Abstract:
  This book chapter describes a novel approach to training machine learning
systems by means of a hybrid computer setup i.e. a digital computer tightly
coupled with an analog computer. As an example a reinforcement learning system
is trained to balance an inverted pendulum which is simulated on an analog
computer, thus demonstrating a solution to the major challenge of adequately
simulating the environment for reinforcement learning.


Abstract:
  In the present work, we tackle the regular language indexing problem by first
studying the hierarchy of $p$-sortable languages: regular languages accepted by
automata of width $p$. We show that the hierarchy is strict and does not
collapse, and provide (exponential in $p$) upper and lower bounds relating the
minimum widths of equivalent NFAs and DFAs. Our bounds indicate the importance
of being able to index NFAs, as they enable indexing regular languages with
much faster and smaller indexes. Our second contribution solves precisely this
problem, optimally: we devise a polynomial-time algorithm that indexes any NFA
with the optimal value $p$ for its width, without explicitly computing $p$
(NP-hard to find). In particular, this implies that we can index in polynomial
time the well-studied case $p=1$ (Wheeler NFAs). More in general, in polynomial
time we can build an index breaking the worst-case conditional lower bound of
$\Omega(|P| m)$, whenever the input NFA's width is $p \in o(\sqrt{m})$.


Abstract:
  The modern search for extraterrestrial intelligence (SETI) began with the
seminal publications of Cocconi & Morrison (1959) and Schwartz & Townes (1961),
who proposed to search for narrow-band signals in the radio spectrum, and for
optical laser pulses. Over the last six decades, more than one hundred
dedicated search programs have targeted these wavelengths; all with null
results. All of these campaigns searched for classical communications, that is,
for a significant number of photons above a noise threshold; with the
assumption of a pattern encoded in time and/or frequency space. I argue that
future searches should also target quantum communications. They are preferred
over classical communications with regards to security and information
efficiency, and they would have escaped detection in all previous searches. The
measurement of Fock state photons or squeezed light would indicate the
artificiality of a signal. I show that quantum coherence is feasible over
interstellar distances, and explain for the first time how astronomers can
search for quantum transmissions sent by ETI to Earth, using commercially
available telescopes and receiver equipment.


Abstract:
  Entanglement entropy (EE) in interacting field theories has two important
issues: renormalization of UV divergences and non-Gaussianity of the vacuum. In
this letter, we investigate them in the framework of the two-particle
irreducible formalism. In particular, we consider EE of a half space in an
interacting scalar field theory. It is formulated as $\mathbb{Z}_M$ gauge
theory on Feynman diagrams: $\mathbb{Z}_M$ fluxes are assigned on plaquettes
and summed to obtain EE. Some configurations of fluxes are interpreted as
twists of propagators and vertices. The former gives a Gaussian part of EE
written in terms of a renormalized 2-point function while the latter reflects
non-Gaussianity of the vacuum.


Abstract:
  It is known that the Cabibbo-Kobayashi-Maskawa (CKM) $n\times n$ matrix can
be represented by a real matrix iff there is no CP-violation, and then the
Jarlskog invariants vanish. We investigate sufficient conditions for the
opposite statement to hold, paying particular attention to degenerate cases. We
find that higher Jarlskog invariants are needed for $n\geq 4$. One generic
sufficient condition is provided by the existence of a so-called echelon cross.


Abstract:
  Quantum contextuality takes an important place amongst the concepts of
quantum computing that bring an advantage over its classical counterpart. For a
large class of contextuality proofs, aka. observable-based proofs of the
Kochen-Specker Theorem, we first formulate the contextuality property as the
absence of solutions to a linear system. Then we explain why subgeometries of
binary symplectic polar spaces are candidates for contextuality proofs. We
report first results of a software that generates these subgeometries and
decides their contextuality. The proofs we consider involve more contexts and
observables than the smallest known proofs. This intermediate size property of
those proofs is interesting for experimental tests, but could also be
interesting in quantum game theory.


Abstract:
  A q-Gauss-Newton algorithm is an iterative procedure that solves nonlinear
unconstrained optimization problems based on minimization of the sum squared
errors of the objective function residuals. Main advantage of the algorithm is
that it approximates matrix of q-second order derivatives with the first-order
q-Jacobian matrix. For that reason, the algorithm is much faster than
q-steepest descent algorithms. The convergence of q-GN method is assured only
when the initial guess is close enough to the solution. In this paper the
influence of the parameter q to the non-linear problem solving is presented
through three examples. The results show that the q-GD algorithm finds an
optimal solution and speeds up the iterative procedure.


Abstract:
  We study the runtime verification of hyperproperties, expressed in the
temporal logic HyperLTL, as a means to inspect a system with respect to
security polices. Runtime monitors for hyperproperties analyze trace logs that
are organized by common prefixes in the form of a tree-shaped Kripke structure,
or are organized both by common prefixes and by common suffixes in the form of
an acyclic Kripke structure. Unlike runtime verification techniques for trace
properties, where the monitor tracks the state of the specification but usually
does not need to store traces, a monitor for hyperproperties repeatedly model
checks the growing Kripke structure. This calls for a rigorous complexity
analysis of the model checking problem over tree-shaped and acyclic Kripke
structures. We show that for trees, the complexity in the size of the Kripke
structure is L-complete independently of the number of quantifier alternations
in the HyperLTL formula. For acyclic Kripke structures, the complexity is
PSPACE-complete (in the level of the polynomial hierarchy that corresponds to
the number of quantifier alternations). The combined complexity in the size of
the Kripke structure and the length of the HyperLTL formula is PSPACE-complete
for both trees and acyclic Kripke structures, and is as low as NC for the
relevant case of trees and alternation-free HyperLTL formulas. Thus, the size
and shape of both the Kripke structure and the formula have significant impact
on the complexity of the model checking problem.


Abstract:
  Resource allocation is investigated for offloading computational-intensive
tasks in multi-hop mobile edge computing (MEC) system. The envisioned system
has both the cooperative access points (AP) with the computing capability and
the MEC servers. A user-device (UD) therefore first uploads a computing task to
the nearest AP, and the AP can either locally process the received task or
offload to MEC server. In order to utilize the radio resource blocks (RRBs) in
the APs efficiently, we exploit the non-orthogonal multiple access for
offloading the tasks from the UDs to the AP(s). For the considered NOMA-enabled
multi-hop MEC computing system, our objective is to minimize both the latency
and energy consumption of the system jointly. Towards this goal, a joint
optimization problem is formulated by taking the offloading decision of the
APs, the scheduling among the UDs, RRBs, and APs, and UDs' transmit power
allocation into account. To solve this problem efficiently, (i) a conflict
graph-based approach is devised that solves the scheduling among the UDs, APs,
and RRBs, the transmit power control, and the APs' computation resource
allocation jointly, and (ii) a low-complexity pruning graph-based approach is
also devised. The efficiency of the proposed graph-based approaches over
several benchmark schemes is verified via extensive simulations.


Abstract:
  Decentralized optimization over time-varying graphs has been increasingly
common in modern machine learning with massive data stored on millions of
mobile devices, such as in federated learning. This paper revisits the widely
used accelerated gradient tracking and extends it to time-varying graphs. We
prove the $O((\frac{\gamma}{1-\sigma_{\gamma}})^2\sqrt{\frac{L}{\epsilon}})$
and
$O((\frac{\gamma}{1-\sigma_{\gamma}})^{1.5}\sqrt{\frac{L}{\mu}}\log\frac{1}{\epsilon})$
complexities for the practical single loop accelerated gradient tracking over
time-varying graphs when the problems are nonstrongly convex and strongly
convex, respectively, where $\gamma$ and $\sigma_{\gamma}$ are two common
constants charactering the network connectivity, $\epsilon$ is the desired
precision, and $L$ and $\mu$ are the smoothness and strong convexity constants,
respectively. Our complexities improve significantly over the ones of
$O(\frac{1}{\epsilon^{5/7}})$ and
$O((\frac{L}{\mu})^{5/7}\frac{1}{(1-\sigma)^{1.5}}\log\frac{1}{\epsilon})$,
respectively, which were proved in the original literature only for static
graphs, where $\frac{1}{1-\sigma}$ equals $\frac{\gamma}{1-\sigma_{\gamma}}$
when the network is time-invariant. When combining with a multiple consensus
subroutine, the dependence on the network connectivity constants can be further
improved to $O(1)$ and $O(\frac{\gamma}{1-\sigma_{\gamma}})$ for the
computation and communication complexities, respectively. When the network is
static, by employing the Chebyshev acceleration, our complexities exactly match
the lower bounds without hiding any poly-logarithmic factor for both
nonstrongly convex and strongly convex problems.


Abstract:
  Motivated by questions of Fouvry and Rudnick on the distribution of Gaussian
primes, we develop a very general setting in which one can study inequities in
the distribution of analogues of primes through analytic properties of
infinitely many $L$-functions. In particular, we give a heuristic argument for
the following claim : for more than half of the prime numbers that can be
written as a sum of two square, the odd square is the square of a positive
integer congruent to $1 \bmod 4$.


Abstract:
  Methane ebullition (bubbling) from lake sediments is an important methane
flux into the atmosphere. Previous studies have focused on the open-water
season, showing that temperature variations, pressure fluctuations and
wind-induced currents can affect ebullition. However, ebullition surveys during
the ice-cover are rare despite the prevalence of seasonally ice-covered lakes,
and the factors controlling ebullition are poorly understood. Here, we present
a month-long, high frequency record of acoustic ebullition data from an
ice-covered lake. The record shows that ebullition occurs almost exclusively
when atmospheric pressure drops below a threshold that is approximately equal
to the long-term average pressure. The intensity of ebullition is proportional
to the amount by which the pressure drops below this threshold. In addition,
field measurements of turbidity, in conjunction with laboratory experiments,
provide evidence that ebullition is responsible for previously unexplained
elevated levels of turbidity during ice-cover.


Abstract:
  We present SrvfNet, a generative deep learning framework for the joint
multiple alignment of large collections of functional data comprising
square-root velocity functions (SRVF) to their templates. Our proposed
framework is fully unsupervised and is capable of aligning to a predefined
template as well as jointly predicting an optimal template from data while
simultaneously achieving alignment. Our network is constructed as a generative
encoder-decoder architecture comprising fully-connected layers capable of
producing a distribution space of the warping functions. We demonstrate the
strength of our framework by validating it on synthetic data as well as
diffusion profiles from magnetic resonance imaging (MRI) data.


Abstract:
  We study the time evolution of the excess value of capacity of entanglement
between a locally excited state and ground state in free, massless fermionic
theory and free Yang-Mills theory in four spacetime dimensions. Capacity has
non-trivial time evolution and is sensitive to the partial entanglement
structure, and shows a universal peak at early times. We define a quantity, the
normalized "Page time", which measures the timescale when capacity reaches its
peak. This quantity turns out to be a characteristic property of the inserted
operator. This firmly establishes capacity as a valuable measure of
entanglement structure of an operator, especially at early times similar in
spirit to the Renyi entropies at late times. Interestingly, the time evolution
of capacity closely resembles its evolution in microcanonical and canonical
ensemble of the replica wormhole model in the context of the black hole
information paradox.


Abstract:
  We have performed density-matrix renormalization group studies of a square
lattice $t$-$J$ model with small hole doping, $\delta\ll 1$, on long 4 and 6
leg cylinders. We include frustration in the form of a second-neighbor exchange
coupling, $J_2 = J_1/2$, such that the undoped ($\delta=0$) "parent" state is a
quantum spin liquid. In contrast to the relatively short range superconducting
(SC) correlations that have been observed in recent studies of the 6-leg
cylinder in the absence of frustration, we find power law SC correlations with
a Luttinger exponent, $K_{sc} \approx 1$, consistent with a strongly diverging
SC susceptibility, $\chi \sim T^{-(2-K_{sc})}$ as the temperature $T\to 0$. The
spin-spin correlations - as in the undoped state - fall exponentially
suggesting that the SC "pairing" correlations evolve smoothly from the
insulating parent state.


Abstract:
  We find a phenomenon in a non-gravitational gauge theory analogous to the
replica wormhole in a quantum gravity theory. We consider a reservoir of a
scalar field coupled with a gauge theory contained in a region with a boundary
by an axion-like coupling. When the replica trick is used to compute the
entanglement entropy for a subregion in the reservoir, a tuple of instantons
distributed across the replica sheets gives a non-perturbative contribution. As
an explicit and solvable example, we consider a discrete scalar field coupled
to a 2d pure gauge theory and observe how the replica instantons reproduce the
entropy directly calculated from the reduced density matrix. In addition, we
notice that the entanglement entropy can detect the confinement of a 2d gauge
theory.


Abstract:
  GRB200522A is a short duration gamma-ray burst (GRB) at redshift $z$=0.554
characterized by a bright infrared counterpart. A possible, although not
unambiguous, interpretation of the observed emission is the onset of a luminous
kilonova powered by a rapidly rotating and highly-magnetized neutron star,
known as magnetar. A bright radio flare, arising from the interaction of the
kilonova ejecta with the surrounding medium, is a prediction of this model.
Whereas the available dataset remains open to multiple interpretations (e.g.
afterglow, r-process kilonova, magnetar-powered kilonova), long-term radio
monitoring of this burst may be key to discriminate between models. We present
our late-time upper limit on the radio emission of GRB200522A, carried out with
the Karl G. Jansky Very Large Array at 288 days after the burst. For kilonova
ejecta with energy $E_{\rm ej} \approx 10^{53} \rm erg$, as expected for a
long-lived magnetar remnant, we can already rule out ejecta masses $M_{\rm ej}
\lesssim0.03 \mathrm{M}_\odot$ for the most likely range of circumburst
densities $n\gtrsim 10^{-3}$ cm$^{-3}$. Observations on timescales of
$\approx$3-10 yr after the merger will probe larger ejecta masses up to $M_{\rm
ej} \sim 0.1 \mathrm{M}_\odot$, providing a robust test to the magnetar
scenario.


Abstract:
  Most real-world datasets are inherently heterogeneous graphs, which involve a
diversity of node and relation types. Heterogeneous graph embedding is to learn
the structure and semantic information from the graph, and then embed it into
the low-dimensional node representation. Existing methods usually capture the
composite relation of a heterogeneous graph by defining metapath, which
represent a semantic of the graph. However, these methods either ignore node
attributes, or discard the local and global information of the graph, or only
consider one metapath. To address these limitations, we propose a
Metapaths-guided Neighbors-aggregated Heterogeneous Graph Neural Network(MHN)
to improve performance. Specially, MHN employs node base embedding to
encapsulate node attributes, BFS and DFS neighbors aggregation within a
metapath to capture local and global information, and metapaths aggregation to
combine different semantics of the heterogeneous graph. We conduct extensive
experiments for the proposed MHN on three real-world heterogeneous graph
datasets, including node classification, link prediction and online A/B test on
Alibaba mobile application. Results demonstrate that MHN performs better than
other state-of-the-art baselines.


Abstract:
  Universal quantifiers occur frequently in proof obligations produced by
program verifiers, for instance, to axiomatize uninterpreted functions and to
express properties of arrays. SMT-based verifiers typically reason about them
via E-matching, an SMT algorithm that requires syntactic matching patterns to
guide the quantifier instantiations. Devising good matching patterns is
challenging. In particular, overly restrictive patterns may lead to spurious
verification errors if the quantifiers needed for a proof are not instantiated;
they may also conceal unsoundness caused by inconsistent axiomatizations. In
this paper, we present the first technique that identifies and helps the users
remedy the effects of overly restrictive matching patterns. We designed a novel
algorithm to synthesize missing triggering terms required to complete a proof.
Tool developers can use this information to refine their matching patterns and
prevent similar verification errors, or to fix a detected unsoundness.


Abstract:
  The popularity of 3D displays has risen drastically over the past few decades
but these displays are still merely a novelty compared to their true potential.
The development has mostly focused on Head Mounted Displays (HMD) development
for Virtual Reality and in general ignored non-HMD 3D displays. This is due to
the inherent difficulty in the creation of these displays and their
impracticability in general use due to cost, performance, and lack of
meaningful use cases. In fairness to the hardware manufacturers who have made
striking innovations in this field, there has been a dereliction of duty of
software developers and researchers in terms of developing software to best
utilize these displays.
  This paper will seek to identify what areas of future software development
could mitigate this dereliction. To achieve this goal, the paper will first
examine the current state of the art and perform a comparative analysis on
different types of 3D displays, from this analysis a clear researcher gap
exists in terms of software development for Light field displays which are the
current state of the art of non-HMD-based 3D displays.
  The paper will then outline six distinct areas where the context-awareness
concept will allow for non-HMD-based 3D displays in particular light field
displays that can not only compete but surpass their HMD-based brethren for
many specific use cases.


Abstract:
  The evolution of the biosphere unfolds as a luxuriant generative process of
new living forms and functions. Organisms adapt to their environment, exploit
novel opportunities that are created in this continuous blooming dynamics.
Affordances play a fundamental role in the evolution of the biosphere, for
organisms can exploit them for new morphological and behavioral adaptations
achieved by heritable variations and selection. This way, the opportunities
offered by affordances are then actualized as ever novel adaptations. In this
paper we maintain that affordances elude a formalization that relies on set
theory: we argue that it is not possible to apply set theory to affordances,
therefore we cannot devise a set-based mathematical theory of the diachronic
evolution of the biosphere.


Abstract:
  This paper proposes a deep unfitted Nitsche method for computing elliptic
interface problems with high contrasts in high dimensions. To capture
discontinuities of the solution caused by interfaces, we reformulate the
problem as an energy minimization involving two weakly coupled components. This
enables us to train two deep neural networks to represent two components of the
solution in high-dimensional. The curse of dimensionality is alleviated by
using the Monte-Carlo method to discretize the unfitted Nitsche energy
function. We present several numerical examples to show the performance of the
proposed method.


Abstract:
  In this paper, we propose a novel multi-color balance method for reducing
color distortions caused by lighting effects. The proposed method allows us to
adjust three target-colors chosen by a user in an input image so that each
target color is the same as the corresponding destination (benchmark) one. In
contrast, white balancing is a typical technique for reducing the color
distortions, however, they cannot remove lighting effects on colors other than
white. In an experiment, the proposed method is demonstrated to be able to
remove lighting effects on selected three colors, and is compared with existing
white balance adjustments.


Abstract:
  Because of the ultrafast and photon-driven nature of the transport in their
active region, we demonstrate that quantum cascade lasers can be operated as
resonantly amplified terahertz detectors. Tunable responsivities up to 50 V/W
and noise equivalent powers down to 100 pW/sqrt(Hz) are demonstrated at 4.7
THz. Constant peak responsivities with respect to the detector temperature are
observed up to 80K. Thanks to the sub-ps intersubband lifetime electrical
bandwidths larger than 20 GHz can be obtained, allowing the detection of
optical beatnotes from quantum cascade THz frequency combs.


Abstract:
  An almost self-centered graph is a connected graph of order $n$ with exactly
$n-2$ central vertices, and an almost peripheral graph is a connected graph of
order $n$ with exactly $n-1$ peripheral vertices. We determine (1) the maximum
girth of an almost self-centered graph of order $n;$ (2) the maximum
independence number of an almost self-centered graph of order $n$ and radius
$r;$ (3) the minimum order of a $k$-regular almost self-centered graph and (4)
the maximum size of an almost peripheral graph of order $n;$ (5) which numbers
are possible for the maximum degree of an almost peripheral graph of order $n;$
(6) the maximum number of vertices of maximum degree in an almost peripheral
graph of order $n$ whose maximum degree is the second largest possible.
Whenever the extremal graphs have a neat form, we also describe them.


Abstract:
  We develop a formalism for constructing stochastic upper bounds on the
expected full sample risk for supervised classification tasks via the Hilbert
coresets approach within a transductive framework. We explicitly compute tight
and meaningful bounds for complex datasets and complex hypothesis classes such
as state-of-the-art deep neural network architectures. The bounds we develop
exhibit nice properties: i) the bounds are non-uniform in the hypothesis space,
ii) in many practical examples, the bounds become effectively deterministic by
appropriate choice of prior and training data-dependent posterior distributions
on the hypothesis space, and iii) the bounds become significantly better with
increase in the size of the training set. We also lay out some ideas to explore
for future research.


Abstract:
  Traditional approaches for data anonymization consider relational data and
textual data independently. We propose rx-anon, an anonymization approach for
heterogeneous semi-structured documents composed of relational and textual
attributes. We map sensitive terms extracted from the text to the structured
data. This allows us to use concepts like k-anonymity to generate a joined,
privacy-preserved version of the heterogeneous data input. We introduce the
concept of redundant sensitive information to consistently anonymize the
heterogeneous data. To control the influence of anonymization over unstructured
textual data versus structured data attributes, we introduce a modified,
parameterized Mondrian algorithm. The parameter $\lambda$ allows to give
different weight on the relational and textual attributes during the
anonymization process. We evaluate our approach with two real-world datasets
using a Normalized Certainty Penalty score, adapted to the problem of jointly
anonymizing relational and textual data. The results show that our approach is
capable of reducing information loss by using the tuning parameter to control
the Mondrian partitioning while guaranteeing k-anonymity for relational
attributes as well as for sensitive terms. As rx-anon is a framework approach,
it can be reused and extended by other anonymization algorithms, privacy
models, and textual similarity metrics.


Abstract:
  There are several challenges in creating an electronic archery scoring system
using computer vision techniques. Variability of light, reconstruction of the
target from several images, variability of target configuration, and filtering
noise were significant challenges during the creation of this scoring system.
This paper discusses the approach used to determine where an arrow hits a
target, for any possible single or set of targets and provides an algorithm
that balances the difficulty of robust arrow detection while retaining the
required accuracy.


Abstract:
  Facial expressions are the most common universal forms of body language. In
the past few years, automatic facial expression recognition (FER) has been an
active field of research. However, it is still a challenging task due to
different uncertainties and complications. Nevertheless, efficiency and
performance are yet essential aspects for building robust systems. We proposed
two models, EmoXNet which is an ensemble learning technique for learning
convoluted facial representations, and EmoXNetLite which is a distillation
technique that is useful for transferring the knowledge from our ensemble model
to an efficient deep neural network using label-smoothen soft labels for able
to effectively detect expressions in real-time. Both of the techniques
performed quite well, where the ensemble model (EmoXNet) helped to achieve
85.07% test accuracy on FER2013 with FER+ annotations and 86.25% test accuracy
on RAF-DB. Moreover, the distilled model (EmoXNetLite) showed 82.07% test
accuracy on FER2013 with FER+ annotations and 81.78% test accuracy on RAF-DB.
Results show that our models seem to generalize well on new data and are
learned to focus on relevant facial representations for expressions
recognition.


Abstract:
  We study the propagation of wavepackets along weakly curved interfaces
between topologically distinct media. Our Hamiltonian is an adiabatic
modulation of Dirac operators omnipresent in the topological insulators
literature. Using explicit formulas for straight edges, we construct a family
of solutions that propagates, for long times, unidirectionally and
dispersion-free along the curved edge. We illustrate our results through
various numerical simulations.


Abstract:
  We study the robust double auction mechanisms, that is, the double auction
mechanisms that satisfy dominant strategy incentive compatibility, ex-post
individual rationality, ex-post budget balance and feasibility. We first
establish that the price in any deterministic robust mechanism does not depend
on the valuations of the trading players. We next establish that, with the
non-bossiness assumption, the price in any deterministic robust mechanism does
not depend on players' valuations at all, whether trading or non-trading, i.e.,
the price is posted in advance. Our main result is a characterization result
that, with the non-bossiness assumption along with other assumptions on the
properties of the mechanism, the posted price mechanism with an exogenous
rationing rule is the only deterministic robust double auction mechanism. We
also show that, even without the non-bossiness assumption, it is quite
difficult to find a reasonable robust double auction mechanism other than the
posted price mechanism with rationing.


Abstract:
  The risk for severe illness and mortality from COVID-19 significantly
increases with age. As a result, age-stratified modeling for COVID-19 dynamics
is the key to study how to reduce hospitalizations and mortality from COVID-19.
By taking advantage of network theory, we develop an age-stratified epidemic
model for COVID-19 in complex contact networks. Specifically, we present an
extension of standard SEIR (susceptible-exposed-infectious-removed)
compartmental model, called age-stratified SEAHIR
(susceptible-exposedasymptomatic-hospitalized-infectious-removed) model, to
capture the spread of COVID-19 over multitype random networks with general
degree distributions. We derive several key epidemiological metrics and then
propose an age-stratified vaccination strategy to decrease the mortality and
hospitalizations. Through extensive study, we discover that the outcome of
vaccination prioritization depends on the reproduction number R0. Specifically,
the elderly should be prioritized only when R0 is relatively high. If ongoing
intervention policies, such as universal masking, could suppress R0 at a
relatively low level, prioritizing the high-transmission age group (i.e.,
adults aged 20-39) is most effective to reduce both mortality and
hospitalizations. These conclusions provide useful recommendations for
age-based vaccination prioritization for COVID-19.


Abstract:
  Large samples of experimentally produced graphene are polycrystalline. For
the study of this material, it helps to have realistic computer samples that
are also polycrystalline. A common approach to produce such samples in computer
simulations is based on the method of Wooten, Winer, and Weaire, originally
introduced for the simulation of amorphous silicon. We introduce an early
rejection variation of their method, applied to graphene, which exploits the
local nature of the structural changes to achieve a significant speed-up in the
relaxation of the material, without compromising the dynamics. We test it on a
3,200 atoms sample, obtaining a speedup between one and two orders of
magnitude. We also introduce a further variation called early decision
specifically for relaxing large samples even faster and we test it on two
samples of 10,024 and 20,000 atoms, obtaining a further speed-up of an order of
magnitude. Furthermore, we provide a graphical manipulation tool to remove
unwanted artifacts in a sample, such as bond crossings.


Abstract:
  Popular blockchains such as Ethereum and several others execute complex
transactions in blocks through user-defined scripts known as smart contracts.
Serial execution of smart contract transactions/atomic-units (AUs) fails to
harness the multiprocessing power offered by the prevalence of multi-core
processors. By adding concurrency to the execution of AUs, we can achieve
better efficiency and higher throughput.
  In this paper, we develop a concurrent miner that proposes a block by
executing the AUs concurrently using optimistic Software Transactional Memory
systems (STMs). It captures the independent AUs in a concurrent bin and
dependent AUs in the block graph (BG) efficiently. Later, we propose a
concurrent validator that re-executes the same AUs concurrently and
deterministically using a concurrent bin followed by a BG given by the miner to
verify the proposed block. We rigorously prove the correctness of concurrent
execution of AUs and achieve significant performance gain over the
state-of-the-art.


Abstract:
  In this work we study a system of two galaxies, Astarte and Adonis, at z
$\sim $2 when the Universe was undergoing its peak of star formation activity.
Astarte is a dusty star-forming galaxy at the massive-end of the main sequence
(MS) and Adonis is a less-massive, bright in ultraviolet (UV), companion galaxy
with an optical spectroscopic redshift. We analyse the physical properties of
this system, and probe the gas mass of Astarte with its ALMA CO emission, to
investigate whether this ultra-massive galaxy is quenching or not. We use
CIGALE - a spectral energy distribution modeling code - to derive the key
physical properties of Astarte and Adonis, mainly their star formation rates
(SFRs), stellar masses, and dust luminosities. We inspect the variation of the
physical parameters depending on the assumed dust attenuation law. We also
estimate the molecular gas mass of Astarte from its CO emission, using
different $\alpha_{CO}$ and transition ratios ($r_{31}$) and discuss the
implication of the various assumptions on the gas mass derivation. We find that
Astarte exhibits a MS-like star formation activity, while Adonis is undergoing
a strong starburst (SB) phase. The molecular gas mass of Astarte is far below
the gas fraction of typical star-forming galaxies at z=2. This low gas content
and high SFR, result in a depletion time of $0.22\pm0.07$ Gyrs, slightly
shorter than what is expected for a MS galaxy at this redshift. The CO
luminosity versus the total IR luminosity suggests a MS-like activity assuming
a galactic conversion factor and a low transition ratio. The SFR of Astarte is
of the same order using different attenuation laws, unlike its stellar mass
that increases using shallow attenuation laws. We discuss these properties and
suggest that Astarte might be experiencing a recent decrease of star formation
activity and is quenching through the MS following a SB epoch.


Abstract:
  Quantum coherences, correlations and collective effects can be harnessed to
the advantage of quantum batteries. Here, we introduce a feasible structure
engineering scheme that is applicable to spin-based open quantum batteries. Our
scheme, which builds solely upon a modulation of spin energy gaps, allows
engineered quantum batteries to exploit spin-spin correlations for mitigating
environment-induced aging. As a result of this advantage, an engineered quantum
battery can preserve relatively more energy as compared with its non-engineered
counterpart over the course of the storage phase. Particularly, the excess in
stored energy is independent of system size. This implies a scale-invariant
passive protection strategy, which we demonstrate on an engineered quantum
battery with staggered spin energy gaps. Our findings establish structure
engineering as a useful route for advancing quantum batteries, and bring new
perspectives on efficient quantum battery designs.


Abstract:
  Predicting the binding of viral peptides to the major histocompatibility
complex with machine learning can potentially extend the computational
immunology toolkit for vaccine development, and serve as a key component in the
fight against a pandemic. In this work, we adapt and extend USMPep, a recently
proposed, conceptually simple prediction algorithm based on recurrent neural
networks. Most notably, we combine regressors (binding affinity data) and
classifiers (mass spectrometry data) from qualitatively different data sources
to obtain a more comprehensive prediction tool. We evaluate the performance on
a recently released SARS-CoV-2 dataset with binding stability measurements.
USMPep not only sets new benchmarks on selected single alleles, but
consistently turns out to be among the best-performing methods or, for some
metrics, to be even the overall best-performing method for this task.


Abstract:
  Shift scheduling impacts healthcare workers' well-being because it sets the
frame for their social life and recreational activities. Since it is complex
and time-consuming, it has become a target for automation. However, existing
systems mostly focus on improving efficiency. The workers' needs and their
active participation do not play a pronounced role. Contrasting this trend, we
designed a social practice-based, worker-centered, and well-being-oriented
self-scheduling system which gives healthcare workers more control during shift
planning. In a following nine month appropriation study, we found that workers
who were cautious about their social standing in the group or who had a more
spontaneous personal lifestyle used our system less often than others.
Moreover, we revealed several conflict prevention practices and suggest to
shift the focus away from a competitive shift distribution paradigm towards
supporting these pro-social practices. We conclude with guidelines to support
individual planning practices, self-leadership, and for dealing with conflicts.


Abstract:
  The cost of a partitioned fluid-structure interaction scheme is typically
assessed by the number of coupling iterations required per time step, while
ignoring the Newton loops within the nonlinear sub-solvers. In this work, we
discuss why these single-field iterations deserve more attention when
evaluating the coupling's efficiency and how to find the optimal number of
Newton steps per coupling iteration.


Abstract:
  High-dimensional expanders generalize the notion of expander graphs to
higher-dimensional simplicial complexes. In contrast to expander graphs, only a
handful of high-dimensional expander constructions have been proposed, and no
elementary combinatorial construction with near-optimal expansion is known. In
this paper, we introduce an improved combinatorial high-dimensional expander
construction, by modifying a previous construction of Liu, Mohanty, and Yang
(ITCS 2020), which is based on a high-dimensional variant of a tensor product.
Our construction achieves a spectral gap of $\Omega(\frac{1}{k^2})$ for random
walks on the $k$-dimensional faces, which is only quadratically worse than the
optimal bound of $\Theta(\frac{1}{k})$. Previous combinatorial constructions,
including that of Liu, Mohanty, and Yang, only achieved a spectral gap that is
exponentially small in $k$. We also present reasoning that suggests our
construction is optimal among similar product-based constructions.


Abstract:
  We introduce a novel multi-resolution Localized Orthogonal Decomposition
(LOD) for time-harmonic acoustic scattering problems that can be modeled by the
Helmholtz equation. The method merges the concepts of LOD and operator-adapted
wavelets (gamblets) and proves its applicability for a class of complex-valued,
non-hermitian and indefinite problems. It computes hierarchical bases that
block-diagonalize the Helmholtz operator and thereby decouples the
discretization scales. Sparsity is preserved by a novel localization strategy
that improves stability properties even in the elliptic case. We present a
rigorous stability and a-priori error analysis of the proposed method for
homogeneous media. In addition, we investigate the fast solvability of the
blocks by a standard iterative method. A sequence of numerical experiments
illustrates the sharpness of the theoretical findings and demonstrates the
applicability to scattering problems in heterogeneous media.


Abstract:
  In this paper, we introduce the concept of the (higher order) Appell-Carlitz
numbers which unifies the definitions of several special numbers in positive
characteristic, such as the Bernoulli-Carlitz numbers and the Cauchy-Carlitz
numbers.Their generating function is usually named Hurwitz series in the
function field arithmetic. By using Hasse-Teichm\"uller derivatives, we also
obtain several properties of the (higher order) Appell-Carlitz numbers,
including a recurrence formula, two closed forms expressions, and a determinant
expression.
  The recurrence formula implies Carlitz's recurrence formula for
Bernoulli-Carlitz numbers. Two closed from expressions implies the
corresponding results for Bernoulli-Carlitz and Cauchy-Carlitz numbers . The
determinant expression implies the corresponding results for Bernoulli-Carlitz
and Cauchy-Carlitz numbers, which are analogues of the classical determinant
expressions of Bernoulli and Cauchy numbers stated in an article by Glaisher in
1875.


Abstract:
  We study the phase controlled transmission properties in a compound system
consisting of a 3D copper cavity and an yttrium iron garnet (YIG) sphere. By
tuning the relative phase of the magnon pumping and cavity probe tones,
constructive and destructive interferences occur periodically, which strongly
modify both the cavity field transmission spectra and the group delay of light.
Moreover, the tunable amplitude ratio between pump-probe tones allows us to
further improve the signal absorption or amplification, accompanied by either
significantly enhanced optical advance or delay. Both the phase and
amplitude-ratio can be used to realize in-situ tunable and switchable fast-slow
light. The tunable phase and amplitude-ratio lead to the zero reflection of the
transmitted light and an abrupt fast-slow light transition. Our results confirm
that direct magnon pumping through the coupling loops provides a versatile
route to achieve controllable signal transmission, storage, and communication,
which can be further expanded to the quantum regime, realizing coherent-state
processing or quantum-limited precise measurements.


Abstract:
  After showing the efficiency of feedforward networks to estimate control in
high dimension in the global optimization of some storages problems, we develop
a modification of an algorithm based on some dynamic programming principle. We
show that classical feedforward networks are not effective to estimate Bellman
values for reservoir problems and we propose some neural networks giving far
better results. At last, we develop a new algorithm mixing LP resolution and
conditional cuts calculated by neural networks to solve some stochastic linear
problems.


Abstract:
  In modern networks, the use of drones as mobile base stations (MBSs) has been
discussed for coverage flexibility. However, the realization of drone-based
networks raises several issues. One of the critical issues is drones are
extremely power-hungry. To overcome this, we need to characterize a new type of
drones, so-called charging drones, which can deliver energy to MBS drones.
Motivated by the fact that the charging drones also need to be charged, we
deploy ground-mounted charging towers for delivering energy to the charging
drones. We introduce a new energy-efficiency maximization problem, which is
partitioned into two independently separable tasks. More specifically, as our
first optimization task, two-stage charging matching is proposed due to the
inherent nature of our network model, where the first matching aims to schedule
between charging towers and charging drones while the second matching solves
the scheduling between charging drones and MBS drones. We analyze how to
convert the formulation containing non-convex terms to another one only with
convex terms. As our second optimization task, each MBS drone conducts
energy-aware time-average transmit power allocation minimization subject to
stability via Lyapunov optimization. Our solutions enable the MBS drones to
extend their lifetimes; in turn, network coverage-time can be extended.


Abstract:
  Scientific research changed profoundly over the last 30 years, in all its
aspects. Scientific publishing has changed as well, mainly because of the
strong increased number of submitted papers and because of the appearance of
Open Access journals and publishers. We propose some reflections on these
issues.


Abstract:
  In marginally jammed solids confined by walls, we calculate the particle and
ensemble averaged value of an order parameter, $\left<\Psi(r)\right>$, as a
function of the distance to the wall, $r$. Being a microscopic indicator of
structural disorder and particle mobility in solids, $\Psi$ is by definition
the response of the mean square particle displacement to the increase of
temperature in the harmonic approximation and can be directly calculated from
the normal modes of vibration of the zero-temperature solids. We find that, in
confined jammed solids, $\left<\Psi(r)\right>$ curves at different pressures
can collapse onto the same master curve following a scaling function,
indicating the criticality of the jamming transition. The scaling collapse
suggests a diverging length scale and marginal instability at the jamming
transition, which should be accessible to sophisticatedly designed experiments.
Moreover, $\left<\Psi(r)\right>$ is found to be significantly suppressed when
approaching the wall and anisotropic in directions perpendicular and parallel
to the wall. This finding can be applied to understand the $r$-dependence and
anisotropy of the structural relaxation in confined supercooled liquids,
providing another example of understanding or predicting behaviors of
supercooled liquids from the perspective of the zero-temperature amorphous
solids.


Abstract:
  In this paper we present a two-step neural network model to separate
detections of solar system objects from optical and electronic artifacts in
data obtained with the "Asteroid Terrestrial-impact Last Alert System" (ATLAS),
a near-Earth asteroid sky survey system [arXiv:1802.00879]. A convolutional
neural network [arXiv:1807.10912] is used to classify small "postage-stamp"
images of candidate detections of astronomical sources into eight classes,
followed by a multi-layered perceptron that provides a probability that a
temporal sequence of four candidate detections represents a real astronomical
source. The goal of this work is to reduce the time delay between Near-Earth
Object (NEO) detections and submission to the Minor Planet Center. Due to the
rare and hazardous nature of NEOs [Harris and D'Abramo, 2015], a low false
negative rate is a priority for the model. We show that the model reaches
99.6\% accuracy on real asteroids in ATLAS data with a 0.4\% false negative
rate. Deployment of this model on ATLAS has reduced the amount of NEO
candidates that astronomers must screen by 90%, thereby bringing ATLAS one step
closer to full autonomy.


Abstract:
  We study wireless networks where signal propagation delays are multiples of a
time interval. Such a network can be modelled as a weighted hypergraph. The
link scheduling problem of such a wireless network is closely related to the
independent sets of the periodic hypergraph induced by the weighted hypergraph.
As the periodic graph has infinitely many vertices, existing characterizations
of graph independent sets cannot be applied to study link scheduling
efficiently. To characterize the rate region of link scheduling, a directed
graph of finite size called scheduling graph is derived to capture a certain
conditional independence property of link scheduling over time. A
collision-free schedule is equivalent to a path in the scheduling graph, and
hence the rate region is equivalent to the convex hull of the rate vectors
associated with the cycles of the scheduling graph. With the maximum
independent set problem as a special case, calculating the whole rate region is
NP hard and also hard to approximate. We derive two algorithms that benefit
from a partial order on the paths in the scheduling graph, and can potentially
find schedules that are not dominated by the existing cycle enumerating
algorithms running in a given time. The first algorithm calculates the rate
region incrementally in the cycle lengths so that a subset of the rate region
corresponding to short cycles can be obtained efficiently. The second algorithm
enumerates cycles associated with a maximal subgraph of the scheduling graph.
In addition to scheduling a wireless network, the independent sets of periodic
hypergraphs also find applications in some operational research problems.


Abstract:
  Cross-document event coreference resolution is a foundational task for NLP
applications involving multi-text processing. However, existing corpora for
this task are scarce and relatively small, while annotating only modest-size
clusters of documents belonging to the same topic. To complement these
resources and enhance future research, we present Wikipedia Event Coreference
(WEC), an efficient methodology for gathering a large-scale dataset for
cross-document event coreference from Wikipedia, where coreference links are
not restricted within predefined topics. We apply this methodology to the
English Wikipedia and extract our large-scale WEC-Eng dataset. Notably, our
dataset creation method is generic and can be applied with relatively little
effort to other Wikipedia languages. To set baseline results, we develop an
algorithm that adapts components of state-of-the-art models for within-document
coreference resolution to the cross-document setting. Our model is suitably
efficient and outperforms previously published state-of-the-art results for the
task.


Abstract:
  Few-layered transition metal dichalcogenides (TMDs) are increasingly popular
materials for optoelectronics and catalysis. Amongst the various types of TMDs
available today, rhenium-chalcogenides (ReX2) stand out due to their remarkable
electronic structure, such as the occurrence of anisotropic excitons and
potential direct bandgap behavior throughout multi-layered stacks. In this
letter, we have analyzed the nature and dynamics of charge carriers in highly
crystalline liquid-phase exfoliated ReS2, using a unique combination of optical
pump-THz probe and broadband transient absorption spectroscopy. Two distinct
time regimes are identified, both of which are dominated by unbound charge
carriers despite the high exciton binding energy. In the first time regime, the
unbound charge carriers cause an increase and a broadening of the exciton
absorption band. In the second time regime, a peculiar narrowing of the
excitonic absorption profile is observed, which we assign to the presence of
built-in fields and/or charged defects. Our results pave the way to analyze
spectrally complex transient absorption measurements on layered TMD materials
and indicate the potential for ReS2 to produce mobile free charge carriers, a
feat relevant for photovoltaic applications.


Abstract:
  We propose a new framework, inspired by random matrix theory, for analyzing
the dynamics of stochastic gradient descent (SGD) when both number of samples
and dimensions are large. This framework applies to any fixed stepsize and the
finite sum setting. Using this new framework, we show that the dynamics of SGD
on a least squares problem with random data become deterministic in the large
sample and dimensional limit. Furthermore, the limiting dynamics are governed
by a Volterra integral equation. This model predicts that SGD undergoes a phase
transition at an explicitly given critical stepsize that ultimately affects its
convergence rate, which we also verify experimentally. Finally, when input data
is isotropic, we provide explicit expressions for the dynamics and average-case
convergence rates (i.e., the complexity of an algorithm averaged over all
possible inputs). These rates show significant improvement over the worst-case
complexities.


Abstract:
  This paper describes our method for tuning a transformer-based pretrained
model, to adaptation with Reliable Intelligence Identification on Vietnamese
SNSs problem. We also proposed a model that combines bert-base pretrained
models with some metadata features, such as the number of comments, number of
likes, images of SNS documents,... to improved results for VLSP shared task:
Reliable Intelligence Identification on Vietnamese SNSs. With appropriate
training techniques, our model is able to achieve 0.9392 ROC-AUC on public test
set and the final version settles at top 2 ROC-AUC (0.9513) on private test
set.


Abstract:
  This paper presents the first model-free, simulator-free reinforcement
learning algorithm for Constrained Markov Decision Processes (CMDPs) with
sublinear regret and zero constraint violation. The algorithm is named Triple-Q
because it includes three key components: a Q-function (also called
action-value function) for the cumulative reward, a Q-function for the
cumulative utility for the constraint, and a virtual-Queue that
(over)-estimates the cumulative constraint violation. Under Triple-Q, at each
step, an action is chosen based on the pseudo-Q-value that is a combination of
the three "Q" values. The algorithm updates the reward and utility Q-values
with learning rates that depend on the visit counts to the corresponding
(state, action) pairs and are periodically reset. In the episodic CMDP setting,
Triple-Q achieves $\tilde{\cal O}\left(\frac{1 }{\delta}H^4
S^{\frac{1}{2}}A^{\frac{1}{2}}K^{\frac{4}{5}} \right)$ regret, where $K$ is the
total number of episodes, $H$ is the number of steps in each episode, $S$ is
the number of states, $A$ is the number of actions, and $\delta$ is Slater's
constant. Furthermore, Triple-Q guarantees zero constraint violation, both on
expectation and with a high probability, when $K$ is sufficiently large.
Finally, the computational complexity of Triple-Q is similar to SARSA for
unconstrained MDPs and is computationally efficient.


Abstract:
  During the image acquisition process, noise is usually added to the data
mainly due to physical limitations of the acquisition sensor, and also
regarding imprecisions during the data transmission and manipulation. In that
sense, the resultant image needs to be processed to attenuate its noise without
losing details. Non-learning-based strategies such as filter-based and noise
prior modeling have been adopted to solve the image denoising problem.
Nowadays, learning-based denoising techniques showed to be much more effective
and flexible approaches, such as Residual Convolutional Neural Networks. Here,
we propose a new learning-based non-blind denoising technique named Attention
Residual Convolutional Neural Network (ARCNN), and its extension to blind
denoising named Flexible Attention Residual Convolutional Neural Network
(FARCNN). The proposed methods try to learn the underlying noise expectation
using an Attention-Residual mechanism. Experiments on public datasets corrupted
by different levels of Gaussian and Poisson noise support the effectiveness of
the proposed approaches against some state-of-the-art image denoising methods.
ARCNN achieved an overall average PSNR results of around 0.44dB and 0.96dB for
Gaussian and Poisson denoising, respectively FARCNN presented very consistent
results, even with slightly worsen performance compared to ARCNN.


Abstract:
  The COVID-19 pandemic has proved to be one of the most disruptive public
health emergencies in recent memory. Among non-pharmaceutical interventions,
social distancing and lockdown measures are some of the most common tools
employed by governments around the world to combat the disease. While
mathematical models of COVID-19 are ubiquitous, few have leveraged network
theory in a general way to explain the mechanics of social distancing. In this
paper, we build on existing network models for heterogeneous, clustered
networks with random link activation/deletion dynamics to put forth realistic
mechanisms of social distancing using piecewise constant activation/deletion
rates. We find our models are capable of rich qualitative behavior, and offer
meaningful insight with relatively few intervention parameters. In particular,
we find that the severity of social distancing interventions and when they
begin have more impact than how long it takes for the interventions to take
full effect.


Abstract:
  Kubelka-Munk (K-M) theory has been successfully used to estimate pigment
concentrations in the pigment mixtures of modern paintings in spectral imagery.
In this study the single-constant K-M theory has been utilized for the
classification of green pigments in the Selden Map of China, a navigational map
of the South China Sea likely created in the early seventeenth century.
Hyperspectral data of the map was collected at the Bodleian Library, University
of Oxford, and can be used to estimate the pigment diversity, and spatial
distribution, within the map. This work seeks to assess the utility of
analyzing the data in the K/S space from Kubelka-Munk theory, as opposed to the
traditional reflectance domain. We estimate the dimensionality of the data and
extract endmembers in the reflectance domain. Then we perform linear unmixing
to estimate abundances in the K/S space, and following Bai, et al. (2017), we
perform a classification in the abundance space. Finally, due to the lack of
ground truth labels, the classification accuracy was estimated by computing the
mean spectrum of each class as the representative signature of that class, and
calculating the root mean squared error with all the pixels in that class to
create a spatial representation of the error. This highlights both the
magnitude of, and any spatial pattern in, the errors, indicating if a
particular pigment is not well modeled in this approach.


Abstract:
  Objective: The automatic discrimination between the coughing sounds produced
by patients with tuberculosis (TB) and those produced by patients with other
lung ailments.
  Approach: We present experiments based on a dataset of 1358 forced cough
recordings obtained in a developing-world clinic from 16 patients with
confirmed active pulmonary TB and 35 patients suffering from respiratory
conditions suggestive of TB but confirmed to be TB negative. Using nested
cross-validation, we have trained and evaluated five machine learning
classifiers: logistic regression (LR), support vector machines (SVM), k-nearest
neighbour (KNN), multilayer perceptrons (MLP) and convolutional neural networks
(CNN).
  Main Results: Although classification is possible in all cases, the best
performance is achieved using LR. In combination with feature selection by
sequential forward selection (SFS), our best LR system achieves an area under
the ROC curve (AUC) of 0.94 using 23 features selected from a set of 78
high-resolution mel-frequency cepstral coefficients (MFCCs). This system
achieves a sensitivity of 93\% at a specificity of 95\% and thus exceeds the
90\% sensitivity at 70\% specificity specification considered by the World
Health Organisation (WHO) as a minimal requirement for a community-based TB
triage test.
  Significance: The automatic classification of cough audio sounds, when
applied to symptomatic patients requiring investigation for TB, can meet the
WHO triage specifications for the identification of patients who should undergo
expensive molecular downstream testing. This makes it a promising and viable
means of low cost, easily deployable frontline screening for TB, which can
benefit especially developing countries with a heavy TB burden.


Abstract:
  Conformance checking techniques aim to collate observed process behavior with
normative/modeled process models. The majority of existing approaches focuses
on completed process executions, i.e., offline conformance checking. Recently,
novel approaches have been designed to monitor ongoing processes, i.e., online
conformance checking. Such techniques detect deviations of an ongoing process
execution from a normative process model at the moment they occur. Thereby,
countermeasures can be taken immediately to prevent a process deviation from
causing further, undesired consequences. Most online approaches only allow to
detect approximations of deviations. This causes the problem of falsely
detected deviations, i.e., detected deviations that are actually no deviations.
We have, therefore, recently introduced a novel approach to compute exact
conformance checking results in an online environment. In this paper, we focus
on the practical application and present a scalable, distributed implementation
of the proposed online conformance checking approach. Moreover, we present two
extensions to said approach to reduce its computational effort and its
practical applicability. We evaluate our implementation using data sets
capturing the execution of real processes.


Abstract:
  Young stellar objects are observed to have large X-ray fluxes and are thought
to produce commensurate luminosities in energetic particles (cosmic rays). This
particle radiation, in turn, can synthesize short-lived radioactive nuclei
through spallation. With a focus on $^{26}$Al, this paper estimates the
expected abundances of radioactive nulcei produced by spallation during the
epoch of planet formation. In this model, cosmic rays are accelerated near the
inner truncation radii of circumstellar disks, $r_{\scriptstyle X}\approx0.1$
AU, where intense magnetic activity takes place. For planets forming in this
region, radioactive abundances can be enhanced over the values inferred for the
early solar system (from meteoritic measurements) by factors of $\sim10-20$.
These short-lived radioactive nuclei influence the process of planet formation
and the properties of planets in several ways. The minimum size required for
planetesimals to become fully molten decreases with increasing levels of
radioactive enrichment, and such melting leads to loss of volatile components
including water. Planets produced with an enhanced radioactive inventory have
significant internal luminosity which can be comparable to that provided by the
host star; this additional heating affects both atmospheric mass loss and
chemical composition. Finally, the habitable zone of red dwarf stars is
coincident with the magnetic reconnection region, so that planets forming at
those locations will experience maximum exposure to particle radiation, and
subsequent depletion of volatiles.


Abstract:
  Using the recently developed time-dependent Landauer-B\"uttiker formalism and
Jefimenko's retarded solutions to the Maxwell equations, we show how to compute
the time-dependent electromagnetic field produced by the charge and current
densities in nanojunctions out of equilibrium. We then apply this formalism to
a benzene ring junction, and show that geometry-dependent quantum interference
effects can be used to control the magnetic field in the vicinity of the
molecule. Then, treating the molecular junction as a quantum emitter, we
demonstrate clear signatures of the local molecular geometry in the non-local
radiated power.


Abstract:
  The perfect case for time-domain investigations are active galactic nuclei
(AGNs) since they are luminous objects that show strong variability. Key result
from the studies of AGNs variability is the estimated mass of a supermassive
black hole (SMBH), which resides in the center of an AGN. Moreover, the
spectral variability of AGN can be used to study the structure and physics of
the broad line region, which in general can be hardly directly observed. Here
we review the current status of AGNs variability investigations in Serbia, in
the perspectives of the present and future monitoring campaigns.


Abstract:
  In this article, we investigate the problem of state reconstruction of
four-level quantum systems. A realistic scenario is considered with measurement
results distorted by random unitary operators. Two frames which define
injective measurements are applied and compared. By introducing arbitrary
rotations, we can test the performance of the framework versus the amount of
experimental noise. The results of numerical simulations are depicted on graphs
and discussed. In particular, a class of entangled states is reconstructed. The
concurrence is used as a figure of merit in order to quantify how well
entanglement is preserved through noisy measurements.


Abstract:
  A data representation for system behavior telemetry for scalable big data
security analytics is presented, affording telemetry consumers comprehensive
visibility into workloads at reduced storage and processing overheads. The new
abstraction, SysFlow, is a compact open data format that lifts the
representation of system activities into a flow-centric, object-relational
mapping that records how applications interact with their environment, relating
processes to file accesses, network activities, and runtime information. The
telemetry format supports single-event and volumetric flow representations of
process control flows, file interactions, and network communications.
Evaluation on enterprise-grade benchmarks shows that SysFlow facilitates deeper
introspection into attack kill chains while yielding traces orders of magnitude
smaller than current state-of-the-art system telemetry approaches --
drastically reducing storage requirements and enabling feature-filled system
analytics, process-level provenance tracking, and long-term data archival for
cyber threat discovery and forensic analysis on historical data.


Abstract:
  We introduce a novel class of time integrators for dispersive equations which
allow us to reproduce the dynamics of the solution from the classical $
\varepsilon = 1$ up to long wave limit regime $ \varepsilon \ll 1 $ on the
natural time scale of the PDE $t= \mathcal{O}(\frac{1}{\varepsilon})$. Most
notably our new schemes converge with rates at order $\tau \varepsilon$ over
long times $t= \frac{1}{\varepsilon}$.


Abstract:
  The goal of this paper is an analysis of the geometry of billiards in
ellipses, based on properties of confocal central conics. The extended sides of
the billiards meet at points which are located on confocal ellipses and
hyperbolas. They define the associated Poncelet grid. If a billiard is periodic
then it closes for any choice of the initial vertex on the ellipse. This gives
rise to a continuous variation of billiards which is called billiard's motion
though it is neither a Euclidean nor a projective motion. The extension of this
motion to the associated Poncelet grid leads to new insights and invariants.


Abstract:
  This paper corrects the characterisation of biautomatic groups presented in
Lemma 2.5.5 in the book Word Processing in Groups by Epstein et al. We present
a counterexample to the lemma, and we reformulate the lemma to give a valid
characterisation of biautomatic groups.


Abstract:
  Data from the space missions {\it Gaia}, {\it Kepler}, {\it CoRoT} and {\it
TESS}, make it possible to compare parallax and asteroseismic distances. From
the ratio of two densities $\rho_{\rm sca}/\rho_{\pi}$, we obtain an empirical
relation $f_{\Delta \nu}$ between the asteroseismic large frequency separation
and mean density, which is important for more accurate stellar mass and radius.
This expression for main-sequence (MS) and subgiant stars with $K$-band
magnitude is very close to the one obtained from interior MS models by
Y{\i}ld{\i}z, \c{C}elik \& Kayhan. We also discuss the effects of effective
temperature and parallax offset as the source of the difference between
asteroseismic and non-asteroseismic stellar parameters. We have obtained our
best results for about 3500 red giants (RGs) by using 2MASS data and model
values for $f_{\Delta \nu}$ from Sharma et al. Another unknown scaling
parameter $f_{\nu_{\rm max}}$ comes from the relationship between the frequency
of maximum amplitude and gravity. Using different combinations of $f_{\nu_{\rm
max}}$ and the parallax offset, we find that the parallax offset is generally a
function of distance. The situation where this slope disappears is accepted as
the most reasonable solution. By a very careful comparison of asteroseismic and
non-asteroseismic parameters, we obtain very precise values for the parallax
offset and $f_{\nu_{\rm max}}$ for RGs of $-0.0463\pm0.0007$ mas and
$1.003\pm0.001$, respectively. Our results for mass and radius are in perfect
agreement with those of APOKASC-2: the mass and radius of $\sim$3500 RGs are in
the range of about 0.8-1.8 M$_{\odot}$ (96 per cent) and 3.8-38 R$_{\odot}$,
respectively.


Abstract:
  In this paper, we investigate the problem of prescribing Webster scalar
curvatures on compact pseudo-Hermitian manifolds. In terms of the method of
upper and lower solutions and the perturbation theory of self-adjoint
operators, we can describe some sets of Webster scalar curvature functions
which can be realized through pointwise CR conformal deformations and CR
conformally equivalent deformations respectively from a given pseudo-Hermitian
structure.


Abstract:
  We study normal directions to facets of the Newton polytope of the
discriminant of the Laurent polynomial system via the tropical approach. We use
the combinatorial construction proposed by Dickenstein, Feichtner and Sturmfels
for the tropicalization of algebraic varieties admitting a parametrization by a
linear map followed by a monomial map.


Abstract:
  While polarisation sensing is vital in many areas of research, with
applications spanning from microscopy to aerospace, traditional approaches are
limited by method-related error amplification or accumulation, placing
fundamental limitations on precision and accuracy in single-shot polarimetry.
Here, we put forward a new measurement paradigm to circumvent this, introducing
the notion of a universal full Poincar\'e generator to map all polarisation
analyser states into a single vectorially structured light field, allowing all
vector components to be analysed in a single-shot with theoretically
user-defined precision. To demonstrate the advantage of our approach, we use a
common GRIN optic as our mapping device and show mean errors of <1% for each
vector component, enhancing the sensitivity by around three times, allowing us
to sense weak polarisation aberrations not measurable by traditional
single-shot techniques. Our work paves the way for next-generation polarimetry,
impacting a wide variety of applications relying on weak vector measurement.


Abstract:
  This paper establishes sufficient conditions that force a graph to contain a
bipartite subgraph with a given structural property. In particular, let $\beta$
be any of the following graph parameters: Hadwiger number, Haj\'{o}s number,
treewidth, pathwidth, and treedepth. In each case, we show that there exists a
function $f$ such that every graph $G$ with $\beta(G)\geq f(k)$ contains a
bipartite subgraph $\hat{G}\subseteq G$ with $\beta(\hat{G})\geq k$.


Abstract:
  We present MAPFF1.0, a determination of unpolarised charged-pion
fragmentation functions (FFs) from a set of single-inclusive $e^+e^-$
annihilation and lepton-nucleon semi-inclusive deep-inelastic-scattering
(SIDIS) data. FFs are parametrised in terms of a neural network (NN) and fitted
to data exploiting the knowledge of the analytic derivative of the NN itself
w.r.t. its free parameters. Uncertainties on the FFs are determined by means of
the Monte Carlo sampling method properly accounting for all sources of
experimental uncertainties, including that of parton distribution functions.
Theoretical predictions for the relevant observables, as well as evolution
effects, are computed to next-to-leading order (NLO) accuracy in perturbative
QCD. We exploit the flavour sensitivity of the SIDIS measurements delivered by
the HERMES and COMPASS experiments to determine a minimally-biased set of seven
independent FF combinations. Moreover, we discuss the quality of the fit to the
SIDIS data with low virtuality $Q^2$ showing that, as expected, low-$Q^2$ SIDIS
measurements are generally harder to describe within a NLO-accurate
perturbative framework.


Abstract:
  Let $G$ be a $2$-generated group. The generating graph $\Gamma(G)$ is the
graph whose vertices are the elements of $G$ and where two vertices $g_1$ and
$g_2$ are adjacent if $G = \langle g_1, g_2 \rangle.$ This graph encodes the
combinatorial structure of the distribution of generating pairs across $G.$ In
this paper we study some graph theoretic properties of $\Gamma(G)$, with
particular emphasis on those properties that can be formulated in terms of
forbidden induced subgraphs. In particular we investigate when the generating
graph $\Gamma(G)$ is a cograph (giving a complete description when $G$ is
soluble) and when it is perfect (giving a complete description when $G$ is
nilpotent and proving, among the others, that $\Gamma(S_n)$ and $\Gamma(A_n)$
are perfect if and only if $n\leq 4$). Finally we prove that for a finite group
$G$, the properties that $\Gamma(G)$ is split, chordal or $C_4$-free are
equivalent.


Abstract:
  In this work, we develop a systematic method of constructing flat-band models
with and without band crossings. Our construction scheme utilizes the symmetry
and spatial shape of a compact localized state (CLS) and also the singularity
of the flat-band wave function obtained by a Fourier transform of the CLS
(FT-CLS). In order to construct a flat-band model systematically using these
ingredients, we first choose a CLS with a specific symmetry representation in a
given lattice. Then, the singularity of FT-CLS indicates whether the resulting
flat band exhibits a band crossing point or not. A tight-binding Hamiltonian
with the flat band corresponding to the FT-CLS is obtained by introducing a set
of basis molecular orbitals, which are orthogonal to the FT-CLS. Our
construction scheme can be systematically applied to any lattice so that it
provides a powerful theoretical framework to study exotic properties of both
gapped and gapless flat bands arising from their wave function singularities.


Abstract:
  One important feature of sunspots is the presence of light bridges. These
structures are elongated and bright (as compared to the umbra) features that
seem to be related to the formation and evolution of sunspots. In this work, we
studied the long-term evolution and the stratification of different atmospheric
parameters of three light bridges formed in the same host sunspot by different
mechanisms. To accomplish this, we used data taken with the GREGOR Infrared
Spectrograph installed at the GREGOR telescope. These data were inverted to
infer the physical parameters of the atmosphere where the observed spectral
profiles were formed of the three light bridges. We find that, in general, the
behaviour of the three light bridges is typical of this kind of structure with
the magnetic field strength, inclination, and temperature values between the
values at the umbra and the penumbra. We also find that they are of a
significantly non-magnetic character (particularly at the axis of the light
bridges) as it is deduced from the filling factor. In addition, within the
common behaviour of the physical properties of light bridges, we observe that
each one exhibits a particular behaviour. Another interesting result is that
the light bridge cools down, the magnetic field decreases, and the magnetic
field lines get more inclined higher in the atmosphere. Finally, we studied the
magnetic and non-magnetic line-of-sight velocities of the light bridges. The
former shows that the magnetic component is at rest and, interestingly, its
variation with optical depth shows a bi-modal behaviour. For the line-of-sight
velocity of the non-magnetic component, we see that the core of the light
bridge is at rest or with shallow upflows and clear downflows sinking through
the edges.


Abstract:
  A vast concourse of events and phenomena occur in nature that may be
interrelated by a entropy-maximization technique that provides a comprehensible
explanation of a range of physical problems, integrating in a new framework the
universal tendency of energy to a minimum and entropy to a maximum. The outcome
is a modification of Newton's dynamical equation of motion, grounding the
principles of mechanics on the concepts of energy and entropy, instead on the
usual definition of force, integrating into a consistent framework the
description of translation and vortical motion. The new method offers a fresh
approach to traditional problems and can be applied with advantage in the
solution of variational problems.


Abstract:
  The generalized Langevin equation (GLE) overcomes the limiting Markov
approximation of the Langevin equation by an incorporated memory kernel and can
be used to model various stochastic processes in many fields of science ranging
from climate modeling over neuroscience to finance. Generally, Bayesian
estimation facilitates the determination of both suitable model parameters and
their credibility for a measured time series in a straightforward way. In this
work we develop a realization of this estimation technique for the GLE in the
case of white noise. We assume piecewise constant drift and diffusion functions
and represent the characteristics of the data set by only a few coefficients,
which leads to a numerically efficient procedure. The kernel function is an
arbitrary time-discrete function with a fixed length $K$. We show how to
determine a reasonable value of $K$ based on the data. We illustrate the
abilities of both the method and the model by an example from turbulence.


Abstract:
  Complex multivariate time series arise in many fields, ranging from computer
vision to robotics or medicine. Often we are interested in the independent
underlying factors that give rise to the high-dimensional data we are
observing. While many models have been introduced to learn such disentangled
representations, only few attempt to explicitly exploit the structure of
sequential data. We investigate the disentanglement properties of Gaussian
process variational autoencoders, a class of models recently introduced that
have been successful in different tasks on time series data. Our model exploits
the temporal structure of the data by modeling each latent channel with a GP
prior and employing a structured variational distribution that can capture
dependencies in time. We demonstrate the competitiveness of our approach
against state-of-the-art unsupervised and weakly-supervised disentanglement
methods on a benchmark task. Moreover, we provide evidence that we can learn
meaningful disentangled representations on real-world medical time series data.


Abstract:
  In this paper we derive some Edmundson-Lah-Ribari\v{c} type inequalities for
positive linear functionals and 3-convex functions. Main results are applied to
the generalized f-divergence functional. Examples with Zipf Mandelbrot law are
used to illustrate the results. In addition, obtained results are utilized in
constructing some families of exponentially convex functions and Stolarsky-type
means.


Abstract:
  We provide the rigorous derivation of the wave kinetic equation from the
cubic nonlinear Schr\"odinger (NLS) equation at the kinetic timescale, under a
particular scaling law that describes the limiting process. This solves a main
conjecture in the theory of wave turbulence, i.e. the kinetic theory of
nonlinear wave systems. Our result is the wave analog of Lanford's theorem on
the derivation of the Boltzmann kinetic equation from particle systems, where
in both cases one takes the thermodynamic limit as the size of the system
diverges to infinity, and as the interaction strength of waves or radius of
particles vanishes to $0$, according to a particular scaling law
(Boltzmann-Grad in the particle case).
  More precisely, in dimensions $d\geq 3$, we consider the (NLS) equation in a
large box of size $L$ with a weak nonlinearity of strength $\alpha$. In the
limit $L\to\infty$ and $\alpha\to 0$, under the scaling law $\alpha\sim
L^{-1}$, we show that the long-time behavior of (NLS) is statistically
described by the wave kinetic equation, with well justified approximation, up
to times that are $O(1)$ (i.e independent of $L$ and $\alpha$) multiples of the
kinetic timescale $T_{\text{kin}}\sim \alpha^{-2}$. This is the first result of
its kind for any nonlinear dispersive system.


Abstract:
  Fixed points in three dimensions described by conformal field theories with
$MN_{m,n}= O(m)^n\rtimes S_n$ global symmetry have extensive applications in
critical phenomena. Associated experimental data for $m=n=2$ suggest the
existence of two non-trivial fixed points, while the $\varepsilon$ expansion
predicts only one, resulting in a puzzling state of affairs. A recent numerical
conformal bootstrap study has found two kinks for small values of the
parameters $m$ and $n$, with critical exponents in good agreement with
experimental determinations in the $m=n=2$ case. In this paper we investigate
the fate of the corresponding fixed points as we vary the parameters $m$ and
$n$. We find that one family of kinks approaches a perturbative limit as $m$
increases, and using large spin perturbation theory we construct a large $m$
expansion that fits well with the numerical data. This new expansion, akin to
the large $N$ expansion of critical $O(N)$ models, is compatible with the fixed
point found in the $\varepsilon$ expansion. For the other family of kinks, we
find that it persists only for $n=2$, where for large $m$ it approaches a
non-perturbative limit with $\Delta_\phi\approx 0.75$. We investigate the
spectrum in the case $MN_{100,2}$ and find consistency with expectations from
the lightcone bootstrap.


Abstract:
  A major challenge in applying machine learning to automated theorem proving
is the scarcity of training data, which is a key ingredient in training
successful deep learning models. To tackle this problem, we propose an approach
that relies on training purely with synthetically generated theorems, without
any human data aside from axioms. We use these theorems to train a
neurally-guided saturation-based prover. Our neural prover outperforms the
state-of-the-art E-prover on this synthetic data in both time and search steps,
and shows significant transfer to the unseen human-written theorems from the
TPTP library, where it solves 72\% of first-order problems without equality.


Abstract:
  Graph embeddings are low dimensional representations of nodes, edges or whole
graphs. Such representations allow for data in a network format to be used
along with machine learning models for a variety of tasks (e.g., node
classification), where using a similarity matrix would be impractical. In
recent years, many methods for graph embedding generation have been created
based on the idea of random walks. We propose MultiWalk, a framework that uses
an ensemble of these methods to generate the embeddings. Our experiments show
that the proposed framework, using an ensemble composed of two state-of-the-art
methods, can generate embeddings that perform better in classification tasks
than each method in isolation.


Abstract:
  The Muon g-2 experiment at FERMILAB has confirmed the muon anomalous magnetic
moment anomaly with an error bar 15% smaller and a different central value
compared with the previous Brookhaven result. The combined results from
FERMILAB and Brookhaven show a difference with theory at a significance of
$4.2\sigma$, strongly indicating the presence of new physics. In light of this
new result, we discuss a Two Higgs Doublet model augmented by an Abelian gauge
symmetry that can simultaneously accommodate a light dark matter candidate and
$(g-2)_\mu$, in agreement with existing bounds.


Abstract:
  We propose and demonstrate a scalable scheme for the simultaneous
determination of internal and motional states in trapped ions with single-site
resolution. The scheme is applied to the study of polaritonic excitations in
the Jaynes- Cummings Hubbard model with trapped ions, in which the internal and
motional states of the ions are strongly correlated. We observe quantum phase
transitions of polaritonic excitations in two ions by directly evaluating their
variances per ion site. Our work establishes an essential technological method
for large-scale quantum simulations of polaritonic systems.


Abstract:
  We revisit the renormalizable polynomial inflection point model of inflation,
focusing on the small field scenario which can be treated fully analytically.
In particular, the running of the spectral index is predicted to be $\alpha =
-1.43 \times 10^{-3} +5.56 \times 10^{-5} \left(N_{\rm CMB}-65 \right)$, which
might be tested in future. We also analyze reheating through perturbative
inflaton decays to either fermionic or bosonic final states via a trilinear
coupling. The lower bound on the reheating temperature from successful Big Bang
nucleosynthesis gives lower bounds for these couplings; on the other hand
radiative stability of the inflaton potential leads to upper bounds. In
combination this leads to a lower bound on the location $\phi_0$ of the near
inflection point, $\phi_0 > 3 \cdot 10^{-5}$ in Planckian units. The Hubble
parameter during inflation can be as low as $H_{\rm inf} \sim 1$ MeV, or as
high as $\sim 10^{10}$ GeV. Similarly, the reheating temperature can lie
between its lower bound of $\sim 4$ MeV and about $4 \cdot 10^8 \ (10^{11})$
GeV for fermionic (bosonic) inflaton decays. We finally speculate on the
"prehistory" of the universe in this scenario, which might have included an
epoch of eternal inflation.


Abstract:
  The dynamical history of stars influences the formation and evolution of
planets significantly. To explore the influence of dynamical history on planet
formation and evolution from observations, we assume that stars who experienced
significantly different dynamical histories tend to have different relative
velocities. Utilizing the accurate Gaia-Kepler Stellar Properties Catalog, we
select single main-sequence stars and divide these stars into three groups
according to their relative velocities, i.e. high-V, medium-V, and low-V stars.
After considering the known biases from Kepler data and adopting prior and
posterior correction to minimize the influence of stellar properties on planet
occurrence rate, we find that high-V stars have a lower occurrence rate of
super-Earths and sub-Neptunes (1--4 R$_{\rm \oplus}$, P<100 days) and higher
occurrence rate of sub-Earth (0.5--1 R$_{ \oplus}$, P<30 days) than low-V
stars. Additionally, high-V stars have a lower occurrence rate of hot Jupiter
sized planets (4--20 R$_{\oplus}$, P<10 days) and a slightly higher occurrence
rate of warm or cold Jupiter sized planets (4--20 R$_{\oplus}$, 10<P<400 days).
After investigating the multiplicity and eccentricity, we find that high-V
planet hosts prefer a higher fraction of multi-planets systems and lower
average eccentricity, which is consistent with the eccentricity-multiplicity
dichotomy of Kepler planetary systems. All these statistical results favor the
scenario that the high-V stars with large relative velocity may experience
fewer gravitational events, while the low-V stars may be influenced by stellar
clustering significantly.


Abstract:
  Determining the mechanism by which high-mass stars are formed is essential
for our understanding of the energy budget and chemical evolution of galaxies.
By using the New IRAM KIDs Array 2 (NIKA2) camera on the Institut de Radio
Astronomie Millim\'etrique (IRAM) 30-m telescope, we have conducted
high-sensitivity and large-scale mapping of a fraction of the Galactic plane in
order to search for signatures of the transition between the high- and low-mass
star-forming modes. Here, we present the first results from the Galactic Star
Formation with NIKA2 (GASTON) project, a Large Programme at the IRAM 30-m
telescope which is mapping $\approx$2 deg$^2$ of the inner Galactic plane (GP),
centred on $\ell$=23.9$^\circ$, $b$=0.05$^\circ$, as well as targets in Taurus
and Ophiuchus in 1.15 and 2.00 mm continuum wavebands. In this paper we present
the first of the GASTON GP data taken, and present initial science results. We
conduct an extraction of structures from the 1.15 mm maps using a dendrogram
analysis and, by comparison to the compact source catalogues from Herschel
survey data, we identify a population of 321 previously-undetected clumps.
Approximately 80 per cent of these new clumps are 70 $\mu$m-quiet, and may be
considered as starless candidates. We find that this new population of clumps
are less massive and cooler, on average, than clumps that have already been
identified. Further, by classifying the full sample of clumps based upon their
infrared-bright fraction - an indicator of evolutionary stage - we find
evidence for clump mass growth, supporting models of clump-fed high-mass star
formation.


Abstract:
  Intersection patterns of convex sets in $\mathbb{R}^d$ have the remarkable
property that for $d+1 \le k \le \ell$, in any sufficiently large family of
convex sets in $\mathbb{R}^d$, if a constant fraction of the $k$-element
subfamilies have nonempty intersection, then a constant fraction of the
$\ell$-element subfamilies must also have nonempty intersection. Here, we prove
that a similar phenomenon holds for any topological set system $\mathcal{F}$ in
$\mathbb{R}^d$. Quantitatively, our bounds depend on how complicated the
intersection of $\ell$ elements of $\mathcal{F}$ can be, as measured by the sum
of the $\lceil\frac{d}2\rceil$ first Betti numbers. As an application, we
improve the fractional Helly number of set systems with bounded topological
complexity due to the third author, from a Ramsey number down to $d+1$. We also
shed some light on a conjecture of Kalai and Meshulam on intersection patterns
of sets with bounded homological VC dimension. A key ingredient in our proof is
the use of the stair convexity of Bukh, Matou\v{s}ek and Nivash to recast a
simplicial complex as a homological minor of a cubical complex.


Abstract:
  In this book chapter, we study a problem of distributed content caching in an
ultra-dense edge caching network (UDCN), in which a large number of small base
stations (SBSs) prefetch popular files to cope with the ever-growing user
demand in 5G and beyond. In a UDCN, even a small misprediction of user demand
may render a large amount of prefetched data obsolete. Furtherproacmore, the
interference variance is high due to the short inter-SBS distances, making it
difficult to quantify data downloading rates. Lastly, since the caching
decision of each SBS interacts with those of all other SBSs, the problem
complexity of exponentially increases with the number of SBSs, which is unfit
for UDCNs. To resolve such challenging issues while reflecting time-varying and
location-dependent user demand, we leverage mean-field game (MFG) theory
through which each SBS interacts only with a single virtual SBS whose state is
drawn from the state distribution of the entire SBS population, i.e.,
mean-field (MF) distribution. This MF approximation asymptotically guarantees
achieving the epsilon Nash equilibrium as the number of SBSs approaches
infinity. To describe such an MFG-theoretic caching framework, this chapter
aims to provide a brief review of MFG, and demonstrate its effectiveness for
UDCNs.


Abstract:
  Information overload is a prevalent challenge in many high-value domains. A
prominent case in point is the explosion of the biomedical literature on
COVID-19, which swelled to hundreds of thousands of papers in a matter of
months. In general, biomedical literature expands by two papers every minute,
totalling over a million new papers every year. Search in the biomedical realm,
and many other vertical domains is challenging due to the scarcity of direct
supervision from click logs. Self-supervised learning has emerged as a
promising direction to overcome the annotation bottleneck. We propose a general
approach for vertical search based on domain-specific pretraining and present a
case study for the biomedical domain. Despite being substantially simpler and
not using any relevance labels for training or development, our method performs
comparably or better than the best systems in the official TREC-COVID
evaluation, a COVID-related biomedical search competition. Using distributed
computing in modern cloud infrastructure, our system can scale to tens of
millions of articles on PubMed and has been deployed as Microsoft Biomedical
Search, a new search experience for biomedical literature:
https://aka.ms/biomedsearch.


Abstract:
  We analyze the thermodynamic Casimir effect occurring in a gas of
non-interacting bosons confined by two parallel walls with a strongly
anisotropic dispersion inherited from an underlying lattice. In the direction
perpendicular to the confining walls the standard quadratic dispersion is
replaced by the term $|{\bf p}|^{\alpha}$ with $\alpha \geq 2$ treated as a
parameter. We derive a closed, analytical expression for the Casimir force
depending on the dimensionality $d$ and the exponent $\alpha$, and analyze it
for thermodynamic states in which the Bose-Einstein condensate is present. For
$\alpha\in\{4,6,8,\dots\}$ the exponent governing the decay of the Casimir
force with increasing distance between the walls becomes modified and the
Casimir amplitude $\Delta_{\alpha}(d)$ exhibits oscillations of sign as a
function of $d$. Otherwise we find that $\Delta_{\alpha}(d)$ features
singularities when viewed as a function of $d$ and $\alpha$. Recovering the
known previous results for the isotropic limit $\alpha=2$ turns out to occur
via a cancellation of singular terms.


Abstract:
  The classic searches for supersymmetry have not given any strong indication
for new physics. Therefore CMS is designing dedicated searches to target the
more difficult and specific supersymmetry scenarios. This contribution present
three such recent searches based on 13 TeV proton-proton collisions recorded
with the CMS detector in 2016, 2017 and 2018: a search for heavy gluinos
cascading via heavy next-to-lightest neutralino in final states with boosted Z
bosons and missing transverse momentum; a search for compressed supersymmetry
in final states with soft taus; and a search for compressed, long-lived
charginos in hadronic final states with disappearing tracks.


Abstract:
  More than a decade has passed since the definition of Globular Cluster (GC)
changed, and now we know that they host Multiple Populations (MPs). But few GCs
do not share that behaviour and Ruprecht 106 is one of these clusters. We
analyzed thirteen member red giant branch stars using spectra in the wavelength
range 6120-6405 Angstroms obtained through the GIRAFFE Spectrograph, mounted at
UT2 telescope at Paranal, as well as the whole cluster using C, V, R and I
photometry obtained through the Swope telescope at Las Campanas. Atmospheric
parameters were determined from the photometry to determine Fe and Na
abundances. A photometric analysis searching for MPs was also carried out. Both
analyses confirm that Ruprecht 106 is indeed one on the few GCs to host Simple
Stellar Population, in agreement with previous studies. Finally, a dynamical
study concerning its orbits was carried out to analyze the possible extra
galactic origin of the Cluster. The orbital integration indicates that this GC
belongs to the inner halo, while an Energy plane shows that it cannot be
accurately associated with any known extragalactic progenitor.


Abstract:
  Phase gradient metagratings/metasurfaces (PGMs) have provided a new paradigm
for light manipulations. In this work, we will show the existence of gauge
invariance in PGMs, i.e., the diffraction law of PGMs is independent of the
choice of initial value of abrupt phase shift that induces the phase gradient.
This gauge invariance ensures the well-studied ordinary metallic grating that
can be regarded as a PGM, with its diffraction properties that can fully
predicted by generalized diffraction law with phase gradient. The generalized
diffraction law presents a new insight for the famous effect of Wood's
Anomalies and Rayleigh conjecture.


Abstract:
  Hydraulic blockage of cross-drainage structures such as culverts is
considered one of main contributor in triggering urban flash floods. However,
due to lack of during floods data and highly non-linear nature of debris
interaction, conventional modelling for hydraulic blockage is not possible.
This paper proposes to use machine learning regression analysis for the
prediction of hydraulic blockage. Relevant data has been collected by
performing a scaled in-lab study and replicating different blockage scenarios.
From the regression analysis, Artificial Neural Network (ANN) was reported best
in hydraulic blockage prediction with $R^2$ of 0.89. With deployment of
hydraulic sensors in smart cities, and availability of Big Data, regression
analysis may prove helpful in addressing the blockage detection problem which
is difficult to counter using conventional experimental and hydrological
approaches.


Abstract:
  Bi-objective search is a well-known algorithmic problem, concerned with
finding a set of optimal solutions in a two-dimensional domain. This problem
has a wide variety of applications such as planning in transport systems or
optimal control in energy systems. Recently, bi-objective A*-based search
(BOA*) has shown state-of-the-art performance in large networks. This paper
develops a bi-directional and parallel variant of BOA*, enriched with several
speed-up heuristics. Our experimental results on 1,000 benchmark cases show
that our bi-directional A* algorithm for bi-objective search (BOBA*) can
optimally solve all of the benchmark cases within the time limit, outperforming
the state of the art BOA*, bi-objective Dijkstra and bi-directional
bi-objective Dijkstra by an average runtime improvement of a factor of five
over all of the benchmark instances.


Abstract:
  For AI technology to fulfill its full promises, we must have effective means
to ensure Responsible AI behavior and curtail potential irresponsible use,
e.g., in areas of privacy protection, human autonomy, robustness, and
prevention of biases and discrimination in automated decision making. Recent
literature in the field has identified serious shortcomings of narrow
technology focused and formalism-oriented research and has proposed an
interdisciplinary approach that brings the social context into the scope of
study. In this paper, we take a sociotechnical approach to propose a more
expansive framework of thinking about the Responsible AI challenges in both
technical and social context. Effective solutions need to bridge the gap
between a technical system with the social system that it will be deployed to.
To this end, we propose human agency and regulation as main mechanisms of
intervention and propose a decentralized computational infrastructure, or a set
of public utilities, as the computational means to bridge this gap. A
decentralized infrastructure is uniquely suited for meeting this challenge and
enable technical solutions and social institutions in a mutually reinforcing
dynamic to achieve Responsible AI goals. Our approach is novel in its
sociotechnical approach and its aim in tackling the structural issues that
cannot be solved within the narrow confines of AI technical research. We then
explore possible features of the proposed infrastructure and discuss how it may
help solve example problems recently studied in the field.