title:
Distributed Vehicular Dynamic Spectrum Access for Platooning
  Environments

abstract:
  In this paper, we propose a distributed Vehicular Dynamic Spectrum Access
(VDSA) framework for vehicles operating in platoon formations. Given the
potential for significant congestion in licensed frequency bands for vehicular
applications such as 5.9 GHz. Our approach proposes to offload part of the
intra-platoon data traffic to spectral white-spaces in order to enhance
vehicular connectivity in support of on-road operations. To enable VDSA, a
Bumblebee-based decision making process is employed which is based on the
behavioral models of animals, is employed to provide a means of distributed
transmission band selection. Simulation results show the distributed VDSA
framework improves the leader packets reception ratio by 5%, thus indicating
its potential to increase in reliability of intra-platoon communications.


title: A comprehensive guide to the physics and usage of PYTHIA 8.3

abstract:
  This manual describes the PYTHIA 8.3 event generator, the most recent version
of an evolving physics tool used to answer fundamental questions in particle
physics. The program is most often used to generate high-energy-physics
collision "events", i.e. sets of particles produced in association with the
collision of two incoming high-energy particles, but has several uses beyond
that. The guiding philosophy is to produce and reproduce properties of
experimentally obtained collisions as accurately as possible. The program
includes a wide ranges of reactions within and beyond the Standard Model, and
extending to heavy ion physics. Emphasis is put on phenomena where strong
interactions play a major role. The manual contains both pedagogical and
practical components. All included physics models are described in enough
detail to allow the user to obtain a cursory overview of used assumptions and
approximations, enabling an informed evaluation of the program output. A number
of the most central algorithms are described in enough detail that the main
results of the program can be reproduced independently, allowing further
development of existing models or the addition of new ones. Finally, a chapter
dedicated fully to the user is included towards the end, providing pedagogical
examples of standard use cases, and a detailed description of a number of
external interfaces. The program code, the online manual, and the latest
version of this print manual can be found on the PYTHIA web page:
https://www.pythia.org/


title:
An Algorithm for Solving Solvable Polynomial Equations of Arbitrary
  Degree by Radicals

abstract:
  This work provides a method(an algorithm) for solving the solvable unary
algebraic equation $f(x)=0$ ($f(x)\in\mathbb{Q}[x]$) of arbitrary degree and
obtaining the exact radical roots. This method requires that we know the Galois
group as the permutation group of the roots of $f(x)$ and the approximate roots
with sufficient precision beforehand. Of course, the approximate roots are not
necessary but can help reduce the quantity of computation. The algorithm
complexity is approximately proportional to the 4th power of the size of the
Galois group of $f(x)$. The whole algorithm doesn't need to deal with
tremendous polynomials or reduce symmetric polynomials.


title:
Exact solutions to a carsharing pricing and relocation problem under
  uncertainty

abstract:
  In this article we study the problem of jointly deciding carsharing prices
and vehicle relocations. We consider carsharing services operating in the
context of multi-modal urban transportation systems. Pricing decisions take
into account the availability of alternative transport modes, and customer
preferences with respect to these. In order to account for the inherent
uncertainty in customer preferences, the problem is formulated as a
mixed-integer two-stage stochastic program with integer decision variables at
both stages. We propose an exact solution method for the problem based on the
integer L-Shaped method which exploits an efficient exact algorithm for the
solution of the subproblems. Tests on artificial instances based on the city of
Milan illustrate that the method can solve, or find good solutions to,
moderately sized instances for which a commercial solver fails. Furthermore,
our results suggest that, by adjusting prices between different zones of the
city, the operator can attract significantly more demand than with a fixed
pricing scheme and that such a pricing scheme, coupled with a sufficiently
large fleet, significantly reduces the relevance of staff-based relocations. A
number of issues, that remain to be addressed in future research, are pointed
out in our conclusions.


title:
Radio Environment Maps for Dynamic Frequency Selection in V2X
  Communications

abstract:
  In this paper, we investigate the concept of database supported Vehicular
Dynamic Spectrum Access (VDSA) for platooning. As various researchers show that
the 5.9 GHz band, devoted for Intelligent Transportation Systems, may suffer
from congestion of the channel, we propose to offload part of this traffic to
white-spaces with the guidance of the active database system. In our work, we
describe our measurement campaign which delivered data for population of the
dedicated radio environment map. Once the map is created, it was used in three
proposed algorithms for VDSA: an optimal and two pragmatic approaches.


title:
Intrinsic ferromagnetism and restrictive thermodynamic stability in
  MA$_2$N$_4$ and Janus VSiGeN$_4$ monolayers

abstract:
  The seminal experimental discovery of remarkably stable MoSi$_2$N$_4$
monolayer has led to a handful of predicted magnetic 2D materials in the
MA$_2$Z$_4$ family (M = transition metals, A = Si, Ge, and Z = N, P, As). These
magnetic monolayers were predicted to be dynamically stable, but none of them
has been synthesized to date. In this work, from the first-principles
thermodynamic stability analysis, we demonstrate that only the nitrides are
thermodynamically stable under N-rich conditions. Based on this finding, we
propose two new ferromagnetic, semiconducting Janus monolayers in the family:
VSiGeN$_4$ and VSiSnN$_4$. They are both dynamically and thermally stable but
only the former is further thermodynamically stable. Intriguingly, unlike those
easy-plane ferromagnetic MA$_2$Z$_4$ monolayers, the Janus VSiSnN$_4$ monolayer
exhibits a long-range ferromagnetic order with T$_c$ $\sim$173 K. These two
emerging Janus magnetic semiconductors offer new opportunities for studying 2D
magnetism and spin control for spintronics applications.


title:
Analysis of Disfluencies for automatic detection of Mild Cognitive
  Impartment: a deep learning approach

abstract:
  The so-called Mild Cognitive Impairment (MCI) or cognitive loss appears in a
previous stage before Alzheimer's Disease (AD), but it does not seem
sufficiently severe to interfere in independent abilities of daily life, so it
usually does not receive an appropriate diagnosis. Its detection is a
challenging issue to be addressed by medical specialists. This work presents a
novel proposal based on automatic analysis of speech and disfluencies aimed at
supporting MCI diagnosis. The approach includes deep learning by means of
Convolutional Neural Networks (CNN) and non-linear multifeature modelling.
Moreover, to select the most relevant features non-parametric Mann-Whitney
U-testt and Support Vector Machine Attribute (SVM) evaluation are used.


title:
Expectation values of polynomials and moments on general compact Lie
  groups

abstract:
  We develop a powerful framework to calculate expectation values of
polynomials and moments on compact Lie groups based on elementary
representation-theoretic arguments and an integration by parts formula. In the
setting of lattice gauge theory, we generalize expectation value formulas for
products of Wilson loops by Chatterjee and Jafarov to arbitrary compact Lie
groups, and study explicit examples for many classical compact Lie groups and
the exceptional Lie group $G_2$. Extending classical results by Collins and
L\'evy, we use our framework to derive expectation value formulas of
polynomials of matrix coefficients under the Haar measure, Brownian motion, and
the Wilson action. In particular, we construct Weingarten functions for general
compact Lie groups by studying the underlying tensor invariants, and apply this
to $\mathrm{SU}(N)$ and $G_2$.


title: The second shifted difference of partitions and its applications

abstract:
  A number of recent papers have estimated ratios of the partition function
$p(n-j)/p(n)$, which appears in many applications. Here, we prove an
easy-to-use effective bound on these ratios. Using this, we then study second
shifted difference of partitions, $f(j,n):= p(n) -2p(n-j) +p(n-2j)$, and give
another easy-to-use estimate of $f(j,n)$. As applications of these, we prove a
shifted convexity property of $p(n)$, as well as giving new estimates of the
$k$-rank partition function $N_k(m,n)$ and non-$k$-ary partitions along with
their differences.


title:
Pointwise convergence in nilmanifolds along smooth functions of
  polynomial growth

abstract:
  We study the equidistribution of orbits of the form $b_1^{a_1(n)}...
b_k^{a_k(n)}\Gamma$ in a nilmanifold $X$, where the sequences $a_i(n)$ arise
from smooth functions of polynomial growth belonging to a Hardy field. We show
that under certain assumptions on the growth rates of the functions
$a_1,...,a_k$, these orbits are uniformly distributed on some subnilmanifold of
the space $X$. As an application of these results and in combination with the
Host-Kra structure theorem for measure preserving systems, as well as some
recent seminorm estimates of the author for ergodic averages concerning Hardy
field functions, we deduce a norm convergence result for multiple ergodic
averages. Our method mainly relies on an equidistribution result of Green-Tao
on finite polynomial orbits of a nilmanifold.


title: Diagnosis of Schizophrenia: A comprehensive evaluation

abstract:
  Machine learning models have been successfully employed in the diagnosis of
Schizophrenia disease. The impact of classification models and the feature
selection techniques on the diagnosis of Schizophrenia have not been evaluated.
Here, we sought to access the performance of classification models along with
different feature selection approaches on the structural magnetic resonance
imaging data. The data consist of 72 subjects with Schizophrenia and 74 healthy
control subjects. We evaluated different classification algorithms based on
support vector machine (SVM), random forest, kernel ridge regression and
randomized neural networks. Moreover, we evaluated T-Test, Receiver Operator
Characteristics (ROC), Wilcoxon, entropy, Bhattacharyya, Minimum Redundancy
Maximum Relevance (MRMR) and Neighbourhood Component Analysis (NCA) as the
feature selection techniques. Based on the evaluation, SVM based models with
Gaussian kernel proved better compared to other classification models and
Wilcoxon feature selection emerged as the best feature selection approach.
Moreover, in terms of data modality the performance on integration of the grey
matter and white matter proved better compared to the performance on the grey
and white matter individually. Our evaluation showed that classification
algorithms along with the feature selection approaches impact the diagnosis of
Schizophrenia disease. This indicates that proper selection of the features and
the classification models can improve the diagnosis of Schizophrenia.


title: Dense Residual Networks for Gaze Mapping on Indian Roads

abstract:
  In the recent past, greater accessibility to powerful computational resources
has enabled progress in the field of Deep Learning and Computer Vision to grow
by leaps and bounds. This in consequence has lent progress to the domain of
Autonomous Driving and Navigation Systems. Most of the present research work
has been focused on driving scenarios in the European or American roads. Our
paper draws special attention to the Indian driving context. To this effect, we
propose a novel architecture, DR-Gaze, which is used to map the driver's gaze
onto the road. We compare our results with previous works and state-of-the-art
results on the DGAZE dataset. Our code will be made publicly available upon
acceptance of our paper.


title: Nonlinear prediction with neural nets in ADPCM

abstract:
  In the last years there has been a growing interest for nonlinear speech
models. Several works have been published revealing the better performance of
nonlinear techniques, but little attention has been dedicated to the
implementation of the nonlinear model into real applications. This work is
focused on the study of the behaviour of a nonlinear predictive model based on
neural nets, in a speech waveform coder. Our novel scheme obtains an
improvement in SEGSNR between 1 and 2 dB for an adaptive quantization ranging
from 2 to 5 bits.


title:
Exclusive $J/\psi$ photoproduction in ultraperipheral Pb+Pb collisions
  at the LHC to next-to-leading order perturbative QCD

abstract:
  We present the first next-to-leading-order (NLO) perturbative QCD (pQCD)
study of rapidity-differential cross sections of coherent exclusive
photoproduction of $J/\psi$ mesons in heavy-ion ultraperipheral collisions
(UPCs) at the LHC, $d\sigma/dy(\text{Pb} + \text{Pb} \rightarrow \text{Pb} +
J/\psi + \text{Pb})$. For this, we account for the photon-nucleon NLO cross
sections at the forward limit, the $t$ dependence using a standard nuclear form
factor, and the photon fluxes of the colliding nuclei. Approximating the
generalized parton distributions with their forward-limit parton distribution
functions (PDFs), we quantify the NLO contributions in the cross sections, show
that the real part of the amplitude and quark-PDF contributions must not be
neglected, quantify the uncertainties arising from the scale-choice and PDFs,
and compare our results with ALICE, CMS and LHCb $J/\psi$ photoproduction data
in Pb+Pb UPCs, exclusive $J/\psi$ photoproduction data from HERA, and LHCb data
in p+p. The scale dependence in $d\sigma/dy(\text{Pb} + \text{Pb} \rightarrow
\text{Pb} + J/\psi + \text{Pb})$ is significant, but we can find a scale-choice
that reproduces the Pb+Pb UPC data both at 2.76 and 5.02 TeV collision
energies. This process has traditionally been suggested to be a direct probe of
nuclear gluon distributions. We show that the situation changes rather
dramatically from LO to NLO: the NLO cross sections reflect the nuclear effects
of both gluons and quarks in a complicated manner where the relative signs of
the LO and NLO terms in the amplitude play a significant role.


title: Speaker recognition with a MLP classifier and LPCC codebook

abstract:
  This paper improves the speaker recognition rates of a MLP classifier and
LPCC codebook alone, using a linear combination between both methods. In
simulations we have obtained an improvement of 4.7% over a LPCC codebook of 32
vectors and 1.5% for a codebook of 128 vectors (error rate drops from 3.68% to
2.1%). Also we propose an efficient algorithm that reduces the computational
complexity of the LPCC-VQ system by a factor of 4.


title: Solution of the Conformable Angular Equation of the Schrodinger Equation

abstract:
  In this work, the conformable Schrodinger equation in spherical coordinates
is separated into two parts; radial and angular part, the angular part of the
Schrodinger equation is solved. The normalized Spherical harmonics function is
obtained as a solution of the angular part


title: Deterministic KPZ equations with nonlocal "gradient terms"

abstract:
  The main goal of this paper is to prove existence and non-existence results
for deterministic Kardar-Parisi-Zhang type equations involving non-local
"gradient terms". More precisely, let $\Omega \subset \mathbb{R}^N$, $N \geq
2$, be a bounded domain with boundary $\partial \Omega$ of class $C^2$. For $s
\in (0,1)$, we consider problems of the form \[ \tag{KPZ} \left\{
\begin{aligned} (-\Delta)^s u & = \mu(x) |\mathbb{D}(u)|^q + \lambda f(x),
\quad && \mbox{ in } \Omega,\\ u & = 0, && \mbox{ in } \mathbb{R}^N \setminus
\Omega, \end{aligned} \right. \] where $q > 1$ and $\lambda > 0$ are real
parameters, $f$ belongs to a suitable Lebesgue space, $\mu$ belongs to
$L^{\infty}(\Omega)$ and $\mathbb{D}$ represents a nonlocal "gradient term".
Depending on the size of $\lambda > 0$, we derive existence and non-existence
results. In particular, we solve several open problems posed in [4, Section 6]
and [2, Section 7].


title:
Preparation of ultra-cold atomic-ensemble arrays using time-multiplexed
  optical tweezers

abstract:
  We use optical tweezers based on time-multiplexed acousto-optic deflectors to
trap ultra-cold cesium atoms in one-dimensional arrays of atomic ensembles. For
temperatures between 2.5 $\mu$K and 50 nK we study the maximal time between
optical tweezer pulses that retains the number of atoms in a single trap. This
time provides an estimate on the maximal number of sites in an array of
time-multiplexed optical tweezers. We demonstrate evaporative cooling of atoms
in arrays of up to 25 optical tweezer traps and the preparation of atoms in a
box potential. Additionally, we demonstrate three different protocols for the
preparation of atomic-ensemble arrays by transfer from an expanding ultra-cold
atomic cloud. These result in the preparation of arrays of up to 74 atomic
ensembles consisting of $\sim$100 atoms on average.


title:
Distributing Collaborative Multi-Robot Planning with Gaussian Belief
  Propagation

abstract:
  Precise coordinated planning enables safe and highly efficient motion when
many robots must work together in tight spaces, but this would normally require
centralised control of all devices which is difficult to scale. We demonstrate
a new purely distributed technique based on Gaussian Belief Propagation on
multi-robot planning problems formulated by a generic factor graph defining
dynamics and collision constraints. We show that our method allows extremely
high performance collaborative planning in a simulated road traffic scenario,
where vehicles are able to cross each other at a busy multi-lane junction while
maintaining much higher average speeds than alternative distributed planning
techniques. We encourage the reader to view the accompanying video
demonstration to this work at https://youtu.be/5d4LXbxgxaY.


title:
Spectrality of random convolutions generated by finitely many Hadamard
  triples

abstract:
  Let $\{(N_j, B_j, L_j): 1 \le j \le m\}$ be finitely many Hadamard triples in
$\mathbb{R}$. Given a sequence of positive integers $\{n_k\}_{k=1}^\infty$ and
$\omega=(\omega_k)_{k=1}^\infty \in \{1,2,\cdots, m\}^\mathbb{N}$, let
$\mu_{\omega,\{n_k\}}$ be a random infinite convolution given by
$$\mu_{\omega,\{n_k\}} = \delta_{N_{\omega_1}^{-n_1} B_{\omega_1}} *
\delta_{N_{\omega_1}^{-n_1} N_{\omega_2}^{-n_2} B_{\omega_2}} * \cdots *
\delta_{N_{\omega_1}^{-n_1} N_{\omega_2}^{-n_2} \cdots N_{\omega_k}^{-n_k}
B_{\omega_k} }* \cdots. $$ We study the spectrality of $\mu_{\omega,\{ n_k\}}$,
and we show that if the Hadamard triples $\{(N_j, B_j, L_j): 1 \le j \le m\}$
satisfy that $\mathrm{gcd}(B_j - B_j)=1$ for $1 \le j \le m$, then the random
convolution $\mu_{\omega,\{n_k\}}$ is a spectral measure for every $\omega \in
\{1,2,\cdots, m\}^\mathbb{N}$. This implies that we may find a set
$\Lambda_{\omega,\{n_k\}}\subseteq \mathbb{R}$ such that $\big\{ e_\lambda(x) =
e^{-2\pi i \lambda x}: \lambda \in \Lambda_{\omega,\{n_k\}} \big\}$ forms an
orthonormal basis for $L^2(\mu_{\omega,\{ n_k\}})$.


title:
Direct probing of phonon mode specific electron-phonon scatterings in
  two-dimensional semiconductor transition metal dichalcogenides: Symmetry and
  Berry phase

abstract:
  Electron-phonon scatterings in solid-state systems are pivotal processes in
determining many key physical quantities such as charge carrier mobilities and
thermal conductivities. Here, we report on the direct probing of phonon mode
specific electron-phonon scatterings in layered semiconducting transition metal
dichalcogenides WSe2, MoSe2, WS2, and MoS2 through inelastic electron tunneling
spectroscopy measurements, quantum transport simulations, and density
functional calculation. We experimentally and theoretically characterize
momentum-conserving single- and two-phonon electron-phonon scatterings
involving up to as many as eight individual phonon modes in mono- and bilayer
films, among which transverse, longitudinal acoustic and optical, and flexural
optical phonons play significant roles in quantum charge flows. Moreover, we
observe that two-phonon inelastic electron tunneling processes, which are
confirmed to be generic in all four semiconducting layers, are governed by
layer-number dependent symmetry, quantum interference, and geometric Berry
phase.


title:
Automatic Detection of Accretion Bursts in Young Stellar Objects: a New
  Algorithm for Long--Term Sky Surveys

abstract:
  Young stellar objects in their pre-main sequence phase are characterized by
irregular changes in brightness, generally attributed to an increase of the
mass accretion rate due to various kind of instabilities occurring in the
circumstellar disk. In the era of large surveys aimed to monitor the sky, we
present a pipeline to detect irregular bursts, in particular EXors-like ( EX
Lupi type eruptive variables), in the light curves. The procedure follows a
heuristic approach and is tested against the light curves already collected for
a few objects presently recognized as bona fide or candidate EXors.


title: Injection tolerances for AWAKE Run 2c

abstract:
  The AWAKE project at CERN makes use of a proton-driven plasma wakefield to
accelerate a witness electron bunch. While Run 1 relied on selective trapping
of a long electron bunch, Run~2c will use a short witness bunch to allow better
control of the accelerated bunch parameters. This work investigates the
tolerances for injection into such a quasilinear wake through simulation. A
figure of merit based on a potential high-energy application for AWAKE is
developed to provide a single metric for the accelerated bunch, allowing
different configurations to be easily compared and providing a quantitative
basis for future design decisions. Further, it is shown that the figure of
merit naturally gives rise to constraints on the tunability and stability of
the initial witness bunch parameters.


title: PetaVolts per meter Plasmonics: Snowmass21 White Paper

abstract:
  Plasmonic modes offer the potential to achieve PetaVolts per meter fields,
that would transform the current paradigm in collider development in addition
to non-collider searches in fundamental physics. PetaVolts per meter plasmonics
relies on collective oscillations of the free electron Fermi gas inherent in
the conduction band of materials that have a suitable combination of
constituent atoms and ionic lattice structure. As the conduction band free
electron density, at equilibrium, can be as high as $\rm 10^{24}cm^{-3}$,
electromagnetic fields of the order of $\rm 0.1 \sqrt{\rm n_0(10^{24}cm^{-3})}
~ PVm^{-1}$ can be sustained by plasmonic modes. Engineered materials not only
allow highly tunable material properties but quite critically make it possible
to overcome disruptive instabilities that dominate the interactions in bulk
media. Due to rapid shielding by the free electron Fermi gas, dielectric
effects are strongly suppressed. Because the ionic lattice, the corresponding
electronic energy bands and the free electron gas are governed by quantum
mechanical effects, comparisons with plasmas are merely notional. Based on this
framework, it is critical to address various challenges that underlie PetaVolts
per meter plasmonics including stable excitation of plasmonic modes while
accounting for their effects on the ionic lattice and the electronic energy
band structure over femtosecond timescales. We summarize the ongoing
theoretical and experimental efforts as well as map out strategies for the
future. Extreme plasmonic fields can shape the future by not only bringing tens
of TeV to multi-PeV center-of-mass-energies within reach but also by opening
novel pathways in non-collider HEP. In view of this promise, we invite the
scientific community to help realize the immense potential of PV/m plasmonics
and call for significant expansion of the US and international R\&D program.


title:
High-resolution Iterative Feedback Network for Camouflaged Object
  Detection

abstract:
  Spotting camouflaged objects that are visually assimilated into the
background is tricky for both object detection algorithms and humans who are
usually confused or cheated by the perfectly intrinsic similarities between the
foreground objects and the background surroundings. To tackle this challenge,
we aim to extract the high-resolution texture details to avoid the detail
degradation that causes blurred vision in edges and boundaries. We introduce a
novel HitNet to refine the low-resolution representations by high-resolution
features in an iterative feedback manner, essentially a global loop-based
connection among the multi-scale resolutions. In addition, an iterative
feedback loss is proposed to impose more constraints on each feedback
connection. Extensive experiments on four challenging datasets demonstrate that
our \ourmodel~breaks the performance bottleneck and achieves significant
improvements compared with 29 state-of-the-art methods. To address the data
scarcity in camouflaged scenarios, we provide an application example by
employing cross-domain learning to extract the features that can reflect the
camouflaged object properties and embed the features into salient objects,
thereby generating more camouflaged training samples from the diverse salient
object datasets The code will be available at
https://github.com/HUuxiaobin/HitNet.


title:
Nanoflare distributions over solar cycle 24 based on SDO/AIA
  differential emission measure observations

abstract:
  Nanoflares in quiet-Sun regions during solar cycle 24 are studied with the
best available plasma diagnostics to derive their energy distribution and
contribution to coronal heating during different levels of solar activity.
Extreme ultraviolet (EUV) filters of the Atmospheric Imaging Assembly (AIA)
onboard the Solar Dynamics Observatory (SDO) are used. We analyze 30 AIA/SDO
image series between 2011 and 2018, each covering a $400 \times 400$ arcsec
quiet-Sun field-of-view over two hours with a 12-second cadence. Differential
emission measure (DEM) analysis is used to derive the emission measure (EM) and
temperature evolution for each pixel. We detect nanoflares as EM-enhancements
using a threshold-based algorithm and derive their thermal energy from the DEM
observations. Nanoflare energy distributions follow power-laws that show slight
variations in steepness ($\alpha =$ 2.02 to 2.47) but no correlation to the
solar activity level. The combined nanoflare distribution of all data sets
covers five orders of magnitude in event energies ($10^{24} \mathrm{~to~}
10^{29} \mathrm{~erg}$) with a power-law index $\alpha=2.28 \pm0.03$. The
derived mean energy flux of $(3.7\pm 1.6)\times
10^4\mathrm{~erg~cm^{-2}~s^{-1}}$ is one order of magnitude smaller than the
coronal heating requirement. We find no correlation between the derived energy
flux and solar activity. Analysis of the spatial distribution reveals clusters
of high energy flux (up to $3\times 10^5 \mathrm{~erg~cm^{-2}~s^{-1}}$)
surrounded by extended regions with lower activity. Comparisons with
magnetograms from the Helioseismic and Magnetic Imager (HMI) demonstrate that
high-activity clusters are located preferentially in the magnetic network and
above regions of enhanced magnetic flux density.


title:
Assessing the Impact of Hydrogen Absorption on the Characteristics of
  the Galactic Center Excess

abstract:
  We present a new reconstruction of the distribution of atomic hydrogen in the
inner Galaxy that is based on explicit radiation-transport modelling of line
and continuum emission and a gas-flow model in the barred Galaxy that provides
distance resolution for lines of sight toward the Galactic Center. The main
benefits of the new gas model are, a), the ability to reproduce the negative
line signals seen with the H$I$4PI survey and, b), the accounting for gas that
primarily manifests itself through absorption.
  We apply the new model of Galactic atomic hydrogen to an analysis of the
diffuse gamma-ray emission from the inner Galaxy, for which an excess at a few
GeV was reported that may be related to dark matter. We find with high
significance an improved fit to the diffuse gamma-ray emission observed with
the Fermi-LAT, if our new H$I$ model is used to estimate the cosmic-ray induced
diffuse gamma-ray emission. The fit still requires a nuclear bulge at high
significance. Once this is included there is no evidence for a dark-matter
signal, be it cuspy or cored. But an additional so-called boxy bulge is still
favoured by the data. This finding is robust under the variation of various
parameters, for example the excitation temperature of atomic hydrogen, and a
number of tests for systematic issues.


title:
Bounds on Wasserstein distances between continuous distributions using
  independent samples

abstract:
  The plug-in estimator of the Wasserstein distance is known to be
conservative, however its usefulness is severely limited when the distributions
are similar as its bias does not decay to zero with the true Wasserstein
distance. We propose a linear combination of plug-in estimators for the squared
2-Wasserstein distance with a reduced bias that decays to zero with the true
distance. The new estimator is provably conservative provided one distribution
is appropriately overdispersed with respect the other, and is unbiased when the
distributions are equal. We apply it to approximately bound from above the
2-Wasserstein distance between the target and current distribution in Markov
chain Monte Carlo, running multiple identically distributed chains which start,
and remain, overdispersed with respect to the target. Our bound consistently
outperforms the current state-of-the-art bound, which uses coupling, improving
mixing time bounds by up to an order of magnitude.


title: Entanglement Witness for the Weak Equivalence Principle

abstract:
  The Einstein equivalence principle is based on the equality of gravitational
mass and inertial mass, which has led to the universality of a free-fall
concept. The principle has been extremely well tested so far and has been
tested with a great precision. However, all these tests and the corresponding
arguments are based on a classical setup where the notion of position and
velocity of the mass is associated with a classical value as opposed to the
quantum entities. Here, we will provide a simple protocol based on creating
Schr\"odinger Cat states in a laboratory to test the fully quantum regime of
the equivalence principle where both matter and gravity are treated at par as a
quantum entity. We will argue that such a quantum protocol is unique with
regard to testing especially the weak equivalence principle via witnessing
quantum entanglement.


title: On Neural Network Equivalence Checking using SMT Solvers

abstract:
  Two pretrained neural networks are deemed equivalent if they yield similar
outputs for the same inputs. Equivalence checking of neural networks is of
great importance, due to its utility in replacing learning-enabled components
with equivalent ones, when there is need to fulfill additional requirements or
to address security threats, as is the case for example when using knowledge
distillation, adversarial training etc. SMT solvers can potentially provide
solutions to the problem of neural network equivalence checking that will be
sound and complete, but as it is expected any such solution is associated with
significant limitations with respect to the size of neural networks to be
checked. This work presents a first SMT-based encoding of the equivalence
checking problem, explores its utility and limitations and proposes avenues for
future research and improvements towards more scalable and practically
applicable solutions. We present experimental results that shed light to the
aforementioned issues, for diverse types of neural network models (classifiers
and regression networks) and equivalence criteria, towards a general and
application-independent equivalence checking approach.


title:
Decade-long time-monitoring of candidate Luminous Blue Variable Stars in
  the two very metal-deficient compact dwarf galaxies DDO 68 and PHL 293B

abstract:
  We have studied the spectral time variations of candidate luminous blue
variable stars (cLBV) in two low-metallicity blue compact dwarf galaxies, DDO
68 and PHL 293B. The LBV in DDO 68, located in HII region #3, shows an
outburst, with an increase of more than 1000 times in Halpha luminosity during
the period 2008-2010. The broad emission of the HI and HeI lines display a P
Cygni profile, with a relatively constant terminal velocity of ~800 km/s,
reaching a maximum luminosity L(Halpha) of ~2x10^38 erg/s, with a FWHM of
~1000-1200 km/s. On the other hand, since the discovery of a cLBV in 2001 in
PHL 293B, the fluxes of the broad components and the broad-to-narrow flux
ratios of the HI and HeI emission lines in this galaxy have remained nearly
constant over 16 years, with small variations. The luminosity of the broad
Halpha component varies between ~2x10^38 erg/s and ~10^39 erg/s, with the FWHM
varying in the range ~500-1500 km/s. Unusually persistent P Cygni features are
clearly visible until the end of 2020 despite a decrease of the broad-to-narrow
flux ratio in the most recent years. A terminal velocity of ~800 km/s is
measured from the P Cygni profile, similar to the one in DDO 68, although the
latter is 3.7 more metal-deficient than PHL 293B. The relative constancy of the
broad Halpha luminosity in PHL 293B suggests that it is due to a long-lived
stellar transient of type LBV/SN IIn.


title: Dehn twists and the Nielsen realization problem for spin 4-manifolds

abstract:
  We prove that, for a closed oriented smooth spin 4-manifold $X$ with
$\sigma(X)<0$ and $H^{1}(X;\mathbb{Z}/2)=0$, the Dehn twist about a
$(-2)$-sphere in $X$ is not homotopic to any finite order diffeomorphism. In
particular, we negatively answer the Nielsen realization problem for each group
generated by the mapping class of a Dehn twist. We also show that there is a
difference between the Nielsen realization problems in the topological category
and smooth category for connected sums of copies of $K3$ and $S^{2} \times
S^{2}$. The main ingredient of the proofs is Y. Kato's 10/8-type inequality for
involutions.


title:
QS-Craft: Learning to Quantize, Scrabble and Craft for Conditional Human
  Motion Animation

abstract:
  This paper studies the task of conditional Human Motion Animation (cHMA).
Given a source image and a driving video, the model should animate the new
frame sequence, in which the person in the source image should perform a
similar motion as the pose sequence from the driving video. Despite the success
of Generative Adversarial Network (GANs) methods in image and video synthesis,
it is still very challenging to conduct cHMA due to the difficulty in
efficiently utilizing the conditional guided information such as images or
poses, and generating images of good visual quality. To this end, this paper
proposes a novel model of learning to Quantize, Scrabble, and Craft (QS-Craft)
for conditional human motion animation. The key novelties come from the newly
introduced three key steps: quantize, scrabble and craft. Particularly, our
QS-Craft employs transformer in its structure to utilize the attention
architectures. The guided information is represented as a pose coordinate
sequence extracted from the driving videos. Extensive experiments on human
motion datasets validate the efficacy of our model.


title:
Semi-Targeted Model Poisoning Attack on Federated Learning via Backward
  Error Analysis

abstract:
  Model poisoning attacks on federated learning (FL) intrude in the entire
system via compromising an edge model, resulting in malfunctioning of machine
learning models. Such compromised models are tampered with to perform
adversary-desired behaviors. In particular, we considered a semi-targeted
situation where the source class is predetermined however the target class is
not. The goal is to cause the global classifier to misclassify data of the
source class. Though approaches such as label flipping have been adopted to
inject poisoned parameters into FL, it has been shown that their performances
are usually class-sensitive varying with different target classes applied.
Typically, an attack can become less effective when shifting to a different
target class. To overcome this challenge, we propose the Attacking
Distance-aware Attack (ADA) to enhance a poisoning attack by finding the
optimized target class in the feature space. Moreover, we studied a more
challenging situation where an adversary had limited prior knowledge about a
client's data. To tackle this problem, ADA deduces pair-wise distances between
different classes in the latent feature space from shared model parameters
based on the backward error analysis. We performed extensive empirical
evaluations on ADA by varying the factor of attacking frequency in three
different image classification tasks. As a result, ADA succeeded in increasing
the attack performance by 1.8 times in the most challenging case with an
attacking frequency of 0.01.


title:
A complete characterization of normal cones and extreme points for
  $p$-boxes

abstract:
  Probability boxes, also known as $p$-boxes, correspond to sets of probability
distributions bounded by a pair of distribution functions. They fall into the
class of models known as imprecise probabilities. One of the central questions
related to imprecise probabilities are the intervals of values corresponding to
expectations of random variables, and especially the interval bounds. In
general, those are attained in extremal points of credal sets, which denote
convex sets of compatible probabilistic models. The aim of this paper is a
characterization and identification of extreme points corresponding to
$p$-boxes on finite domains. To accomplish this, we utilize the concept of
normal cones. In the settings of imprecise probabilities, those correspond to
sets of random variables whose extremal expectations are attained in a common
extreme point. Our main results include a characterization all possible normal
cones of $p$-boxes, their relation with extreme points, and the identification
of adjacency structure on the collection of normal cones, closely related to
the adjacency structure in the set of extreme points.


title: Multi-Source Domain Adaptation Based on Federated Knowledge Alignment

abstract:
  Federated Learning (FL) facilitates distributed model learning to protect
users' privacy. In the absence of labels for a new user's data, the knowledge
transfer in FL allows a learned global model to adapt to the new samples
quickly. The multi-source domain adaptation in FL aims to improve the model's
generality in a target domain by learning domain-invariant features from
different clients. In this paper, we propose Federated Knowledge Alignment
(FedKA) that aligns features from different clients and those of the target
task. We identify two types of negative transfer arising in multi-source domain
adaptation of FL and demonstrate how FedKA can alleviate such negative
transfers with the help of a global features disentangler enhanced by embedding
matching. To further facilitate representation learning of the target task, we
devise a federated voting mechanism to provide labels for samples from the
target domain via a consensus from querying local models and fine-tune the
global model with these labeled samples. Extensive experiments, including an
ablation study, on an image classification task of Digit-Five and a text
sentiment classification task of Amazon Review, show that FedKA could be
augmented to existing FL algorithms to improve the generality of the learned
model for tackling a new task.


title: A Method for Estimating Individual Socioeconomic Status of Twitter Users

abstract:
  The rise of social media and computational social science (CSS) has opened
countless opportunities to explore social science questions with new forms of
data and methods. However, CSS research on socioeconomic inequality, a
fundamental problem in sociology, has been constrained due to the lack of
individual-level socioeconomic status (SES) measures in digital trace data. We
propose a new approach to address this problem. Following Bourdieu, we argue
that the commercial and entertainment accounts that Twitter users follow
reflect their economic and cultural capital and hence, we can use these
followings to infer the users' SES. Inspired by political science approaches to
inferring social media users' political ideology, we develop a method that uses
correspondence analysis to project official Twitter accounts and their
followers onto a linear SES scale. Using this method, we estimate the SES of
3,482,657 Twitter users who follow the Twitter accounts of 339 supermarkets and
department stores, clothing and speciality retailers, chain restaurants,
newspapers and news channels, sports, and TV shows in the United States. We
validate our estimates with data on audience composition from the Facebook
Marketing API, self-reported job titles on users' Twitter profiles, and a small
survey sample. The results show reasonable correlations between our SES
estimates and the standard proxies for SES: education, occupational class, and
income at the aggregate level and weaker but still significant correlations at
the individual level. The proposed method opens new opportunities for
innovative social research on inequality on Twitter and similar online
platforms.


title:
Look for the Change: Learning Object States and State-Modifying Actions
  from Untrimmed Web Videos

abstract:
  Human actions often induce changes of object states such as "cutting an
apple", "cleaning shoes" or "pouring coffee". In this paper, we seek to
temporally localize object states (e.g. "empty" and "full" cup) together with
the corresponding state-modifying actions ("pouring coffee") in long uncurated
videos with minimal supervision. The contributions of this work are threefold.
First, we develop a self-supervised model for jointly learning state-modifying
actions together with the corresponding object states from an uncurated set of
videos from the Internet. The model is self-supervised by the causal ordering
signal, i.e. initial object state $\rightarrow$ manipulating action
$\rightarrow$ end state. Second, to cope with noisy uncurated training data,
our model incorporates a noise adaptive weighting module supervised by a small
number of annotated still images, that allows to efficiently filter out
irrelevant videos during training. Third, we collect a new dataset with more
than 2600 hours of video and 34 thousand changes of object states, and manually
annotate a part of this data to validate our approach. Our results demonstrate
substantial improvements over prior work in both action and object
state-recognition in video.


title: Skew-product decomposition of Brownian motion on ellipsoid

abstract:
  In this article we obtain a skew-product decomposition of a Brownian motion
on an ellipsoid of dimension $n$ in a Euclidean space of dimension $n+1$. We
only consider such ellipsoid whose restriction to first $n$ dimensions is a
sphere and its last coordinate depends on a variable parameter. We prove that
the projection of this Brownian motion on to the last coordinate is, after a
suitable transformation, a Wright-Fisher diffusion process with atypical
selection coefficient.


title:
Learning Relation-Specific Representations for Few-shot Knowledge Graph
  Completion

abstract:
  Recent years have witnessed increasing interest in few-shot knowledge graph
completion (FKGC), which aims to infer unseen query triples for a few-shot
relation using a handful of reference triples of the relation. The primary
focus of existing FKGC methods lies in learning the relation representations
that can reflect the common information shared by the query and reference
triples. To this end, these methods learn the embeddings of entities with their
direct neighbors, and use the concatenation of the entity embeddings as the
relation representations. However, the entity embeddings learned only from
direct neighborhoods may have low expressiveness when the entity has sparse
neighbors or shares a common local neighborhood with other entities. Moreover,
the embeddings of two entities are insufficient to represent the semantic
information of their relationship, especially when they have multiple
relations. To address these issues, we propose a Relation-Specific Context
Learning (RSCL) framework, which exploits graph contexts of triples to capture
the semantic information of relations and entities simultaneously.
Specifically, we first extract graph contexts for each triple, which can
provide long-term entity-relation dependencies. To model the graph contexts, we
then develop a hierarchical relation-specific learner to learn global and local
relation-specific representations for relations by capturing contextualized
information of triples and incorporating local information of entities.
Finally, we utilize the learned representations to predict the likelihood of
the query triples. Experimental results on two public datasets demonstrate that
RSCL outperforms state-of-the-art FKGC methods.


title:
Control of light-atom solitons and atomic transport by optical vortex
  beams propagating through a Bose-Einstein Condensate

abstract:
  We model propagation of far-red-detuned optical vortex beams through a
Bose-Einstein Condensate using nonlinear Schr\"odinger and Gross-Pitaevskii
equations. We show the formation of coupled light/atomic solitons that rotate
azimuthally before moving off tangentially, carrying angular momentum. The
number, and velocity, of solitons, depends on the orbital angular momentum of
the optical field. Using a Bessel-Gauss beam increases radial confinement so
that solitons can rotate with fixed azimuthal velocity. Our model provides a
highly controllable method of channelling a BEC and atomic transport.


title:
Irreducible modules of toroidal Lie algebras arising from
  $\phi_\epsilon$-coordinated modules of vertex algebras

abstract:
  In this paper, for every $\epsilon\in \mathbb{Z}$, we introduce an extension
of the 2-toroidal Lie algebra by certain derivations. Based on the
$\phi_\epsilon$-coordinated modules theory for vertex algebras, we give an
explicit realization of a class of irreducible highest weight modules for this
extended toroidal Lie algebra. When $\epsilon=1$, this affords a realization of
certain irreducible modules for the toroidal extended affine Lie algebras first
constructed by Billig.


title: Neutrino interactions with ultralight axion-like dark matter

abstract:
  In this work, we study the propagation of high energy neutrinos produced in
extragalactic sources including the effect of a possible interaction with
ultralight axion-like particles (UALP) with a mass $m_a\sim 10^{-22} {\rm eV}$
as the constituents of dark matter (DM) under the assumption that their
coupling to neutrinos is dominant. We compute the cross section and describe
the propagation of a diffuse neutrino flux using transport equations for each
mass eigenstate. This allows us to obtain the neutrino fluxes of the different
flavors to be observed at the Earth with neutrino telescopes under different
assumptions for the flavor composition emitted at the sources and for a normal
ordering (NO) or an inverted ordering (IO) of the neutrino masses. If the
coupling of neutrinos with UALPs is the same for all flavors ($g_{\nu a}$), we
find that interactions change the flavor composition of neutrinos arriving on
Earth for $g_{\nu a}\gtrsim 0.5\,{\rm GeV}^{-1}$, causing the electron(muon)
flavor to dominate in the NO(IO) case for neutrino energies above $\sim
10^5\,{\rm GeV}$. Although current data on the flavor ratios suggest that
interactions with UALP DM do not take place within the range of coupling
studied (particularly in the NO case) more data is needed to improve the
precision of the experimentally measured flavor composition.


title:
Relation between spectra of Narain CFTs and properties of associated
  boolean functions

abstract:
  Recently, the construction of Narain CFT from a certain class of quantum
error correcting codes has been discovered. In particular, the spectral gap of
Narain CFT corresponds to the binary distance of the code, not the genuine
Hamming distance. In this paper, we show that the binary distance is identical
to the so-called EPC distance of the boolean function uniquely associated with
the quantum code. Therefore, seeking Narain CFT with high spectral gap is
equivalent to getting a boolean function with high EPC distance. Furthermore,
this problem can be addressed by finding lower Peak-to-Average Power ratio
(PAR) with respect to the binary truth table of the boolean function. Though
this is neither sufficient nor necessary condition for high EPC distance, we
construct some examples of relatively high EPC distances referring to the
constructions for lower PAR. We also see that codes with high distance are
related to induced graphs with low independence numbers.


title: Far from equilibrium relaxation in the weak coupling limit

abstract:
  A common assumption is that a large system, weakly coupled to a thermal
environment through its boundaries, relaxes quasi-statically towards the new
equilibrium even when the temperature of the environment changes abruptly. Here
we show how this intuitive picture can break down in discrete energy systems,
even in the case of infinitely weak coupling. We provide an example in the
Ising antiferromagnetic chain, showing how the interaction among degrees of
freedom can create corrugated energy landscapes that are responsible for
far-from-equilibrium and anomalous relaxations.


title:
Confronting a set of Skyrme and $\chi_{EFT}$ predictions for the crust
  of neutron stars

abstract:
  With the improved accuracy of neutron star observational data, it is
necessary to derive new equation of state where the crust and the core are
consistently calculated within a unified approach. For this purpose we describe
non-uniform matter in the crust of neutron stars employing a compressible
liquid-drop model, where the bulk and the neutron fluid terms are given from
the same model as the one describing uniform matter present in the core. We
then generate a set of fifteen unified equations of state for cold catalyzed
neutron stars built on realistic modelings of the nuclear interaction, which
belongs to two main groups: the first one derives from the phenomenological
Skyrme interaction and the second one from $\chi_{EFT}$ Hamiltonians. The
confrontation of these model predictions allows us to investigate the model
dependence for the crust properties, and in particular the effect of neutron
matter at low density. The new set of unified equations of state is available
at the CompOSE repository.


title: Silicon-carbide for integrated photonics

abstract:
  The recent progress in chip-scale integrated photonics has stimulated the
rapid development of material platforms with desired optical properties. Among
the different material platforms that are currently investigated, the
third-generation semiconductor, silicon carbide (SiC), offers the broadest
range of functionalities, including wide bandgap, high optical nonlinearities,
high refractive index, and CMOS-compatible device fabrication process. Here, we
provide an overview of SiC-based integrated photonics, presenting the latest
progress on investigating its basic optical and optoelectronic properties, as
well as the recent developments in the fabrication of several typical
approaches for light confinement structures that form the basic building blocks
for low-loss, high functional and industry-compatible integrated photonic
platform. Moreover, recent works employing SiC as optically-readable spin hosts
for quantum information applications are also summarized and discussed. As a
still-developing integrated photonic platform, the prospects and challenges of
SiC material platform in the field of integrated photonics are also discussed,
followed by potential solutions.


title: Semantic State Estimation in Cloth Manipulation Tasks

abstract:
  Understanding of deformable object manipulations such as textiles is a
challenge due to the complexity and high dimensionality of the problem.
Particularly, the lack of a generic representation of semantic states (e.g.,
\textit{crumpled}, \textit{diagonally folded}) during a continuous manipulation
process introduces an obstacle to identify the manipulation type. In this
paper, we aim to solve the problem of semantic state estimation in cloth
manipulation tasks. For this purpose, we introduce a new large-scale
fully-annotated RGB image dataset showing various human demonstrations of
different complicated cloth manipulations. We provide a set of baseline deep
networks and benchmark them on the problem of semantic state estimation using
our proposed dataset. Furthermore, we investigate the scalability of our
semantic state estimation framework in robot monitoring tasks of long and
complex cloth manipulations.


title: Learning Operators with Mesh-Informed Neural Networks

abstract:
  Thanks to their universal approximation properties and new efficient training
strategies, Deep Neural Networks are becoming a valuable tool for the
approximation of mathematical operators. In the present work, we introduce
Mesh-Informed Neural Networks (MINNs), a class of architectures specifically
tailored to handle mesh based functional data, and thus of particular interest
for reduced order modeling of parametrized Partial Differential Equations
(PDEs). The driving idea behind MINNs is to embed hidden layers into discrete
functional spaces of increasing complexity, obtained through a sequence of
meshes defined over the underlying spatial domain. The approach leads to a
natural pruning strategy which enables the design of sparse architectures that
are able to learn general nonlinear operators. We assess this strategy through
an extensive set of numerical experiments, ranging from nonlocal operators to
nonlinear diffusion PDEs, where MINNs are compared to classical fully connected
Deep Neural Networks. Our results show that MINNs can handle functional data
defined on general domains of any shape, while ensuring reduced training times,
lower computational costs, and better generalization capabilities, thus making
MINNs very well-suited for demanding applications such as Reduced Order
Modeling and Uncertainty Quantification for PDEs.


title:
Performance Evaluation of Machine Learning-based Algorithm and Taguchi
  Algorithm for the Determination of the Hardness Value of the Friction Stir
  Welded AA 6262 Joints at a Nugget Zone

abstract:
  Nowadays, industry 4.0 plays a tremendous role in the manufacturing
industries for increasing the amount of data and accuracy in modern
manufacturing systems. Thanks to artificial intelligence, particularly machine
learning, big data analytics have dramatically amended, and manufacturers
easily exploit organized and unorganized data. This study utilized hybrid
optimization algorithms to find friction stir welding and optimal hardness
value at the nugget zone. A similar AA 6262 material was used and welded in a
butt joint configuration. Tool rotational speed (RPM), tool traverse speed
(mm/min), and the plane depth (mm) are used as controllable parameters and
optimized using Taguchi L9, Random Forest, and XG Boost machine learning tools.
Analysis of variance was also conducted at a 95% confidence interval for
identifying the significant parameters. The result indicated that the
coefficient of determination from Taguchi L9 orthogonal array is 0.91 obtained
while Random Forest and XG Boost algorithm imparted 0.62 and 0.65,
respectively.


title:
Methodology for development of scientific software and test frameworks
  in function of precision of the expected results

abstract:
  This dissertation focuses on the development process of scientific software.
It presents a methodology that has emerged over time during development of
Monte Carlo tools for high energy physics experiments. A short description of
the physics background needed to understand the subjects presented in this
dissertation is included and the different types of software created for the
physics experiments are outlined. Challenges related to the scientific software
development are presented.
  The development process of several projects is described. The development of
subsequent milestones of these projects follow the cycle of improving the
physics model, describing the model using mathematical formalism, implementing
the model with numerical approximations, creating the software framework,
documenting and validating results. The relation between increased precision of
the results and increased complexity of tests and test frameworks is also
demonstrated based on these projects. The subject of scientific software
testing is addressed and the taxonomy of the scientific software tests is
presented including testing techniques used in the development of this
software.
  Author of this dissertation co-authored tools presented in it. Some of these
tools have been introduced into the HEP community. Some gained large user base
and are in active use by the community. Some of them are part of analyses
performed by experiments around Large Hadron Collider. The analysis of the
development process of these tools can help estimate the effort needed to
improve the design and precision of complex algorithms.


title:
Noncommutativity and nonassociativity of type II superstring with
  coordinate dependent RR field

abstract:
  In this paper we will consider noncommutativity that arises from bosonic
T-dualization of type II superstring in presence of Ramond-Ramond (RR) field,
which linearly depends on the bosonic coordinates $x^\mu$. The derivative of
the RR field $C^{\alpha\beta}_\mu$ is infinitesimal. We will employ generalized
Buscher procedure that can be applied to cases that have coordinate dependent
background fields. Bosonic part of newly obtained T-dual theory is non-local.
It is defined in non-geometric space spanned by Lagrange multipliers $y_\mu$.
We will apply generalized Buscher procedure once more on T-dual theory and
prove that original theory can be salvaged. Finally, we will use T-dual
transformation laws along with Poisson brackets of original theory to derive
Poisson bracket structure of T-dual theory and nonassociativity relation.
Noncommutativity parameter depends on the supercoordinates $x^\mu$,
$\theta^\alpha$ and $\bar\theta^\alpha$, while nonassociativity parameter is a
constant tensor containing infinitesimal $C^{\alpha\beta}_\mu$.


title: Weakly-Supervised Salient Object Detection Using Point Supervison

abstract:
  Current state-of-the-art saliency detection models rely heavily on large
datasets of accurate pixel-wise annotations, but manually labeling pixels is
time-consuming and labor-intensive. There are some weakly supervised methods
developed for alleviating the problem, such as image label, bounding box label,
and scribble label, while point label still has not been explored in this
field. In this paper, we propose a novel weakly-supervised salient object
detection method using point supervision. To infer the saliency map, we first
design an adaptive masked flood filling algorithm to generate pseudo labels.
Then we develop a transformer-based point-supervised saliency detection model
to produce the first round of saliency maps. However, due to the sparseness of
the label, the weakly supervised model tends to degenerate into a general
foreground detection model. To address this issue, we propose a Non-Salient
Suppression (NSS) method to optimize the erroneous saliency maps generated in
the first round and leverage them for the second round of training. Moreover,
we build a new point-supervised dataset (P-DUTS) by relabeling the DUTS
dataset. In P-DUTS, there is only one labeled point for each salient object.
Comprehensive experiments on five largest benchmark datasets demonstrate our
method outperforms the previous state-of-the-art methods trained with the
stronger supervision and even surpass several fully supervised state-of-the-art
models. The code is available at: https://github.com/shuyonggao/PSOD.


title:
Transferring Multi-Agent Reinforcement Learning Policies for Autonomous
  Driving using Sim-to-Real

abstract:
  Autonomous Driving requires high levels of coordination and collaboration
between agents. Achieving effective coordination in multi-agent systems is a
difficult task that remains largely unresolved. Multi-Agent Reinforcement
Learning has arisen as a powerful method to accomplish this task because it
considers the interaction between agents and also allows for decentralized
training -- which makes it highly scalable. However, transferring policies from
simulation to the real world is a big challenge, even for single-agent
applications. Multi-agent systems add additional complexities to the
Sim-to-Real gap due to agent collaboration and environment synchronization. In
this paper, we propose a method to transfer multi-agent autonomous driving
policies to the real world. For this, we create a multi-agent environment that
imitates the dynamics of the Duckietown multi-robot testbed, and train
multi-agent policies using the MAPPO algorithm with different levels of domain
randomization. We then transfer the trained policies to the Duckietown testbed
and compare the use of the MAPPO algorithm against a traditional rule-based
method. We show that the rewards of the transferred policies with MAPPO and
domain randomization are, on average, 1.85 times superior to the rule-based
method. Moreover, we show that different levels of parameter randomization have
a substantial impact on the Sim-to-Real gap.


title: Fine-Grained Scene Graph Generation with Data Transfer

abstract:
  Scene graph generation (SGG) aims to extract (subject, predicate, object)
triplets in images. Recent works have made a steady progress on SGG, and
provide useful tools for high-level vision and language understanding. However,
due to the data distribution problems including long-tail distribution and
semantic ambiguity, the predictions of current SGG models tend to collapse to
several frequent but uninformative predicates (e.g., \textit{on}, \textit{at}),
which limits practical application of these models in downstream tasks. To deal
with the problems above, we propose a novel Internal and External Data Transfer
(IETrans) method, which can be applied in a play-and-plug fashion and expanded
to large SGG with 1,807 predicate classes. Our IETrans tries to relieve the
data distribution problem by automatically creating an enhanced dataset that
provides more sufficient and coherent annotations for all predicates. By
training on the transferred dataset, a Neural Motif model doubles the macro
performance while maintaining competitive micro performance. The data and code
for this paper are publicly available at
\url{https://github.com/waxnkw/IETrans-SGG.pytorch}


title:
Supercharacters for parabolic contractions of finite groups of A,B,C,D
  Lie types

abstract:
  We construct supercharacter theories for the finite groups constructed by
parabolic constraction from simple groups of $A,B,C,D$ Lie types. In terms of
rook placements in the root systems we classify supecharacters and
superclasses.


title:
Is Vanilla Policy Gradient Overlooked? Analyzing Deep Reinforcement
  Learning for Hanabi

abstract:
  In pursuit of enhanced multi-agent collaboration, we analyze several
on-policy deep reinforcement learning algorithms in the recently published
Hanabi benchmark. Our research suggests a perhaps counter-intuitive finding,
where Proximal Policy Optimization (PPO) is outperformed by Vanilla Policy
Gradient over multiple random seeds in a simplified environment of the
multi-agent cooperative card game. In our analysis of this behavior we look
into Hanabi-specific metrics and hypothesize a reason for PPO's plateau. In
addition, we provide proofs for the maximum length of a perfect game (71 turns)
and any game (89 turns). Our code can be found at:
https://github.com/bramgrooten/DeepRL-for-Hanabi


title: Kondo Effect in a Spin-3/2 Fermi Gas

abstract:
  We investigate the Kondo effect of a spin-3/2 Fermi gas and give a detailed
calculation of the impurity resistance and ground state energy based on the s-d
exchange model. It is found that the impurity resistance increases
logarithmically with the decrease of temperature in the case of
antiferromagnetic coupling similar to the spin-1/2 system but has a larger
resistance minimum value due to the increase of spin scattering channels. In
the case of antiferromagnetic interaction, the ground state is still the Kondo
singlet state while the septuplet state has the lowest energy for ferromagnetic
coupling. And with the same antiferromagnetic s-d coupling parameter, the
energy of the Kondo singlet state is lower than spin-1/2, which indicates that
the larger spin, the easier it is to enter the Kondo-screened phase. This
provides some theoretical support for the realization of the Kondo effect with
ultra-cold atoms.


title:
A Decentralised Multi-Agent Reinforcement Learning Approach for the
  Same-Day Delivery Problem

abstract:
  Same-Day Delivery services are becoming increasingly popular in recent years.
These have been usually modelled by previous studies as a certain class of
Dynamic Vehicle Routing Problem (DVRP) where goods must be delivered from a
depot to a set of customers in the same day that the orders were placed.
Adaptive exact solution methods for DVRPs can become intractable even for small
problem instances. In this paper, we formulate the SDDP as a Markov Decision
Process (MDP) and solve it using a parameter-sharing Deep Q-Network, which
corresponds to a decentralised Multi-Agent Reinforcement Learning (MARL)
approach. For this, we create a multi-agent grid-based SDD environment,
consisting of multiple vehicles, a central depot and dynamic order generation.
In addition, we introduce zone-specific order generation and reward
probabilities. We compare the performance of our proposed MARL approach against
a Mixed Inter Programming (MIP) solution. Results show that our proposed MARL
framework performs on par with MIP-based policy when the number of orders is
relatively low. For problem instances with higher order arrival rates,
computational results show that the MARL approach underperforms the MIP by up
to 30%. The performance gap between both methods becomes smaller when
zone-specific parameters are employed. The gap is reduced from 30% to 3% for a
5x5 grid scenario with 30 orders. Execution time results indicate that the MARL
approach is, on average, 65 times faster than the MIP-based policy, and
therefore may be more advantageous for real-time control, at least for
small-sized instances.


title: Sur les espaces homog\`enes de Borovoi-Kunyavski\u{\i}

abstract:
  We establish the Hasse principle and the weak approximation property for
certain homogeneous spaces of $\mathrm{SL}_m$ whose geometric stabilizer is of
nilpotency class 2, which were constructed by Borovoi and Kunyavski\u{\i}.
These homogeneous spaces verify thus a conjecture of Colliot-Th\'el\`ene
concerning Brauer-Manin obstruction for geometrically rationally connected
varieties. -- Nous \'etablissons le principe de Hasse et l'approximation faible
pour certains espaces homog\`enes de $\mathrm{SL}_m$ \`a stabilisateur
g\'eom\'etrique nilpotent de classe 2, construits par Borovoi et
Kunyavski\u{\i}. Ces espaces homog\`enes v\'erifient donc une conjecture de
Colliot-Th\'el\`ene concernant l'obstruction de Brauer-Manin pour les
vari\'et\'es g\'eom\'etriquement rationnellement connexes.


title: Channel Self-Supervision for Online Knowledge Distillation

abstract:
  Recently, researchers have shown an increased interest in the online
knowledge distillation. Adopting an one-stage and end-to-end training fashion,
online knowledge distillation uses aggregated intermediated predictions of
multiple peer models for training. However, the absence of a powerful teacher
model may result in the homogeneity problem between group peers, affecting the
effectiveness of group distillation adversely. In this paper, we propose a
novel online knowledge distillation method, \textbf{C}hannel
\textbf{S}elf-\textbf{S}upervision for Online Knowledge Distillation (CSS),
which structures diversity in terms of input, target, and network to alleviate
the homogenization problem. Specifically, we construct a dual-network
multi-branch structure and enhance inter-branch diversity through
self-supervised learning, adopting the feature-level transformation and
augmenting the corresponding labels. Meanwhile, the dual network structure has
a larger space of independent parameters to resist the homogenization problem
during distillation. Extensive quantitative experiments on CIFAR-100 illustrate
that our method provides greater diversity than OKDDip and we also give pretty
performance improvement, even over the state-of-the-art such as PCL. The
results on three fine-grained datasets (StanfordDogs, StanfordCars,
CUB-200-211) also show the significant generalization capability of our
approach.


title:
Active control of thermal emission by graphene-nanowire coupled
  plasmonic metasurfaces

abstract:
  Metasurfaces, together with graphene plasmonics, have become prominent for
the emissivity control in thermal engineering, both passively through changing
the geometric parameters and packing density of the metasurfaces, and actively
through graphene gating or doping. We demonstrate a graphene-nanowire coupled
plasmonic metasurface utilizing the hybrid localized surface plasmon modes of
the nanowire array and graphene. The nanowire array makes the hybrid surface
plasmon mode localized, allowing a free-space excitation. The single layer
graphene, via the gating between the underneath mirror and a top electrode, can
actively tune the spectral emissivity by almost 90%. In addition, the hybrid
plasmon mode provides an extra degree of freedom to modulate the p-polarized
emissivity with a five-fold enhancement, especially for large emission angles.


title:
Enhancing HEP research in predominantly undergraduate institutions and
  community colleges

abstract:
  The long-term success of HEP lies in expanding inclusiveness beyond national
labs and academic research institutions to a vast community of predominantly
undergraduate institutions (PUI) and community colleges (CC). Institutions such
as PUIs and CCs offer an early starting point in the pipeline that can mitigate
issues of lack of diversity and underrepresented participation of different
groups in HEP. However, there are many underlying systemic, structural, and
cultural challenges that need to be addressed collectively. Experimental
collaborations are largely populated by national labs and research-focused
academic institutions (non-PUIs). The faculty at PUIs and CCs have a high
teaching load that is detrimental to their research participation. In addition,
there is a lack of guidance, access, and tough competition for securing
research funding. The students also suffer from a lack of research
infrastructure and technical equipment that can only be found at national labs
and larger universities. There are existing successful efforts to enhance the
HEP research experience of students and faculty members. This paper discusses
ways to leverage these to provide more research opportunities and establish a
sustainable national program targeting specifically the issues faced by
communities at PUIs and CCs. The need for research mentoring and skill building
for faculty members is also laid out. The changes discussed in this paper would
make a direct impact on the current spectrum of challenges.


title:
Classification of global solutions of a free boundary problem in the
  plane

abstract:
  We classify nontrivial, nonnegative, positively homogeneous solutions of the
equation \begin{equation*} \Delta u=\gamma u^{\gamma-1} \end{equation*} in the
plane.
  The problem is motivated by the analysis of the classical Alt-Phillips free
boundary problem, but considered here with negative exponents $\gamma$.
  The proof relies on several bespoke results for ordinary differential
equations.


title: Bayesian Learning of Graph Substructures

abstract:
  Graphical models provide a powerful methodology for learning the conditional
independence structure in multivariate data. Inference is often focused on
estimating individual edges in the latent graph. Nonetheless, there is
increasing interest in inferring more complex structures, such as communities,
for multiple reasons, including more effective information retrieval and better
interpretability. Stochastic blockmodels offer a powerful tool to detect such
structure in a network. We thus propose to exploit advances in random graph
theory and embed them within the graphical models framework. A consequence of
this approach is the propagation of the uncertainty in graph estimation to
large-scale structure learning. We consider Bayesian nonparametric stochastic
blockmodels as priors on the graph. We extend such models to consider
clique-based blocks and to multiple graph settings introducing a novel prior
process based on a dependent Dirichlet process. Moreover, we devise a tailored
computation strategy of Bayes factors for block structure based on the
Savage-Dickey ratio to test for presence of larger structure in a graph. We
demonstrate our approach in simulations as well as on real data applications in
finance and transcriptomics.


title: Facilitating Non-HEP Career Transition

abstract:
  About two-third of Physics PhDs establish careers outside of academia and the
national laboratories in areas like Software, Instrumentation, Data Science,
Finance, Healthcare, Journalism, Public Policy and Non-Governmental
Organization. Skills and knowledge developed during HEPA (High Energy Physics
and Astrophysics) research as an undergraduate, graduate or a postdoc level
(collectively called early career) have been long sought after in industry.
These skills are complex problem solving abilities, software programming, data
analysis, math, statistics and scientific writing, to name a few. Given that a
vast majority transition to the industry jobs, existing paths for such
transition should be strengthened and new ways of facilitating it be identified
and developed. A strong engagement between HEPA and its alumni would be a
pre-requisite for this. It might also lead to creative ways to reverse the
"brain drain" by encouraging alumni to collaborate on HEPA research projects or
possibly come back full time to research. We motivate and discuss below several
actionable recommendations by which HEPA institutions as well as HEPA faculty
mentors can strengthen both ability to identify non-HEP career opportunities
for students and post-docs as well as help more fully develop skills such as
effective networking, resume building, project management, risk assessment,
budget planning, to name a few. This will help prepare early career HEPA
scientists for successfully transitioning from academia to the diverse array of
non-traditional careers available. HEPA alumni can play a pivotal role by
engaging in this process.


title:
Analytical and numerical modeling of reconfigurable reflecting
  metasurfaces with capacitive memory

abstract:
  In this article, we develop analytical-numerical models for reconfigurable
reflecting metasurfaces formed by chessboard-patterned arrays of metallic
patches. These patch arrays are loaded with varactor diodes in order to enable
surface impedance and reflection phase control. Two types of analytical models
are considered. The first model based on the effective medium approach is used
to predict the metasurface reflectivity. The second model is the Bloch wave
dispersion model for the same structure understood as a two-dimensional
transmission line metamaterial. The latter model is used to study ways to
suppress parasitic resonances in finite-size beamforming metasurfaces. We
validate the developed analytical models with full-wave numerical simulations.
Finally, we outline a design of the metasurface control network with capacitive
memory that may allow for independent programming of individual unit cells of
the beamforming metasurface.


title: TS-Reconfiguration of $k$-Path Vertex Covers in Caterpillars

abstract:
  A $k$-path vertex cover ($k$-PVC) of a graph $G$ is a vertex subset $I$ such
that each path on $k$ vertices in $G$ contains at least one member of $I$.
Imagine that a token is placed on each vertex of a $k$-PVC. Given two $k$-PVCs
$I, J$ of a graph $G$, the $k$-Path Vertex Cover Reconfiguration ($k$-PVCR)
under Token Sliding ($\mathsf{TS}$) problem asks if there is a sequence of
$k$-PVCs between $I$ and $J$ where each intermediate member is obtained from
its predecessor by sliding a token from some vertex to one of its unoccupied
neighbors. This problem is known to be $\mathtt{PSPACE}$-complete even for
planar graphs of maximum degree $3$ and bounded treewidth and can be solved in
polynomial time for paths and cycles. Its complexity for trees remains unknown.
In this paper, for $k \geq 4$, we present a polynomial-time algorithm that
solves $k$-PVCR under $\mathsf{TS}$ for caterpillars (i.e., trees formed by
attaching leaves to a path).


title: Chiral fermions, dimensional regularization, and the trace anomaly

abstract:
  We investigate the trace anomaly of a chiral fermion in dimensional
regularization, considering in detail the simplest case of coupling to an
abelian gauge field. We apply the Breitenlohner-Maison/'t Hooft-Veltman
prescription for dealing with the chiral matrix $\gamma^5$ and verify that no
parity-odd term arises in the trace anomaly. The issue treated here is
analogous to that of a chiral fermion in curved spacetime, discussed in recent
literature. The advantage of having a simplified background minimizes the
amount of algebraic calculations and allows pinpointing better the subtle
points carried by dimensional regularization.


title:
Are You Misinformed? A Study of Covid-Related Fake News in Bengali on
  Facebook

abstract:
  Our opinions and views of life can be shaped by how we perceive the opinions
of others on social media like Facebook. This dependence has increased during
COVID-19 periods when we have fewer means to connect with others. However, fake
news related to COVID-19 has become a significant problem on Facebook. Bengali
is the seventh most spoken language worldwide, yet we are aware of no previous
research that studied the prevalence of COVID-19 related fake news in Bengali
on Facebook. In this paper, we develop machine learning models to detect fake
news in Bengali automatically. The best performing model is BERT, with an
F1-score of 0.97. We apply BERT on all Facebook Bengali posts related to
COVID-19. We find 10 topics in the COVID-19 Bengali fake news grouped into
three categories: System (e.g., medical system), belief (e.g., religious
rituals), and social (e.g., scientific awareness).


title:
Improving Meta-learning for Low-resource Text Classification and
  Generation via Memory Imitation

abstract:
  Building models of natural language processing (NLP) is challenging in
low-resource scenarios where only limited data are available.
Optimization-based meta-learning algorithms achieve promising results in
low-resource scenarios by adapting a well-generalized model initialization to
handle new tasks. Nonetheless, these approaches suffer from the memorization
overfitting issue, where the model tends to memorize the meta-training tasks
while ignoring support sets when adapting to new tasks. To address this issue,
we propose a memory imitation meta-learning (MemIML) method that enhances the
model's reliance on support sets for task adaptation. Specifically, we
introduce a task-specific memory module to store support set information and
construct an imitation module to force query sets to imitate the behaviors of
some representative support-set samples stored in the memory. A theoretical
analysis is provided to prove the effectiveness of our method, and empirical
results also demonstrate that our method outperforms competitive baselines on
both text classification and generation tasks.


title:
Higher-order generalized uncertainty principle applied to gravitational
  baryogenesis

abstract:
  The gravitational baryogenesis plays an important role in the study of the
baryon asymmetry. However, the original mechanism of gravitational baryogenesis
in the radiation dominated era leads to the asymmetry factor $\eta$ is equal to
zero, which indicates this mechanism may not generate a sufficient baryon
asymmetry for the standard cosmological model. In this manuscript, we
investigate the gravitational baryogenesis for the generation of baryon
asymmetry in the early Universe by using an new higher-order generalized
uncertainty principle (GUP). It is demonstrated that the entropy and Friedman
equation of the Universe deviate from the original cases due to the effect of
the higher-order GUP. Those modifications break the thermal equilibrium of the
Universe and in turn produces a non-zero asymmetry factor $\eta $. In
particular, our results satisfy all three Sakharov conditions, which indicates
that the scheme of explaining baryon asymmetry in the framework of higher-order
GUP is feasible. In addition, confronting our theoretical results with the
observational results, we constraint the GUP parameter $\beta_0$, whose bound
between $8.4 \times {10^{10}} \sim 1.1 \times {10^{13}}$.


title: Geometry of elliptic normal curves of degree 6

abstract:
  In our work we focus on the geometry of elliptic normal curves of degree 6
embedded in $\mathbb{P}^5$. We determine the space of quadric hypersurfaces
through an elliptic normal curve of degree 6 and find the explicit equations of
generators of $I(\text{Sec}(C_6))$. We study the images $C_p$ and $C_{pq}$ of a
sextic $C_6$ under the projection from a general point $P \in \mathbb{P}^5$ and
a general line $\overline{PQ} \subset \mathbb{P}^5$. In particular, we show
that $C_p$ is $k$-normal for all $k \geq 2$ and $I(C_p)$ is generated by three
homogeneous polynomials of degree 2 and two homogeneous polynomials of degree
3. We then show that $C_{pq}$ is $k$-normal for all $k \geq 3$ and $I(C_{pq})$
is generated by two homogeneous polynomials of degree 3 and three homogeneous
polynomials of degree 4.


title:
The Research Based Learning -- STEM Learning Activities: The Use of
  r-Dynamic Coloring to Improve the Students Metaliteracy in Solving a
  Tessellation Decoration Problem

abstract:
  The metaliteracy ability is a very important in today's life, especially to
live in the disruptive technology era. Metaliteracy is an ability that goes
beyond metacognition and technological literacy. However, the students'
metalliteracy ability is still relatively low. One of the causes of the low
ability is due to the learning model that has been applied so far has not been
able to bring out this ability. Therefore, in this study, an RBL learning model
that is integrated with the STEM approach will be applied in solving the
r-Dynamic coloring problem. By r-dynamic coloring, we mean a proper k-coloring
c of G if for every vertex v in V (G) satisfies the number of neighbours color
is greater then min{r,d(v)}. The minimum k such that G has an r-dynamic
coloring with k colors is called an r-dynamic chromatic number, denoted by
r(G). By using the r-dynamic coloring technique, we will improve students'
metaliteracy in solving the tessellation decoration problem. Therefore, in this
research, the syntax of learning activities of the Research-Based Learning and
STEM approach will be developed including the assesment indicator of the
metaliteracy ability.


title:
Selectivity in Ligand Functionalization of Photocatalytic Metal Oxide
  Nanoparticles for Phase Transfer and Self-assembly Applications

abstract:
  Functionalization of photocatalytic metal oxide nanoparticles of TiO2, ZnO,
WO3 and CuO with amine-terminated (oleylamine) and thiol-terminated
(1-dodecanethiol) alkyl chained ligands was studied under ambient conditions. A
high selectivity was observed in the binding specificity of a ligand towards
nanoparticles of these different oxides. It was observed that oleylamine binds
stably to only TiO2 and WO3, while 1-dodecanethiol binds stably only to ZnO and
CuO. Similarly, polar to non-polar solvent phase transfer of TiO2 and WO3
nanoparticles could be achieved by using oleylamine, but not by
1-dodecanethiol, while the contrary holds for ZnO and CuO. The surface
chemistry of ligand functionalized nanoparticles was probed by ATR-FTIR
spectroscopy, that enabled to elucidate the occupation of the ligands at the
active sites. The photo-stability of the ligands on the nanoparticle surface
was determined by the photocatalytic self-cleaning properties of the material.
While TiO2 and WO3 degrade the ligands within 24 hours under both UV and
visible light, ligands on ZnO and CuO remain unaffected. The gathered insights
are also highly relevant from an application point of view. As an example,
since the ligand functionalized nanoparticles are hydrophobic in nature, they
can thus be self-assembled at the air-water interface, for obtaining
nanoparticle films with demonstrated photocatalytic as well as anti-fogging
properties.


title: Subset Sum in $O(n^{16}\log(n))$

abstract:
  This extensive revision of my paper "Description of an $O(\text{poly}(n))$
Algorithm for NP-Complete Combinatorial Problems" will dramatically simplify
the content of the original paper by solving subset-sum instead of $3$-SAT. I
will first define the "product-derivative" method which will be used to
generate a system of equations for solving unknown polynomial coefficients.
Then I will describe the "Dragonfly" algorithm usable to solve subset-sum in
$O(n^{16}\log(n))$ which is itself composed of a set of symbolic algebra steps
on monic polynomials to convert a subset, $S_T$, of a set of positive integers,
$S$, with a given target sum, $T$ into a polynomial with roots corresponding to
the elements of $S_T$.


title: Rip models in Ricci-Gauss-Bonnet Cosmology

abstract:
  The ultimate fate of the Universe or the possible occurrence of future
singularity to interpret the cosmic accelerating expansion phenomena has been
discussed in this paper. The gravitational theory comprising of Ricci scalar
$R$ and Gauss-Bonnet invariant $\mathcal{G}$, known as $f(R,\mathcal{G})$
gravity has been considered in the quadratic form. Three models with the Hubble
parameter that represents finite and infinite future time are presented. The
physical and geometrical parameters of the models are analyzed. Also, the
properties of modified gravitational theory have been examined. The fate of the
Universe evolution in this study confronts with neither little or pseudo rip
nor the future singularity. The perturbed evolution equations are formulated in
the scalar perturbation approach and the stability of the models are shown.


title: Robust Action Gap Increasing with Clipped Advantage Learning

abstract:
  Advantage Learning (AL) seeks to increase the action gap between the optimal
action and its competitors, so as to improve the robustness to estimation
errors. However, the method becomes problematic when the optimal action induced
by the approximated value function does not agree with the true optimal action.
In this paper, we present a novel method, named clipped Advantage Learning
(clipped AL), to address this issue. The method is inspired by our observation
that increasing the action gap blindly for all given samples while not taking
their necessities into account could accumulate more errors in the performance
loss bound, leading to a slow value convergence, and to avoid that, we should
adjust the advantage value adaptively. We show that our simple clipped AL
operator not only enjoys fast convergence guarantee but also retains proper
action gaps, hence achieving a good balance between the large action gap and
the fast convergence. The feasibility and effectiveness of the proposed method
are verified empirically on several RL benchmarks with promising performance.


title: CNNs and Transformers Perceive Hybrid Images Similar to Humans

abstract:
  Hybrid images is a technique to generate images with two interpretations that
change as a function of viewing distance. It has been utilized to study
multiscale processing of images by the human visual system. Using 63,000 hybrid
images across 10 fruit categories, here we show that predictions of deep
learning vision models qualitatively matches with the human perception of these
images. Our results provide yet another evidence in support of the hypothesis
that Convolutional Neural Networks (CNNs) and Transformers are good at modeling
the feedforward sweep of information in the ventral stream of visual cortex.
Code and data is available at https://github.com/aliborji/hybrid_images.git.


title:
The collision frequencies of charged particles in the complex plasmas
  with the non-Maxwellian velocity distributions

abstract:
  We study the collision frequencies of charged particles in the complex
plasmas with the non-Maxwellian velocity distributions. The average collision
frequencies of electron-ion, electron-electron and ion-ion are derived in the
two-parameter (r,s) and the three-parameter (a,r,s) velocity distributions,
respectively. We show that these average collision frequencies in the complex
plasmas depend strongly on the parameters in the non-Maxwellian distributions
and thus are significant deviations from those in the plasmas with a Maxwell
velocity distribution. Numerical analyses are made of the effects of the
parameters on the average collision frequencies. The results have important
effect on transport coefficients and their properties of charged particles in
the highly ionized complex plasmas with the non-Maxwell distributions.


title:
Current and future constraints on cosmology and modified gravitational
  wave friction from binary black holes

abstract:
  In this proceedings, we are interested in dark gravitational wave standard
sirens and their use for cosmology and for constraining modified gravity
theories. Due to the extra friction term introduced in their propagation
equation those theories predict different luminosity distances for
electromagnetic and gravitational waves (GWs). This effect can be parametrized
by the two variables $\Xi_0$ and $n$, that can be measured from gravitational
wave observations, and specifically from the binary black hole (BBH) mergers
detected by LIGO and Virgo. By fitting jointly BBH population models in mass
and redshift, the cosmological parameters, and the modified GW luminosity
distance to $\sim$ 60 signals observed during the first three LIGO/Virgo
observation runs, we conclude that general relativity is consistently the
preferred model. The future observation runs O4 and O5 are also considered.
Using the same approach, we forecast a measurement uncertainty on the modified
gravity parameter $\Xi_0$ of $51\%$ with O4, and $20\%$ with O4 and O5,
respectively if GR is the correct theory of gravity. However, we underline that
there are strong correlations between astrophysical, cosmological and modified
gravity parameters, possibly leading to bias if wrong priors are assumed.


title:
Discussion on a new extension of the FGM copula with application in
  reliability

abstract:
  A new extended Farlie-Gumbel-Morgenstern copula recently studied by Ebaid et
al. [Comm. Statist. Theory Methods, (2020)] is reviewed. The reported
admissible range for the copula parameter a is incorrect and some typos are
also found in their paper. Corrections to the admissible range of the copula
parameter a and typos are presented here.


title:
Silver-Gold Bimetallic Alloy versus Core-Shell Nanoparticles:
  Implications for Plasmonic Enhancement and Photothermal Applications

abstract:
  Bimetallic plasmonic nanoparticles enable tuning of the optical response and
chemical stability by variation of the composition. The present numerical
simulation study compares Ag-Au alloy, Ag@Au core-shell, and Au@Ag core-shell
bimetallic plasmonic nanoparticles of both spherical and anisotropic
(nanotriangle and nanorods) shapes. By studying both spherical and anisotropic
(with LSPR in the near-infrared region) shapes, cases with and without
interband transitions of Au can be decoupled. Explicit comparisons are
facilitated by numerical models supported by careful validation and examination
of optical constants of Au-Ag alloys reported in literature. Although both
Au-Ag core-shell and alloy nanoparticles exhibit an intermediary optical
response between that of pure Ag and Au nanoparticles, there are noticeable
differences in the spectral characteristics. Also, the effect of the bimetallic
constitution in anisotropic nanoparticles is starkly different from that in
spherical nanoparticles due to the absence of Au interband transitions in the
former case. In general, the improved chemical stability of Ag nanoparticles by
incorporation of Au comes with a cost of reduction in plasmonic enhancement,
also applicable to anisotropic nanoparticles with a weaker effect. A
photothermal heat transfer study confirms that increased absorption by the
incorporation of Au in spherical Ag nanoparticles also results in an increased
steady state temperature. On the other hand, anisotropic nanoparticles are
inherently better absorbers, hence better photothermal sources and their
photothermal properties are apparently not strongly affected by the
incorporation of one metal in the other.


title: Twin Weisfeiler-Lehman: High Expressive GNNs for Graph Classification

abstract:
  The expressive power of message passing GNNs is upper-bounded by
Weisfeiler-Lehman (WL) test. To achieve high expressive GNNs beyond WL test, we
propose a novel graph isomorphism test method, namely Twin-WL, which
simultaneously passes node labels and node identities rather than only passes
node label as WL. The identity-passing mechanism encodes complete structure
information of rooted subgraph, and thus Twin-WL can offer extra power beyond
WL at distinguishing graph structures. Based on Twin-WL, we implement two
Twin-GNNs for graph classification via defining readout function over rooted
subgraph: one simply readouts the size of rooted subgraph and the other
readouts rich structure information of subgraph following a GNN-style. We prove
that the two Twin-GNNs both have higher expressive power than traditional
message passing GNNs. Experiments also demonstrate the Twin-GNNs significantly
outperform state-of-the-art methods at the task of graph classification.


title: Meta-attention for ViT-backed Continual Learning

abstract:
  Continual learning is a longstanding research topic due to its crucial role
in tackling continually arriving tasks. Up to now, the study of continual
learning in computer vision is mainly restricted to convolutional neural
networks (CNNs). However, recently there is a tendency that the newly emerging
vision transformers (ViTs) are gradually dominating the field of computer
vision, which leaves CNN-based continual learning lagging behind as they can
suffer from severe performance degradation if straightforwardly applied to
ViTs. In this paper, we study ViT-backed continual learning to strive for
higher performance riding on recent advances of ViTs. Inspired by mask-based
continual learning methods in CNNs, where a mask is learned per task to adapt
the pre-trained ViT to the new task, we propose MEta-ATtention (MEAT), i.e.,
attention to self-attention, to adapt a pre-trained ViT to new tasks without
sacrificing performance on already learned tasks. Unlike prior mask-based
methods like Piggyback, where all parameters are associated with corresponding
masks, MEAT leverages the characteristics of ViTs and only masks a portion of
its parameters. It renders MEAT more efficient and effective with less overhead
and higher accuracy. Extensive experiments demonstrate that MEAT exhibits
significant superiority to its state-of-the-art CNN counterparts, with 4.0~6.0%
absolute boosts in accuracy. Our code has been released at
https://github.com/zju-vipa/MEAT-TIL.


title:
Piecewise Constant Parameters Identification Under Finite Excitation
  Condition: Time Alertness Preservation, Exponential Convergence, Robustness
  and Applications

abstract:
  The scope of this research is the identification of piecewise constant
parameters of linear regression equations under the finite excitation
condition. Such an equation is considered as a switched system, which
identification usually consists of three main steps: a switching time instant
detection, choice of the most appropriate model from the known set or
generation of a new one, online adjustment of the chosen model parameters.
Compared to the known methods, to make the computational burden lower and
simplify the stability analysis, we use only one model to identify all
switching states of the regression. So, the proposed identification procedure
includes only two main approaches. The first one is a new estimation algorithm
to detect switching time and preserve time alertness, which is based on a
well-known DREM procedure and ensures adjustable detection delay. Unlike
existing solutions, it does not involve an offline operation of data monitoring
and stacking. The second one is the adaptive law, which provides element-wise
monotonous exponential convergence of the regression parameters to their true
values over the time range between two consecutive switches. Its convergence
condition is that the regressor is finitely exciting somewhere inside such time
interval. The robustness of the proposed identification procedure to the
influence of external disturbances is analytically proved. Its effectiveness is
demonstrated via numerical experiments, in which both abstract regressions and
a second-order plant model are used.