Abstract:
  In this study, we investigate the spatially nonuniform-temperature effects on
the QCD chiral phase transition in the heavy-ion collisions. Since the QCD
effective theory and the Ising model belong to the same universality class, we
start our discussion by mimicking the QCD effective potential with an
Ising-like effective potential. In contrast to the dynamical slowing down
effects which delays the phase transition from quark-gluon-plasma to hadron
gas, the spatially nonuniform-temperature effects show a possibility to lift
the phase transition temperature. Besides, both the fluctuations and the
correlation length are enhanced in the phase transition region. Furthermore,
the critical phenomena is strongly suppressed like as the critical slowing down
effects. The underlying mechanism is the nonzero-momentum mode fluctuations of
the order parameter induced by the nonuniform temperature. Our study provides a
method to evaluate the nonuniform-temperature effects, and illustrate its
potential influence on analyzing the QCD phase transition signals at RHIC.


Abstract:
  Earth observation offers new insight into anthropogenic changes to nature,
and how these changes are effecting (and are effected by) the built environment
and the real economy. With the global availability of medium-resolution
(10-30m) synthetic aperture radar (SAR) Sentinel-1 and multispectral Sentinel-2
imagery, machine learning can be employed to offer these insights at scale,
unbiased to the reporting of companies and countries. In this paper, I
introduce DeepSentinel, a data pipeline and experimentation framework for
producing general-purpose semantic embeddings of paired Sentinel-1 and
Sentinel-2 imagery. I document the development of an extensible corpus of
labelled and unlabelled imagery for the purposes of sensor fusion research.
With this new dataset I develop a set of experiments applying popular
self-supervision methods and encoder architectures to a land cover
classification problem. Tile2vec spatial encoding with a self-attention enabled
ResNet model outperforms deeper ResNet variants as well as pretraining with
variational autoencoding and contrastive loss. All supporting and derived data
and code are made publicly available.


Abstract:
  Low gain avalanche detectors can measure charged particle fluences with high
speed and spatial precision, and are a promising technology for radiation
monitoring and dosimetry. A detector has been tested in a medical linac where
single particles were observed with a time resolution of 50\,ps. The integrated
response is similar to a standard ionising chamber but with a spatial precision
twenty times finer, and a temporal precision over 100 million times better,
with the capability to measure the charge deposited by a single linac pulse.
The unprecedented resolving power allows the structure of the $\sim 3\,\mu$s
linac pulses to be viewed and the 350\,ps sub-pulses in the train to be
observed.


Abstract:
  For the first time, simultaneous broadband spectral and timing study of the
atoll source 4U 1705-44 was performed using AstroSat Soft X-ray Telescope (SXT)
and Large Area X-ray Proportional Counter (LAXPC) data (0.8-70 keV). Based on
the HID, the source was in the soft banana state during these observations.
Spectral modeling was performed using the full reflection framework and an
inner disk radii of 14 Rg was obtained. A hard powerlaw tail was noticed in the
soft state and hot component fluxes and varying powerlaw indices point towards
a varying corona/sub-keplerian flow. Based on the spectral fits the boundary
layer radius and magnetospheric radius were constrained to be $\sim$ 14-18 km
and $\sim$ 9-19 km respectively. Cross- Correlation Function studies were
performed between the 0.8-3 keV soft SXT lightcurve and 10-20 keV hard LAXPC
lightcurve and correlated and anticorrelated lags were found, which was used to
constrain the coronal height to 0.6-20 km (\b{eta}=0.1). Since the inner disk
radius is not varying during the observations, we conclude that the detected
lags are possibly caused by a varying structure of corona/boundary layer in the
inner region of the accretion disk. Based on the observations, a geometrical
model is proposed for explaining the detected lags in the atoll source 4U
1705-44.


Abstract:
  UI design is an integral part of software development. For many developers
who do not have much UI design experience, exposing them to a large database of
real-application UI designs can help them quickly build up a realistic
understanding of the design space for a software feature and get design
inspirations from existing applications. However, existing keyword-based,
image-similarity-based, and component-matching-based methods cannot reliably
find relevant high-fidelity UI designs in a large database alike to the UI
wireframe that the developers sketch, in face of the great variations in UI
designs. In this article, we propose a deep-learning-based UI design search
engine to fill in the gap. The key innovation of our search engine is to train
a wireframe image autoencoder using a large database of real-application UI
designs, without the need for labeling relevant UI designs. We implement our
approach for Android UI design search, and conduct extensive experiments with
artificially created relevant UI designs and human evaluation of UI design
search results. Our experiments confirm the superior performance of our search
engine over existing image-similarity or component-matching-based methods and
demonstrate the usefulness of our search engine in real-world UI design tasks.


Abstract:
  Medical diagnostic robot systems have been paid more and more attention due
to its objectivity and accuracy. The diagnosis of mild cognitive impairment
(MCI) is considered an effective means to prevent Alzheimer's disease (AD).
Doctors diagnose MCI based on various clinical examinations, which are
expensive and the diagnosis results rely on the knowledge of doctors.
Therefore, it is necessary to develop a robot diagnostic system to eliminate
the influence of human factors and obtain a higher accuracy rate. In this
paper, we propose a novel Group Feature Domain Adversarial Neural Network
(GF-DANN) for amnestic MCI (aMCI) diagnosis, which involves two important
modules. A Group Feature Extraction (GFE) module is proposed to reduce
individual differences by learning group-level features through adversarial
learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed
to reduce the distribution difference between the source and target domain in a
domain adaption way. On three types of data set, GF-DANN achieves the best
accuracy compared with classic machine learning and deep learning methods. On
the DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and the
sensitivity and specificity are 90% and 89%. In addition, by comparing three
EEG data collection paradigms, our results demonstrate that the DMS paradigm
has the potential to build an aMCI diagnose robot system.


Abstract:
  Automatic differentiation (AD) is an important family of algorithms which
enables derivative based optimization. We show that AD can be simply
implemented with effects and handlers by doing so in the Frank language. By
considering how our implementation behaves in Frank's operational semantics, we
show how our code performs the dynamic creation of programs during evaluation.


Abstract:
  The wire stretching measurement was completed on the prototype Double Quarter
Wave (DQW) crab cavity for operation practice and calibration of the
measurement system. Four locations were defined to be on the electrical center
plane of the crab cavity, and survey of the wire indicated all are on the same
plane. The successful measurement validated the wire stretching system built at
Brookhaven National Lab. The offset of the four wire locations to the fitted
plane provided the error of the measurement.


Abstract:
  Topological order in solid state systems is often calculated from the
integration of an appropriate curvature function over the entire Brillouin
zone. At topological phase transitions where the single particle spectral gap
closes, the curvature function diverges and changes sign at certain high
symmetry points in the Brillouin zone. These generic properties suggest the
introduction of a supervised machine learning scheme that uses only the
curvature function at the high symmetry points as input data. We apply this
scheme to a variety of interacting topological insulators in different
dimensions and symmetry classes, and demonstrate that an artificial neural
network trained with the noninteracting data can accurately predict all
topological phases in the interacting cases with very little numerical effort.
Intriguingly, the method uncovers a ubiquitous interaction-induced topological
quantum multicriticality in the examples studied.


Abstract:
  This paper aims at reviewing and analysing the method of reflections. The
latter is an iterative procedure designed to linear boundary value problems set
in multiply connected domains. Being based on a decomposition of the domain
boundary, this method is particularly well-suited to numerical solvers relying
on integral representation formulas. For the parallel and sequential forms of
the method appearing in the literature, we propose a general abstract
formulation in a given Hilbert setting and interpret the procedure in terms of
subspace corrections. We then prove the unconditional convergence of the
sequential form and propose a modification of the parallel one that makes it
unconditionally converging. An alternative proof of convergence is provided in
a case which does not fit into the previous framework. We finally present some
numerical tests.


Abstract:
  All the elements heavier than Fe are produced either by slow (-s) or rapid
(-r) neutron-capture process. Neutron density prevailing in the stellar sites
is one of the major factors that determines the type of neutron-capture
processes. We present the results based on the estimates of corrected value of
absolute carbon abundance, [C/N] ratio, carbon isotopic ratio and [hs/ls] ratio
obtained from the high resolution spectral analysis of six stars that include
both CH stars and CEMP stars. All the stars show enhancement of neutron-capture
elements. Location of these objects in the A(C) vs. [Fe/H] diagram shows that
they are Group I objects, with external origin of carbon and neutron-capture
elements. Low values of carbon isotopic ratios estimated for these objects may
also be attributed to some external sources. As the carbon isotopic ratio is a
good indicator of mixing, we have used the estimates of 12C/13C ratios to
examine the occurance of mixing in the stars. While the object HD 30443 might
have experienced an extra mixing process that usually occurs after red giant
branch (RGB) bump for stars with log(L/L0) > 2.0, the remaining objects do not
show any evidence of having undergone any such mixing process. The higher
values of [C/N] ratios obtained for these objects also indicate that none of
these objects have experienced any strong internal mixing processes. Based on
the estimated abundances of carbon and the neutron-capture elements, and the
abundance ratios, we have classified the objects into different groups. While
the objects HE 0110-0406, HD 30443 and CD-38 2151 are found to be CEMP-s stars,
HE 0308-1612 and HD 176021 show characteristic properties of CH stars with
moderate enhancement of carbon. The object CD-28 1082 with enhancement of both
r- and s-process elements is found to belong to the CEMP-r/s group.


Abstract:
  This paper presents a brief overview of how astronomy can help society in
COVID-19 times, and the lessons that come from studying our place in the
Universe and the global coordination of scientific and outreach activities.
Several examples coordinated by the International Astronomical Union are
presented.


Abstract:
  Point-spread function (PSF) estimation in spatially undersampled images is
challenging because large pixels average fine-scale spatial information. This
is problematic when fine-resolution details are necessary, as in optimal
photometry where knowledge of the illumination pattern beyond the native
spatial resolution of the image may be required. Here, we introduce a method of
PSF reconstruction where point sources are artificially sampled beyond the
native resolution of an image and combined together via stacking to return a
finely sampled estimate of the PSF. This estimate is then deconvolved from the
pixel-gridding function to return a superresolution kernel that can be used for
optimally weighted photometry. We benchmark against the < 1% photometric error
requirement of the upcoming SPHEREx mission to assess performance in a concrete
example. We find that standard methods like Richardson--Lucy deconvolution are
not sufficient to achieve this stringent requirement. We investigate a more
advanced method with significant heritage in image analysis called iterative
back-projection (IBP) and demonstrate it using idealized Gaussian cases and
simulated SPHEREx images. In testing this method on real images recorded by the
LORRI instrument on New Horizons, we are able to identify systematic pointing
drift. Our IBP-derived PSF kernels allow photometric accuracy significantly
better than the requirement in individual SPHEREx exposures. This PSF
reconstruction method is broadly applicable to a variety of problems and
combines computationally simple techniques in a way that is robust to
complicating factors such as severe undersampling, spatially complex PSFs,
noise, crowded fields, or limited source numbers.


Abstract:
  We present an accurate user-encounter trace generator based on analytical
models. Our method generates traces of intercontact times faster than models
that explicitly generate mobility traces. We use this trace generator to study
the characteristics of pair-wise intercontact-time distributions and visualize,
using simulations, how they combine to form the aggregate intercontact-time
distribution. Finally, we apply our trace-generation model to the epidemic
routing protocol.


Abstract:
  The need to accurately estimate the speed of road vehicles is becoming
increasingly important for at least two main reasons. First, the number of
speed cameras installed worldwide has been growing in recent years, as the
introduction and enforcement of appropriate speed limits is considered one of
the most effective means to increase the road safety. Second, traffic
monitoring and forecasting in road networks plays a fundamental role to enhance
traffic, emissions and energy consumption in smart cities, being the speed of
the vehicles one of the most relevant parameters of the traffic state. Among
the technologies available for the accurate detection of vehicle speed, the use
of vision-based systems brings great challenges to be solved, but also great
potential advantages, such as the drastic reduction of costs due to the absence
of expensive range sensors, and the possibility of identifying vehicles
accurately. This paper provides a review of vision-based vehicle speed
estimation. We describe the terminology, the application domains, and propose a
complete taxonomy of a large selection of works that categorizes all stages
involved. An overview of performance evaluation metrics and available datasets
is provided. Finally, we discuss current limitations and future directions.


Abstract:
  We present a new compositional approach to compositional game theory (CGT)
based upon Arrows, a concept originally from functional programming, closely
related to Tambara modules, and operators to build new Arrows from old. We
model equilibria as a bimodule over an Arrow and define an operator to build a
new Arrow from such a bimodule over an existing Arrow. We also model strategies
as graded Arrows and define an operator which builds a new Arrow by taking the
colimit of a graded Arrow. A final operator builds a graded Arrow from a graded
bimodule. We use this compositional approach to CGT to show how known and
previously unknown variants of open games can be proven to form symmetric
monoidal categories.


Abstract:
  We find an asymptotic enumeration formula for the number of simple
$r$-uniform hypergraphs with a given degree sequence, when the number of edges
is sufficiently large. The formula is given in terms of the solution of a
system of equations. We give sufficient conditions on the degree sequence which
guarantee existence of a solution to this system. Furthermore, we solve the
system and give an explicit asymptotic formula when the degree sequence is
close to regular. This allows us to establish several properties of the degree
sequence of a random $r$-uniform hypergraph with a given number of edges. More
specifically, we compare the degree sequence of a random $r$-uniform hypergraph
with a given number edges to certain models involving sequences of binomial or
hypergeometric random variables conditioned on their sum.


Abstract:
  Precise determination and assessment of bladder cancer (BC) extent of muscle
invasion involvement guides proper risk stratification and personalized therapy
selection. In this context, segmentation of both bladder walls and cancer are
of pivotal importance, as it provides invaluable information to stage the
primary tumour. Hence, multi region segmentation on patients presenting with
symptoms of bladder tumours using deep learning heralds a new level of staging
accuracy and prediction of the biologic behaviour of the tumour. Nevertheless,
despite the success of these models in other medical problems, progress in
multi region bladder segmentation is still at a nascent stage, with just a
handful of works tackling a multi region scenario. Furthermore, most existing
approaches systematically follow prior literature in other clinical problems,
without casting a doubt on the validity of these methods on bladder
segmentation, which may present different challenges. Inspired by this, we
provide an in-depth look at bladder cancer segmentation using deep learning
models. The critical determinants for accurate differentiation of muscle
invasive disease, current status of deep learning based bladder segmentation,
lessons and limitations of prior work are highlighted.


Abstract:
  In this paper we address the topic of a possible path to standardize the ICT
services expected to be delivered by the so-called "Port of the Future". How
the most relevant technologies and Information Systems are used by the Port
Communities for their businesses is discussed together with a detailed analysis
of the on-going actions carried on by Standard Setting Organizations.
Considering the examples given by the C-ITS Platform and the C-Roads programme
at EU level, a proposal of contents to be considered in a comprehensive
standardization action is given. The innovation services are therefore grouped
into four bundles: (i) Vessel \& Marine Navigation, (ii) e-Freight \&
(Intermodal) Logistics, (iii) Passenger Transport, (iv) Environmental
sustainability. The standardized version of these applications will be finally
labeled as C-Port services. Alongside the standardization plan, a proposal for
ranking the ports on the basis of a specially-defined C- Port vector is
discussed with the purpose of addressing the well-known lack of consensus
around the mathematical definition of the Smart Port Index. Considering the
good practice and the background offered by the Port of Livorno in terms of
innovation actions, the prospected final user applications are then labeled as
Day 1, Day 1.5, and Day 2 services in consideration of the technical and
commercial gaps to be filled. As a case study about the evolution in the C-Port
vector experienced by the Port of Livorno in the last years will also be
discussed.


Abstract:
  We study the homologous collapse of stellar nuclear core, the virial theorem
for hadron collisional relaxations, and photon productions from hadron
collisions. We thus show the gravo-thermal dynamical process that transforms
gravitational energy to photon energy. The process is energetically and
entropically favourable. The total baryon number conservation, Euler equation
for energy-momentum conservation and Poisson's equation for gravitational
potential are adopted to describe homologous core collapses. The virial theorem
determines the hadron collision energy gain from gravitational potential. The
hadronic photon production rate determines the photon energy density. The time
scales of macroscopic and microscopic processes are studied to verify
approximations. As a result, we show the formation of opaque photon-pair
spheres, whose total energy, size, temperature and number density, accounting
for the main energetic features of Gamma-Ray Burst progenitors. We obtain the
intrinsic correlations of these quantities. They depend only on the averaged
thermal index of the stellar core. We discuss the possibility to confront them
with observational data.


Abstract:
  The etching process is one of the most important processes in semiconductor
manufacturing. We have introduced the state-of-the-art deep learning model to
predict the etching profiles. However, the significant problems violating
physics have been found through various techniques such as explainable
artificial intelligence and representation of prediction uncertainty. To
address this problem, this paper presents a novel approach to apply the
inductive biases for etching process. We demonstrate that our approach fits the
measurement faster than physical simulator while following the physical
behavior. Our approach would bring a new opportunity for better etching process
with higher accuracy and lower cost.


Abstract:
  An isoparametric family in the unit sphere consists of parallel isoparametric
hypersurfaces and their two focal submanifolds. The present paper has two
parts. The first part investigates topology of the isoparametric families,
namely the homotopy, homeomorphism, or diffeomorphism types, parallelizability,
as well as the Lusternik-Schnirelmann category. This part extends substantially
the results of Q.M.Wang in \cite{Wa88}. The second part is concerned with their
curvatures, more precisely, we determine when they have non-negative sectional
curvatures or positive Ricci curvatures with the induced metric.


Abstract:
  We rigorously compute the integrable system for the limiting
$(N\rightarrow\infty)$ distribution function of the extreme momentum of $N$
noninteracting fermions when confined to an anharmonic trap $V(q)=q^{2n}$ for
$n\in\mathbb{Z}_{\geq 1}$ at positive temperature. More precisely, the edge
momentum statistics in the harmonic trap $n=1$ are known to obey the weak
asymmetric KPZ crossover law which is realized via the finite temperature Airy
kernel determinant or equivalently via a Painlev\'e-II integro-differential
transcendent, cf. \cite{LW,ACQ}. For general $n\geq 2$, a novel higher order
finite temperature Airy kernel has recently emerged in physics literature
\cite{DMS} and we show that the corresponding edge law in momentum space is now
governed by a distinguished Painlev\'e-II integro-differential hierarchy. Our
analysis is based on operator-valued Riemann-Hilbert techniques which produce a
Lax pair for an operator-valued Painlev\'e-II ODE system that naturally encodes
the aforementioned hierarchy. As byproduct, we establish a connection of the
integro-differential Painlev\'e-II hierarchy to a novel integro-differential
mKdV hierarchy.


Abstract:
  We propose a novel type of a Bose-Hubbard ladder model based on an open
quantum-gas--cavity-QED setup to study the physics of dynamical gauge
potentials. Atomic tunneling along opposite directions in the two legs of the
ladder is mediated by photon scattering from transverse pump lasers to two
distinct cavity modes. The resulting interplay between cavity photon
dissipation and the optomechanical atomic back-action then induces an
average-density-dependent dynamical gauge field. The dissipation-stabilized
steady-state atomic motion along the legs of the ladder leads either to a pure
chiral current, screening the induced dynamical magnetic field as in the
Meissner effect, or generates simultaneously chiral and particle currents. For
sufficiently strong pump the system enters into a dynamically unstable regime
exhibiting limit-cycle and period-doubled oscillations. Intriguingly, an
electromotive force is induced in this dynamical regime as expected from an
interpretation based on Faraday's law of induction for the time-dependent
synthetic magnetic flux.


Abstract:
  In 2006, Biere, Jussila, and Sinz made the key observation that the
underlying logic behind algorithms for constructing Reduced, Ordered Binary
Decision Diagrams (BDDs) can be encoded as steps in a proof in the extended
resolution logical framework. Through this, a BDD-based Boolean satisfiability
(SAT) solver can generate a checkable proof of unsatisfiability for a set of
clauses. Such a proof indicates that the formula is truly unsatisfiable without
requiring the user to trust the BDD package or the SAT solver built on top of
it.
  We extend their work to enable arbitrary existential quantification of the
formula variables, a critical capability for BDD-based SAT solvers. We
demonstrate the utility of this approach by applying a prototype solver to
several problems that are very challenging for search-based SAT solvers,
obtaining polynomially sized proofs on benchmarks for parity formulas, as well
as the Urquhart, mutilated chessboard, and pigeonhole problems.


Abstract:
  Ever since Machine Learning as a Service (MLaaS) emerges as a viable business
that utilizes deep learning models to generate lucrative revenue, Intellectual
Property Right (IPR) has become a major concern because these deep learning
models can easily be replicated, shared, and re-distributed by any unauthorized
third parties. To the best of our knowledge, one of the prominent deep learning
models - Generative Adversarial Networks (GANs) which has been widely used to
create photorealistic image are totally unprotected despite the existence of
pioneering IPR protection methodology for Convolutional Neural Networks (CNNs).
This paper therefore presents a complete protection framework in both black-box
and white-box settings to enforce IPR protection on GANs. Empirically, we show
that the proposed method does not compromise the original GANs performance
(i.e. image generation, image super-resolution, style transfer), and at the
same time, it is able to withstand both removal and ambiguity attacks against
embedded watermarks.


Abstract:
  To obtain the accurate transient states of the big scale natural gas pipeline
networks under the bad data and non-zero mean noises conditions, a robust
Kalman filter-based dynamic state estimation method is proposed using the
linearized gas pipeline transient flow equations in this paper. Firstly, the
dynamic state estimation model is built. Since the gas pipeline transient flow
equations are less than the states, the boundary conditions are used as
supplementary constraints to predict the transient states. To increase the
measurement redundancy, the zero mass flow rate constraints at the sink nodes
are taken as virtual measurements. Secondly, to ensure the stability under bad
data condition, the robust Kalman filter algorithm is proposed by introducing a
time-varying scalar matrix to regulate the measurement error variances
correctly according to the innovation vector at every time step. At last, the
proposed method is applied to a 30-node gas pipeline networks in several kinds
of measurement conditions. The simulation shows that the proposed robust
dynamic state estimation can decrease the effects of bad data and achieve
better estimating results.


Abstract:
  The Alcubierre warp drive metric is a spacetime construction where a massive
particle located inside a spacetime distortion, called warp bubble, travels at
velocities arbitrarily higher than the velocity of light. This theoretically
constructed spacetime geometry is a consequence of general relativity where
global superluminal velocities, also known as warp speeds, are possible,
whereas local speeds are limited to subluminal ones as required by special
relativity. In this work we analyze the solutions of the Einstein equations
having charged dust energy-momentum tensor as source for warp velocities. The
Einstein equations with the cosmological constant are written and all solutions
having energy-momentum tensor components for electromagnetic fields generated
by charged dust are presented, as well as the respective energy conditions. The
results show an interplay between the energy conditions and the electromagnetic
field such that in some cases the former can be satisfied by both positive and
negative matter density. In other cases the dominant and null energy conditions
are violated. A result connecting the electric energy density with the
cosmological constant is also presented, as well as the effects of the
electromagnetic field on the bubble dynamics.


Abstract:
  Annotated data has become the most important bottleneck in training accurate
machine learning models, especially for areas that require domain expertise. A
recent approach to deal with the above issue proposes using natural language
explanations instead of labeling individual data points, thereby increasing
human annotators' efficiency as well as decreasing costs substantially. This
paper focuses on the task of turning these natural language descriptions into
Python labeling functions by following a novel approach to semantic parsing
with pre-trained text-to-text Transformers. In a series of experiments our
approach achieves a new state of the art on the semantic parsing benchmark
CoNaLa, surpassing the previous best approach by 3.7 BLEU points. Furthermore,
on a manually constructed dataset of natural language descriptions-labeling
functions pairs we achieve a BLEU of 0.39. Our approach can be regarded as a
stepping stone towards models that are taught how to label in natural language,
instead of being provided specific labeled samples. Our code, constructed
dataset and models are available at
https://github.com/ypapanik/t5-for-code-generation.


Abstract:
  For sensitive text data to be shared among NLP researchers and practitioners,
shared documents need to comply with data protection and privacy laws. There is
hence a growing interest in automated approaches for text anonymization.
However, measuring such methods' performance is challenging: missing a single
identifying attribute can reveal an individual's identity. In this paper, we
draw attention to this problem and argue that researchers and practitioners
developing automated text anonymization systems should carefully assess whether
their evaluation methods truly reflect the system's ability to protect
individuals from being re-identified. We then propose TILD, a set of evaluation
criteria that comprises an anonymization method's technical performance, the
information loss resulting from its anonymization, and the human ability to
de-anonymize redacted documents. These criteria may facilitate progress towards
a standardized way for measuring anonymization performance.


Abstract:
  Octupole deformations and related collective excitations are analyzed using
the framework of nuclear density functional theory. Axially-symmetric
quadrupole-octupole constrained self-consistent mean-field (SCMF) calculations
with a choice of universal energy density functional and a pairing interaction
are performed for Xe, Ba, and Ce isotopes from proton-rich to neutron-rich
regions, and neutron-rich Se, Kr, and Sr isotopes, in which enhanced octupole
correlations are expected to occur. Low-energy positive- and negative-parity
spectra and transition strengths are computed by solving the
quadrupole-octupole collective Hamiltonian, with the inertia parameters and
collective potential determined by the constrained SCMF calculations.
Octupole-deformed equilibrium states are found in the potential energy surfaces
of the Ba and Ce isotopes with $N\approx 56$ and 88. The evolution of
spectroscopic properties indicates enhanced octupole correlations in the
regions corresponding to $N\approx Z\approx 56$, $Z\approx 88$ and $Z\approx
56$, and $N\approx 56$ and $Z\approx 34$. The average $\beta_{30}$ deformation
parameter and its fluctuation exhibit signatures of octupole shape phase
transition around $N=56$ and 88.


Abstract:
  We study the images of the Markov measures under transformations generated by
the Mealy automata. We find conditions under which the image measure is
absolutely continuous or singular relative to the Markov measure. Also, we
determine statistical properties of the image of a generic sequence.


Abstract:
  The bidomain model is the standard model for cardiac electrophysiology. In
this paper, we investigate the instability and asymptotic behavior of planar
fronts and planar pulses of the bidomain Allen-Cahn equation and the bidomain
FitzHugh-Nagumo equation in two spatial dimension. In previous work, it was
shown that planar fronts of the bidomain Allen-Cahn equation can become
unstable in contrast to the classical Allen-Cahn equation. We find that, after
the planar front is destabilized, a rotating zigzag front develops whose shape
can be explained by simple geometric arguments using a suitable Frank diagram.
We also show that the Hopf bifurcation through which the front becomes unstable
can be either supercritical or subcritical, by demonstrating a parameter regime
in which a stable planar front and zigzag front can coexist. In our
computational studies of the bidomain FitzHugh-Nagumo pulse solution, we show
that the pulses can also become unstable much like the bidomain Allen-Cahn
fronts. However, unlike the bidomain Allen-Cahn case, the destabilized pulse
does not necessarily develop into a zigzag pulse. For certain choice of
parameters, the destabilized pulse can disintegrate entirely. These studies are
made possible by the development of a numerical scheme that allows for the
accurate computation of the bidomain equation in a two dimensional strip domain
of infinite extent.


Abstract:
  Joint Energy-based Model (JEM) of Grathwohl et al. shows that a standard
softmax classifier can be reinterpreted as an energy-based model (EBM) for the
joint distribution p(x,y); the resulting model can be optimized to improve
calibration, robustness, and out-of-distribution detection, while generating
samples rivaling the quality of recent GAN-based approaches. However, the
softmax classifier that JEM exploits is inherently discriminative and its
latent feature space is not well formulated as probabilistic distributions,
which may hinder its potential for image generation and incur training
instability. We hypothesize that generative classifiers, such as Linear
Discriminant Analysis (LDA), might be more suitable for image generation since
generative classifiers model the data generation process explicitly. This paper
therefore investigates an LDA classifier for image classification and
generation. In particular, the Max-Mahalanobis Classifier (MMC), a special case
of LDA, fits our goal very well. We show that our Generative MMC (GMMC) can be
trained discriminatively, generatively, or jointly for image classification and
generation. Extensive experiments on multiple datasets show that GMMC achieves
state-of-the-art discriminative and generative performances, while
outperforming JEM in calibration, adversarial robustness, and
out-of-distribution detection by a significant margin. Our source code is
available at https://github.com/sndnyang/GMMC.


Abstract:
  In this paper, we propose a novel joint intelligent trajectory design and
resource allocation algorithm based on user's mobility and their requested
services for unmanned aerial vehicles (UAVs) assisted networks, where UAVs act
as nodes of a network function virtualization (NFV) enabled network. Our
objective is to maximize energy efficiency and minimize the average delay on
all services by allocating the limited radio and NFV resources. In addition,
due to the traffic conditions and mobility of users, we let some Virtual
Network Functions (VNFs) to migrate from their current locations to other
locations to satisfy the Quality of Service requirements. We formulate our
problem to find near-optimal locations of UAVs, transmit power, subcarrier
assignment, placement, and scheduling the requested service's functions over
the UAVs and perform suitable VNF migration. Then we propose a novel
Hierarchical Hybrid Continuous and Discrete Action (HHCDA) deep reinforcement
learning method to solve our problem. Finally, the convergence and
computational complexity of the proposed algorithm and its performance analyzed
for different parameters. Simulation results show that our proposed HHCDA
method decreases the request reject rate and average delay by 31.5% and 20% and
increases the energy efficiency by 40% compared to DDPG method.


Abstract:
  Let $Y$ be a smooth hypersurface in a projective irreducible holomorphic
symplectic manifold X of dimension 2n. The characteristic foliation $F$ is the
kernel of the symplectic form restricted to Y. In this article we prove that a
generic leaf of the characteristic foliation is dense in Y if Y has positive
Beauville-Bogomolov-Fujiki square.


Abstract:
  This paper presents our findings for using activity modeling for simulation
(validation), model checking (verification), and execution purposes. Each is
needed to tackle system complexity and further research into behavioral
modeling. We argue different models implicate different understandings and
expectations. We emphasize some distinctions with demonstrations using the
Discrete Event System Specification with an exemplary model. In particular, the
continuous-time base in models helps observe some inherent limitations and
strengths in acquiring each capability. The temporal characterization of input,
output, and state, or the lack thereof, is at the core of developing behavioral
specifications. We use DEVS to arrive at the capability of validating
simulations for activity models. We use Constrained-DEVS for the verification
of activity models. We show how some executions can be derived, whether from
the specification itself or with considerations for simulation and model
checking.


Abstract:
  Urbanization of an area is known to increase the temperature of the
surrounding area. This phenomenon -- a so-called urban heat island (UHI) --
occurs at a local level over a period of time and has lasting impacts for
historical data analysis. We propose a methodology to examine if long-term
changes in temperature increases and decreases across time exist (and to what
extent) at the local level for a given set of temperature readings at various
locations. Specifically, we propose a Bayesian change point model for
spatio-temporally dependent data where we select the number of change points at
each location using a "forwards" selection process using deviance information
criteria (DIC). We then fit the selected model and examine the linear slopes
across time to quantify changes in long-term temperature behavior. We show the
utility of this model and method using a synthetic data set and temperature
measurements from eight stations in Utah consisting of daily temperature data
for 60 years.


Abstract:
  Variational quantum eigensolver (VQE) that solves the ground state of a
Hamiltonian is a promising application of noisy intermediate-scale quantum
devices. The quantum alternating operator Ansatz (QAOA) is one of the most
widely studied Ans\"atze for this purpose and solves many Hamiltonians
reliably. However, because of inherited symmetries from the Hamiltonian, the
QAOA is not necessarily good at solving problems with symmetry broken nature.
In this paper, we propose a variational Ansatz with symmetry breaking layers
for solving those systems. Notably, our Ansatz finds a constant-depth circuit
for solving a symmetry broken ground state which is impossible for the QAOA. We
also propose a simple learning technique that can choose a particular symmetry
broken state among degenerate ground states.


Abstract:
  Here we consider the image of the principal minor map of symmetric matrices
over an arbitrary unique factorization domain $R$. By exploiting a connection
with symmetric determinantal representations, we characterize the image of the
principal minor map through the condition that certain polynomials coming from
so-called Rayleigh differences are squares in the polynomial ring over $R$. In
almost all cases, one can characterize the image of the principal minor map
using the orbit of Cayley's hyperdeterminant under the action of $(SL_2(R))^{n}
\rtimes S_{n}$. Over the complex numbers, this recovers a characterization of
Oeding from 2011, and over the reals, the orbit of a single additional
quadratic inequality suffices to cut out the image. Applications to other
symmetric determinantal representations are also discussed.


Abstract:
  Due to the importance of the lower bounding distances and the attractiveness
of symbolic representations, the family of symbolic aggregate approximations
(SAX) has been used extensively for encoding time series data. However, typical
SAX-based methods rely on two restrictive assumptions; the Gaussian
distribution and equiprobable symbols. This paper proposes two novel
data-driven SAX-based symbolic representations, distinguished by their
discretization steps. The first representation, oriented for general data
compaction and indexing scenarios, is based on the combination of kernel
density estimation and Lloyd-Max quantization to minimize the information loss
and mean squared error in the discretization step. The second method, oriented
for high-level mining tasks, employs the Mean-Shift clustering method and is
shown to enhance anomaly detection in the lower-dimensional space. Besides, we
verify on a theoretical basis a previously observed phenomenon of the intrinsic
process that results in a lower than the expected variance of the intermediate
piecewise aggregate approximation. This phenomenon causes an additional
information loss but can be avoided with a simple modification. The proposed
representations possess all the attractive properties of the conventional SAX
method. Furthermore, experimental evaluation on real-world datasets
demonstrates their superiority compared to the traditional SAX and an
alternative data-driven SAX variant.


Abstract:
  In this paper, we focus on the rendezvous problem for the autonomous
cooperative landing of an unmanned aerial vehicle (UAV) on an unmanned surface
vehicle (USV). These heterogeneous agents with nonlinear dynamics are
dynamically decoupled but share a common cooperative rendezvous task. The
underlying control scheme is based on the Distributed Model Predictive Control
(MPC). One of our main contributions is a rendezvous algorithm with an online
update rule of the rendezvous location. The algorithm requires that agents
update the rendezvous location only when they are not guaranteed to reach it.
Therefore, the exchange of information occurs aperiodically and proposed
algorithm improves the communication efficiency. Furthermore, we prove the
recursive feasibility of the algorithm. The simulation results show the
effectiveness of our algorithm applied to the problem of autonomous cooperative
landing.


Abstract:
  ASCENT is a fully automated methodology for extracting and consolidating
commonsense assertions from web contents (Nguyen et al., WWW 2021). It advances
traditional triple-based commonsense knowledge representation by capturing
semantic facets like locations and purposes, and composite concepts, i.e.,
subgroups and related aspects of subjects. In this demo, we present a web
portal that allows users to understand its construction process, explore its
content, and observe its impact in the use case of question answering. The demo
website and an introductory video are both available online.


Abstract:
  In the context of statistical learning, the Information Bottleneck method
seeks a right balance between accuracy and generalization capability through a
suitable tradeoff between compression complexity, measured by minimum
description length, and distortion evaluated under logarithmic loss measure. In
this paper, we study a variation of the problem, called scalable information
bottleneck, in which the encoder outputs multiple descriptions of the
observation with increasingly richer features. The model, which is of
successive-refinement type with degraded side information streams at the
decoders, is motivated by some application scenarios that require varying
levels of accuracy depending on the allowed (or targeted) level of complexity.
We establish an analytic characterization of the optimal relevance-complexity
region for vector Gaussian sources. Then, we derive a variational inference
type algorithm for general sources with unknown distribution; and show means of
parametrizing it using neural networks. Finally, we provide experimental
results on the MNIST dataset which illustrate that the proposed method
generalizes better to unseen data during the training phase.


Abstract:
  This paper is intended to solve the nonconvex $\ell_{p}$-ball constrained
nonlinear optimization problems. An iteratively reweighted method is proposed,
which solves a sequence of weighted $\ell_{1}$-ball projection subproblems. At
each iteration, the next iterate is obtained by moving along the negative
gradient with a stepsize and then projecting the resulted point onto the
weighted $\ell_{1}$ ball to approximate the $\ell_{p}$ ball. Specifically, if
the current iterate is in the interior of the feasible set, then the weighted
$\ell_{1}$ ball is formed by linearizing the $\ell_{p}$ norm at the current
iterate. If the current iterate is on the boundary of the feasible set, then
the weighted $\ell_{1}$ ball is formed differently by keeping those zero
components in the current iterate still zero. In our analysis, we prove that
the generated iterates converge to a first-order stationary point. Numerical
experiments demonstrate the effectiveness of the proposed method.


Abstract:
  We study black and white hole analogues in Weyl semimetals with inhomogenous
nodal tilts. We study how the presence of a microscopic lattice, giving rise to
low-energy fermion doubler states at large momenta that are not present for
elementary particles, affects the analogy between Weyl Hamiltonians and general
relativity. Using a microscopic tight-binding lattice model, we find the
doubler states to give rise to Hawking fragmentation and Hawking attenuation of
wavepackets by the analogue event horizon. These phenomena depend on an
analogue Hawking temperature, and can be measured in metamaterials and solids,
as we confirm by numerical simulations.


Abstract:
  In learning-phase clinical trials in drug development, adaptive designs can
be efficient and highly informative when used appropriately. In this article,
we extend the multiple comparison procedures with modeling techniques (MCP-Mod)
procedure with generalized multiple contrast tests (GMCTs) to two-stage
adaptive designs for establishing proof-of-concept. The results of an interim
analysis of first-stage data are used to adapt the candidate dose-response
models and the dosages studied in the second stage. GMCTs are used in both
stages to obtain stage-wise p-values, which are then combined to determine an
overall p-value. An alternative approach is also considered that combines the
t-statistics across stages, employing the conditional rejection probability
(CRP) principle to preserve the Type I error probability. Simulation studies
demonstrate that the adaptive designs are advantageous compared to the
corresponding tests in a non-adaptive design if the selection of the candidate
set of dose-response models is not well informed by evidence from preclinical
and early-phase studies.


Abstract:
  Type- and token-based embedding architectures are still competing in lexical
semantic change detection. The recent success of type-based models in
SemEval-2020 Task 1 has raised the question why the success of token-based
models on a variety of other NLP tasks does not translate to our field. We
investigate the influence of a range of variables on clusterings of BERT
vectors and show that its low performance is largely due to orthographic
information on the target word, which is encoded even in the higher layers of
BERT representations. By reducing the influence of orthography we considerably
improve BERT's performance.


Abstract:
  With 1.85 billion tons produced per year, steel is the most important
material class in terms of volume and environmental impact. While steel is a
sustainability enabler, for instance through lightweight design, magnetic
devices, and efficient turbines, its primary production is not. For 3000 years,
iron has been reduced from ores using carbon. Today 2.1 tons CO2 are produced
per ton of steel, causing 30% of the global CO2 emissions in the manufacturing
sector, which translates to 6.5% of the global CO2 emissions. These numbers
qualify iron- and steel-making as the largest single industrial greenhouse gas
emission source. The envisaged future industrial route to mitigate these CO2
emissions targets green hydrogen as a reductant. Although this reaction has
been studied for decades, its kinetics is not well understood, particularly
during the wustite reduction step which is dramatically slower than the
hematite reduction. Many rate-limiting factors of this reaction are set by the
micro- and nanostructure as well as the local chemistry of the ores. Their
quantification allows knowledge-driven ore preparation and process optimization
to make the hydrogen-based reduction of iron ores commercially viable, enabling
the required massive CO2 mitigation to ease global warming. Here, we report on
a multi-scale structure and composition analysis of iron reduced from hematite
with pure H2, reaching down to near-atomic scale.


Abstract:
  In the past, the cumulative metallicity distribution function (CMDF) turned
out as a useful tool to constrain the accretion history of various components
of the Milky Way. In this Letter, by means of analytical, leaky-box chemical
evolution models (i.e. including both infall and galactic outflows) we study
the CMDF of local star-forming galaxies that follow two fundamental empirical
scaling relations, namely the mass-metallicity and main sequence relations. Our
analysis shows that galactic winds, which are dominant mostly in low-mass
systems, play a fundamental role in shaping this function and, in particular,
in determining its steepness and curvature. We show that the CMDF of low-mass
(M$_{\star}$/M$_{\odot} \le 10^{9.5}$) and high-mass
(M$_{\star}$/M$_{\odot}$>10$^{10.5}$) galaxies deviate substantially from the
results of a 'closed-box' model, as the evolution of the former (latter)
systems is mostly dominated by outflows (infall). In the context of galactic
downsizing, we show that downward-concave CMDFs (associated with systems with
extremely small infall timescales and with very strong winds) are more frequent
in low-mass galaxies, which include larger fractions of young systems and
present more substantial deviations from equilibrium between gas accretion and
reprocessing (either via star formation or winds).


Abstract:
  The problem of robustness under location deformations for deep convolutional
neural networks (DCNNs) is of great theoretical and practical interest. This
issue has been studied in pioneering works, especially for scattering-type
architectures, for deformation vector fields $\tau(x)$ with some regularity -
at least $C^1$. Here we address this issue for any field $\tau\in
L^\infty(\mathbb{R}^d;\mathbb{R}^d)$, without any additional regularity
assumption, hence including the case of wild irregular deformations such as a
noise on the pixel location of an image. We prove that for signals in
multiresolution approximation spaces $U_s$ at scale $s$, whenever the network
is Lipschitz continuous (regardless of its architecture), stability in $L^2$
holds in the regime $\|\tau\|_{L^\infty}/s\ll 1$, essentially as a consequence
of the uncertainty principle. When $\|\tau\|_{L^\infty}/s\gg 1$ instability can
occur even for well-structured DCNNs such as the wavelet scattering networks,
and we provide a sharp upper bound for the asymptotic growth rate. The
stability results are then extended to signals in the Besov space
$B^{d/2}_{2,1}$ tailored to the given multiresolution approximation. We also
consider the case of more general time-frequency deformations. Finally, we
provide stochastic versions of the aforementioned results, namely we study the
issue of stability in mean when $\tau(x)$ is modeled as a random field (not
bounded, in general) with with identically distributed variables $|\tau(x)|$,
$x\in\mathbb{R}^d$.


Abstract:
  We study endomorphisms and derivations of infinite dimensional cyclic Leibniz
algebra.


Abstract:
  This paper presents an experimental and numerical investigation of
solid-liquid fluidized beds consisting of bonded spheres in very narrow tubes,
i.e., when the ratio between the tube and grain diameters is small. In narrow
beds, high confinement effects have proved to induce crystallization, jamming
and different patterns, which can be intensified or modified if some grains are
bonded together. In order to investigate that, we produced duos and trios of
bonded aluminum spheres with diameter of 4.8 mm, and formed beds consisting
either of 150-300 duos or 100-200 trios in a 25.4 mm-ID pipe, which were
submitted to water velocities above those necessary for fluidization. For the
experiments, we filmed the bed with high-speed and conventional cameras and
processed the images, obtaining measurements at both the bed and grain scales.
For the numerical part, we computed the bed evolution for the same conditions
with a CFD-DEM (computational fluid dynamics - discrete element method) code.
Our results show distinct motions for individual duos and trios, and different
structures within the bed. We also found that jamming may occur suddenly for
trios, where even the microscopic motion (fluctuation at the grain scale)
stops, calling into question the fluidization conditions for those cases.


Abstract:
  Star formation in galaxies is regulated by the heating and cooling in the
interstellar medium. In particular, the processing of molecular gas into stars
will depend strongly on the ratio of gas heating to gas cooling in the neutral
gas around sites of recent star-formation. In this work, we combine
mid-infrared (mid-IR) observations of Polycyclic Aromatic Hydrocarbons (PAHs),
the dominant heating mechanism of gas in the interstellar medium (ISM), with [C
II], [O I], and [Si II] fine-structure emission, the strongest cooling channels
in dense, neutral gas. The ratio of IR cooling line emission to PAH emission
measures the photoelectric efficiency, a property of the ISM which dictates how
much energy carried by ultraviolet photons gets transferred into the gas. We
find that star-forming, IR luminous galaxies in the Great Observatories All-Sky
LIRG Survey (GOALS) with high IR surface densities have low photoelectric
efficiencies. These systems also have, on average, higher ratios of radiation
field strength to gas densities, and larger average dust grain size
distributions. The data support a scenario in which the most compact galaxies
have more young star-forming regions per unit area, which exhibit less
efficient gas heating. These conditions may be more common at high-z, and may
help explain the higher star-formation rates at cosmic noon. We make
predictions on how this can be investigated with JWST.


Abstract:
  Nowadays, sensors play a major role in several contexts like science,
industry and daily life which benefit of their use. However, the retrieved
information must be reliable. Anomalies in the behavior of sensors can give
rise to critical consequences such as ruining a scientific project or
jeopardizing the quality of the production in industrial production lines. One
of the more subtle kind of anomalies are uncalibrations. An uncalibration is
said to take place when the sensor is not adjusted or standardized by
calibration according to a ground truth value. In this work, an online
machine-learning based uncalibration detector for temperature, humidity and
pressure sensors was developed. This solution integrates an Artificial Neural
Network as main component which learns from the behavior of the sensors under
calibrated conditions. Then, after trained and deployed, it detects
uncalibrations once they take place. The obtained results show that the
proposed solution is able to detect uncalibrations for deviation values of 0.25
degrees, 1% RH and 1.5 Pa, respectively. This solution can be adapted to
different contexts by means of transfer learning, whose application allows for
the addition of new sensors, the deployment into new environments and the
retraining of the model with minimum amounts of data.


Abstract:
  360{\deg} videos a.k.a. spherical videos are getting popular among users
nevertheless, omnidirectional view of these videos demands high bandwidth and
processing power at the end devices. Recently proposed viewport aware streaming
mechanisms can reduce the amount of data transmitted by streaming a limited
portion of the frame covering the current user viewport (VP). However, they
still suffer from sending a high amount of redundant data, as the fixed tile
mechanisms can not provide finer granularity to the user VP. Though making the
tiles smaller can provide a finer granularity for user viewport, high encoding
overhead incurred. To overcome this trade-off, in this paper, we present a
computational geometric approach based adaptive tiling mechanism named VAD360,
which takes visual attention information on the 360{\deg} video frame as the
input and provide a suitable non-overlapping variable size tile cover on the
frame. Experimental results shows that VAD360 can save up to 31.1% of pixel
redundancy before compression and 35.4% of bandwidth saving compared to
recently proposed fixed tile configurations, providing tile schemes within
0.98($\pm$0.11)s time frame.


Abstract:
  While there has been significant progress towards modelling coherence in
written discourse, the work in modelling spoken discourse coherence has been
quite limited. Unlike the coherence in text, coherence in spoken discourse is
also dependent on the prosodic and acoustic patterns in speech. In this paper,
we model coherence in spoken discourse with audio-based coherence models. We
perform experiments with four coherence-related tasks with spoken discourses.
In our experiments, we evaluate machine-generated speech against the speech
delivered by expert human speakers. We also compare the spoken discourses
generated by human language learners of varying language proficiency levels.
Our results show that incorporating the audio modality along with the text
benefits the coherence models in performing downstream coherence related tasks
with spoken discourses.


Abstract:
  Short textual descriptions of entities provide summaries of their key
attributes and have been shown to be useful sources of background knowledge for
tasks such as entity linking and question answering. However, generating entity
descriptions, especially for new and long-tail entities, can be challenging
since relevant information is often scattered across multiple sources with
varied content and style. We introduce DESCGEN: given mentions spread over
multiple documents, the goal is to generate an entity summary description.
DESCGEN consists of 37K entity descriptions from Wikipedia and Fandom, each
paired with nine evidence documents on average. The documents were collected
using a combination of entity linking and hyperlinks to the Wikipedia and
Fandom entity pages, which together provide high-quality distant supervision.
The resulting summaries are more abstractive than those found in existing
datasets and provide a better proxy for the challenge of describing new and
emerging entities. We also propose a two-stage extract-then-generate baseline
and show that there exists a large gap (19.9% in ROUGE-L) between
state-of-the-art models and human performance, suggesting that the data will
support significant future work.


Abstract:
  In this paper, we determine the detectability of eclipsing binary star
companions from eclipse timing variations using the Kepler mission dataset.
Extensive and precise stellar time-series photometry from space-based missions
enable searches for binary star companions. However, due to the large datasets
and computational resources involved, these searches would benefit from
guidance from detection simulations. Our simulations start with and benefit
from the use of empirical Kepler mission data, into which we inject third
bodies to predict the resulting timing of binary star eclipses. We find that
the orbital eccentricity of the third body and the orbital period of the host
binary star are the key factors in detecting companions. Target brightness is
also likely to be a factor in detecting companions. Detectable third body
masses and periods can be efficiently bound using just two equations. Our
results enable the setting of realistic expectations when planning searches for
eclipsing binary star planetary and brown dwarf companions. Our results also
suggest the brown dwarf desert is real rather than observational selection.


Abstract:
  In a bid to resolve lingering problems in cosmology, more focus is being
tilted towards cosmological models in which physical constants of nature are
not necessarily real constants but vary with cosmic time. In this paper, we
study a cosmological model in nonlinear electrodynamics with Newton's
gravitational constant $G$, which is not a constant but varies in terms of a
power law of the scale factor of the universe. Hence, the evolution of the
scale factor $a(t)$ is studied in this model, which depends on a fine-tuning
term of nonlinear electrodynamics, $\alpha$. Finally, we verify the stability
of the model using the speed of sound.


Abstract:
  The jet composition and radiative efficiency of GRBs are poorly constrained
from the data. If the jet composition is matter-dominated (i.e. a fireball),
the GRB prompt emission spectra would include a dominant thermal component
originating from the fireball photosphere, and a non-thermal component
presumably originating from internal shocks whose radii are greater than the
photosphere radius. We propose a method to directly dissect the GRB fireball
energy budget into three components and measure their values by combining the
prompt emission and early afterglow data. The measured parameters include the
initial dimensionless specific enthalpy density ($\eta$), bulk Lorentz factors
at the photosphere radius ($\Gamma_{\rm ph}$) and before fireball deceleration
($\Gamma_0$), the amount of mass loading ($M$), as well as the GRB radiative
efficiency ($\eta_\gamma$). All the parameters can be derived from the data for
a GRB with a dominant thermal spectral component, a deceleration bump feature
in the early afterglow lightcurve, and a measured redshift. The results only
weakly depend on the density $n$ of the interstellar medium when the
composition ${\cal Y}$ parameter (typically unity) is specified.


Abstract:
  The interaction of polycyclic aromatic hydrocarbons (PAHs) with vacuum
ultraviolet (VUV) photons triggers the emission of the well-known aromatic
infrared bands (AIBs) but other mechanisms such as fragmentation can be
involved in this interaction. Fragmentation leads to selection effects that
favor specific sizes and structures. We investigate the impact of aliphatic
bonds on the VUV photo-stability of PAH cations under conditions applicable for
photodissociation regions (PDRs). Cations of pyrene (C16H10) and coronene
(C24H12) derivatives containing aliphatic bonds (methyl or ethyl sidegroups,
superhydrogenation) are submitted to VUV (10.5 eV) photons over long timescales
(~1000 s) in the cryogenic PIRENEA setup. The fragmentation cascades are
analyzed with a simple kinetics model; fragmentation pathways, rates and
branching ratios are derived. Aliphatic PAH derivatives are found to have a
higher fragmentation rate and carbon loss compared to regular PAHs. The
fragmentation of PAHs with alkylated sidegroups forms species with peripheral
pentagonal cycles, which can be more stable than the bare PAH cations. This
stability is quantified and the most stable species, for which there is an
effective competition of the fragmentation with isomerization and radiative
cooling are identified. This work supports a scenario in which the evaporation
of nanograins with a mixed aliphatic and aromatic composition followed by VUV
photoprocessing results in both the production of the carriers of the 3.4um AIB
by methyl sidegroups and in an abundant source of small hydrocarbons at the
border of PDRs. An additional side effect is the efficient formation of stable
PAHs that contain some peripheral pentagonal rings. Our experiments also
support the role of isomerization processes in PAH photofragmentation,
including the H-migration process, which could lead to an additional
contribution to the 3.4um AIB.


Abstract:
  The paper presents two variants of a Krylov-Simplex iterative method that
combines Krylov and simplex iterations to minimize the residual $r = b-Ax$. The
first method minimizes $\|r\|_\infty$, i.e. maximum of the absolute residuals.
The second minimizes $\|r\|_1$, and finds the solution with the least absolute
residuals. Both methods search for an optimal solution $x_k$ in a Krylov
subspace which results in a small linear programming problem. A specialized
simplex algorithm solves this projected problem and finds the optimal linear
combination of Krylov basis vectors to approximate the solution. The resulting
simplex algorithm requires the solution of a series of small dense linear
systems that only differ by rank-one updates. The $QR$ factorization of these
matrices is updated each iteration. We demonstrate the effectiveness of the
methods with numerical experiments.


Abstract:
  A general framework for solving nonlinear least squares problems without the
employment of derivatives is proposed in the present paper together with a new
general global convergence theory. With the aim to cope with the case in which
the number of variables is big (for the standards of derivative-free
optimization), two dimension-reduction procedures are introduced. One of them
is based on iterative subspace minimization and the other one is based on
spline interpolation with variable nodes. Each iteration based on those
procedures is followed by an acceleration step inspired in the Sequential
Secant Method. The practical motivation for this work is the estimation of
parameters in Hydraulic models applied to dam breaking problems. Numerical
examples of the application of the new method to those problems are given.


Abstract:
  We establish the existence of chimera states, simultaneously supporting
synchronous and asynchronous dynamics, in a network consisting of two
symmetrically linked star subnetworks consisting of identical oscillators with
shear and Kuramoto--Sakaguchi coupling. We show that the chimera states may be
metastable or asymptotically stable. If the intra-star coupling strength is of
order $\varepsilon$, the chimera states persist on time scales at least of
order $1/\varepsilon$ in general, and on time-scales at least of order
$1/\varepsilon^2$ if the intra-star coupling is of Kuramoto--Sakaguchi type. If
the intra-star coupling configuration is sparse, the chimeras are
asymptotically stable. The analysis relies on a combination of dimensional
reduction using a M\"obius symmetry group and techniques from averaging theory
and normal hyperbolicity.


Abstract:
  The paper explains the method to determine a short unit cell attached to any
hyperplane given by its integer vector $\mathbf{p}$. Equivalently, it gives all
the solutions of the $N$-dimensional Bezout identity associated with the
coordinates of $\mathbf{p}$.


Abstract:
  We investigate two-sided bounds for operator norms of random matrices with
unhomogenous independent entries. We formulate a lower bound for Rademacher
matrices and conjecture that it may be reversed up to a universal constant. We
show that our conjecture holds up to $\log\log n$ factor for randomized
$n\times n$ circulant matrices and double logarithm may be eliminated under
some mild additional assumptions on the coefficients.


Abstract:
  Generative adversarial networks (GANs) can be trained to generate 3D image
data, which is useful for design optimisation. However, this conventionally
requires 3D training data, which is challenging to obtain. 2D imaging
techniques tend to be faster, higher resolution, better at phase identification
and more widely available. Here, we introduce a generative adversarial network
architecture, SliceGAN, which is able to synthesise high fidelity 3D datasets
using a single representative 2D image. This is especially relevant for the
task of material microstructure generation, as a cross-sectional micrograph can
contain sufficient information to statistically reconstruct 3D samples. Our
architecture implements the concept of uniform information density, which both
ensures that generated volumes are equally high quality at all points in space,
and that arbitrarily large volumes can be generated. SliceGAN has been
successfully trained on a diverse set of materials, demonstrating the
widespread applicability of this tool. The quality of generated micrographs is
shown through a statistical comparison of synthetic and real datasets of a
battery electrode in terms of key microstructural metrics. Finally, we find
that the generation time for a $10^8$ voxel volume is on the order of a few
seconds, yielding a path for future studies into high-throughput
microstructural optimisation.


Abstract:
  Displays are an indispensable medium to visually convey information in our
daily life. Although conventional dye-based color displays have been rigorously
advanced by world leading companies, critical issues still remain. For
instance, color fading and wavelength-limited resolution restrict further
developments. Plasmonic colors emerging from resonant interactions between
light and metallic nanostructures can overcome these restrictions. With dynamic
characteristics enabled by functional materials, dynamic plasmonic coloration
may find a variety of applications in display technologies. In this review, we
elucidate basic concepts for dynamic plasmonic color generation and highlight
recent advances. In particular, we devote our review to a selection of dynamic
controls endowed by functional materials, including magnesium, liquid crystals,
electrochromic polymers, and phase change materials. We also discuss their
performance in view of potential applications in current display technologies.


Abstract:
  Industrial wireless sensor networks are becoming crucial for modern
manufacturing. If the sensors in those networks are mobile, the position
information, besides the sensor data itself, can be of high relevance. E.g.
this position information can increase the trustability of a wireless sensor
measurement by assuring that the sensor is not physically removed, off track,
or otherwise compromised.
  In certain applications, localization information at cell-level, whether the
sensor is inside or outside a room or cell, is sufficient. For this,
localization using Received Signal Strength Indicator (RSSI) measurements is
very popular since RSSI values are available in almost all existing
technologies and no direct interaction with the mobile sensor node and its
communication in the network is needed. For this scenario, we propose methods
to improve the robustness and accuracy of common machine learning classifiers,
by using features based on short-term moments and a second classification stage
using Hidden Markov Models. With the data from an extensive measurement
campaign, we show the applicability of our method and achieve a cell-level
localization accuracy of 93.5\%.


Abstract:
  Multiple object tracking is a challenging problem in computer vision due to
difficulty in dealing with motion prediction, occlusion handling, and object
re-identification. Many recent algorithms use motion and appearance cues to
overcome these challenges. But using appearance cues increases the computation
cost notably and therefore the speed of the algorithm decreases significantly
which makes them inappropriate for online applications. In contrast, there are
algorithms that only use motion cues to increase speed, especially for online
applications. But these algorithms cannot handle occlusions and re-identify
lost objects. In this paper, a novel online multiple object tracking algorithm
is presented that only uses geometric cues of objects to tackle the occlusion
and reidentification challenges simultaneously. As a result, it decreases the
identity switch and fragmentation metrics. Experimental results show that the
proposed algorithm could decrease identity switch by 40% and fragmentation by
28% compared to the state of the art online tracking algorithms. The code is
also publicly available.


Abstract:
  This paper proposes a new method to provide the exponential convergence of
both the parameter and tracking errors of the composite adaptive control system
without the requirement of the regressor persistent excitation (PE). Instead,
the composite adaptation law obtained in this paper requires the regressor to
be finitely exciting (FE) to guarantee the above-mentioned properties. Unlike
known solutions, not only does it relax the PE requirement, but also it
functions effectively under the condition of a jump change of the plant
uncertainty parameters. To derive such an adaptation law, an integral filter of
regressor with damping and resetting is proposed. It provides the required
properties of the control system, and its output signal is bounded even when
its input is subjected to noise and disturbances. A rigorous analytical proof
of all mentioned properties of the developed adaptation law is presented. Such
law is compared with the known composite ones relaxing the PE requirement. The
wing-rock problem is used for the modeling of the developed composite MRAC
system. The obtained results fully support the theoretical analysis and
demonstrate the advantages of the proposed method.


Abstract:
  Distributed training across several quantum computers could significantly
improve the training time and if we could share the learned model, not the
data, it could potentially improve the data privacy as the training would
happen where the data is located. However, to the best of our knowledge, no
work has been done in quantum machine learning (QML) in federation setting yet.
In this work, we present the federated training on hybrid quantum-classical
machine learning models although our framework could be generalized to pure
quantum machine learning model. Specifically, we consider the quantum neural
network (QNN) coupled with classical pre-trained convolutional model. Our
distributed federated learning scheme demonstrated almost the same level of
trained model accuracies and yet significantly faster distributed training. It
demonstrates a promising future research direction for scaling and privacy
aspects.


Abstract:
  We propose a gauged $U(1)_{B-L}$ extended two Higgs doublet model to explain
both neutrino mass and lepton anomalous magnetic moments ($g-2$). Neutrino mass
is generated via an inverse seesaw mechanism by introducing singlet fermions.
Especially, we update the result of muon $g-2$ in light of the very recent
report by E989 experiment at Fermilab, indicating $a_\mu^{\rm
FNAL}=116592040(54)\times 10^{-11}$. Combining BNL result, we have the
following deviation from the standard model prediction $\Delta a_\mu=(2.51\pm
5.9)\times10^{-10}$ at 4.2 $\sigma$. Thanks to an appropriate assignment for
$U(1)_{B-L}\times Z_2$ symmetry and larger $(20\lesssim)\tan\beta$ that is
favoured by type-X model, we realize natural hierarchies among neutral
fermions. The lepton anomalous magnetic moments can be induced at the one loop
level by introducing an iso-spin singlet singly-charged boson. This charged
scalar plays a significant role in evading chiral suppression of these
phenomenologies. We show sizable lepton ($g-2$) can be obtained after
satisfying all the flavour constraints, such as $\mu\to e\gamma$ and flavour
conserving leptonic $Z$ boson decays.


Abstract:
  The widespread diffusion of electric mobility requires a contextual expansion
of the charging infrastructure. An extended collection and processing of
information regarding charging of electric vehicles may turn each electric
vehicle charging station into a valuable source of streaming data. Charging
point operators may profit from all these data for optimizing their operation
and planning activities. In such a scenario, big data and machine learning
techniques would allow valorizing real-time data coming from electric vehicle
charging stations. This paper presents an architecture able to deal with data
streams from a charging infrastructure, with the final aim to forecast electric
charging station availability after a set amount of minutes from present time.
Both batch data regarding past charges and real-time data streams are used to
train a streaming logistic regression model, to take into account recurrent
past situations and unexpected actual events. The streaming model performs
better than a model trained only using historical data. The results highlight
the importance of constantly updating the predictive model parameters in order
to adapt to changing conditions and always provide accurate forecasts.


Abstract:
  We review various characterizations of uniform convexity and smoothness on
norm balls in finite-dimensional spaces and connect results stemming from the
geometry of Banach spaces with \textit{scaling inequalities} used in analysing
the convergence of optimization methods. In particular, we establish local
versions of these conditions to provide sharper insights on a recent body of
complexity results in learning theory, online learning, or offline
optimization, which rely on the strong convexity of the feasible set. While
they have a significant impact on complexity, these strong convexity or uniform
convexity properties of feasible sets are not exploited as thoroughly as their
functional counterparts, and this work is an effort to correct this imbalance.
We conclude with some practical examples in optimization and machine learning
where leveraging these conditions and localized assumptions lead to new
complexity results.


Abstract:
  Open-world novelty occurs when the rules of an environment can change
abruptly, such as when a game player encounters "house rules". To address
open-world novelty, game playing agents must be able to detect when novelty is
injected, and to quickly adapt to the new rules. We propose a model-based
reinforcement learning approach where game state and rules are represented as
knowledge graphs. The knowledge graph representation of the state and rules
allows novelty to be detected as changes in the knowledge graph, assists with
the training of deep reinforcement learners, and enables imagination-based
re-training where the agent uses the knowledge graph to perform look-ahead.


Abstract:
  Gravitational-wave (GW) catalogs are rapidly increasing in number, allowing
for robust statistical analyses of the population of compact binaries.
Nonetheless, GW inference of cosmology has typically relied on additional
electromagnetic counterparts or galaxy catalogs. I present a new probe of
cosmological modifications of general relativity with GW data only. I focus on
deviations of the GW luminosity distance constrained with the astrophysical
population of binary black holes (BBHs). The three key observables are 1) the
number of events as a function of luminosity distance, 2) the stochastic GW
background of unresolved binaries and 3) the location of any feature in the
source mass distribution, such as the pair instability supernova (PISN) gap.
Despite a priori degeneracies between modified gravity and the unknown
evolution of the merger rate and source masses, a large damping of the GW
amplitude could be falsifiable since as redshift grows it reduces the events
and lowers the edges of the PISN gap, which is against standard astrophysical
expectations. Applying a hierarchical Bayesian analysis to the current
LIGO--Virgo catalog (GWTC-2), the strongest constraints to date are placed on
deviations from the GW luminosity distance, finding $c_{_M}=-3.2^{+3.4}_{-2.0}$
at $68\%$ C.L., which is $\sim10$ times better than multi-messenger GW170817
bounds. These modifications also affects the determination of the BBH masses,
which is crucial to accommodate the high-mass binary GW190521 away from the
PISN gap. In this analysis it is found that the maximum mass of $99\%$ of the
population shifts to lower masses with increased uncertainty,
$m_{99\%}=46.2^{+11.4}_{-9.1}M_\odot$ at $68\%$ C.L. Testing gravity at large
scales with the population of BBHs will become increasingly relevant with
future catalogs, providing an independent and self-contained test of the
standard cosmological model.


Abstract:
  The recent availability of low-power neural accelerator hardware, combined
with improvements in end-to-end neural facial recognition algorithms provides,
enabling technology for on-device facial authentication. The present research
work examines the effects of directional lighting on a State-of-Art(SoA) neural
face recognizer. A synthetic re-lighting technique is used to augment data
samples due to the lack of public data-sets with sufficient directional
lighting variations. Top lighting and its variants (top-left, top-right) are
found to have minimal effect on accuracy, while bottom-left or bottom-right
directional lighting has the most pronounced effects. Following the fine-tuning
of network weights, the face recognition model is shown to achieve close to the
original Receiver Operating Characteristic curve (ROC)performance across all
lighting conditions and demonstrates an ability to generalize beyond the
lighting augmentations used in the fine-tuning data-set. This work shows that
an SoA neural face recognition model can be tuned to compensate for directional
lighting effects, removing the need for a pre-processing step before applying
facial recognition.


Abstract:
  In this work, we have adopted gravitational decoupling by Minimal Geometric
Deformation (MGD) approach and have developed an anisotropic version of
well-known Tolman VII isotropic solution in the framework of $f(R, T)$ gravity,
where $R$ is Ricci scalar and $T$ is trace of energy momentum tensor. The set
of field equations has been developed with respect to total energy momentum
tensor, which combines effective energy momentum tensor in $f(R,T)$ gravity and
additional source $\phi_{ij}$. Following MGD approach, the set of field
equations has been separated into two sections. One section represents $f(R,
T)$ field equations, while the other is related to the source $\phi_{ij}$. The
matching conditions for inner and outer geometry have also been discussed and
an anisotropic solution has been developed using mimic constraint for radial
pressure. In order to check viability of the solution, we have considered
observation data of three different compact star models, named PSR J1614-2230,
PSR 1937+21 and SAX J1808.4-3658 and have discussed thermodynamical properties
analytically and graphically.
  The energy conditions are found to be satisfied for the three compact stars.
The stability analysis has been presented through causality condition and
Herrera's cracking concept, which ensures physical acceptability of the
solution.


Abstract:
  Artificial Intelligence (AI) can augment and sometimes even replace human
cognition. Inspired by efforts to value human agency alongside productivity, we
discuss the benefits of solving Citizen Science (CS) tasks with Hybrid
Intelligence (HI), a synergetic mixture of human and artificial intelligence.
Currently there is no clear framework or methodology on how to create such an
effective mixture. Due to the unique participant-centered set of values and the
abundance of tasks drawing upon both human common sense and complex 21st
century skills, we believe that the field of CS offers an invaluable testbed
for the development of HI and human-centered AI of the 21st century, while
benefiting CS as well. In order to investigate this potential, we first relate
CS to adjacent computational disciplines. Then, we demonstrate that CS projects
can be grouped according to their potential for HI-enhancement by examining two
key dimensions: the level of digitization and the amount of knowledge or
experience required for participation. Finally, we propose a framework for
types of human-AI interaction in CS based on established criteria of HI. This
"HI lens" provides the CS community with an overview of several ways to utilize
the combination of AI and human intelligence in their projects. It also allows
the AI community to gain ideas on how developing AI in CS projects can further
their own field.


Abstract:
  The goal of this paper is to present a methodology for the computation of
invariant tori in Hamiltonian systems combining flow map methods,
parameterization methods, and symplectic geometry. While flow map methods
reduce the dimension of the tori to be computed by one (avoiding Poincare
maps), parameterization methods reduce the cost of a single step of the derived
Newton-like method to be proportional to the cost of a FFT. Symplectic
properties lead to some magic cancellations that make the methods work. The
multiple shooting version of the methods are applied to the computation of
invariant tori and their invariant bundles around librational equilibrium
points of the Restricted Three Body Problem. The invariant bundles are the
first order approximations of the corresponding invariant manifolds, commonly
known as the whiskers, which are very important in the dynamical organization
and have important applications in space mission design.


Abstract:
  Trusted Execution Environments (TEEs) are used to protect sensitive data and
run secure execution for security-critical applications, by providing an
environment isolated from the rest of the system. However, over the last few
years, TEEs have been proven weak, as either TEEs built upon security-oriented
hardware extensions (e.g., Arm TrustZone) or resorting to dedicated secure
elements were exploited multiple times. In this project, we introduce Trusted
Execution Environments On-Demand (TEEOD), a novel TEE design that leverages the
programmable logic (PL) in the heterogeneous system on chips (SoC) as the
secure execution environment. Unlike other TEE designs, TEEOD can provide
high-bandwidth connections and physical on-chip isolation. We implemented a
proof-of-concept (PoC) implementation targeting an Ultra96-V2 platform. The
conducted evaluation demonstrated TEEOD can host up to 6 simultaneous enclaves
with a resource usage per enclave of 7.0%, 3.8%, and 15.3% of the total LUTs,
FFs, and BRAMS, respectively. To demonstrate the practicability of TEEOD in
real-world applications, we successfully run a legacy open-source Bitcoin
wallet.


Abstract:
  In this paper, we investigate the worst-case robust beamforming design and
resource block (RB) assignment problem for total transmit power minimization of
the central controller while guaranteeing each robot's transmission with target
number of data bits and within required ultra-low latency and extremely high
reliability. By using the property of the independence of each robot's
beamformer design, we can obtain the equivalent power control design form of
the original beamforming design. The binary RB mapping indicators are
transformed into continuous ones with additional $\ell_0$-norm constraints to
promote sparsity on each RB. A novel non-convex penalty (NCP) approach is
applied to solve such $\ell_0$-norm constraints. Numerical results demonstrate
the superiority of the NCP approach to the well-known reweighted $\ell_1$
method in terms of the optimized power consumption, convergence rate and
robustness to channel realizations. Also, the impacts of latency, reliability,
number of transmit antennas and channel uncertainty on the system performance
are revealed.


Abstract:
  Unsupervised anomaly detection could be crucial in future analyses searching
for rare phenomena in large datasets, as for example collected at the LHC. To
this end, we introduce a physics inspired variational autoencoder (VAE)
architecture which performs competitively and robustly on the LHC Olympics
Machine Learning Challenge datasets. We demonstrate how embedding some physical
observables directly into the VAE latent space, while at the same time keeping
the classifier manifestly agnostic to them, can help to identify and
characterise features in measured spectra as caused by the presence of
anomalies in a dataset.


Abstract:
  We study the fundamental differences that separate: Litecoin; Bitcoin Gold;
Bitcoin Cash; Ethereum; and Zcash from Bitcoin, and draw analysis to how these
features are appreciated by the market, to ultimately make an inference as to
how future successful cryptocurrencies may behave. We use Google Trend data, as
well as price, volume and market capitalization data sourced from
coinmarketcap.com to support this analysis. We find that Litecoin's shorter
block times offer benefits in commerce, but drawbacks in the mining process
through orphaned blocks. Zcash holds a niche use for anonymous transactions,
benefitting areas of the world lacking in economic freedom. Bitcoin Cash
suffers from centralization in the mining process, while the greater
decentralization of Bitcoin Gold has generally left it to stagnate. Ether's
greater functionality offers the greatest threat to Bitcoin's dominance in the
market. A coin that incorporates several of these features can be technically
better than Bitcoin, but the first-to-marketadvantage of Bitcoin should keep
its dominant position in the market.


Abstract:
  Theories with massive gravitons have peculiarity called the van
Dam-Veltman-Zakharov discontinuity in that the massive theory propagator does
not go to the massless graviton propagator in the zero graviton mass limit.
This results in large deviation in Newtons law for massive graviton theories
even when the graviton mass vanishes. We test the vDVZ in massive graviton
theories for single graviton vertex process namely the gravitational radiation
from a classical source. We calculate the gravitational radiation from compact
binaries using the perturbative Feynman diagram method. We perform this
calculation for Einstein's gravity with massless gravitons and verify that the
Feynman diagram calculation reproduces the quadrupole formula. Using the same
procedure we calculate the gravitational radiation for three massive graviton
theories: (1) the Fierz-Pauli theory (2) the modified Fierz-Pauli theory
without the vDVZ discontinuity and (3) the Dvali-Gabadadze-Porrati theory with
a momentum dependent graviton mass. We put limits on the graviton mass in each
of these theories from observations of binary pulsar timings.


Abstract:
  Nowadays, researchers have moved to platforms like Twitter to spread
information about their ideas and empirical evidence. Recent studies have shown
that social media affects the scientific impact of a paper. However, these
studies only utilize the tweet counts to represent Twitter activity. In this
paper, we propose TweetPap, a large-scale dataset that introduces temporal
information of citation/tweets and the metadata of the tweets to quantify and
understand the discourse of scientific papers on social media. The dataset is
publicly available at https://github.com/lingo-iitgn/TweetPap


Abstract:
  We study Landau gauge gluon propagators in two-color QCD at finite quark
chemical potential ($\mu_q$) and temperature ($T$). We include medium
polarization effects at one-loop by quarks into massive gluon propagators, and
compared the analytic results with the available lattice data. We particularly
focus on the high density phase of color-singlet diquark condensates whose
critical temperature is $\sim 100$ MeV with weak dependence on $\mu_q$. At zero
temperature the color singlet condensates protect the IR limit of electric and
magnetic gluon propagators from the medium screening effects. At finite
temperature, this behavior remains true for the magnetic sector, but the
electric screening mass should be generated by thermal, and hence gapless,
particles which are unbound from the diquark condensates. Treating thermal
excitations as quasi-quarks, we found that the electric screening develops too
fast compared to the lattice results. Beyond the critical temperature for
diquark condensates the analytic results are consistent with the lattice
results.


Abstract:
  We have obtained the first constraint of the parameter space of
Scalar-Tensor-Vector-Gravity using the motion of the S2-star around the
supermassive black hole at the centre of the Milky Way, and we did not find any
serious tension with General Relativity. We used the Schwarzschild-like metric
of Scalar-Tensor-Vector-Gravity to predict the orbital motion of S2-star, and
to compare it with the publicly available astrometric data, which include 145
measurements of the positions, 44 measurements of the radial velocities of
S2-star along its orbit, and the recent measurement of the orbital precession.
We employed a Monte Carlo Markov Chain algorithm to explore the parameter
space, and constrained the only one additional parameter of
Scalar-Tensor-Vector-Gravity to $\alpha \lesssim 0.410$ at 99,7$\%$ confidence
level, where $\alpha=0$ reduces this modified theory of gravity to General
Relativity.


Abstract:
  Integrated photonics has enabled signal synthesis, modulation and conversion
using photonic integrated circuits (PIC). Many materials have been developed,
among which silicon nitride (Si$_3$N$_4$) has emerged as a leading platform
particularly for nonlinear photonics. Low-loss Si$_3$N$_4$ PIC has been widely
used for frequency comb generation, narrow-linewidth lasers, microwave
photonics, photonic computing networks, and even surface-electrode ion traps.
Yet, among all demonstrated functionalities for Si$_3$N$_4$ integrated
photonics, optical non-reciprocal devices, such as isolators and circulators,
have not been achieved. Conventionally, they are realized based on Faraday
effect of magneto-optic materials under external magnetic field. However, it
has been challenging to integrate magneto-optic materials that are not
CMOS-compatible and that require bulky external magnet. Here, we demonstrate a
magnetic-free optical isolator based on aluminum nitride (AlN) piezoelectric
modulators monolithically integrated on ultralow-loss Si$_3$N$_4$ PIC. The
transmission reciprocity is broken by spatio-temporal modulation of a
Si$_3$N$_4$ microring resonator with three AlN bulk acoustic wave resonators
that are driven with a rotational phase. This design creates an effective
rotating acoustic wave that allows indirect interband transition in only one
direction among a pair of strongly coupled optical modes. Maximum of 10 dB
isolation is achieved under 100 mW RF power applied to each actuator, with
minimum insertion loss of 0.1 dB. The isolation remains constant over nearly 30
dB dynamic range of optical input power, showing excellent optical linearity.
Our integrated, linear, magnetic-free, electrically driven optical isolator
could become key building blocks for integrated lasers, chip-scale LiDAR
engines, as well as optical interfaces for superconducting circuits.


Abstract:
  Automated construction of surface geometries of cardiac structures from
volumetric medical images is important for a number of clinical applications.
While deep-learning-based approaches have demonstrated promising reconstruction
precision, these approaches have mostly focused on voxel-wise segmentation
followed by surface reconstruction and post-processing techniques. However,
such approaches suffer from a number of limitations including disconnected
regions or incorrect surface topology due to erroneous segmentation and
stair-case artifacts due to limited segmentation resolution. We propose a novel
deep-learning-based approach that directly predicts whole heart surface meshes
from volumetric CT and MR image data. Our approach leverages a graph
convolutional neural network to predict deformation on mesh vertices from a
pre-defined mesh template to reconstruct multiple anatomical structures in a 3D
image volume. Our method demonstrated promising performance of generating whole
heart reconstructions with as good or better accuracy than prior
deep-learning-based methods on both CT and MR data. Furthermore, by deforming a
template mesh, our method can generate whole heart geometries with better
anatomical consistency and produce high-resolution geometries from lower
resolution input image data. Our method was also able to produce temporally
consistent surface mesh predictions for heart motion from CT or MR cine
sequences, and therefore can potentially be applied for efficiently
constructing 4D whole heart dynamics. Our code and pre-trained networks are
available at https://github.com/fkong7/MeshDeformNet


Abstract:
  Extreme multi-label classification (XMC) aims to learn a model that can tag
data points with a subset of relevant labels from an extremely large label set.
Real world e-commerce applications like personalized recommendations and
product advertising can be formulated as XMC problems, where the objective is
to predict for a user a small subset of items from a catalog of several million
products. For such applications, a common approach is to organize these labels
into a tree, enabling training and inference times that are logarithmic in the
number of labels. While training a model once a label tree is available is well
studied, designing the structure of the tree is a difficult task that is not
yet well understood, and can dramatically impact both model latency and
statistical performance. Existing approaches to tree construction fall at an
extreme point, either optimizing exclusively for statistical performance, or
for latency. We propose an efficient information theory inspired algorithm to
construct intermediary operating points that trade off between the benefits of
both. Our algorithm enables interpolation between these objectives, which was
not previously possible. We corroborate our theoretical analysis with numerical
results, showing that on the Wiki-500K benchmark dataset our method can reduce
a proxy for expected latency by up to 28% while maintaining the same accuracy
as Parabel. On several datasets derived from e-commerce customer logs, our
modified label tree is able to improve this expected latency metric by up to
20% while maintaining the same accuracy. Finally, we discuss challenges in
realizing these latency improvements in deployed models.