actor-oriented relational event model and a dyad-oriented relational event
model. We show how to apply Bayesian regularization methods for these models
and provide insights about which method works best and guidelines how to apply
them in practice. Our results show that shrinkage priors can reduce Type-I
errors while keeping reasonably high predictive performance and yielding
parsimonious models to explain social network behavior.


title:
A universal lower bound for certain quadratic integrals of automorphic
  L-functions

abstract:
  We obtain uniform lower bounds, true for all automorphic L-functions L(s)
associated to cuspidal representations of GL(m,A) where A denotes the adeles of
the rationals Q, of the integral on the vertical line (Re(s)=1/2) of the
absolute value squared of L(s)/s; and also of L(s)/(s-s_0) when s_0 is a zero
of the L-function on the critical line. Several variants are also obtained in
small degrees m, for the vertical integrals at different abscissas in the
critical strip. For the estimates required to prove convergence, we are led to
generalise a result of Friedlander-Iwaniec (Can. J. Math. 57,2005). We obtain
new results on the abscissa of convergence of the L-series. Finally, a problem
is posed about the behaviour of the quadratic integral when s_0 tends to
infinity, in particular for the Riemann zeta function.


title:
Adaptively Re-weighting Multi-Loss Untrained Transformer for Sparse-View
  Cone-Beam CT Reconstruction

abstract:
  Cone-Beam Computed Tomography (CBCT) has been proven useful in diagnosis, but
how to shorten scanning time with lower radiation dosage and how to efficiently
reconstruct 3D image remain as the main issues for clinical practice. The
recent development of tomographic image reconstruction on sparse-view
measurements employs deep neural networks in a supervised way to tackle such
issues, whereas the success of model training requires quantity and quality of
the given paired measurements/images. We propose a novel untrained Transformer
to fit the CBCT inverse solver without training data. It is mainly comprised of
an untrained 3D Transformer of billions of network weights and a multi-level
loss function with variable weights. Unlike conventional deep neural networks
(DNNs), there is no requirement of training steps in our approach. Upon
observing the hardship of optimising Transformer, the variable weights within
the loss function are designed to automatically update together with the
iteration process, ultimately stabilising its optimisation. We evaluate the
proposed approach on two publicly available datasets: SPARE and Walnut. The
results show a significant performance improvement on image quality metrics
with streak artefact reduction in the visualisation. We also provide a clinical
report by an experienced radiologist to assess our reconstructed images in a
diagnosis point of view. The source code and the optimised models are available
from the corresponding author on request at the moment.


title:
Approximating elements of the middle third Cantor set with dyadic
  rationals

abstract:
  Let $C$ be the middle third Cantor set and $\mu$ be the $\frac{\log 2}{\log
3}$-dimensional Hausdorff measure restricted to $C$. In this paper we study
approximations of elements of $C$ by dyadic rationals. Our main result implies
that for $\mu$ almost every $x\in C$ we have $$\#\left\{1\leq n\leq
N:\left|x-\frac{p}{2^n}\right| \leq \frac{1}{n^{0.01}\cdot 2^{n}}\textrm{ for
some }p\in\mathbb{N}\right\}\sim 2\sum_{n=1}^{N}n^{-0.01}.$$ This improves upon
a recent result of Allen, Chow, and Yu which gives a sub-logarithmic
improvement over the trivial approximation rate.


title: Monoidal Reverse Differential Categories

abstract:
  Cartesian reverse differential categories (CRDCs) are a recently defined
structure which categorically model the reverse differentiation operations used
in supervised learning. Here we define a related structure called a monoidal
reverse differential category, prove important results about its relationship
to CRDCs, and provide examples of both structures, including examples coming
from models of quantum computation.


title: Set of uniqueness for Cantorvals

abstract:
  The main result is that the celebrated Guthrie-Nymann's Cantorval has
comeager set of uniqueness. On the other hand many other Cantorvals have meager
set of uniqueness.


title:
Diagrammatic quantum Monte Carlo toward the calculation of transport
  properties in disordered semiconductors

abstract:
  A new diagrammatic quantum Monte Carlo approach is proposed to deal with the
imaginary time propagator involving both dynamic disorder (i.e.,
electron-phonon interactions) and static disorder of local or nonlocal nature
in a unified and numerically exact way. The establishment of the whole
framework relies on a general reciprocal-space expression and a generalized
Wick's theorem for the static disorder. Since the numerical cost is independent
of the system size, various physical quantities such as the thermally averaged
coherence, Matsubara one-particle Green's function and current autocorrelation
function can be efficiently evaluated in the thermodynamic limit (infinite in
the system size). The validity and performance of the proposed approach are
systematically examined in a broad parameter regimes. This approach, combined
with proper numerical analytic continuation methods and first-principles
calculations, is expected to be a versatile tool toward the calculation of
various transport properties like mobilities in realistic semiconductors
involving multiple electronic energy bands, high-frequency optical and
low-frequency acoustic phonons, different forms of dynamic and static
disorders, anisotropy, etc.


title:
Prompt-based Pre-trained Model for Personality and Interpersonal
  Reactivity Prediction

abstract:
  This paper describes our proposed method for the Workshop on Computational
Approaches to Subjectivity, Sentiment & Social Media Analysis (WASSA) 2022
shared task on Personality Prediction (PER) and Reactivity Index Prediction
(IRI). In this paper, we adopt the prompt-based learning method with the
pre-trained language model to accomplish these tasks. Specifically, the prompt
is designed to provide knowledge of the extra personalized information for
enhancing the pre-trained model. Data augmentation and model ensemble are
adopted for obtaining better results. Moreover, we also provided the online
software demonstration and the codes of the software for further research.


title: A Deep Learning Framework to Reconstruct Face under Mask

abstract:
  While deep learning-based image reconstruction methods have shown significant
success in removing objects from pictures, they have yet to achieve acceptable
results for attributing consistency to gender, ethnicity, expression, and other
characteristics like the topological structure of the face. The purpose of this
work is to extract the mask region from a masked image and rebuild the area
that has been detected. This problem is complex because (i) it is difficult to
determine the gender of an image hidden behind a mask, which causes the network
to become confused and reconstruct the male face as a female or vice versa;
(ii) we may receive images from multiple angles, making it extremely difficult
to maintain the actual shape, topological structure of the face and a natural
image; and (iii) there are problems with various mask forms because, in some
cases, the area of the mask cannot be anticipated precisely; certain parts of
the mask remain on the face after completion. To solve this complex task, we
split the problem into three phases: landmark detection, object detection for
the targeted mask area, and inpainting the addressed mask region. To begin, to
solve the first problem, we have used gender classification, which detects the
actual gender behind a mask, then we detect the landmark of the masked facial
image. Second, we identified the non-face item, i.e., the mask, and used the
Mask R-CNN network to create the binary mask of the observed mask area.
Thirdly, we developed an inpainting network that uses anticipated landmarks to
create realistic images. To segment the mask, this article uses a mask R-CNN
and offers a binary segmentation map for identifying the mask area.
Additionally, we generated the image utilizing landmarks as structural guidance
through a GAN-based network. The studies presented in this paper use the FFHQ
and CelebA datasets.


title:
Geometry of finite-time thermodynamic cycles with anisotropic thermal
  fluctuations

abstract:
  In contrast to the classical concept of a Carnot engine that alternates
contact between heat baths of different temperatures, naturally occurring
processes usually harvest energy from anisotropy, being exposed simultaneously
to chemical and thermal fluctuations of different intensities. In these cases,
the enabling mechanism responsible for transduction of energy is the presence
of a non-equilibrium steady state (NESS). A suitable stochastic model for such
a phenomenon is the Brownian gyrator -- a two-degree of freedom stochastically
driven system that exchanges energy and heat with the environment. In this
context, we present a geometric view of the energy harvesting mechanism, from a
stochastic control perspective, that entails a forced periodic trajectory of
the system state on the thermodynamic manifold. Dissipation and work output are
expressed accordingly as path integrals of a controlled process, and
fundamental limitations on power and efficiency are expressed in geometric
terms via a relationship to an isoperimetric problem. The theory is presented
for high-order systems far from equilibrium and beyond the linear response
regime.


title:
Reconciling Power Law Slopes in Solar Flare and Nanoflare Size
  Distributions

abstract:
  We unify the power laws of size distributions of solar flare and nanoflare
energies. We present three models that predict the power law slopes $\alpha_E$
of flare energies defined in terms of the 2-D and 3-D fractal dimensions ($D_A,
D_V$): (i) The spatio-temporal standard SOC model, defined by the power law
slope $\alpha_{E1}=1+2/(D_V+2)=(13/9)\approx 1.44$; (ii) the 2-D thermal energy
model, $\alpha_{E2}=1+2/D_A=(7/3)\approx 2.33$, and (iii) the 3-D thermal
energy model, $\alpha_{E3}=1+2/D_V=(9/5)\approx 1.80$. The theoretical
predictions of energies are consistent with the observational values of these
three groups, i.e., $\alpha_{E1}=1.47 \pm 0.07$; $\alpha_{E2}=2.38 \pm 0.09$,
and $\alpha_{E3}=1.80 \pm 0.18$. These results corroborate that the energy of
nanoflares does not diverge at small energies, since $(\alpha_{E1}<2$) and
$(\alpha_{E3}<2)$, except for the unphyiscal 2-D model $(\alpha_{E2}>2)$. This
conclusion adds an additional argument against the scenario of coronal heating
by nanoflares.


title: CroMo: Cross-Modal Learning for Monocular Depth Estimation

abstract:
  Learning-based depth estimation has witnessed recent progress in multiple
directions; from self-supervision using monocular video to supervised methods
offering highest accuracy. Complementary to supervision, further boosts to
performance and robustness are gained by combining information from multiple
signals. In this paper we systematically investigate key trade-offs associated
with sensor and modality design choices as well as related model training
strategies. Our study leads us to a new method, capable of connecting
modality-specific advantages from polarisation, Time-of-Flight and
structured-light inputs. We propose a novel pipeline capable of estimating
depth from monocular polarisation for which we evaluate various training
signals. The inversion of differentiable analytic models thereby connects scene
geometry with polarisation and ToF signals and enables self-supervised and
cross-modal learning. In the absence of existing multimodal datasets, we
examine our approach with a custom-made multi-modal camera rig and collect
CroMo; the first dataset to consist of synchronized stereo polarisation,
indirect ToF and structured-light depth, captured at video rates. Extensive
experiments on challenging video scenes confirm both qualitative and
quantitative pipeline advantages where we are able to outperform competitive
monocular depth estimation method.


title: An Algorithmic Introduction to Savings Circles

abstract:
  Rotating savings and credit associations (roscas) are informal financial
organizations common in settings where communities have reduced access to
formal financial institutions. In a rosca, a fixed group of participants
regularly contribute sums of money to a pot. This pot is then allocated
periodically using lottery, aftermarket, or auction mechanisms. Roscas are
empirically well-studied in economics. They are, however, challenging to study
theoretically due to their dynamic nature. Typical economic analyses of roscas
stop at coarse ordinal welfare comparisons to other credit allocation
mechanisms, leaving much of roscas' ubiquity unexplained. In this work, we take
an algorithmic perspective on the study of roscas. Building on techniques from
the price of anarchy literature, we present worst-case welfare approximation
guarantees. We further experimentally compare the welfare of outcomes as key
features of the environment vary. These cardinal welfare analyses further
rationalize the prevalence of roscas. We conclude by discussing several other
promising avenues.


title: A Context-Aware Feature Fusion Framework for Punctuation Restoration

abstract:
  To accomplish the punctuation restoration task, most existing approaches
focused on leveraging extra information (e.g., part-of-speech tags) or
addressing the class imbalance problem. Recent works have widely applied the
transformer-based language models and significantly improved their
effectiveness. To the best of our knowledge, an inherent issue has remained
neglected: the attention of individual heads in the transformer will be diluted
or powerless while feeding the long non-punctuation utterances. Since those
previous contexts, not the followings, are comparatively more valuable to the
current position, it's hard to achieve a good balance by independent attention.
In this paper, we propose a novel Feature Fusion framework based on two-type
Attentions (FFA) to alleviate the shortage. It introduces a two-stream
architecture. One module involves interaction between attention heads to
encourage the communication, and another masked attention module captures the
dependent feature representation. Then, it aggregates two feature embeddings to
fuse information and enhances context-awareness. The experiments on the popular
benchmark dataset IWSLT demonstrate that our approach is effective. Without
additional data, it obtains comparable performance to the current
state-of-the-art models.


title: Well-posedness for magnetoviscoelastic fluids in 3D

abstract:
  We show that the system of equations describing a magnetoviscoelastic fluid
in three dimensions can be cast as a quasilinear parabolic system. Using the
theory of maximal $L_p$-regularity, we establish existence and uniqueness of
local strong solutions and we show that each solution is smooth (in fact
analytic) in space and time. Moreover, we give a complete characterization of
the set of equilibria and show that solutions that start out close to an
equilibrium exist globally and converge to a (possibly different) equilibrium.
Finally, we show that every solution that is eventually bounded in the topology
of the state space exists globally and converges to an equilibrium.


title:
Dynamical robustness of complex networks subject to long-range
  connectivity

abstract:
  In spite of a few attempts in understanding the dynamical robustness of
complex networks, this extremely important subject of research is still in its
dawn as compared to the other dynamical processes on networks. We hereby
consider the concept of long-range interactions among the dynamical units of
complex networks and demonstrate for the first time that such a characteristic
can have noteworthy impacts on the dynamical robustness of networked systems,
regardless of the underlying network topology. We present a comprehensive
analysis of this phenomenon on top of diverse network architectures. Such
dynamical damages being able to substantially affect the network performance,
determining mechanisms that boost the robustness of networks becomes a
fundamental question. In this work, we put forward a prescription based upon
self-feedback that can efficiently resurrect global rhythmicity of complex
networks composed of active and inactive dynamical units, and thus can enhance
the network robustness. We have been able to delineate the whole proposition
analytically while dealing with all d-path adjacency matrices, having an
excellent agreement with the numerical results. For the numerical computations,
we examine scale-free networks, Watts-Strogatz small world model and also
Erd\"os-R\'enyi random network, along with Landau-Stuart oscillators for
casting the local dynamics.


title: Towards constructivising the Freyd-Mitchell embedding theorem

abstract:
  The aim of the paper is to first point out that the classical proof of the
Freyd-Mitchell Embedding Theorem does not work in CZF; then, to propose an
alternative embedding of a small abelian category into the category of sheaves
of modules over a ringed space, which works constructively. It is necessary to
mention that this work has been initially inspired by Erik Palmgren, who
unexpectedly passed away in November 2019: I'm very grateful to him for having
shared with me his intuitions, and for having supervised the realization of the
first half of the paper.


title:
A fast randomized algorithm for computing a hybrid CUR-type Tucker
  decomposition

abstract:
  The paper develops a fast randomized algorithm for computing a hybrid
CUR-type decomposition of tensors in the Tucker representation. Specifically,
to obtain the factor matrices, random sampling techniques are utilized to
accelerate the procedure of constructing the classical matrix decompositions,
that are, the interpolatory decomposition and singular value decomposition.
Compared with the non-random algorithm, the proposed algorithm has advantages
in speed with lower computational cost while keeping a high degree of accuracy.
We establish a detailed probabilistic error analysis for the algorithm and
provide numerical results that show the promise of our approach.


title: Balanced shifted tableaux

abstract:
  We introduce balanced shifted tableaux, as an analogue of balanced tableaux
of Edelman and Greene, from the perspective of root systems of type B and C. We
show that they are equinumerous to standard Young tableaux of the corresponding
shifted shape by presenting an explicit bijection.


title:
Reassessing the potential of TlCl for laser cooling experiments via
  four-component correlated electronic structure calculations

abstract:
  The TlCl molecule has previously been investigated theoretically and proposed
as promising candidates for laser cooling searches [X. Yuan et. al. J. Chem.
Phys., 149, 094306, 2018]. From these results, the cooling process, which would
proceed by transitions between a\sup{3}{\Pi}\sup{+}\sub{0} and
X\sup{1}{\Sigma}\sup{+}\sub{0} states, had as potential bottleneck the long
lifetime (6.04 {\mu}s) of the excited state a\sup{3}{\Pi}\sup{+}\sub{0} , that
would prohibit experimentally control the slowing region. Here, we revisit this
system by employing four-component Multireference Configuration Interaction
(MRCI) calculations, and investigate the effect of such approaches on the
calculated transition moments between a\sup{3}{\Pi}\sup{+}\sub{0} and
a\sup{3}{\Pi}\sub{1} excited states of TlCl as well as TlF, the latter serving
as a benchmark between theory and experiment. Wherever possible, MRCI results
have been cross-validated by, and turned out to be consistent with,
four-component equation of motion coupled-cluster (EOM-CC) and polarization
propagator (PP) calculations. We find that the results of TlF are very closed
to experiment values, while for TlCl the lifetime of the a3{\Pi}+0 state is now
estimated to be 175 ns, which is much shorter than previous calculations
indicated, thus yielding a different, more favorable cooling dynamics. By
solving the rate-equation numerically, we provide evidence that TlCl could have
cooling properties similar to those of TlF. Our investigations also point to
the potential benefits of enhancing the stimulated radiation in optical cycle
to improve cooling efficiency.


title:
The nonexistence of expansive polycyclic group actions on the circle
  $\mathbb S^1$

abstract:
  We show that the circle $\mathbb S^1$ admits no expansive polycyclic group
actions.


title:
On predictive inference for intractable models via approximate Bayesian
  computation

abstract:
  Approximate Bayesian computation (ABC) is commonly used for parameter
estimation and model comparison for intractable simulator-based models whose
likelihood function cannot be evaluated. In this paper we instead investigate
the feasibility of ABC as a generic approximate method for predictive
inference, in particular, for computing the posterior predictive distribution
of future observations or missing data of interest. We consider three
complementary ABC approaches for this goal, each based on different assumptions
regarding which predictive density of the intractable model can be sampled
from. The case where only simulation from the joint density of the observed and
future data given the model parameters can be used for inference is given
particular attention and it is shown that the ideal summary statistic in this
setting is minimal predictive sufficient instead of merely minimal sufficient
(in the ordinary sense). An ABC prediction approach that takes advantage of a
certain latent variable representation is also investigated. We additionally
show how common ABC sampling algorithms can be used in the predictive settings
considered. Our main results are first illustrated by using simple time-series
models that facilitate analytical treatment, and later by using two common
intractable dynamic models.


title: Quantum and Semi-Quantum Lottery: Strategies and Advantages

abstract:
  Lottery is a game in which multiple players take chances in the hope of
getting some rewards in cash or kind. In addition, from the time of the early
civilizations, lottery has also been considered as an apposite method to
allocate scarce resources. Technically, any scheme for lottery needs to be fair
and secure, but none of the classical schemes for lottery are unconditionally
secure and fair. As fairness demands complete unpredictability of the outcome
of the lottery, it essentially requires perfect randomness. Quantum mechanics
not only guarantees the generation of perfect randomness, it can also provide
unconditional security. Motivated by these facts, a set of strategies for
performing lottery using different type of quantum resources (e.g., single
photon states, and entangled states) are proposed here, and it's established
that the proposed strategies leads to unconditionally secure and fair lottery
schemes. A scheme for semi-quantum lottery that allows some classical users to
participate in the lottery involving quantum resources is also proposed and the
merits and demerits of all the proposed schemes are critically analysed. Its
also established that the level of security is intrinsically related to the
type of quantum resources being utilized. Further, its shown that the proposed
schemes can be experimentally realized using currently available technology,
and that may herald a new era of commercial lottery.


title: Quantum-enhanced Markov chain Monte Carlo

abstract:
  Sampling from complicated probability distributions is a hard computational
problem arising in many fields, including statistical physics, optimization,
and machine learning. Quantum computers have recently been used to sample from
complicated distributions that are hard to sample from classically, but which
seldom arise in applications. Here we introduce a quantum algorithm to sample
from distributions that pose a bottleneck in several applications, which we
implement on a superconducting quantum processor. The algorithm performs Markov
chain Monte Carlo (MCMC), a popular iterative sampling technique, to sample
from the Boltzmann distribution of classical Ising models. In each step, the
quantum processor explores the model in superposition to propose a random move,
which is then accepted or rejected by a classical computer and returned to the
quantum processor, ensuring convergence to the desired Boltzmann distribution.
We find that this quantum algorithm converges in fewer iterations than common
classical MCMC alternatives on relevant problem instances, both in simulations
and experiments. It therefore opens a new path for quantum computers to solve
useful--not merely difficult--problems in the near term.


title: Effective decoupling of ferromagnetic sublattices by frustration

abstract:
  Magnetic frustration in ferromagnetic metallic systems is unusual due to the
long-range and symmetric nature of the exchange interactions. In this work we
prove that it is possible to obtain a highly frustrated ferromagnetic phase in
a multi-sublattices cubic structure through a fine tuning of the magnetic
interactions. This peculiar state is achieved in Ni-Mn-(In, Sn) Heusler alloys
and results in the effective decoupling of their two intertwined ferromagnetic
sublattices. One sublattice is ferromagnetic long range ordered below the
macroscopic Curie temperature (TC ) whereas the second one remains disordered
until a crossover to a polarized state occurs at T << TC . This result points
out that a fine engineering of the magnetic interactions in metallic systems
can lead to interesting novel and emergent phenomena.


title:
An Example of the SAM+ Algorithm for Learning Action Models for
  Stochastic Worlds

abstract:
  In this technical report, we provide a complete example of running the SAM+
algorithm, an algorithm for learning stochastic planning action models, on a
simplified PPDDL version of the Coffee problem. We provide a very brief
description of the SAM+ algorithm and detailed description of our simplified
version of the Coffee domain, and then describe the results of running it on
the simplified Coffee domain.


title: Hamiltonian perturbations in contact Floer homology

abstract:
  We study the contact Floer homology ${\rm HF}_*(W, h)$ introduced by
Merry-Uljarevi\'c, which associates a Floer-type homology theory to a Liouville
domain $W$ and a contact Hamiltonian $h$ on its boundary. The main results
investigate the behavior of ${\rm HF}_*(W, h)$ under the perturbations of the
input contact Hamiltonian $h$. In particular, we provide sufficient conditions
that guarantee ${\rm HF}_*(W, h)$ to be invariant under the perturbations. This
can be regarded as a contact geometry analogue of the continuation and
bifurcation maps along the Hamiltonian perturbations of Hamiltonian Floer
homology in symplectic geometry. As an application, we give an algebraic proof
of a rigidity result concerning the positive loops of contactomorphisms for a
wide class of contact manifolds.


title: Quantum Logic Enhanced Sensing in Solid-State Spin Ensembles

abstract:
  We demonstrate quantum logic enhanced sensitivity for a macroscopic ensemble
of solid-state, hybrid two-qubit sensors. We achieve a factor of 30 improvement
in signal-to-noise ratio, translating to a sensitivity enhancement exceeding an
order of magnitude. Using the electronic spins of nitrogen vacancy (NV) centers
in diamond as sensors, we leverage the on-site nitrogen nuclear spins of the NV
centers as memory qubits, in combination with homogeneous bias and control
fields, ensuring that all of the ${\sim}10^9$ two-qubit sensors are
sufficiently identical to permit global control of the NV ensemble spin states.
We find quantum logic sensitivity enhancement for multiple measurement
protocols with varying optimal sensing intervals, including XY8 dynamical
decoupling and correlation spectroscopy, using a synthetic AC magnetic field.
The results are independent of the nature of the target signal and broadly
applicable to metrology using NV centers and other solid-state ensembles. This
work provides a benchmark for macroscopic ensembles of quantum sensors that
employ quantum logic or quantum error correction algorithms for enhanced
sensitivity.


title:
Block-Level Interference Exploitation Precoding without Symbol-by-Symbol
  Optimization

abstract:
  Symbol-level precoding (SLP) based on the concept of constructive
interference (CI) is shown to be superior to traditional block-level precoding
(BLP), however at the cost of a symbol-by-symbol optimization during the
precoding design. In this paper, we propose a CI-based block-level precoding
(CI-BLP) scheme for the downlink transmission of a multi-user multiple-input
single-output (MU-MISO) communication system, where we design a constant
precoding matrix to a block of symbol slots to exploit CI for each symbol slot
simultaneously. A single optimization problem is formulated to maximize the
minimum CI effect over the entire block, thus reducing the computational cost
of traditional SLP as the optimization problem only needs to be solved once per
block. By leveraging the Karush-Kuhn-Tucker (KKT) conditions and the dual
problem formulation, the original optimization problem is finally shown to be
equivalent to a quadratic programming (QP) over a simplex. Numerical results
validate our derivations and exhibit superior performance for the proposed
CI-BLP scheme over traditional BLP and SLP methods, thanks to the relaxed
block-level power constraint.


title: The Tur\'an number of the Cartesian product of graphs

abstract:
  Recently, Domagoj Brada\v{c}, Oliver Janzer, Benny Sudakov and Istv\'an Tomon
have proved that the Tur\'an number of $2$-dimensional grids is
$\Theta(n^{3/2})$, or more general,
$\mathrm{ex}\left(n,T\square{P}\right)=\Theta(n^{3/2})$, where $T$ is a
non-trivial tree, $P$ is a non-trivial path, and $T\square{P}$ denotes the
Cartesian product. In their proof, they exhibited a novel way of using the
tensor power trick, which has lots of potential in Tur\'an type problems. By
the end of their proof, they conjectured that
$\mathrm{ex}\left(n,T\square{R}\right)=\Theta(n^{3/2})$ for non-trivial trees
$T$ and $R$. This paper is an extension based on their work, we successfully
prove the above conjecture by adapting their approach.


title:
Author Multidisciplinarity and Disciplinary Roles in Field of Study
  Networks

abstract:
  When studying large research corpora, "distant reading" methods are vital to
understand the topics and trends in the corresponding research space. In
particular, given the recognised benefits of multidisciplinary research, it may
be important to map schools or communities of diverse research topics, and to
understand the multidisciplinary role that topics play within and between these
communities. This work proposes Field of Study (FoS) networks as a novel
network representation for use in scientometric analysis. We describe the
formation of FoS networks, which relate research topics according to the
authors who publish in them, from corpora of articles in which fields of study
can be identified. FoS networks are particularly useful for the distant reading
of large datasets of research papers when analysed through the lens of
exploring multidisciplinary science. In an evolving scientific landscape,
modular communities in FoS networks offer an alternative categorisation
strategy for research topics and sub-disciplines, when compared to traditional
prescribed discipline classification schemes. Furthermore, structural role
analysis of FoS networks can highlight important characteristics of topics in
such communities. To support this, we present two case studies which explore
multidisciplinary research in corpora of varying size and scope; namely, 6,323
articles relating to network science research and 4,184,011 articles relating
to research on the COVID-19-pandemic.


title:
A Spatial-Temporal Attention Multi-Graph Convolution Network for
  Ride-Hailing Demand Prediction Based on Periodicity with Offset

abstract:
  Ride-hailing service is becoming a leading part in urban transportation. To
improve the efficiency of ride-hailing service, accurate prediction of
transportation demand is a fundamental challenge. In this paper, we tackle this
problem from both aspects of network structure and data-set formulation. For
network design, we propose a spatial-temporal attention multi-graph convolution
network (STA-MGCN). A spatial-temporal layer in STA-MGCN is developed to
capture the temporal correlations by temporal attention mechanism and temporal
gate convolution, and the spatial correlations by multigraph convolution. A
feature cluster layer is introduced to learn latent regional functions and to
reduce the computation burden. For the data-set formulation, we develop a novel
approach which considers the transportation feature of periodicity with offset.
Instead of only using history data during the same time period, the history
order demand in forward and backward neighboring time periods from yesterday
and last week are also included. Extensive experiments on the three real-world
datasets of New-York, Chicago and Chengdu show that the proposed algorithm
achieves the state-of-the-art performance for ride-hailing demand prediction.


title:
The interplay between diversity and noise in an excitable cell network
  model

abstract:
  We study the interplay between diversity and noise in a 3D network of
FitzHugh-Nagumo elements, with topology and dimensions chosen to model a
pancreatic beta-cell cluster, as an example of an excitable cell network. Our
results show that diversity and noise are not equivalent sources of disorder
but have different effects on network dynamics. Their synchronization
mechanisms may act independently of one another or synergistically, depending
on the mean value of the diversity distribution compared to the intrinsic
oscillatory range of the network elements.


title:
Kinetic phase diagram for nucleation and growth of competing crystal
  polymorphs in charged colloidal suspensions

abstract:
  We determine the full kinetic phase diagram for nucleation and growth of
crystal phases in a suspension of charged colloids. We calculate nucleation
barrier heights for face-centered cubic (fcc) and body-centered cubic (bcc)
crystal phases for varying screening lengths and supersaturations using the
seeding approach in extensive simulations. Using classical nucleation theory,
we determine for the entire metastable fluid region the crystal polymorph with
the lowest nucleation barrier. Surprisingly, we find a regime close to the
triple point where metastable bcc can form due to a lower nucleation barrier,
even though fcc is the stable phase. For higher supersaturation, we find that
the difference in barrier heights decreases and we observe a mix of hexagonal
close-packed (hcp), fcc and bcc structures in the growth of crystalline seeds
as well as in spontaneously formed crystals.


title:
Photoredox Processes in the Aggregation and Gelation of
  Electron-responsive Supramolecular Polymers Based on Viologens

abstract:
  Viologen-based ditopic bis-pyridinyl-triazole bidentate ligands self-assemble
in the presence of palladium ions into supramolecular polymers whose structure
is imposed by the directed formation of coordination bonds. Light-irradiation
of these electron-responsive supramolecular materials triggers a photo-induced
electron transfer yielding isolated {\pi}-radicals and dimers of radicals. The
photoreduction events and the associated dimerization steps trigger a
large-scale reorganization occurring within the supramolecular network yielding
aggregates or gels depending on the irradiation conditions (power, duration).
Detailed electrochemical, spectro-electrochemical and photochemical analyses
were conducted to understand the mechanisms at stakes in these light-induced
aggregation and gelation.


title:
Double Negative Control Inference in Test-Negative Design Studies of
  Vaccine Effectiveness

abstract:
  The test-negative design (TND) has become a standard approach to evaluate
vaccine effectiveness against the risk of acquiring infectious diseases in
real-world settings, such as Influenza, Rotavirus, Dengue fever, and more
recently COVID-19. In a TND study, individuals who experience symptoms and seek
care are recruited and tested for the infectious disease which defines cases
and controls. Despite TND's potential to reduce unobserved differences in
healthcare seeking behavior (HSB) between vaccinated and unvaccinated subjects,
it remains subject to various potential biases. First, residual confounding
bias may remain due to unobserved HSB, occupation as healthcare worker, or
previous infection history. Second, because selection into the TND sample is a
common consequence of infection and HSB, collider stratification bias may exist
when conditioning the analysis on testing, which further induces confounding by
latent HSB. In this paper, we present a novel approach to identify and estimate
vaccine effectiveness in the target population by carefully leveraging a pair
of negative control exposure and outcome variables to account for potential
hidden bias in TND studies. We illustrate our proposed method with extensive
simulation and an application to study COVID-19 vaccine effectiveness using
data from the University of Michigan Health System.


title:
Exact formula and asymptotic behavior for the expected number of
  inversions in a random permutation avoiding a pattern of length three

abstract:
  For $\tau\in S_3$, let $S_n(\tau)$ denote the set of permutations in $S_n$
which avoid the pattern $\tau$, and let $E_n^\tau$ denote the expectation with
respect to the uniformly random probability measure on $S_n(\tau)$. Let
$\mathcal{I}_n(\sigma)$ denote the number of inversions in $\sigma\in S_n$. We
study $E_n^\tau\mathcal{I}_n$ for $\tau\in\{231,132,213,312\}\subset S_3$. We
prove that $$
E_n^{231}\mathcal{I}_n=E_n^{312}\mathcal{I}_n=\frac12\frac{n!(n+1)!4^n}{(2n)!}-\frac12(3n+1),
$$ and that $$
E_n^{132}\mathcal{I}_n=E_n^{213}\mathcal{I}_n=\frac12(n-1)n-E_n^{231}\mathcal{I}_n.
$$ From the first equation it follows that $$
E_n^{231}\mathcal{I}_n=E_n^{312}\mathcal{I}_n\sim\frac{\sqrt\pi}2n^\frac32. $$
We also show that the variance $\text{Var}_{P_n^{\tau}}(\mathcal{I}_n)$ of
$\mathcal{I}_n$ under $P_n^\tau$ satisfies $$
\text{Var}_{P_n^{\tau}}(\mathcal{I}_n)\sim (\frac56-\frac\pi4)n^3\approx
0.048n^3,\ \text{for}\ \tau\in\{231,132,213,312\}. $$


title:
Emergence of Fermi arcs and novel magnetic splitting in an
  antiferromagnet

abstract:
  The Fermi arcs are signatures of exotic states in solids because they defy
conventional concept of Fermi surfaces as closed contours in momentum space.
Fermi arcs were first discovered in cuprates, and caused by the pseudogap. Weyl
semimetals provided another way to generate Fermi arcs by breaking either the
time reversal symmetry (TRS) or inversion symmetry of a 3D Dirac semimetal,
which can result in a Weyl semimetal with pairs of Weyl nodes that have
opposite chirality. The bulk-boundary correspondence associated with the Chern
number leads to the emergence of Fermi arcs on the boundary. Here, we present
experimental evidence that pairs of magnetically split hole- and electron-like
Fermi arcs emerge below the Neel temperature, in the antiferromagnetic (AFM)
state of cubic NdBi due to a novel band splitting effect. Whereas TRS is broken
by the AFM order, both inversion and nonsymmorphic TRS are preserved in the
bulk, precluding the possibility of a Weyl semimetal. The observed magnetic
splitting is highly unusual, as it creates bands of opposing curvature, that
changes with temperature and follows the antiferromagnetic order parameter.
This is completely different from previously reported cases of magnetic
splittings such as traditional Zeeman and Rashba, where the curvature of the
bands is preserved. Therefore, our finding represents a new Fermionic state
created by new type of magnetic band splitting in the presence of a long-range
AFM order that are not readily explained by existing theoretical ideas.


title: Dilaton Solutions for Laboratory Constraints and Lunar Laser Ranging

abstract:
  We derive approximate analytical solutions to the environment-dependent
dilaton field theory equations in the presence of a one or two mirror system or
a sphere. The one-dimensional equations of motion are integrated for each
system. The solutions obtained herein can be applied to \textit{q}BOUNCE
experiments, neutron interferometry and for the calculation of the dilaton
field induced "Casimir force" in the \textsc{Cannex} experiment as well as for
Lunar Laser Ranging. They are typical of the Damour-Polyakov screening
mechanism whereby deviations from General Relativity are suppressed by a
vanishingly small direct coupling of the dilaton to matter in dense
environments.


title: Sampling Theorems for Unsupervised Learning in Linear Inverse Problems

abstract:
  Solving a linear inverse problem requires knowledge about the underlying
signal model. In many applications, this model is a priori unknown and has to
be learned from data. However, it is impossible to learn the model using
observations obtained via a single incomplete measurement operator, as there is
no information outside the range of the inverse operator, resulting in a
chicken-and-egg problem: to learn the model we need reconstructed signals, but
to reconstruct the signals we need to know the model. Two ways to overcome this
limitation are using multiple measurement operators or assuming that the signal
model is invariant to a certain group action. In this paper, we present
necessary and sufficient sampling conditions for learning the signal model from
partial measurements which only depend on the dimension of the model, and the
number of operators or properties of the group action that the model is
invariant to. As our results are agnostic of the learning algorithm, they shed
light into the fundamental limitations of learning from incomplete data and
have implications in a wide range set of practical algorithms, such as
dictionary learning, matrix completion and deep neural networks.


title: Refine-Net: Normal Refinement Neural Network for Noisy Point Clouds

abstract:
  Point normal, as an intrinsic geometric property of 3D objects, not only
serves conventional geometric tasks such as surface consolidation and
reconstruction, but also facilitates cutting-edge learning-based techniques for
shape analysis and generation. In this paper, we propose a normal refinement
network, called Refine-Net, to predict accurate normals for noisy point clouds.
Traditional normal estimation wisdom heavily depends on priors such as surface
shapes or noise distributions, while learning-based solutions settle for single
types of hand-crafted features. Differently, our network is designed to refine
the initial normal of each point by extracting additional information from
multiple feature representations. To this end, several feature modules are
developed and incorporated into Refine-Net by a novel connection module.
Besides the overall network architecture of Refine-Net, we propose a new
multi-scale fitting patch selection scheme for the initial normal estimation,
by absorbing geometry domain knowledge. Also, Refine-Net is a generic normal
estimation framework: 1) point normals obtained from other methods can be
further refined, and 2) any feature module related to the surface geometric
structures can be potentially integrated into the framework. Qualitative and
quantitative evaluations demonstrate the clear superiority of Refine-Net over
the state-of-the-arts on both synthetic and real-scanned datasets. Our code is
available at https://github.com/hrzhou2/refinenet.


title: A Survey on Cross-Lingual Summarization

abstract:
  Cross-lingual summarization is the task of generating a summary in one
language (e.g., English) for the given document(s) in a different language
(e.g., Chinese). Under the globalization background, this task has attracted
increasing attention of the computational linguistics community. Nevertheless,
there still remains a lack of comprehensive review for this task. Therefore, we
present the first systematic critical review on the datasets, approaches and
challenges in this field. Specifically, we carefully organize existing datasets
and approaches according to different construction methods and solution
paradigms, respectively. For each type of datasets or approaches, we thoroughly
introduce and summarize previous efforts and further compare them with each
other to provide deeper analyses. In the end, we also discuss promising
directions and offer our thoughts to facilitate future research. This survey is
for both beginners and experts in cross-lingual summarization, and we hope it
will serve as a starting point as well as a source of new ideas for researchers
and engineers interested in this area.


title: Emergent rheotaxis of shape-changing swimmers in Poiseuille flow

abstract:
  A simple model for the motion of shape-changing swimmers in Poiseuille flow
was recently proposed and numerically explored by Omori et al. (2022). These
explorations hinted that a small number of interacting mechanics can drive
long-time behaviours in this model, cast in the context of the well-studied
alga Chlamydomonas and its rheotactic behaviours in such flows. Here, we
explore this model analytically via a multiple-scale asymptotic analysis,
seeking to formally identify the causal factors that shape the behaviour of
these swimmers in Poiseuille flow. By capturing the evolution of a
Hamiltonian-like quantity, we reveal the origins of the long-term drift in a
single swimmer-dependent constant, whose sign determines the eventual behaviour
of the swimmer. This constant captures the nonlinear interaction between the
oscillatory speed and effective hydrodynamic shape of deforming swimmers,
driving drift either towards or away from rheotaxis.


title: Exact methods and lower bounds for the Oven Scheduling Problem

abstract:
  The Oven Scheduling Problem (OSP) is a new parallel batch scheduling problem
that arises in the area of electronic component manufacturing. Jobs need to be
scheduled to one of several ovens and may be processed simultaneously in one
batch if they have compatible requirements. The scheduling of jobs must respect
several constraints concerning eligibility and availability of ovens, release
dates of jobs, setup times between batches as well as oven capacities. Running
the ovens is highly energy-intensive and thus the main objective, besides
finishing jobs on time, is to minimize the cumulative batch processing time
across all ovens. This objective distinguishes the OSP from other batch
processing problems which typically minimize objectives related to makespan,
tardiness or lateness.
  We propose to solve this NP-hard scheduling problem via constraint
programming (CP) and integer linear programming (ILP) and present corresponding
models. For an experimental evaluation, we introduce a multi-parameter random
instance generator to provide a diverse set of problem instances. Using
state-of-the-art solvers, we evaluate the quality and compare the performance
of our CP- and ILP-models. We show that our models can find feasible solutions
for instances of realistic size, many of those being provably optimal or nearly
optimal solutions. Finally, we derive theoretical lower bounds on the solution
cost of feasible solutions to the OSP; these can be computed within a few
seconds. We show that these lower bounds are competitive with those derived by
state-of-the-art solvers.


title: Isoperimetric inequalities in finitely generated groups

abstract:
  To each finitely generated group $G$, we associate a quasi-isometric
invariant called the isoperimetric spectrum of $G$. If $G$ is finitely
presented, our invariant is closely related to the Dehn function of $G$. The
main goal of this paper is to initiate the study of isoperimetric spectra of
finitely generated (but not necessarily finitely presented) groups. In
particular, we compute the isoperimetric spectrum of small cancellation groups,
certain wreath products, and free Burnside groups of sufficiently large odd
exponent. We also address some natural questions on the structure of the poset
of isoperimetric spectra. As an application, we show that there exist
$2^{\aleph_0}$ pairwise non-quasi-isometric finitely generated groups of finite
exponent.


title:
Dative epitaxy of commensurate monocrystalline covalent-van der Waals
  moir\'e supercrystal

abstract:
  Realizing van der Waals (vdW) epitaxy in the 80s represents a breakthrough
that circumvents the stringent lattice matching and processing compatibility
requirements in conventional covalent heteroepitaxy. However, due to the weak
vdW interactions, there is little control over film qualities by the substrate.
Typically, discrete domains with a spread of misorientation angles are formed,
limiting the applicability of vdW epitaxy. Here we report the epitaxial growth
of monocrystalline, covalent Cr5Te8 2D crystals on monolayer vdW WSe2 by
chemical vapor deposition, driven by interfacial dative bond formation. The
lattice of Cr5Te8, with a lateral dimension of a few ten microns, is fully
commensurate with that of WSe2 via 3 x 3 (Cr5Te8)-7 x 7 (WSe2) supercell
matching, forming a single crystalline moire superlattice. Our work has
established a conceptually distinct paradigm of thin film epitaxy termed dative
epitaxy, which takes full advantage of covalent epitaxy with chemical bonding
for fixing the atomic registry and crystal orientation, while circumventing its
stringent lattice matching and processing compatibility requirements;
conversely, it ensures the full flexibility of vdW epitaxy, while avoiding its
poor orientation control. Cr5Te8 2D crystals grown by dative epitaxy exhibit
square magnetic hysteresis, suggesting minimized interfacial defects that can
serve as pinning sites.


title: Navigation with Probabilistic Safety Constraints: Convex Formulation

abstract:
  We consider the problem of navigation with safety constraints. The safety
constraints are probabilistic, where a given set is assigned a degree of
safety, a number between zero and one, with zero being safe and one being
unsafe. The deterministic unsafe set will arise as a particular case of the
proposed probabilistic description of safety. We provide a convex formulation
to the navigation problem with probabilistic safety constraints. The convex
formulation is made possible by lifting the navigation problem in the dual
space of density using linear transfer operator theory methods involving
Perron-Frobenius and Koopman operators. The convex formulation leads to an
infinite-dimensional feasibility problem for probabilistic safety verification
and control design. The finite-dimensional approximation of the optimization
problem relies on the data-driven approximation of the linear transfer
operator.


title: CoMeFa: Compute-in-Memory Blocks for FPGAs

abstract:
  Block RAMs (BRAMs) are the storage houses of FPGAs, providing extensive
on-chip memory bandwidth to the compute units implemented using Logic Blocks
(LBs) and Digital Signal Processing (DSP) slices. We propose modifying BRAMs to
convert them to CoMeFa (Compute-In-Memory Blocks for FPGAs) RAMs. These RAMs
provide highly-parallel compute-in-memory by combining computation and storage
capabilities in one block. CoMeFa RAMs utilize the true dual port nature of
FPGA BRAMs and contain multiple programmable single-bit bit-serial processing
elements. CoMeFa RAMs can be used to compute in any precision, which is
extremely important for evolving applications like Deep Learning. Adding CoMeFa
RAMs to FPGAs significantly increases their compute density. We explore and
propose two architectures of these RAMs: CoMeFa-D (optimized for delay) and
CoMeFa-A (optimized for area). Compared to existing proposals, CoMeFa RAMs do
not require changing the underlying SRAM technology like simultaneously
activating multiple rows on the same port, and are practical to implement.
CoMeFa RAMs are versatile blocks that find applications in numerous diverse
parallel applications like Deep Learning, signal processing, databases, etc. By
augmenting an Intel Arria-10-like FPGA with CoMeFa-D (CoMeFa-A) RAMs at the
cost of 3.8% (1.2%) area, and with algorithmic improvements and efficient
mapping, we observe a geomean speedup of 2.5x (1.8x), across several
representative benchmarks. Replacing all or some BRAMs with CoMeFa RAMs in
FPGAs can make them better accelerators of modern compute-intensive workloads.


title: Semi-Supervised Graph Learning Meets Dimensionality Reduction

abstract:
  Semi-supervised learning (SSL) has recently received increased attention from
machine learning researchers. By enabling effective propagation of known labels
in graph-based deep learning (GDL) algorithms, SSL is poised to become an
increasingly used technique in GDL in the coming years. However, there are
currently few explorations in the graph-based SSL literature on exploiting
classical dimensionality reduction techniques for improved label propagation.
In this work, we investigate the use of dimensionality reduction techniques
such as PCA, t-SNE, and UMAP to see their effect on the performance of graph
neural networks (GNNs) designed for semi-supervised propagation of node labels.
Our study makes use of benchmark semi-supervised GDL datasets such as the Cora
and Citeseer datasets to allow meaningful comparisons of the representations
learned by each algorithm when paired with a dimensionality reduction
technique. Our comprehensive benchmarks and clustering visualizations
quantitatively and qualitatively demonstrate that, under certain conditions,
employing a priori and a posteriori dimensionality reduction to GNN inputs and
outputs, respectively, can simultaneously improve the effectiveness of
semi-supervised node label propagation and node clustering. Our source code is
freely available on GitHub.


title:
Variations and extensions of the Gaussian concentration inequality, Part
  II

abstract:
  Pisier's version of the Gaussian concentration inequality is transformed and
used to prove deviation inequalities for locally Lipschitz functions with
respect to heavy tailed product measures on Euclidean space. The approach is,
in our opinion, more direct than much of the modern theory of concentration of
measure (i.e. Poincar\'{e} and log-Sobolev inequalities, estimating moments
etc.).


title:
Computational historical linguistics and language diversity in South
  Asia

abstract:
  South Asia is home to a plethora of languages, many of which severely lack
access to new language technologies. This linguistic diversity also results in
a research environment conducive to the study of comparative, contact, and
historical linguistics -- fields which necessitate the gathering of extensive
data from many languages. We claim that data scatteredness (rather than
scarcity) is the primary obstacle in the development of South Asian language
technology, and suggest that the study of language history is uniquely aligned
with surmounting this obstacle. We review recent developments in and at the
intersection of South Asian NLP and historical-comparative linguistics,
describing our and others' current efforts in this area. We also offer new
strategies towards breaking the data barrier.


title: On Elser's conjecture and the topology of $U$-nucleus complex

abstract:
  Dorpalen-Barry et al. proved Elser's conjecture about sign of Elser's number
by interpreting them as certain sums of reduced Euler characteristics of an
abstract simplicial complex known as $U$-nucleus complex. We prove a conjecture
posed by them regarding the homology of $U$-nucleus complex.


title:
Designing Stress-Adaptive Dense Suspensions using Dynamic Covalent
  Chemistry

abstract:
  The non-Newtonian behaviors of dense suspensions are central to their use in
technological and industrial applications and arise from a network of
particle-particle contacts that dynamically adapts to imposed shear. Reported
herein are studies aimed at explor-ing how dynamic covalent chemistry between
particles and the polymeric solvent can be used to tailor such stress-adaptive
contact networks leading to their unusual rheological behaviors. Specifically,
a room temperature dynamic thia-Michael bond is employed to rationally tune the
equilibrium constant (Keq) of the polymeric solvent to the particle interface.
It is demonstrated that low Keq leads to shear thinning while high Keq produces
antithixotropy, a rare phenomenon where the viscosity increases with shearing
time. It is proposed that an increase in Keq increases the polymer graft
density at the particle surface and that antithixotropy primar-ily arises from
partial debonding of the polymeric graft/solvent from the particle surface and
the formation of polymer bridges between particles. Thus, the implementation of
dynamic covalent chemistry provides a new molecular handle with which to tailor
the macroscopic rheology of suspensions by introducing programable time
dependence. These studies open the door to energy absorbing materials that not
only sense mechanical inputs and adjust their dissipation as a function of time
or shear rate but can switch between these two modalities on demand.


title:
A note on the number of triangles in graphs without the suspension of a
  path on four vertices

abstract:
  The suspension of the path $P_4$ consists of a $P_4$ and an additional vertex
connected to each of the four vertices, and is denoted by $\hat{P_4}$. The
largest number of triangles in a $\hat{P_4}$-free $n$-vertex graph is denoted
by $ex(n,K_3,\hat{P_4})$. Mubayi and Mukherjee in 2020 showed that $
ex(n,K_3,\hat{P_4})= n^2/8+O(n)$. We show that for sufficiently large $n$,
$ex(n,K_3,\hat{P_4})=\lfloor n^2/8\rfloor$.


title: Configurations of higher orders

abstract:
  This paper begins by extending the notion of a combinatorial configuration of
points and lines to a combinatorial configuration of points and planes that we
refer to as configurations of order $2$. We then proceed to investigate a
further extension to the notion of points and $k$-planes ($k$-dimensional
hyperplanes) which we refer to as configurations of order $k$. We present a
number of general examples such as stacked configurations of order $k$ -
intuitively layering lower order configurations - and product configurations of
order $k$. We discuss many analogues of standard configurations such as dual
configurations, isomorphisms, graphical representations, and when a
configuration is geometric. We focus mostly on configurations of order $2$ and
specifically compute the number of possible symmetric configurations of order
$2$ when each plane contains $3$ points for small values on $n$ - the total
number of points in the configuration.


title: A Deep Learning Approach to Probabilistic Forecasting of Weather

abstract:
  We discuss an approach to probabilistic forecasting based on two chained
machine-learning steps: a dimensional reduction step that learns a reduction
map of predictor information to a low-dimensional space in a manner designed to
preserve information about forecast quantities; and a density estimation step
that uses the probabilistic machine learning technique of normalizing flows to
compute the joint probability density of reduced predictors and forecast
quantities. This joint density is then renormalized to produce the conditional
forecast distribution. In this method, probabilistic calibration testing plays
the role of a regularization procedure, preventing overfitting in the second
step, while effective dimensional reduction from the first step is the source
of forecast sharpness. We verify the method using a 22-year 1-hour cadence time
series of Weather Research and Forecasting (WRF) simulation data of surface
wind on a grid.


title: Poincar\'e inequalities on graphs

abstract:
  We prove local $L^p$-Poincar\'e inequalities, $ p\in[1,\infty]$, on
quasiconvex sets in infinite graphs endowed with a family of locally doubling
measures, and global $L^p$-Poincar\'e inequalities on connected sets for flow
measures on trees. We also discuss the optimality of our results.


title: Multi-label Transformer for Action Unit Detection

abstract:
  Action Unit (AU) Detection is the branch of affective computing that aims at
recognizing unitary facial muscular movements. It is key to unlock unbiased
computational face representations and has therefore aroused great interest in
the past few years. One of the main obstacles toward building efficient deep
learning based AU detection system is the lack of wide facial image databases
annotated by AU experts. In that extent the ABAW challenge paves the way toward
better AU detection as it involves a 2M frames AU annotated dataset. In this
paper, we present our submission to the ABAW3 challenge. In a nutshell, we
applied a multi-label detection transformer that leverage multi-head attention
to learn which part of the face image is the most relevant to predict each AU.


title:
Stability of convergence rates: Kernel interpolation on non-Lipschitz
  domains

abstract:
  Error estimates for kernel interpolation in Reproducing Kernel Hilbert Spaces
(RKHS) usually assume quite restrictive properties on the shape of the domain,
especially in the case of infinitely smooth kernels like the popular Gaussian
kernel.
  In this paper we leverage an analysis of greedy kernel algorithms to prove
that it is possible to obtain convergence results (in the number of
interpolation points) for kernel interpolation for arbitrary domains $\Omega
\subset \mathbb{R}^d$, thus allowing for non-Lipschitz domains including e.g.
cusps and irregular boundaries. Especially we show that, when going to a
smaller domain $\tilde{\Omega} \subset \Omega \subset \mathbb{R}^d$, the
convergence rate does not deteriorate - i.e. the convergence rates are stable
with respect to going to a subset.
  The impact of this result is explained on the examples of kernels of finite
as well as infinite smoothness like the Gaussian kernel. A comparison to
approximation in Sobolev spaces is drawn, where the shape of the domain
$\Omega$ has an impact on the approximation properties. Numerical experiments
illustrate and confirm the experiments.


title: Pathways: Asynchronous Distributed Dataflow for ML

abstract:
  We present the design of a new large scale orchestration layer for
accelerators. Our system, Pathways, is explicitly designed to enable
exploration of new systems and ML research ideas, while retaining state of the
art performance for current models. Pathways uses a sharded dataflow graph of
asynchronous operators that consume and produce futures, and efficiently
gang-schedules heterogeneous parallel computations on thousands of accelerators
while coordinating data transfers over their dedicated interconnects. Pathways
makes use of a novel asynchronous distributed dataflow design that lets the
control plane execute in parallel despite dependencies in the data plane. This
design, with careful engineering, allows Pathways to adopt a single-controller
model that makes it easier to express complex new parallelism patterns. We
demonstrate that Pathways can achieve performance parity (~100% accelerator
utilization) with state-of-the-art systems when running SPMD computations over
2048 TPUs, while also delivering throughput comparable to the SPMD case for
Transformer models that are pipelined across 16 stages, or sharded across two
islands of accelerators connected over a data center network.


title: Ordering Regular Languages and Automata: Complexity

abstract:
  Given an order of the underlying alphabet we can lift it to the states of a
finite deterministic automaton: to compare states we use the order of the
strings reaching them. When the order on strings is the co-lexicographic one
\emph{and} this order turns out to be total, the DFA is called Wheeler. This
recently introduced class of automata -- the \emph{Wheeler automata} --
constitute an important data-structure for languages, since it allows the
design and implementation of a very efficient tool-set of storage mechanisms
for the transition function, supporting a large variety of substring queries.
  In this context it is natural to consider the class of regular languages
accepted by Wheeler automata, i.e. the Wheeler languages. An inspiring result
in this area is the following: it has been shown that, as opposed to the
general case, the classic determinization by powerset construction is
\emph{polynomial} on Wheeler automata. As a consequence, most classical
problems, when considered on this class of automata, turn out to be "easy" --
that is, solvable in polynomial time.
  In this paper we consider computational problems related to Wheelerness, but
starting from non-deterministic automata. We also consider the case of
\emph{reduced} non-deterministic ones -- a class of NFA where recognizing
Wheelerness is still polynomial, as for DFA's. Our collection of results shows
that moving towards non-determinism is, in most cases, a dangerous path leading
quickly to intractability.
  Moreover, we start a study of "state complexity" related to Wheeler DFA and
languages, proving that the classic construction for the intersection of
languages turns out to be computationally simpler on Wheeler DFA than in the
general case. We also provide a construction for the minimum Wheeler DFA
recognizing a given Wheeler language.


title:
LASED: A Laser-Atom Interaction Simulator derived from Quantum
  Electrodynamics

abstract:
  A laser-atom interaction simulator derived from quantum electrodynamics
(LASED) is presented, which has been developed in the python programming
language. LASED allows a user to calculate the time evolution of a
laser-excited atomic system. The model allows for any laser polarization, a
Gaussian laser beam profile, a rotation of the reference frame chosen to define
the states, and an averaging over the Doppler profile of an atomic beam.
Examples of simulations using LASED are presented for excitation of calcium
from the 4$^1S_0$ state to the 4$^1P_1$ state, for excitation from the helium
3$^1D_2$ state excited by electron impact to the 10$^1P_1$ state, and for laser
excitation of caesium via the $D_2$ line.


title:
Dynamically Refined Regularization for Improving Cross-corpora Hate
  Speech Detection

abstract:
  Hate speech classifiers exhibit substantial performance degradation when
evaluated on datasets different from the source. This is due to learning
spurious correlations between words that are not necessarily relevant to
hateful language, and hate speech labels from the training corpus. Previous
work has attempted to mitigate this problem by regularizing specific terms from
pre-defined static dictionaries. While this has been demonstrated to improve
the generalizability of classifiers, the coverage of such methods is limited
and the dictionaries require regular manual updates from human experts. In this
paper, we propose to automatically identify and reduce spurious correlations
using attribution methods with dynamic refinement of the list of terms that
need to be regularized during training. Our approach is flexible and improves
the cross-corpora performance over previous work independently and in
combination with pre-defined dictionaries.


title:
Socially Fair Mitigation of Misinformation on Social Networks via
  Constraint Stochastic Optimization

abstract:
  Recent social networks' misinformation mitigation approaches tend to
investigate how to reduce misinformation by considering a whole-network
statistical scale. However, unbalanced misinformation exposures among
individuals urge to study fair allocation of mitigation resources. Moreover,
the network has random dynamics which change over time. Therefore, we introduce
a stochastic and non-stationary knapsack problem, and we apply its resolution
to mitigate misinformation in social network campaigns. We further propose a
generic misinformation mitigation algorithm that is robust to different social
networks' misinformation statistics, allowing a promising impact in real-world
scenarios. A novel loss function ensures fair mitigation among users. We
achieve fairness by intelligently allocating a mitigation incentivization
budget to the knapsack, and optimizing the loss function. To this end, a team
of Learning Automata (LA) drives the budget allocation. Each LA is associated
with a user and learns to minimize its exposure to misinformation by performing
a non-stationary and stochastic walk over its state space. Our results show how
our LA-based method is robust and outperforms similar misinformation mitigation
methods in how the mitigation is fairly influencing the network users.


title: Causal Inference in High Dimensions -- Without Sparsity

abstract:
  We revisit the classical causal inference problem of estimating the average
treatment effect in the presence of fully observed, high dimensional
confounding variables, where the number of confounders $d$ is of the same order
as the sample size $n$. To make the problem tractable, we posit a generalized
linear model for the effect of the confounders on the treatment assignment and
outcomes, but do not assume any sparsity. Instead, we only require the
magnitude of confounding to remain non-degenerate. Despite making parametric
assumptions, this setting is a useful surrogate for some machine learning
methods used to adjust for confounding in two-stage methods. In particular, the
estimation of the first stage adds variance that does not vanish, forcing us to
confront terms in the asymptotic expansion that normally are brushed aside as
finite sample defects. Using new theoretical results and simulations, we
compare G-computation, inverse propensity weighting (IPW), and two common
doubly robust estimators -- augmented IPW (AIPW) and targeted maximum
likelihood estimation (TMLE). When the outcome model estimates are unbiased,
G-computation outperforms the other estimators in both bias and variance. Among
the doubly robust estimators, the TMLE estimator has the lowest variance.
Existing theoretical results do not explain this advantage because the TMLE and
AIPW estimators have the same asymptotic influence function. However, our model
emphasizes differences in performance between these estimators beyond
first-order asymptotics.


title:
Holographic model for light quarks in anisotropic hot dense QGP with
  external magnetic field

abstract:
  We present a five-dimensional twice anisotropic holographic model supported
by Einstein-dilaton-three-Maxwell action describing light quarks. The first of
the Maxwell fields provides finite chemical potential. The second Maxwell field
serves for anisotropy, characterizing spatial anisotropy of the QGP produced in
heavy-ion collisions (HIC). The third Maxwell field is related to a magnetic
field that appears in HIC. The dependence of the 5-dim black hole solution and
confinement/deconfinement phase diagram on this magnetic field is considered.
The effect of the inverse magnetic catalyses for light quarks phase diagram of
is obtained. Positions of critical end points are found. We also study the
behavior of the conductivities for light quarks for both isotropic and
anisotropic cases and show that behaviour of the conductivity near critical
points essentially depend on quark masses, meanwhile at high temperature they
are similar.


title:
String order melting of spin-1 particle chains in superconducting
  transmons using optimal control

abstract:
  Utilizing optimal control to simulate a model Hamiltonian is an emerging
strategy that leverages the intrinsic physics of a device with digital quantum
simulation methods. Here we evaluate optimal control for probing the
non-equilibrium properties of symmetry-protected topological (SPT) states
simulated with superconducting hardware. Assuming a tunable transmon
architecture, we cast evolution of these SPT states as a series of one- and
two-site pulse optimization problems that are solved in the presence of leakage
constraints. From the generated pulses, we numerical simulate time-dependent
melting of the perturbed SPT string order across a six-site model with an
average state infidelity of $10^{-3}$. The feasibility of these pulses as well
as their efficient application indicate that high-fidelity simulations of
string-order melting are within reach of current quantum computing systems.


title:
Multi-q Origin of Unconventional Surface States in a High-Symmetry
  Lattice

abstract:
  A variety of complex magnetic structures in a high-symmetry lattice can arise
from a superposition of well-defined magnetic wave vectors. These "multi-q"
structures have garnered much attention because their non-collinear magnetic
moment patterns can generate interesting real-space spin textures such as
skyrmions. However, the role multi-q structures play in the topology of
electronic bands in momentum space has remained rather elusive. In this work,
we show that 2q and 3q antiferromagnetic structures in a cubic lattice with
band inversion, such as NdBi, can induce unconventional surface state pairs in
the band-folding bulk gap. Our density functional theory calculations match
well with the recent experimental observation of unconventional surface state
hole Fermi arcs and electron pockets below the magnetic transition temperature,
which can not be explained with its previously reported single-q structure. We
further show that these multi-q structures have also 3D Dirac and Weyl nodal
features. Our work reveals the special role that multi-q structures can play in
the development of new quantum states and suggests that NdBi is a multi-q
antiferromagnet.