title:
Estimating Individualized Treatment Rules in Longitudinal Studies with
  Covariate-Driven Observation Times

abstract:
  The sequential treatment decisions made by physicians to treat chronic
diseases are formalized in the statistical literature as dynamic treatment
regimes. To date, methods for dynamic treatment regimes have been developed
under the assumption that observation times, i.e., treatment and outcome
monitoring times, are determined by study investigators. That assumption is
often not satisfied in electronic health records data in which the outcome, the
observation times, and the treatment mechanism are associated with patients'
characteristics. The treatment and observation processes can lead to spurious
associations between the treatment of interest and the outcome to be optimized
under the dynamic treatment regime if not adequately considered in the
analysis. We address these associations by incorporating two inverse weights
that are functions of a patient's covariates into dynamic weighted ordinary
least squares to develop optimal single stage dynamic treatment regimes, known
as individualized treatment rules. We show empirically that our methodology
yields consistent, multiply robust estimators. In a cohort of new users of
antidepressant drugs from the United Kingdom's Clinical Practice Research
Datalink, the proposed method is used to develop an optimal treatment rule that
chooses between two antidepressants to optimize a utility function related to
the change in body mass index.


title:
Performance evaluation of baseline-dependent averaging based
  onfull-scale SKA1-LOW simulation

abstract:
  The Square Kilometre Array (SKA) is the largest radio interferometer under
construction in the world. Its immense amount of visibility data poses a
considerable challenge to the subsequent processing by the science data
processor (SDP). Baseline dependent averaging (BDA), which reduces the amount
of visibility data based on the baseline distribution of the radio
interferometer, has become a focus of SKA SDP development. This paper developed
and implemented a full-featured BDA module based on Radio Astronomy Simulation,
Calibration and Imaging Library (RASCIL). Simulated observations were then
performed with RASCIL based on a full-scale SKA1-LOW configuration. The
performance of the BDA was systematically investigated and evaluated based on
the simulated data. The experimental results presented that the amount of
visibility data is reduced by about 50\% to 85\% for different time intervals
($\Delta t_{max}$). In addition, different $\Delta t_{max}$ have a significant
effect on the imaging quality. The smaller the $\Delta t_{max}$, the smaller
the degradation of the imaging quality.


title: Set-homogeneous hypergraphs

abstract:
  A $k$-uniform hypergraph $M$ is set-homogeneous if it is countable (possibly
finite) and whenever two finite induced subhypergraphs $U,V$ are isomorphic
there is $g\in Aut(M)$ with $U^g=V$; the hypergraph $M$ is said to be
homogeneous if in addition every isomorphism between finite induced
subhypergraphs extends to an automorphism. We give four examples of countably
infinite set-homogeneous $k$-uniform hypergraphs which are not homogeneous (two
with $k=3$, one with $k=4$, and one with $k=6$). Evidence is also given that
these may be the only ones, up to complementation. For example, for $k=3$ there
is just one countably infinite $k$-uniform hypergraph whose automorphism group
is not 2-transitive, and there is none for $k=4$. We also give an example of a
finite set-homogeneous 3-uniform hypergraph which is not homogeneous.


title:
Nonlinear optical heating of all-dielectric super-cavity: efficient
  light-to-heat conversion through giant thermorefractive bistability

abstract:
  Optical heating of resonant nanostructures is one of the key issues in modern
nanophotonics, being either harmful or desirable effect depending on the
applications. Despite a linear regime of light-to-heat conversion is
well-studied both for metal and semiconductor resonant systems generalized as
critical coupling condition, the clear strategy to optimize optical heating
upon high-intensity light irradiation is still missing. In this work, we
propose a simple analytical model for such problem taking into account material
properties changes caused by the heating. It allows us to derive a new general
critical coupling condition for the nonlinear case, requiring counterintuitive
initial spectral mismatch between the pumping light frequency and resonant one.
Basing on the suggested strategy, we develop an optimized design for efficient
nonlinear optical heating, which employs a cylindrical nanoparticle supporting
quasi bound state in the continuum mode (quasi-BIC or so-called `super-cavity
mode') excited by the incident azimuthal vector beam. Our approach provides a
background for various nonlinear experiments related to optical heating and
bistability, where self-action of the intense laser beam can change resonant
properties of the irradiated nanostructure.


title: Illuminating the Space of Enemies Through MAP-Elites

abstract:
  Action-Adventure games have several challenges to overcome, where the most
common are enemies. The enemies' goal is to hinder the players' progression by
taking life points, and the way they hinder this progress is distinct for
different kinds of enemies. In this context, this paper introduces an extended
version of an evolutionary approach for procedurally generating enemies that
target the enemy's difficulty as the goal. Our approach advances the enemy
generation research by incorporating a MAP-Elites population to generate
diverse enemies without losing quality. The computational experiment showed the
method converged most enemies in the MAP-Elites in less than a second for most
cases. Besides, we experimented with players who played an Action-Adventure
game prototype with enemies we generated. This experiment showed that the
players enjoyed most levels they played, and we successfully created enemies
perceived as easy, medium, or hard to face.


title:
A Galerkin FE method for elliptic optimal control problem governed by 2D
  space-fractional PDEs

abstract:
  In this paper, we propose a Galerkin finite element method for the elliptic
optimal control problem governed by the Riesz space-fractional PDEs on 2D
domains with control variable being discretized by variational discretization
technique. The optimality condition is derived and priori error estimates of
control, costate and state variables are successfully established. Numerical
test is carried out to illustrate the accuracy performance of this approach.


title:
Search for Higgs boson pair production in the four b quark final state
  in proton-proton collisions at $\sqrt{s}$ = 13 TeV

abstract:
  A search for pairs of Higgs bosons produced via gluon and vector boson fusion
is presented, focusing on the four b quark final state. The data sample
consists of proton-proton collisions at a center-of-mass energy of 13 TeV,
collected with the CMS detector at the LHC, and corresponds to an integrated
luminosity of 138 fb$^{-1}$. No deviation from the background-only hypothesis
is observed. A 95% confidence level upper limit on the Higgs boson pair
production cross section is observed at 3.9 times the standard model prediction
for an expected value of 7.8. Constraints are also set on the modifiers of the
Higgs field self-coupling, $\kappa_\lambda$, and of the coupling of two Higgs
bosons to two vector bosons, $\kappa_\mathrm{2V}$. The observed (expected)
allowed intervals at the 95% confidence level are $-$2.3 $\lt \kappa_\lambda
\lt$ 9.4 ($-$5.0 $\lt \kappa_\lambda \lt$ 12.0) and $-$0.1 $\lt
\kappa_\mathrm{2V} \lt$ 2.2 ($-$0.4 $\lt \kappa_\mathrm{2V} \lt$ 2.5). These
are the most stringent observed constraints to date on the HH production cross
section and on the $\kappa_\mathrm{2V}$ coupling.


title: Identifying OCRs in cfDNA WGS Data by Correlation Clustering

abstract:
  Open chromatin regions (OCRs) are of the most important epigenetic features
that play important role in cell regulating mechanisms and accordingly their
investigation is of high interest and importance for disease diagnosis and
monitoring particularly in cancer related studies. In recent decade, the
emergence of liquid biopsy(LP) has huge positive impact on cancer early
detection and to improve the accuracy and specificity of LP related tests
requires well-established experimental and bioinformatic pipeline analysis.
Accordingly, applying a multimodal analysis that investigates different types
of genomic and epigenomic features from the same sample may lead to better
performance along with reduced experimental cost and time. Due to the critical
role of cell free DNA(cfDNA) in cancer detection, we aimed to propose a
computational approach to target the OCR prediction task as an epigenetic
analysis from cfDNA whole genome sequence(WGS) file. Here, by considering the
combination of several mathematical concepts which includes the signal
processing, graph, and correlation clustering algorithms we were able to use
unique read depth counts from a cfDNA WGS bam file to predict the possible
OCRs. It is worth mentioning this is a very challenging computational problem
to accurately detect OCRs from plasma cfDNA-seq data due to the existence of
several confounding factors such as technical and biological variations
however, assessment of the obtained results based on other experimentally
validated databases showed the decent concordance and accuracy.


title:
Evaluation of Neuromorphic Spike Encoding of Sound Using Information
  Theory

abstract:
  The problem of spike encoding of sound consists in transforming a sound
waveform into spikes. It is of interest in many domains, including the
development of audio-based spiking neural networks, where it is the first and
most crucial stage of processing. Many algorithms have been proposed to perform
spike encoding of sound. However, a systematic approach to quantitatively
evaluate their performance is currently lacking. We propose the use of an
information-theoretic framework to solve this problem. Specifically, we
evaluate the coding efficiency of four spike encoding algorithms on two coding
tasks that consist of coding the fundamental characteristics of sound:
frequency and amplitude. The algorithms investigated are: Independent Spike
Coding, Send-on-Delta coding, Ben's Spiker Algorithm, and Leaky
Integrate-and-Fire coding. Using the tools of information theory, we estimate
the information that the spikes carry on relevant aspects of an input stimulus.
We find disparities in the coding efficiencies of the algorithms, where Leaky
Integrate-and-Fire coding performs best. The information-theoretic analysis of
their performance on these coding tasks provides insight on the encoding of
richer and more complex sound stimuli.


title: Synchronization of frequency combs by optical injection

abstract:
  Optical frequency combs based on semiconductor lasers are a promising
technology for monolithic integration of dual-comb spectrometers. However, the
stabilization of the offset frequency fceo remains a challenging feat due the
lack of octave-spanning spectra. In a dual-comb configuration, the uncorrelated
jitter of the offset frequencies leads to a non-periodic signal resulting in
broadened beatnotes with a limited signal-to-noise ratio (SNR). Hence,
expensive data acquisition schemes and complex signal processing are currently
required. Here, we show that the offset frequencies of two frequency combs can
be synchronized by optical injection locking, which allows full
phase-stabilization when combined with electrical injection. A single comb line
isolated via an optical Vernier filter serves as Master oscillator for
injection locking. The resulting dual-comb signal is periodic and stable over
thousands of periods. This enables coherent averaging using analog electronics,
which increases the SNR and reduces the data size by one and three orders of
magnitude, respectively. The presented method will enable fully
phase-stabilized dual-comb spectrometers by leveraging on integrated optical
filters and provides access for measuring and stabilizing fceo.


title: Minimal non-orientable matroids in a projective plane

abstract:
  We construct a new family of minimal non-orientable matroids of rank three.
Some of these matroids embed in Desarguesian projective planes. This answers a
question of Ziegler: for every prime power $q$, find a minimal non-orientable
submatroid of the projective plane over the $q$-element field.


title:
Time-correlated Photons from a In$_{0.5}$Ga$_{0.5}$P Photonic Crystal
  Cavity on a Silicon Chip

abstract:
  Time-correlated photon pairs are generated by triply-resonant
Four-Wave-Mixing in a In$_{0.5}$Ga$_{0.5}$P Photonic Crystal cavitiy. Maximal
efficiency is reached by actively compensating the residual spectral
misalignment of the cavity modes. The generation rate reaches 5 MHz in cavities
with Q-factor $\approx 4\times 10^4$, more than one order of magnitude larger
than what is measured using ring resonators with similar Q factors fabricated
on the same chip. The Photonic Crystal source is integrated on a Si photonic
circuit, an important asset for applications in quantum technologies.


title: Multi-Channel FFT Architectures Designed via Folding and Interleaving

abstract:
  Computing the FFT of a single channel is well understood in the literature.
However, computing the FFT of multiple channels in a systematic manner has not
been fully addressed. This paper presents a framework to design a family of
multi-channel FFT architectures using {\em folding} and {\em interleaving}.
Three distinct multi-channel FFT architectures are presented in this paper.
These architectures differ in the input and output preprocessing steps and are
based on different folding sets, i.e., different orders of execution.


title: Maximal coin-walker entanglement in a ballistic quantum walk

abstract:
  We report the position-inhomogeneous quantum walk (IQW) can be utilized to
produce the maximal high dimensional entanglement while maintaining the
quadratic speedup spread of the wave-function. Our calculations show that the
maximal coin-walker entanglement can be generated in any odd steps or
asymptotically in even steps, and the nearly maximal entanglement can be
obtained in even steps after $2$. We implement the IQW by a stable
resource-saving time-bin optical network, in which a polarization Sagnac loop
is employed to realize the precisely tunable phase shift. Our approach opens up
an efficient way for high-dimensional entanglement engineering as well as
promotes investigations on the role of coin-walker interactions in QW based
applications.


title: CALCS 2021 Shared Task: Machine Translation for Code-Switched Data

abstract:
  To date, efforts in the code-switching literature have focused for the most
part on language identification, POS, NER, and syntactic parsing. In this
paper, we address machine translation for code-switched social media data. We
create a community shared task. We provide two modalities for participation:
supervised and unsupervised. For the supervised setting, participants are
challenged to translate English into Hindi-English (Eng-Hinglish) in a single
direction. For the unsupervised setting, we provide the following language
pairs: English and Spanish-English (Eng-Spanglish), and English and Modern
Standard Arabic-Egyptian Arabic (Eng-MSAEA) in both directions. We share
insights and challenges in curating the "into" code-switching language
evaluation data. Further, we provide baselines for all language pairs in the
shared task. The leaderboard for the shared task comprises 12 individual system
submissions corresponding to 5 different teams. The best performance achieved
is 12.67% BLEU score for English to Hinglish and 25.72% BLEU score for MSAEA to
English.


title: ValAsp: a tool for data validation in Answer Set Programming

abstract:
  The development of complex software requires tools promoting fail-fast
approaches, so that bugs and unexpected behavior can be quickly identified and
fixed. Tools for data validation may save the day of computer programmers. In
fact, processing invalid data is a waste of resources at best, and a drama at
worst if the problem remains unnoticed and wrong results are used for business.
Answer Set Programming (ASP) is not an exception, but the quest for better and
better performance resulted in systems that essentially do not validate data.
Even under the simplistic assumption that input/output data are eventually
validated by external tools, invalid data may appear in other portions of the
program, and go undetected until some other module of the designed software
suddenly breaks. This paper formalizes the problem of data validation for ASP
programs, introduces a language to specify data validation, and presents
\textsc{valasp}, a tool to inject data validation in ordinary programs. The
proposed approach promotes fail-fast techniques at coding time without imposing
any lag on the deployed system if data are pretended to be valid. Validation
can be specified in terms of statements using YAML, ASP and Python.
Additionally, the proposed approach opens the possibility to use ASP for
validating data of imperative programming languages. Under consideration for
acceptance in TPLP.


title:
A criterion for asymptotic stability of general fractional-order linear
  time-invariant systems with incommensurate orders

abstract:
  A criterion on the asymptotic stability of fractional-order systems with
incomensurate orders is proposed in this paper. Existing methods always assume
order parameters be rational numbers or the ratios of any two orders be
rational numbers. In engineering applications, order parameters are more likely
to be uncertain which may be real numbers. Furthermore, the boundary of the
stable parameter region is determined, which decomposes parameter space into
the finite number of connected regions. All systems whose parameters belong to
the same region have the same stability. Each region only needs checking one
point to determine the stability of the region. The method established in this
paper involves low computational complexity and clearly gives the relationship
between order parameters and stability. Some examples show the advantages of
this method.


title: Variational methods for some singular stochastic elliptic PDEs

abstract:
  We use some tools from nonlinear analysis to study two examples of singular
stochastic elliptic PDEs that cannot be solved by the contraction principle or
the Schauder fixed point theorem. Let $\xi$ stand for a spatial white noise on
a closed Riemannian surface $S$. We prove the existence of a solution to the
equation $$ (-\Delta + a)u = f(u) + \xi u $$ with a potential $a\in L^p(S)$ and
$p>1$, and $f$ subject to growth conditions. Under an additional parity
condition on $f$ -- met for instance when $f(u) = u\vert u\vert^\ell$, with
$\ell$ an even innteger, we further prove that this equation has infinitely
many solutions, in stark contrast with all the well-posedness results that have
been proved so far for singular stochastic PDEs under a small parameter
assumption. This kind of results is obtained by seeing the equation as
characterizing the critical points of an energy functional based on the
Anderson operator $H=\Delta + \xi$ and by resorting to variants of the mountain
pass theorem. There are however some interesting equations that cannot be
characterized as the critical points of an energy functional. Such is the case
of the singular Choquard-Pecard equation on $\mathbb{T}^2$ $$ (-\Delta + a)u =
\big(w\star f(u)\big)g(u) + \xi u $$ One can use Ghoussoub's machinery of
self-dual functionals to prove the existence of a solution to that equation as
the minimum of a self-dual strongly coercive functional under proper
assumptions on the coefficients $a,w,f$ and $g$.


title: Rotating Micro-Spheres for Adsorption Monitoring at a Fluid Interface

abstract:
  Hypothesis: A broad range of phenomena, such as emulsification and emulsion
stability, foam formation or liquid evaporation, are closely related to the
dynamics of adsorbing colloidal particles. Elucidation of the mechanisms
implied is key to a correct design of many different types of materials.
  Experiments: Microspheres forced to rotate near a fluid interface exhibit a
roto-translational hydrodynamic mechanism that is hindered by capillary torques
as soon as the particles protrude the interface. Under these conditions, the
time evolution in the ratio of moving spheres provides a direct description of
the adsorption kinetics, while microscopy monitoring of particle
acceleration\deceleration informs about the adsorption\desorption dynamics. In
this work, the proposed strategy is applied at an air/water interface loaded
with spherical magnetic particles negatively charged, forced to rotate by the
action of a rotating magnetic field.
  Findings: The proposed method enables the adsorption/desorption dynamics to
be followed during the earliest phase of the process, when desorption of a
small fraction of particles is detected, as well as to estimate approximated
values of the adsorption/desorption constants. The results obtained show that
the addition of a monovalent salt or a cationic (anionic) surfactant promotes
(inhibits) both adsorption and formation of permanent bonds between particles.


title: Arbitrary coherent distributions in a programmable quantum walk

abstract:
  The coherent superposition of position states in a quantum walk (QW) can be
precisely engineered towards the desired distributions to meet the need of
quantum information applications. The coherent distribution can make full use
of quantum parallel in computation and simulation. Particularly, the uniform
superposition provides the robust non-locality, which has wide applications
such as the generation of genuine multi-bit random numbers without
post-processing. We experimentally demonstrate that the rich dynamics featured
with arbitrary coherent distributions can be obtained by introducing different
sets of the time- and position-dependent operations. Such a QW is realized by a
resource-constant and flexible optical circuit, in which the variable operation
is executed based on a Sagnac interferometer in an intrinsically stable and
precisely controlled way. Our results contribute to the practical realization
of quantum-walk-based quantum computation, quantum simulations and quantum
information protocols.


title:
Confidence-rich Localization and Mapping based on Particle Filter for
  Robotic Exploration

abstract:
  This paper mainly studies the localization and mapping of range sensing
robots in the confidence-rich map (CRM), a dense environmental representation
with continuous belief, and then extends to information-theoretic exploration
to reduce the pose uncertainty. Most previous works about active simultaneous
localization and mapping (SLAM) and exploration always assumed the known robot
poses or utilized inaccurate information metrics to approximate pose
uncertainty, resulting in imbalanced exploration performance and efficiency in
the unknown environment. This inspires us to extend the confidence-rich mutual
information (CRMI) with measurable pose uncertainty. Specifically, we propose a
Rao-Blackwellized particle filter-based localization and mapping scheme
(RBPF-CLAM) for CRMs, then we develop a new closed-form weighting method to
improve the localization accuracy without scan matching. We further compute the
uncertain CRMI (UCRMI) with the weighted particles by a more accurate
approximation. Simulations and experimental evaluations show the localization
accuracy and exploration performance of the proposed methods in unstructured
and confined scenes.


title:
Bispectral analysis of nonlinear mixing in a periodically driven
  Korteweg-de Vries system

abstract:
  The nonlinear response of a periodically driven Korteweg-de Vries model
system is studied using a variety of nonlinear drivers and compared to previous
results obtained for a purely time-dependent sinusoidal driver [Phys. Plasmas
27, 113701 (2020)]. It is found that a nonlinear driver in the form of a
cnoidal square wave or a travelling wave driver produces a spectral response
that is closer to experimental observations [Phys. Rev. Lett. 92, 085001
(2004)] than that predicted by the simple sinusoidal driver. Using a bispectral
analysis, we also firmly establish that the nature of the nonlinear
oscillations, due to the interaction between the periodic source and the
inherent collective mode of the system, is predominantly governed by a
three-wave mixing process. Furthermore, by studying the variation in the mixing
pattern, from a broad to a sparse frequency spectrum, as a function of the
driver frequency and its functional form, we propose a means of tailoring the
nature of such patterns. Our results could find useful applications in the
experimental interpretation and manipulation of nonlinear wave mixing patterns
in weakly nonlinear and dispersive plasma systems or similar phenomena in
neutral fluids.


title:
Formation and Immediate Deformation of a Small Filament Through
  Intermittent Magnetic Interactions

abstract:
  It is generally believed that filament formation involves a process of the
accumulation of magnetic energy. However, in this paper we discuss the idea
that filaments will not erupt and will only deform when the stored magnetic
energy is released gradually. Combining high-quality observations from Solar
Dynamics Observatory and other instruments, we present the formation and
immediate deformation of a small filament (F1) in the active region (AR) 12760
on 28-30 April 2020. Before the filament formation, three successive dipoles
quickly emerged with separation motions in the center of AR 12760. Due to the
magnetic interaction between magnetic dipoles and pre-existing positive
polarities, coronal brightenings consequently appeared in the overlying
atmosphere. Subsequently, because of the continuous cancellation of magnetic
flux that happened around the adjacent ends of F1 and another nearby filament
(F2), the magnetic reconections occurred intermittently occurred between F1 and
F2. Finally, F1 lessened in the shear, and F2 became shorter. All the results
show that the formation of F1 was closely associated with intermittent
interactions between the sequence of emerging dipoles and pre-existing magnetic
polarities, and the immediate deformation of F1 was intimately related to
intermittent interactions between F1 and F2. We also suggest that the
intermittent magnetic interactions driven by the continuous magnetic activities
(magnetic-flux emergence, cancellation, and convergence) play an important role
in the formation and deformation of filaments.


title:
Teaching Drones on the Fly: Can Emotional Feedback Serve as Learning
  Signal for Training Artificial Agents?

abstract:
  We investigate whether naturalistic emotional human feedback can be directly
exploited as a reward signal for training artificial agents via interactive
human-in-the-loop reinforcement learning. To answer this question, we devise an
experimental setting inspired by animal training, in which human test subjects
interactively teach an emulated drone agent their desired
command-action-mapping by providing emotional feedback on the drone's action
selections. We present a first empirical proof-of-concept study and analysis
confirming that human facial emotion expression can be directly exploited as
reward signal in such interactive learning settings. Thereby, we contribute
empirical findings towards more naturalistic and intuitive forms of
reinforcement learning especially designed for non-expert users.


title:
An Unsupervised Attentive-Adversarial Learning Framework for Single
  Image Deraining

abstract:
  Single image deraining has been an important topic in low-level computer
vision tasks. The atmospheric veiling effect (which is generated by rain
accumulation, similar to fog) usually appears with the rain. Most deep
learning-based single image deraining methods mainly focus on rain streak
removal by disregarding this effect, which leads to low-quality deraining
performance. In addition, these methods are trained only on synthetic data,
hence they do not take into account real-world rainy images. To address the
above issues, we propose a novel unsupervised attentive-adversarial learning
framework (UALF) for single image deraining that trains on both synthetic and
real rainy images while simultaneously capturing both rain streaks and rain
accumulation features. UALF consists of a Rain-fog2Clean (R2C) transformation
block and a Clean2Rain-fog (C2R) transformation block. In R2C, to better
characterize the rain-fog fusion feature and to achieve high-quality deraining
performance, we employ an attention rain-fog feature extraction network (ARFE)
to exploit the self-similarity of global and local rain-fog information by
learning the spatial feature correlations. Moreover, to improve the
transformation ability of C2R, we design a rain-fog feature decoupling and
reorganization network (RFDR) by embedding a rainy image degradation model and
a mixed discriminator to preserve richer texture details. Extensive experiments
on benchmark rain-fog and rain datasets show that UALF outperforms
state-of-the-art deraining methods. We also conduct defogging performance
evaluation experiments to further demonstrate the effectiveness of UALF


title: Models with (broken) Z2 symmetries

abstract:
  I discuss the allowed parameter space as well as collider prospects of
several new physics models containing (broken) Z2 symmetries. I focus on
effects of current constraints on these, and touch on several future collider
options.


title:
Decision Problems in a Logic for Reasoning about Reconfigurable
  Distributed Systems

abstract:
  We consider a logic used to describe sets of configurations of distributed
systems, whose network topologies can be changed at runtime, by reconfiguration
programs. The logic uses inductive definitions to describe networks with an
unbounded number of components and interactions, written using a multiplicative
conjunction, reminiscent of Bunched Implications and Separation Logic. We study
the complexity of the satisfiability and entailment problems for the
configuration logic under consideration. Additionally, we consider robustness
properties, such as tightness (are all interactions entirely connected to
components?) and degree boundedness (is every component involved in a bounded
number of interactions?), the latter being an ingredient for decidability of
entailments.


title:
Polytopic Matrix Factorization: Determinant Maximization Based Criterion
  and Identifiability

abstract:
  We introduce Polytopic Matrix Factorization (PMF) as a novel data
decomposition approach. In this new framework, we model input data as unknown
linear transformations of some latent vectors drawn from a polytope. In this
sense, the article considers a semi-structured data model, in which the input
matrix is modeled as the product of a full column rank matrix and a matrix
containing samples from a polytope as its column vectors. The choice of
polytope reflects the presumed features of the latent components and their
mutual relationships. As the factorization criterion, we propose the
determinant maximization (Det-Max) for the sample autocorrelation matrix of the
latent vectors. We introduce a sufficient condition for identifiability, which
requires that the convex hull of the latent vectors contains the maximum volume
inscribed ellipsoid of the polytope with a particular tightness constraint.
Based on the Det-Max criterion and the proposed identifiability condition, we
show that all polytopes that satisfy a particular symmetry restriction qualify
for the PMF framework. Having infinitely many polytope choices provides a form
of flexibility in characterizing latent vectors. In particular, it is possible
to define latent vectors with heterogeneous features, enabling the assignment
of attributes such as nonnegativity and sparsity at the subvector level. The
article offers examples illustrating the connection between polytope choices
and the corresponding feature representations.


title: A comment on Bell's Theorem Logical Consistency

abstract:
  In their recent paper, Lambare and Franco correctly claim that Bell
deterministic model and inequalities may be derived using only local causality,
perfect correlations and measurement independence, without talking about joint
probabilities. However, they do not understand and they are not alone, that
measurement independence has nothing to do with freedom of choice and no
conspiracy. Measurement independence should be called noncontextuality, because
it allows implementing random variables, describing incompatible random
experiments, on a unique probability space, on which they are jointly
distributed. Such implementation defines a probabilistic coupling, which we
explain in detail in this paper. Their frequentist proof fails, if the
probabilistic coupling and joint probabilities do not exist. We construct a
probabilistic coupling for their counterexample to prove, that there is no
contradiction with Fine Theorem. Nobody questions Bell Theorem logical
consistency and nobody claims that Fine disproved Bell Theorem. Various
metaphysical assumptions, such as local realism, classicality or counterfactual
definiteness may motivate a choice of a probabilistic model. However, once a
model is chosen, its meaning and its implications may only be discussed
rigorously in a probabilistic framework. Bell inequalities are violated in
various Bell Tests, for us, it proves that that hidden variables depend on
settings confirming contextual character of quantum observables and an active
role played by measuring instruments. Bell was a realist, thus he thought that
he had to choose between nonlocality and super-determinism. From two bad
choices he chose nonlocality. Today he would probably choose contextuality.


title:
The prebiotic molecular inventory of Serpens SMM1: II. The building
  blocks of peptide chains

abstract:
  This work aims to constrain the abundances of interstellar amides, by
searching for this group of prebiotic molecules in the intermediate-mass
protostar Serpens SMM1-a. ALMA observations are conducted toward Serpens SMM1.
A spectrum is extracted toward the SMM1-a position and analyzed with the CASSIS
line analysis software for the presence of characteristic rotational lines of a
number of amides and other molecules. NH$_{2}$CHO, NH$_{2}$CHO $\nu_{12}$=1,
NH$_{2}^{13}$CHO, CH$_{3}$C(O)NH$_{2}$ $\nu$=0,1, CH$_{2}$DOH, CH$_{3}$CHO, and
CH$_{3}$C(O)CH$_{3}$ are securely detected, while trans-NHDCHO, NH$_{2}$CDO,
CH$_{3}$NHCHO $\nu$=0,1, CH$_{3}$COOH, and HOCH$_{2}$CHO are tentatively
identified. The results of this work are compared with detections presented in
the literature. A uniform CH$_{3}$C(O)NH$_{2}$/NH$_{2}$CHO ratio is found for a
group of interstellar sources with vast physical differences. A similar ratio
is seen for CH$_{3}$NHCHO, based on a smaller data sample. The D/H ratio of
NH$_{2}$CHO is about 1--3\% and is close to values found in the low-mass source
IRAS~16293--2422B. The formation of CH$_{3}$C(O)NH$_{2}$ and NH$_{2}$CHO is
likely linked. Formation of these molecules on grain surfaces during the dark
cloud stage is a likely scenario. The high D/H ratio of NH$_{2}$CHO is also
seen as an indication that these molecules are formed on icy dust grains. As a
direct consequence, amides are expected to be present in the most pristine
material from which planetary systems form, thus providing a reservoir of
prebiotic material.


title: Retrolensing by two photon spheres of a black-bounce spacetime

abstract:
  We investigate retrolensing by two photon spheres in a novel black-bounce
spacetime suggested by Lobo et al. which can correspond to a Schwarzschild
black hole, a regular black hole, and a traversable wormhole including an
Ellis-Bronnikov wormhole. In a case, the wormhole has a throat which acts as a
photon sphere and it has another photon sphere outside of the throat. With the
sun as a light source, an observer, and the wormhole are lined up in this
order, sunlight reflected slightly outside of the throat and barely outside and
inside of the outer photon sphere can reach the observer. We show that the
light rays reflected by the outer photon sphere are dominant in retrolensing
light curves in the case.


title: Anisotropy of Kondo-lattice coherence in momentum space for CeCoIn5

abstract:
  We study the electronic and phononic excitations of heavy-fermion metal
CeCoIn$_5$ by polarization-resolved Raman spectroscopy to explore the
Kondo-lattice coherence. Below the coherence temperature T*\,=\,45\,K, the
continuum of electronic excitations in the XY scattering geometry is suppressed
at frequencies below 50\,cm$^{-1}$, whereas the low-frequency continuum in the
X'Y' geometry exhibits no change across T*. We relate the suppression to the
reduced electron-electron scattering rate resulting from the coherence effect.
The presence of suppression in the XY geometry and absence of it in the X'Y'
geometry implies that the $\alpha$ and $\beta$ bands become coherent below T*,
whereas the $\gamma$ band remains largely incoherent down to 10\,K. Moreover,
two optical phonon modes exhibit anomalies in their temperature dependence of
the frequency and linewidth below T*, which results from developing coherent
spectral weight near the Fermi level and reduced electron-phonon scattering
rate. Our results further support the key role of anisotropic hybridization in
CeCoIn$_5$.


title: Finite distributive lattices, polyominoes and ideals of K\"onig type

abstract:
  Finite distributive lattices whose join-meet ideals are of K\"onig type will
be classified. Furthermore, a class of polyominoes whose polyomino ideals are
of K\"onig type will be studied.


title:
Multi-task Safe Reinforcement Learning for Navigating Intersections in
  Dense Traffic

abstract:
  Multi-task intersection navigation including the unprotected turning left,
turning right, and going straight in dense traffic is still a challenging task
for autonomous driving. For the human driver, the negotiation skill with other
interactive vehicles is the key to guarantee safety and efficiency. However, it
is hard to balance the safety and efficiency of the autonomous vehicle for
multi-task intersection navigation. In this paper, we formulate a multi-task
safe reinforcement learning with social attention to improve the safety and
efficiency when interacting with other traffic participants. Specifically, the
social attention module is used to focus on the states of negotiation vehicles.
In addition, a safety layer is added to the multi-task reinforcement learning
framework to guarantee safe negotiation. We compare the experiments in the
simulator SUMO with abundant traffic flows and CARLA with high-fidelity vehicle
models, which both show that the proposed algorithm can improve safety with
consistent traffic efficiency for multi-task intersection navigation.


title: The $m$-bipartite Ramsey number $BR_m(K_{2,2},K_{5,5})$

abstract:
  The bipartite Ramsey number $BR(H_1,H_2,\ldots,H_k)$, is the smallest
positive integer $b$, such that each $k$-decomposition of $E(K_{b,b})$ contains
$H_i$ in the $i$-th class for some $i, 1\leq i\leq k$. As another view of
bipartite Ramsey numbers, for given two bipartite graphs $H_1$ and $H_2$ and a
positive integer $m$, the $m$-bipartite Ramsey number $BR_m(H_1, H_2)$, is
defined as the least integer $n$, such that any subgraph of $K_{m,n}$ say $H$,
results in $H_1\subseteq H$ or $H_2\subseteq \overline{H}$. The size of
$BR_m(K_{2,2}, K_{3,3})$, $BR_m(K_{2,2}, K_{4,4})$ for each $m$, and the size
of $BR_m(K_{3,3}, K_{3,3})$ for some $m$, have been determined in several
papers up to now. Also, it is shown that $BR(K_{2,2}, K_{5,5})=17$. In this
article, we compute the size of $BR_m(K_{2,2}, K_{5,5})$ for some $m\geq 2$.


title:
Navigating Conceptual Space; A new take on Artificial General
  Intelligence

abstract:
  Edward C. Tolman found reinforcement learning unsatisfactory for explaining
intelligence and proposed a clear distinction between learning and behavior.
Tolman's ideas on latent learning and cognitive maps eventually led to what is
now known as conceptual space, a geometric representation where concepts and
ideas can form points or shapes.Active navigation between ideas - reasoning -
can be expressed directly as purposive navigation in conceptual space.
Assimilating the theory of conceptual space from modern neuroscience, we
propose autonomous navigation as a valid approach for emulated cognition.
However, achieving autonomous navigation in high-dimensional Euclidean spaces
is not trivial in technology. In this work, we explore whether neoRL navigation
is up for the task; adopting Kaelbling's concerns for efficient robot
navigation, we test whether the neoRL approach is general across navigational
modalities, compositional across considerations of experience, and effective
when learning in multiple Euclidean dimensions. We find neoRL learning to be
more resemblant of biological learning than of RL in AI, and propose neoRL
navigation of conceptual space as a plausible new path toward emulated
cognition.


title: Experimental demonstration of composite pulses on IBM's quantum computer

abstract:
  We perform comprehensive experimental tests of various composite pulse
sequences using one of open-access IBM's quantum processors, based on
superconducting transmon qubits. We implement explicit pulse control of the
qubit by making use of the opportunity of low-level access to the backend,
provided by IBM Quantum. We obtain the excitation profiles for a huge variety
of broadband, narrowband, and passband composite pulses, producing any
pre-chosen target probabilities, ranging from zero to one. We also test
universal composite pulses which compensate errors in any experimental
parameter. In all experiments, we find excellent agreement between theoretical
and experimental excitation profiles. This proves both the composite pulses as
a very efficient and flexible quantum control tool and the high quality of the
IBM quantum processor. As an extreme example, we test and observe a pronounced
narrowband excitation profile for a composite sequence of as many as 1001
pulses.


title:
Echofilter: A Deep Learning Segmentation Model Improves the Automation,
  Standardization, and Timeliness for Post-Processing Echosounder Data in Tidal
  Energy Streams

abstract:
  Understanding the abundance and distribution of fish in tidal energy streams
is important for assessing the risk presented by the introduction of tidal
energy devices into the habitat. However, the impressive tidal currents that
make sites favorable for tidal energy development are often highly turbulent
and entrain air into the water, complicating the interpretation of echosounder
data. The portion of the water column contaminated by returns from entrained
air must be excluded from data used for biological analyses. Application of a
single algorithm to identify the depth-of-penetration of entrained-air is
insufficient for a boundary that is discontinuous, depth-dynamic, porous, and
widely variable across the tidal flow speeds which can range from 0 to 5m/s.
Using a case study at a tidal energy demonstration site in the Bay of Fundy, we
describe the development and application of deep learning models that produce a
pronounced, consistent, substantial, and measurable improvement of the
automated detection of the extent to which entrained-air has penetrated the
water column.
  Our model, Echofilter, was highly responsive to the dynamic range of
turbulence conditions and sensitive to the fine-scale nuances in the boundary
position, producing an entrained-air boundary line with an average error of
0.32m on mobile downfacing and 0.5-1.0m on stationary upfacing data. The
model's annotations had a high level of agreement with the human segmentation
(mobile downfacing Jaccard index: 98.8%; stationary upfacing: 93-95%). This
resulted in a 50% reduction in the time required for manual edits compared to
the time required to manually edit the line placed by currently available
algorithms. Because of the improved initial automated placement, the
implementation of the models generated a marked increase in the standardization
and repeatability of line placement.


title: Region-Based Semantic Factorization in GANs

abstract:
  Despite the rapid advancement of semantic discovery in the latent space of
Generative Adversarial Networks (GANs), existing approaches either are limited
to finding global attributes or rely on a number of segmentation masks to
identify local attributes. In this work, we present a highly efficient
algorithm to factorize the latent semantics learned by GANs concerning an
arbitrary image region. Concretely, we revisit the task of local manipulation
with pre-trained GANs and formulate region-based semantic discovery as a dual
optimization problem. Through an appropriately defined generalized Rayleigh
quotient, we manage to solve such a problem without any annotations or
training. Experimental results on various state-of-the-art GAN models
demonstrate the effectiveness of our approach, as well as its superiority over
prior arts regarding precise control, region robustness, speed of
implementation, and simplicity of use.


title: Three in One: The VLBI Radio View of the X-ray Quasar RX J1456.0+5048

abstract:
  RX J1456.0+5048 is a prominent X-ray source detected by ROSAT. There is
~100-mJy level radio emission associated with the X-ray source. However,
interferometric observations with increasing angular resolution revealed that
three distinct objects located within 2 arcmin are responsible for the measured
total flux density. Whether these radio sources lining up in the sky are
physically associated or just seen close to each other in projection is not
immediately clear. In fact, incorrect cross-identification of the X-ray,
optical and radio sources can already be found in the literature. Here we
summarise the current knowledge about this intriguing group of objects, where
two of the three sources show compact radio emission detected with very long
baseline interferometry (VLBI). We present a VLBI image of one of them for the
first time, based on archival European VLBI Network (EVN) data taken at 5 GHz.


title:
An Oracle Gradient Regularized Newton Method for Quadratic Measurements
  Regression

abstract:
  Recently, recovering an unknown signal from quadratic measurements has gained
popularity because it includes many interesting applications as special cases
such as phase retrieval, fusion frame phase retrieval, and positive
operator-valued measure. In this paper, by employing the least squares approach
to reconstruct the signal, we establish the non-asymptotic statistical property
showing that the gap between the estimator and the true signal is vanished in
the noiseless case and is bounded in the noisy case by an error rate of
$O(\sqrt{p\log(1+2n)/n})$, where $n$ and $p$ are the number of measurements and
the dimension of the signal, respectively. We develop a gradient regularized
Newton method (GRNM) to solve the least squares problem and prove that it
converges to a unique local minimum at a superlinear rate under certain mild
conditions. In addition to the deterministic results, GRNM can reconstruct the
true signal exactly for the noiseless case and achieve the above error rate
with a high probability for the noisy case. Numerical experiments demonstrate
the GRNM performs nicely in terms of high order of recovery accuracy, faster
computational speed, and strong recovery capability.


title: MSSNet: Multi-Scale-Stage Network for Single Image Deblurring

abstract:
  Most of traditional single image deblurring methods before deep learning
adopt a coarse-to-fine scheme that estimates a sharp image at a coarse scale
and progressively refines it at finer scales. While this scheme has also been
adopted to several deep learning-based approaches, recently a number of
single-scale approaches have been introduced showing superior performance to
previous coarse-to-fine approaches both in quality and computation time. In
this paper, we revisit the coarse-to-fine scheme, and analyze defects of
previous coarse-to-fine approaches that degrade their performance. Based on the
analysis, we propose Multi-Scale-Stage Network (MSSNet), a novel deep
learning-based approach to single image deblurring that adopts our remedies to
the defects. Specifically, MSSNet adopts three novel technical components:
stage configuration reflecting blur scales, an inter-scale information
propagation scheme, and a pixel-shuffle-based multi-scale scheme. Our
experiments show that MSSNet achieves the state-of-the-art performance in terms
of quality, network size, and computation time.


title:
The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player
  Multi-Armed Bandits with no Communication

abstract:
  We study the stochastic multi-player multi-armed bandit problem. In this
problem, $m$ players cooperate to maximize their total reward from $K > m$
arms. However the players cannot communicate and are penalized (e.g. receive no
reward) if they pull the same arm at the same time. We ask whether it is
possible to obtain optimal instance-dependent regret $\tilde{O}(1/\Delta)$
where $\Delta$ is the gap between the $m$-th and $m+1$-st best arms. Such
guarantees were recently achieved in a model allowing the players to implicitly
communicate through intentional collisions. We show that with no communication
at all, such guarantees are, surprisingly, not achievable. In fact, obtaining
the optimal $\tilde{O}(1/\Delta)$ regret for some regimes of $\Delta$
necessarily implies strictly sub-optimal regret in other regimes. Our main
result is a complete characterization of the Pareto optimal instance-dependent
trade-offs that are possible with no communication. Our algorithm generalizes
that of Bubeck, Budzinski, and the second author and enjoys the same strong
no-collision property, while our lower bound is based on a topological
obstruction and holds even under full information.


title: Simultaneous approximation of translation operators

abstract:
  We prove the existence of entire functions that achieve universal
approximations on certain countable sequences of translation operators .


title: Superkicks and the photon angular and linear momentum density

abstract:
  We address a problem of proper definition of momentum density for spatially
structured electromagnetic fields. We show that the expressions for the
momentum and angular momentum obtained locally are not the same when one uses
the canonical energy-momentum tensor instead of the symmetric Belinfante
energy-momentum tensor in electrodynamics. This has important consequences for
interaction of matter with structured light, for example, twisted photons; and
would give drastically different results for forces and angular momenta induced
on small test objects. We show, with numerical estimates of the size of the
effects, situations where the canonical and symmetrized forms induce very
different torques or (superkick) recoil momenta on small objects or atomic
rotors, over a broad range of circumstances.


title: On the asymptotic behavior of solutions to a structure acoustics model

abstract:
  This article concerns the long term behavior of solutions to a structural
acoustic model consisting of a semilinear wave equation defined on a smooth
bounded domain $\Omega\subset\mathbb{R}^3$ which is coupled with a Berger plate
equation acting on a flat portion of the boundary of $\Omega$. The system is
influenced by several competing forces, in particular a source term acting on
the wave equation which is allowed to have a supercritical exponent.
  Our results build upon those obtained by Becklin and Rammaha [8]. With some
restrictions on the parameters in the system and with careful analysis
involving the Nehari manifold we obtain global existence of potential well
solutions and establish either exponential or algebraic decay rates of energy,
dependent upon the behavior of the damping terms. The main novelty in this work
lies in our stabilization estimate, which notably does not generate lower-order
terms. Consequently, the proof of the main result is shorter and more concise.


title:
Survey of Machine Learning Based Intrusion Detection Methods for
  Internet of Medical Things

abstract:
  Internet of Medical Things (IoMT) represents an application of the Internet
of Things, where health professionals perform remote analysis of physiological
data collected using sensors that are associated with patients, allowing
real-time and permanent monitoring of the patient's health condition and the
detection of possible diseases at an early stage. However, the use of wireless
communication for data transfer exposes this data to cyberattacks, and the
sensitive and private nature of this data may represent a prime interest for
attackers. The use of traditional security methods on equipment that is limited
in terms of storage and computing capacity is ineffective. In this context, we
have performed a comprehensive survey to investigate the use of the intrusion
detection system based on machine learning (ML) for IoMT security. We presented
the generic three-layer architecture of IoMT, the security requirement of IoMT
security. We review the various threats that can affect IoMT security and
identify the advantage, disadvantages, methods, and datasets used in each
solution based on ML. Then we provide some challenges and limitations of
applying ML on each layer of IoMT, which can serve as direction for future
study.


title: Superconductivity in Bi based Bi2PdPt

abstract:
  We report synthesis and properties of superconducting Bi$_2$PdPt, a new
member of Bi-Pd based compounds known for their simultaneous existence of
topological surfaces states and superconductivity. It crystallizes in a
hexagonal structure having space group $P6_3/mmc$. A detailed investigation of
the properties via transport, magnetization, and specific heat measurements
confirm bulk superconductivity with transition temperature, \textit{T}$_{C}$ =
4.0(1) K in moderate coupling limit.


title:
Eigensolutions and Thermodynamic Properties of Kratzer plus generalized
  Morse Potential

abstract:
  In this study, we apply the parametric Nikiforov-Uvarov method to obtain the
bound state solution of Schrodinger wave equation in the presence of Kratzer
plus generalized Morse potential (KPGM). The energy eigen equation and the
corresponding normalised wave function were obtained in closed form. The
resulting energy eigen equation were use to study partition function and other
thermodynamic properties such as vibrational mean energy, vibrational specific
heat capacity, vibrational mean free energy and vibrational entropy for the
proposed potential as applied to lithium hydride diatomic molecule. The
thermodynamic plots obtained were in excellent agreement to work of existing
literatures. The wave function and probability density plots for the diatomic
molecules were obtained through a well designed and implemented maple
programme.


title: The heat flow conjecture for random matrices

abstract:
  Recent results by various authors have established a "model deformation
phenomenon" in random matrix theory. Specifically, it is possible to construct
pairs of random matrix models such that the limiting eigenvalue distributions
are connected by push-forward under an explicitly constructible map of the
plane to itself. In this paper, we argue that the analogous transformation at
the finite-$N$ level can be accomplished by applying an appropriate heat flow
to the characteristic polynomial of the first model.
  Let the "second moment" of a random polynomial $p$ denote the expectation
value of the square of the absolute value of $p.$ We find certain pairs of
random matrix models and we apply a certain heat-type operator to the
characteristic polynomial $p_{1}$ of the first model, giving a new polynomial
$q.$ We prove that the second moment of $q$ is equal to the second moment of
the characteristic polynomial $p_{2}$ of the second model. This result leads to
several conjectures of the following sort: when $N$ is large, the zeros of $q$
have the same bulk distribution as the zeros of $p_{2},$ namely the eigenvalues
of the second random matrix model. At a more refined level, we conjecture that,
as the characteristic polynomial of the first model evolves under the
appropriate heat flow, its zeros will evolve close to the characteristic curves
of a certain PDE. All conjectures are formulated in "additive" and
"multiplicative" forms.
  As a special case, suppose we apply the standard heat operator for time $1/N$
to the characteristic polynomial $p$ of an $N\times N$ GUE matrix, giving a new
polynomial $q.$ We conjecture that the zeros of $q$ will be asymptotically
uniformly distributed over the unit disk. That is, the heat operator converts
the distribution of zeros from semicircular to circular.


title: Detection of Stealthy Adversaries for Networked Unmanned Aerial Vehicles

abstract:
  A network of unmanned aerial vehicles (UAVs) provides distributed coverage,
reconfigurability, and maneuverability in performing complex cooperative tasks.
However, it relies on wireless communications that can be susceptible to cyber
adversaries and intrusions, disrupting the entire network's operation. This
paper develops model-based centralized and decentralized observer techniques
for detecting a class of stealthy intrusions, namely zero-dynamics and covert
attacks, on networked UAVs in formation control settings. The centralized
observer that runs in a control center leverages switching in the UAVs'
communication topology for attack detection, and the decentralized observers,
implemented onboard each UAV in the network, use the model of networked UAVs
and locally available measurements. Experimental results are provided to show
the effectiveness of the proposed detection schemes in different case studies.


title:
Reward Modeling for Mitigating Toxicity in Transformer-based Language
  Models

abstract:
  Transformer-based language models are able to generate fluent text and be
efficiently adapted across various natural language generation tasks. However,
language models that are pretrained on large unlabeled web text corpora have
been shown to suffer from degenerating toxic content and social bias behaviors,
consequently hindering their safe deployment. Various detoxification methods
were proposed to mitigate the language model's toxicity; however, these methods
struggled to detoxify language models when conditioned on prompts that contain
specific social identities related to gender, race, or religion. In this study,
we propose Reinforce-Detoxify; A reinforcement learning-based method for
mitigating toxicity in language models. We address the challenge of safety in
language models and propose a new reward model that is able to detect toxic
content and mitigate unintended bias towards social identities in toxicity
prediction. The experiments demonstrate that the Reinforce-Detoxify method for
language model detoxification outperforms existing detoxification approaches in
automatic evaluation metrics, indicating the ability of our approach in
language model detoxification and less prone to unintended bias toward social
identities in generated content.


title:
Structure of $180^\circ$ ferroelectric domain walls in $\mathrm{HfO_2}$
  and $\mathrm{ZrO_2}$

abstract:
  We report two series of structures representing two types of $180^\circ$
ferroelectric domain walls in $\mathrm{HfO_2}$ and $\mathrm{ZrO_2}$. We model
the domain structures with different width by density functional theory
calculations. The results reveal the dependency of domain wall energy and
polarization on domain width. We show how the directions of polarization of
ferroelectric domains can be determined from experimentally observed Hf atoms,
despite the fact that the knowledge about the positions of O atoms is missing.
The two series of structures thus provide more thorough interpretation and
hence deeper understanding of experimental observations by transmission
electron microscopy.


title:
Accurate Prediction and Uncertainty Estimation using Decoupled
  Prediction Interval Networks

abstract:
  We propose a network architecture capable of reliably estimating uncertainty
of regression based predictions without sacrificing accuracy. The current
state-of-the-art uncertainty algorithms either fall short of achieving
prediction accuracy comparable to the mean square error optimization or
underestimate the variance of network predictions. We propose a decoupled
network architecture that is capable of accomplishing both at the same time. We
achieve this by breaking down the learning of prediction and prediction
interval (PI) estimations into a two-stage training process. We use a custom
loss function for learning a PI range around optimized mean estimation with a
desired coverage of a proportion of the target labels within the PI range. We
compare the proposed method with current state-of-the-art uncertainty
quantification algorithms on synthetic datasets and UCI benchmarks, reducing
the error in the predictions by 23 to 34% while maintaining 95% Prediction
Interval Coverage Probability (PICP) for 7 out of 9 UCI benchmark datasets. We
also examine the quality of our predictive uncertainty by evaluating on Active
Learning and demonstrating 17 to 36% error reduction on UCI benchmarks.


title:
A primal-dual splitting algorithm for composite monotone inclusions with
  minimal lifting

abstract:
  In this work, we study resolvent splitting algorithms for solving composite
monotone inclusion problems. The objective of these general problems is finding
a zero in the sum of maximally monotone operators composed with linear
operators. Our main contribution is establishing the first primal-dual
splitting algorithm for composite monotone inclusions with minimal lifting.
Specifically, the proposed scheme reduces the dimension of the product space
where the underlying fixed point operator is defined, in comparison to other
algorithms, without requiring additional evaluations of the resolvent
operators. We prove the convergence of this new algorithm and analyze its
performance in a problem arising in image deblurring and denoising. This work
also contributes to the theory of resolvent splitting algorithms by extending
the minimal lifting theorem recently proved by Malitsky and Tam to schemes with
resolvent parameters.


title: A local-to-global analysis of Gelfand-Fuks cohomology

abstract:
  We present a novel proof technique to construct the Gelfand-Fuks spectral
sequence for diagonal Chevalley-Eilenberg cohomology of vector fields on a
smooth manifold, performing a local-to-global analysis through a notion of
generalized good covers from the theory of factorization algebras and
cosheaves. This approach yields a unified way to deal with the problem of
comparing "sheaf-like" data over different Cartesian powers of the manifold,
and is easily generalized to the study of other cohomology theories associated
to geometric objects. Independently, we lay out a detailed and easily
accessible exposition on the continuous Chevalley-Eilenberg cohomology of
formal vector fields and of vector fields on Euclidean space, modernizing and
elaborating on well-established literature on the subject by Bott, Fuks, and
Gelfand.


title: Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning

abstract:
  Off-policy evaluation and learning (OPE/L) use offline observational data to
make better decisions, which is crucial in applications where experimentation
is necessarily limited. OPE/L is nonetheless sensitive to discrepancies between
the data-generating environment and that where policies are deployed. Recent
work proposed distributionally robust OPE/L (DROPE/L) to remedy this, but the
proposal relies on inverse-propensity weighting, whose regret rates may
deteriorate if propensities are estimated and whose variance is suboptimal even
if not. For vanilla OPE/L, this is solved by doubly robust (DR) methods, but
they do not naturally extend to the more complex DROPE/L, which involves a
worst-case expectation. In this paper, we propose the first DR algorithms for
DROPE/L with KL-divergence uncertainty sets. For evaluation, we propose
Localized Doubly Robust DROPE (LDR$^2$OPE) and prove its semiparametric
efficiency under weak product rates conditions. Notably, thanks to a
localization technique, LDR$^2$OPE only requires fitting a small number of
regressions, just like DR methods for vanilla OPE. For learning, we propose
Continuum Doubly Robust DROPL (CDR$^2$OPL) and show that, under a product rate
condition involving a continuum of regressions, it enjoys a fast regret rate of
$\mathcal{O}(N^{-1/2})$ even when unknown propensities are nonparametrically
estimated. We further extend our results to general $f$-divergence uncertainty
sets. We illustrate the advantage of our algorithms in simulations.


title:
Light-induced dimension crossover in 1T-TiSe$_2$ dictated by excitonic
  correlations

abstract:
  In low-dimensional systems with strong electronic correlations, the
application of an ultrashort laser pulse often yields novel phases that are
otherwise inaccessible. The central challenge in understanding such phenomena
is to determine how dimensionality and many-body correlations together govern
the pathway of a non-adiabatic transition. To this end, we examine a layered
compound, 1T-TiSe$_2$, whose three-dimensional charge-density-wave (3D CDW)
state also features exciton condensation due to strong electron-hole
interactions. We find that photoexcitation suppresses the equilibrium 3D CDW
while creating a nonequilibrium 2D CDW. Remarkably, the dimension reduction
does not occur unless bound electron-hole pairs are broken. This relation
suggests that excitonic correlations maintain the out-of-plane CDW coherence,
settling a long-standing debate over their role in the CDW transition. Our
findings demonstrate how optical manipulation of electronic interaction enables
one to control the dimensionality of a broken-symmetry order, paving the way
for realizing other emergent states in strongly correlated systems.


title:
Evaluation of voltage magnitude based unbalance metric for low voltage
  distribution networks

abstract:
  Voltage unbalance in distribution networks (DN) is expected to grow with
increasing penetration of single-phase distributed generation and single-phase
loads such as electric vehicle chargers. Unbalance mitigation will be a
significant concern as voltage unbalance leads to increased losses, reduced
motor and inverter efficiency, and becomes a limiting factor for DN operation.
The true definition of the unbalance metric needs phasor measurements of
network voltage and current. However, such phasor measurements are generally
not available in real life and as such approximate definitions are widely used
due to their simplicity. This work aims to compare the true voltage unbalance
definition and approximate unbalance metrics derived from phase voltage
magnitude, as phase voltage magnitudes are commonly measured by digital
metering infrastructure. For the comparison, multi-period power flow
simulations are performed for 161 Spanish distribution feeders with R/X ratios
varying from 2.87 to 14.68. We observe that phase magnitude-based unbalance
metrics reasonably approximate the true unbalance for higher R/X ratios with a
varying load power factor in a DN. Furthermore, the approximate unbalance
metrics slightly improve for a low DN power factor due to the increase in DN
unbalance. Therefore, the phase magnitude-based unbalance metric can be
utilized for approximating DN unbalance.


title:
$d$-th roots of determinants of generators of $\mathcal{C}_6$-subgroups
  of $\mathrm{GL}(d, q)$ in $\mathbb{F}_q$

abstract:
  The maximal subgroups of the finite classical groups can be partitioned into
nine classes according to Aschbacher's theorem. We are concerned here with the
class $\mathcal{C}_6$ consisting of normalisers of extraspecial groups or of
two-groups of symplectic type. In "Constructing maximal subgroups of classical
groups", Holt and Roney-Dougal give explicit constructions for generators of
these subgroups. Namely, for $\mathrm{SL}(d, q)$, this is done by first
describing generators of the respective normalisers in $\mathrm{GL}(d, q)$ and
afterwards appropriately scaling them so that they lie in $\mathrm{SL}(d, q)$.
Effectively, this comes down to finding a $d$-th root of the determinants of
the generators in $\mathbb{F}_q$.
  In Proposition 9.5 of loc.cit., it is claimed that this can be done in almost
all cases with only a few exceptions, but no detailed explanation or analysis
is given why this is true or when exactly this is possible. This paper works
out the missing details by examining the relevant determinants and giving a
complete description of the cases in which they are $d$-th powers in
$\mathbb{F}_q$. Furthermore, it turns out that these determinants are actually
already $1$ in many cases so that one can omit the scaling entirely -
particularly for large $q$, this may save some time in practical computations.


title: Truncated Diffusion Probabilistic Models

abstract:
  Employing a forward Markov diffusion chain to gradually map the data to a
noise distribution, diffusion probabilistic models learn how to generate the
data by inferring a reverse Markov diffusion chain to invert the forward
diffusion process. To achieve competitive data generation performance, they
demand a long diffusion chain that makes them computationally intensive in not
only training but also generation. To significantly improve the computation
efficiency, we propose to truncate the forward diffusion chain by abolishing
the requirement of diffusing the data to random noise. Consequently, we start
the inverse diffusion chain from an implicit generative distribution, rather
than random noise, and learn its parameters by matching it to the distribution
of the data corrupted by the truncated forward diffusion chain. Experimental
results show our truncated diffusion probabilistic models provide consistent
improvements over the non-truncated ones in terms of the generation performance
and the number of required inverse diffusion steps.


title:
Suggestive evidence for Coherent Elastic Neutrino-Nucleus Scattering
  from reactor antineutrinos

abstract:
  The 96.4 day exposure of a 3 kg ultra-low noise germanium detector to the
high flux of antineutrinos from a power nuclear reactor is described. A very
strong preference for the presence of a coherent elastic neutrino-nucleus
scattering (CE$\nu$NS) component in the data is found, when compared to a
background-only model. No such effect is visible in 25 days of operation during
reactor outages. The best-fit CE$\nu$NS signal is in good agreement with
expectations based on a recent characterization of germanium response to
sub-keV nuclear recoils. Deviations of order 60\% from the Standard Model
CE$\nu$NS prediction can be excluded using present data. Standing uncertainties
in models of germanium quenching factor, neutrino energy spectrum, and
background are examined.


title: A Regularized Implicit Policy for Offline Reinforcement Learning

abstract:
  Offline reinforcement learning enables learning from a fixed dataset, without
further interactions with the environment. The lack of environmental
interactions makes the policy training vulnerable to state-action pairs far
from the training dataset and prone to missing rewarding actions. For training
more effective agents, we propose a framework that supports learning a flexible
yet well-regularized fully-implicit policy. We further propose a simple
modification to the classical policy-matching methods for regularizing with
respect to the dual form of the Jensen--Shannon divergence and the integral
probability metrics. We theoretically show the correctness of the
policy-matching approach, and the correctness and a good finite-sample property
of our modification. An effective instantiation of our framework through the
GAN structure is provided, together with techniques to explicitly smooth the
state-action mapping for robust generalization beyond the static dataset.
Extensive experiments and ablation study on the D4RL dataset validate our
framework and the effectiveness of our algorithmic designs.


title: Generalized Optimistic Methods for Convex-Concave Saddle Point Problems

abstract:
  The optimistic gradient method has seen increasing popularity as an efficient
first-order method for solving convex-concave saddle point problems. To analyze
its iteration complexity, a recent work [arXiv:1901.08511] proposed an
interesting perspective that interprets the optimistic gradient method as an
approximation to the proximal point method. In this paper, we follow this
approach and distill the underlying idea of optimism to propose a generalized
optimistic method, which encompasses the optimistic gradient method as a
special case. Our general framework can handle constrained saddle point
problems with composite objective functions and can work with arbitrary norms
with compatible Bregman distances. Moreover, we also develop an adaptive line
search scheme to select the stepsizes without knowledge of the smoothness
coefficients. We instantiate our method with first-order, second-order and
higher-order oracles and give sharp global iteration complexity bounds. When
the objective function is convex-concave, we show that the averaged iterates of
our $p$-th-order method ($p\geq 1$) converge at a rate of
$\mathcal{O}(1/N^\frac{p+1}{2})$. When the objective function is further
strongly-convex-strongly-concave, we prove a complexity bound of
$\mathcal{O}(\frac{L_1}{\mu}\log\frac{1}{\epsilon})$ for our first-order method
and a bound of $\mathcal{O}((L_p
D^\frac{p-1}{2}/\mu)^{\frac{2}{p+1}}+\log\log\frac{1}{\epsilon})$ for our
$p$-th-order method ($p\geq 2$) respectively, where $L_p$ ($p\geq 1$) is the
Lipschitz constant of the $p$-th-order derivative, $\mu$ is the strongly-convex
parameter, and $D$ is the initial Bregman distance to the saddle point.
Moreover, our line search scheme provably only requires an almost constant
number of calls to a subproblem solver per iteration on average, making our
first-order and second-order methods particularly amenable to implementation.


title: Finite maximal codes and factorizations of cyclic groups

abstract:
  Variable-length codes are the bases of the free submonoids of a free monoid.
There are some important longstanding open questions about the structure of
finite maximal codes, namely the factorization conjecture and the triangle
conjecture, proposed by Perrin and Sch\"{u}tzemberger. The latter concerns
finite codes $Y$ which are subsets of $a^* B a^*$, where $a$ is a letter and
$B$ is an alphabet not containing $a$. A structural property of finite maximal
codes has recently been shown by Zhang and Shum. It exhibits a relationship
between finite maximal codes and factorizations of cyclic groups. With the aim
of highlighting the links between this result and other older ones on maximal
and factorizing codes, we give a simpler and a new proof of this result. As a
consequence, we prove that for any finite maximal code $X \subseteq (B \cup \{a
\})^*$ containing the word $a^{pq}$, where $p,q$ are prime numbers, $X \cap a^*
B a^*$ satisfies the triangle conjecture. Let $n$ be a positive integer that is
a product of at most two prime numbers. We also prove that it is decidable
whether a finite code $Y \cup a^{n} \subseteq a^* B a^* \cup a^*$ is included
in a finite maximal code and that, if this holds, $Y \cup a^{n}$ is included in
a code that also satisfies the factorization conjecture.


title: Gabber rigidity in hermitian K-theory

abstract:
  We note that Gabber's rigidity theorem for the algebraic K-theory of
henselian pairs also holds true for hermitian K-theory with respect to
arbitrary form parameters.


title:
Families of relatively exact Lagrangians, free loop spaces and
  generalised homology

abstract:
  We prove that (under appropriate orientation conditions, depending on $R$) a
Hamiltonian isotopy $\psi^1$ of a symplectic manifold $(M, \omega)$ fixing a
relatively exact Lagrangian $L$ setwise must act trivially on $R_*(L)$, where
$R_*$ is some generalised homology theory. We use a strategy inspired by that
of Hu, Lalonde and Leclercq (\cite{Hu-Lalonde-Leclercq}), who proved an
analogous result over $\mathbb{Z}/2$ and over $\mathbb{Z}$ under stronger
orientation assumptions. However the differences in our approaches let us
deduce that if $L$ is a homotopy sphere, $\psi^1|_L$ is homotopic to the
identity. Our technical set-up differs from both theirs and that of Cohen,
Jones and Segal (\cite{Cohen-Jones-Segal, Cohen}).
  We also prove (under similar conditions) that $\psi^1|_L$ acts trivially on
$R_*(\mathcal{L} L)$, where $\mathcal{L} L$ is the free loop space of $L$. From
this we deduce that when $L$ is a surface or a $K(\pi, 1)$, $\psi^1|_L$ is
homotopic to the identity.
  Using methods of \cite{Lalonde-McDuff}, we also show that given a family of
Lagrangians all of which are Hamiltonian isotopic to $L$ over a sphere or a
torus, the associated fibre bundle cohomologically splits over $\mathbb{Z}/2$.


title: Entangled, Spin-polarized Excitons from Singlet Fission in a Rigid Dimer

abstract:
  Singlet fission, a process that splits a singlet exciton into a biexciton,
has promise in quantum information. We report time-resolved electron
paramagnetic resonance measurements on a molecule, TIPS-BP1$'$, designed to
exhibit strongly state-selective relaxation to specific magnetic spin
sublevels. The resulting optically pumped "spin polarization" is a nearly pure
initial state from the ensemble. The long-lived spin coherences modulate the
signal intrinsically, allowing a new measurement scheme that substantially
removes noise and uncertainty in the magnetic resonance spectra. A nonadiabatic
transition theory with a minimal number of spectroscopic parameters allows the
quantitative assignment and interpretation of the spectra. The rigid,
covalently bound dimer, TIPS-BP1$'$, supports persistent spin coherences at
temperatures far higher than those used in conventional quantum hardware.


title:
Simple Genetic Operators are Universal Approximators of Probability
  Distributions (and other Advantages of Expressive Encodings)

abstract:
  This paper characterizes the inherent power of evolutionary algorithms. This
power depends on the computational properties of the genetic encoding. With
some encodings, two parents recombined with a simple crossover operator can
sample from an arbitrary distribution of child phenotypes. Such encodings are
termed \emph{expressive encodings} in this paper. Universal function
approximators, including popular evolutionary substrates of genetic programming
and neural networks, can be used to construct expressive encodings. Remarkably,
this approach need not be applied only to domains where the phenotype is a
function: Expressivity can be achieved even when optimizing static structures,
such as binary vectors. Such simpler settings make it possible to characterize
expressive encodings theoretically: Across a variety of test problems,
expressive encodings are shown to achieve up to super-exponential convergence
speed-ups over the standard direct encoding. The conclusion is that, across
evolutionary computation areas as diverse as genetic programming,
neuroevolution, genetic algorithms, and theory, expressive encodings can be a
key to understanding and realizing the full power of evolution.


title: HEWES: Heisenberg-Euler Weak-Field Expansion Simulator

abstract:
  A numerical simulation code of nonlinear effective light-light interactions
in the weak-field expansion of the famous Heisenberg-Euler theory of quantum
electrodynamics is presented. The field of application is the quantum vacuum,
where these interactions can be triggered by high-intensity laser pulse
collisions, causing a variety of intriguing effects. Since theoretical
approaches are limited to approximations and manageable configurations, and the
experimental requirements for the detection of these signals are high, the need
for support from the numerical side is apparent.


title: A note on wormholes as compact stellar objects

abstract:
  A Morris-Thorne wormhole can only be sustained by violating the null energy
condition, thereby requiring the existence of "exotic matter," a requirement
that many researchers consider unphysical. Using a mostly qualitative approach,
it is proposed in this note that the basic problems can be overcome by purely
relativistic considerations. The implication is that a Morris-Thorne wormhole
is likely to be a compact stellar object.


title: Scaling of acceleration statistics in high Reynolds number turbulence

abstract:
  The scaling of acceleration statistics in turbulence is examined by combining
data from the literature with new data from well-resolved direct numerical
simulations of isotropic turbulence, significantly extending the Reynolds
number range. The acceleration variance at higher Reynolds numbers departs from
previous predictions based on multifractal models, which characterize
Lagrangian intermittency as a naive extension of Eulerian intermittency. The
disagreement is even more prominent for higher-order moments of the
acceleration. Instead, starting from a known exact relation, we relate the
scaling of acceleration variance to that of Eulerian fourth-order velocity
gradient and velocity increment statistics. This prediction is in excellent
agreement with the variance data. Our work highlights the need for models that
consider Lagrangian intermittency independent of the Eulerian counterpart.


title:
Combining Node-RED and Openwhisk for Pattern-based Development and
  Execution of Complex FaaS Workflows

abstract:
  Modern cloud computing advances have been pressing application modernization
in the last 15 years, stressing the need for application redesign towards the
use of more distributed and ephemeral resources. From the initial IaaS and PaaS
approaches, to microservices and now to the serverless model (and especially
the Function as a Service approach), new challenges arise constantly for
application developers. This paper presents a design and development
environment that aims to ease application evolution and migration to the new
FaaS model, based on the widely used Node-RED open source tool. The goal of the
environment is to enable a more user friendly and abstract function and
workflow creation for complex FaaS applications. To this end, it bypasses
workflow description and function reuse limitations of the current FaaS
platforms, by providing an extendable, pattern-enriched palette of ready-made,
reusable functionality that can be combined in arbitrary ways. The environment
embeds seamless DevOps processes for generating the deployable artefacts (i.e.
functions and images) of the FaaS platform (Openwhisk). Annotation mechanisms
are also available for the developer to dictate diverse execution options or
management guidelines towards the deployment and operation stacks. The
evaluation is based on case studies of indicative scenarios, including
creating, registering and executing functions and flows based on the Node-RED
runtime, embedding of existing legacy code in a FaaS environment, parallelizing
a workload, collecting data at the edge and creating function orchestrators to
accompany the application. For the latter, a detailed argumentation is provided
as to why this process should not be constrained by the "double billing"
principle of FaaS.


title:
Energy reconstruction of hadronic showers at the CERN PS and SPS using
  the Semi-Digital Hadronic Calorimeter

abstract:
  The CALICE Semi-Digital Hadronic CALorimeter (SDHCAL) is the first
technological prototype in a family of high-granularity calorimeters developed
by the CALICE Collaboration to equip the experiments of future lepton
colliders. The SDHCAL is a sampling calorimeter using stainless steel for
absorber and Glass Resistive Plate Chambers (GRPC) as a sensitive medium. The
GRPC are read out by 1~cm $\times$ 1~cm pickup pads combined to a
multi-threshold electronics. The prototype was exposed to hadron beams in both
the CERN PS and the SPS beamlines in 2015 allowing the test of the SDHCAL in a
large energy range from 3~GeV to 80~GeV. After introducing the method used to
select the hadrons of our data and reject the muon and electron contamination,
we present the energy reconstruction approach that we apply to the data
collected from both beamlines and we discuss the response linearity and the
energy resolution of the SDHCAL. The results obtained in the two beamlines
confirm the excellent SDHCAL performance observed with the data collected with
the same prototype in the SPS beamline in 2012. They also show the stability of
the SDHCAL in different beam conditions and different time periods.


title: Scalable Fine-Grained Parallel Cycle Enumeration Algorithms

abstract:
  This paper investigates scalable parallelisation of state-of-the-art cycle
enumeration algorithms by Johnson and Read-Tarjan along with their applications
to temporal graphs. We provide a comprehensive theoretical analysis of various
parallel versions of these algorithms and evaluate their performance on
multi-core processors. We show that a straightforward coarse-grained
parallelisation approach is not scalable and suffers from load imbalance
issues. To eliminate the load imbalance, we modify the Johnson and the
Read-Tarjan algorithms to exploit finer-grained parallelism. We show that our
fine-grained parallel Read-Tarjan algorithm is theoretically work efficient --
i.e., it does no more work than its serial version. However, our fine-grained
parallel Johnson algorithm does not share this property. Yet, in practice, our
fine-grained parallel Johnson algorithm outperforms our fine-grained parallel
Read-Tarjan algorithm. In any case, both of our contributed fine-grained
parallel algorithms are scalable, meaning that they can effectively utilise an
increasing number of software threads, which we prove theoretically and
demonstrate through extensive experiments. On a cluster of multi-core CPUs with
$256$ physical cores that can execute $1024$ simultaneous threads, our
fine-grained parallel Johnson and Read-Tarjan algorithms are respectively up to
$435\times$ and $470\times$ faster than their single-threaded versions. On the
same compute cluster, our fine-grained parallel algorithms are on average an
order of magnitude faster than their coarse-grained parallel counterparts.


title: Local Decomposition of Hexahedral Singular Nodes into Singular Curves

abstract:
  Hexahedral (hex) meshing is a long studied topic in geometry processing with
many fascinating and challenging associated problems. Hex meshes vary in
complexity from structured to unstructured depending on application or domain
of interest. Fully structured meshes require that all interior mesh edges are
adjacent to exactly four hexes. Edges not satisfying this criteria are
considered singular and indicate an unstructured hex mesh. Singular edges join
together into singular curves that either form closed cycles, end on the mesh
boundary, or end at a singular node, a complex junction of more than two
singular curves. While all hex meshes with singularities are unstructured,
those with more complex singular nodes tend to have more distorted elements and
smaller scaled Jacobian values. In this work, we study the topology of singular
nodes. We show that all eight of the most common singular nodes are
decomposable into just singular curves. We further show that all singular
nodes, regardless of edge valence, are locally decomposable. Finally we
demonstrate these decompositions on hex meshes, thereby decreasing their
distortion and converting all singular nodes into singular curves. With this
decomposition, the enigmatic complexity of 3D singular nodes becomes
effectively 2D.


title:
The Dimension of the Moduli Space of Pointed Algebraic Curves of Low
  Genus

abstract:
  We explicitly compute the moduli space pointed algebraic curves with a given
numerical semigroup as Weierstrass semigroup for many cases of genus at most
seven and determine the dimension for all semigroups of genus seven.


title: A Variance-Reduced Stochastic Accelerated Primal Dual Algorithm

abstract:
  In this work, we consider strongly convex strongly concave (SCSC) saddle
point (SP) problems
$\min_{x\in\mathbb{R}^{d_x}}\max_{y\in\mathbb{R}^{d_y}}f(x,y)$ where $f$ is
$L$-smooth, $f(.,y)$ is $\mu$-strongly convex for every $y$, and $f(x,.)$ is
$\mu$-strongly concave for every $x$. Such problems arise frequently in machine
learning in the context of robust empirical risk minimization (ERM), e.g.
$\textit{distributionally robust}$ ERM, where partial gradients are estimated
using mini-batches of data points. Assuming we have access to an unbiased
stochastic first-order oracle we consider the stochastic accelerated primal
dual (SAPD) algorithm recently introduced in Zhang et al. [2021] for SCSC SP
problems as a robust method against gradient noise. In particular, SAPD
recovers the well-known stochastic gradient descent ascent (SGDA) as a special
case when the momentum parameter is set to zero and can achieve an accelerated
rate when the momentum parameter is properly tuned, i.e., improving the $\kappa
\triangleq L/\mu$ dependence from $\kappa^2$ for SGDA to $\kappa$. We propose
efficient variance-reduction strategies for SAPD based on Richardson-Romberg
extrapolation and show that our method improves upon SAPD both in practice and
in theory.


title: Is there an aesthetic component of language?

abstract:
  Speakers of all human languages make use of grammatical devices to express
attributional qualities, feelings, and opinions as well as to provide
meta-commentary on topics in discourse. In general, linguists refer to this
category as 'expressives'in spite of the fact that defining exactly what
'expressives' are remains elusive. The elusiveness of expressives has given
rise to considerable speculation about the nature of expressivity as a
linguistic principle. Specifically, several scholars have pointed out the
'special' or 'unusual' nature of expressives vis-a-vis 'normal' or 'natural'
morpho-syntax.


title: Fundamental Limitations on Gain of THz QCL

abstract:
  We analyze the main physical processes in quantum cascade lasers with spatial
separation between the region of photon radiation and LO phonon emission
providing depopulation of the lower level of the optical transition. Our
purpose is to find reasons of reduction of the population inversion at low
photon energy and search for ways of its increase. The expression for the
population inversion is obtained from equation for simplified density matrix.
This allows us, on one hand, to take into account coherence of tunneling
between different levels and, on the other hand, to understand its role in
transition probabilities in a simple way. We come to the conclusion that
population inversion in THz lasers can be significantly increased by
optimization of tunneling matrix element between the two regions and LO phonon
emission time. The optimal value of the matrix element is smaller than its
maximal possible value. As well, the optimal LO phonon emission time is larger
than the time reached at LO phonon resonant emission.


title:
Parallel Sampling for Efficient High-dimensional Bayesian Network
  Structure Learning

abstract:
  Score-based algorithms that learn the structure of Bayesian networks can be
used for both exact and approximate solutions. While approximate learning
scales better with the number of variables, it can be computationally expensive
in the presence of high dimensional data. This paper describes an approximate
algorithm that performs parallel sampling on Candidate Parent Sets (CPSs), and
can be viewed as an extension of MINOBS which is a state-of-the-art algorithm
for structure learning from high dimensional data. The modified algorithm,
which we call Parallel Sampling MINOBS (PS-MINOBS), constructs the graph by
sampling CPSs for each variable. Sampling is performed in parallel under the
assumption the distribution of CPSs is half-normal when ordered by Bayesian
score for each variable. Sampling from a half-normal distribution ensures that
the CPSs sampled are likely to be those which produce the higher scores.
Empirical results show that, in most cases, the proposed algorithm discovers
higher score structures than MINOBS when both algorithms are restricted to the
same runtime limit.


title: Dismantling the information flow in complex interconnected systems

abstract:
  Microscopic structural damage, such as lesions in neural systems or
disruptions in urban transportation networks, can impair the dynamics crucial
for systems' functionality, such as electrochemical signals or human flows, or
any other type of information exchange, respectively, at larger topological
scales. Damage is usually modeled by progressive removal of components or
connections and, consequently, systems' robustness is assessed in terms of how
fast their structure fragments into disconnected sub-systems. Yet, this
approach fails to capture how damage hinders the propagation of information
across scales, since system function can be degraded even in absence of
fragmentation -- e.g., pathological yet structurally integrated human brain.
Here, we probe the response to damage of dynamical processes on the top of
complex networks, to study how such an information flow is affected. We find
that removal of nodes central for network connectivity might have insignificant
effects, challenging the traditional assumption that structural metrics alone
are sufficient to gain insights about how complex systems operate. Using a
damaging protocol explicitly accounting for flow dynamics, we analyze synthetic
and empirical systems, from biological to infrastructural ones, and show that
it is possible to drive the system towards functional fragmentation before full
structural disintegration.


title:
Constructive stability results in interpolation inequalities and
  explicit improvements of decay rates of fast diffusion equations

abstract:
  We provide a scheme of a recent stability result for a family of
Gagliardo-Nirenberg-Sobolev (GNS) inequalities, which is equivalent to an
improved entropy - entropy production inequality associated with an appropriate
fast diffusion equation (FDE) written in self-similar variables. This result
can be rephrased as an improved decay rate of the entropy of the solution of
(FDE) for well prepared initial data. There is a family of
Caffarelli-Kohn-Nirenberg (CKN) inequalities which has a very similar
structure. When the exponents are in a range for which the optimal functions
for (CKN) are radially symmetric, we investigate how the methods for (GNS) can
be extended to (CKN). In particular, we prove that the solutions of the
evolution equation associated to (CKN) also satisfy an improved decay rate of
the entropy, after an explicit delay. However, the improved rate is obtained
without assuming that initial data are well prepared, which is a major
difference with the (GNS) case.


title:
MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain
  Acronym Extraction

abstract:
  Acronym extraction is the task of identifying acronyms and their expanded
forms in texts that is necessary for various NLP applications. Despite major
progress for this task in recent years, one limitation of existing AE research
is that they are limited to the English language and certain domains (i.e.,
scientific and biomedical). As such, challenges of AE in other languages and
domains is mainly unexplored. Lacking annotated datasets in multiple languages
and domains has been a major issue to hinder research in this area. To address
this limitation, we propose a new dataset for multilingual multi-domain AE.
Specifically, 27,200 sentences in 6 typologically different languages and 2
domains, i.e., Legal and Scientific, is manually annotated for AE. Our
extensive experiments on the proposed dataset show that AE in different
languages and different learning settings has unique challenges, emphasizing
the necessity of further research on multilingual and multi-domain AE.


title:
SemEval 2022 Task 12: Symlink- Linking Mathematical Symbols to their
  Descriptions

abstract:
  Given the increasing number of livestreaming videos, automatic speech
recognition and post-processing for livestreaming video transcripts are crucial
for efficient data management as well as knowledge mining. A key step in this
process is punctuation restoration which restores fundamental text structures
such as phrase and sentence boundaries from the video transcripts. This work
presents a new human-annotated corpus, called BehancePR, for punctuation
restoration in livestreaming video transcripts. Our experiments on BehancePR
demonstrate the challenges of punctuation restoration for this domain.
Furthermore, we show that popular natural language processing toolkits are
incapable of detecting sentence boundary on non-punctuated transcripts of
livestreaming videos, calling for more research effort to develop robust models
for this area.


title: SIDIS Measurement with A=3 Nuclei

abstract:
  We introduced a new experimental effort at Jefferson Lab (JLab) to precisely
measure the ratios of charged pion electroproduction in Semi-Inclusive Deep
Inelastic Scattering (SIDIS) from $^2$D, $^3$He, and $^3$H targets. This
conditionally approved experiment (C12-21-004) aims to run in Hall-B using the
standard CLAS12 configuration and a new target system developed for the
approved quasi-elastic experiment (E12-20-005). In this data-driven study, we
will measure the cross-sections as a function of ($x$, $Q^2$, $z$, $P_T$) to
allow the extraction of the unpolarized parton distribution functions (PDFs),
transverse momentum distributions (TMDs) and fragmentation functions (FFs) in A
= 3 nuclei. By using super-ratios of pion yields of SIDIS off light nuclei over
a wide $x_B$ range, we will search for evidence of a flavor dependence in the
EMC effect, giving us new insights on the effect of the nuclear environment on
valance quarks. Double ratios between the A = 3 mirror nuclei can provide a
direct measurement of the d/u ratios at large x thanks to their similar and
well-understood nuclear corrections. With the utilization of mirror nuclei, the
CLAS12's large kinematic range, and high precision, we will be able to maintain
the sensitivity to the underlying physics but dramatically decrease the nuclear
uncertainties due to attenuation and hadronization in heavy nuclei.