title:
Transient impact from the Nash equilibrium of a permanent market impact
  game

abstract:
  A large body of empirical literature has shown that market impact of
financial prices is transient. However, from a theoretical standpoint, the
origin of this temporary nature is still unclear. We show that an implied
transient impact arises from the Nash equilibrium between a directional trader
and one arbitrageur in a market impact game with fixed and permanent impact.
The implied impact is the one that can be empirically inferred from the
directional trader's trading profile and price reaction to order flow.
Specifically, we propose two approaches to derive the functional form of the
decay kernel of the Transient Impact Model, one of the most popular empirical
models for transient impact, from the behaviour of the directional trader at
the Nash equilibrium. The first is based on the relationship between past order
flow and future price change, while in the second we solve an inverse optimal
execution problem. We show that in the first approach the implied kernel is
unique, while in the second case infinite solutions exist and a linear kernel
can always be inferred.


title: Finite Free Point Processes

abstract:
  We use techniques from finite free probability to analyze matrix processes
related to eigenvalues, singular values, and generalized singular values of
random matrices. The models we use are quite basic and the analysis consists
entirely of expected characteristic polynomials. A number of our results match
known results in random matrix theory, however our main result (regarding
generalized singular values) seems to be more general than any of the standard
random matrix processes (Hermite/Laguerre/Jacobi) in the field. To test this,
we perform a series of simulations of this new process that, on the one hand,
confirms that this process can exhibit behavior not seen in the standard random
matrix processes, but on the other hand provides evidence that the true
behavior is captured quite well by our techniques. This, coupled with the fact
that we are able to compute the same statistics for this new model that we are
for the standard models, suggests that further investigation could be both
interesting and fruitful.


title:
Explicit spacetime-symmetry breaking and the dynamics of primordial
  fields

abstract:
  We study the effects of explicit spacetime-symmetry breaking on primordial
tensor fluctuations using an effective-field theory for Lorentz/CPT violation.
We find that for a sufficiently small coefficient for Lorentz violation, our
solutions track the GR solutions very closely; remarkably, we find that for
this approach to be valid during radiation domination, we must impose that the
coefficient for Lorentz violation is no larger than $T_{CMB}/(1K) 10^{-9}$. We
also find an indirect constraint on the same coefficient related to the scale
factor when demanding that the subhorizon modes must oscillate.


title:
The Distribution of Occupational Tasks in the United States:
  Implications for a Diverse and Aging Population

abstract:
  We document the age-race-gender intersectionality in the distribution of
occupational tasks in the United States. We also investigate how the task
content of work changed from the early-2000s to the late-2010s for different
age-race/ethnicity-gender groups. Using the Occupation Information Network
(O*NET) and pooled cross-sectional data from the American Community Survey
(ACS) we examine how the tasks that workers perform vary with age and over
time. We find that White men transition to occupations high in non-routine
cognitive tasks early in their careers, whereas Hispanic and Black men work
mostly in physically demanding jobs over their entire working lives. Routine
manual tasks increased dramatically for 55-67 year-old workers, except for
Asian men and women. Policymakers will soon be challenged by financial stress
on entitlement programs, reforms could have disproportionate effects on gender
and racial/ethnic groups due to inequality in the distribution of occupational
tasks.


title:
CUP: Curriculum Learning based Prompt Tuning for Implicit Event Argument
  Extraction

abstract:
  Implicit event argument extraction (EAE) aims to identify arguments that
could scatter over the document. Most previous work focuses on learning the
direct relations between arguments and the given trigger, while the implicit
relations with long-range dependency are not well studied. Moreover, recent
neural network based approaches rely on a large amount of labeled data for
training, which is unavailable due to the high labelling cost. In this paper,
we propose a Curriculum learning based Prompt tuning (CUP) approach, which
resolves implicit EAE by four learning stages. The stages are defined according
to the relations with the trigger node in a semantic graph, which well captures
the long-range dependency between arguments and the trigger. In addition, we
integrate a prompt-based encoder-decoder model to elicit related knowledge from
pre-trained language models (PLMs) in each stage, where the prompt templates
are adapted with the learning progress to enhance the reasoning for arguments.
Experimental results on two well-known benchmark datasets show the great
advantages of our proposed approach. In particular, we outperform the
state-of-the-art models in both fully-supervised and low-data scenarios.


title:
Relation-guided acoustic scene classification aided with event
  embeddings

abstract:
  In real life, acoustic scenes and audio events are naturally correlated.
Humans instinctively rely on fine-grained audio events as well as the overall
sound characteristics to distinguish diverse acoustic scenes. Yet, most
previous approaches treat acoustic scene classification (ASC) and audio event
classification (AEC) as two independent tasks. A few studies on scene and event
joint classification either use synthetic audio datasets that hardly match the
real world, or simply use the multi-task framework to perform two tasks at the
same time. Neither of these two ways makes full use of the implicit and
inherent relation between fine-grained events and coarse-grained scenes. To
this end, this paper proposes a relation-guided ASC (RGASC) model to further
exploit and coordinate the scene-event relation for the mutual benefit of scene
and event recognition. The TUT Urban Acoustic Scenes 2018 dataset (TUT2018) is
annotated with pseudo labels of events by a simple and efficient audio-related
pre-trained model PANN, which is one of the state-of-the-art AEC models. Then,
a prior scene-event relation matrix is defined as the average probability of
the presence of each event type in each scene class. Finally, the two-tower
RGASC model is jointly trained on the real-life dataset TUT2018 for both scene
and event classification. The following results are achieved. 1) RGASC
effectively coordinates the true information of coarse-grained scenes and the
pseudo information of fine-grained events. 2) The event embeddings learned from
pseudo labels under the guidance of prior scene-event relations help reduce the
confusion between similar acoustic scenes. 3) Compared with other
(non-ensemble) methods, RGASC improves the scene classification accuracy on the
real-life dataset.


title: Long Range, Large Charge, Large $N$

abstract:
  We study operators with large charge $j$ in the $d$-dimensional $O(N)$ model
with long range interactions that decrease with the distance as $1/r^{d+s}$,
where $s$ is a continuous parameter. We consider the double scaling limit of
large $N$, large $j$ with $j/N=\hat{j}$ fixed, and identify the semiclassical
saddle point that captures the two-point function of the large charge operators
in this limit. The solution is given in terms of certain ladder conformal
integrals that have recently appeared in the literature on fishnet models. We
find that the scaling dimensions for general $s$ interpolate between $\Delta_j
\sim \frac{(d-s)}{2}j$ at small $\hat{j}$ and $\Delta_j \sim \frac{(d+s)}{2}j$
at large $\hat{j}$, which is a qualitatively different behavior from the one
found in the short range version of the $O(N)$ model. We also derive results
for the structure constants and 4-point functions with two large charge and one
or two finite charge operators. Using a description of the long range models as
defects in a higher dimensional local free field theory, we also obtain the
scaling dimensions in a complementary way, by mapping the problem to a cylinder
in the presence of a chemical potential for the conserved charge.


title:
Is Your Toxicity My Toxicity? Exploring the Impact of Rater Identity on
  Toxicity Annotation

abstract:
  Machine learning models are commonly used to detect toxicity in online
conversations. These models are trained on datasets annotated by human raters.
We explore how raters' self-described identities impact how they annotate
toxicity in online comments. We first define the concept of specialized rater
pools: rater pools formed based on raters' self-described identities, rather
than at random. We formed three such rater pools for this study--specialized
rater pools of raters from the U.S. who identify as African American, LGBTQ,
and those who identify as neither. Each of these rater pools annotated the same
set of comments, which contains many references to these identity groups. We
found that rater identity is a statistically significant factor in how raters
will annotate toxicity for identity-related annotations. Using preliminary
content analysis, we examined the comments with the most disagreement between
rater pools and found nuanced differences in the toxicity annotations. Next, we
trained models on the annotations from each of the different rater pools, and
compared the scores of these models on comments from several test sets.
Finally, we discuss how using raters that self-identify with the subjects of
comments can create more inclusive machine learning models, and provide more
nuanced ratings than those by random raters.


title:
A note on Galois representations valued in reductive groups with open
  image

abstract:
  Let $G$ be a split reductive group with $\dim Z(G) \leq 1$. We show that for
any prime $p$ that is large enough relative to $G$, there is a finitely
ramified Galois representation $\rho \colon \Gamma_{\mathbb Q} \to G(\mathbb
Z_p)$ with open image. We also show that for any given integer $e$, if the
index of irregularity of $p$ is at most $e$ and if $p$ is large enough relative
to $G$ and $e$, then there is a Galois representation $\Gamma_{\mathbb Q} \to
G(\mathbb Z_p)$ ramified only at $p$ with open image, generalizing a theorem of
A. Ray. The first type of Galois representation is constructed by lifting a
suitable Galois representation into $G(\mathbb F_p)$ using a lifting theorem of
Fakhruddin--Khare--Patrikis, and the second type of Galois representation is
constructed using a variant of Ray's argument.


title: Large mode-2 internal solitary waves in three-layer flows

abstract:
  In this paper, we investigate mode-2 solitary waves in a three-layer
stratified flow model. Travelling wave solutions to both the fully nonlinear
problem (Euler equations), and a three-layer extension of the strongly
nonlinear two-layer Miyata-Choi-Camassa equations are found numerically. Mode-2
solitary waves with speeds slower than the linear mode-1 long-wave speed are
typically expected to be generalised solitary waves: that is, their tails
consist of a resonant mode-one periodic wave train. Herein we evidence the
existence of mode-2 embedded solitary waves, that is, we show that for specific
values of the parameters, the amplitude of the oscillations in the tail are
zero. For sufficiently thick middle layers, we also find branches of mode-2
solitary waves with speeds that exceed the mode-1 linear long wave speed.
Further, we show how some forms of the embedded solitary waves are intimately
linked to the conjugate states of the problem.


title: Domain Adaptation meets Individual Fairness. And they get along

abstract:
  Many instances of algorithmic bias are caused by distributional shifts. For
example, machine learning (ML) models often perform worse on demographic groups
that are underrepresented in the training data. In this paper, we leverage this
connection between algorithmic fairness and distribution shifts to show that
algorithmic fairness interventions can help ML models overcome distribution
shifts, and that domain adaptation methods (for overcoming distribution shifts)
can mitigate algorithmic biases. In particular, we show that (i) enforcing
suitable notions of individual fairness (IF) can improve the
out-of-distribution accuracy of ML models, and that (ii) it is possible to
adapt representation alignment methods for domain adaptation to enforce
(individual) fairness. The former is unexpected because IF interventions were
not developed with distribution shifts in mind. The latter is also unexpected
because representation alignment is not a common approach in the IF literature.


title:
Statistical inference for the two-sample problem under likelihood ratio
  ordering, with application to the ROC curve estimation

abstract:
  Likelihood ratio ordering has been identified as a reasonable assumption in
the two-sample problem in many practical scenarios. With this assumption,
statisticians have proposed various methods in the estimation of the
distributions of subpopulations, which consequently benefit the downstream
inferences, such as the ROC curve and the associated summary statistic
estimation. In this paper, under the likelihood ratio ordering assumption, we
first propose a Bernstein polynomial method to model the distributions of both
samples; we then estimate the distributions by the maximum empirical likelihood
principle. The ROC curve estimate and the associated summary statistics are
obtained subsequently. We compare the performance of our method with existing
methods by extensive simulation studies. The application of our method is
illustrated by a real-data example.


title:
Preserve Pre-trained Knowledge: Transfer Learning With Self-Distillation
  For Action Recognition

abstract:
  Video-based action recognition is one of the most popular topics in computer
vision. With recent advances of selfsupervised video representation learning
approaches, action recognition usually follows a two-stage training framework,
i.e., self-supervised pre-training on large-scale unlabeled sets and transfer
learning on a downstream labeled set. However, catastrophic forgetting of the
pre-trained knowledge becomes the main issue in the downstream transfer
learning of action recognition, resulting in a sub-optimal solution. In this
paper, to alleviate the above issue, we propose a novel transfer learning
approach that combines self-distillation in fine-tuning to preserve knowledge
from the pre-trained model learned from the large-scale dataset. Specifically,
we fix the encoder from the last epoch as the teacher model to guide the
training of the encoder from the current epoch in the transfer learning. With
such a simple yet effective learning strategy, we outperform state-of-the-art
methods on widely used UCF101 and HMDB51 datasets in action recognition task.


title:
Dynamic modeling of spike count data with Conway-Maxwell Poisson
  variability

abstract:
  In many areas of the brain, neural spiking activity covaries with features of
the external world, such as sensory stimuli or an animal's movement.
Experimental findings suggest that the variability of neural activity changes
over time and may provide information about the external world beyond the
information provided by the average neural activity. To flexibly track
time-varying neural response properties, here we developed a dynamic model with
Conway-Maxwell Poisson (CMP) observations. The CMP distribution can flexibly
describe firing patterns that are both under- and over-dispersed relative to
the Poisson distribution. Here we track parameters of the CMP distribution as
they vary over time. Using simulations, we show that a normal approximation can
accurately track dynamics in state vectors for both the centering and shape
parameters ($\lambda$ and $\nu$). We then fit our model to neural data from
neurons in primary visual cortex and "place cells" in the hippocampus. We find
that this method out-performs previous dynamic models based on the Poisson
distribution. The dynamic CMP model provides a flexible framework for tracking
time-varying non-Poisson count data and may also have applications beyond
neuroscience.


title:
The Best of Both Worlds: Combining Model-based and Nonparametric
  Approaches for 3D Human Body Estimation

abstract:
  Nonparametric based methods have recently shown promising results in
reconstructing human bodies from monocular images while model-based methods can
help correct these estimates and improve prediction. However, estimating model
parameters from global image features may lead to noticeable misalignment
between the estimated meshes and image evidence. To address this issue and
leverage the best of both worlds, we propose a framework of three consecutive
modules. A dense map prediction module explicitly establishes the dense UV
correspondence between the image evidence and each part of the body model. The
inverse kinematics module refines the key point prediction and generates a
posed template mesh. Finally, a UV inpainting module relies on the
corresponding feature, prediction and the posed template, and completes the
predictions of occluded body shape. Our framework leverages the best of
non-parametric and model-based methods and is also robust to partial occlusion.
Experiments demonstrate that our framework outperforms existing 3D human
estimation methods on multiple public benchmarks.


title: Geometry of symmetric spaces of type EIII

abstract:
  In this paper we generalize Atsuyama result on the geometry of symmetric
spaces of type EIII to the case of arbitrary fields of characteristic not 2 or
3. As an application we prove a variant of "Chain lemma" for microweight tori
in groups of type E6.


title: Textual Stylistic Variation: Choices, Genres and Individuals

abstract:
  This chapter argues for more informed target metrics for the statistical
processing of stylistic variation in text collections. Much as operationalised
relevance proved a useful goal to strive for in information retrieval, research
in textual stylistics, whether application oriented or philologically inclined,
needs goals formulated in terms of pertinence, relevance, and utility - notions
that agree with reader experience of text. Differences readers are aware of are
mostly based on utility - not on textual characteristics per se. Mostly,
readers report stylistic differences in terms of genres. Genres, while vague
and undefined, are well-established and talked about: very early on, readers
learn to distinguish genres. This chapter discusses variation given by genre,
and contrasts it to variation occasioned by individual choice.


title:
An Early Fault Detection Method of Rotating Machines Based on Multiple
  Feature Fusion with Stacking Architecture

abstract:
  Early fault detection (EFD) of rotating machines is important to decrease the
maintenance cost and improve the mechanical system stability. One of the key
points of EFD is developing a generic model to extract robust and
discriminative features from different equipment for early fault detection.
Most existing EFD methods focus on learning fault representation by one type of
feature. However, a combination of multiple features can capture a more
comprehensive representation of system state. In this paper, we propose an EFD
method based on multiple feature fusion with stacking architecture (M2FSA). The
proposed method can extract generic and discriminiative features to detect
early faults by combining time domain (TD), frequency domain (FD), and
time-frequency domain (TFD) features. In order to unify the dimensions of the
different domain features, Stacked Denoising Autoencoder (SDAE) is utilized to
learn deep features in three domains. The architecture of the proposed M2FSA
consists of two layers. The first layer contains three base models, whose
corresponding inputs are different deep features. The outputs of the first
layer are concatenated to generate the input to the second layer, which
consists of a meta model. The proposed method is tested on three bearing
datasets. The results demonstrate that the proposed method is better than
existing methods both in sensibility and reliability.


title:
Conventions and Mutual Expectations -- understanding sources for web
  genres

abstract:
  Genres can be understood in many different ways. They are often perceived as
a primarily sociological construction, or, alternatively, as a
stylostatistically observable objective characteristic of texts. The latter
view is more common in the research field of information and language
technology. These two views can be quite compatible and can inform each other;
this present investigation discusses knowledge sources for studying genre
variation and change by observing reader and author behaviour rather than
performing analyses on the information objects themselves.


title:
Controlled Power System Separation Using Generator PMU Data and System
  Kinetic Energy

abstract:
  With the growing number of severe system disturbances and blackouts around
the world, controlled system separation is becoming an increasingly important
system integrity protection scheme (SIPS) to save the electric power system
from a complete or partial disintegration. A successful controlled splitting
approach should at least tackle the following two well-known and interrelated
problems: "when to split?" and "where to split?". Multiple previous
publications consider these problems separately, and even those pursuing a
combined approach propose solutions with limited applicability. In this paper,
we are proposing a novel PMU-based detector of loss of synchronism that
utilizes generator rotor angles and speeds to promptly detect rotor angle
instabilities over a wide area. Moreover, we are showing how our loss of
synchronism detection principle can be coupled with the known controlled
splitting techniques to form an integrated defense scheme against unintentional
loss of synchronism. The performance of this wide-area SIPS is demonstrated on
the IEEE 39-bus test power system for various types of unstable conditions.


title:
Scaling theory of wave confinement in classical and quantum periodic
  systems

abstract:
  Functional defects in periodic media confine waves - acoustic,
electromagnetic, electronic, spin, etc. - in various dimensions, depending on
the structure of the defect. While defects are usually modelled by a
superlattice with a typical band-structure representation of energy levels,
determining the confinement associated with a given band is highly non-trivial
and no analytical method is known to date. Therefore, we propose a rigorous
method to classify the dimensionality of the confinement. Starting from the
confinement energy and the mode volume, we use finite-size scaling to find that
ratios of these quantities to certain powers yield the confinement
dimensionality of each band. This classification has negligible additional
computational costs compared to a band structure calculation and is valid for
any type of wave in both quantum and classical regimes, and any dimension. In
the quantum case, we illustrate our method on electronic confinement in 2D
hexagonal BN with a nitrogen vacancy, which confirms the previous results. In
the classical case, we study a three-dimensional photonic band gap cavity
superlattice, where we identify novel acceptor-like behavior.


title:
Relativistic electron precipitation by EMIC waves: importance of
  nonlinear resonant effects

abstract:
  Relativistic electron losses in Earth's radiation belts are usually
attributed to electron resonant scattering by electromagnetic waves. One of the
most important wave mode for such scattering is the electromagnetic ion
cyclotron (EMIC) mode. Within the quasi-linear diffusion framework, the
cyclotron resonance of relativistic electrons with EMIC waves results in very
fast electron precipitation to the atmosphere. However, wave intensities often
exceed the threshold for nonlinear resonant interaction, and such intense EMIC
waves have been shown to transport electrons away from the loss cone due to the
force bunching effect. In this study we investigate if this transport can block
electron precipitation. We combine test particle simulations, low-altitude
ELFIN observations of EMIC-driven electron precipitation, and ground-based EMIC
observations. Comparing simulations and observations, we show that, despite of
the low pitch-angle electrons being transported away from the loss cone, the
scattering at higher pitch angles results in the loss cone filling and electron
precipitation.


title:
A probabilistic scheme for semilinear nonlocal diffusion equations with
  volume constraints

abstract:
  This work presents a probabilistic scheme for solving semilinear nonlocal
diffusion equations with volume constraints and integrable kernels. The
nonlocal model of interest is defined by a time-dependent semilinear partial
integro-differential equation (PIDE), in which the integro-differential
operator consists of both local convection-diffusion and nonlocal diffusion
operators. Our numerical scheme is based on the direct approximation of the
nonlinear Feynman-Kac formula that establishes a link between nonlinear PIDEs
and stochastic differential equations. The exploitation of the Feynman-Kac
representation successfully avoids solving dense linear systems arising from
nonlocality operators. Compared with existing stochastic approaches, our method
can achieve first-order convergence after balancing the temporal and spatial
discretization errors, which is a significant improvement of existing
probabilistic/stochastic methods for nonlocal diffusion problems. Error
analysis of our numerical scheme is established. The effectiveness of our
approach is shown in two numerical examples. The first example considers a
three-dimensional nonlocal diffusion equation to numerically verify the error
analysis results. The second example presents a physics problem motivated by
the study of heat transport in magnetically confined fusion plasmas.


title: Accurate non-stationary short-term traffic flow prediction method

abstract:
  Precise and timely traffic flow prediction plays a critical role in
developing intelligent transportation systems and has attracted considerable
attention in recent decades. Despite the significant progress in this area
brought by deep learning, challenges remain. Traffic flows usually change
dramatically in a short period, which prevents the current methods from
accurately capturing the future trend and likely causes the over-fitting
problem, leading to unsatisfied accuracy. To this end, this paper proposes a
Long Short-Term Memory (LSTM) based method that can forecast the short-term
traffic flow precisely and avoid local optimum problems during training.
Specifically, instead of using the non-stationary raw traffic data directly, we
first decompose them into sub-components, where each one is less noisy than the
original input. Afterward, Sample Entropy (SE) is employed to merge similar
components to reduce the computation cost. The merged features are fed into the
LSTM, and we then introduce a spatiotemporal module to consider the neighboring
relationships in the recombined signals to avoid strong autocorrelation. During
training, we utilize the Grey Wolf Algorithm (GWO) to optimize the parameters
of LSTM, which overcome the overfitting issue. We conduct the experiments on a
UK public highway traffic flow dataset, and the results show that the proposed
method performs favorably against other state-of-the-art methods with better
adaption performance on extreme outliers, delay effects, and trend-changing
responses.


title: Scheduling for Multi-Phase Parallelizable Jobs

abstract:
  With multiple identical unit speed servers, the online problem of scheduling
jobs that migrate between two phases, limitedly parallelizable or completely
sequential, and choosing their respective speeds to minimize the total flow
time is considered. In the limited parallelizable regime, allocating $k$
servers to a job, the speed extracted is $k^{1/\alpha}, \alpha>1$, a
sub-linear, concave speedup function, while in the sequential phase, a job can
be processed by at most one server with a maximum speed of unity. A LCFS based
algorithm is proposed for scheduling jobs which always assigns equal speed to
the jobs that are in the same phase (limitedly parallelizable/sequential), and
is shown to have a constant (dependent only on $\alpha > 1$) competitive ratio.
For the special case when all jobs are available beforehand, improved
competitive ratio is obtained.


title:
Preparing Arbitrary Continuous Functions in Quantum Registers With
  Logarithmic Complexity

abstract:
  Quantum computers will be able solve important problems with significant
polynomial and exponential speedups over their classical counterparts, for
instance in option pricing in finance, and in real-space molecular chemistry
simulations. However, key applications can only achieve their potential speedup
if their inputs are prepared efficiently. We effectively solve the important
problem of efficiently preparing quantum states following arbitrary continuous
(as well as more general) functions with complexity logarithmic in the desired
resolution, and with rigorous error bounds. This is enabled by the development
of a fundamental subroutine based off of the simulation of rank-1 projectors.
Combined with diverse techniques from quantum information processing, this
subroutine enables us to present a broad set of tools for solving practical
tasks, such as state preparation, numerical integration of Lipschitz continuous
functions, and superior sampling from probability density functions. As a
result, our work has significant implications in a wide range of applications,
for instance in financial forecasting, and in quantum simulation.


title:
Relationship between Gamma-ray loudness and X-ray spectra of Radio
  Galaxies

abstract:
  The {\it Fermi} satellite has detected $\sim$60 radio galaxies (RGs). In this
study, we investigate the difference in the properties of X-ray spectra between
GeV-loud RGs and GeV-quiet RGs. Our sample comprises 68 objects: 36 RGs
detected with {\it Fermi} and 32 RGs not detected with gamma rays. We analyzed
the X-ray spectra of these 68 objects using data from the {\it XMM-Newton},
{\it Chandra}, {\it NuSTAR}, and {\it Swift} satellites. Our results show that
most GeV-loud RGs do not exhibit significant absorption, while $\sim$50\% of
the GeV-quiet RGs exhibit significant absorption. This suggests that the jet of
GeV-loud RGs is viewed from a small angle, and thus the emission is not easily
blocked by the torus. Moreover, we reported that RGs with a heavy absorption
are mostly in the X-ray luminosity range of $10^{43}-10^{45}$erg s$^{-1}$;
however, few RGs with lower and higher luminosity suffer from heavy absorption.
This is the same trend as that of Seyfert galaxies.


title:
Study of maximum electron energy of sub-PeV pulsar wind nebulae by
  multiwavelength modelling

abstract:
  Recently, the Large High Altitude Air Shower Observatory (LHAASO) reported
discovery of 12 ultrahigh-energy (UHE; $\mathrm{\varepsilon} \ge 100$ TeV)
gamma-ray sources located in the Galactic plane. Few of these UHE gamma-ray
emitting regions are in spatial coincidence with pulsar wind nebula (PWN)
objects. We consider a sample of five sources; two of them are LHAASO sources
(LHAASO J1908+0621 and LHAASO J2226+6057) and the remaining three are GeV-TeV
gamma-ray emitters. In addition, their X-ray and radio observations or upper
limits are also available for these objects. We study multiwavelength radiation
from these sources by considering a PWN origin, where the emission is powered
by time-dependent spin-down luminosity of the associated pulsars. In this one
zone, time-dependent leptonic emission model, the electron population is
calculated at different times under the radiative (synchrotron and
inverse-Compton) and adiabatic cooling. We estimate the upper limits on the
minimum Lorentz factor of the electrons and it also infers the minimum value of
the pair-multiplicity of charged pairs. Further, the maximum value of the
electron Lorentz factor is estimated by the maximum observed photon energy in
the sub-PeV range. In the special case of HESS J1640-465, a higher energy
density of the stellar photons is required to fit gamma-ray data compared to
the standard IR/CMB background used in the PWNe modelling. We also discuss the
possible modification in the model parameters, if the escape of particles is
allowed from the pulsar wind nebula. For example, we consider LHAASO 1908+0621
and discuss qualitatively the impact of escape of particles from this source.


title:
Two-Particle Tight-Binding Description of Higher-Harmonic Generation in
  Semiconductor Nanostructures

abstract:
  We develop a quantum mechanical theory to describe the optical response of
semiconductor nanostructures with a particular emphasis on higher-order
harmonic Generation. Based on a tight-binding approach we take all two-particle
correlations into account thus describing the creation, evolution and
annihilation of electron and holes. In the limiting case of bulk materials, we
obtain the same precision as that achieved by solving the well-established
semiconductor Bloch Equations. For semiconducting structures of finite extent,
we also incorporate the surrounding space thus enabling a description of
electron emission. In addition, we incorporate different relaxation mechanisms
such as dephasing and damping of intraband currents. Moreover, the advantage of
our description is that, starting from extremely precise material data as e.g.,
from tight-binding parameters obtained from density-functional-theory
calculations, we obtain a numerical description being by far less
computationally challenging and resource-demanding as comparable ab-initio
approaches, e.g., those based on time-dependent density functional theory.


title: Deep Learning with Logical Constraints

abstract:
  In recent years, there has been an increasing interest in exploiting
logically specified background knowledge in order to obtain neural models (i)
with a better performance, (ii) able to learn from less data, and/or (iii)
guaranteed to be compliant with the background knowledge itself, e.g., for
safety-critical applications. In this survey, we retrace such works and
categorize them based on (i) the logical language that they use to express the
background knowledge and (ii) the goals that they achieve.


title:
Atomic structure of amorphous SiN: combining Car-Parrinello and
  Born-Oppenheimer first-principles molecular dynamics

abstract:
  First-principles molecular dynamics is employed to describe the atomic
structure of amorphous SiN, a non-stoichiometric compound belonging to the
Si$_x$N$_{y}$ family. To produce the amorphous state via the cooling of the
liquid, both the Car-Parrinello and the Born-Oppenheimer approaches are
exploited to obtain a system featuring sizeable atomic mobility. At high
temperatures, due to the peculiar electronic structure of SiN, exhibiting gap
closing effects, the Car-Parrinello methodology could not be followed since
non-adiabatic effects involving the ionic and electronic degrees of freedom do
occur. This shortcoming was surmounted by resorting to the Born-Oppenheimer
approach allowing to achieve significant ionic diffusion at $T$= 2500 K. From
this highly diffusive sample, an amorphous state at room temperature was
obtained with a quenching rate of 10 K/ps. Four different models were created,
differing by their sizes and the thermal cycles. We found that the subnetwork
of atoms N has the same environment than in the stoichiometric material
Si$_3$N$_4$ since N is mostly threefold coordinated with Si. Si atoms can also
be found coordinated to four N atoms as in Si$_3$N$_4$, but a substantial
fraction of them forms homopolar bonds with one, two, three and even four Si.
Our results are not too dissimilar from former models available in the
litterature but they feature a higher statistical accuracy and refer more
precisely to room temperature as the reference thermodynamical condition for
the analysis of the structure in the amorphous state.


title:
Deep vs. Shallow Learning: A Benchmark Study in Low Magnitude Earthquake
  Detection

abstract:
  While deep learning models have seen recent high uptake in the geosciences,
and are appealing in their ability to learn from minimally processed input
data, as black box models they do not provide an easy means to understand how a
decision is reached, which in safety-critical tasks especially can be
problematical. An alternative route is to use simpler, more transparent white
box models, in which task-specific feature construction replaces the more
opaque feature discovery process performed automatically within deep learning
models. Using data from the Groningen Gas Field in the Netherlands, we build on
an existing logistic regression model by the addition of four further features
discovered using elastic net driven data mining within the catch22 time series
analysis package. We then evaluate the performance of the augmented logistic
regression model relative to a deep (CNN) model, pre-trained on the Groningen
data, on progressively increasing noise-to-signal ratios. We discover that, for
each ratio, our logistic regression model correctly detects every earthquake,
while the deep model fails to detect nearly 20 % of seismic events, thus
justifying at least a degree of caution in the application of deep models,
especially to data with higher noise-to-signal ratios.


title:
Isotropization and Evolution of Energy-Containing Eddies in Solar Wind
  Turbulence: Parker Solar Probe, Helios 1, ACE, WIND, and Voyager 1

abstract:
  We examine the radial evolution of correlation lengths perpendicular
(\(\lambda_C^{\perp}\)) and parallel (\(\lambda_C^{\parallel}\)) to the
magnetic-field direction, computed from solar wind magnetic-field data measured
by Parker Solar Probe (PSP) during its first eight orbits, Helios 1, Advanced
Composition Explorer (ACE), WIND, and Voyager 1 spacecraft. Correlation lengths
are grouped by an interval's alignment angle; the angle between the
magnetic-field and solar wind velocity vectors (\(\Theta_{\rm BV}\)). Parallel
and perpendicular angular channels correspond to angles
\(0^{\circ}~<~\Theta_{\rm BV}~<~40^{\circ}\) and \(50^{\circ}~<~\Theta_{\rm
BV}~<~90^{\circ}\), respectively. We observe an anisotropy in the inner
heliosphere within 0.40~au, with \(\lambda_C^{\parallel} / \lambda_C^{\perp}
\approx 0.75\) at 0.10~au. This anisotropy reduces with increasing heliocentric
distance and the correlation lengths roughly isotropize within 1~au. Results
from ACE and WIND support a reversal of the anisotropy, such that
\(\lambda_C^{\parallel} /\lambda_C^{\perp} \approx 1.29\) at 1~au. The ratio
does not appear to change significantly beyond 1~au, although the small number
of parallel intervals in the Voyager dataset precludes unambiguous conclusions
from being drawn. This study provides insights regarding the radial evolution
of the large, most energetic interacting turbulent fluctuations in the
heliosphere. We also emphasize the importance of tracking the changes in
sampling direction in PSP measurements as the spacecraft approaches the Sun,
when using these data to study the radial evolution of turbulence. This can
prove to be vital in understanding the more complex dynamics of the solar wind
in the inner heliosphere and can assist in improving related simulations.


title: On Finite Analogs of Schmidt's Problem and Its Variants

abstract:
  We refine Schmidt's problem and a partition identity related to 2-color
partitions which we will refer to as Uncu-Andrews-Paule theorem. We will
approach the problem using Boulet-Stanley weights and a formula on
Rogers-Szeg\H{o} polynomials by Berkovich-Warnaar, and present various
Schmidt's problem alike theorems and their refinements. Our new Schmidt type
results include the use of even-indexed parts' sums, alternating sum of parts,
and hook lengths as well as the odd-indexed parts' sum which appears in the
original Schmidt's problem. We also translate some of our Schmidt's problem
alike relations to weighted partition counts with multiplicative weights in
relation to Rogers-Ramanujan partitions.


title:
Integral Global Optimality Conditions and an Algorithm for
  Multiobjective Problems

abstract:
  In this work, we propose integral global optimality conditions for
multiobjective problems not necessarily differentiable. The integral
characterization, already known for single objective problems, are extended to
multiobjective problems by weighted sum and Chebyshev weighted scalarizations.
Using this last scalarization, we propose an algorithm for obtaining an
approximation of the weak Pareto front whose effectiveness is illustrated by
solving a collection of multiobjective test problems.


title:
The F\"oppl-von K\'arm\'an equations of elastic plates with initial
  stress

abstract:
  Initially stressed plates are widely used in modern fabrication techniques,
such as additive manufacturing and UV lithography, for their tunable morphology
by application of external stimuli. In this work, we propose a formal
asymptotic derivation of the F\"{o}ppl-von K\'{a}rm\'{a}n equations for an
elastic plate with initial stresses, using the constitutive theory of nonlinear
elastic solids with initial stresses under the assumptions of incompressibility
and material isotropy. Compared to existing works, our approach allows to
determine the morphological transitions of the elastic plate without
prescribing the underlying target metric of the unstressed state of the elastic
body. We explicitly solve the derived FvK equations in some physical problems
of engineering interest, discussing how the initial stress distribution drives
the emergence of spontaneous curvatures within the deformed plate. The proposed
mathematical framework can be used to tailor shape on demand, with applications
in several engineering fields ranging from soft robotics to 4D printing.


title:
Generalized Fisher-Darmois-Koopman-Pitman Theorem and Rao-Blackwell Type
  Estimators for Power-Law Distributions

abstract:
  This paper generalizes the notion of sufficiency for estimation problems
beyond maximum likelihood. In particular, we consider estimation problems based
on Jones et al. and Basu et al. likelihood functions that are popular among
distance-based robust inference methods. We first characterize the probability
distributions that always have a fixed number of sufficient statistics with
respect to these likelihood functions. These distributions are power-law
extensions of the usual exponential family and contain Student distributions as
special case. We then extend the notion of minimal sufficient statistics and
compute it for these power-law families. Finally, we establish a Rao-Blackwell
type theorem for finding best estimators for a power-law family. This enables
us to derive certain generalized Cram\'er-Rao lower bounds for power-law
families.


title: Topological edge states of quasiperiodic elastic metasurfaces

abstract:
  In this work, we investigate the dynamic behavior and the topological
properties of quasiperiodic elastic metasurfaces, namely arrays of mechanical
oscillators arranged over the free surface of an elastic half-space according
to a quasiperiodic spatial distribution. An ad-hoc multiple scattering
formulation is developed to describe the dynamic interaction between Rayleigh
waves and a generic array of surface resonators. The approach allows to
calculate the spectrum of natural frequencies of the quasiperiodic metasurface
which reveals a fractal distribution of the frequency gaps reminiscent of the
Hofstadter butterfly. These gaps have nontrivial topological properties and can
host Rayleigh-like edge modes. We demonstrate that such topologically protected
edge modes can be driven from one boundary to the opposite of the array by a
smooth variation of the phason, a parameter which modulates the geometry of the
array. Topological elastic waveguides designed on these principles provide new
opportunities in surface acoustic wave engineering for vibration control,
energy harvesting, and lossless signal transport, among others.


title:
Persistent Laplacian projected Omicron BA.4 and BA.5 to become new
  dominating variants

abstract:
  Due to its high transmissibility, Omicron BA.1 ousted the Delta variant to
become a dominating variant in late 2021 and was replaced by more transmissible
Omicron BA.2 in March 2022. An important question is which new variants will
dominate in the future. Topology-based deep learning models have had tremendous
success in forecasting emerging variants in the past. However, topology is
insensitive to homotopic shape variations in virus-human protein-protein
binding, which are crucial to viral evolution and transmission. This challenge
is tackled with persistent Laplacian, which is able to capture both the
topology and shape of data. Persistent Laplacian-based deep learning models are
developed to systematically evaluate variant infectivity. Our comparative
analysis of Alpha, Beta, Gamma, Delta, Lambda, Mu, and Omicron BA.1, BA.1.1,
BA.2, BA.2.11, BA.2.12.1, BA.3, BA.4, and BA.5 unveils that Omicron BA.2.11,
BA.2.12.1, BA.3, BA.4, and BA.5 are more contagious than BA.2. In particular,
BA.4 and BA.5 are about 36\% more infectious than BA.2 and are projected to
become new dominating variants by natural selection. Moreover, the proposed
models outperform the state-of-the-art methods on three major benchmark
datasets for mutation-induced protein-protein binding free energy changes.


title:
Performance Analysis of Cooperative Relaying for Multi-Antenna RF
  Transmissions over THz Wireless Link

abstract:
  Recent research has focused on single antenna radio-frequency (RF) and
terahertz (THz) wireless systems to mix the access link with the backhaul. In
this paper, we evaluate the performance of a mixed RF-THz system employing
multiple antenna-assisted access point (AP) for the RF link and single-antenna
THz transmissions. We employ an equal gain combining (EGC) receiver at the AP
and use the fixed-gain amplify and forward (AF) relaying protocol to interface
the RF and THz links. We derive analytical expressions for probability density
function (PDF) and cumulative distribution function (CDF) of the end-to-end SNR
for the considered system assuming independent and non-identically distributed
(i.ni.d.) $\alpha$-$\mu$ distribution to model for both RF and THz channels and
pointing errors in the THz link. We analyze the system performance using the
outage probability, average bit error rate (BER), and ergodic capacity
involving bivariate Fox's H-function. We use the residue method to develop
asymptotic analysis using Gamma functions to show the impact of the various
channel and system parameters on the outage probability and average BER in the
high SNR regime. We use computer simulations to depict the scaling of the
performance with an increase in the number of antennas at the AP for signal
reception in the access link.


title: Generalized Reference Kernel for One-class Classification

abstract:
  In this paper, we formulate a new generalized reference kernel hoping to
improve the original base kernel using a set of reference vectors. Depending on
the selected reference vectors, our formulation shows similarities to
approximate kernels, random mappings, and Non-linear Projection Trick. Focusing
on small-scale one-class classification, our analysis and experimental results
show that the new formulation provides approaches to regularize, adjust the
rank, and incorporate additional information into the kernel itself, leading to
improved one-class classification accuracy.


title:
A Novel Hybrid Backscatter and Conventional Algorithm for Multi-Hop IoT
  Networks

abstract:
  This paper investigates a multi-hop cognitive radio network in terms of
end-to-end bit delivery. The network exploits backscatter communication
(BackCom) and harvest-then-transmit (HTT) mode in a hybrid manner. Such a
network can be used in internet of things (IoT) applications in which IoT users
coexist with a primary network (PN) and use the primary spectrum to transmit
data in both BackCom and HTT modes. Besides, such users can harvest energy from
the primary signals. A novel hybrid backscatter and conventional transmission
(HBCT) algorithm is proposed in order to maximize end-to-end bit delivery by
jointly optimizing time and power allocations. For this goal, we formulate a
non-convex optimization problem. Next, we transform the problem into a convex
one and develop a new analytical formulation by which we calculate the optimal
power and time allocation in closed-form equations. The numerical results
demonstrate the superiority of HBCT compared with current schemes.


title: Phase Vortex Lattices in Neutron Interferometry

abstract:
  First observed in optics Orbital Angular Momentum (OAM) of photons has become
ubiquitous in physics. Seeing applications in quantum communications, astronomy
and many other areas. OAM has also been observed in massive free particles,
such as electrons, atoms and neutrons. In the latter case, however, there is
some ambiguity on whether any of the observed OAM is intrinsic to the neutrons
or whether it is simply an extrinsic beam property, since the dimensions of the
phase vortex (order cm) previously observed were much larger than the neutron
coherence length (order 100 nm). To address this concern a method using
magnetic prisms was developed to generate a lattice of smaller vortices.
However, realistically achievable magnetic potentials are too small to generate
vortices on the order of the neutron coherence length. We have generalized this
method, such that the strong nuclear potential can be exploited, enabling
generation of smaller vortices. We demonstrate the generation of a vortex
lattice using strongly interacting aluminium prisms in a nested loop neutron
interferometer.


title:
Existence of inter coupled structural, electronic and magnetic states in
  Sm$ _{2} $NiMnO$ _{6} $ double perovskite

abstract:
  Coupling between different interactions allows to control physical aspects in
multifunctional materials by perturbing any of the degrees of freedom. Here, we
aim to probe the correlation among structural, electronic and magnetic
observables of Sm$ _{2} $NiMnO$ _{6} $ ferromagnetic insulator double
perovskite. Our employed methodology includes thermal evolution of synchrotron
X-ray diffraction, near edge and extended edge hard X-ray absorption
spectroscopy and bulk magnetometry. The magnetic ordering in SNMO adopts two
transitions, at T$ _{C} $=159.6K due to ferromagnetic arrangement of Ni-Mn
sublattice and at T$ _{d} $=34.1K because of anti-parallel alignment of
polarized Sm paramagnetic moments with respect to Ni-Mn network. The global as
well as local crystal structure of SNMO undergoes isostructural transitions
across T$ _{C} $ and T$ _{d} $, observed by means of temperature dependent
variation in Ni/Mn-O, Ni-Mn bonding characters and super exchange angle in
Ni-O-Mn linkage. Hybridization between Ni, Mn 3\textit{d}, O 2\textit{p}
electronic states is also modified in the vicinity of magnetic transition. On
the other hand, the signature of Ni/Mn anti-site disorders are evidenced from
local structure and magnetization analysis. The change in crystal environments
governs the magnetic response by imposing alteration in metal - ligand orbital
overlap. Utilizing these complimentary probes we have found that structural,
electronic and magnetic states are inter-coupled in SNMO which makes it a
potential platform for technological usage.


title: Can Information Behaviour Inform Machine Learning?

abstract:
  The objective of this paper is to explore the opportunities for human
information behaviour research to inform and influence the field of machine
learning and the resulting machine information behaviour. Using the development
of foundation models in machine learning as an example, the paper illustrates
how human information behaviour research can bring to machine learning a more
nuanced view of information and informing, a better understanding of
information need and how that affects the communication among people and
systems, guidance on the nature of context and how to operationalize that in
models and systems, and insights into bias, misinformation, and
marginalization. Despite their clear differences, the fields of information
behaviour and machine learning share many common objectives, paradigms, and key
research questions. The example of foundation models illustrates that human
information behaviour research has much to offer in addressing some of the
challenges emerging in the nascent area of machine information behaviour.


title: Enumeration Classes Defined by Circuits

abstract:
  We refine the complexity landscape for enumeration problems by introducing
very low classes defined by using Boolean circuits as enumerators. We locate
well-known enumeration problems, e.g., from graph theory, Gray code
enumeration, and propositional satisfiability in our classes. In this way we
obtain a framework to distinguish between the complexity of different problems
known to be in $\mathbf{DelayP}$, for which a formal way of comparison was not
possible to this day.


title: Distinguished varieties in the polydisc

abstract:
  We characterize all distinguished varieties in the polydisc $\mathbb D^n$ and
thus generalize the seminal work due to Agler and M\raise.45ex\hbox{c}Carthy on
distinguished varieties in the bidisc. We show that a distinguished variety in
$\mathbb D^n$ is a part of an affine algebraic curve which is a set-theoretic
complete intersection and can be represented by Taylor joint spectrum of $n-1$
commuting linear complex matrix pencils satisfying certain conditions.


title: COUCH: Towards Controllable Human-Chair Interactions

abstract:
  Humans interact with an object in many different ways by making contact at
different locations, creating a highly complex motion space that can be
difficult to learn, particularly when synthesizing such human interactions in a
controllable manner. Existing works on synthesizing human scene interaction
focus on the high-level control of action but do not consider the fine-grained
control of motion. In this work, we study the problem of synthesizing scene
interactions conditioned on different contact positions on the object. As a
testbed to investigate this new problem, we focus on human-chair interaction as
one of the most common actions which exhibit large variability in terms of
contacts. We propose a novel synthesis framework COUCH that plans ahead the
motion by predicting contact-aware control signals of the hands, which are then
used to synthesize contact-conditioned interactions. Furthermore, we contribute
a large human-chair interaction dataset with clean annotations, the COUCH
Dataset. Our method shows significant quantitative and qualitative improvements
over existing methods for human-object interactions. More importantly, our
method enables control of the motion through user-specified or automatically
predicted contacts.


title: Improved tests of entanglement and Bell inequalities with LHC tops

abstract:
  We discuss quantum entanglement in top pair production at the LHC. Near the
$t \bar t$ threshold, entanglement observables are enhanced by suppressing the
contribution of $q \bar q$ subprocesses, which is achieved by a simple cut on
the velocity of the $t \bar t$ system in the laboratory frame. Furthermore, we
design new observables that directly measure the relevant combinations of
$t\bar t$ spin correlation coefficients involved in the measurement of
entanglement and Bell inequalities. As a result, the statistical sensitivity is
enhanced, up to a factor of 7 for Bell inequalities near threshold.


title: Extremality and rigidity for scalar curvature in dimension four

abstract:
  Following Gromov, a Riemannian manifold is called area-extremal if any
modification that increases scalar curvature must decrease the area of some
tangent 2-plane. We prove that large classes of compact 4-manifolds, with or
without boundary, with nonnegative sectional curvature are area-extremal. We
also show that all regions of positive sectional curvature on 4-manifolds are
locally area-extremal. These results are obtained analyzing sections in the
kernel of a twisted Dirac operator constructed from pairs of metrics, and using
the Finsler--Thorpe trick for sectional curvature bounds in dimension 4.


title:
Topology and morphology design of spherically reconfigurable homogeneous
  Modular Soft Robots (MSoRos)

abstract:
  Imagine a swarm of terrestrial robots that can explore an environment, and,
upon completion of this task, reconfigure into a spherical ball and roll out.
This dimensional change alters the dynamics of locomotion and can assist them
to maneuver variable terrains. The sphere-plane reconfiguration is equivalent
to projecting a spherical shell onto a plane, an operation which is not
possible without distortions. Fortunately, soft materials have potential to
adapt to this disparity of the Gaussian curvatures. Modular Soft Robots
(MSoRos) have promise of achieving dimensional change by exploiting their
continuum and deformable nature. We present topology and morphology design of
MSoRos capable of reconfiguring between spherical and planar configurations.
Our approach is based in geometry, where a platonic solid determines the number
of modules required for plane-to-sphere reconfiguration and the radius of the
resulting sphere, e.g., four `tetrahedron-based' or six `cube-based' MSoRos are
required for spherical reconfiguration. The methodology involves: (1)inverse
orthographic projection of a `module-topology curve' onto the circumscribing
sphere to generate the spherical topology,(2)azimuthal projection of the
spherical topology onto a tangent plane at the center of the module resulting
in the planar topology, and (3)adjusting the limb stiffness and curling ability
by manipulating the geometry of cavities to realize a physical finite-width,
Motor-Tendon Actuated MSoRo. The topology design is shown to be scale
invariant, i.e., scaling of base platonic solid is reflected linearly in
spherical and planar topologies. The module-topology curve is optimized for the
reconfiguration and locomotion ability using a metric that quantifies
sphere-to-plane distortion. The geometry of the cavity optimizes for the limb
stiffness and curling ability without compromising the actuator's structural
integrity.


title:
A sensitive search for water masers associated with star formation
  regions in the Local Group galaxy NGC 6822

abstract:
  We report the results of a sensitive search for water maser emission in the
Local Group Galaxy NGC 6822 with the Karl G. Jansky Very Large Array. The
observations provide tentative single-epoch detections of four candidates,
associated with two infrared-bright star formation regions (Hubble I/III and
Hubble IV). The candidate maser detections are all offset from the velocity
range where strong emission from HI neutral gas is observed toward NGC 6822,
with the closest offset by 40 kms$^{-1}$. Our observations include the location
of NL1K, a previous tentative water maser detection in NGC 6822. We do not
detect any emission from this location with a sensitivity limit approximately a
factor of 5 better than the original Sardina Radio Telescope observations.


title:
Heterogeneous graph neural network for power allocation in
  multicarrier-division duplex cell-free massive MIMO systems

abstract:
  In-band full duplex-based cell-free (IBFD-CF) systems suffer from severe
interference problem including self-interference (SI) and cross-link
interference (CLI), especially when cell-free (CF) systems are operated in a
distributed way. To this end, we propose multicarrier-division duplex (MDD) as
an enabler for full-duplex (FD)-style operation in distributed CF massive MIMO
systems, where DL and UL transmissions take place simultaneously at the same
frequency band but mutually orthogonal subcarrier sets. In order to maximize
the spectral efficiency (SE) in the proposed systems, we present heterogeneous
graph neural network specific for CF systems (CF-HGNN), which consists of an
adaptive node embedding layer, meta-path based message passing, meta-path based
attention and downstream power allocation learning. In particular, the adaptive
node embedding layer can handle the varying number of access points (APs),
mobile stations (MSs) and subcarriers, and the involved attention mechanism
enables each AP/MS node in CF-HGNN to aggregate the information from
interfering path and communication path with different priorities. Numerical
results show that CF-HGNN is capable of using $10^4$ times less operation time
to achieve the 99% performance of the SE of quadratic transform and successive
convex approximation (QT-SCA). Additionally, CF-HGNN also significantly
outperforms unfair greedy method in terms of SE performance. Furthermore,
CF-HGNN exhibits good adaptivity to varying number of nodes and subcarriers,
and also generalization ability to different sizes of CF network.


title: Variable AGN in the GALEX Time Domain Survey

abstract:
  We searched the Northern Hemisphere Fields of the GALEX Time-Domain Survey
(TDS) for galaxies with UV variability indicative of active galactic nuclei
(AGNs). We identified 48 high-probability candidate AGNs from a parent sample
of 1819 galaxies in the NASA Sloan Atlas (NSA) catalog. We further
characterized these systems using optical spectroscopic diagnostics, WISE IR
color selection criteria, and spectral energy distribution (SED) modeling. Of
the 48 candidates, eight were identified as AGNs from optical emission lines,
two were identified by their IR colors, and 28 were identified through spectral
energy decomposition. Observational biases of each selection method are
discussed in connecting these AGNs subsamples to another. By selecting AGNs
based on UV variability, we also identified six low-mass AGNs candidates, all
of which would have been missed by spectroscopic selection.


title:
Large-Scale Multi-Document Summarization with Information Extraction and
  Compression

abstract:
  We develop an abstractive summarization framework independent of labeled data
for multiple heterogeneous documents. Unlike existing multi-document
summarization methods, our framework processes documents telling different
stories instead of documents on the same topic. We also enhance an existing
sentence fusion method with a uni-directional language model to prioritize
fused sentences with higher sentence probability with the goal of increasing
readability. Lastly, we construct a total of twelve dataset variations based on
CNN/Daily Mail and the NewsRoom datasets, where each document group contains a
large and diverse collection of documents to evaluate the performance of our
model in comparison with other baseline systems. Our experiments demonstrate
that our framework outperforms current state-of-the-art methods in this more
generic setting.


title:
Effect of stochastic resetting on Brownian motion with stochastic
  diffusion coefficient

abstract:
  We study the dynamics of a Brownian motion with a diffusion coefficient which
evolves stochastically. We first study this process in arbitrary dimensions and
find the scaling form and the corresponding scaling function of the position
distribution. We find that the tails of the distribution have exponential tails
with a ballistic scaling. We then introduce the resetting dynamics where, at a
constant rate, both the position and the diffusion coefficient are reset to
zero. This eventually leads to a nonequilibrium stationary state, which we
study in arbitrary dimensions. In stark contrast to ordinary Brownian motion
under resetting, the stationary position distribution in one dimension has a
logarithmic divergence at the origin. For higher dimensions, however, the
divergence disappears and the distribution attains a dimension-dependent
constant value at the origin, which we compute exactly. The distribution has a
generic stretched exponential tail in all dimensions.
  We also study the approach to the stationary state and find that, as time
increases, an inner core region around the origin attains the stationary state,
while the outside region still has a transient distribution -- this inner
stationary region grows $\sim t^2$, i.e., with a constant acceleration, much
faster than ordinary Brownian motion.


title:
Federated Semi-Supervised Classification of Multimedia Flows for 3D
  Networks

abstract:
  Automatic traffic classification is increasingly becoming important in
traffic engineering, as the current trend of encrypting transport information
(e.g., behind HTTP-encrypted tunnels) prevents intermediate nodes from
accessing end-to-end packet headers. However, this information is crucial for
traffic shaping, network slicing, and Quality of Service (QoS) management, for
preventing network intrusion, and for anomaly detection. 3D networks offer
multiple routes that can guarantee different levels of QoS. Therefore, service
classification and separation are essential to guarantee the required QoS level
to each traffic sub-flow through the appropriate network trunk. In this paper,
a federated feature selection and feature reduction learning scheme is proposed
to classify network traffic in a semi-supervised cooperative manner. The
federated gateways of 3D network help to enhance the global knowledge of
network traffic to improve the accuracy of anomaly and intrusion detection and
service identification of a new traffic flow.


title: Gender Bias in Masked Language Models for Multiple Languages

abstract:
  Masked Language Models (MLMs) pre-trained by predicting masked tokens on
large corpora have been used successfully in natural language processing tasks
for a variety of languages. Unfortunately, it was reported that MLMs also learn
discriminative biases regarding attributes such as gender and race. Because
most studies have focused on MLMs in English, the bias of MLMs in other
languages has rarely been investigated. Manual annotation of evaluation data
for languages other than English has been challenging due to the cost and
difficulty in recruiting annotators. Moreover, the existing bias evaluation
methods require the stereotypical sentence pairs consisting of the same context
with attribute words (e.g. He/She is a nurse). We propose Multilingual Bias
Evaluation (MBE) score, to evaluate bias in various languages using only
English attribute word lists and parallel corpora between the target language
and English without requiring manually annotated data. We evaluated MLMs in
eight languages using the MBE and confirmed that gender-related biases are
encoded in MLMs for all those languages. We manually created datasets for
gender bias in Japanese and Russian to evaluate the validity of the MBE. The
results show that the bias scores reported by the MBE significantly correlates
with that computed from the above manually created datasets and the existing
English datasets for gender bias.


title:
Melting of gold by ultrashort laser pulses: Advanced two-temperature
  modeling and comparison with surface damage experiments

abstract:
  The ultrafast laser-induced solid-liquid phase transition in metals is still
not clearly understood and its accurate quantitative description remains a
challenge. Here we systematically investigated, both experimentally and
theoretically, the melting of gold by single femto- and picosecond
near-infrared laser pulses. Two laser systems with wavelengths of 800 and 1030
nm and pulse durations ranging from 124 fs to 7 ps were used and the damage and
ablation thresholds were determined for each irradiation condition. The
theoretical analysis was based on two-temperature modeling. Different
expressions for the electron-lattice coupling rate and contribution of
ballistic electrons were examined. In addition, the number of free electrons
involved in the optical response is suggested to be dependent on the laser
intensity and the influence of the fraction of involved electrons on the damage
threshold was investigated. Only one combination of modelling parameters was
able to describe consistently all the measured damage thresholds. Physical
arguments are presented to explain the modeling results.


title: Full exceptional collections for anticanonical log del Pezzo surfaces

abstract:
  Motivated by homological mirror symmetry, this paper constructs explicit full
exceptional collections for the canonical stacks associated with the series of
log del Pezzo surfaces constructed by Johnson and Koll\'ar. These surfaces have
cyclic quotient, non-Gorenstein, singularities. The construction involves both
the $\mathrm{GL}(2,\mathbb{C})$ McKay correspondence, and the study of the
minimal resolutions of the surfaces, which are birational to degree two del
Pezzo surfaces. We show that a degree two del Pezzo surface arises in this way
if and only if it admits a generalized Eckardt point, and in the course of the
paper we classify the blow-ups of $\mathbb{P}^2$ giving rise to them. Our
result on the adjoints of the functor of Ishii-Ueda applies to any finite small
subgroup of $\mathrm{GL}(2,\mathbb{C})$.


title:
Unravelling the Innermost Jet Structure of OJ 287 with the First
  GMVA+ALMA Observations

abstract:
  We present the first very-long-baseline interferometric (VLBI) observations
of the blazar OJ287 carried out jointly with the Global Millimeter VLBI Array
(GMVA) and the phased Atacama Large Millimeter/submillimeter Array (ALMA) at
3.5 mm on April 2, 2017. Participation of phased-ALMA not only has improved the
GMVA north-south resolution by a factor of ~3, but also has enabled fringe
detection with signal-to-noise ratios up to 300 at baselines longer than 2
G{\lambda}. The high sensitivity has motivated us to image the data with the
newly developed regularized maximum likelihood imaging methods, revealing the
innermost jet structure with unprecedentedly high angular resolution. Our
images reveal a compact and twisted jet extending along the northwest direction
with two bends within the inner 200 {\mu}as that resembles a precessing jet in
projection. The component at the southeastern end shows a compact morphology
and high brightness temperature, and is identified as the VLBI core. An
extended jet feature that lies at ~200 {\mu}as northwest of the core shows a
conical shape in both total and linearly polarized intensity, and a bimodal
distribution of the linear polarization electric vector position angle. We
discuss the nature of this feature by comparing our observations with models
and simulations of oblique and recollimation shocks with various magnetic field
configurations. Our high-fidelity images also enabled us to search for possible
jet features from the secondary supermassive black hole (SMBH) and test the
SMBH binary hypothesis proposed for this source.


title: Thermalization by off-shell processes: the virtues of small virtuality

abstract:
  We study the thermalization of a scalar field $\Phi$ coupled to two other
scalar fields $\chi_{1,2}$ that constitute a bath in thermal equilibrium. For a
range of masses the $\Phi$ propagator features threshold and infrared
divergences, a vanishing residue at the (quasi) particle pole and vanishing
\emph{on-shell} decay rates thereby preventing the equilibration of $\Phi$ with
the bath via on-shell processes. Inspired by the theory of quantum open systems
we obtain a quantum master equation for the reduced density matrix of $\Phi$
that includes the time dependence of bath correlations, yielding time dependent
rates in the dynamics of relaxation and allowing virtual processes of small
virtuality $\propto 1/t$ at long time $t$. These \emph{off-shell} processes
lead to thermalization despite vanishing S-matrix rates. In the case of
threshold divergences we find that a thermal fixed point is approached as
$e^{-\sqrt{t/t^*}}$ with the relaxation time $t^*$ becoming shorter at high
temperature as a consequence of stimulated emission and absorption. In the
infrared case, the thermal fixed point is approached as $e^{-\gamma(t)}$, where
$\gamma(t)$ features a crossover between a $\propto \ln(t)$ and a $\propto t$
behavior for $t \gg 1/T$. The vanishing of the residue and the crossover in
relaxational dynamics in this case is strikingly reminiscent of the
orthogonality catastrophe in heavy impurity systems. The results yield more
general lessons on thermalization via virtual processes.


title:
Quasinormal modes, quasibound states, scalar clouds, and superradiant
  instabilities of a Kerr-like black hole

abstract:
  We use the continued fraction method to determine the eigenfrequencies
associated with a scalar field around a Kerr-like black hole. The Kerr-like
metric considered in this article is a subclass of the general parametrization
of axisymmetric black holes proposed by Konoplya, Rezzolla and Zhidenko. In
addition to its mass $M$ and specific angular momentum $a$, the black hole
depends on a third parameter $\eta$, called the deformation parameter. We
investigate how the deformation parameter affects the quasinormal modes and the
quasibound states of a massive scalar field around the black hole. In
particular, we compute the time scales associated with the superradiant
instabilities of the scalar field in such a spacetime. The properties of
stationary scalar clouds that could be formed by these instabilities are also
discussed.


title: The free field representation for the $GL(1|1)$ WZW model revisited

abstract:
  The $GL(1|1)$ WZW model in the free field realization that uses the $bc$
system is revisited. By bosonizing the $bc$ system we describe the
Neveu--Schwarz and Ramon sector modules $\mathcal
V^{\text{NS}}_{en}=\bigoplus_{l\in\mathbb Z}\mathcal V^l_{en}$ and $\mathcal
V^{\text{R}}_{en}=\bigoplus_{l\in\mathbb Z+{1\over2}}\mathcal V^l_{en}$ in
terms of the subspaces of a given fermion number $l$. We show that there are
two sectors of mutually local operators, each consists of all Neveu--Schwarz
operators and of Ramon operators with either integer or half-integer spins.
Conformal blocks and structure constants are found for operators that
correspond the highest weight vectors of the spaces $\mathcal V^l_{en}$. The
crossing and braiding matrices are considered and the hexagon and pentagon
equations are shown to be satisfied for typical modules. The degenerate case of
conformal blocks with atypical (logarithmic) modules as intermediate states is
considered. The known conformal block decomposition of correlation functions in
the degenerate case is shown to be related to the degeneration splitting in the
crossing and braiding relations. The scalar product in atypical modules is
discussed. The decomposition of unity in the full correlation functions in the
degenerate case in terms of this scalar product is explained.


title: Vertex spanning planar Laman graphs in triangulated surfaces

abstract:
  We prove that every triangulation of either of the torus, projective plane
and Klein bottle, contains a vertex-spanning planar Laman graph as a
subcomplex. Invoking a result of Kir{\'a}ly, we conclude that every
$1$-skeleton of a triangulation of a surface of nonnegative Euler
characteristic has a rigid realization in the plane using at most 26 locations
for the vertices.


title: Hybrid Equations of State for Neutron Stars with Hyperons and Deltas

abstract:
  In this contribution, we describe new chemically-equilibrated charge-neutral
hybrid equations of state for neutron stars. They present a first-order phase
transition to quark matter and differentiate by the particle population
considered and how these particles interact. While some equations of state
contain just nucleons and up, down-quarks, others also contain hyperons, Delta
baryons, and strange quarks. The hybrid equations of state, together with
corresponding hadronic ones, are available on the CompOSE repository and can be
used for different astrophysical applications.


title: Compactifications of horospheric products

abstract:
  We define and study a new compactification, called the height
compactification of the horospheric product of two infinite trees. We will
provide a complete description of this compactification. In particular, we show
that this compactification is isomorphic to the Busemann compactification when
all the vertices of both trees have degrees of at least three, which also leads
to a precise description of the Busemann functions in terms of the points in
the geometric compactification of each tree. We will discuss an application to
the asymptotic behavior of integrable ergodic cocycles with values in the
isometry group of such horospheric product.


title: Experimental quantum pattern recognition in IBMQ and diamond NVs

abstract:
  One of the most promising applications of quantum computing is the processing
of graphical data like images. Here, we investigate the possibility of
realizing a quantum pattern recognition protocol based on swap test, and use
the IBMQ noisy intermediate-scale quantum (NISQ) devices to verify the idea. We
find that with a two-qubit protocol, swap test can efficiently detect the
similarity between two patterns with good fidelity, though for three or more
qubits the noise in the real devices becomes detrimental. To mitigate this
noise effect, we resort to destructive swap test, which shows an improved
performance for three-qubit states. Due to limited cloud access to larger IBMQ
processors, we take a segment-wise approach to apply the destructive swap test
on higher dimensional images. In this case, we define an average overlap
measure which shows faithfulness to distinguish between two very different or
very similar patterns when simulated on real IBMQ processors. As test images,
we use binary images with simple patterns, greyscale MNIST numbers and MNIST
fashion images, as well as binary images of human blood vessel obtained from
magnetic resonance imaging (MRI). We also present an experimental set up for
applying destructive swap test using the nitrogen vacancy centre (NVs) in
diamond. Our experimental data show high fidelity for single qubit states.
Lastly, we propose a protocol inspired from quantum associative memory, which
works in an analogous way to supervised learning for performing quantum pattern
recognition using destructive swap test.


title:
Game-Theoretic Planning for Autonomous Driving among Risk-Aware Human
  Drivers

abstract:
  We present a novel approach for risk-aware planning with human agents in
multi-agent traffic scenarios. Our approach takes into account the wide range
of human driver behaviors on the road, from aggressive maneuvers like speeding
and overtaking, to conservative traits like driving slowly and conforming to
the right-most lane. In our approach, we learn a mapping from a data-driven
human driver behavior model called the CMetric to a driver's entropic risk
preference. We then use the derived risk preference within a game-theoretic
risk-sensitive planner to model risk-aware interactions among human drivers and
an autonomous vehicle in various traffic scenarios. We demonstrate our method
in a merging scenario, where our results show that the final trajectories
obtained from the risk-aware planner generate desirable emergent behaviors.
Particularly, our planner recognizes aggressive human drivers and yields to
them while maintaining a greater distance from them. In a user study,
participants were able to distinguish between aggressive and conservative
simulated drivers based on trajectories generated from our risk-sensitive
planner. We also observe that aggressive human driving results in more frequent
lane-changing in the planner. Finally, we compare the performance of our
modified risk-aware planner with existing methods and show that modeling human
driver behavior leads to safer navigation.


title: QC-LDPC Codes from Difference Matrices and Difference Covering Arrays

abstract:
  We give a framework for generalizing LDPC code constructions that use
Transversal Designs or related structures such as mutually orthogonal Latin
squares. Our construction offers a broader range of code lengths and codes
rates. Similar earlier constructions rely on the existence of finite fields of
order a power of a prime. In contrast the LDPC codes constructed here are based
on difference matrices and difference covering arrays, structures available for
any order $a$. They satisfy the RC constraint and have, for $a$ odd, length
$a^2$ and rate $1-\frac{4a-3}{a^2}$, and for $a$ even, length $a^2-a$ and rate
at least $1-\frac{4a-6}{a^2-a}$. When $3$ does not divide $a$, these LDPC codes
have stopping distance at least $8$. When $a$ is odd and both $3$ and $5$ do
not divide $a$, our construction delivers an infinite family of QC-LDPC codes
with minimum distance at least $10$. The simplicity of the construction allows
us to theoretically verify these properties and analytically determine lower
bounds for the minimum distance and stopping distance of the code. The BER and
FER performance of our codes over AWGN (via simulation) is at the least
equivalent to codes constructed previously, while in some cases significantly
outperforming them.


title: Strategic Behavior under Context Misalignment

abstract:
  We study the behavioral implications of Rationality and Common Strong Belief
in Rationality (RCSBR) with contextual assumptions allowing players to
entertain misaligned beliefs, i.e., players can hold beliefs concerning their
opponents' beliefs where there is no opponent holding those very beliefs.
Taking the analysts' perspective, we distinguish the infinite hierarchies of
beliefs actually held by players ("real types") from those that are a byproduct
of players' hierarchies ("imaginary types") by introducing the notion of
separating type structure. We characterize the behavioral implications of RCSBR
for the real types across all separating type structures via a family of
subsets of Full Strong Best-Reply Sets of Battigalli & Friedenberg (2012). By
allowing misalignment, in dynamic games we can obtain behavioral predictions
inconsistent with RCSBR (in the standard framework), contrary to the case of
belief-based analyses for static games--a difference due to the dichotomy
"non-monotonic vs. monotonic" reasoning.


title: Parity and Partition of the Rational Numbers

abstract:
  We define an extension of parity from the integers to the rational numbers.
Three parity classes are found -- even, odd and `none'. Using the 2-adic
valuation, we partition the rationals into subgroups with a rich algebraic
structure. The natural density provides a means of distinguishing the sizes of
countably infinite sets. The Calkin-Wilf tree has a remarkably simple parity
pattern, with the sequence `odd/none/even' repeating indefinitely. This pattern
means that the three parity classes have equal natural density in the
rationals. A similar result holds for the Stern-Brocot tree.


title: Adversarial Plannning

abstract:
  Planning algorithms are used in computational systems to direct autonomous
behavior. In a canonical application, for example, planning for autonomous
vehicles is used to automate the static or continuous planning towards
performance, resource management, or functional goals (e.g., arriving at the
destination, managing fuel fuel consumption). Existing planning algorithms
assume non-adversarial settings; a least-cost plan is developed based on
available environmental information (i.e., the input instance). Yet, it is
unclear how such algorithms will perform in the face of adversaries attempting
to thwart the planner. In this paper, we explore the security of planning
algorithms used in cyber- and cyber-physical systems. We present two
$\textit{adversarial planning}$ algorithms-one static and one adaptive-that
perturb input planning instances to maximize cost (often substantially so). We
evaluate the performance of the algorithms against two dominant planning
algorithms used in commercial applications (D* Lite and Fast Downward) and show
both are vulnerable to extremely limited adversarial action. Here, experiments
show that an adversary is able to increase plan costs in 66.9% of instances by
only removing a single action from the actions space (D* Lite) and render 70%
of instances from an international planning competition unsolvable by removing
only three actions (Fast Forward). Finally, we show that finding an optimal
perturbation in any search-based planning system is NP-hard.


title: Origin and evolution of dust-obscured galaxies in galaxy mergers

abstract:
  Dust Obscured Galaxies (DOGs), which are observationally characterized as
faint in the optical and bright in the infrared, are the final stage of galaxy
mergers and are essential objects in the evolution of galaxies and active
galactic nuclei (AGNs). However, the relationship between torus-scale gas
dynamics around AGNs and DOGs lifetime remain unclear. We obtained evolution of
the spectral energy distributions (SEDs) of a galaxy merger system with AGN
feedback, from post-processed pseudo-observations based on an N-body/Smoothed
Particle Hydrodynamics (SPH) simulation. We focused on a late stage merger of
two identical galaxies with a supermassive black hole (SMBH) of 10$^8$
M$_\odot$. We found that the infrared luminosity of the system reaches ultra-
and hyper-luminous infrared galaxy classes (10$^{12}$ and 10$^{13}$ L$_\odot$,
respectively). The DOGs phase corresponds to a state in which the AGNs are
buried in dense gas and dust, with the infrared luminosity exceeding 3.3
$\times$ 10$^{12}$ L$_\odot$. We also identified the sub-categories of DOGs,
namely bump and power-law DOGs from the SEDs and their evolution. The bump DOGs
tend to evolve to power-law DOGs on several Myrs. We found that contribution
from the hot dust around the nucleus in the infrared radiation is essential for
identifying the system as a power-law DOG; the gas and dust distribute
non-spherically around the nucleus, therefore, the observed properties of DOGs
depend on the viewing angle. In our model, the lifetime of merger-driven DOGs
is less than 4 Myrs, suggesting that the observed DOGs phase is a brief aspect
of galaxy mergers.


title:
The sky is blue, and other reasons quantum mechanics is not
  underdetermined by evidence

abstract:
  I criticize the widely-defended view that the quantum measurement problem is
an example of underdetermination of theory by evidence: more specifically, the
view that the unmodified, unitary quantum formalism (interpreted following
Everett) is empirically indistinguishable from Bohmian Mechanics and from
dynamical-collapse theories like the GRW or CSL theories. I argue that there as
yet no empirically successful generalization of either theory to interacting
quantum field theory and so the apparent underdetermination is broken by a very
large class of quantum experiments that require field theory somewhere in their
description. The class of quantum experiments reproducible by either is much
smaller than is commonly recognized and excludes many of the most iconic
successes of quantum mechanics, including the quantitative account of Rayleigh
scattering that explains the color of the sky. I respond to various arguments
to the contrary in the recent literature.


title:
Fisher matrix for the one-loop galaxy power spectrum: measuring
  expansion and growth rates without assuming a cosmological model

abstract:
  We introduce a methodology to extend the Fisher matrix forecasts to mildly
non-linear scales without the need of selecting a cosmological model. We make
use of standard non-linear perturbation theory for biased tracers complemented
by counterterms, and assume that the cosmological distances can be measured
accurately with standard candles. Instead of choosing a specific model, we
parametrize the linear power spectrum and the growth rate in several $k$ and
$z$ bins. We show that one can then obtain model-independent constraints of the
expansion rate $H(z)/H_0$ and the growth rate $f(k,z)$, besides the bias
functions. We apply the technique to both Euclid and DESI public specifications
in the redshift range $0.6-1.8$ and show that the precision on $H(z)$ from
increasing the cut-off scale improves abruptly for $k_{\rm max} > 0.17\,h$/Mpc
and reaches subpercent values for $k_{\rm max} \approx 0.3\,h$/Mpc. Overall,
the gain in precision when going from $k_{\rm max} = 0.1\,h$/Mpc to $k_{\rm
max} = 0.3\,h$/Mpc is around one order of magnitude. The growth rate has in
general much weaker constraints, unless is assumed to be $k$-independent. In
such case, the gain is similar to the one for $H(z)$ and one can reach
uncertainties around 5--10\% at each $z$-bin. We also discuss how neglecting
the non-linear corrections can have a large effect on the constraints even for
$k_{\rm max}=0.1\,h/$Mpc, unless one has independent strong prior information
on the non-linear parameters.


title:
Budgeted Classification with Rejection: An Evolutionary Method with
  Multiple Objectives

abstract:
  Classification systems are often deployed in resource-constrained settings
where labels must be assigned to inputs on a budget of time, memory, etc.
Budgeted, sequential classifiers (BSCs) address these scenarios by processing
inputs through a sequence of partial feature acquisition and evaluation steps
with early-exit options. This allows for an efficient evaluation of inputs that
prevents unneeded feature acquisition. To approximate an intractable
combinatorial problem, current approaches to budgeted classification rely on
well-behaved loss functions that account for two primary objectives (processing
cost and error). These approaches offer improved efficiency over traditional
classifiers but are limited by analytic constraints in formulation and do not
manage additional performance objectives. Notably, such methods do not
explicitly account for an important aspect of real-time detection systems --
the fraction of "accepted" predictions satisfying a confidence criterion
imposed by a risk-averse monitor.
  We propose a problem-specific genetic algorithm to build budgeted, sequential
classifiers with confidence-based reject options. Three objectives -- accuracy,
processing time/cost, and coverage -- are considered. The algorithm emphasizes
Pareto efficiency while accounting for a notion of aggregate performance via a
unique scalarization. Experiments show our method can quickly find globally
Pareto optimal solutions in very large search spaces and is competitive with
existing approaches while offering advantages for selective, budgeted
deployment scenarios.


title:
Frequency analysis of KIC 1573174 : shedding light on the nature of HADS
  stars

abstract:
  We propose that KIC 1573174 is a quadruple-mode $\delta$ Scuti star with
pulsation amplitudes between those of the HADS (high-amplitude Delta Scuti
star) group and average low-amplitude pulsators. The radial modes detected in
this star provide a unique opportunity to exploit asteroseismic techniques up
to their limits. Detailed frequency analysis is given for the light curve from
the Kepler mission. The variation of the light curve is dominated by the
strongest mode with a frequency of F0 = 7.3975 $\rm{d^{-1}}$, as shown by
Fourier analysis of long cadence data (Q1-Q17, spanning 1460 days), indicating
that the target is a $\delta$ Scuti star. The other three independent modes
with F1 = 9.4397 d$^{-1}$, F2 = 12.1225 d$^{-1}$ and F3 = 14.3577 d$^{-1}$,
have ratios of $P_{1}$ / $P_{0}$, $P_{2}$ / $P_{0}$ and $P_{3}$ / $P_{0}$
estimated as 0.783, 0.610 and 0.515, which indicate that KIC 1573174 is a
quadruple-mode $\delta$ Scuti star. A different approach has been used to
determine the $O-C$ through the study of phase modulation. The change of period
$(1/P)~dP/dt$ is obtained resulting in $-1.14 \times 10^{-6}~\text{yr}^{-1}$
and $-4.48 \times 10^{-6}~\text{yr}^{-1}$ for F0 and F1 respectively. Based on
frequency parameters (i.e., F0, F1, F2, and F3), a series of theoretical models
were conducted by employing the stellar evolution code MESA. The ratio of
observed $f_{1}/f_{2}$ is larger than that of the model, which may be caused by
the rotation of the star. We suggest high-resolution spectral observation is
highly desired in the future to further constrain models.


title: Studies of the X(3872) at Belle II

abstract:
  \begin{abstract}
  The X(3872) is one of the most puzzling resonances ever observed. First seen
by the Belle Collaboration in 2003, it solicited the effort of a hundred of
experimental physicists and dozens of theorists, who nowadays are trying yet to
shed light on the nature of this peculiar resonant state. It was seen in
several decay modes and different production mechanisms, and confirmed by
several experiments, so it is well established, and recently is addressed as
the $\chi_{c1} (3872)$. Here we report about a re-discovery of the X(3872) with
early Belle II data, and discuss plans for future measurements once the full
integrated planned luminosity will be achieved by Belle II.


title:
Pricing Path-dependent Options under Stochastic Volatility via Mellin
  Transform

abstract:
  In this paper, we derive closed-form formulas of first-order approximation
for down-and-out barrier and floating strike lookback put option prices under a
stochastic volatility model, by using an asymptotic approach. To find the
explicit closed-form formulas for the zero-order term and the first-order
correction term, we use Mellin transform. We also conduct a sensitivity
analysis on these formulas, and compare the option prices calculated by them
with those generated by Monte-Carlo simulation.


title: Time and G\"odel: Fuzzy temporal reasoning in PSPACE

abstract:
  We investigate a non-classical version of linear temporal logic whose
propositional fragment is G\"odel--Dummett logic (which is well known both as a
superintuitionistic logic and a t-norm fuzzy logic). We define the logic using
two natural semantics, a real-valued semantics and a bi-relational semantics,
and show that these indeed define one and the same logic. Although this G\"odel
temporal logic does not have any form of the finite model property for these
two semantics, we show that every falsifiable formula is falsifiable on a
finite quasimodel, which yields decidability of the logic. We then strengthen
this result by showing that this G\"odel temporal logic is PSPACE-complete.


title: Inflation from a chaotic potential with a step

abstract:
  In this work we study the effects on relevant observational parameters of an
inflationary universe from a chaotic potential with a step. We evolve
numerically the perturbations equations within both cold and warm inflation. On
the one hand, in a cold inflation scenario we analyse the scalar power spectrum
$P_{\mathcal{R}}$ in terms of the number of e-folds $N_{e}$, and in terms of
the ratio $k/k_{0}$, where $k_{0}$ is our pivot scale. We show how
$P_{\mathcal{R}}$ oscillates around $0.2< k/k_{0} < 20$. Also, we present the
evolution of two relevant parameters: the scalar spectral index $n_{s}$ and the
tensor-to-scalar ratio $r$. Actually, more than one region of $(n_{s},r)$ lies
within the observable window (Planck 2018). On the other hand, in the warm
inflationary instance we present two examples, both related to previous
samples. We examine the evolution of the scalar power spectrum in terms of
$N_{e}$, where it can be observed a growth of its amplitude with respect to
bigger $k/k_{0}$. This result might imply that a warm step potential enhances
the evolution of $P_{\mathcal{R}}$ at larger fluctuation scales. Finally, one
key aspect of this research was to contrast the features of an inflationary
potential between both paradigms; and in fact they show similarities and
differences among various cases. One remarkable upshot in both scenarios is
that certain fluctuation scales are not longer "freeze in" on super-horizon
scales. Thus, this outcome might suggest that purely adiabatic primordial
density perturbations no longer holds at the time of the step. Nonetheless, a
deeper study is needed to assure that entropy production is indeed happening at
super-horizon scales. And on the other hand, warm inflation could function as a
screening mechanism capable of smoothing the feature of the inflationary
potential.


title: Higgs inflation and scalar weak gravity conjecture

abstract:
  In this article, we intend to find a specific model which can satisfy the
further refining dS swampland conjecture and scalar weak gravity conjecture
(SWGC) simultaneously, in particular, Higgs inflation model and its two
extensions: Higgs-Dilaton model and Palatini Higgs inflation. We find that
although Higgs inflation model and Higgs-Dilaton model could satisfy the
further refining dS swampland conjecture, the two models cannot satisfy SWGC
and its strong version. While Palatini Higgs inflation could satisfy these
conjectures simultaneously. Therefore Palatini Higgs inflation could be a
"real" inflation model of the universe.


title: A Simple Bootstrap Method for Panel Data Inferences

abstract:
  In this paper, we propose a simple dependent wild bootstrap procedure for us
to establish valid inferences for a wide class of panel data models including
those with interactive fixed effects. The proposed method allows for the error
components having weak correlation over both dimensions, and
heteroskedasticity. The asymptotic properties are established under a set of
simple and general conditions, and bridge the literature of bootstrap methods
and the literature of HAC approaches for panel data models. The new findings
fill some gaps left by the bulk literature of the block bootstrap based panel
data studies. Finally, we show the superiority of our approach over several
natural competitors using extensive numerical studies.