Abstract:
  The study of anisotropic harmonic flow coefficients $ v_{n}$(n=2,3,4) is
performed in Xe-Xe collisions at $\sqrt{s_{NN}}$ = 5.44 TeV under Monte Carlo
HYDJET++ model (HYDrodynamics plus JETs) framework. Anisotropic flow of
identified particles and correlation between the azimuthal harmonic flow
amplitudes is presented. Here, we have considered body-body and tip-tip type of
geometrical configurations for Xe-Xe collision systems. The kinematic ranges
$|\eta|<0.8$, $0<p_{T}<5.0$ GeV/c, and $|\delta / \eta|> 2$ are considered. The
results have been shown for seven classes of centrality and compared with the
ALICE experimental data. The anisotropic flow of identified charged particles
show a strong centrality dependence. Mass ordering is observed for
$v_{2},v_{3}$ and $v_{4}$. Mass ordering is different for different ranges of
transverse momentum $p_{T}$. Strong correlation is observed between
$v_{3}-v_{2}$, $v_{4}-v_{2}$, and $v_{4}-v_{3}$. Such correlation is centrality
dependent and is different in different centrality windows. The anisotropic
flow coefficients show a clear dependence on the total charged particle
multiplicity. HYDJET++ model justifies experimental data well enough.


Abstract:
  Here we investigate the temperature dependence of anomalous Hall effect in
Hf/GdFeCo/MgO sheet film and Hall bar device. The magnetic compensation
temperature ($T_{comp}$) for the sheet film and device is found to be ~240 K
and ~118 K, respectively. In sheet film, spin-flopping is witnessed at a
considerably lower field, 0.6 T, close to $T_{comp}$. The AHE hysteresis loops
in the sheet film have a single loop whereas in the Hall bar device, hystereses
consist of triple loops are observed just above the Tcomp. Moreover, the
temperature-dependent anomalous Hall resistance ($R_\mathrm{AHE}$) responds
unusually when a perpendicular magnetic field is applied while recording the
$R_\mathrm{AHE}$. The zero-field $R_\mathrm{AHE}$ scan suggests the Hall signal
generates solely from the FeCo moment. However, the behavior of 3 T-field
$R_\mathrm{AHE}$ scan in which the $R_\mathrm{AHE}$ drops close to zero near
the $T_{comp}$ seems to be following the net magnetization response of the
device, is explained by considering the low field spin-flopping around the
compensation temperature. The results presented here give important insight to
understand the complex AHE behavior of ferrimagnets for their spintronic
applications.


Abstract:
  Significant efforts have been expended in the research and development of a
database management system (DBMS) that has a wide range of applications for
managing an enormous collection of multisource, heterogeneous, complex, or
growing data. Besides the primary function (i.e., create, delete, and update),
a practical and impeccable DBMS can interact with users through information
selection, that is, querying with their targets. Previous querying algorithms,
such as frequent itemset querying and sequential pattern querying (SPQ) have
focused on the measurement of frequency, which does not involve the concept of
utility, which is helpful for users to discover more informative patterns. To
apply the querying technology for wider applications, we incorporate utility
into target-oriented SPQ and formulate the task of targeted utility-oriented
sequence querying. To address the proposed problem, we develop a novel
algorithm, namely targeted high-utility sequence querying (TUSQ), based on two
novel upper bounds suffix remain utility and terminated descendants utility as
well as a vertical Last Instance Table structure. For further efficiency, TUSQ
relies on a projection technology utilizing a compact data structure called the
targeted chain. An extensive experimental study conducted on several real and
synthetic datasets shows that the proposed algorithm outperformed the designed
baseline algorithm in terms of runtime, memory consumption, and candidate
filtering.


Abstract:
  Wikidata has been increasingly adopted by many communities for a wide variety
of applications, which demand high-quality knowledge to deliver successful
results. In this paper, we develop a framework to detect and analyze
low-quality statements in Wikidata by shedding light on the current practices
exercised by the community. We explore three indicators of data quality in
Wikidata, based on: 1) community consensus on the currently recorded knowledge,
assuming that statements that have been removed and not added back are
implicitly agreed to be of low quality; 2) statements that have been
deprecated; and 3) constraint violations in the data. We combine these
indicators to detect low-quality statements, revealing challenges with
duplicate entities, missing triples, violated type rules, and taxonomic
distinctions. Our findings complement ongoing efforts by the Wikidata community
to improve data quality, aiming to make it easier for users and editors to find
and correct mistakes.


Abstract:
  Graph convolution networks (GCN), which recently becomes new state-of-the-art
method for graph node classification, recommendation and other applications,
has not been successfully applied to industrial-scale search engine yet. In
this proposal, we introduce our approach, namely SearchGCN, for embedding-based
candidate retrieval in one of the largest e-commerce search engine in the
world. Empirical studies demonstrate that SearchGCN learns better embedding
representations than existing methods, especially for long tail queries and
items. Thus, SearchGCN has been deployed into JD.com's search production since
July 2020.


Abstract:
  We study the hop-constrained s-t path enumeration (HcPE) problem, which takes
a graph $G$, two distinct vertices $s,t$ and a hop constraint $k$ as input, and
outputs all paths from $s$ to $t$ whose length is at most $k$. The
state-of-the-art algorithms suffer from severe performance issues caused by the
costly pruning operations during enumeration for the workloads with the large
search space. Consequently, these algorithms hardly meet the real-time
constraints of many online applications. In this paper, we propose PathEnum, an
efficient index-based algorithm towards real-time HcPE. For an input query,
PathEnum first builds a light-weight index aiming to reduce the number of edges
involved in the enumeration, and develops efficient index-based approaches for
enumeration, one based on depth-first search and the other based on joins. We
further develop a query optimizer based on a join-based cost model to optimize
the search order. We conduct experiments with 15 real-world graphs. Our
experiment results show that PathEnum outperforms the state-of-the-art
approaches by orders of magnitude in terms of the query time, throughput and
response time.


Abstract:
  In this paper, we investigate two dimensional subsonic and subsonic-sonic
spiral flows outside a porous body. The existence and uniqueness of the
subsonic spiral flow are obtained via variational formulation. The optimal
decay rate at far fields is also derived by the Kelvin's transformation and
some elliptic estimates. By extracting spiral subsonic solutions as the
approximate sequences, we obtain the spiral subsonic-sonic limit solution. The
main ingredients of our analysis are methods of calculus of variations, the
theory of second-order quasilinear equations and the compactness framework.


Abstract:
  We study a non-Hermitian and non-unitary version of the two-dimensional
Chalker-Coddington network model with balanced gain and loss. This model
belongs to the class D^dagger with particle-hole symmetry^dagger and hosts both
the non-Hermitian skin effect as well as exceptional points. By calculating its
two-terminal transmission, we find a novel contact effect induced by the skin
effect, which results in a non-quantized transmission for chiral edge states.
In addition, the model exhibits an insulator to 'supermetal' transition, across
which the transmission changes from exponentially decaying with system size to
exponentially growing with system size. In the clean system, the critical point
separating insulator from supermetal is characterized by a non-Hermitian Dirac
point that produces a quantized critical transmission of 4, instead of the
value of 1 expected in Hermitian systems. This change in critical transmission
is a consequence of the balanced gain and loss. When adding disorder to the
system, we find a critical exponent for the divergence of the localization
length \nu \approx 1, which is the same as that characterizing the universality
class of two-dimensional Hermitian systems in class D. Our work provides a
novel way of exploring the localization behavior of non-Hermitian systems, by
using network models, which in the past proved versatile tools to describe
Hermitian physics.


Abstract:
  Road accident can be triggered by wet road because it decreases skid
resistance. To prevent the road accident, detecting road surface abnomality is
highly useful. In this paper, we propose the deep learning based cost-effective
real-time anomaly detection architecture, naming with non-compression
auto-encoder (NCAE). The proposed architecture can reflect forward and backward
causality of time series information via convolutional operation. Moreover, the
above architecture shows higher anomaly detection performance of published
anomaly detection model via experiments. We conclude that NCAE as a
cutting-edge model for road surface anomaly detection with 4.20\% higher AUROC
and 2.99 times faster decision than before.


Abstract:
  We outline the construction of a molecular system that could, in principle,
implement a thermodynamically reversible Universal Turing Machine (UTM). By
proposing a concrete-albeit idealised-design and operational protocol, we
reveal fundamental challenges that arise when attempting to implement arbitrary
computations reversibly. Firstly, the requirements of thermodynamic
reversibility inevitably lead to an intricate design. Secondly,
thermodynamically reversible UTMs, unlike simpler devices, must also be
logically reversible. Finally, implementing multiple distinct computations in
parallel is necessary to take the cost of external control per computation to
zero, but this approach is complicated the distinct halting times of different
computations.


Abstract:
  Quantum annealing is an emerging technology with the potential to solve some
of the computational challenges that remain unresolved as we approach an era
beyond Moore's Law. In this work, we investigate the capabilities of the
quantum annealers of D-Wave Systems, Inc., for computing a certain type of
Boolean tensor decomposition called Boolean Hierarchical Tucker Network (BHTN).
Boolean tensor decomposition problems ask for finding a decomposition of a
high-dimensional tensor with categorical, [true, false], values, as a product
of smaller Boolean core tensors. As the BHTN decompositions are usually not
exact, we aim to approximate an input high-dimensional tensor by a product of
lower-dimensional tensors such that the difference between both is minimized in
some norm. We show that BHTN can be calculated as a sequence of optimization
problems suitable for the D-Wave 2000Q quantum annealer. Although current
technology is still fairly restricted in the problems they can address, we show
that a complex problem such as BHTN can be solved efficiently and accurately.


Abstract:
  With the aim of understanding the role of outflows in star formation, we
performed a statistical study of the physical parameters of outflows in eleven
massive protoclusters associated with ultra-compact HII regions. A total of 106
outflow lobes are identified in these protoclusters using the ALMA CO (3-2),
HCN (4-3) and HCO+ (4-3) line observations. Although the position angles of
outflow lobes do not differ in these three tracers, HCN and HCO+ tend to detect
lower terminal velocity of the identified outflows compared to CO. The majority
of the outflows in our targets are young with typical dynamical time-scales of
10^2-10^4 years, and are mostly composed of low-mass outflows along with at
least one high-mass outflow in each target. An anti-correlation of outflow rate
with dynamical time-scale indicates that the outflow rate possibly decreases
with time. Also, a rising trend of dynamical time-scale with the mass of the
associated core hints that the massive cores might have longer accretion
histories than the low mass cores. Estimation of different energies in these
protoclusters shows that outflows studied here cannot account for the
generation of the observed turbulence, but can sustain the turbulence at the
current epoch as the energy injection rate from the outflows is similar to the
estimated dissipation rate.


Abstract:
  In recent years, deep learning-based automated personality trait detection
has received a lot of attention, especially now, due to the massive digital
footprints of an individual. Moreover, many researchers have demonstrated that
there is a strong link between personality traits and emotions. In this paper,
we build on the known correlation between personality traits and emotional
behaviors, and propose a novel multitask learning framework, SoGMTL that
simultaneously predicts both of them. We also empirically evaluate and discuss
different information-sharing mechanisms between the two tasks. To ensure the
high quality of the learning process, we adopt a MAML-like framework for model
optimization. Our more computationally efficient CNN-based multitask model
achieves the state-of-the-art performance across multiple famous personality
and emotion datasets, even outperforming Language Model based models.


Abstract:
  Most of the recent deep reinforcement learning advances take an RL-centric
perspective and focus on refinements of the training objective. We diverge from
this view and show we can recover the performance of these developments not by
changing the objective, but by regularising the value-function estimator.
Constraining the Lipschitz constant of a single layer using spectral
normalisation is sufficient to elevate the performance of a Categorical-DQN
agent to that of a more elaborated \rainbow{} agent on the challenging Atari
domain. We conduct ablation studies to disentangle the various effects
normalisation has on the learning dynamics and show that is sufficient to
modulate the parameter updates to recover most of the performance of spectral
normalisation. These findings hint towards the need to also focus on the neural
component and its learning dynamics to tackle the peculiarities of Deep
Reinforcement Learning.


Abstract:
  We revisit a supersymmetric string model for space-time foam, in which
bosonic open-string states, such as photons, can possess
quantum-gravity-induced velocity fluctuations in vacuum. We argue that the
suggestion of light speed variation with lower bound from gamma-ray burst
photon time delays can serve as a support for this string-inspired framework,
through connecting the experimental finding with model predictions. We also
derive the value of the effective quantum-gravity mass in this framework, and
give a qualitative study on the model-dependent coefficients. Constraints from
birefringent effects and/or photon decays, including the novel $\gamma$-decay
constraint obtained here from the latest Tibet AS$\gamma$ near-PeV photon, are
also found to be consistent with predictions in such a quantum-gravity scheme.
Future observation that can testify further the theory is suggested.


Abstract:
  Few-shot object detection is an imperative and long-lasting problem due to
the inherent long-tail distribution of real-world data. Its performance is
largely affected by the data scarcity of novel classes. But the semantic
relation between the novel classes and the base classes is constant regardless
of the data availability. In this work, we investigate utilizing this semantic
relation together with the visual information and introduce explicit relation
reasoning into the learning of novel object detection. Specifically, we
represent each class concept by a semantic embedding learned from a large
corpus of text. The detector is trained to project the image representations of
objects into this embedding space. We also identify the problems of trivially
using the raw embeddings with a heuristic knowledge graph and propose to
augment the embeddings with a dynamic relation graph. As a result, our few-shot
detector, termed SRR-FSD, is robust and stable to the variation of shots of
novel objects. Experiments show that SRR-FSD can achieve competitive results at
higher shots, and more importantly, a significantly better performance given
both lower explicit and implicit shots. The benchmark protocol with implicit
shots removed from the pretrained classification dataset can serve as a more
realistic setting for future research.


Abstract:
  The paper considers a distributed version of deep reinforcement learning
(DRL) for multi-agent decision-making process in the paradigm of federated
learning. Since the deep neural network models in federated learning are
trained locally and aggregated iteratively through a central server, frequent
information exchange incurs a large amount of communication overheads. Besides,
due to the heterogeneity of agents, Markov state transition trajectories from
different agents are usually unsynchronized within the same time interval,
which will further influence the convergence bound of the aggregated deep
neural network models. Therefore, it is of vital importance to reasonably
evaluate the effectiveness of different optimization methods. Accordingly, this
paper proposes a utility function to consider the balance between reducing
communication overheads and improving convergence performance. Meanwhile, this
paper develops two new optimization methods on top of variation-aware periodic
averaging methods: 1) the decay-based method which gradually decreases the
weight of the model's local gradients within the progress of local updating,
and 2) the consensus-based method which introduces the consensus algorithm into
federated learning for the exchange of the model's local gradients. This paper
also provides novel convergence guarantees for both developed methods and
demonstrates their effectiveness and efficiency through theoretical analysis
and numerical simulation results.


Abstract:
  Scaling the cyber hunt problem poses several key technical challenges.
Detecting and characterizing cyber threats at scale in large enterprise
networks is hard because of the vast quantity and complexity of the data that
must be analyzed as adversaries deploy varied and evolving tactics to
accomplish their goals. There is a great need to automate all aspects, and,
indeed, the workflow of cyber hunting. AI offers many ways to support this. We
have developed the WILEE system that automates cyber threat hunting by
translating high-level threat descriptions into many possible concrete
implementations. Both the (high-level) abstract and (low-level) concrete
implementations are represented using a custom domain specific language (DSL).
WILEE uses the implementations along with other logic, also written in the DSL,
to automatically generate queries to confirm (or refute) any hypotheses tied to
the potential adversarial workflows represented at various layers of
abstraction.


Abstract:
  Few-shot learning aims to correctly recognize query samples from unseen
classes given a limited number of support samples, often by relying on global
embeddings of images. In this paper, we propose to equip the backbone network
with an attention agent, which is trained by reinforcement learning. The policy
gradient algorithm is employed to train the agent towards adaptively localizing
the representative regions on feature maps over time. We further design a
reward function based on the prediction of the held-out data, thus helping the
attention mechanism to generalize better across the unseen classes. The
extensive experiments show, with the help of the reinforced attention, that our
embedding network has the capability to progressively generate a more
discriminative representation in few-shot learning. Moreover, experiments on
the task of image classification also show the effectiveness of the proposed
design.


Abstract:
  A few years ago, the first example of a closed manifold admitting an Anosov
diffeomorphism but no expanding map was given. Unfortunately, this example is
not explicit and is high-dimensional, although its exact dimension is unknown
due to the type of construction. In this paper, we present a family of concrete
12-dimensional nilmanifolds with an Anosov diffeomorphism but no expanding map,
where nilmanifolds are defined as the quotient of a 1-connected nilpotent Lie
group by a cocompact lattice. We show that this family has the smallest
possible dimension in the class of infra-nilmanifolds, which is conjectured to
be the only type of manifolds admitting Anosov diffeomorphisms up to
homeomorphism. The proof shows how to construct positive gradings from the
eigenvalues of the Anosov diffeomorphism under some additional assumptions
related to the rank, using the action of the Galois group on these algebraic
units.


Abstract:
  3D snapshot microscopy enables fast volumetric imaging by capturing a 3D
volume in a single 2D camera image, and has found a variety of biological
applications such as whole brain imaging of fast neural activity in larval
zebrafish. The optimal microscope design for this optical 3D-to-2D encoding is
both sample- and task-dependent, with no general solution known. Highly
programmable optical elements create new possibilities for sample-specific
computational optimization of microscope parameters, e.g. tuning the collection
of light for a given sample structure. We perform such optimization with deep
learning, using a differentiable wave-optics simulation of light propagation
through a programmable microscope and a neural network to reconstruct volumes
from the microscope image. We introduce a class of global kernel Fourier
convolutional neural networks which can efficiently decode information from
multiple depths in the volume, globally encoded across a 3D snapshot image. We
show that our proposed networks succeed in large field of view volume
reconstruction and microscope parameter optimization where traditional networks
fail. We also show that our networks outperform the state-of-the-art learned
reconstruction algorithms for lensless computational photography.


Abstract:
  Chiral symmetry represents a fundamental concept lying at the core of
particle and nuclear physics. Its spontaneous breaking in vacuum can be
exploited to distinguish chiral hadronic partners, whose masses differ. In
fact, the features of this breaking serve as guiding principles for the
construction of effective approaches of QCD at low energies, e.g., the chiral
perturbation theory, the linear sigma model, the
(Polyakov)--Nambu--Jona-Lasinio model, etc. At high temperatures/densities
chiral symmetry can be restored bringing the chiral partners to be nearly
degenerated in mass. At vanishing baryochemical potential, such restoration
follows a smooth transition, and the chiral companions reach this degeneration
above the transition temperature. In this work I review how different
realizations of chiral partner degeneracy arise in different effective
theories/models of QCD. I distinguish the cases where the chiral states are
either fundamental degrees of freedom or (dynamically-generated) composed
states. In particular, I discuss the intriguing case in which chiral symmetry
restoration involves more than two chiral partners, recently addressed in the
literature.


Abstract:
  The interplay between time-reversal symmetry (TRS) and band topology plays a
crucial role in topological states of quantum matter. In
time-reversal-invariant (TRI) systems, the inversion of spin-degenerate bands
with opposite parity leads to nontrivial topological states, such as
topological insulators and Dirac semimetals. When the TRS is broken, the
exchange field induces spin splitting of the bands. The inversion of a pair of
spin-splitting subbands can generate more exotic topological states, such as
quantum anomalous Hall insulators and magnetic Weyl semimetals. So far, such
topological phase transitions driven by the TRS breaking have not been
visualized. In this work, using angle-resolved photoemission spectroscopy, we
have demonstrated that the TRS breaking induces a band inversion of a pair of
spin-splitting subbands at the TRI points of Brillouin zone in EuB$_6$, when a
long-range ferromagnetic order is developed. The dramatic changes in the
electronic structure result in a topological phase transition from a TRI
ordinary insulator state to a TRS-broken topological semimetal (TSM) state.
Remarkably, the magnetic TSM state has an ideal electronic structure, in which
the band crossings are located at the Fermi level without any interference from
other bands. Our findings not only reveal the topological phase transition
driven by the TRS breaking, but also provide an excellent platform to explore
novel physical behavior in the magnetic topological states of quantum matter.


Abstract:
  The exploitation of syntactic graphs (SyGs) as a word's context has been
shown to be beneficial for distributional semantic models (DSMs), both at the
level of individual word representations and in deriving phrasal
representations via composition. However, notwithstanding the potential
performance benefit, the syntactically-aware DSMs proposed to date have huge
numbers of parameters (compared to conventional DSMs) and suffer from data
sparsity. Furthermore, the encoding of the SyG links (i.e., the syntactic
relations) has been largely limited to linear maps. The knowledge graphs'
literature, on the other hand, has proposed light-weight models employing
different geometric transformations (GTs) to encode edges in a knowledge graph
(KG). Our work explores the possibility of adopting this family of models to
encode SyGs. Furthermore, we investigate which GT better encodes syntactic
relations, so that these representations can be used to enhance phrase-level
composition via syntactic contextualisation.


Abstract:
  We present a new insight into the propagation of ion magnetoacoustic and
neutral acoustic waves in a magnetic arcade in the lower solar atmosphere. By
means of numerical simulations, we aim to: (a) study two-fluid waves
propagating in a magnetic arcade embedded in the partially-ionized, lower solar
atmosphere; and (b) investigate the impact of the background magnetic field
configuration on the observed wave-periods. We consider a 2D approximation of
the gravitationally stratified and partially-ionized lower solar atmosphere
consisting of ion+electron and neutral fluids that are coupled by ion-neutral
collisions. In this model, the convection below the photosphere is responsible
for the excitation of ion magnetoacoustic-gravity and neutral acoustic-gravity
waves. We find that in the solar photosphere, where ions and neutrals are
strongly coupled by collisions, ion magnetoacoustic-gravity and neutral
acoustic-gravity waves have periods ranging from 250 s to 350 s. In the
chromosphere, where the collisional coupling is weak, the wave characteristics
strongly depend on the magnetic field configuration. Above the foot-points of
the considered arcade, the plasma is dominated by a vertical magnetic field
along which ion magnetoacoustic-gravity waves propagate. These waves exhibit a
broad range of periods with the most prominent periods of 180 s, 220 s, and 300
s. Above the main loop of the solar arcade, where mostly horizontal magnetic
field lines guide ion magnetoacoustic-gravity waves, the main spectral power
reduces to the period of about 180 s and longer wave-periods do not exist. Our
results are in agreement with the recent observational data reported by
Wi\'sniewska et al. (2016) and Kayshap et al. (2018).


Abstract:
  Let $\Sigma$ be a compact convex hypersurface in ${\bf R}^{2n}$ which is
P-cyclic symmetric, i.e., $x\in \Sigma$ implies $Px\in\Sigma$ with P being a
$2n\times2n$ symplectic orthogonal matrix and satisfying $P^k=I_{2n}$,
$ker(P^l-I_{2n})=0$ for $1\leq l< k$, where $n, k\geq2$. In this paper, we
prove that there exist at least $n$ geometrically distinct closed
characteristics on $\Sigma$, which solves a longstanding conjecture about the
multiplicity of closed characteristics for a broad class of compact convex
hypersurfaces with symmetries(cf.,Page 235 of \cite{Eke1}). Based on the proof,
we further prove that if the number of geometrically distinct closed
characteristics on $\Sigma$ is finite, then at least $2[\frac{n}{2}]$ of them
are non-hyperbolic; and if the number of geometrically distinct closed
characteristics on $\Sigma$ is exactly $n$ and $k\geq3$, then all of them are
P-cyclic symmetric, where a closed characteristic $(\tau, y)$ on $\Sigma$ is
called P-cyclic symmetric if $y({\bf R})=Py({\bf R})$.


Abstract:
  In this paper, we obtain the weighted boundedness for the local
multi(sub)linear Hardy-Littlewood maximal operators and local multilinear
fractional integral operators associated with the local Muckenhoupt weights on
Gaussian measure spaces. We deal with these problems by introducing a new
pointwise equivalent "radial" definitions of these local operators. Moreover
using a similar approach, we also get the weighted boundedness for the local
fractional maximal operators with rough kernel and local fractional integral
operators with rough kernel on Gaussian measure spaces.


Abstract:
  Synchronization has been the subject of intense research during decades
mainly focused on determining the structural and dynamical conditions driving a
set of interacting units to a coherent state globally stable. However, little
attention has been paid to the description of the dynamical development of each
individual networked unit in the process towards the synchronization of the
whole ensemble. In this paper, we show how in a network of identical dynamical
systems, nodes belonging to the same degree class differentiate in the same
manner visiting a sequence of states of diverse complexity along the route to
synchronization independently on the global network structure. In particular,
we observe, just after interaction starts pulling orbits from the initially
uncoupled attractor, a general reduction of the complexity of the dynamics of
all units being more pronounced in those with higher connectivity. In the weak
coupling regime, when synchronization starts to build up, there is an increase
in the dynamical complexity whose maximum is achieved, in general, first in the
hubs due to their earlier synchronization with the mean field. For very strong
coupling, just before complete synchronization, we found a hierarchical
dynamical differentiation with lower degree nodes being the ones exhibiting the
largest complexity departure. We unveil how this differentiation route holds
for several models of nonlinear dynamics including toroidal chaos and how it
depends on the coupling function. This study provides new insights to
understand better strategies for network identification and control or to
devise effective methods for network inference.


Abstract:
  This project is based on a mathematical model of erythropoiesis for anemia,
which consists of five hyperbolic population equations describing the
production of red blood cells under treatment with epoetin-alfa (EPO). Extended
dynamic mode decomposition (EDMD) is utilized to approximate the non-linear
dynamical systems by linear ones. This allows for efficient and reliable
strategies based on a combination of EDMD and model predictive control (MPC),
which produces results comparable with the one obtained in past publications
for the original model.


Abstract:
  We propose a simple and efficient real-space approach for the calculation of
the ground-state energies of Wigner crystals in 1, 2, and 3 dimensions. To be
precise, we calculate the first two terms in the asymptotic expansion of the
total energy per electron which correspond to the classical energy and the
harmonic correction due to the zero-point motion of the Wigner crystals,
respectively. Our approach employs Clifford periodic boundary conditions to
simulate the infinite electron gas and a renormalized distance to evaluate the
Coulomb potential. This allows us to calculate the energies unambiguously and
with a higher precision than those reported in the literature. Our results are
in agreement with the literature values with the exception of harmonic
correction of the 2-dimensional Wigner crystal for which we find a significant
difference. Although we focus on the ground state, i.e., the triangular lattice
and the body-centered cubic lattice, in two and three dimensions, respectively,
we also report the classical energies of several other common lattice
structures.


Abstract:
  Large matrices are often accessed as a row-order stream. We consider the
setting where rows are time-sensitive (i.e. they expire), which can be
described by the sliding-window row-order model, and provide the first
$(1+\epsilon)$-approximation of Schatten $p$-norms in this setting. Our main
technical contribution is a proof that Schatten $p$-norms in row-order streams
are smooth, and thus fit the smooth-histograms technique of Braverman and
Ostrovsky (FOCS 2007) for sliding-window streams.


Abstract:
  Smart devices, such as smartphones, wearables, robots, and others, can
collect vast amounts of data from their environment. This data is suitable for
training machine learning models, which can significantly improve their
behavior, and therefore, the user experience. Federated learning is a young and
popular framework that allows multiple distributed devices to train deep
learning models collaboratively while preserving data privacy. Nevertheless,
this approach may not be optimal for scenarios where data distribution is
non-identical among the participants or changes over time, causing what is
known as concept drift. Little research has yet been done in this field, but
this kind of situation is quite frequent in real life and poses new challenges
to both continual and federated learning. Therefore, in this work, we present a
new method, called Concept-Drift-Aware Federated Averaging (CDA-FedAvg). Our
proposal is an extension of the most popular federated algorithm, Federated
Averaging (FedAvg), enhancing it for continual adaptation under concept drift.
We empirically demonstrate the weaknesses of regular FedAvg and prove that
CDA-FedAvg outperforms it in this type of scenario.


Abstract:
  Exotic tiling patterns of quasicrystals have motivated extensive studies of
quantum phenomena such as critical states and phasons. Nevertheless, the
systematic understanding of the Landau levels of quasicrystals in the presence
of the magnetic field has not been established yet. One of the main obstacles
is the complication of the quasiperiodic tilings without periodic length
scales, thus it has been thought that the system cannot possess any universal
features of the Landau levels. In this paper, contrary to these assertions, we
develop a generic theory of the Landau levels for quasicrystals. Focusing on
the two dimensional quasicrystals with rotational symmetries, we highlight that
quasiperiodic tilings induce anomalous Landau levels where electrons are
localized near the rotational symmetry centers. Interestingly, the localization
length of these Landau levels has a universal dependence on n for quasicrystals
with n-fold rotational symmetry. Furthermore, macroscopically degenerate zero
energy Landau levels are present due to the chiral symmetry of the rhombic
tilings. In this case, each Landau level forms an independent island where
electrons are trapped at given fields, but with field control, the interference
between the islands gives rise to an abrupt change in the local density of
states. Our work provide a general scheme to understand the electron
localization behavior of the Landau levels in quasicrystals.


Abstract:
  The gas content of the complete compilation of Local Group dwarf galaxies
(119 within 2 Mpc) is presented using HI survey data. Within the virial radius
of the Milky Way (224 kpc here), 53 of 55 dwarf galaxies are devoid of gas to
limits of M$_{\rm HI}<10^4$ M$_\odot$. Within the virial radius of M31 (266
kpc), 27 of 30 dwarf galaxies are devoid of gas (with limits typically $<10^5$
M$_\odot$). Beyond the virial radii of the Milky Way and M31, the majority of
the dwarf galaxies have detected HI gas and have HI masses higher than the
limits. When the relationship between gas content and distance is investigated
using a Local Group virial radius, more of the non-detected dwarf galaxies are
within this radius (85$\pm1$ of the 93 non-detected dwarf galaxies) than within
the virial radii of the Milky Way and M31. Using the Gaia proper motion
measurements available for 38 dwarf galaxies, the minimum gas density required
to completely strip them of gas is calculated. Halo densities between $10^{-5}$
and $5 \times 10^{-4}$ cm$^{-3}$ are typically required for instantaneous
stripping at perigalacticon. When compared to halo density with radius
expectations from simulations and observations, 80% of the dwarf galaxies with
proper motions are consistent with being stripped by ram pressure at Milky Way
pericenter. The results suggest a diffuse gaseous galactic halo medium is
important in quenching dwarf galaxies, and that a Local Group medium also
potentially plays a role.


Abstract:
  We introduce a visual motion segmentation method employing spherical geometry
for fisheye cameras and automoated driving. Three commonly used geometric
constraints in pin-hole imagery (the positive height, positive depth and
epipolar constraints) are reformulated to spherical coordinates, making them
invariant to specific camera configurations as long as the camera calibration
is known. A fourth constraint, known as the anti-parallel constraint, is added
to resolve motion-parallax ambiguity, to support the detection of moving
objects undergoing parallel or near-parallel motion with respect to the host
vehicle. A final constraint constraint is described, known as the spherical
three-view constraint, is described though not employed in our proposed
algorithm. Results are presented and analyzed that demonstrate that the
proposal is an effective motion segmentation approach for direct employment on
fisheye imagery.


Abstract:
  Most of the functions performed by astrocytes in brain information processing
are related to calcium waves. Experimental studies involving calcium waves
present discrepant results, leading to gaps in the full understanding of the
functions of these cells. The use of mathematical models help to understand the
experimental results, identifying chemical mechanisms involved in calcium waves
and the limits of experimental methods. The model is diffusion-based and uses
receptors and channels as boundary conditions. The computer program developed
was prepared to allow the study of complex geometries, with several astrocytes,
each of them with several branches. The code structure allows easy adaptation
to various experimental situations in which the model can be compared. The code
was deposited in the ModelDB repository, and will be under number 266795 after
publication. A sensitivity analysis showed the relative significance of the
parameters and identifies the ideal range of values for each one. We showed
that several sets of values can lead to the same calcium signaling dynamics.
This encourages the questioning of parameters to model calcium signaling in
astrocytes that are commonly used in the literature, and it suggests better
experimental planning. In the final part of the work, the effects produced by
the endoplasmic reticulum when located close to the extremities of the branches
were evaluated. We conclude that when they are located close to the region of
the glutamatergic stimulus, they favor local calcium dynamics. By contrast,
when they are located at points away from the stimulated region, they
accelerate the global spread of signaling.


Abstract:
  Result relevance prediction is an essential task of e-commerce search engines
to boost the utility of search engines and ensure smooth user experience. The
last few years eyewitnessed a flurry of research on the use of
Transformer-style models and deep text-match models to improve relevance.
However, these two types of models ignored the inherent bipartite network
structures that are ubiquitous in e-commerce search logs, making these models
ineffective. We propose in this paper a novel Second-order Relevance, which is
fundamentally different from the previous First-order Relevance, to improve
result relevance prediction. We design, for the first time, an end-to-end
First-and-Second-order Relevance prediction model for e-commerce item
relevance. The model is augmented by the neighborhood structures of bipartite
networks that are built using the information of user behavioral feedback,
including clicks and purchases. To ensure that edges accurately encode
relevance information, we introduce external knowledge generated from BERT to
refine the network of user behaviors. This allows the new model to integrate
information from neighboring items and queries, which are highly relevant to
the focus query-item pair under consideration. Results of offline experiments
showed that the new model significantly improved the prediction accuracy in
terms of human relevance judgment. An ablation study showed that the
First-and-Second-order model gained a 4.3% average gain over the First-order
model. Results of an online A/B test revealed that the new model derived more
commercial benefits compared to the base model.


Abstract:
  We apply the multi-particle fields model to calculate the differential
cross-section d{\sigma}/dt of elastic proton-proton scattering. This problem
includes the calculation of multidimensional integrals arising from the loop
Feynman diagrams. We demonstrated how these integrals can be reduced with
Laplace's method to one- and two-dimensional integrals which can be calculated
numerically. The obtained result qualitatively describe the minimum in
differential cross-section dependency d{\sigma}/dt(t).


Abstract:
  Let $p(z)$ be a nonconstant polynomial and $\beta(z)$ be a small entire
function of $e^{p(z)}$ in the sense of Nevanlinna. We first describe the growth
behavior of the entire function $H(z):=e^{p(z)}\int_0^{z}\beta(t)e^{-p(t)}dt$
on the complex plane $\mathbb{C}$. As an application, we solve entire solutions
of Tumura--Clunie type differential equation
$f(z)^n+P(z,f)=b_1(z)e^{p_1(z)}+b_2(z)e^{p_2(z)}$, where $b_1(z)$ and $b_2(z)$
are nonzero polynomials, $p_1(z)$ and $p_2(z)$ are two polynomials of the same
degree~$k\geq 1$ and $P(z,f)$ is a differential polynomial in $f$ of degree
$\leq n-1$ with meromorphic functions of order~$<k$ as coefficients. These
results allow us to determine all solutions with relatively few zeros of the
second-order differential equation
$f''-[b_1(z)e^{p_1(z)}+b_2(z)e^{p_2(z)}+b_3(z)]f=0$, where $b_3(z)$ is a
polynomial. We also prove a theorem on certain first-order linear differential
equation related to complex dynamics.


Abstract:
  This work is focused on the system-level performance of a broadcast network.
Since all transmitters in a broadcast network transmit the identical signal,
received signals from multiple transmitters can be combined to improve system
performance. We develop a stochastic geometry based analytical framework to
derive the coverage of a typical receiver. We show that there may exist an
optimal connectivity radius that maximizes the rate coverage. Our analysis
includes the fact that users may have their individual content/advertisement
preferences. We assume that there are multiple classes of users with each user
class prefers a particular type of content/advertisements and the users will
pay the network only when then can see content aligned with their interest. The
operator may choose to transmit multiple contents simultaneously to cater more
users' interests to increase its revenue. We present revenue models to study
the impact of the number of contents on the operator revenue. We consider two
scenarios for users' distribution: one where users' interest depends on their
geographical location and the one where it doesn't. With the help of numerical
results and analysis, we show the impact of various parameters including
content granularity, connectivity radius, and rate threshold and present
important design insights.


Abstract:
  Differential cross sections for the Drell-Yan process, including Z boson
production, using the dimuon decay channel are measured in proton-lead (pPb)
collisions at a nucleon-nucleon centre-of-mass energy of 8.16 TeV. A data
sample recorded with the CMS detector at the LHC is used, corresponding to an
integrated luminosity of 173 nb$^{-1}$. The differential cross section as a
function of the dimuon mass is measured in the range 15-600 GeV, for the first
time in proton-nucleus collisions. It is also reported as a function of dimuon
rapidity over the mass ranges 15-60 GeV and 60-120 GeV, and ratios for the
p-going over the Pb-going beam directions are built. In both mass ranges, the
differential cross sections as functions of the dimuon transverse momentum
$p_\mathrm{T}$ and of a geometric variable $\phi^*$ are measured, where
$\phi^*$ highly correlates with $p_\mathrm{T}$ but is determined with higher
precision. In the Z mass region, the rapidity dependence of the data indicate a
modification of the distribution of partons within a lead nucleus as compared
to the proton case. The data are more precise than predictions based upon
current models of parton distributions.


Abstract:
  In this paper, we study {\bf twisted Milnor hypersurfaces} and compute their
$\hat A$-genus and Atiyah-Singer-Milnor $\alpha$-invariant. Our tool to compute
the $\alpha$-invariant is Zhang's analytic Rokhlin congruence formula. We also
give some applications about group actions and metrics of positive scalar
curvature on twisted Milnor hypersurfaces.


Abstract:
  We propose magnetically arrested disks (MADs) in quiescent black-hole (BH)
binaries as the origin of the multiwavelength emission, and argue that this
class of sources can dominate the cosmic-ray spectrum around the knee. X-ray
luminosities of Galactic BH binaries in the quiescent state are far below the
Eddington luminosity, and thus, radiatively inefficient accretion flows (RIAFs)
are formed in the inner region. Strong thermal and turbulent pressures in RIAFs
produce outflows, which can create large-scale poloidal magnetic fields. These
fields are carried to the vicinity of the BH by the rapid inflow motion,
forming a MAD. Inside the MAD, non-thermal protons and electrons are naturally
accelerated by magnetic reconnections or stochastic acceleration by turbulence.
Both thermal and non-thermal electrons emit broadband photons via synchrotron
emission, which are broadly consistent with the optical and X-ray data of the
quiescent BH X-ray binaries. Moreover, protons are accelerated up to PeV
energies and diffusively escape from these MADs, which can account for the
cosmic-ray intensity around the knee energy.


Abstract:
  In this paper, a hybrid model for single-crystal Shape Memory Alloy (SMA)
wire actuators is presented. The result is based on a mathematical
reformulation of the M\"uller-Achenbach-Seelecke (MAS) model, which provides an
accurate and interconnection-oriented description of the SMA hysteretic
response. The strong nonlinearity and high numerical stiffness of the MAS
model, however, hinder its practical use for simulation and control of complex
SMA-driven systems. The main idea behind the hybrid reformulation is based on
dividing the mechanical hysteresis of the SMA into five operating modes, each
one representing a different physical state of the material. By properly
deriving the switching conditions among those modes in a physically-consistent
way, the MAS model is effectively reformulated within a hybrid dynamical
setting. The main advantage of the hybrid reformulation is the possibility of
describing the material dynamics with a simplified set of state equations while
maintaining all benefits of the physics-based description offered by the MAS
model After describing the novel approach, simulation studies are conducted on
a flexible robotic module actuated by protagonist-antagonist SMA wires. Through
comparative numerical analysis, it is shown how the hybrid model provides the
same accuracy as the MAS model while saving up to 80% of the simulation time.
Moreover, the new modeling framework opens up the possibility of addressing SMA
control from a hybrid systems perspective.


Abstract:
  We determine the Weierstrass semigroup $H(P_\infty,P_1,\ldots,P_m)$ at
several rational points on the maximal curves which cannot be covered by the
Hermitian curve introduced by Tafazolian, Teher\'an-Herrera, and Torres.
Furthermore, we present some conditions to find pure gaps. We use this
semigroup to obtain AG codes with better relative parameters than comparable
one-point AG codes arising from these curves.


Abstract:
  We present here a simple mathematical model that provides a successful
strategy, quantitatively, to ending the continued championship futility
experienced by Canadian Hockey Teams. Competitive Intransitivity is used here
as a simple predictive framework to capture how investing strategically, under
a uniform salary cap, in just 3 independently variable aspects of the sport
(such as Offence, Defence, and a Goaltender), by just 3 Hockey Teams applying
differing salary priorities (such as Montreal, Boston, and New York), can lead
to rich and perhaps surprisingly unexpected outcomes in play, similar to
rolling intransitive dice together in a series of head-to-head games. A
possibly fortunate conclusion of this analysis is the prediction that for any
Team's chosen strategy (such as New York's), a counter strategy within the same
salary cap can be adopted by a playoff opponent (such as Montreal) which will
prove victorious over a long playoff series, enabling a pathway to end
prolonged championship futility.


Abstract:
  We introduce Dynabench, an open-source platform for dynamic dataset creation
and model benchmarking. Dynabench runs in a web browser and supports
human-and-model-in-the-loop dataset creation: annotators seek to create
examples that a target model will misclassify, but that another person will
not. In this paper, we argue that Dynabench addresses a critical need in our
community: contemporary models quickly achieve outstanding performance on
benchmark tasks but nonetheless fail on simple challenge examples and falter in
real-world scenarios. With Dynabench, dataset creation, model development, and
model assessment can directly inform each other, leading to more robust and
informative benchmarks. We report on four initial NLP tasks, illustrating these
concepts and highlighting the promise of the platform, and address potential
objections to dynamic benchmarking as a new standard for the field.


Abstract:
  Due to the wide adoption of social media platforms like Facebook, Twitter,
etc., there is an emerging need of detecting online posts that can go against
the community acceptance standards. The hostility detection task has been well
explored for resource-rich languages like English, but is unexplored for
resource-constrained languages like Hindidue to the unavailability of large
suitable data. We view this hostility detection as a multi-label multi-class
classification problem. We propose an effective neural network-based technique
for hostility detection in Hindi posts. We leverage pre-trained multilingual
Bidirectional Encoder Representations of Transformer (mBERT) to obtain the
contextual representations of Hindi posts. We have performed extensive
experiments including different pre-processing techniques, pre-trained models,
neural architectures, hybrid strategies, etc. Our best performing neural
classifier model includes One-vs-the-Rest approach where we obtained 92.60%,
81.14%,69.59%, 75.29% and 73.01% F1 scores for hostile, fake, hate, offensive,
and defamation labels respectively. The proposed model outperformed the
existing baseline models and emerged as the state-of-the-art model for
detecting hostility in the Hindi posts.


Abstract:
  We study the Wishart-Sachdev-Ye-Kitaev (WSYK) model consisting of two
$\hat{q}$-body Sachdev-Ye-Kitaev (SYK) models with general complex couplings,
one the Hermitian conjugate of the other, living in off-diagonal blocks of a
larger WSYK Hamiltonian. The spectrum is positive with a hard edge at zero
energy. We employ diagrammatic and combinatorial techniques to compute
analytically the low-order moments of the Hamiltonian. In the limit of large
number $N$ of Majoranas, we have found striking similarities with the moments
of the weight function of the Al-Salam-Chihara $Q$-Laguerre polynomials. For
$\hat{q} = 3, 4$, the $Q$-Laguerre prediction, with $Q=Q(\hat{q},N)$ also
computed analytically, agrees well with exact diagonalization results for $30 <
N \leq 34$ while we observe some deviations for $\hat q = 2$. The most salient
feature of the spectral density is that, for odd $\hat{q}$, low-energy
excitations grow as a stretched exponential, with a functional form different
from that of the supersymmetric SYK model. For $\hat q = 4$, a detailed
analysis of level statistics reveals quantum chaotic dynamics even for time
scales substantially shorter than the Heisenberg time. More specifically, the
spacing ratios in the bulk of the spectrum and the microscopic spectral density
and the number variance close to the hard edge are very well approximated by
that of an ensemble of random matrices that, depending on $N$, belong to the
chiral or superconducting universality classes. In particular, we report the
first realization of level statistics belonging to the chGUE universality
class, which completes the tenfold-way classification in the SYK model.


Abstract:
  Data from clinical real-world settings is characterized by variability in
quality, machine-type, setting, and source. One of the primary goals of medical
computer vision is to develop and validate artificial intelligence (AI) based
algorithms on real-world data enabling clinical translations. However, despite
the exponential growth in AI based applications in healthcare, specifically in
ophthalmology, translations to clinical settings remain challenging. Limited
access to adequate and diverse real-world data inhibits the development and
validation of translatable algorithms. In this paper, we present a new
multi-modal longitudinal ophthalmic imaging dataset, the Illinois Ophthalmic
Database Atlas (I-ODA), with the goal of advancing state-of-the-art computer
vision applications in ophthalmology, and improving upon the translatable
capacity of AI based applications across different clinical settings. We
present the infrastructure employed to collect, annotate, and anonymize images
from multiple sources, demonstrating the complexity of real-world retrospective
data and its limitations. I-ODA includes 12 imaging modalities with a total of
3,668,649 ophthalmic images of 33,876 individuals from the Department of
Ophthalmology and Visual Sciences at the Illinois Eye and Ear Infirmary of the
University of Illinois Chicago (UIC) over the course of 12 years.


Abstract:
  We explore an anomaly-free ${\textrm U}(1)$ gauge extended beyond the
Standard model (BSM) framework, to account for the baryon asymmetry of the
Universe, along with arranging for tiny neutrino mass. Neutrino masses are
generated via higher-dimensional operators (HDOs) involving three right-handed
neutrinos (RHNs) with gauge charges ($4$, $4$ and $-5$ respectively) and two
BSM scalars. This is an attractive framework as it can accommodate a keV scale
dark matter, with the lightest RHN being the candidate. The remaining two RHNs
are quasi-degenerate at the TeV-scale, actively participating in the process of
resonant leptogenesis through their decay governed by the same set of HDOs. The
RHNs being at the TeV scale, make this framework relevant for studying flavored
resonant leptogenesis. This TeV-scale resonant leptogenesis, after satisfying
the neutrino oscillation data, leads to interesting predictions on the Yukawa
sector of the model HDOs. The thermal evolution of the baryon asymmetry has
followed the experimental results rather accurately in that corner of parameter
space. As a matter of fact, this TeV-scale framework which in principle relies
on the low scale resonant leptogenesis typically leads to predictions that
potentially can be tested at the colliders. In particular, we consider the
same-sign dilepton signature that arises from the RHN pair production through
the decay of heavy gauge boson of the extra ${\textrm U}(1)$.


Abstract:
  We prove that random hypergraphs are asymptotically almost surely resiliently
Hamiltonian. Specifically, for any $\gamma>0$ and $k\ge3$, we show that
asymptotically almost surely, every subgraph of the binomial random $k$-uniform
hypergraph $G^{(k)}\big(n,n^{\gamma-1}\big)$ in which all $(k-1)$-sets are
contained in at least $\big(\tfrac12+2\gamma\big)pn$ edges has a tight Hamilton
cycle. This is a cyclic ordering of the $n$ vertices such that each consecutive
$k$ vertices forms an edge.


Abstract:
  Language resources are necessary for language processing,but building them is
costly, involves many researches from different areas and needs constant
updating. In this paper, we describe the crosslingual framework used for
developing the Multilingual Central Repository (MCR), a multilingual knowledge
base that includes wordnets of Basque, Catalan, English, Galician, Portuguese,
Spanish and the following ontologies: Base Concepts, Top Ontology, WordNet
Domains and Suggested Upper Merged Ontology. We present the story of MCR, its
state in 2017 and the developed tools.


Abstract:
  Contrastive learning has delivered impressive results for various tasks in
the self-supervised regime. However, existing approaches optimize for learning
representations specific to downstream scenarios, i.e., \textit{global}
representations suitable for tasks such as classification or \textit{local}
representations for tasks such as detection and localization. While they
produce satisfactory results in the intended downstream scenarios, they often
fail to generalize to tasks that they were not originally designed for. In this
work, we propose to learn video representations that generalize to both the
tasks which require global semantic information (e.g., classification) and the
tasks that require local fine-grained spatio-temporal information (e.g.,
localization). We achieve this by optimizing two contrastive objectives that
together encourage our model to learn global-local visual information given
audio signals. We show that the two objectives mutually improve the
generalizability of the learned global-local representations, significantly
outperforming their disjointly learned counterparts. We demonstrate our
approach on various tasks including action/sound classification, lip reading,
deepfake detection, event and sound localization
(https://github.com/yunyikristy/global\_local).


Abstract:
  When applying imitation learning techniques to fit a policy from expert
demonstrations, one can take advantage of prior stability/robustness
assumptions on the expert's policy and incorporate such control-theoretic prior
knowledge explicitly into the learning process. In this paper, we formulate the
imitation learning of linear policies as a constrained optimization problem,
and present efficient methods which can be used to enforce stability and
robustness constraints during the learning processes. Specifically, we show
that one can guarantee the closed-loop stability and robustness by posing
linear matrix inequality (LMI) constraints on the fitted policy. Then both the
projected gradient descent method and the alternating direction method of
multipliers (ADMM) method can be applied to solve the resulting constrained
policy fitting problem. Finally, we provide numerical results to demonstrate
the effectiveness of our methods in producing linear polices with various
stability and robustness guarantees.


Abstract:
  We explore the presence of active galactic nuclei (AGN)/black hole (BH) in
Green Pea galaxies (GPs), motivated by the presence of high ionization emission
lines such as HeII and [NeIII] in their optical spectra. In order to identify
AGN candidates, we used mid-infrared (MIR) photometric observations from the
all-sky Wide-field Infrared Survey Explorer (WISE) mission for a sample of 516
GPs. We select 58 GPs as candidate AGN based on a stringent 3-band WISE color
diagnostic. Using multi-epoch photometry of W1 and W2 bands from the
WISE/NEOWISE-R observations, we find 38 GPs showing significant variability in
both the WISE bands. Four of these were selected as AGN by the WISE 3-band
color diagnostic as well. Interestingly, we find a high fraction of MIR
variable sources among GPs which demonstrates the uniqueness and importance of
studying these extreme objects. Through this work, we demonstrate that
photometric variability is a promising tool to select AGN that may be missed by
other selection techniques (including optical emission-line ratios and X-ray
emission) in star-formation dominated, low-mass, low-metallicity galaxies.


Abstract:
  Second language (L2) English learners often find it difficult to improve
their pronunciations due to the lack of expressive and personalized corrective
feedback. In this paper, we present Pronunciation Teacher (PTeacher), a
Computer-Aided Pronunciation Training (CAPT) system that provides personalized
exaggerated audio-visual corrective feedback for mispronunciations. Though the
effectiveness of exaggerated feedback has been demonstrated, it is still
unclear how to define the appropriate degrees of exaggeration when interacting
with individual learners. To fill in this gap, we interview 100 L2 English
learners and 22 professional native teachers to understand their needs and
experiences. Three critical metrics are proposed for both learners and teachers
to identify the best exaggeration levels in both audio and visual modalities.
Additionally, we incorporate the personalized dynamic feedback mechanism given
the English proficiency of learners. Based on the obtained insights, a
comprehensive interactive pronunciation training course is designed to help L2
learners rectify mispronunciations in a more perceptible, understandable, and
discriminative manner. Extensive user studies demonstrate that our system
significantly promotes the learners' learning efficiency.


Abstract:
  Applying the concept of S-convergence, based on averaging in the spirit of
Strong Law of Large Numbers, the vanishing viscosity solutions of the Euler
system are studied. We show how to efficiently compute a viscosity solution of
the Euler system as the S-limit of numerical solutions obtained by the
Viscosity Finite Volume method. Theoretical results are illustrated by
numerical simulations of the Kelvin--Helmholtz instability problem.


Abstract:
  In this paper, we propose our enhanced approach to create a dedicated corpus
for Algerian Arabic newspapers comments. The developed approach has to enhance
an existing approach by the enrichment of the available corpus and the
inclusion of the annotation step by following the Model Annotate Train Test
Evaluate Revise (MATTER) approach. A corpus is created by collecting comments
from web sites of three well know Algerian newspapers. Three classifiers,
support vector machines, na{\"i}ve Bayes, and k-nearest neighbors, were used
for classification of comments into positive and negative classes. To identify
the influence of the stemming in the obtained results, the classification was
tested with and without stemming. Obtained results show that stemming does not
enhance considerably the classification due to the nature of Algerian comments
tied to Algerian Arabic Dialect. The promising results constitute a motivation
for us to improve our approach especially in dealing with non Arabic sentences,
especially Dialectal and French ones.


Abstract:
  We describe a technique to measure photon pair joint spectra by detecting the
time-correlation beat note when non-degenerate photon pairs interfere at a
beamsplitter. The technique implements a temporal analog of the Ghosh-Mandel
effect with one photon counter and a time-resolved Hong-Ou-Mandel interference
with two. It is well suited to characterize pairs of photons, each of which can
interact with a single atomic species, as required to study recently predicted
photon-photon interaction in sub-wavelength atomic arrays. With this technique,
we characterize photon pairs from cavity-enhanced parametric downconversion
with a bandwidth $\approx$ 5 MHz and frequency separation of $\sim$ 200 MHz
near the D$_1$ line of atomic Rb.


Abstract:
  We consider the problem of linear regression from strategic data sources with
a public good component, i.e., when data is provided by strategic agents who
seek to minimize an individual provision cost for increasing their data's
precision while benefiting from the model's overall precision. In contrast to
previous works, our model tackles the case where there is uncertainty on the
attributes characterizing the agents' data -- a critical aspect of the problem
when the number of agents is large. We provide a characterization of the game's
equilibrium, which reveals an interesting connection with optimal design.
Subsequently, we focus on the asymptotic behavior of the covariance of the
linear regression parameters estimated via generalized least squares as the
number of data sources becomes large. We provide upper and lower bounds for
this covariance matrix and we show that, when the agents' provision costs are
superlinear, the model's covariance converges to zero but at a slower rate
relative to virtually all learning problems with exogenous data. On the other
hand, if the agents' provision costs are linear, this covariance fails to
converge. This shows that even the basic property of consistency of generalized
least squares estimators is compromised when the data sources are strategic.


Abstract:
  The supernova remnant (SNR) 3C 397 is thought to originate from a Type Ia
supernova (SN Ia) explosion of a near-Chandrasekhar-mass ($M_{\rm Ch}$)
progenitor, based on the enhanced abundances of Mn and Ni revealed by previous
X-ray study with Suzaku. Here we report follow-up XMM-Newton observations of
this SNR, conducted with the aim of investigating the detailed spatial
distribution of the Fe-peak elements. We have discovered an ejecta clump with
extremely high abundances of Ti and Cr, in addition to Mn, Fe, and Ni, in the
southern part of the SNR. The Fe mass of this ejecta clump is estimated to be
$\sim$ 0.06 $M_{\odot}$, under the assumption of a typical Fe yield for SNe Ia
(i.e., $\sim$ 0.8 $M_{\odot}$). The observed mass ratios among the Fe-peak
elements and Ti require substantial neutronization that is achieved only in the
innermost regions of a near-$M_{\rm Ch}$ SN Ia with a central density of
$\rho_c \sim 5 \times 10^9$ g cm$^{-3}$, significantly higher than typically
assumed for standard near-$M_{\rm Ch}$ SNe Ia ($\rho_c \sim 2 \times 10^9$ g
cm$^{-3}$). The overproduction of the neutron-rich isotopes (e.g., $^{50}$Ti
and $^{54}$Cr) is significant in such high-$\rho_c$ SNe Ia, with respect to the
solar composition. Therefore, if 3C 397 is a typical high-$\rho_c$ near-$M_{\rm
Ch}$ SN Ia remnant, the solar abundances of these isotopes could be reproduced
by the mixture of the high- and low-$\rho_c$ near-$M_{\rm Ch}$ and sub-$M_{\rm
Ch}$ Type Ia events, with $\lesssim$ 20 % being high-$\rho_c$ near-$M_{\rm
Ch}$.


Abstract:
  We present the results of the X-ray flaring activity of 1ES 1959+650 during
October 25-26, 2017 using AstroSat observations. The source was variable in the
X-ray. We investigated the evolution of the X-ray spectral properties of the
source by dividing the total observation period ($\sim 130$ ksecs) into time
segments of 5 ksecs, and fitting the SXT and LAXPC spectra for each segment.
Synchrotron emission of a broken power-law particle density model provided a
better fit than the log-parabola one. The X-ray flux and the normalised
particle density at an energy less than the break one, were found to
anti-correlate with the index before the break. However, a stronger correlation
between the density and index was obtained when a delay of $\sim 60$ ksec was
introduced. The amplitude of the normalised particle density variation $|\Delta
n_\gamma/n_\gamma| \sim 0.1$ was found to be less than that of the index
$\Delta \Gamma \sim 0.5$. We model the amplitudes and the time delay in a
scenario where the particle acceleration time-scale varies on a time-scale
comparable to itself. In this framework, the rest frame acceleration time-scale
is estimated to be $\sim 1.97\times10^{5}$ secs and the emission region size to
be $\sim 6.73\times 10^{15}$ cms.


Abstract:
  In this paper we consider the problem to reconstruct a $k$-uniform hypergraph
from its line graph. In general this problem is hard. We solve this problem
when the number of hyperedges containing any pair of vertices is bounded. Given
an integer sequence, constructing a $k$-uniform hypergraph with that as its
degree sequence is NP-complete. Here we show that for constant integer
sequences the question can be answered in polynomial time using Baranyai's
theorem.


Abstract:
  We quantitatively investigate the dependence of central galaxy HI mass
($M_{\rm HI}$) on the stellar mass ($M_\ast$), halo mass ($M_{\rm h}$), star
formation rate (SFR), and central stellar surface density within 1 kpc
($\Sigma_1$), taking advantage of the HI spectra stacking technique using both
the Arecibo Fast Legacy ALFA Survey and the Sloan Digital Sky Survey. We find
that the shapes of $M_{\rm HI}$-$M_{\rm h}$ and $M_{\rm HI}$-$M_\ast$ relations
are remarkably similar for both star-forming and quenched galaxies, with
massive quenched galaxies having constantly lower HI masses of around 0.6 dex.
This similarity strongly suggests that neither halo mass nor stellar mass is
the direct cause of quenching, but rather the depletion of HI reservoir. While
the HI reservoir for low-mass galaxies of $M_\ast<10^{10.5}M_\odot$ strongly
increases with $M_{\rm h}$, more massive galaxies show no significant
dependence of $M_{\rm HI}$ on $M_{\rm h}$, indicating the effect of halo to
determine the smooth cold gas accretion. We find that the star formation and
quenching of central galaxies are directly regulated by the available HI
reservoir, with an average relation of ${\rm SFR}\propto M_{\rm
HI}^{2.75}/M_\ast^{0.40}$, implying a quasi-steady state of star formation. We
further confirm that galaxies are depleted of their HI reservoir once they drop
off the star-formation main sequence and there is a very tight and consistent
correlation between $M_{\rm HI}$ and $\Sigma_1$ in this phase, with $M_{\rm
HI}\propto\Sigma_1^{-2}$. This result is in consistent with the
compaction-triggered quenching scenario, with galaxies going through three
evolutionary phases of cold gas accretion, compaction and post-compaction, and
quenching.


Abstract:
  The competent programmer hypothesis states that most programmers are
competent enough to create correct or almost correct source code. Because this
implies that bugs should usually manifest through small variations of the
correct code, the competent programmer hypothesis is one of the fundamental
assumptions of mutation testing. Unfortunately, it is still unclear if the
competent programmer hypothesis holds and past research presents contradictory
claims. Within this article, we provide a new perspective on the competent
programmer hypothesis and its relation to mutation testing. We try to re-create
real-world bugs through chains of mutations to understand if there is a direct
link between mutation testing and bugs. The lengths of these paths help us to
understand if the source code is really almost correct, or if large variations
are required. Our results indicate that while the competent programmer
hypothesis seems to be true, mutation testing is missing important operators to
generate representative real-world bugs.


Abstract:
  Demanding that charged Nariai black holes in (quasi-)de Sitter space decay
without becoming super-extremal implies a lower bound on the masses of charged
particles, known as the Festina Lente (FL) bound. In this paper we fix the
$\mathcal{O}(1)$ constant in the bound and elucidate various aspects of it, as
well as extensions to $d>4$ and to situations with scalar potentials and
dilatonic couplings. We also discuss phenomenological implications of FL
including an explanation of why the Higgs potential cannot have a local minimum
at the origin, thus explaining why the weak force must be broken. For
constructions of meta-stable dS involving anti-brane uplift scenarios, even
though the throat region is consistent with FL, the bound implies that we
cannot have any light charged matter fields coming from any far away region in
the compactified geometry, contrary to the fact that they are typically
expected to arise in these scenarios. This strongly suggests that introduction
of warped anti-branes in the throat cannot be decoupled from the bulk dynamics
as is commonly assumed. Finally, we provide some evidence that in certain
situations the FL bound can have implications even with gravity decoupled and
illustrate this in the context of non-compact throats.


Abstract:
  From small screenshots to large videos, documents take up a bulk of space in
a modern smartphone. Documents in a phone can accumulate from various sources,
and with the high storage capacity of mobiles, hundreds of documents are
accumulated in a short period. However, searching or managing documents remains
an onerous task, since most search methods depend on meta-information or only
text in a document. In this paper, we showcase that a single modality is
insufficient for classification and present a novel pipeline to classify
documents on-device, thus preventing any private user data transfer to server.
For this task, we integrate an open-source library for Optical Character
Recognition (OCR) and our novel model architecture in the pipeline. We optimise
the model for size, a necessary metric for on-device inference. We benchmark
our classification model with a standard multimodal dataset FOOD-101 and
showcase competitive results with the previous State of the Art with 30% model
compression.


Abstract:
  We consider a novel formulation of the dynamic pricing and demand learning
problem, where the evolution of demand in response to posted prices is governed
by a stochastic variant of the popular Bass model with parameters $\alpha,
\beta$ that are linked to the so-called "innovation" and "imitation" effects.
Unlike the more commonly used i.i.d. and contextual demand models, in this
model the posted price not only affects the demand and the revenue in the
current round but also the future evolution of demand, and hence the fraction
of potential market size $m$ that can be ultimately captured. In this paper, we
consider the more challenging incomplete information problem where dynamic
pricing is applied in conjunction with learning the unknown parameters, with
the objective of optimizing the cumulative revenues over a given selling
horizon of length $T$. Equivalently, the goal is to minimize the regret which
measures the revenue loss of the algorithm relative to the optimal expected
revenue achievable under the stochastic Bass model with market size $m$ and
time horizon $T$. Our main contribution is the development of an algorithm that
satisfies a high probability regret guarantee of order $\tilde O(m^{2/3})$;
where the market size $m$ is known a priori. Moreover, we show that no
algorithm can incur smaller order of loss by deriving a matching lower bound.
Unlike most regret analysis results, in the present problem the market size $m$
is the fundamental driver of the complexity; our lower bound in fact, indicates
that for any fixed $\alpha, \beta$, most non-trivial instances of the problem
have constant $T$ and large $m$. We believe that this insight sets the problem
of dynamic pricing under the Bass model apart from the typical i.i.d. setting
and multi-armed bandit based models for dynamic pricing, which typically focus
only on the asymptotics with respect to time horizon $T$.


Abstract:
  We focus on the realistic maximization of the uplink minimum
signal-to-interference-plus-noise ratio (SINR) of a general multiple-input
single-output (MISO) system assisted by an intelligent reflecting surface (IRS)
in the large system limit accounting for HIs. In particular, we introduce the
HIs at both the IRS (IRS-HIs) and the transceiver HIs (AT-HIs), usually
neglected despite their inevitable impact. Specifically, the deterministic
equivalent analysis enables the derivation of the asymptotic weighted
maximum-minimum SINR with HIs by jointly optimizing the HIs-aware receiver, the
transmit power, and the reflect beamforming matrix (RBM). Notably, we obtain
the optimal power allocation and reflect beamforming matrix with low overhead
instead of their frequent necessary computation in conventional MIMO systems
based on the instantaneous channel information. Monte Carlo simulations verify
the analytical results which show the insightful interplay among the key
parameters and the degradation of the performance due to HIs.


Abstract:
  It was suggested that a programmable matter system (composed of multiple
computationally weak mobile particles) should remain connected at all times
since otherwise, reconnection is difficult and may be impossible. At the same
time, it was not clear that allowing the system to disconnect carried a
significant advantage in terms of time complexity. We demonstrate for a
fundamental task, that of leader election, an algorithm where the system
disconnects and then reconnects automatically in a non-trivial way (particles
can move far away from their former neighbors and later reconnect to others).
Moreover, the runtime of the temporarily disconnecting deterministic leader
election algorithm is linear in the diameter. Hence, the disconnecting --
reconnecting algorithm is as fast as previous randomized algorithms. When
comparing to previous deterministic algorithms, we note that some of the
previous work assumed weaker schedulers. Still, the runtime of all the previous
deterministic algorithms that did not assume special shapes of the particle
system (shapes with no holes) was at least quadratic in $n$, where $n$ is the
number of particles in the system. (Moreover, the new algorithm is even faster
in some parameters than the deterministic algorithms that did assume special
initial shapes.)
  Since leader election is an important module in algorithms for various other
tasks, the presented algorithm can be useful for speeding up other algorithms
under the assumption of a strong scheduler. This leaves open the question: "can
a deterministic algorithm be as fast as the randomized ones also under weaker
schedulers?"


Abstract:
  We discuss the prospects of gravitational lensing of gravitational waves
(GWs) coming from core-collapse supernovae (CCSN). As the CCSN GW signal can
only be detected from within our own Galaxy and the local group by current and
upcoming ground-based GW detectors, we focus on microlensing. We introduce a
new technique based on analysis of the power spectrum and association of peaks
of the power spectrum with the peaks of the amplification factor to identify
lensed signals. We validate our method by applying it on the CCSN-like mock
signals lensed by a point mass lens. We find that the lensed and unlensed
signal can be differentiated using the association of peaks by more than one
sigma for lens masses larger than 150 solar masses. We also study the
correlation integral between the power spectra and corresponding amplification
factor. This statistical approach is able to differentiate between unlensed and
lensed signals for lenses as small as 15 solar masses. Further, we demonstrate
that this method can be used to estimate the mass of a lens in case the signal
is lensed. The power spectrum based analysis is general and can be applied to
any broad band signal and is especially useful for incoherent signals.


Abstract:
  Recently superconductivity was discovered in the Kagome metal AV3Sb5 (A = K,
Rb, and Cs), which has an ideal Kagome lattice of vanadium. These V-based
superconductors also host charge density wave (CDW) and topological nontrivial
band structure. Here we report the ultralow-temperature thermal conductivity
and high pressure resistance measurements on CsV3Sb5 with Tc = 2.5 K, the
highest among AV3Sb5. A finite residual linear term of thermal conductivity at
zero magnetic field and its rapid increase in fields suggest nodal
superconductivity. By applying pressure, the Tc of CsV3Sb5 increases first,
then decreases to lower than 0.3 K at 11.4 GPa, showing a clear first
superconducting dome peaked around 0.8 GPa. Above 11.4 GPa, superconductivity
re-emerges, suggesting a second superconducting dome. Both nodal
superconductivity and superconducting domes point to unconventional
superconductivity in this V-based superconductor. While our finding of nodal
superconductivity puts a strong constrain on the pairing state of the first
dome, which should be related to the CDW instability, the superconductivity of
the second dome may present another exotic pairing state in this ideal Kagome
lattice of vanadium.


Abstract:
  Scientific journals are currently the primary medium used by researchers to
report their research findings. The transformation of print journals into
e-journals has simplified the process of submissions to journals and also their
access has become wider. Journals are usually published by commercial
publishers, learned societies as well as Universities. There are different
number of journals published from different countries. This paper attempts to
explore whether the number of journals published from a country influences its
research output. Scopus master journal list is analysed to identify journals
published from 50 selected countries with significant volume of research
output. The following relationship are analysed: (a) number of journals from a
country and its research output, (b) growth rate of journals and research
output for different countries, (c) global share of journals and research
output for different countries, and (d) subject area-wise number of journals
and research output in that subject area for different countries. Factors like
journal packing density are also analysed. The results obtained show that for
majority of the countries, the number of journals is positively correlated to
their research output volume, though some other factors also play a role in
growth of research output. The study at the end presents a discussion of the
analytical outcomes and provides useful suggestions on policy perspectives for
different countries.


Abstract:
  Superluminal tunneling of light through a barrier has attracted broad
interest in the last several decades. Despite the observation of such phenomena
in various systems, it has been under intensive debate whether the transmitted
light truly carry the information of the original pulse. Here we report
observation of anomalous time response for terahertz electromagnetic pulses
passing through thin metal films, with the pulse shape of the transmitted beam
faithfully resembling that of the incident beam. A causal theoretical analysis
is developed to explain the experiments, though the theory of Special
Relativity may confront a challenge in this exceptional circumstance. These
findings may facilitate future applications in high-speed optical communication
or signal transmission, and may reshape our fundamental understanding about the
tunneling of light.


Abstract:
  Deep Neural Networks have achieved unprecedented success in the field of face
recognition such that any individual can crawl the data of others from the
Internet without their explicit permission for the purpose of training
high-precision face recognition models, creating a serious violation of
privacy. Recently, a well-known system named Fawkes (published in USENIX
Security 2020) claimed this privacy threat can be neutralized by uploading
cloaked user images instead of their original images. In this paper, we present
Oriole, a system that combines the advantages of data poisoning attacks and
evasion attacks, to thwart the protection offered by Fawkes, by training the
attacker face recognition model with multi-cloaked images generated by Oriole.
Consequently, the face recognition accuracy of the attack model is maintained
and the weaknesses of Fawkes are revealed. Experimental results show that our
proposed Oriole system is able to effectively interfere with the performance of
the Fawkes system to achieve promising attacking results. Our ablation study
highlights multiple principal factors that affect the performance of the Oriole
system, including the DSSIM perturbation budget, the ratio of leaked clean user
images, and the numbers of multi-cloaks for each uncloaked image. We also
identify and discuss at length the vulnerabilities of Fawkes. We hope that the
new methodology presented in this paper will inform the security community of a
need to design more robust privacy-preserving deep learning models.


Abstract:
  Quantum correlations, in particular those, which enable to violate a Bell
inequality \cite{BELL}, open a way to advantage in certain communication tasks.
However, the main difficulty in harnessing quantumness is its fragility to,
e.g, noise or loss of particles. We study the persistency of Bell correlations
of GHZ based mixtures and Dicke states. For the former, we consider quantum
communication complexity reduction (QCCR) scheme, and propose new Bell
inequalities (BIs), which can be used in that scheme for higher persistency in
the limit of large number of particles $N$. In case of Dicke states, we show
that persistency can reach $0.482N$, significantly more than reported in
previous studies.


Abstract:
  Neural network modules conditioned by known priors can be effectively trained
and combined to represent systems with nonlinear dynamics. This work explores a
novel formulation for data-efficient learning of deep control-oriented
nonlinear dynamical models by embedding local model structure and constraints.
The proposed method consists of neural network blocks that represent input,
state, and output dynamics with constraints placed on the network weights and
system variables. For handling partially observable dynamical systems, we
utilize a state observer neural network to estimate the states of the system's
latent dynamics. We evaluate the performance of the proposed architecture and
training methods on system identification tasks for three nonlinear systems: a
continuous stirred tank reactor, a two tank interacting system, and an
aerodynamics body. Models optimized with a few thousand system state
observations accurately represent system dynamics in open loop simulation over
thousands of time steps from a single set of initial conditions. Experimental
results demonstrate an order of magnitude reduction in open-loop simulation
mean squared error for our constrained, block-structured neural models when
compared to traditional unstructured and unconstrained neural network models.


Abstract:
  Third-generation gravitational wave detectors, such as the Einstein Telescope
and Cosmic Explorer, will detect a bunch of gravitational-wave (GW) signals
originating from the coalescence of binary neutron star (BNS) and binary black
hole (BBH) systems out to the higher redshifts, $z\sim 5-10$. There is a
potential concern that some of the GW signals detected at a high statistical
significance eventually overlap with each other, and the parameter estimation
of such an overlapping system can differ from the one expected from a single
event. Also, there are certainly overlapping systems in which one of the
overlapping events has a low signal-to-noise ratio $\lesssim 4$, and is thus
unable to be clearly detected. Those system will potentially be misidentified
with a single GW event, and the estimated parameters of binary GWs can be
biased. We estimate the occurrence rate of those overlapping events. We find
that the numbers of overlapping events are $\sim 200$ per day for BNSs and a
few per hour for BBHs. Then we study the statistical impacts of these
overlapping GWs on a parameter estimation based on the Fisher matrix analysis.
Our finding is that the overlapping signals produce neither large statistical
errors nor serious systematic biases on the parameters of binary systems,
unless the coalescence time and the redshifted chirp masses of the two
overlapping GWs are very close to each other, i.e.,
$|\mathcal{M}_{z1}-\mathcal{M}_{z2}|\lesssim10^{-4} \,(10^{-1})\,M_\odot$ and
$|t_{\rm c1}-t_{\rm c2}|\lesssim10^{-2}\,(10^{-1})$\,s for BNSs (BBHs). The
occurrence rate of such a closely overlapping event is shown to be much smaller
than one per year with the third-generation detectors.


Abstract:
  We present the concept of a magnetless Reflective Gyrotropic Spatial Isolator
(RGSI) metasurface. This is a birefringent metasurface that reflects vertically
polarized incident waves into a horizontally polarized waves, and absorbs
horizontally polarized incident waves, hence providing isolation between the
two orthogonal polarization. We first synthesize the metasurface using surface
susceptibility-based Generalized Sheet Transition Conditions~(GSTCs). We then
propose a mirror-backed metaparticle implementation of this metasurface, where
transistor-loaded resonators provide the desired magnetless nonreciprocal
response. Finally, we demonstrate the metasurface by full-wave simulation
results. The proposed RGSI metasurface may be used in various electromagnetic
applications, and may also serve as a step towards more sophisticated
magnetless nonreciprocal metasurface systems.


Abstract:
  In this article, we aim to recover locally conservative and $H(div)$
conforming fluxes for the linear Cut Finite Element Solution with Nitsche's
method for Poisson problems with Dirichlet boundary condition. The computation
of the conservative flux in the Raviart-Thomas space is completely local and
does not require to solve any mixed problem. The $L^2$-norm of the difference
between the numerical flux and the recovered flux can then be used as a
posteriori error estimator in the adaptive mesh refinement procedure.
Theoretically we are able to prove the global reliability and local efficiency.
The theoretical results are verified in the numerical results. Moreover, in the
numerical results we also observe optimal convergence rate for the flux error.


Abstract:
  We perform explorative analyses of the 3D gluon content of the proton via a
study of (un)polarized twist-2 gluon TMDs, calculated in a spectator model for
the parent nucleon. Our approach encodes a flexible parametrization for the
spectator-mass density, suited to describe both moderate and small-$x$ effects.
All these prospective developments are relevant in the investigation of the
gluon dynamics inside nucleons and nuclei, which constitutes one of the major
goals of new-generation colliding machines, as the EIC, the HL-LHC and NICA.


Abstract:
  The strong alignment of small-scale turbulent Alfv\'enic motions with the
direction of the magnetic field that percolates the small-scale eddies and
imprints the direction of the magnetic field is a property that follows from
the MHD theory and the theory of turbulent reconnection. The Alfv\'enic eddies
mix magnetic fields perpendicular to the direction of the local magnetic field,
and this type of motion is used to trace magnetic fields with the velocity
gradient technique (VGT). The other type of turbulent motion, fast modes,
induces anisotropies orthogonal to Alfv\'enic eddies and interferes with the
tracing of the magnetic field with the VGT. We report a new effect, i.e., in a
magnetically dominated low-\beta subsonic medium, fast modes are very
intermittent, and in a volume, with a small filling factor the fast modes
dominate other turbulent motions. We identify these localized regions as the
cause of the occasional change of direction of gradients in our synthetic
observations. We show that the new technique of measuring the gradients of
gradient amplitudes suppresses the contribution from the fast-mode-dominated
regions, improving the magnetic field tracing. In addition, we show that the
distortion of the gradient measurements by fast modes is also applicable to the
synchrotron intensity gradients, but the effect is reduced compared to the VGT.


Abstract:
  This paper presents an approach to improve the forecast of computational
fluid dynamics (CFD) simulations of urban air pollution using deep learning,
and most specifically adversarial training. This adversarial approach aims to
reduce the divergence of the forecasts from the underlying physical model. Our
two-step method integrates a Principal Components Analysis (PCA) based
adversarial autoencoder (PC-AAE) with adversarial Long short-term memory (LSTM)
networks. Once the reduced-order model (ROM) of the CFD solution is obtained
via PCA, an adversarial autoencoder is used on the principal components time
series. Subsequentially, a Long Short-Term Memory network (LSTM) is
adversarially trained on the latent space produced by the PC-AAE to make
forecasts. Once trained, the adversarially trained LSTM outperforms a LSTM
trained in a classical way. The study area is in South London, including
three-dimensional velocity vectors in a busy traffic junction.


Abstract:
  Promising searches for new physics beyond the current Standard Model (SM) of
particle physics are feasible through isotope-shift spectroscopy, which is
sensitive to a hypothetical fifth force between the neutrons of the nucleus and
the electrons of the shell. Such an interaction would be mediated by a new
particle which could in principle be associated with dark matter. In so-called
King plots, the mass-scaled frequency shifts of two optical transitions are
plotted against each other for a series of isotopes. Subtle deviations from the
expected linearity could reveal such a fifth force. Here, we study
experimentally and theoretically six transitions in highly charged ions of Ca,
an element with five stable isotopes of zero nuclear spin. Some of the
transitions are suitable for upcoming high-precision coherent laser
spectroscopy and optical clocks. Our results provide a sufficient number of
clock transitions for -- in combination with those of singly charged Ca$^+$ --
application of the generalized King plot method. This will allow future
high-precision measurements to remove higher-order SM-related nonlinearities
and open a new door to yet more sensitive searches for unknown forces and
particles.


Abstract:
  This paper and accompanying Python and C++ Framework is the product of the
authors perceived problems with narrow (Discrimination based) AI. (Artificial
Intelligence) The Framework attempts to develop a genetic transfer of
experience through potential structural expressions using a common
regulation/exchange value (energy) to create a model whereby neural
architecture and all unit processes are co-dependently developed by genetic and
real time signal processing influences; successful routes are defined by
stability of the spike distribution per epoch which is influenced by
genetically encoded morphological development biases.These principles are aimed
towards creating a diverse and robust network that is capable of adapting to
general tasks by training within a simulation designed for transfer learning to
other mediums at scale.


Abstract:
  Relativistic runaway electron avalanches (RREAs) imply a large multiplication
of high energy electrons (~1 MeV). Two factors are necessary for this
phenomenon: a high electric field sustained over a large distance and an
energetic particle to serve as a seed. The former sustains particle energies as
they keep colliding and lose energy randomly, and the latter serves as a
multiplication starting point that promotes avalanches. RREA is usually
connected to both terrestrial gamma-ray flashes (TGFs) and gamma-ray glows
(also known as Thunderstorm Ground Enhancement (TGE) when detected at ground
level) as possible generation mechanism of both events, but the current
knowledge does not provide a clear relationship between these events (TGF and
TGE), beyond their possible common source mechanism, still as they have
different characteristics. In particular, their timescales differ by several
orders of magnitude. This work shows that chain reactions by TGF byproducts can
continue for the timescale of gamma-ray glows and even provide energetic
particles as seeds for RREAs of gamma-ray glows.


Abstract:
  We generalize the correspondence between theories and monads with arities of
arXiv:1101.3064 to $\infty$-categories. Additionally, we introduce the notion
of complete theories that is unique to the $\infty$-categorical case and
provide a completion construction for a certain class of theories. Along the
way we also develop the necessary technical material related to the flagged
bicategory of correspondences and lax functor in the $\infty$-categorical
context.


Abstract:
  We report on the experimental observation of mirror enhanced directional
surface enhanced Raman scattering (SERS) from a self-assembled monolayer of
molecules coupled to a nanowire-nanoparticle (NW-NP) junction on a mirror in
remote excitation configuration. Placing NW-NP junction on a metallic mirror
generates multiple gap plasmon modes which have unique momentum space
scattering signatures. We perform Fourier plane imaging of SERS from NW-NP on a
mirror to understand the effect of multiple hotspots on molecular emission. We
systematically study the effect of ground plane on the directionality of
emission from NW-NP junction and show that the presence of a mirror drastically
reduces angular spread of emission. The effect of multiple hotspots in the
geometry on directionality of molecular emission is studied using 3D numerical
simulations. The results presented here will have implications in understanding
plasmon hybridization in the momentum space and its effects on molecular
emission.