Abstract:
  Aluminum monochloride (AlCl) has been proposed as an excellent candidate for
laser cooling. Here we present absorption spectroscopy measurements on the
$A^1\Pi \leftarrow X^1\Sigma^+$ transition in AlCl inside a cryogenic helium
buffer-gas beam cell. The high resolution absorption data enables a rigorous,
quantitative comparison with our high-level ab initio calculations of the
electronic and rovibronic energies, providing a comprehensive picture of the
AlCl quantum structure. The combination of high resolution spectral data and
theory permits the evaluation of spectroscopic constants and associated
properties, like equilibrium bond length, with an order of magnitude higher
precision. Based on the measured molecular equilibrium constants of the
$A^1\Pi$ state, we estimate a Franck-Condon factor of the $A^1\Pi \leftarrow
X^1\Sigma^+$ of 99.88%, which confirms that AlCl is amenable to laser cooling.


Abstract:
  Rich and poor galaxy clusters have the same measured halo metallicity,
0.35-0.4 $Z_\odot$, despite being an order of magnitude apart in stellar
fraction, $M_*/M_{gas}$. The measured ICM metallicity in rich clusters cannot
be explained by the visible stellar population as stars make up only 5% of the
total gas mass. The independence of metallicity from $M_*/M_{gas}$ suggests an
external and universal source of metals such as an Early Enrichment Population
(EEP). Galaxy cluster RX J1416.4+2315 has the lowest stellar fraction known,
$M_*/M_{gas}=0.015\pm0.003$, and here we improve the halo metallicity
determination using archival $\textit{Chandra}$ and $\textit{XMM Newton}$
observations. We determine the ICM metallicity of RXJ to be $0.336\pm0.058$
$Z_\odot$ within $0.3<R/R_{500}<0.75$, excluding the central galaxy. We combine
this measurement with other clusters with a wider range of $M_*/M_{gas}$
resulting in the fit of $Z_{tot}=(0.37\pm0.01)+(0.13\pm 0.17)(M_*/M_{gas})$.
This fit is largely independent of $M_*/M_{gas}$, and shows that for a low
$M_*/M_{gas}$ system the observed stellar population can make only 10-20% of
the total metals. We quantify the Fe contribution of the EEP further by
adopting a standard Fe yield for visible stellar population, and find that
$Z_{EEP}=(0.36\pm0.01)-(1.44\pm0.17)(M_*/M_{gas})$. To account for the observed
Fe mass, a supernova rate of $6.6\pm3.0$ SNe yr$^{-1}$ (Type Ia) and
$5.0\pm2.5$ SNe yr$^{-1}$ (core collapse) is required over the redshift range
$3<z<10$ for a single galaxy cluster with mass $\sim3\times10^{14}$ $M_\odot$
at z=0. These supernovae might be visible in JWST observation of high redshift
clusters and protoclusters.


Abstract:
  The Peaks of Eternal Light (PELs), that are largely unshaded regions mostly
at the lunar south pole, have been suggested as a source of solar power for
mining the water and other volatiles in the nearby permanently dark regions. As
mining is a power-intensive activity, it is interesting to estimate the maximum
solar power that could be generated at the PELs. Here we use average percentage
illumination maps for a range of heights above the local topography from 2 m to
2 km to determine the total power available as a function of time of lunar day.
Overshadowing of highly illuminated areas by towers placed in sunward locations
(at a given time of day) limits the total power to much smaller values than the
highly illuminated area would suggest. We find that for near-term realizable
towers (up to 20 m), the upper limit to the time-averaged power available is
~55 MW at >70% illumination, and ~6 MW at >90% illumination. For the more
distant future a maximum time-averaged power of order 21000 MW at >70%
illumination could be realizable for towers up to 2 km in height, and ~5270 MW,
respectively, at 90% illumination. Towers 1 km high provide about a factor 2.7
times less power. The variation with lunar time of day ranges from a factor of
1.1 to ~ 3.


Abstract:
  Examining effectiveness of institutional scientific coalitions can inform
future policies. This is a study on the structure of scientific collaborations
in three cities in central Germany. Since 1995, the three universities of this
region have formed and maintained a coalition which led to the establishment of
an interdisciplinary center in 2012, i.e., German Center for Integrative
Biodiversity Research (iDiv). We investigate whether the impact of the former
coalition is evident in the region's structure of scientific collaborations and
the scientific output of the new center. Using publications data from
1996-2018, we build co-authorship networks and identify the most cohesive
communities in terms of collaboration, and compare them with communities
identified based on publications presented as the scientific outcome of the
coalition and new center on their website. Our results show that despite the
highly cohesive structure of collaborations presented on the coalition website,
there is still much potential to be realized. The newly established center has
bridged the member institutions but not to a particularly strong level. We see
that geographical proximity, collaboration policies, funding, and
organizational structure alone do not ensure prosperous scientific
collaboration structures. When new center's scientific output is compared with
its regional context, observed trends become less conspicuous. Nevertheless,
the level of success the coalition achieved could inform policy makers
regarding other regions' scientific development plans.


Abstract:
  The purpose of this paper is two-fold: we present some matrix inequalities of
log-majorization type for eigenvalues indexed by a sequence; we then apply our
main theorem to generalize and improve the Hua-Marcus' inequalities. Our
results are stronger and more general than the existing ones.


Abstract:
  We study the dynamics of periodically-kicked many-body systems away from the
high-frequency regime, and discuss a family of Floquet systems where the notion
of prethermalization can be naturally extended to intermediate and low driving
frequencies. We investigate numerically the dynamics of both integrable and
nonintegrable systems, and report on the formation of a long-lived prethermal
plateau, akin to the high-frequency limit, where the system thermalizes with
respect to an effective Hamiltonian captured by the inverse-frequency expansion
(IFE). Unlike the high-frequency regime, we find that the relevant heating
times are model dependent: we analyze the stability of the prethermal plateau
to small perturbations in the drive period, and show that, in a spin chain
whose IFE is intractable, the plateau duration is insensitive to the
perturbation strength, in contrast to a chain where the IFE admits the
resummation of an entire subseries. Infinitesimal perturbations are enough to
restore the ergodic properties of the system, and decrease residual finite-size
effects. Although the regime where the Floquet system leaves the prethermal
plateau and starts heating up to infinite temperature is not captured by the
IFE, we provide evidence that the evolved subsystem is described well by a
thermal state w.r.t.~the IFE Hamiltonian, with a gradually changing
temperature, in accord with the Eigenstate Thermalization Hypothesis.


Abstract:
  6G and beyond networks tend towards fully intelligent and adaptive design in
order to provide better operational agility in maintaining universal wireless
access and supporting a wide range of services and use cases while dealing with
network complexity efficiently. Such enhanced network agility will require
developing a self-evolving capability in designing both the network
architecture and resource management to intelligently utilize resources, reduce
operational costs, and achieve the coveted quality of service (QoS). To enable
this capability, the necessity of considering an integrated vertical
heterogeneous network (VHetNet) architecture appears to be inevitable due to
its high inherent agility. Moreover, employing an intelligent framework is
another crucial requirement for self-evolving networks to deal with real-time
network optimization problems. Hence, in this work, to provide a better insight
on network architecture design in support of self-evolving networks, we
highlight the merits of integrated VHetNet architecture while proposing an
intelligent framework for self-evolving integrated vertical heterogeneous
networks (SEI-VHetNets). The impact of the challenges associated with
SEI-VHetNet architecture, on network management is also studied considering a
generalized network model. Furthermore, the current literature on network
management of integrated VHetNets along with the recent advancements in
artificial intelligence (AI)/machine learning (ML) solutions are discussed.
Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are
identified. Finally, the potential future research directions for advancing the
autonomous and self-evolving capabilities of SEI-VHetNets are discussed.


Abstract:
  We report on finite bias spectroscopy measurements of the two-electron
spectrum in a gate defined bilayer graphene (BLG) quantum dot for varying
magnetic fields. The spin and valley degree of freedom in BLG give rise to
multiplets of 6 orbital symmetric and 10 orbital anti-symmetric states. We find
that orbital symmetric states are lower in energy and separated by $\approx 0.4
- 0.8$ meV from orbital anti-symmetric states. The symmetric multiplet exhibits
an additional energy splitting of its 6 states of $\approx 0.15 - 0.5$ meV due
to lattice scale interactions. The experimental observations are supported by
theoretical calculations, which allow to determine that inter-valley scattering
and 'current-current' interaction constants are of the same magnitude in BLG.


Abstract:
  When an electron passes through a chiral molecule there is a high probability
for a correlation between the momentum and spin of the charge, thus leading to
spin polarized current. This phenomenon is known as the chiral induced spin
selectivity (CISS) effect. One of the most surprising experimental results
recently demonstrated is that magnetization reversal in a ferromagnet (FM) with
perpendicular anisotropy can be realized solely by chemisorbing a chiral
molecular monolayer without applying any current or external magnetic field.
This result raises the currently open question of whether this effect is due to
the bonding event, held by the ferromagnet, or a long timescale effect
stabilized by exchange interactions. In this work we have performed vectorial
magnetic field measurements of the magnetization reorientation of a
ferromagnetic layer exhibiting perpendicular anisotropy due to CISS using
nitrogen-vacancy centers in diamond, and followed the time dynamics of this
effect. In parallel, we have measured the molecular monolayer tilt angle in
order to find a correlation between the time dependence of the magnetization
re-orientation and the change of the tilt angle of the molecular monolayer. We
have identified that changes in the magnetization direction correspond to
changes of the molecular monolayer tilt angle, providing evidence for a
long-timescale characteristic of the induced magnetization reorientation. This
suggests that the CISS effect has an effect over long-timescales which we
attribute to exchange interactions. These results offer significant insights
into the fundamental processes underlying the CISS effect, contributing to the
implementation of CISS in state-of-the-art applications such as spintronic and
magnetic memory devices.


Abstract:
  We describe a solvable model of a quantum transition in a single band model
involving a change in the size of the electron Fermi surface without any
symmetry breaking. In a model with electron density $1-p$, we find a 'large'
Fermi surface state with the conventional Luttinger volume $1-p$ of electrons
for $p>p_c$, and a first order transition to a 'small' Fermi surface state with
a non-Luttinger volume $p$ of holes for $p<p_c$. As required by extended
Luttinger theorems, the small Fermi surface state also has fractionalized
spinon excitations. The model has electrons with strong local interactions in a
single band; after a canonical transformation, the interactions are transferred
to a coupling to two layers of ancilla qubits, as proposed by Zhang and Sachdev
(Phys. Rev. Research ${\bf 2}$, 023172 (2020)). Solvability is achieved by
employing random exchange interactions within the ancilla layers, and taking
the large $M$ limit with SU($M$) spin symmetry, as in the Sachdev-Ye-Kitaev
models. The local electron spectral function of the small Fermi surface phase
displays a particle-hole asymmetric pseudogap, and maps onto the spectral
function of a lightly doped Kondo insulator of a Kondo-Heisenberg lattice
model. We discuss connections to the physics of the hole-doped cuprates: the
asymmetric pseudogap observed in STM, and the sudden change from incoherent to
coherent anti-nodal spectra observed recently in photoemission. A holographic
analogy to wormhole transitions between multiple black holes is briefly noted.


Abstract:
  The compliance of IoT platforms to quality is paramount to achieve users
satisfaction. Currently, we do not have a comprehensive set of guidelines to
appraise and select the most suitable IoT platform architectures that meet
relevant criteria. This paper is a tentative response to this critical
knowledge gap where we adopted the design science research approach to develop
a novel evaluation framework. Our research, on the one hand, stimulates an
unbiased competition among IoT platform providers and, on the other hand,
establishes a solid foundation for IoT platform consumers to make informed
decisions in this multiplicity. The application of the framework is illustrated
in example scenarios. Moreover, lessons learned from applying design science
research are shared.


Abstract:
  With the open-source revolution, source codes are now more easily accessible
than ever. This has, however, made it easier for malicious users and
institutions to copy the code without giving regards to the license, or credit
to the original author. Therefore, source code author identification is a
critical task with paramount importance. In this paper, we propose ICodeNet - a
hierarchical neural network that can be used for source code file-level tasks.
The ICodeNet processes source code in image format and is employed for the task
of per file author identification. The ICodeNet consists of an ImageNet trained
VGG encoder followed by a shallow neural network. The shallow network is based
either on CNN or LSTM. Different variations of models are evaluated on a source
code author classification dataset. We have also compared our image-based
hierarchical neural network model with simple image-based CNN architecture and
text-based CNN and LSTM models to highlight its novelty and efficiency.


Abstract:
  With the continuous increase of the spectrum and antennas, endogenous sensing
is now possible in the fifth generation and future wireless communication
systems. However, sensing is a highly complex task for a heterogeneous
communication network with massive connections. Seeking multi-domain
cooperation is necessary. In this article, we present an integrated sensing and
communication (ISAC) system that performs active, passive, and interactive
sensing in different stages of communication through hardware and software. We
also propose different methods about how multi-user and multi-frequency band
cooperate to further enhance the ISAC system's performance. Finally, we
elaborate on the advantages of multi-domain cooperation from the physical layer
to the network layer for the ISAC system.


Abstract:
  We consider large-scale industrial computer model calibration, combining
multi-output simulation with limited physical observation, involved in the
development of a honeycomb seal. Toward that end, we adopt a localized sampling
and emulation strategy called "on-site surrogates (OSSs)", designed to cope
with the amalgamated challenges of high-dimensional inputs, large-scale
simulation campaigns, and nonstationary response surfaces. In previous
applications, OSSs were one-at-a-time affairs for multiple outputs. We
demonstrate that this leads to dissonance in calibration efforts for a common
parameter set across outputs for the honeycomb. Instead, a conceptually
straightforward, but implementationally intricate, principal-components
representation, adapted from ordinary Gaussian process surrogate modeling to
the OSS setting, can resolve this tension. With a two-pronged -
optimization-based and fully Bayesian - approach, we show how pooled
information across outputs can reduce uncertainty and enhance (statistical and
computational) efficiency in calibrated parameters for the honeycomb relative
to the previous, "data-poor" univariate analog.


Abstract:
  This article is devoted to the study of a nonlinear and nonlocal parabolic
equation introduced by Stefan Steinerberger to study the roots of polynomials
under differentiation; it also appeared in a work by Dimitri Shlyakhtenko and
Terence Tao on free convolution. Rafael Granero-Belinch\'on obtained a global
well-posedness result for positive initial data small enough in a Wiener space,
and recently Alexander Kiselev and Changhui Tan proved a global well-posedness
result for any positive initial data in the Sobolev space $H^s(\mathbb{S})$
with $s>3/2$. In this paper, we consider the Cauchy problem in the critical
space $H^{1/2}(\mathbb{S})$. Two interesting new features, at this level of
regularity, are that the equation can be written in the form $$ \partial_t
u+V\partial_x u+\gamma \Lambda u=0, $$ where $\gamma$ is non-negative but not
bounded from below and $V/\sqrt{\gamma}$ is not bounded. Therefore, the
equation is only weakly parabolic. We prove that nevertheless the Cauchy
problem is well posed locally in time and that the solutions are smooth for
positive times. Combining this with the results of Kiselev and Tan, this gives
a global well-posedness result for any positive initial data in
$H^{1/2}(\mathbb{S})$. Our proof relies on sharp commutator estimates and
introduces a strategy to prove a local well-posedness result in a situation
where the lifespan depends on the profile of the initial data and not only on
its norm.


Abstract:
  This paper considers optimization of smooth nonconvex functionals in smooth
infinite dimensional spaces. A H\"older gradient descent algorithm is first
proposed for finding approximate first-order points of regularized polynomial
functionals. This method is then applied to analyze the evaluation complexity
of an adaptive regularization method which searches for approximate first-order
points of functionals with $\beta$-H\"older continuous derivatives. It is shown
that finding an $\epsilon$-approximate first-order point requires at most
$O(\epsilon^{-\frac{p+\beta}{p+\beta-1}})$ evaluations of the functional and
its first $p$ derivatives.


Abstract:
  In this article a recognition principle for $\infty$-loop pairs of spaces of
connective commutative algebra spectra over connective commutative ring spectra
is proved. This is done by generalizing the classical recognition principle for
connective commutative ring spectra using relative operads. The machinery of
idempotent quasiadjunctions is used to handle the model theoretical aspects of
the proof.


Abstract:
  Building on the recent computation of the cohomology rings of smooth toric
varieties and partial quotients of moment-angle complexes, we investigate the
naturality properties of the resulting isomorphism between the cohomology of
such a space and the torsion product involving the Stanley-Reisner ring. If 2
is invertible in the chosen coefficient ring, then the isomorphism is natural
with respect to toric morphisms, which for partial quotients are defined in
analogy with toric varieties. In general there are deformation terms that we
describe explicitly.


Abstract:
  Intelligent reflecting surfaces (IRS) can improve the physical layer security
(PLS) by providing a controllable wireless environment. In this paper, we
propose a novel PLS technique with the help of IRS implemented by an
intelligent mirror array for the visible light communication (VLC) system.
First, for the IRS aided VLC system containing an access point (AP), a
legitimate user and an eavesdropper, the IRS channel gain and a lower bound of
the achievable secrecy rate are derived. Further, to enhance the IRS channel
gain of the legitimate user while restricting the IRS channel gain of the
eavesdropper, we formulate an achievable secrecy rate maximization problem for
the proposed IRS-aided PLS technique to find the optimal orientations of
mirrors. Since the sensitivity of mirrors' orientations on the IRS channel gain
makes the optimization problem hard to solve, we transform the original problem
into a reflected spot position optimization problem and solve it by a particle
swarm optimization (PSO) algorithm. Our simulation results show that secrecy
performance can be significantly improved by adding an IRS in a VLC system.


Abstract:
  We consider infinite matrices obtained by restricting Hardy integral kernels
to natural numbers. For a suitable class of Hardy kernels we describe the
absolutely continuous spectrum, the essential spectrum and the asymptotic
spectral density of these matrices.


Abstract:
  We study the problem of finding the best approximate circuit that is the
closest (in some pertinent metric) to a target circuit, and which satisfies a
number of hardware constraints, like gate alphabet and connectivity. We look at
the problem in the CNOT+rotation gate set from a mathematical programming
standpoint, offering contributions both in terms of understanding the
mathematics of the problem and its efficient solution. Among the results that
we present, we are able to derive a 14-CNOT 4-qubit Toffoli decomposition from
scratch, and show that the Quantum Shannon Decomposition can be compressed by a
factor of two without practical loss of fidelity.


Abstract:
  Augmented Reality (AR) enriches a user's real environment by adding spatially
aligned virtual objects (3D models, 2D textures, textual annotations, etc) by
means of special display technologies. These are either worn on the body or
placed in the working environment. From a technical point of view, AR faces
three major challenges: (1) to generate a high quality rendering, (2) to
precisely register (in position and orientation) the virtual objects (VOs) with
the real environment, and (3) to do so in interactive real-time (Regenbrecht,
Wagner, and Baratoff, 2002). The goal is to create the impression that the VOs
are part of the real environment. Therefore, and similar to definitions of
virtual reality (Steuer, 1992), it makes sense to define AR from a
psychological point of view: Augmented Reality conveys the impression that VOs
are present in the real environment. In order to evaluate how well this goal is
reached, a psychological measurement of this type of presence is necessary. In
the following, we will describe technological features of AR systems that make
a special questionnaire version necessary, describe our approach to the
questionnaire development, and the data collection strategy. Finally we will
present first results of the application of the questionnaire in a recent study
with 385 participants.


Abstract:
  High precision measurements of the polarized electron beam-spin asymmetry in
semi-inclusive deep inelastic scattering (SIDIS) from the proton have been
performed using a 10.6 GeV incident electron beam and the CLAS12 spectrometer
at Jefferson Lab. We report here the first multidimensional study of single
$\pi^+$ SIDIS data over a large kinematic range in $z$, $x_B$, $P_T$ and
virtualities $Q^2$ ranging from 1 GeV$^2$ up to 7 GeV$^2$. In particular, the
structure function ratio $F_{LU}^{\sin\phi}/F_{UU}$ has been determined, where
$F_{LU}^{\sin\phi}$ is a twist-3 quantity that can reveal novel properties of
quark-gluon correlations within the nucleon. The impact of the data on the
evolving understanding of the underlying reaction mechanisms and their
kinematic variation is explored using theoretical models for the different
contributing transverse momentum dependent parton distribution functions.


Abstract:
  Bloom Filter is a probabilistic data structure for the membership query, and
it has been intensely experimented in various fields to reduce memory
consumption and enhance a system's performance. Bloom Filter is classified into
two key categories: counting Bloom Filter (CBF), and non-counting Bloom Filter.
CBF has a higher false positive probability than standard Bloom Filter (SBF),
i.e., CBF uses a higher memory footprint than SBF. But CBF can address the
issue of the false negative probability. Notably, SBF is also false negative
free, but it cannot support delete operations like CBF. To address these
issues, we present a novel counting Bloom Filter based on SBF and 2D Bloom
Filter, called countBF. countBF uses a modified murmur hash function to enhance
its various requirements, which is experimentally evaluated. Our experimental
results show that countBF uses $1.96\times$ and $7.85\times$ less memory than
SBF and CBF respectively, while preserving lower false positive probability and
execution time than both SBF and CBF. The overall accuracy of countBF is
$99.999921$, and it proves the superiority of countBF over SBF and CBF. Also,
we compare with other state-of-the-art counting Bloom Filters.


Abstract:
  We have systematically constructed the general structure of the gauge boson
self-energy and the effective propagator in presence of a nontrivial background
like hot magnetized material medium. Based on this as well as the general
structure of fermion propagator in weakly magnetized medium we have calculated
pressure of deconfined QCD matter within HTL approximation.


Abstract:
  Using fixed point characterization, we develop a new goodness of fit test for
uniform distribution. We also discuss how the right censored observations can
be incorporated in the proposed test procedure. We study the asymptotic
properties of the proposed test statistics. A Monte Carlo simulation is carried
out to evaluate the finite sample performance of the tests. We illustrate the
test procedures using real data sets.


Abstract:
  The spectral efficiency of wireless networks can be made nearly infinitely
large by deploying many antennas, but the deployment of very many antennas
requires new topologies beyond the compact and discrete antenna arrays used by
conventional base stations. In this paper, we consider the large intelligent
surface scenario where small antennas are deployed on a large and dense
two-dimensional grid. Building on the heritage of MIMO, we first analyze the
beamwidth and sidelobes when transmitting from large intelligent surfaces. We
compare different precoding schemes and determine how to optimize the transmit
power with respect to different utility functions.


Abstract:
  Stars form in cold dense cores showing subsonic velocity dispersions. The
parental molecular clouds display higher temperatures and supersonic velocity
dispersions. The transition from core to cloud has been observed in velocity
dispersion, but temperature and abundance variations are unknown. We aim to
study the transition from cores to ambient cloud in temperature and velocity
dispersion using a single tracer.
  We use NH3 (1,1) and (2,2) maps in L1688 from the Green Bank Ammonia Survey,
smoothed to 1', and determine the physical properties from fits. We identify
the coherent cores and study the changes in temperature and velocity dispersion
from cores to the surrounding cloud. We obtain a kinetic temperature map
tracing the extended cloud, improving from previous maps tracing mostly the
cores. The cloud is 4-6 K warmer than the cores, and shows a larger velocity
dispersion (diff. = 0.15-0.25 km/s). Comparing to Herschel-based measurements,
we find that cores show kinetic temperature $\approx$1.8 K lower than the dust
temperature; while the gas temperature is higher than the dust temperature in
the cloud. We find an average p-NH3 fractional abundance (with respect to H2)
of $(4.2\pm0.2) \times 10^{-9}$ towards the coherent cores, and $(1.4\pm0.1)
\times 10^{-9}$ outside the core boundaries. Using stacked spectra, we detect
two components, one narrow and one broad, towards cores and their
neighbourhoods. We find the turbulence in the narrow component to be correlated
to the size of the structure (Pearson-r=0.54). With these unresolved regional
measurements, we obtain a turbulence-size relation of ${\sigma}_{v,NT}\propto
r^{0.5}$, similar to previous findings using multiple tracers.
  We discover that the subsonic component extends up to 0.15 pc beyond the
typical coherent boundaries, unveiling larger extents of the coherent cores and
showing gradual transition to coherence over ~0.2 pc.


Abstract:
  We propose a Goodness of Causal Fit (GCF) measure which depends on Pearl "do"
interventions. This is different from a measure of Goodness of Fit (GF), which
does not use interventions. Given a DAG set ${\cal G}$, to find a good $G\in
{\cal G}$, we propose plotting $GCF(G)$ versus $GF(G)$ for all $G\in {\cal G}$,
and finding a graph $G\in {\cal G}$ with a large amount of both types of
goodness.


Abstract:
  Speech sound disorders are a common communication impairment in childhood.
Because speech disorders can negatively affect the lives and the development of
children, clinical intervention is often recommended. To help with diagnosis
and treatment, clinicians use instrumented methods such as spectrograms or
ultrasound tongue imaging to analyse speech articulations. Analysis with these
methods can be laborious for clinicians, therefore there is growing interest in
its automation. In this paper, we investigate the contribution of ultrasound
tongue imaging for the automatic detection of speech articulation errors. Our
systems are trained on typically developing child speech and augmented with a
database of adult speech using audio and ultrasound. Evaluation on typically
developing speech indicates that pre-training on adult speech and jointly using
ultrasound and audio gives the best results with an accuracy of 86.9%. To
evaluate on disordered speech, we collect pronunciation scores from experienced
speech and language therapists, focusing on cases of velar fronting and gliding
of /r/. The scores show good inter-annotator agreement for velar fronting, but
not for gliding errors. For automatic velar fronting error detection, the best
results are obtained when jointly using ultrasound and audio. The best system
correctly detects 86.6% of the errors identified by experienced clinicians. Out
of all the segments identified as errors by the best system, 73.2% match errors
identified by clinicians. Results on automatic gliding detection are harder to
interpret due to poor inter-annotator agreement, but appear promising. Overall
findings suggest that automatic detection of speech articulation errors has
potential to be integrated into ultrasound intervention software for
automatically quantifying progress during speech therapy.


Abstract:
  Using concepts from integral geometry, we propose a definition for a local
coarse-grained curvature tensor that is well-defined on polygonal surfaces.
This coarse-grained curvature tensor shows fast convergence to the curvature
tensor of smooth surfaces, capturing with accuracy not only the principal
curvatures but also the principal directions of curvature. Thanks to the
additivity of the integrated curvature tensor, coarse-graining procedures can
be implemented to compute it over arbitrary patches of polygons. When computed
for a closed surface, the integrated curvature tensor is identical to a rank-2
Minkowski tensor. We also provide an algorithm to extend an existing C++
package, that can be used to compute efficiently local curvature tensors on
triangulated surfaces.


Abstract:
  We present the results of a near-infrared (NIR) monitoring program carried
out between 1999 and 2005 to determine the variability properties of the
Arecibo sample of OH/IR stars. The sample consists of 385 IRAS-selected
Asymptotic Giant Branch (AGB) candidates, for which their O-rich chemistry has
been proven by the detection of 1612 MHz OH maser emission. The monitoring data
was complemented by data collected from public optical and NIR surveys. We
fitted the light curves obtained in the optical and NIR bands with a model
using an asymmetric cosine function, and derived a period for 345 sources (90%
of the sample). Based on their variability properties, most of the Arecibo
sources are classified as long-period large-amplitude variable stars (LPLAV),
4% as (candidate) post-AGB stars, and 3% remain unclassified although they are
likely post-AGB stars or highly obscured AGB stars. The period distribution of
the LPLAVs peaks at 400 days, with periods between 300 and 800 days for most of
the sources, and has a long tail up to 2100 days. Typically, the amplitudes are
between 1 and 3 mag in the NIR and between 2 and 6 mag in the optical. We find
correlations between periods and amplitudes, with larger amplitudes associated
to longer periods, as well as between the period and the infrared colours, with
the longer periods linked to the redder sources. Among the post-AGB stars, the
light curve of IRAS 19566+3423 was exceptional, showing a large systematic
increase in K-band brightness over 7 years.


Abstract:
  The effect of Ca and Zn in solid solution on the critical resolved shear
stress (CRSS) of <a> basal slip, tensile twinning and <c+a> pyramidal slip in
Mg alloys has been measured through compression tests on single crystal
micropillars with different orientations. The solute atoms increased the CRSS
for basal slip to ~ 13.5 MPa, while the CRSS for pyramidal slip was lower than
85 MPa, reducing significantly the plastic anisotropy in comparison with pure
Mg. Moreover, the CRSSs for twin nucleation and growth were very similar (~ 37
MPa) and the large value of the CRSS for twin growth hindered the growth of
twins during thermo-mechanical processing. Finally, evidence of <a> prismatic
slip and cross-slip between basal and prismatic dislocations was found. It is
concluded that the reduction of plastic anisotropy, the activation of different
slip systems and cross-slip and the weak basal texture promoted by the large
CRSS for twin growth are responsible for the improved ductility and formability
of Mg-Ca-Zn alloys.


Abstract:
  We propose a particle-based method to simulate thin-film fluid that jointly
facilitates aggressive surface deformation and vigorous tangential flows. We
build our dynamics model from the surface tension driven Navier-Stokes equation
with the dimensionality reduced using the asymptotic lubrication theory and
customize a set of differential operators based on the weakly compressible
Smoothed Particle Hydrodynamics (SPH) for evolving pointset surfaces. The key
insight is that the compressible nature of SPH, which is unfavorable in its
typical usage, is helpful in our application to co-evolve the thickness,
calculate the surface tension, and enforce the fluid incompressibility on a
thin film. In this way, we are able to two-way couple the surface deformation
with the in-plane flows in a physically based manner. We can simulate complex
vortical swirls, fingering effects due to Rayleigh-Taylor instability,
capillary waves, Newton's interference fringes, and the Marangoni effect on
liberally deforming surfaces by presenting both realistic visual results and
numerical validations. The particle-based nature of our system also enables it
to conveniently handle topology changes and codimension transitions, allowing
us to marry the thin-film simulation with a wide gamut of 3D phenomena, such as
pinch-off of unstable catenoids, dripping under gravity, merging of droplets,
as well as bubble rupture.


Abstract:
  Chromatin moves dynamically inside the cell nucleus, and its motion is often
correlated with gene functions such as DNA recombination and transcription. A
recent study has shown that during early embryogenesis of the nematode,
Caenorhabiditis elegans, the chromatin motion markedly decreases. However, the
underlying mechanism for this transition has yet to be elucidated. We
systematically investigated the impact of nuclear size to demonstrate that it
is indeed a decisive factor in chromatin mobility. To this end, we established
a method to quantify chromatin motion inside the nucleus, while excluding the
contribution of the movement of the nucleus itself, which allowed us to extract
the intrinsic mean-squared displacement (iMSD) of individual chromosomal loci
in moving nuclei from the correlated motion of two loci. We show that a simple
theoretical description, which takes into account the topological constraints
of chromatin polymers, can quantitatively describe the relationship between the
nucleus size and the chromatin motion in vivo. Our results emphasize a
regulatory role of nuclear size in restricting chromatin motion, and a generic
polymer physics model plays a guiding role in capturing this essential feature.


Abstract:
  Non-linear model predictive control (nMPC) is a powerful approach to control
complex robots (such as humanoids, quadrupeds, or unmanned aerial manipulators
(UAMs)) as it brings important advantages over other existing techniques. The
full-body dynamics, along with the prediction capability of the optimal control
problem (OCP) solved at the core of the controller, allows to actuate the robot
in line with its dynamics. This fact enhances the robot capabilities and
allows, e.g., to perform intricate maneuvers at high dynamics while optimizing
the amount of energy used. Despite the many similarities between humanoids or
quadrupeds and UAMs, full-body torque-level nMPC has rarely been applied to
UAMs.
  This paper provides a thorough description of how to use such techniques in
the field of aerial manipulation. We give a detailed explanation of the
different parts involved in the OCP, from the UAM dynamical model to the
residuals in the cost function. We develop and compare three different nMPC
controllers: Weighted MPC, Rail MPC, and Carrot MPC, which differ on the
structure of their OCPs and on how these are updated at every time step. To
validate the proposed framework, we present a wide variety of simulated case
studies. First, we evaluate the trajectory generation problem, i.e., optimal
control problems solved offline, involving different kinds of motions (e.g.,
aggressive maneuvers or contact locomotion) for different types of UAMs. Then,
we assess the performance of the three nMPC controllers, i.e., closed-loop
controllers solved online, through a variety of realistic simulations. For the
benefit of the community, we have made available the source code related to
this work.


Abstract:
  Modular Decomposition focuses on repeatedly identifying a module M (a
collection of vertices that shares exactly the same neighbourhood outside of M)
and collapsing it into a single vertex. This notion of exactitude of
neighbourhood is very strict, especially when dealing with real world graphs.
We study new ways to relax this exactitude condition. However, generalizing
modular decomposition is far from obvious. Most of the previous proposals lose
algebraic properties of modules and thus most of the nice algorithmic
consequences. We introduce the notion of an ({\alpha}, {\beta})-module, a
relaxation that allows a bounded number of errors in each node and maintains
some of the algebraic structure. It leads to a new combinatorial decomposition
with interesting properties. Among the main results in this work, we show that
minimal ({\alpha}, {\beta})-modules can be computed in polynomial time, and
that every graph admits an ({\alpha},{\beta})-modular decomposition tree, thus
generalizing Gallai's Theorem (which corresponds to the case for {\alpha} =
{\beta} = 0). Unfortunately we give evidence that computing such a
decomposition tree can be difficult.


Abstract:
  We study static traversable wormholes obtained by Morris and Thorne in
general relativity (GR) in the framework of a modified theory of gravity. The
modified gravitational action $f(R,T)$ is a function of the Ricci scalar ($R$)
and of the trace of the energy momentum tensor ($T$). For a modified gravity
$f(R,T)=R+\alpha R^{2}+\lambda T$, where $\alpha$ and $\lambda$ are constants,
we obtain wormhole solutions (WH) with normal matter for a relevant shape
functions. The energy conditions are checked at the throat and away from the
throat of the WH. The coupling parameters $\alpha $ and $\lambda$ in the
gravitational action play an important role to accommodate the matter
composition. For a given $\lambda$, WH solutions are found in the presence of
exotic matter at the throat for $\alpha <0$. It is shown that WH exists in the
modified gravity without exotic matter when $\alpha >0$. We consider two
different shape functions to investigate the existence of WH in the presence of
exotic or normal matter. A class of WH solutions exist with anisotropic fluid
provided $\lambda \neq - 8 \pi$. In flat asymptotic regions on both sides of
the throat with anisotropic source and $\lambda= - 8 \pi$, it does not permit
WH as per no go theorem. As $\lambda \rightarrow 0$ in the gravitational
action, all the energy conditions are obeyed with the hybrid shape function
indicating existence of WH with normal matter.


Abstract:
  Cutting plane methods play a significant role in modern solvers for tackling
mixed-integer programming (MIP) problems. Proper selection of cuts would remove
infeasible solutions in the early stage, thus largely reducing the
computational burden without hurting the solution accuracy. However, the major
cut selection approaches heavily rely on heuristics, which strongly depend on
the specific problem at hand and thus limit their generalization capability. In
this paper, we propose a data-driven and generalizable cut selection approach,
named Cut Ranking, in the settings of multiple instance learning. To measure
the quality of the candidate cuts, a scoring function, which takes the
instance-specific cut features as inputs, is trained and applied in cut ranking
and selection. In order to evaluate our method, we conduct extensive
experiments on both synthetic datasets and real-world datasets. Compared with
commonly used heuristics for cut selection, the learning-based policy has shown
to be more effective, and is capable of generalizing over multiple problems
with different properties. Cut Ranking has been deployed in an industrial
solver for large-scale MIPs. In the online A/B testing of the product planning
problems with more than $10^7$ variables and constraints daily, Cut Ranking has
achieved the average speedup ratio of 12.42% over the production solver without
any accuracy loss of solution.


Abstract:
  Democratization of machine learning requires architectures that automatically
adapt to new problems. Neural Differential Equations (NDEs) have emerged as a
popular modeling framework by removing the need for ML practitioners to choose
the number of layers in a recurrent model. While we can control the
computational cost by choosing the number of layers in standard architectures,
in NDEs the number of neural network evaluations for a forward pass can depend
on the number of steps of the adaptive ODE solver. But, can we force the NDE to
learn the version with the least steps while not increasing the training cost?
Current strategies to overcome slow prediction require high order automatic
differentiation, leading to significantly higher training time. We describe a
novel regularization method that uses the internal cost heuristics of adaptive
differential equation solvers combined with discrete adjoint sensitivities to
guide the training process towards learning NDEs that are easier to solve. This
approach opens up the blackbox numerical analysis behind the differential
equation solver's algorithm and directly uses its local error estimates and
stiffness heuristics as cheap and accurate cost estimates. We incorporate our
method without any change in the underlying NDE framework and show that our
method extends beyond Ordinary Differential Equations to accommodate Neural
Stochastic Differential Equations. We demonstrate how our approach can halve
the prediction time and, unlike other methods which can increase the training
time by an order of magnitude, we demonstrate similar reduction in training
times. Together this showcases how the knowledge embedded within
state-of-the-art equation solvers can be used to enhance machine learning.


Abstract:
  Complex numbers are widely used in both classical and quantum physics, and
are indispensable components for describing quantum systems and their dynamical
behavior. Recently, the resource theory of imaginarity has been introduced,
allowing for a systematic study of complex numbers in quantum mechanics and
quantum information theory. In this work we develop theoretical methods for the
resource theory of imaginarity, motivated by recent progress within theories of
entanglement and coherence. We investigate imaginarity quantification, focusing
on the geometric imaginarity and the robustness of imaginarity, and apply these
tools to the state conversion problem in imaginarity theory. Moreover, we
analyze the complexity of real and general operations in optical experiments,
focusing on the number of unfixed wave plates for their implementation. We also
discuss the role of imaginarity for local state discrimination, proving that
any pair of real orthogonal pure states can be discriminated via local real
operations and classical communication. Our study reveals the significance of
complex numbers in quantum physics, and proves that imaginarity is a resource
in optical experiments.


Abstract:
  The distributions of the $k$-th largest level at the soft edge scaling limit
of Gaussian ensembles are some of the most important distributions in random
matrix theory, and their numerical evaluation is a subject of great practical
importance. One numerical method for evaluating the distributions uses the fact
that they can be represented as Fredholm determinants involving the so-called
Airy integral operator. When the spectrum of the integral operator is computed
by discretizing it directly, the eigenvalues are known to at most absolute
precision. Remarkably, the Airy integral operator is an example of a so-called
bispectral operator, which admits a commuting differential operator that shares
the same eigenfunctions. In this manuscript, we develop an efficient numerical
algorithm for evaluating the eigendecomposition of the Airy integral operator
to full relative precision, using the eigendecomposition of the commuting
differential operator. This allows us to rapidly evaluate the distributions of
the $k$-th largest level to full relative precision rapidly everywhere except
in the left tail, where they are computed to absolute precision. In addition,
we characterize the eigenfunctions of the Airy integral operator, and describe
their extremal properties in relation to an uncertainty principle involving the
Airy transform. We observe that the Airy integral operator is fairly universal,
and we describe a separate application to Airy beams in optics. Using the
eigenfunctions, we compute a finite-energy Airy beam that is optimal, in the
sense that the beam is both maximally concentrated, and maximally
non-diffracting and self-accelerating.


Abstract:
  We explore the use of large pretrained language models as few-shot semantic
parsers. The goal in semantic parsing is to generate a structured meaning
representation given a natural language input. However, language models are
trained to generate natural language. To bridge the gap, we use language models
to paraphrase inputs into a controlled sublanguage resembling English that can
be automatically mapped to a target meaning representation. Our results
demonstrate that with only a small amount of data and very little code to
convert into English-like representations, our blueprint for rapidly
bootstrapping semantic parsers leads to surprisingly effective performance on
multiple community tasks, greatly exceeding baseline methods also trained on
the same limited data.


Abstract:
  We study the self-assembly on a spherical surface of a model for a binary
mixture of amphiphilic dimers in the presence of guest particles via Monte
Carlo (MC) computer simulation. All particles have a hard core, but one monomer
of the dimer also interacts with the guest particle by means of a short-range
attractive potential. We observe the formation of aggregates of various shape
as a function of the composition of the mixture and of the size of guest
particles. Our MC simulations are a further step towards a microscopic
understanding of experiments on colloidal aggregation over curved surfaces,
such as oil droplets.


Abstract:
  Character rigging is universally needed in computer graphics but notoriously
laborious. We present a new method, HeterSkinNet, aiming to fully automate such
processes and significantly boost productivity. Given a character mesh and
skeleton as input, our method builds a heterogeneous graph that treats the mesh
vertices and the skeletal bones as nodes of different types and uses graph
convolutions to learn their relationships. To tackle the graph heterogeneity,
we propose a new graph network convolution operator that transfers information
between heterogeneous nodes. The convolution is based on a new distance
HollowDist that quantifies the relations between mesh vertices and bones. We
show that HeterSkinNet is robust for production characters by providing the
ability to incorporate meshes and skeletons with arbitrary topologies and
morphologies (e.g., out-of-body bones, disconnected mesh components, etc.).
Through exhaustive comparisons, we show that HeterSkinNet outperforms
state-of-the-art methods by large margins in terms of rigging accuracy and
naturalness. HeterSkinNet provides a solution for effective and robust
character rigging.


Abstract:
  A classical question in economics is whether complex, randomized selling
protocols can improve a firm's revenue beyond that of simple,
take-it-or-leave-it posted prices. In 1981, Myerson answered this question with
an emphatic ``No'' for a monopolist selling a single good. By contrast, there
is no crisp answer for multiple goods, and a major focus of Bayesian mechanism
design since then has been to understand the power of randomized lotteries over
deterministic pricing in different settings.
  In this paper, we ask the same question for assortment optimization, where
goods have exogenously-fixed prices, and the decision is a set of substitute
goods to offer. To formalize this question, we introduce a Bayesian mechanism
design problem with fixed prices and ordinal preferences, in which assortments
correspond to deterministic mechanisms. Meanwhile, randomized mechanisms
correspond to lotteries whose payment is restricted to equal the fixed price of
the randomly-allocated good. This models the ``fixed-price lotteries'' trending
in designer fashion, a significant departure from traditional lotteries based
on price discounts.
  We first show that for general ordinal distributions, lotteries under this
restriction can still earn greater revenue than any deterministic assortment.
We then derive a natural sufficient condition on the distribution which ensures
the optimality of assortments. Importantly, this sufficient condition captures
commonly-used distributions in the assortment optimization literature,
including Multi-Nomial Logit, Markov Chain, Tversky's Elimination by Aspects,
and mixtures with Independent Demand Models. The takeaway is that unless a firm
has a sophisticated model for consumer choice, fixed-price lotteries are no
better than assortments.


Abstract:
  In this paper we consider the system with $n$ identical elements and one
repairing device. In each time moment only one element works while the rest
elements stay in reserve. The distribution of element repairing period is
exponential, the distribution of element working period is general. We obtain
the asymptotic distribution of the system lifetime under condition of the
element's fast repair.


Abstract:
  We study Schubert polynomials using geometry of infinite-dimensional flag
varieties and degeneracy loci. Applications include Graham-positivity of
coefficients appearing in equivariant coproduct formulas and expansions of
back-stable and enriched Schubert polynomials. We also construct embeddings of
affine and type C flag varieties, study the corresponding pullback maps on
(equivariant) cohomology rings, and give a presentation of the integral
equivariant cohomology of the (type A) affine Grassmannian.


Abstract:
  Recently, we have been witnessing an increasing use of machine learning
methods in self-adaptive systems. Machine learning methods offer a variety of
use cases for supporting self-adaptation, e.g., to keep runtime models up to
date, reduce large adaptation spaces, or update adaptation rules. Yet, since
machine learning methods apply in essence statistical methods, they may have an
impact on the decisions made by a self-adaptive system. Given the wide use of
formal approaches to provide guarantees for the decisions made by self-adaptive
systems, it is important to investigate the impact of applying machine learning
methods when such approaches are used. In this paper, we study one particular
instance that combines linear regression to reduce the adaptation space of a
self-adaptive system with statistical model checking to analyze the resulting
adaptation options. We use computational learning theory to determine a
theoretical bound on the impact of the machine learning method on the
predictions made by the verifier. We illustrate and evaluate the theoretical
result using a scenario of the DeltaIoT artifact. To conclude, we look at
opportunities for future research in this area.


Abstract:
  Optimal sensor placement (SP) usually minimizes an impact measure, such as
the amount of contaminated water or the number of inhabitants affected before
detection. The common choice is to minimize the minimum detection time (MDT)
averaged over a set of contamination events, with contaminant injected at a
different location. Given a SP, propagation is simulated through a hydraulic
software model of the network to obtain spatio-temporal concentrations and the
average MDT. Searching for an optimal SP is NP-hard: even for mid-size
networks, efficient search methods are required, among which evolutionary
approaches are often used. A bi-objective formalization is proposed: minimizing
the average MDT and its standard deviation, that is the risk to detect some
contamination event too late than the average MDT. We propose a data structure
(sort of spatio-temporal heatmap) collecting simulation outcomes for every SP
and particularly suitable for evolutionary optimization. Indeed, the proposed
data structure enabled a convergence analysis of a population-based algorithm,
leading to the identification of indicators for detecting problem-specific
converge issues which could be generalized to other similar problems. We used
Pymoo, a recent Python framework flexible enough to incorporate our problem
specific termination criterion. Results on a benchmark and a real-world network
are presented.


Abstract:
  Managing a pandemic requires continuous dissemination of infectious disease
surveillance data. Legislation permits sharing de-identified patient data;
however, current de-identification approaches are time-consuming and do not
flex with changes in infection rates or population demographics over time. In
this paper, we introduce a framework to dynamically adapt de-identification for
near-real time sharing of patient-level surveillance data. The framework
leverages a simulation mechanism, capable of being applied to any geographic
level, to forecast and manage disclosure risks. We use data from Johns Hopkins
University and the Centers for Disease Control and Prevention to demonstrate
the framework's effectiveness in maintaining the privacy risk below a threshold
based on public health standards for COVID-19 county-level case data from
August 2020 to April 2021. Across all US counties, the framework's approach
meets the threshold for 95.2% of daily data releases, while a policy based on
current de-identification techniques meets the threshold for only 24.6%.


Abstract:
  We consider a quasilinear degenerate parabolic equation driven by the
orthotropic $p-$Laplacian. We prove that local weak solutions are locally
Lipschitz continuous in the spatial variable, uniformly in time.


Abstract:
  Because many of our X-ray telescopes are optimized towards observing faint
sources, observations of bright sources like X-ray binaries in outburst are
often affected by instrumental biases. These effects include dead time and
photon pile-up, which can dramatically change the statistical inference of
physical parameters from these observations. While dead time is difficult to
take into account in a statistically consistent manner, simulating dead
time-affected data is often straightforward. This structure makes the issue of
inferring physical properties from dead time-affected observations fall into a
class of problems common across many scientific disciplines. There is a growing
number of methods to address them under the name of Simulation-Based Inference
(SBI), aided by new developments in density estimation and statistical machine
learning. In this paper, we introduce SBI as a principled way to infer
variability properties from dead time-affected light curves. We use Sequential
Neural Posterior Estimation to estimate the posterior probability for
variability properties. We show that this method can recover variability
parameters on simulated data even when dead time is variable, and present
results of an application of this approach to NuSTAR observations of the
galactic black hole X-ray binary GRS 1915+105.


Abstract:
  Low-complexity beamformer design with practical constraints is an attractive
research area for hybrid analog/digital systems in mm-wave massive
multiple-input multiple-output (MIMO). This paper investigates
interference-aware pre-beamformer (analog beamformer) design for joint spatial
division and multiplexing (JSDM) which is a user-grouping based two-stage
beamforming method. Single-carrier frequency domain equalization (SC-FDE) is
employed in uplink frequency-selective channels. First, unconstrained slowly
changing statistical analog beamformer of each group, namely, generalized
eigenbeamformer (GEB) which has strong interference suppression capability is
designed where the mutual information in reduced dimension is maximized. Then,
constant-modulus constrained approximations of unconstrained beamformer are
obtained by utilizing alternating minimization algorithms for fully connected
arrays and fixed subarrays. In addition, a dynamic subarray algorithm is
proposed where the connections between radio frequency (RF) chains and antennas
are changed with changing channel statistics. Convergence of the proposed
alternating minimization-based algorithms are provided along with their
complexity analysis. It is observed that additional complexity of proposed
algorithms is insignificant for the overall system design. Although most of the
interference is suppressed with the help of proposed constrained beamformers,
there may be some residual interference after the analog beamforming stage.
Therefore, linear minimum mean square error (LMMSE) type digital beamformers,
which take the residual interference in reduced dimension into account, are
proposed instead of zero-forcing (ZF) type. Simulation results verify the
superiority of the proposed interference-aware constrained design over existing
approaches in terms of beampattern, spectral efficiency, outage capacity and
channel estimation accuracy.


Abstract:
  We estimate Weyl sums over the integers with sum of binary digits either
fixed or restricted by some congruence condition. In our proofs we use ideas
that go back to a paper by Banks, Conflitti and the first author (2002).
Moreover, we apply the "main conjecture" on the Vinogradov mean value theorem
which has been established by Bourgain, Demeter and Guth (2016) as well as by
Wooley (2016, 2019). We use our result to give an estimate of the discrepancy
of point sets that are defined by the values of polynomials at arguments having
the sum of binary digits restricted in different ways.


Abstract:
  We consider a dynamic Colonel Blotto game (CBG) in which one of the players
is the learner and has limited troops (budget) to allocate over a finite time
horizon. At each stage, the learner strategically determines the budget
distribution among the battlefields based on past observations. The other
player is the adversary, who chooses its budget allocation strategies randomly
from some fixed unknown distribution. The learner's objective is to minimize
its regret, which is the difference between the payoff of the best mixed
strategy and the realized payoff by following a learning algorithm. The dynamic
CBG is analyzed under the framework of combinatorial bandit and bandit with
knapsacks. We first convert the dynamic CBG with budget constraint to a path
planning problem on a graph. We then devise an efficient dynamic policy for the
learner that uses a combinatorial bandit algorithm Edge on the path planning
graph as a subroutine for another algorithm LagrangeBwK. It is shown that under
the proposed policy, the learner's regret is bounded with high probability by a
term sublinear in time horizon $T$ and polynomial with respect to other
parameters.


Abstract:
  In this paper, we consider a problem of counting rational points near
self-similar sets. Let $n\geq 1$ be an integer. We shall show that for some
self-similar measures on $\mathbb{R}^n$, the set of rational points
$\mathbb{Q}^n$ is 'equidistributed' in a sense that will be introduced in this
paper. This implies that an inhomogeneous Khinchine convergence type result can
be proved for those measures. In particular, for $n=1$ and large enough
integers $p,$ the above holds for the middle-$p$th Cantor measure, i.e. the
natural Hausdorff measure on the set of numbers whose base $p$ expansions do
not have digit $[(p-1)/2].$ Furthermore, we partially proved a conjecture of
Bugeaud and Durand for the middle-$p$th Cantor set and this also answers a
question posed by Levesley, Salp and Velani. Our method includes a fine
analysis of the Fourier coefficients of self-similar measures together with an
Erd\H{o}s-Kahane type argument. We will also provide a numerical argument to
show that $p>10^7$ is sufficient for the above conclusions. In fact, $p\geq 15$
is already enough for most of the above conclusions.


Abstract:
  Recently, significant progress has been made in audio source separation by
the application of deep learning techniques. Current methods that combine both
audio and visual information use 2D representations such as images to guide the
separation process. However, in order to (re)-create acoustically correct
scenes for 3D virtual/augmented reality applications from recordings of real
music ensembles, detailed information about each sound source in the 3D
environment is required. This demand, together with the proliferation of 3D
visual acquisition systems like LiDAR or rgb-depth cameras, stimulates the
creation of models that can guide the audio separation using 3D visual
information. This paper proposes a multi-modal deep learning model to perform
music source separation conditioned on 3D point clouds of music performance
recordings. This model extracts visual features using 3D sparse convolutions,
while audio features are extracted using dense convolutions. A fusion module
combines the extracted features to finally perform the audio source separation.
It is shown, that the presented model can distinguish the musical instruments
from a single 3D point cloud frame, and perform source separation qualitatively
similar to a reference case, where manually assigned instrument labels are
provided.


Abstract:
  Motivation: Protein-ligand affinity prediction is an important part of
structure-based drug design. It includes molecular docking and affinity
prediction. Although molecular dynamics can predict affinity with high accuracy
at present, it is not suitable for large-scale virtual screening. The existing
affinity prediction and evaluation functions based on deep learning mostly rely
on experimentally-determined conformations. Results: We build a predictive
model of protein-ligand affinity through the ResNet neural network with added
attention mechanism. The resulting ResAtom-Score model achieves Pearson's
correlation coefficient R = 0.833 on the CASF-2016 benchmark test set. At the
same time, we evaluated the performance of a variety of existing scoring
functions in combination with ResAtom-Score in the absence of
experimentally-determined conformations. The results show that the use of
{\Delta}VinaRF20 in combination with ResAtom-Score can achieve affinity
prediction close to scoring functions in the presence of
experimentally-determined conformations. These results suggest that ResAtom
system may be used for in silico screening of small molecule ligands with
target proteins in the future. Availability: https://github.com/wyji001/ResAtom


Abstract:
  We consider a planar convex body $C$ and we prove several analogs of Roth's
theorem on irregularities of distribution. When $\partial C$ is $\mathcal{C}%
^{2}$ regardless of curvature, we prove that for every set $\mathcal{P}_{N}$ of
$N$ points in $\mathbb{T}^{2}$ we have the sharp bound \[
\int_{0}^{1}\int_{\mathbb{T}^{2}}\left\vert \mathrm{card}\left(
\mathcal{P}_{N}\mathcal{\cap}\left( \lambda C+t\right) \right) -\lambda
^{2}N\left\vert C\right\vert \right\vert ^{2}~dtd\lambda\geqslant cN^{1/2}\;.
\] When $\partial C$ is only piecewise $\mathcal{C}^{2}$ and is not a polygon
we prove the sharp bound% \[ \int_{0}^{1}\int_{\mathbb{T}^{2}}\left\vert
\mathrm{card}\left( \mathcal{P}_{N}\mathcal{\cap}\left( \lambda C+t\right)
\right) -\lambda ^{2}N\left\vert C\right\vert \right\vert
^{2}~dtd\lambda\geqslant cN^{2/5}. \] We also give a whole range of
intermediate sharp results between $N^{2/5}$ and $N^{1/2}$. Our proofs depend
on a lemma of Cassels-Montgomery, on ad hoc constructions of finite point sets,
and on a geometric type estimate for the average decay of the Fourier transform
of the characteristic function of $C$.


Abstract:
  In this contribution a numerical model is developed to study the time
dynamics of photoluminescence emitted by Tb3+ doped multimode
chalcogenide-selenide glass fibers pumped by laser light at approximately 2
microns. The model consists of a set of partial differential equations (PDEs),
which describe the temporal and spatial evolution of the photon density and
level populations within the fiber. In order to solve numerically the PDEs a
Method of Lines is applied. The modeling parameters are extracted from
measurements and from data available in the literature. The numerical results
obtained support experimental observations. In particular, the developed model
reproduces the discrepancies that are observed between the photoluminescence
decay curves obtained from different points along the fiber. The numerical
analysis is also used to explain the source of these discrepancies.


Abstract:
  We show how to compute annihilators and associated varieties of simple
Harish-Chandra modules for Sp(p,q) with trivial infinitesimal character from a
pair of domino tableaux attached to a parameter for such a module.


Abstract:
  LUX-ZEPLIN (LZ) is a dark matter detector expected to obtain world-leading
sensitivity to weakly interacting massive particles (WIMPs) interacting via
nuclear recoils with a ~7-tonne xenon target mass. This manuscript presents
sensitivity projections to several low-energy signals of the complementary
electron recoil signal type: 1) an effective neutrino magnetic moment and 2) an
effective neutrino millicharge, both for pp-chain solar neutrinos, 3) an axion
flux generated by the Sun, 4) axion-like particles forming the galactic dark
matter, 5) hidden photons, 6) mirror dark matter, and 7) leptophilic dark
matter. World-leading sensitivities are expected in each case, a result of the
large 5.6t 1000d exposure and low expected rate of electron recoil backgrounds
in the $<$100keV energy regime. A consistent signal generation, background
model and profile-likelihood analysis framework is used throughout.


Abstract:
  An accurate force field is the key to the success of all molecular mechanics
simulations on organic polymers and biomolecules. Accuracy beyond density
functional theory is often needed to describe the intermolecular interactions,
while most correlated wavefunction (CW) methods are prohibitively expensive for
large molecules. Therefore, it posts a great challenge to develop an extendible
ab initio force field for large flexible organic molecules at CW level of
accuracy. In this work, we face this challenge by combining the physics-driven
nonbonding potential with a data-driven subgraph neural network bonding model
(named sGNN). Tests on polyethylene glycol polymer chains show that our
strategy is highly accurate and robust for molecules of different sizes.
Therefore, we can develop the force field from small molecular fragments (with
sizes easily accessible to CW methods) and safely transfer it to large
polymers, thus opening a new path to the next-generation organic force fields.


Abstract:
  This paper is concerned with a class of approximate non-linear
transformations that compress solutions of the (generalized) sinh-Gordon
equation into parametrically small regions in two-dimensional spacetime. Given
the sinh-Gordon field near a time-slice, a long Nambu-Goto string can be
constructed in three-dimensional anti-de Sitter space. The string is then
approximated to arbitrary accuracy by a slightly smoothed piecewise linear
string of N segments. The corresponding sinh-Gordon field has a comb-like
structure and its size is controlled by the amount of smoothing applied to the
segmented string. In a (singular) large-N limit, the transformation commutes
with time evolution. As an example, a static cosh-Gordon solution is discussed
in detail. The corresponding smooth and segmented string solutions are obtained
and the compressed cosh-Gordon potential is investigated.


Abstract:
  Market by order (MBO) data - a detailed feed of individual trade instructions
for a given stock on an exchange - is arguably one of the most granular sources
of microstructure information. While limit order books (LOBs) are implicitly
derived from it, MBO data is largely neglected by current academic literature
which focuses primarily on LOB modelling. In this paper, we demonstrate the
utility of MBO data for forecasting high-frequency price movements, providing
an orthogonal source of information to LOB snapshots and expanding the universe
of alpha discovery. We provide the first predictive analysis on MBO data by
carefully introducing the data structure and presenting a specific
normalisation scheme to consider level information in order books and to allow
model training with multiple instruments. Through forecasting experiments using
deep neural networks, we show that while MBO-driven and LOB-driven models
individually provide similar performance, ensembles of the two can lead to
improvements in forecasting accuracy - indicating that MBO data is additive to
LOB-based features.


Abstract:
  We study the groups of automorphisms of rational algebraic surfaces that
admit a relatively minimal pencil of curves of arithmetic genus one over an
algebraically closed field of arbitrary characteristic. In particular, we
classify such surfaces that admit non-trivial automorphisms that act trivially
on the Picard group. As an application, we classify classical Enriques surfaces
in characteristic $2$ that admit non-trivial numerically trivial automorphisms.


Abstract:
  We have realized different honeycomb lattices for microwave photons in the 4
to 8 GHz band using superconducting spiral resonators. Each lattice comprises a
few hundred sites. Two designs have been studied, one leading to two bands
touching at the Dirac points and one where a gap opens at the Dirac points.
Using a scanning laser technique to image the eigenmodes of this new type of
photonic lattices, we are able to reconstruct their band structure. The
measured bands are in excellent agreement with ab initio models that combine
numerical simulations of the electromagnetic properties of the spiral resonator
and analytical calculations.


Abstract:
  In this paper we present our submission for the EACL 2021-Shared Task on
Offensive Language Identification in Dravidian languages. Our final system is
an ensemble of mBERT and XLM-RoBERTa models which leverage task-adaptive
pre-training of multilingual BERT models with a masked language modeling
objective. Our system was ranked 1st for Kannada, 2nd for Malayalam and 3rd for
Tamil.


Abstract:
  We introduce Feynman-like rules to compute quivers for two loops and higher
for the coloured planar $\phi^3$ theory for winding number zero. We demonstrate
this for a few cases.


Abstract:
  Using the programming language Haskell, we introduce an implementation of
propositional calculus, number theory, and a simple imperative language that
can evaluate arithmetic and boolean expressions. Finally, we provide an
implementation of Hoare's logic which will allow us to deduce facts about
programs without the need for a full evaluation.


Abstract:
  We show that several new classes of groups are measure strongly treeable. In
particular, finitely generated groups admitting planar Cayley graphs,
elementarily free groups, and the group of isometries of the hyperbolic plane
and all its closed subgroups. In higher dimensions, we also prove a dichotomy
that the fundamental group of a closed aspherical 3-manifold is either amenable
or has strong ergodic dimension 2. Our main technical tool is a method for
finding measurable treeings of Borel planar graphs by constructing one-ended
spanning subforests in their planar dual. Our techniques for constructing
one-ended spanning subforests also give a complete classification of the
locally finite pmp graphs which admit Borel a.e. one-ended spanning subforests.


Abstract:
  Recent years have witnessed the rapid development of service-oriented
computing technologies. The boom of Web services increases the selection burden
of software developers in developing service-based systems (such as mashups).
How to recommend suitable follow-up component services to develop new mashups
has become a fundamental problem in service-oriented software engineering. Most
of the existing service recommendation approaches are designed for mashup
development in the single-round recommendation scenario. It is hard for them to
update recommendation results in time according to developers' requirements and
behaviors (e.g., instant service selection). To address this issue, we propose
a deep-learning-based interactive service recommendation framework named DLISR,
which aims to capture the interactions among the target mashup, selected
services, and the next service to recommend. Moreover, an attention mechanism
is employed in DLISR to weigh selected services when recommending the next
service. We also design two separate models for learning interactions from the
perspectives of content information and historical invocation information,
respectively, as well as a hybrid model called HISR. Experiments on a
real-world dataset indicate that HISR outperforms several state-of-the-art
service recommendation methods in the online interactive scenario for
developing new mashups iteratively.


Abstract:
  A to-date unsolved and highly limiting safety concern for Ultra High-Field
(UHF) magnetic resonance imaging (MRI) is the deposition of radiofrequency (RF)
power in the body, quantified by the specific absorption rate (SAR), leading to
dangerous tissue heating/damage in the form of local SAR hotspots that cannot
currently be measured/monitored, thereby severely limiting the applicability of
the technology for clinical practice and in regulatory approval. The goal of
this study has been to show proof of concept of an artificial intelligence (AI)
based exam-integrated real-time MRI safety prediction software (MRSaiFE)
facilitating the safe generation of 3T and 7T images by means of accurate local
SAR-monitoring at sub-W/kg levels. We trained the software with a small
database of image as a feasibility study and achieved successful proof of
concept for both field strengths. SAR patterns were predicted with a residual
root mean squared error (RSME) of <11% along with a structural similarity
(SSIM) level of >84% for both field strengths (3T and 7T).


Abstract:
  The motion of charged particles can be interfaced with electric circuitry via
the current induced in nearby pick-up electrodes. Here we show how the
rotational and translational dynamics of levitated objects with arbitrary
charge distributions can be coupled to a circuit and how the latter acts back
on the particle motion. The ensuing cooling rates in series and parallel RLC
circuits are determined, demonstrating that quadrupole ion traps are well
suited for implementing all-electric cooling. We derive the effective
macromotion potential for general trap geometries and demonstrate numerically
how consecutive rotational and translational resistive cooling of a microscale
particle can be achieved in linear Paul traps.


Abstract:
  A great issue of discussion of an infectious disease is its basic
reproduction number R0, which provides an estimation of the contagiousness of
the disease. When R0 > 1, the disease spread will potentially lead to an
outbreak, such that of the ongoing COVID-19 pandemics. During the evolution of
an outbreak, various non-pharmaceutical interventions are employed, the impact
of which is frequently assessed by the reduction that they introduce to the
effective reproduction number Rt; reduction below 1 is an indication of
eventual dying out of the disease spread. Motivated by the fact that R0
essentially expresses the stability of the disease-free equilibrium, in this
work, Rt was examined in the view of timescale analysis. It was shown that
during the evolution of the COVID-19 outbreak in Italy, when various
interventions were in place, Rt had a clear relation with the explosive
timescale characterizing the dynamics of the outbreak. In particular, it is
shown that the existence of an explosive timescale during the progression of
the epidemics implies Rt > 1, while its absence implies Rt < 1. In addition, as
this timescale converges/diverges with the immediately slowest one, Rt
approaches to/withdraws from its threshold value 1. These results suggest that
timescale analysis can be utilized for the assessment of the impact of various
interventions, since it reflects the insight provided by the effective
reproduction number, without being hindered by the selection of the population
model, nor the parameter estimation process followed for model calibration.


Abstract:
  We report the results of magnetic, heat-capacity, electrical and
magnetoresistance measurements on Ho4RhAl and Er4RhAl, characterized by 3 sites
for rare-earths (R). Antiferromagnetic ordering sets in at (T_N =) about 8.8
and 4.0 K respectively. While Ho compound appears to enter into a complex
spin-glass phase at T less thanT_N (at nearly 5 K), spin-glass component
appears to set in essentially almost at T_N for the Er case. The loss of the
spin-disorder contribution in the magnetically ordered state is not pronounced,
mimicking that in Gd2PdSi3, a compound which now attracts interest in the area
of topological Hall effect and magnetic skyrmions, indicating complex Fermi
surface. There is a minimum in the temperature dependence of electrical
resistivity in the case of only Ho above T_N, but significant negative
magnetoresistance is observed over a wide temperature range in the paramagnetic
state increasing with decreasing temperature for both the cases. This finding
establishes that these compounds belong to a select group of intermetallics in
which spin-disorder contribution apparently increases gradually as one
approaches respective TN with decreasing temperature. This could be an
experimental signature for the effect due to classical spin-liquid above T_N.
In view of these properties analogous to those of Gd2PdSi3, it is of interest
to investigate these 4:1:1 compounds further to understand possible
unconventional roles of itinerant electrons, not only in the magnetically
ordered state but also in the paramagnetic state, predicted by some theories in
recent years for which this Gd compound is considered to be a classic
example.Besides, magnetoresistance and isothermal entropy change
(magnetocaloric effect) track each other.


Abstract:
  Invariant inclusive single particle/jet cross sections in p-p collisions can
be factorized in terms of two separable pT dependences, a [pT - sqrt(s)] sector
and an [xR - pT - sqrt(s)] sector, where xR = E/Emax is the radial scaling
variable. Here, we extend our earlier work by analyzing more extensive data to
explore various s-dependent attributes and other systematics of inclusive jet,
photon and single particle inclusive reactions. Approximate power laws in
sqrt(s), pT and xR are found. Physical arguments are given which relate
observations to the underlying physics of parton - parton hard scattering and
the parton distribution functions in the proton. We show that the A(sqrt(s),
pT)-function, introduced in our earlier publication to describe the
pT-dependence of the inclusive cross section, is directly related to the
underlying hard parton-parton scattering for jet production with little
influence from soft physics. In addition to the A-function, we introduce
another function, the F(sqrt(s), xR)-function, which obeys radial scaling for
inclusive jets that offers another test of the underlying parton physics. An
application to heavy ion physics is given where we use our variables to
determine the transparency of cold nuclear matter to penetrating heavy mesons
through the lead nucleus.


Abstract:
  We construct discontinuous point of the Lyapunov exponent of quasiperiodic
Schr\"odinger cocycles in the Gevrey space $G^{s}$ with $s>2$. In contrast, the
Lyapunov exponent has been proved to be continuous in the Gevrey space $G^{s}$
with $s<2$ \cite{klein,cgyz}. This shows that $G^2$ is the transition space for
the continuity of the Lyapunov exponent.


Abstract:
  Bi-periodic patterns of waves that propagate in the x direction with
amplitude variation in the y direction are generated in a laboratory. The
amplitude variation in the y direction is studied within the framework of the
vector (vNLSE) and scalar (sNLSE) nonlinear Schrodinger equations using the
uniform-amplitude, Stokes-like solution of the vNLSE and the Jacobi elliptic
sine function solution of the sNLSE. The wavetrains are generated using the
Stokes-like solution of vNLSE; however, a comparison of both predictions shows
that while they both do a reasonably good job of predicting the observed
amplitude variation in y, the comparison with the elliptic function solution of
the sNLSE has significantly less error. Additionally, for agreement with the
vNLSE solution, a third harmonic in y term from a Stokes-type expansion of
interacting, symmetric wavetrains must be included. There is no evidence of
instability growth in the x-direction, consistent with the work of Segur and
colleagues, who showed that dissipation stabilizes the modulational
instability. There is some extra amplitude variation in y, which is examined
via a qualitative stability calculation that allows symmetry breaking in that
direction.


Abstract:
  In this paper, we introduce the notion of the complete joint Jacobi
polynomial of two linear codes of length $n$ over $\mathbb{F}_q$ and
$\mathbb{Z}_k$. We give the MacWilliams type identity for the complete joint
Jacobi polynomials of codes. We also introduce the concepts of the average
Jacobi polynomial and the average complete joint Jacobi polynomial over
$\mathbb{F}_q$ and $\mathbb{Z}_k$. We give a representation of the average of
the complete joint Jacobi polynomials of two linear codes of length $n$ over
$\mathbb{F}_q$ and $\mathbb{Z}_k$ in terms of the compositions of $n$ and its
distribution in the codes. Further we present a generalization of the
representation for the average of the $(g+1)$-fold complete joint Jacobi
polynomials of codes over $\mathbb{F}_{q}$ and $\mathbb{Z}_{k}$. Finally, we
give the notion of the average Jacobi intersection number of two codes.


Abstract:
  In this paper, the underlying energy flow in rocking dynamics of rigid blocks
is utilized to elucidate their response details and overturning conditions.
Based merely on the law of the conservation of energy, the exact criteria for a
rigid block to overturn in the free vibration regime are established for the
first time in the context of both nonlinear and linear theory. That is, if the
initial conditions of the free oscillation regime fall within the prescribed
stable libration region, then it is a priori determined that the block will not
overturn and vice versa. The analytical solution to the nonlinear free rocking
problem is also derived, utilizing integration techniques originating from the
solution of the nonlinear pendulum. Additionally, pulse-type overturning
spectra are constructed easily by numerical integration of the equation of
motion only during the pulse duration. If the block does not topple during the
forced regime, the angle and angular velocity at the end of the excitations are
employed to assess immediately the safety or unsafety of the block using the
exact overturning nonlinear criterion. Finally, new insights into the stability
regions of the overturning spectra are illuminated and explained in detail
using a color mapping.


Abstract:
  The solar corona is a highly-structured plasma which can reach temperatures
of more than ~2 MK. At low frequencies (decimetric and metric wavelengths),
scattering and refraction of electromagnetic waves are thought to considerably
increase the imaged radio source sizes (up to a few arcminutes). However,
exactly how source size relates to scattering due to turbulence is still
subject to investigation. The theoretical predictions relating source
broadening to propagation effects have not been fully confirmed by observations
due to the rarity of high spatial resolution observations of the solar corona
at low frequencies. Here, the LOw Frequency ARray (LOFAR) was used to observe
the solar corona at 120-180 MHz using baselines of up to ~3.5 km (corresponding
to a resolution of ~1-2') during the partial solar eclipse of 2015 March 20. A
lunar de-occultation technique was used to achieve higher spatial resolution
(~0.6') than that attainable via standard interferometric imaging (~2.4'). This
provides a means of studying the contribution of scattering to apparent source
size broadening. It was found that the de-occultation technique reveals a more
structured quiet corona that is not resolved from standard imaging, implying
scattering may be overestimated in this region when using standard imaging
techniques. However, an active region source was measured to be ~4' using both
de-occultation and standard imaging. This may be explained by the increased
scattering of radio waves by turbulent density fluctuations in active regions,
which is more severe than in the quiet Sun.


Abstract:
  Bound states in the continuum (BICs) in a periodic structure sandwiched
between two homogeneous media have interesting properties and useful
applications in photonics. The topological nature of BICs was previously
revealed based on a topological charge related to the far-field polarization
vector of the surrounding resonant states. Recently, it was established that
when a symmetry-protected BIC (with a nonzero topological charge) is destroyed
by a generic symmetry-breaking perturbation, a pair of circularly polarized
resonant states (CPSs) emerge and the net topological charge is conserved. A
periodic structure can also support propagating BICs with a nonzero wavevector.
These BICs are not protected by symmetry in the sense of symmetry mismatch, but
they need symmetry for their robust existence. Based on a highly accurate
computational method for a periodic array of slightly noncircular cylinders, we
show that a propagating BIC is typically destroyed by a structural perturbation
that breaks only the in-plane inversion symmetry, and when this happens, a pair
of CPSs of opposite handedness emerge so that the net topological charge is
conserved. We also study the generation and annihilation of CPSs when a
structural parameter is varied. It is shown that two CPSs with opposite
topological charge and same handedness, connected to two BICs or in a
continuous branch from one BIC, may collapse and become a CPS with a zero
charge. Our study clarifies the important connection between symmetry and
topological charge conservation.


Abstract:
  In the recording studio, producers of Electronic Dance Music (EDM) spend more
time creating, shaping, mixing and mastering sounds, than with compositional
aspects or arrangement. They tune the sound by close listening and by
leveraging audio metering and audio analysis tools, until they successfully
creat the desired sound aesthetics. DJs of EDM tend to play sets of songs that
meet their sound ideal. We therefore suggest using audio metering and
monitoring tools from the recording studio to analyze EDM, instead of relying
on conventional low-level audio features. We test our novel set of features by
a simple classification task. We attribute songs to DJs who would play the
specific song. This new set of features and the focus on DJ sets is targeted at
EDM as it takes the producer and DJ culture into account. With simple
dimensionality reduction and machine learning these features enable us to
attribute a song to a DJ with an accuracy of 63%. The features from the audio
metering and monitoring tools in the recording studio could serve for many
applications in Music Information Retrieval, such as genre, style and era
classification and music recommendation for both DJs and consumers of
electronic dance music.


Abstract:
  The Non-Fungible Token (NFT) market is mushrooming in recent years. The
concept of NFT originally comes from a token standard of Ethereum, aiming to
distinguish each token with distinguishable signs. This type of token can be
bound with virtual/digital properties as their unique identifications. With
NFTs, all marked properties can be freely traded with customized values
according to their ages, rarity, liquidity, etc. It has greatly stimulated the
prosperity of the decentralized application (DApp) market. At the time of
writing (May 2021), the total money used on completed NFT sales has reached
$34,530,649.86$ USD. The thousandfold return on its increasing market draws
huge attention worldwide. However, the development of the NFT ecosystem is
still in its early stage, and the technologies of NFTs are pre-mature.
Newcomers may get lost in their frenetic evolution due to the lack of
systematic summaries. In this technical report, we explore the NFT ecosystems
in several aspects. We start with an overview of state-of-the-art NFT
solutions, then provide their technical components, protocols, standards, and
desired proprieties. Afterward, we give a security evolution, with discussions
on the perspectives of their design models, opportunities, and challenges. To
the best of our knowledge, this is the first systematic study on the current
NFT ecosystems.


Abstract:
  It is generally taken for granted that our Universe is free of antimatter
objects and domains. This certitude has recently been challenged by the
possible detection of anti-helium nuclei by AMS-02. Should the observation be
confirmed, the existence of nearby antistars would make a plausible hypothesis
to explain the origin of the antinuclei. In this paper we use the 10-years
Fermi Large Area Telescope (LAT) gamma-ray source catalog to set constraints on
the abundance of antistars around the Sun. We identify in the catalog 14
antistar candidates not associated with any objects belonging to established
gamma-ray source classes and with a spectrum compatible with baryon-antibaryon
annihilation. We use them along with an estimate of the LAT sensitivity to
antistars to set upper limits on the local antistar fraction $f_{\bar{\ast}}$
with respect to normal stars. We provide parametric limits as a function of the
closest antistar mass, velocity, and surrounding matter density. We also employ
a novel Monte~Carlo method to set limits for a few hypotheses about the
antistar population. For a population with properties equivalent to those of
regular stars concentrated in the Galactic disk we obtain $f_{\bar{\ast}} < 2.5
\times 10^{-6}$ at 95\% confidence level, which is 20 times more constraining
than limits previously available. For a primordial population of antistars
distributed in the Galactic halo we obtain new local upper limits which
decrease as a function of antistar mass $M$ from $f_{\bar{\ast}} < 0.2$ at 95\%
confidence level for $M = 1 \; M_\odot$ to $f_{\bar{\ast}} < 1.6 \times
10^{-4}$ at 95\% confidence level for $M = 10 \; M_\odot$. By combining these
limits with existing microlensing constraints for lighter objects in the
Magellanic clouds, we infer that a primordial population of halo antistars must
have a density lower than $\mathcal{O}(10^{-5}\;\text{pc}^{-3})$ to
$\mathcal{O}(10^{-2}\;\text{pc}^{-3})$ depending on their masses. Our limits
can constrain models for the origin and propagation of antinuclei in cosmic
rays.


Abstract:
  Entangled states like two-mode squeezed vacuum states are known to give
quantum advantage in the illumination protocol, a method to detect a weakly
reflecting target submerged in a thermal background. We use non-Gaussian
photon-added and subtracted states as probes for the single-shot quantum
illumination both in the presence and absence of noise. Based on the difference
between the Chernoff bounds obtained with the coherent state and the
non-Gaussian state having equal signal strengths, whose positive values are
referred to as a quantum advantage in illumination, we classify the performance
of non-Gaussian states, when photons are added (subtracted) in (from) a single
mode or in (from) both the modes. We highlight the hierarchy among Gaussian and
non-Gaussian states obtained via this method, which is compatible with
correlations per unit signal strength. Interestingly, such hierarchy is
different when comparisons are made only using the Chernoff bounds. The entire
analysis is performed in presence of different noisy apparatus like faulty
twin-beam generator, imperfect photon addition or subtraction as well as with
noisy non-Gaussian probe states.


Abstract:
  We show that the equation of motion of scalar-tensor theory acquires
thermodynamic identity when projected on a generic null surface. The relevant
projection is given by $E_{ab}l^ak^b$, where $E_{ab} =8\pi T_{ab}^{(m)}$
represents the equation motion for gravitational field in presence of external
matter, $l^a$ is the generator of the null surface and $k^a$ is the
corresponding auxiliary null vector. Our analysis is done completely in a
covariant way. Therefore all the thermodynamic quantities are in covariant form
and hence can be used for any specific form of metric adapted to a null
surface. We show this both in Einstein and Jordan frames and find that these
two frames provide equivalent thermodynamic quantities. This is consistent with
the previous findings for a Killing horizon. Also, a concrete proof of the
zeroth law in scalar-tensor theory is provided when the null surface is defined
by a Killing vector.


Abstract:
  We consider the Dirac operator on asymptotically static Lorentzian manifolds
with an odd-dimensional compact Cauchy surface. We prove that if
Atiyah-Patodi-Singer boundary conditions are imposed at infinite times then the
Dirac operator is Fredholm. This generalizes a theorem due to B\"ar-Strohmaier
in the case of finite times, and we also show that the corresponding index
formula extends to the infinite setting. Furthermore, we demonstrate the
existence of a Fredholm inverse which is at the same time a Feynman parametrix
in the sense of Duistermaat-H\"ormander. The proof combines methods from
time-dependent scattering theory with a variant of Egorov's theorem for
pseudo-differential hyperbolic systems.


Abstract:
  Multi-degree Tchebycheffian splines are splines with pieces drawn from
extended (complete) Tchebycheff spaces, which may differ from interval to
interval, and possibly of different dimensions. These are a natural extension
of multi-degree polynomial splines. Under quite mild assumptions, they can be
represented in terms of a so-called MDTB-spline basis; such basis possesses all
the characterizing properties of the classical polynomial B-spline basis. We
present a practical framework to compute MDTB-splines, and provide an
object-oriented implementation in Matlab. The implementation supports the
construction, differentiation, and visualization of MDTB-splines whose pieces
belong to Tchebycheff spaces that are null-spaces of constant-coefficient
linear differential operators. The construction relies on an extraction
operator that maps local Tchebycheffian Bernstein functions to the MDTB-spline
basis of interest.


Abstract:
  A covariant formulation for the Newton-Hooke particle is presented by
following an algorithm developed by us \cite{BMM1, BMM2, BMM3}. It naturally
leads to a coupling with the Newton-Cartan geometry. From this result we
provide an understanding of gravitation in a Newtonian geometric background.
Using Dirac's constrained analysis a canonical formulation for the Newton-Hooke
covariant action is done in both gauge independent and gauge fixed approaches.
While the former helps in identifying the various symmetries of the model, the
latter is able to define the physical variables. From this analysis a path to
canonical quantisation is traced and the Schroedinger equation is derived which
is shown to satisfy various consistency checks. Some consequences of this
equation are briefly mentioned.