Abstract:
  The Gell-Mann$-$Okubo (GMO) mass relations following from
$\text{SU}(3)$-flavor symmetry breaking in the strong sector have been proven
to be of great success for describing hadron masses and classifying them into
multiplets. Nowadays, it is widely believed that baryons being fermions have to
obey GMO relations which are linear in the baryon masses, whilst mesons as
bosons follow quadratic GMO relations. In this work, I challenge the
distinction of GMO relations for baryons and mesons and conclude that both
linear and quadratic GMO relations apply to both baryons and mesons in most
instances (exceptions are the pseudoscalar meson octet and the mass relations
following from heavy quark symmetry). I support this claim by presenting an
in-depth analysis of the GMO relations on a theoretical and experimental level.
On the theoretical side, two approaches to the derivation of the GMO relations
are given and isospin symmetry breaking, electromagnetic contributions, and
heavy quark symmetry are incorporated into the mass relations. Both linear and
quadratic mass relations are checked against experimentally determined hadron
masses. Furthermore, the mass relations are used to classify unassigned
resonances like $\Lambda^+_c(2940)$ and $\Xi_c(3123)$ into multiplets and to
predict the masses of missing particles in yet incomplete multiplets.


Abstract:
  A $d$-dimensional random array on a nonempty set $I$ is a stochastic process
$\boldsymbol{X}=\langle X_s:s\in {I\choose d}\rangle$ indexed by the set
${I\choose d}$ of all $d$-element subsets of $I$. We obtain structural
decompositions of finite, high-dimensional random arrays whose distribution is
invariant under certain symmetries.
  Our first main result is a distributional decomposition of finite,
(approximately) spreadable, high-dimensional random arrays whose entries take
values in a finite set; the two-dimensional case of this result is the finite
version of an infinitary decomposition due to Fremlin and Talagrand. Our second
main result is a physical decomposition of finite, spreadable, high-dimensional
random arrays with square-integrable entries which is the analogue of the
Hoeffding/Efron--Stein decomposition. All proofs are effective.
  We also present applications of these decompositions in the study of
concentration of functions of finite, high-dimensional random arrays.


Abstract:
  A pure excedance in a permutation $\pi=\pi_1\pi_2\ldots \pi_n$ is a position
$i<\pi_i$ such that there is no $j<i$ with $i\leq \pi_j<\pi_i$. We present a
one-to-one correspondence on the symmetric group that transports pure
excedances to descents of special kind. As a byproduct, we prove that the
popularity of pure excedances equals those of pure descents on permutations,
while their distributions are different.


Abstract:
  In this work Data Envelopment Analysis (DEA) is employed in thirty-day
windows to quantify temporal evolution of relative pandemic mitigation
inefficiency of Brazilian municipalities. For each thirty-day window the
results of inefficiency scores of over five thousand Brazilian municipalities
are displayed on maps, to address the spatial distribution of the corresponding
values. This phenomenological spatiotemporal approach reveals location of the
hotspots, in terms of relative pandemic inefficiency of the municipalities, at
different points in time along the pandemic. It is expected that the current
approach may subsidize decision making through comparative analysis of previous
practice, and thus contribute to the future pandemic mitigation efforts.


Abstract:
  We analyse new ALMA observations of the $^{29}$SiO ($\nu$=0, $J$=8$-$7) and
SO$_2$($\nu$=0, $34_{3,31}$$-$$34_{2,32}$) line emissions of the circumstellar
envelope (CSE) of the oxygen-rich AGB star R Dor. With a spatial resolution of
$\sim$2.3 au, they cover distances below $\sim$30 au from the star providing a
link between earlier observations and clarifying some open issues. The main
conclusions are: 1) Rotation is confined below $\sim$15 au from the star, with
velocity reaching a maximum below 10 au and morphology showing no significant
disc-like flattening. 2) In the south-eastern quadrant, a large Doppler
velocity gas stream is studied in more detail than previously possible and its
possible association with an evaporating planetary companion is questioned. 3)
A crude evaluation of the respective contributions of rotation, expansion and
turbulence to the morpho-kinematics is presented. Significant line broadening
occurs below $\sim$12 au from the star and causes the presence of high Doppler
velocity components near the line of sight pointing to the centre of the star.
4) Strong absorption of the continuum emission of the stellar disc and its
immediate dusty environment is observed to extend beyond the disc in the form
of self-absorption. The presence of a cold SiO layer extending up to some 60 au
from the star is shown to be the cause. 5) Line emissions from SO, $^{28}$SiO,
CO and HCN molecules are used to probe the CSE up to some 100 au from the star
and reveal the presence of two broad back-to-back outflows, the morphology of
which is studied in finer detail than in earlier work.


Abstract:
  We observed a strong non-linearity in the system of quasiparticles of a
superconducting aluminum resonator, due to the Cooper-pair breaking from the
absorbed readout power. We observed both negative and positive feedback
effects, controlled by the detuning of the readout frequency, which are able to
alter the relaxation time of quasiparticles by a factor greater than 10. We
estimate that the (70+/-5) % of the total non-linearity of the device is due to
quasiparticles.


Abstract:
  A method for evaluating the relative performance of local, cloud and combined
processing of divisible (i.e. partitionable) data loads is presented. It is
shown how to do this in the context of Amdahl's law. A single level (star)
network operating under each of three fundamental scheduling policies is used
as an example. Applications include mobile computing, cloud computing and
signature searching.


Abstract:
  We prove the Bonnet theorem for statistical manifolds, which states that if a
statistical manifold admits tensors satisfying the Gauss--Codazzi--Ricci
equations, then it is locally embeddable to a flat statistical manifold (or a
Hessian manifold). The proof is based on the notion of statistical embedding to
the product of a vector space and its dual space introduced by Lauritzen. As
another application of Lauritzen's embedding, we show that a statistical
manifold admitting an affine embedding of codimension 1 or 2 is locally
embeddable to a flat statistical manifold of the same codimension.


Abstract:
  In this paper, the problem of load uncertainty in compliance problems is
addressed where the uncertainty is described in the form of a set of finitely
many loading scenarios. Computationally more efficient methods are proposed to
exactly evaluate and differentiate: 1) the mean compliance, or 2) any
scalar-valued function of the individual load compliances such as the weighted
sum of the mean and standard deviation. The computational time complexities of
all the proposed algorithms are analyzed, compared with the naive approaches
and then experimentally verified. Finally, a mean compliance minimization
problem, a risk-averse compliance minimization problem and a maximum compliance
constrained problem are solved to showcase the efficacy of the proposed
algorithms. The maximum compliance constrained problem is solved using the
augmented Lagrangian method and the method proposed for handling scalar-valued
functions of the load compliances, where the scalar-valued function is the
augmented Lagrangian function.


Abstract:
  Face recognition has been extensively studied in computer vision and
artificial intelligence communities in recent years. An important issue of face
recognition is data privacy, which receives more and more public concerns. As a
common privacy-preserving technique, Federated Learning is proposed to train a
model cooperatively without sharing data between parties. However, as far as we
know, it has not been successfully applied in face recognition. This paper
proposes a framework named FedFace to innovate federated learning for face
recognition. Specifically, FedFace relies on two major innovative algorithms,
Partially Federated Momentum (PFM) and Federated Validation (FV). PFM locally
applies an estimated equivalent global momentum to approximating the
centralized momentum-SGD efficiently. FV repeatedly searches for better
federated aggregating weightings via testing the aggregated models on some
private validation datasets, which can improve the model's generalization
ability. The ablation study and extensive experiments validate the
effectiveness of the FedFace method and show that it is comparable to or even
better than the centralized baseline in performance.


Abstract:
  Video game level generation based on machine learning (ML), in particular,
deep generative models, has attracted attention as a technique to automate
level generation. However, applications of existing ML-based level generations
are mostly limited to tile-based level representation. When ML techniques are
applied to game domains with non-tile-based level representation, such as Angry
Birds, where objects in a level are specified by real-valued parameters, ML
often fails to generate playable levels. In this study, we develop a
deep-generative-model-based level generation for the game domain of Angry
Birds. To overcome these drawbacks, we propose a sequential encoding of a level
and process it as text data, whereas existing approaches employ a tile-based
encoding and process it as an image. Experiments show that the proposed level
generator drastically improves the stability and diversity of generated levels
compared with existing approaches. We apply latent variable evolution with the
proposed generator to control the feature of a generated level computed through
an AI agent's play, while keeping the level stable and natural.


Abstract:
  Hydrodynamic instabilities driven by a direct current are analyzed in 2D and
3D relativisticlike systems with the Dyakonov-Shur boundary conditions
supplemented by a boundary condition for temperature. Besides the conventional
Dyakonov-Shur instability for plasmons, we find an entropy wave instability in
both 2D and 3D systems. The entropy wave instability is a manifestation of the
relativisticlike nature of electron quasiparticles and a nontrivial role of the
energy current in such systems. These two instabilities occur for the opposite
directions of fluid flow. While the Dyakonov-Shur instability is characterized
by the plasma frequency in 3D and the system size in 2D, the frequency of the
entropy wave instability is tunable by the system size and the flow velocity.


Abstract:
  Delivering reliable and high-capacity Internet connectivity to high-speed
train users is a challenge. Modern railway cars act as Faraday cages and a
typical train consist comprises several hundreds of users moving at high
velocity. Furthermore, with the global availability of fourth generation (4G)
Long Term Evolution (LTE), user expectations have dramatically increased: it is
expected to be online anytime and anywhere. Demand for mobile high-capacity is
being driven by video and music streaming services, for lower latency and
higher availability by gaming, and for more reliability and even uplink
capacity by mission critical applications. Finally, the life-cycle of the
railway industry is much longer than for telecommunications, which makes
supporting 5G challenging. In this paper, we survey the challenges associated
with delivering high-capacity connectivity to train users, describe potential
options, and highlight how a leading western European operator is tackling
these challenges and preparing for 5G and beyond.


Abstract:
  A common library, TMDlib2, for Transverse-Momentum-Dependent distributions
(TMDs) and unintegrated parton distributions (uPDFs) is described, which allows
for easy access of commonly used TMDs and uPDFs, providing a three-dimensional
(3D) picture of the partonic structure of hadrons. The tool TMDplotter allows
for web-based plotting of distributions implemented in TMDlib2, together with
collinear pdfs as available in LHAPDF.


Abstract:
  This paper develops a framework that models and optimizes the operations of
complex on-orbit servicing infrastructures involving one or more servicers and
orbital depots to provide multiple types of services to a fleet of
geostationary satellites. The proposed method extends the state-of-the-art
space logistics technique by addressing the unique challenges in on-orbit
servicing applications, and integrate it with the Rolling Horizon decision
making approach. The space logistics technique enables modeling of the on-orbit
servicing logistical operations as a Mixed-Integer Linear Program whose optimal
solutions can efficiently be found. The Rolling Horizon approach enables the
assessment of the long-term value of an on-orbit servicing infrastructure by
accounting for the uncertain service needs that arise over time among the
geostationary satellites. Two case studies successfully demonstrate the
effectiveness of the framework for (1) short-term operational scheduling and
(2) long-term strategic decision making for on-orbit servicing architectures
under diverse market conditions.


Abstract:
  We present a tree tensor-network impurity solver suited for general
multiorbital systems. The network is constructed to efficiently capture the
entanglement structure and symmetry of an impurity problem. The solver works
directly on the real-time/frequency axis and generates spectral functions with
energy-independent resolution of the order of one percent of the correlated
bandwidth. Combined with an optimized representation of the impurity bath, it
efficiently solves self-consistent dynamical mean-field equations and
calculates various dynamical correlation functions for systems with
off-diagonal Green's functions. For the archetypal correlated Hund's metal
Sr$_2$RuO$_4$, we show that both the low-energy quasiparticle spectra related
to the van Hove singularity and the high-energy atomic multiplet excitations
can be faithfully resolved. In particular, we show that while the spin-orbit
coupling has only minor effects on the orbital-diagonal one-particle spectral
functions, it has a more profound impact on the low-energy spin and orbital
response functions.


Abstract:
  In this work, we develop an automated method to generate 3D human walking
motion in simulation which is comparable to real-world human motion. At the
core, our work leverages the ability of deep reinforcement learning methods to
learn high-dimensional motor skills while being robust to variations in the
environment dynamics. Our approach iterates between policy learning and
parameter identification to match the real-world bio-mechanical human data. We
present a thorough evaluation of the kinematics, kinetics and ground reaction
forces generated by our learned virtual human agent. We also show that the
method generalizes well across human-subjects with different kinematic
structure and gait-characteristics.


Abstract:
  We derive a covariant expression for the renormalized holographic
entanglement entropy for Conformal Field Theories (CFTs) dual to Quadratic
Curvature Gravity in arbitrary dimensions. This expression is written as the
sum of the bare entanglement entropy functional obtained using standard conical
defect techniques, and a counterterm defined at the boundary of the extremal
surface of the functional. The latter corresponds to the cod-2 self-replicating
part of the extrinsic counterterms when evaluated on the replica orbifold. This
renormalization method isolates the universal terms of the holographic
entanglement entropy functional. We use it to compute the standard C-function
candidate for CFTs of arbitrary dimension, and the type-B anomaly coefficient c
for 4-dimensional CFTs.


Abstract:
  We use a trivial delta method with multiplicative characters for congruence
detection to prove the Weyl bound for GL(2) in $t$-aspect for a holomorphic or
Hecke-Maass cusp form of arbitrary level and nebentypus. This parallels the
work of Aggarwal in 2018, with the difference being multiplicative character
has a more natural connection to the twisted $L$-function. This provides
another view point to understand and explore the trivial and other delta
methods.


Abstract:
  This paper proposes a novel primal heuristic for Mixed Integer Programs, by
employing machine learning techniques. Mixed Integer Programming is a general
technique for formulating combinatorial optimization problems. Inside a solver,
primal heuristics play a critical role in finding good feasible solutions that
enable one to tighten the duality gap from the outset of the Branch-and-Bound
algorithm (B&B), greatly improving its performance by pruning the B&B tree
aggressively. In this paper, we investigate whether effective primal heuristics
can be automatically learned via machine learning. We propose a new method to
represent an optimization problem as a graph, and train a Graph Convolutional
Network on solved problem instances with known optimal solutions. This in turn
can predict the values of decision variables in the optimal solution for an
unseen problem instance of a similar type. The prediction of variable solutions
is then leveraged by a novel configuration of the B&B method, Probabilistic
Branching with guided Depth-first Search (PB-DFS) approach, aiming to find
(near-)optimal solutions quickly. The experimental results show that this new
heuristic can find better primal solutions at a much earlier stage of the
solving process, compared to other state-of-the-art primal heuristics.


Abstract:
  We present the discovery and characterization of two sub-Neptunes in close
orbits, as well as a tentative outer planet of a similar size, orbiting
TOI-1260 - a low metallicity K6V dwarf star. Photometry from TESS yields radii
of $R_{\rm b} = 2.33 \pm 0.10$ $R_{\oplus}$ and $R_{\rm c} = 2.82 \pm 0.15$
$R_{\oplus}$, and periods of 3.13 and 7.49 days for TOI-1260b and TOI-1260c,
respectively. We combined the TESS data with a series of ground-based follow-up
observations to characterize the planetary system. From HARPS-N high-precision
radial velocities we obtain $M_{\rm b} = 8.61_{ - 1.46 } ^ { + 1.36 }$
$M_{\oplus}$ and $M_{\rm c} = 11.84_{ - 3.23 } ^ { + 3.38 }$ $M_{\oplus}$. The
star is moderately active with a complex activity pattern, which necessitated
the use of Gaussian process regression for both the light curve detrending and
the radial velocity modelling, in the latter case guided by suitable activity
indicators. We successfully disentangle the stellar-induced signal from the
planetary signals, underlining the importance and usefulness of the Gaussian
Process approach. We test the system's stability against atmospheric
photoevaporation and find that the TOI-1260 planets are classic examples of the
structure and composition ambiguity typical for the $2-3$ $R_{\oplus}$ range.


Abstract:
  Articulatory information has been shown to be effective in improving the
performance of HMM-based and DNN-based text-to-speech synthesis. Speech
synthesis research focuses traditionally on text-to-speech conversion, when the
input is text or an estimated linguistic representation, and the target is
synthesized speech. However, a research field that has risen in the last decade
is articulation-to-speech synthesis (with a target application of a Silent
Speech Interface, SSI), when the goal is to synthesize speech from some
representation of the movement of the articulatory organs. In this paper, we
extend traditional (vocoder-based) DNN-TTS with articulatory input, estimated
from ultrasound tongue images. We compare text-only, ultrasound-only, and
combined inputs. Using data from eight speakers, we show that that the combined
text and articulatory input can have advantages in limited-data scenarios,
namely, it may increase the naturalness of synthesized speech compared to
single text input. Besides, we analyze the ultrasound tongue recordings of
several speakers, and show that misalignments in the ultrasound transducer
positioning can have a negative effect on the final synthesis performance.


Abstract:
  For many decades now, Bayesian Model Averaging (BMA) has been a popular
framework to systematically account for model uncertainty that arises in
situations when multiple competing models are available to describe the same or
similar physical process. The implementation of this framework, however, comes
with multitude of practical challenges including posterior approximation via
Markov Chain Monte Carlo and numerical integration. We present a Variational
Bayes Inference approach to BMA as a viable alternative to the standard
solutions which avoids many of the aforementioned pitfalls. The proposed method
is 'black box' in the sense that it can be readily applied to many models with
little to no model-specific derivation. We illustrate the utility of our
variational approach on a suite of standard examples and discuss all the
necessary implementation details. Fully documented Python code with all the
examples is provided as well.


Abstract:
  In this paper we study the effect of electroweak sphaleron transition or
electroweak phase transition (EWPT) in balancing baryon excess to the excess
stable quarks of $4^{th}$ generation. Considering the conservation of all
quantum number and charges, sphaleron transition between between baryons,
leptons and $4^{th}$ family of leptons and quarks is possible. We have tried to
established a possible definite relationship between the value and sign of the
$4^{th}$ family excess relative to baryon asymmetry under the framework of
second order EWPT. In passing by we show the small, yet negligible dilution in
the pre-existing dark matter density due the sphaleron transition.


Abstract:
  We prove the existence and $C^{1,\alpha}$ regularity of solutions to nonlocal
fully nonlinear elliptic double obstacle problems. We also obtain boundary
regularity for these problems. The obstacles are assumed to be Lipschitz
semi-concave/semi-convex functions, and we do not require them to be $C^1$. Our
approach is to adapt a penalization method to be applicable to the setting of
nonlocal equations and their viscosity solutions.


Abstract:
  We study the Maximum Independent Set of Rectangles (MISR) problem, where we
are given a set of axis-parallel rectangles in the plane and the goal is to
select a subset of non-overlapping rectangles of maximum cardinality. In a
recent breakthrough, Mitchell [2021] obtained the first constant-factor
approximation algorithm for MISR. His algorithm achieves an approximation ratio
of 10 and it is based on a dynamic program that intuitively recursively
partitions the input plane into special polygons called corner-clipped
rectangles (CCRs), without intersecting certain special horizontal line
segments called fences.
  In this paper, we present a $(2+\epsilon)$-approximation algorithm for MISR
which is also based on a recursive partitioning scheme. First, we use a
partition into a class of axis-parallel polygons with constant complexity each
that are more general than CCRs. This allows us to provide an arguably simpler
analysis and at the same time already improves the approximation ratio to 6.
Then, using a more elaborate charging scheme and a recursive partitioning into
general axis-parallel polygons with constant complexity, we improve our
approximation ratio to $2+\epsilon$. In particular, we construct a recursive
partitioning based on more general fences which can be sequences of up to
$O(1/\epsilon)$ line segments each. This partitioning routine and our other new
ideas may be useful for future work towards a PTAS for MISR.


Abstract:
  GG Carinae is a binary whose primary component is a B[e] supergiant. Using
photometric data from TESS, ASAS, OMC, and ASAS-SN, and spectroscopic data from
the Global Jet Watch to study visible He\,I, Fe\,II and Si\,II emission lines,
we investigate the short-period variations which are exhibited in GG Car. We
find a hitherto neglected periodicity of $1.583156\pm0.0002$\,days that is
present in both its photometry and the radial velocities of its emission lines,
alongside variability at the well-established $\sim$31-day orbital period. We
find that the amplitudes of the shorter-period variations in both photometry
and some of the emission lines are modulated by the orbital phase of the
binary, such that the short-period variations have largest amplitudes when the
binary is at periastron. There are no significant changes in the phases of the
short-period variations over the orbital period. We investigate potential
causes of the 1.583-day variability, and find that the observed period agrees
well with the expected period of the $l=2$ f-mode of the primary given its mass
and radius. We propose that the primary is periodically pulled out of
hydrostatic equilibrium by the quadrupolar tidal forces when the components are
near periastron in the binary's eccentric orbit ($e=0.5$) and the primary
almost fills its Roche lobe. This causes an oscillation at the $l=2$ f-mode
frequency which is damped as the distance between the components increases.


Abstract:
  Highly integrated active nanophotonics addressing both device footprint and
operation speed demands is a key enabling technology for the next generation
optical networks. Plasmonic systems have proven to be a serious contender to
alleviate current performance limitations in electro-optic devices. Here, we
demonstrate a plasmonic optical phased array (OPA) consisting of two
10-$\mu$m-long plasmonic phase shifters, utilized to control the far-field
radiation pattern of two subwavelength-separated emitters for aliasing-free
beam steering with an angular range of $\pm5^\circ$ and flat frequency response
up to 18 GHz (with the potential bandwidth of 1.2 THz). Extreme optical and
electrostatic field confinement with great spatial overlap results in high
phase modulation efficiency ($V_{\pi}L=0.24\: \text{Vcm}$). The demonstrated
approach of using plasmonic lithium niobate technology for optical beam
manipulation offers inertia-free, robust, ultra-compact and high-speed beam
steering.


Abstract:
  We discuss whether a multi-step electroweak phase transition (EWPT) occurs in
two Higgs doublet models (2HDMs). The EWPT is related to interesting phenomena
such as baryogenesis and a gravitational wave from it. We examine parameter
regions in CP-conserving 2HDMs and find certain areas where the multi-step
EWPTs occur. The parameter search shows the multi-step EWPT prefers the scalar
potential with the approximate $Z_2$ symmetry and a mass hierarchy between the
neutral CP-odd and CP-even extra scalar bosons $m_A<m_H$. By contrast, the
multi-step EWPT whose first step is strongly first order favors a mass
hierarchy $m_A>m_H$. In addition, we compute the Higgs trilinear coupling in
the parameter region where the multi-step EWPTs occur, which can be observed at
future colliders. We also discuss a multi-peaked gravitational wave from a
multi-step EWPT.


Abstract:
  We image equilibrium and non-equilibrium transport through a two-dimensional
electronic cavity using scanning gate microscopy (SGM). Injecting electrons
into the cavity through a quantum point contact close to equilibrium, we
raster-scan a weakly invasive tip above the cavity regions and measure the
modulated conductance through the cavity. Varying the electron injection energy
between $\pm$ 2 meV, we observe that conductance minima turn into maxima beyond
the energy threshold of $\pm$ 0.6 meV. This observation bears similarity to
previous measurements by Jura et al. [Jura et al., Phys. Rev. B 82, 155328
(2010)] who used a strongly invasive tip potential to study electron injection
into an open two-dimensional electron gas. This resemblance suggests a similar
microscopic origin based on electron-electron interactions.


Abstract:
  In the Open Data Portal Germany (OPAL) project, a pipeline of the following
data refinement steps has been developed: requirements analysis, data
acquisition, analysis, conversion, integration and selection. 800,000 datasets
in DCAT format have been produced.


Abstract:
  Consider a uniform rooted Cayley tree $T_{n}$ with $n$ vertices and let $m$
cars arrive sequentially, independently, and uniformly on its vertices. Each
car tries to park on its arrival node, and if the spot is already occupied, it
drives towards the root of the tree and parks as soon as possible. Lackner &
Panholzer (arXiv:1504.04972) established a phase transition for this process
when $ m \approx \frac{n}{2}$. In this work, we couple this model with a
variant of the classical Erd\"os-R\'enyi random graph process. This enables us
to describe the phase transition for the size of the components of parked cars
using a modification of the multiplicative coalescent which we name the frozen
multiplicative coalescent. The geometry of critical parked clusters is also
studied. Those trees are very different from Bienaym\'e-Galton-Watson trees and
should converge towards the growth-fragmentation trees canonically associated
to the $3/2$-stable process that already appeared in the study of random planar
maps.


Abstract:
  Let G be a p-solvable group, P a p-subgroup and chi in Irr(G) such that
chi(1)_p \ge |G:P|_p. We prove that the restriction chi_P is a sum of
characters induced from subgroups Q\le P such that chi(1)_p=|G:Q|_p. This
generalizes previous results by Giannelli--Navarro and Giannelli--Sambale on
the number of linear constituents of chi_P. Although this statement does not
hold for arbitrary groups, we conjecture a weaker version which can be seen as
an extension of Brauer--Nesbitt's theorem on characters of p-defect zero. It
also extends a conjecture of Wilde.


Abstract:
  Resonant hadronic systems often exhibit a complicated decay pattern in which
three-body dynamics play a relevant or even dominant role. In this work we
focus on the $a_1(1260)$ resonance. For the first time, the pole position and
branching ratios of a three-body resonance are calculated from lattice QCD
using one-, two-, and three-meson interpolators and a three-body finite-volume
formalism extended to spin and coupled channels. This marks a new milestone for
ab-initio studies of ordinary resonances along with hybrid and exotic hadrons
involving three-body dynamics.


Abstract:
  Few-shot and one-shot learning have been the subject of active and intensive
research in recent years, with mounting evidence pointing to successful
implementation and exploitation of few-shot learning algorithms in practice.
Classical statistical learning theories do not fully explain why few- or
one-shot learning is at all possible since traditional generalisation bounds
normally require large training and testing samples to be meaningful. This
sharply contrasts with numerous examples of successful one- and few-shot
learning systems and applications.
  In this work we present mathematical foundations for a theory of one-shot and
few-shot learning and reveal conditions specifying when such learning schemes
are likely to succeed. Our theory is based on intrinsic properties of
high-dimensional spaces. We show that if the ambient or latent decision space
of a learning machine is sufficiently high-dimensional than a large class of
objects in this space can indeed be easily learned from few examples provided
that certain data non-concentration conditions are met.


Abstract:
  Machine learning (ML) methods are being used in almost every conceivable area
of electronic structure theory and molecular simulation. In particular, ML has
become firmly established in the construction of high-dimensional interatomic
potentials. Not a day goes by without another proof of principle being
published on how ML methods can represent and predict quantum mechanical
properties - be they observable, such as molecular polarizabilities, or not,
such as atomic charges. As ML is becoming pervasive in electronic structure
theory and molecular simulation, we provide an overview of how atomistic
computational modeling is being transformed by the incorporation of ML
approaches. From the perspective of the practitioner in the field, we assess
how common workflows to predict structure, dynamics, and spectroscopy are
affected by ML. Finally, we discuss how a tighter and lasting integration of ML
methods with computational chemistry and materials science can be achieved and
what it will mean for research practice, software development, and postgraduate
training.


Abstract:
  We study the trace class perturbations of the whole-line, discrete Laplacian
and obtain a new bound for the perturbation determinant of the corresponding
non-self-adjoint Jacobi operator. Based on this bound, we refine the
Lieb--Thirring inequality due to Hansmann--Katriel. The spectral enclosure for
such operators is also discussed.


Abstract:
  We develop a new probabilistic method for deriving deviation estimates in
directed planar polymer and percolation models. The key estimates are for exit
points of geodesics as they cross transversal down-right boundaries. These
bounds are of optimal cubic-exponential order. We derive them in the context of
last-passage percolation with exponential weights with near-stationary boundary
conditions. As a result, the probabilistic coupling method is empowered to
treat a variety of problems optimally, which could previously be achieved only
via inputs from integrable probability. As applications in the bulk setting, we
obtain upper bounds of cubic-exponential order for transversal fluctuations of
geodesics, and cube-root upper bounds with a logarithmic correction for
distributional Busemann limits and competition interface limits. Several other
applications are already in the literature.


Abstract:
  In this paper, we prove a result on non-Baire sets in category bases which
when applied together with a result of Grzegorek yeilds a comparatively
stronger version of a decomposition theorem due to Ulam.


Abstract:
  This paper provides three nearly-optimal algorithms for scheduling $t$ jobs
in the $\mathsf{CLIQUE}$ model. First, we present a deterministic scheduling
algorithm that runs in $O(\mathsf{GlobalCongestion} + \mathsf{dilation})$
rounds for jobs that are sufficiently efficient in terms of their memory. The
$\mathsf{dilation}$ is the maximum round complexity of any of the given jobs,
and the $\mathsf{GlobalCongestion}$ is the total number of messages in all jobs
divided by the per-round bandwidth of $n^2$ of the $\mathsf{CLIQUE}$ model.
Both are inherent lower bounds for any scheduling algorithm.
  Then, we present a randomized scheduling algorithm which runs $t$ jobs in
$O(\mathsf{GlobalCongestion} + \mathsf{dilation}\cdot\log{n}+t)$ rounds and
only requires that inputs and outputs do not exceed $O(n\log n)$ bits per node,
which is met by, e.g., almost all graph problems. Lastly, we adjust the
\emph{random-delay-based} scheduling algorithm [Ghaffari, PODC'15] from the
$\mathsf{CLIQUE}$ model and obtain an algorithm that schedules any $t$ jobs in
$O(t / n + \mathsf{LocalCongestion} + \mathsf{dilation}\cdot\log{n})$ rounds,
where the $\mathsf{LocalCongestion}$ relates to the congestion at a single node
of the $\mathsf{CLIQUE}$. We compare this algorithm to the previous approaches
and show their benefit.
  We schedule the set of jobs on-the-fly, without a priori knowledge of its
parameters or the communication patterns of the jobs. In light of the inherent
lower bounds, all of our algorithms are nearly-optimal.
  We exemplify the power of our algorithms by analyzing the message complexity
of the state-of-the-art MIS protocol [Ghaffari, Gouleakis, Konrad, Mitrovic and
Rubinfeld, PODC'18], and we show that we can solve $t$ instances of MIS in $O(t
+ \log\log\Delta\log{n})$ rounds, that is, in $O(1)$ amortized time, for $t\geq
\log\log\Delta\log{n}$.


Abstract:
  The symmetry SU(2) and its geometric Bloch Sphere rendering are familiar for
a qubit (spin-1/2) but extension of symmetries and geometries have been
investigated far less for multiple qubits, even just a pair of them, that are
central to quantum information. In the last two decades, two different
approaches with independent starting points and motivations have come together
for this purpose. One was to develop the unitary time evolution of two or more
qubits for studying quantum correlations, exploiting the relevant Lie algebras
and especially sub-algebras of the Hamiltonians involved, and arriving at
connections to finite projective geometries and combinatorial designs.
Independently, geometers studying projective ring lines and associated finite
geometries have come to parallel conclusions. This review brings together both
the Lie algebraic and group representation perspective of quantum physics and
the geometric algebraic one, along with connections to complex quaternions.
Together, all this may be seen as further development of Felix Klein's Erlangen
Program for symmetries and geometries. In particular, the fifteen generators of
the continuous SU(4) Lie group for two-qubits can be placed in one-to-one
correspondence with finite projective geometries, combinatorial Steiner
designs, and finite quaternionic groups. The very different perspectives may
provide further insight into problems in quantum information. Extensions are
considered for multiple qubits and higher spin or higher dimensional qudits.


Abstract:
  In recent years, many studies have applied wearable devices to capture
psychophysiological data from software developers. However, the current
literature lacks investigations that classify the studies and point out gaps to
be explored. This article, therefore, seeks to present a comprehensive overview
of the literature by classifying and creating a systematic map of the works.
Besides, it seeks to pinpoint research gaps, challenges, and trends. Based on
widely known guidelines, a systematic mapping of the literature was designed
and run to answer eight research questions. After applying a careful filtering
process, we selected 27 representative studies from a sample of 2,084
potentially relevant works retrieved from seven digital libraries. The main
results are: a classification scheme of the published studies was produced;
there is no predominance of the devices used to capture psychophysiological
data; over 50% of the studies have explored indicators related to mental states
and neural activity; and 80% have analyzed composite data to understand the
cognitive load and in the context of understanding debugging programs and
strategies. Our findings can benefit researchers and students by creating a
systematic map of the literature, being a starting point for future research.


Abstract:
  Recent observations in extreme-ultraviolet (EUV) wavelengths reveal a new
late phase in some solar flares, which is seen as a second peak in warm coronal
emissions ($\sim3$ MK) several tens of minutes to a few hours after the soft
X-ray (SXR) peak. The origin of the EUV late phase (ELP) is explained by either
a long-lasting cooling process in the long ELP loops, or a delayed energy
ejection into the ELP loops well after the main flare heating. Using the
observations with the \emph{Solar Dynamics Observatory} (\emph{SDO}), we
investigate the production of the ELP in six homologous flares (F1--F6)
originating from a complex active region (AR) NOAA 11283, with an emphasis on
the emission characteristics of the flares. It is found that the main
production mechanism of the ELP changes from additional heating in flare F1 to
long-lasting cooling in flares F3--F6, with both mechanisms playing a role in
flare F2. The transition is evidenced by an abrupt decrease of the time lag of
the ELP peak, and the long-lasting cooling process in the majority of the
flares is validated by a positive correlation between the flare ribbon fluence
and the ELP peak intensity. We attribute the change in ELP production mechanism
to an enhancement of the envelope magnetic field above the AR, which
facilitates a more prompt and energetic heating of the ELP loops. In addition,
the last and the only confined flare F6 exhibits an extremely large ELP. The
different emission pattern revealed in this flare may reflect a different
energy partitioning inside the ELP loops, which is due to a different magnetic
reconnection process.


Abstract:
  We present a new mass function of galaxy clusters and groups using
optical/near-infrared wavelength spectroscopic and photometric data from the
Observations of Redshift Evolution in Large-Scale Environments (ORELSE) survey.
At $z\sim1$, cluster mass function studies are rare regardless of wavelength
and have never been attempted from an optical/near-infrared perspective. This
work serves as a proof of concept that $z\sim1$ cluster mass functions are
achievable without supplemental X-ray or Sunyaev-Zel'dovich (SZ) data.
Measurements of the cluster mass function provide important contraints on
cosmological parameters and are complementary to other probes. With ORELSE, a
new cluster finding technique based on Voronoi tessellation Monte-Carlo (VMC)
mapping, and rigorous purity and completeness testing, we have obtained
$\sim$240 galaxy overdensity candidates in the redshift range $0.55<z<1.37$ at
a mass range of $13.6<\log(M/M_{\odot})<14.8$. This mass range is comparable to
existing optical cluster mass function studies for the local universe. Our
candidate numbers vary based on the choice of multiple input parameters related
to detection and characterization in our cluster finding algorithm, which we
incorporated into the mass function analysis through a Monte-Carlo scheme. We
find cosmological constraints on the matter density of $\Omega_{m} =
0.250^{+0.104}_{-0.099}$ and on the amplitude of fluctuations of $\sigma_{8} =
1.150^{+0.260}_{-0.163}$. While our $\Omega_{m}$ value is close to concordance,
our $\sigma_{8}$ value is $\sim2\sigma$ higher because of the inflated observed
number densities compared to theoretical mass function models owing to how our
survey targeted overdense regions. With Euclid and several other large,
unbiased optical surveys on the horizon, VMC mapping will enable optical/NIR
cluster cosmology at redshifts much higher than what has been possible before.


Abstract:
  In this work we analyse the parameterised complexity of propositional
inclusion (PINC) and independence logic (PIND). The problems of interest are
model checking (MC) and satisfiability (SAT). The complexity of these problems
is well understood in the classical (non-parameterised) setting. Mahmood and
Meier (FoIKS 2020) recently studied the parameterised complexity of
propositional dependence logic (PDL). As a continuation of their work, we
classify inclusion and independence logic and thereby come closer to completing
the picture with respect to the parametrised complexity for the three most
studied logics in the propositional team semantics setting. We present results
for each problem with respect to 8 different parameterisations. It turns out
that for a team-based logic L such that L-atoms can be evaluated in polynomial
time, then MC parameterised by teamsize is FPT. As a corollary, we get an FPT
membership under the following parameterisations: formula-size, formula-depth,
treewidth, and number of variables. The parameter teamsize shows interesting
behavior for SAT. For PINC, the parameter teamsize is not meaningful, whereas
for PDL and PIND the satisfiability is paraNP-complete. Finally, we prove that
when parameterised by arity, both MC and SAT are paraNP-complete for each of
the considered logics.


Abstract:
  Due to the lower ionospheric thermal pressure and existence of the crustal
magnetism at Mars, the Martian ionopause is expected to behave differently from
the ionopause at Venus. We study the solar wind interaction and pressure
balance at the ionopause of Mars using both in situ and remote sounding
measurements from the MARSIS (Mars Advanced Radar for Subsurface and Ionosphere
Sounding) instrument on the Mars Express orbiter. We show that the magnetic
pressure usually dominates the thermal pressure to hold off the solar wind at
the ionopause at Mars, with only 13% of the cases where the ionospheric thermal
pressure plays a more important role in pressure balance. This percentage at
Venus, however, is up to 65%. We also find that the ionopause altitude at Mars
decreases as the normal component of the solar wind dynamic pressure increases,
similar to the altitude variation of the ionopauses at Venus. Moreover, our
results show that the ionopause thickness at Mars and Venus is mainly
determined by the ion gyromotion and is equivalent to about 5 ion gyroradii.


Abstract:
  The Kaup-Newell (KN) system is a significant integrable system, which reveals
many physical phenomena and natural laws. Especially, since the solutions of
higher-order KN systems are more complex and various than that of second-order
flow KN systems which contain the derivative nonlinear Schrodinger equation
(DNLS), Chen-Lee-Liu equation, Gerdjikov-Ivanov equation and so on, the
research on it will become more and more important. In this paper, we
investigate the third-order flow equation of Kaup-Newell system (TOFKN) with
zero boundary conditions (ZBCs) and non-zero boundary conditions (NZBCs) by
using the Riemann-Hilbert (RH) approach based on the inverse scattering
transformation. Starting from Lax pairs of the TOFKN equation, the direct
scattering problem constructs the analyticity, symmetries, asymptotic behaviors
and discrete spectral properties for the Jost solutions, modified Jost
solutions and scattering matrix. The inverse scattering problems are solved by
establishing the matrix RH problem, and the reconstruction formula of
potential, trace formula and theta condition are also derived correspondingly.
The reflectionless potentials with double poles are exhibited explicitly by
means of determinants. Specifically, vivid plots and dynamics analysis which
contain double-pole soliton solutions for the ZBCs and double-pole dark-bright
soliton solution, breather-breather solutions and breather-breather-dark-bright
interaction solutions for the NZBCs, are demonstrated in details. Furthermore,
compared with the most classical second-order flow KN system which also be
called the DNLS equation, the third-order dispersion and quintic nonlinear term
of the KN system change the trajectory and velocity of solutions.


Abstract:
  In this article, we will review Noether's Theorems and their application in
General Relativity. We will present Noether's Theorems in their original form
and restate them as they are usually applied to physics. Some basic equations
of Special Relativity will be reviewed and contrasted with the equations in
General Relativity. We will be most interested in the role of Noether's
Theorems in conservation laws. Several applications in flat spaces are
examined. A formulation of Noether's Theorems in curved space is presented. Of
particular interest is the conservation of energy in General Relativity. Here,
there is no general form for a conserved tensor, as there is in a flat space
case. Pseudotensor formulations can be found and two examples are given. In
special cases, one can formulate an equation for the conservation of energy.
The standard cosmology is an example.


Abstract:
  Stochastic resonance (SR) is a coherence enhancement effect due to noise that
occurs in periodically-driven nonlinear dynamical systems. A very broad range
of physical and biological systems present this effect such as climate change,
neurons, neural networks, lasers, SQUIDS, and tunnel diodes, among many others.
Early theoretical models of SR dealt only with overdamped bistable oscillators.
Here, we propose a simple model that accounts for SR in an underdamped driven
Duffing oscillator with added white noise. Furthermore, we develop a
theoretical method to predict the effect of white noise on the pump, signal,
and idler responses of a Duffing amplifier. We also calculate the power
spectral density of the response of the Duffing amplifier. This approach may
prove to be useful for assessing the robustness of acoustic, phononic, or
mechanical frequency-comb generation to the presence of noise.


Abstract:
  All-dielectric optical metasurfaces are a workhorse in nano-optics due to
both their ability to manipulate light in different degrees of freedom and
their excellent performance at light frequency conversion. Here, we demonstrate
first-time generation of photon pairs via spontaneous parametric-down
conversion in lithium niobate quantum optical metasurfaces with electric and
magnetic Mie-like resonances at various wavelengths. By engineering the quantum
optical metasurface, we tailor the photon-pair spectrum in a controlled way.
Within a narrow bandwidth around the resonance, the rate of pair production is
enhanced up to two orders of magnitude compared to an unpatterned film of the
same thickness and material. These results enable flat-optics sources of
entangled photons -- a new promising platform for quantum optics experiments.


Abstract:
  We propose and develop an algebraic approach to revealed preference. Our
approach dispenses with non algebraic structure, such as topological
assumptions. We provide algebraic axioms of revealed preference that subsume
previous, classical revealed preference axioms, as well as generate new axioms
for behavioral theories, and show that a data set is rationalizable if and only
if it is consistent with an algebraic axiom.


Abstract:
  The Dirichlet distribution, also known as multivariate beta, is the most used
to analyse frequencies or proportions data. Maximum likelihood is widespread
for estimation of Dirichlet's parameters. However, for small sample sizes, the
maximum likelihood estimator may shows a significant bias. In this paper,
Dirchlet's parameters estimation is obtained through modified score functions
aiming at mean and median bias reduction of the maximum likelihood estimator,
respectively. A simulation study and an application compare the adjusted score
approaches with maximum likelihood.


Abstract:
  Implicit feedback is widely explored by modern recommender systems. Since the
feedback is often sparse and imbalanced, it poses great challenges to the
learning of complex interactions among users and items. Metric learning has
been proposed to capture user-item interactions from implicit feedback, but
existing methods only represent users and items in a single metric space,
ignoring the fact that users can have multiple preferences and items can have
multiple properties, which leads to potential conflicts limiting their
performance in recommendation. To capture the multiple facets of user
preferences and item properties while resolving their potential conflicts, we
propose the novel framework of Multi-fAcet Recommender networks with Spherical
optimization (MARS). By designing a cross-facet similarity measurement, we
project users and items into multiple metric spaces for fine-grained
representation learning, and compare them only in the proper spaces.
Furthermore, we devise a spherical optimization strategy to enhance the
effectiveness and robustness of the multi-facet recommendation framework.
Extensive experiments on six real-world benchmark datasets show drastic
performance gains brought by MARS, which constantly achieves up to 40\%
improvements over the state-of-the-art baselines regarding both HR and nDCG
metrics.


Abstract:
  We have studied the gravitational baryogenesis in f(R) theory of gravity with
an anisotropic Bianchi I space-time. The matter field is considered to be that
of perfect fluid. Two models pertaining to specific form of Ricci scalar have
been presented. The baryon-to-entropy ratio has been derived with the some
specific form of Ricci scalar in the anisotropic background. The gravitational
baryogenesis is examined and its behaviors are studied.


Abstract:
  Due to the practical importance of vehicle routing problems (VRP), there
exists an ever-growing body of research in algorithms and (meta)heuristics for
solving such problems. However, the diversity of VRP domains creates the
separate problem of modeling such problems -- describing the domain entities
(and, in particular, the planning decisions), the set of valid planning
decisions, and the preferences between different plans. In this paper, we
review the approaches for modeling vehicle routing problems. To make the
comparison more straightforward, we formulate several criteria for evaluating
modeling methods reflecting the practical requirements of the development of
optimization algorithms for such problems. Finally, as a result of this
comparison, we discuss several future research avenues in the field of modeling
VRP domains.


Abstract:
  The FeII/MgII line flux ratio has been used as an indicator of the Fe/Mg
abundance ratio in the broad line region (BLR) of active galactic nuclei
(AGNs). On the basis of archival rest-frame UV spectra obtained via the Hubble
Space Telescope and the Sloan Digital Sky Survey, we investigate the FeII/MgII
ratios of type 1 AGNs at z < 2. Over wide dynamic ranges of AGN properties
(i.e., black hole mass, AGN luminosity, and Eddington ratio), we confirm that
the FeII/MgII ratio strongly correlates with Eddington ratio but not with black
hole mass, AGN luminosity, or redshift. Our results suggest that the
metallicity in the BLR are physically related to the accretion activity of
AGNs, but not to the global properties of galaxies (i.e., galaxy mass and
luminosity). With regard to the relation between the BLR metallicity and the
accretion rate of AGNs, we discuss that metal cooling may play an important
role in enhancing the gas inflow into the central region of host galaxies,
resulting in the high accretion rate of AGNs.


Abstract:
  Balancing exploration and exploitation (EE) is a fundamental problem in
contex-tual bandit. One powerful principle for EE trade-off isOptimism in Face
of Uncer-tainty(OFU), in which the agent takes the action according to an upper
confidencebound (UCB) of reward. OFU has achieved (near-)optimal regret bound
for lin-ear/kernel contextual bandits. However, it is in general unknown how to
deriveefficient and effective EE trade-off methods for non-linearcomplex tasks,
suchas contextual bandit with deep neural network as the reward function. In
thispaper, we propose a novel OFU algorithm namedregularized OFU(ROFU). InROFU,
we measure the uncertainty of the reward by a differentiable function
andcompute the upper confidence bound by solving a regularized optimization
prob-lem. We prove that, for multi-armed bandit, kernel contextual bandit and
neuraltangent kernel bandit, ROFU achieves (near-)optimal regret bounds with
certainuncertainty measure, which theoretically justifies its effectiveness on
EE trade-off.Importantly, ROFU admits a very efficient implementation with
gradient-basedoptimizer, which easily extends to general deep neural network
models beyondneural tangent kernel, in sharp contrast with previous OFU
methods. The em-pirical evaluation demonstrates that ROFU works extremelywell
for contextualbandits under various settings.


Abstract:
  In this paper we survey and analyze modern neural-network-based facial
landmark detection algorithms. We focus on approaches that have led to a
significant increase in quality over the past few years on datasets with large
pose and emotion variability, high levels of face occlusions - all of which are
typical in real-world scenarios. We summarize the improvements into categories,
provide quality comparison on difficult and modern in-the-wild datasets: 300-W,
AFLW, WFLW, COFW. Additionally, we compare algorithm speed on CPU, GPU and
Mobile devices. For completeness, we also briefly touch on established methods
with open implementations available. Besides, we cover applications and
vulnerabilities of the landmark detection algorithms. Based on which, we raise
problems that as we hope will lead to further algorithm improvements.


Abstract:
  In this article we consider two particular examples of general construction
proposed in arXiv:2012.15529. We consider the integrable extensions of the
classical elliptic Calogero-Moser model of N particles with spin and the
integrable Euler-Arnold top related to the group SL(N,C). The extended systems
has additional N-1 degrees of freedom and can be described in terms of the
Darboux variables.


Abstract:
  This paper considers an enhancement of the classical iterated penalty Picard
(IPP) method for the incompressible Navier-Stokes equations, where we restrict
our attention to $O(1)$ penalty parameter, and Anderson acceleration (AA) is
used to significantly improve its convergence properties. After showing the
fixed point operator associated with the IPP iteration is Lipschitz continuous
and Lipschitz continuously (Frechet) differentiable, we apply a recently
developed general theory for AA to conclude that IPP enhanced with AA improves
its linear convergence rate by the gain factor associated with the underlying
AA optimization problem. Results for several challenging numerical tests are
given and show that IPP with penalty parameter 1 and enhanced with AA is a very
effective solver.


Abstract:
  Recently, many multi-stream gaze estimation methods have been proposed. They
estimate gaze from eye and face appearances and achieve reasonable accuracy.
However, most of the methods simply concatenate the features extracted from eye
and face appearance. The feature fusion process has been ignored. In this
paper, we propose a novel Adaptive Feature Fusion Network (AFF-Net), which
performs gaze tracking task in mobile tablets. We stack two-eye feature maps
and utilize Squeeze-and-Excitation layers to adaptively fuse two-eye features
according to their similarity on appearance. Meanwhile, we also propose
Adaptive Group Normalization to recalibrate eye features with the guidance of
facial feature. Extensive experiments on both GazeCapture and MPIIFaceGaze
datasets demonstrate consistently superior performance of the proposed method.


Abstract:
  Experiencing images with suitable music can greatly enrich the overall user
experience. The proposed image analysis method treats an artwork image
differently from a photograph image. Automatic image classification is
performed using deep-learning based models. An illustrative analysis showcasing
the ability of our deep-models to inherently learn and utilize perceptually
relevant features when classifying artworks is also presented. The Mean Opinion
Score (MOS) obtained from subjective assessments of the respective image and
recommended music pairs supports the effectiveness of our approach.


Abstract:
  Hyperparameter optimization is a challenging problem in developing deep
neural networks. Decision of transfer layers and trainable layers is a major
task for design of the transfer convolutional neural networks (CNN).
Conventional transfer CNN models are usually manually designed based on
intuition. In this paper, a genetic algorithm is applied to select trainable
layers of the transfer model. The filter criterion is constructed by accuracy
and the counts of the trainable layers. The results show that the method is
competent in this task. The system will converge with a precision of 97% in the
classification of Cats and Dogs datasets, in no more than 15 generations.
Moreover, backward inference according the results of the genetic algorithm
shows that our method can capture the gradient features in network layers,
which plays a part on understanding of the transfer AI models.


Abstract:
  We consider a nonlocal parabolic PDE, which may be regarded as the standard
semilinear heat equation with power nonlinearity, where the nonlinear term is
divided by some Sobolev norm of the solution. In this paper, we are interested
in constructing blowup solutions under some critical regimes. We had a complete
new phenomenon of the blowup speed with a log correction term.


Abstract:
  In this work, a compact model is presented for a 14 nm CMOS-based FinFET
Resonant Body Transistor (fRBT) operating at a frequency of 11.8 GHz and
targeting RF frequency generation/filtering for next generation radio
communication, clocking, and sensing applications. Analysis of the phononic
dispersion characteristics of the device, which informs the model development,
shows the presence of polarization exchange due to the periodic nature of the
back-end-of-line (BEOL) metal PnC. An eigenfrequency-based extraction process,
applicable to resonators based on electrostatic force transduction, has been
used to model the resonance cavity. Augmented forms of the BSIM-CMG (Common
Multi-Gate) model for FinFETs are used to model the drive and sense transistors
in the fRBT. This model framework allows easy integration with the
foundry-supplied process design kits (PDKs) and circuit simulators while being
flexible towards change in transduction mechanisms and device architecture.
Ultimately, the behaviour is validated against RF measured data for the
fabricated fRBT device under different operating conditions, leading to the
demonstration of the first complete model for this class of resonant device
integrated seamlessly in the CMOS stack.


Abstract:
  Paraphrasing is often performed with less concern for controlled style
conversion. Especially for questions and commands, style-variant paraphrasing
can be crucial in tone and manner, which also matters with industrial
applications such as dialog system. In this paper, we attack this issue with a
corpus construction scheme that simultaneously considers the core content and
style of directives, namely intent and formality, for the Korean language.
Utilizing manually generated natural language queries on six daily topics, we
expand the corpus to formal and informal sentences by human rewriting and
transferring. We verify the validity and industrial applicability of our
approach by checking the adequate classification and inference performance that
fit with the fine-tuning approaches, at the same time proposing a supervised
formality transfer task.


Abstract:
  Mobile system-on-chips (SoCs) are growing in their complexity and
heterogeneity (e.g., Arm's Big-Little architecture) to meet the needs of
emerging applications, including games and artificial intelligence. This makes
it very challenging to optimally manage the resources (e.g., controlling the
number and frequency of different types of cores) at runtime to meet the
desired trade-offs among multiple objectives such as performance and energy.
This paper proposes a novel information-theoretic framework referred to as
PaRMIS to create Pareto-optimal resource management policies for given target
applications and design objectives. PaRMIS specifies parametric policies to
manage resources and learns statistical models from candidate policy evaluation
data in the form of target design objective values. The key idea is to select a
candidate policy for evaluation in each iteration guided by statistical models
that maximize the information gain about the true Pareto front. Experiments on
a commercial heterogeneous SoC show that PaRMIS achieves better Pareto fronts
and is easily usable to optimize complex objectives (e.g., performance per
Watt) when compared to prior methods.


Abstract:
  End-to-end semantic role labeling (SRL) has been received increasing
interest. It performs the two subtasks of SRL: predicate identification and
argument role labeling, jointly. Recent work is mostly focused on graph-based
neural models, while the transition-based framework with neural networks which
has been widely used in a number of closely-related tasks, has not been studied
for the joint task yet. In this paper, we present the first work of
transition-based neural models for end-to-end SRL. Our transition model
incrementally discovers all sentential predicates as well as their arguments by
a set of transition actions. The actions of the two subtasks are executed
mutually for full interactions. Besides, we suggest high-order compositions to
extract non-local features, which can enhance the proposed transition model
further. Experimental results on CoNLL09 and Universal Proposition Bank show
that our final model can produce state-of-the-art performance, and meanwhile
keeps highly efficient in decoding. We also conduct detailed experimental
analysis for a deep understanding of our proposed model.


Abstract:
  Tidal effects in capped geometries computed in previous literature display no
dynamics along internal (toroidal) directions. This goes against the intuition
coming from the dual CFT picture. To resolve this tension, we consider a set of
infalling null geodesics in a family of black hole microstate geometries with a
smooth cap at the bottom of a long BTZ-like throat. Using the Penrose limit, we
show that a string following one of these geodesics feels tidal stresses along
all spatial directions, including internal toroidal directions. We find that
the tidal effects along the internal directions are of the same order of
magnitude as those along other, non-internal, directions. Furthermore, these
tidal disruptions oscillate as a function of the distance from the cap -- as a
string falls down the throat, it alternately experiences compression and
stretching. We explain some physical properties of this oscillation and comment
on the dual CFT interpretation.


Abstract:
  The reconstruction of sparse signal is an active area of research. Different
from a typical i.i.d. assumption, this paper considers a non-independent prior
of group structure. For this more practical setup, we propose EM-aided HyGEC, a
new algorithm to address the stability issue and the hyper-parameter issue
facing the other algorithms. The instability problem results from the ill
condition of the transform matrix, while the unavailability of the
hyper-parameters is a ground truth that their values are not known beforehand.
The proposed algorithm is built on the paradigm of HyGAMP (proposed by Rangan
et al.) but we replace its inner engine, the GAMP, by a matrix-insensitive
alternative, the GEC, so that the first issue is solved. For the second issue,
we take expectation-maximization as an outer loop, and together with the inner
engine HyGEC, we learn the value of the hyper-parameters. Effectiveness of the
proposed algorithm is also verified by means of numerical simulations.


Abstract:
  We prove a distribution-theoretic conjecture of Robert Coleman, thereby also
obtaining an explicit description of the complete set of Euler systems for the
multiplicative group over Q.


Abstract:
  This work presents a general paradigm to construct finite combinatorial
objects, with extermal substructures. This is done by observing that there
exist infinite combinatorial structures with desired extermal substructures,
coming from an action of a subgroup of the automorphism group. The crux of our
idea, that we call the closed orbits method is a systematic way to construct a
finite quotient of the infinite structure, containing a simple shadow of the
infinite substructure, which maintains its extermal combinatorial property. In
particular, the shadow of a thin infinite substructure in the cover remains
small in the quotient.
  We give a couple of applications of our paradigm. The first application is to
the well-known question of vertex expansion of number theoretic Ramanujan
graphs. It has been a common belief that such graphs are lossless expanders. We
show that some number theoretic Ramanujan graphs are not even unique neighbor
expanders, which is a weaker notion. This holds, in particular, for the
Morgenstern Ramanujan graphs.
  The second application is in the field of graph quantum ergodicity, where we
produce number theoretic Ramanujan graphs with an eigenfunction of small
support that corresponds to the zero eigenvalue. This again contradicts common
expectations.
  The closed orbits method is based on an established idea from dynamics and
number theory, of studying closed orbits of subgroups. The novelty of this work
is in exploiting this idea to combinatorial questions.


Abstract:
  Mass vaccination offers a promising exit strategy for the COVID-19 pandemic.
However, as vaccination progresses, demands to lift restrictions increase,
despite most of the population remaining susceptible. Using our age-stratified
SEIRD-ICU compartmental model and curated epidemiological and vaccination data,
we quantified the rate (relative to vaccination progress) at which countries
can lift non-pharmaceutical interventions without overwhelming their healthcare
systems. We analyzed scenarios ranging from immediately lifting restrictions
(accepting high mortality and morbidity) to reducing case numbers to a level
where test-trace-and-isolate (TTI) programs efficiently compensate for local
spreading events. In general, the age-dependent vaccination roll-out implies a
transient decrease of more than ten years in the average age of ICU patients
and deceased. The pace of vaccination determines the speed of lifting
restrictions; Taking the European Union (EU) as an example case, all considered
scenarios allow for steadily increasing contacts starting in May 2021 and
relaxing most restrictions by autumn 2021. Throughout summer 2021, only mild
contact restrictions will remain necessary. However, only high vaccine uptake
can prevent further severe waves. Across EU countries, seroprevalence impacts
the long-term success of vaccination campaigns more strongly than age
demographics. In addition, we highlight the need for preventive measures to
reduce contagion in school settings throughout the year 2021, where children
might be drivers of contagion because of them remaining susceptible...


Abstract:
  Presence-only data are a typical occurrence in species distribution modeling.
They include the presence locations and no information on the absence. Their
modeling usually does not account for detection biases. In this work, we aim to
merge three different sources of information to model the presence of marine
mammals. The approach is fully general and it is applied to two species of
dolphins in the Central Tyrrhenian Sea (Italy) as a case study. Data come from
the Italian Environmental Protection Agency (ISPRA) and Sapienza University of
Rome research campaigns, and from a careful selection of social media (SM)
images and videos. We build a Log Gaussian Cox process where different
detection functions describe each data source. For the SM data, we analyze
several choices that allow accounting for detection biases. Our findings allow
for a correct understanding of Stenella coeruleoalba and Tursiops truncatus
distribution in the study area. The results prove that the proposed approach is
broadly applicable, it can be widely used, and it is easily implemented in the
R software using INLA and inlabru. We provide examples' code with simulated
data in the supplementary materials.


Abstract:
  Robust recommendation aims at capturing true preference of users from noisy
data, for which there are two lines of methods have been proposed. One is based
on noise injection, and the other is to adopt the generative model Variational
Auto-encoder (VAE). However, the existing works still face two challenges.
First, the noise injection based methods often draw the noise from a fixed
noise distribution given in advance, while in real world, the noise
distributions of different users and items may differ from each other due to
personal behaviors and item usage patterns. Second, the VAE based models are
not expressive enough to capture the true preference since VAE often yields an
embedding space of a single modal, while in real world, user-item interactions
usually exhibit multi-modality on user preference distribution. In this paper,
we propose a novel model called Dual Adversarial Variational Embedding (DAVE)
for robust recommendation, which can provide personalized noise reduction for
different users and items, and capture the multi-modality of the embedding
space, by combining the advantages of VAE and adversarial training between the
introduced auxiliary discriminators and the variational inference networks. The
extensive experiments conducted on real datasets verify the effectiveness of
DAVE on robust recommendation.


Abstract:
  We bring together topological data analysis, applied category theory, and
machine learning to study multiparameter hierarchical clustering. We begin by
introducing a procedure for flattening multiparameter hierarchical clusterings.
We demonstrate that this procedure is a functor from a category of
multiparameter hierarchical partitions to a category of binary integer
programs. We also include empirical results demonstrating its effectiveness.
Next, we introduce a Bayesian update algorithm for learning clustering
parameters from data. We demonstrate that the composition of this algorithm
with our flattening procedure satisfies a consistency property.


Abstract:
  $K2$ greatly extended $Kepler$'s ability to find new planets, but it was
typically limited to identifying transiting planets with orbital periods below
40 days. While analyzing $K2$ data through the Exoplanet Explorers project,
citizen scientists helped discover one super-Earth and four sub-Neptune sized
planets in the relatively bright ($V=12.21$, $K=10.3$) K2-138 system, all which
orbit near 3:2 mean motion resonances. The $K2$ light curve showed two
additional transit events consistent with a sixth planet. Using $Spitzer$
photometry, we validate the sixth planet's orbital period of $41.966\pm0.006$
days and measure a radius of $3.44^{+0.32}_{-0.31}\,R_{\oplus}$, solidifying
K2-138 as the $K2$ system with the most currently known planets. There is a
sizeable gap between the outer two planets, since the fifth planet in the
system, K2-138 f, orbits at 12.76 days. We explore the possibility of
additional non-transiting planets in the gap between f and g. Due to the
relative brightness of the K2-138 host star, and the near resonance of the
inner planets, K2-138 could be a key benchmark system for both radial velocity
and transit timing variation mass measurements, and indeed radial velocity
masses for the inner four planets have already been obtained. With its five
sub-Neptunes and one super-Earth, the K2-138 system provides a unique test bed
for comparative atmospheric studies of warm to temperate planets of similar
size, dynamical studies of near resonant planets, and models of planet
formation and migration.


Abstract:
  Modern external memory is represented by several device classes. At present,
HDD, SATA SSD and NVMe SSD are widely used. Recently ultra-low latency SSD such
as Intel Optane became available on the market. Each of these types exhibits
it's own pattern for throughput, latency and parallelism. To achieve the
highest performance one has to pick an appropriate I/O interface provided by
the operating system. In this work we present a detailed overview and
evaluation of modern storage reading performance with regard to available Linux
synchronous and asynchronous interfaces. While throughout this work we aim for
the highest throughput we also measure latency and CPU usage. We provide this
report in hope the detailed results could be interesting to both researchers
and practitioners.


Abstract:
  We present the first holographic simulations of non-equilibrium steady state
formation in strongly coupled $\mathcal{N}=4$ SYM theory in 3+1 dimensions. We
initially join together two thermal baths at different temperatures and
chemical potentials and compare the subsequent evolution of the combined system
to analytic solutions of the corresponding Riemann problem and to numeric
solutions of ideal and viscous hydrodynamics. The time evolution of the energy
density that we obtain holographically is consistent with the combination of a
shock and a rarefaction wave: A shock wave moves towards the cold bath, and a
smooth broadening wave towards the hot bath. Between the two waves emerges a
steady state with constant temperature and flow velocity, both of which are
accurately described by a shock+rarefaction wave solution of the Riemann
problem. In the steady state region, a smooth crossover develops between two
regions of different charge density. This is reminiscent of a contact
discontinuity in the Riemann problem. We also obtain results for the
entanglement entropy of regions crossed by shock and rarefaction waves and find
both of them to closely follow the evolution of the energy density.


Abstract:
  It is well known that photons can carry a spatial structure akin to a
"twisted" or "rippled" wavefront. Such structured light fields have sparked
significant interest in both classical and quantum physics, with applications
ranging from dense communications to light-matter interaction. Harnessing the
full advantage of transverse spatial photonic encoding using the
Laguerre-Gaussian (LG) basis in the quantum domain requires control over both
the azimuthal (twisted) and radial (rippled) components of photons. However,
precise measurement of the radial photonic degree-of-freedom has proven to be
experimentally challenging primarily due to its transverse amplitude structure.
Here we demonstrate the generation and certification of full-field
Laguerre-Gaussian entanglement between photons pairs generated by spontaneous
parametric down-conversion in the telecom regime. By precisely tuning the
optical system parameters for state generation and collection, and adopting
recently developed techniques for precise spatial mode measurement, we are able
to certify fidelities up to 85% and entanglement dimensionalities up to 26 in a
43-dimensional radial and azimuthal LG mode space. Furthermore, we study
two-photon quantum correlations between 9 LG mode groups, demonstrating a
correlation structure related to mode group order and inter-modal cross-talk.
In addition, we show how the noise-robustness of high-dimensional entanglement
certification can be significantly increased by using measurements in multiple
LG mutually unbiased bases. Our work demonstrates the potential offered by the
full spatial structure of the two-photon field for enhancing technologies for
quantum information processing and communication.


Abstract:
  Transition and separation are difficult but important problems in the field
of fluid mechanics. Hitherto, separation and transition problems have not been
described accurately in mathematical terms, leading to design errors and
prediction problems in fluid machine engineering. The nonlinear uncertainty
involved in separation and transition makes it difficult to accurately analyze
these phenomena using experimental methods. Thus, new ideas and methods are
required for the mathematical prediction of fluid separation and transition. In
this article, after an axiomatic treatment of fluid mechanics, the concept of
an excited state is derived by generating a fluctuation velocity, and it is
revealed that fluid separation and transition are special forms of this excited
state. This allows us to clarify the state conditions of fluid separation and
transition. Mathematical analysis of the Navier--Stokes equations leads to a
general excited state theorem suitable for flowfields. Finally, the conditions
of separation and transition are derived, and the corresponding general laws
are established. The results presented in this article provide a foundation for
future research on the mechanism of turbulence and the solution of engineering
problems.


Abstract:
  Employment outcomes of resettled refugees depend strongly on where they are
placed inside the host country. While the United States sets refugee capacities
for communities on an annual basis, refugees arrive and must be placed over the
course of the year. We introduce a dynamic allocation system based on two-stage
stochastic programming to improve employment outcomes. Our algorithm is able to
achieve over 98 percent of the hindsight-optimal employment compared to under
90 percent of current greedy-like approaches. This dramatic improvement
persists even when we incorporate a vast array of practical features of the
refugee resettlement process including indivisible families, batching, and
uncertainty with respect to the number of future arrivals. Our algorithm is now
part of the Annie MOORE optimization software used by a leading American
refugee resettlement agency.


Abstract:
  Using the Atacama Large Millimeter/submillimeter Array (ALMA), we
investigated a peculiar millimeter source MMS 3 located in the Orion Molecular
Cloud 3 (OMC-3) region in the 1.3 mm continuum, CO ($J$=2-1), SiO ($J$=5-4),
C$^{18}$O ($J$=2-1), N$_2$D$^+$ ($J$=3-2), and DCN ($J$=3-2) emissions. With
the ALMA high angular resolution ($\sim$0''.2), we detected a very compact and
highly centrally condensed continuum emission with a size of 0''.45 $\times$
0''.32 (P.A.=0.22$^\circ$). The peak position coincides with the locations of
previously reported $Spitzer$/IRAC and X-ray sources within their positional
uncertainties. We also detected an envelope with a diameter of $\sim$6800 au
(P.A.=75$^\circ$) in the C$^{18}$O ($J$=2-1) emission. Moreover, a bipolar
outflow was detected in the CO ($J$=2-1) emission for the first time. The
outflow elongates roughly perpendicular to the long axis of the envelope
detected in the C$^{18}$O ($J$=2-1) emission. Compact high-velocity CO gas in
the (red-shifted) velocity range of 22-30 km s$^{-1}$, presumably tracing a
jet, was detected near the 1.3 mm continuum peak. A compact and faint
red-shifted SiO emission was marginally detected on the CO outflow lobe. The
physical quantities of the outflow in MMS 3 are relatively smaller than those
in other sources in the OMC-3 region. The centrally condensed object associated
with the near-infrared and X-ray sources, the flattened envelope, and the faint
outflow indicate that MMS 3 harbors a low mass protostar with an age of
$\sim$10$^3$ yr.


Abstract:
  Parametric stochastic simulators are ubiquitous in science, often featuring
high-dimensional input parameters and/or an intractable likelihood. Performing
Bayesian parameter inference in this context can be challenging. We present a
neural simulation-based inference algorithm which simultaneously offers
simulation efficiency and fast empirical posterior testability, which is unique
among modern algorithms. Our approach is simulation efficient by simultaneously
estimating low-dimensional marginal posteriors instead of the joint posterior
and by proposing simulations targeted to an observation of interest via a prior
suitably truncated by an indicator function. Furthermore, by estimating a
locally amortized posterior our algorithm enables efficient empirical tests of
the robustness of the inference results. Since scientists cannot access the
ground truth, these tests are necessary for trusting inference in real-world
applications. We perform experiments on a marginalized version of the
simulation-based inference benchmark and two complex and narrow posteriors,
highlighting the simulator efficiency of our algorithm as well as the quality
of the estimated marginal posteriors.


Abstract:
  Flow and storage volume regulation is essential for the adequate transport
and management of energy resources in district heating systems. In this letter,
we propose a novel and suitably tailored -- decentralized -- adaptive control
scheme addressing this problem whilst offering closed-loop stability
guarantees. We focus on a system configuration comprising multiple heat
producers, consumers and storage tanks exchanging energy through a common
distribution network, which are features of modern and prospective district
heating installations. {\color{black}The proposed controller is based on
passivity, backstepping and (indirect) adaptive control theory.


Abstract:
  Metallic ion adsorption on surfactant aggregates were studied with Molecular
dynamics simulations. Using ionic salts, such as lead sulfate (PbSO$_4$) and
aluminum sulfate [Al$_2$(SO$_4$)$_3$], adsorption of lead and aluminum were
investigated at different salt concentrations and different surfactant
aggregates (micelles) sizes. The micelles were constructed with spherical
shapes composed of sodium dodecyl sulfate (SDS) anionic surfactants. The
electrostatic interactions between the positive ions and the negative SDS
headgroups promote capture of the metal particles on the aggregate surface.
Metal adsorption was analyzed in terms of radial density profiles, partial pair
distribution functions and adsorption isotherms. It is showed that SDS micelles
adsorb better lead than aluminum ions regardless of the size of the aggregates
and salt concentrations.


Abstract:
  In large infrastructures such as dams, which have a relatively high economic
value, ensuring the proper operation of the associated hydraulic facilities in
different operating conditions is of utmost importance. To ensure the correct
and successful operation of the dam's hydraulic equipment and prevent possible
damages, including gates and downstream tunnel, to build laboratory models and
perform some tests are essential (the advancement of the smart sensors based on
artificial intelligence is essential). One of the causes of damage to dam
bottom outlets is cavitation in downstream and between the gates, which can
impact on dam facilities, and air aeration can be a solution to improve it. In
the present study, six dams in different provinces in Iran has been chosen to
evaluate the air entrainment in the downstream tunnel experimentally. Three
artificial neural networks (ANN) based machine learning (ML) algorithms are
used to model and predict the air aeration in the bottom outlet. The proposed
models are trained with genetic algorithms (GA), particle swarm optimization
(PSO), i.e., ANN-GA, ANN-PSO, and ANFIS-PSO. Two hydrodynamic variables, namely
volume rate and opening percentage of the gate, are used as inputs into all
bottom outlet models. The results showed that the most optimal model is
ANFIS-PSO to predict the dependent value compared with ANN-GA and ANN-PSO. The
importance of the volume rate and opening percentage of the dams' gate
parameters is more effective for suitable air aeration.


Abstract:
  Concept normalization in free-form texts is a crucial step in every
text-mining pipeline. Neural architectures based on Bidirectional Encoder
Representations from Transformers (BERT) have achieved state-of-the-art results
in the biomedical domain. In the context of drug discovery and development,
clinical trials are necessary to establish the efficacy and safety of drugs. We
investigate the effectiveness of transferring concept normalization from the
general biomedical domain to the clinical trials domain in a zero-shot setting
with an absence of labeled data. We propose a simple and effective two-stage
neural approach based on fine-tuned BERT architectures. In the first stage, we
train a metric learning model that optimizes relative similarity of mentions
and concepts via triplet loss. The model is trained on available labeled
corpora of scientific abstracts to obtain vector embeddings of concept names
and entity mentions from texts. In the second stage, we find the closest
concept name representation in an embedding space to a given clinical mention.
We evaluated several models, including state-of-the-art architectures, on a
dataset of abstracts and a real-world dataset of trial records with
interventions and conditions mapped to drug and disease terminologies.
Extensive experiments validate the effectiveness of our approach in knowledge
transfer from the scientific literature to clinical trials.


Abstract:
  Invertible networks have various benefits for image denoising since they are
lightweight, information-lossless, and memory-saving during back-propagation.
However, applying invertible models to remove noise is challenging because the
input is noisy, and the reversed output is clean, following two different
distributions. We propose an invertible denoising network, InvDN, to address
this challenge. InvDN transforms the noisy input into a low-resolution clean
image and a latent representation containing noise. To discard noise and
restore the clean image, InvDN replaces the noisy latent representation with
another one sampled from a prior distribution during reversion. The denoising
performance of InvDN is better than all the existing competitive models,
achieving a new state-of-the-art result for the SIDD dataset while enjoying
less run time. Moreover, the size of InvDN is far smaller, only having 4.2% of
the number of parameters compared to the most recently proposed DANet. Further,
via manipulating the noisy latent representation, InvDN is also able to
generate noise more similar to the original one. Our code is available at:
https://github.com/Yang-Liu1082/InvDN.git.


Abstract:
  Traditional goal-oriented dialogue systems rely on various components such as
natural language understanding, dialogue state tracking, policy learning and
response generation. Training each component requires annotations which are
hard to obtain for every new domain, limiting scalability of such systems.
Similarly, rule-based dialogue systems require extensive writing and
maintenance of rules and do not scale either. End-to-End dialogue systems, on
the other hand, do not require module-specific annotations but need a large
amount of data for training. To overcome these problems, in this demo, we
present Alexa Conversations, a new approach for building goal-oriented dialogue
systems that is scalable, extensible as well as data efficient. The components
of this system are trained in a data-driven manner, but instead of collecting
annotated conversations for training, we generate them using a novel dialogue
simulator based on a few seed dialogues and specifications of APIs and entities
provided by the developer. Our approach provides out-of-the-box support for
natural conversational phenomena like entity sharing across turns or users
changing their mind during conversation without requiring developers to provide
any such dialogue flows. We exemplify our approach using a simple pizza
ordering task and showcase its value in reducing the developer burden for
creating a robust experience. Finally, we evaluate our system using a typical
movie ticket booking task and show that the dialogue simulator is an essential
component of the system that leads to over $50\%$ improvement in turn-level
action signature prediction accuracy.


Abstract:
  The coupling behavior of time scales of structural relaxation is investigated
on the basis of five different response functions for 1,2,6-hexanetriol, a
hydrogen-bonded liquid with a minor secondary contribution, and
2,6,10,15,19,23-hexamethyl-tetracosane (squalane), a van der Waals bonded
liquid with a prominent secondary relaxation process. Time scales of structural
relaxation are derived as inverse peak frequencies for each investigated
response function. For 1,2,6-hexanetriol, the time-scale indices are
temperature-independent, while a decoupling of time scales is observed for
squalane in accordance with literature. An alternative evaluation approach is
made on the squalane data, extracting time scales from the terminal relaxation
mode instead of the peak position, and in this case temperature-independent
coupling is also found for squalane, despite its strong secondary relaxation
contribution. Interestingly, the very same ordering of
response-function-specific time scales is observed for these two liquids, which
is also consistent with the observation made for simple van der Waals bonded
liquids reported previously [Jakobsen \textit{et al.}, J. Chem. Phys.
\textbf{136}, 081102 (2012)]. This time-scale ordering is based on the
following response functions, from fast to slow dynamics: shear modulus, bulk
modulus, dielectric permittivity, longitudinal thermal expansivity coefficient,
and longitudinal specific heat. These findings indicate a general relation
between the time scales of different response functions and, as inter-molecuar
interactions apparently play a subordinate role, suggest a rather generic
nature of the process of structural relaxation.


Abstract:
  We present the results from a spectroscopic monitoring campaign to obtain
reverberation-mapping measurements and investigate the broad-line region
kinematics for active galactic nuclei (AGN) of Mrk~817 and NGC~7469. This
campaign was undertaken with the Lijiang 2.4-meter telescope, the median
spectroscopic sampling is 2.0 days for Mrk~817 and 1.0 days for NGC~7469. We
detect time lags of the broad emission lines including H$\beta$, H$\gamma$,
He~{\sc ii} and He~{\sc i} for both AGNs, and including Fe~{\sc ii} for Mrk~817
with respect to the varying AGN continuum at 5100~\AA. Investigating the
relationship between line widths and time lags of the broad emission lines, we
find that the BLR dynamics of Mrk~817 and NGC~7469 are consistent with the
virial prediction. We estimate the masses of central supermassive black hole
(SMBH) and the accretion rates of both AGNs. Using the data of this campaign,
we construct the velocity-resolved lag profiles of the broad H$\gamma$,
H$\beta$, and He~{\sc i} lines for Mrk~817, which show almost the same
kinematic signatures that the time lags in the red wing are slightly larger
than the time lags in the blue wing. For NGC~7469, we only clearly construct
the velocity-resolved lag profiles of the broad H$\gamma$ and H$\beta$, which
show very similar kinematic signatures to the BLR of Mrk~817. These signatures
indicate that the BLR of Keplerian motion in both AGNs seemingly has outflowing
components during the monitoring period. We discuss the kinematics of the BLR
and the measurements including SMBH mass and accretion rates.


Abstract:
  In the context of generalized descriptive set theory, we systematically
compare and analyze various notions of Polish-like spaces and standard
$\kappa$-Borel spaces for $\kappa$ an uncountable (regular) cardinal satisfying
$\kappa^{<\kappa} = \kappa$. As a result, we obtain a solid framework where one
can develop the theory in full generality. We also provide natural
characterizations of the generalized Cantor and Baire spaces. Some of the
results obtained considerably extend previous work from [Coskey-Schlicht 2016,
Galeotti 2019, Luecke-Schlicht 2015], and answer some questions contained
therein.


Abstract:
  Borexino is a 280-ton liquid scintillator detector located at the Laboratori
Nazionali del Gran Sasso in Italy. Since the start of its data taking in May
2007, it has provided several measurements of low-energy neutrinos from various
sources. At the base of its success, lie unprecedented levels of radio-purity
and extensive thermal stabilisation, both resulting from a years-long effort of
the collaboration. Solar neutrinos, emitted in the hydrogen-to-helium fusion in
the solar core, are important for the understanding of our star, as well as
neutrino properties. Borexino is the only experiment that has performed a
complete spectroscopy of the \emph{pp} chain solar neutrinos (with the
exception of the \emph{hep} neutrinos contributing to the total flux at
$10^{-5}$ level), through the detection of \emph{pp}, $^7$Be, \emph{pep}, and
$^8$B solar neutrinos and has experimentally confirmed the existence of the CNO
fusion cycle in the Sun. Borexino has also detected geoneutrinos, antineutrinos
from the decays of long-lived radioactive elements inside the Earth, that can
be exploited as a new and unique tool to study our planet. This paper reviews
the most recent Borexino results on solar and geoneutrinos, from highlighting
the key elements of the analyses up to the discussion and interpretation of the
results for neutrino, solar, and geophysics.


Abstract:
  In this paper, we will see that the Cartesian product of two 2-Banach spaces
is also 2-Banach space and discuss some properties of closed linear operator in
linear 2-normed space. We also describe the concept of different types of
continuity of b-linear functional and derive the Uniform Boundedness Principle
and Hahn-Banach extension theorem for b-linear functionals in the case of
linear 2-normed spaces. We also introduce the notion of weak*convergence for
the sequence of bounded b-linear functionals relative to linear 2-normed space.


Abstract:
  We examine the collective states of run-and-tumble active matter disks driven
over a periodic obstacle array. When the drive is applied along a symmetry
direction of the array, we find a clog-free uniform liquid state for low
activity, while at higher activity, the density becomes increasingly
heterogeneous and an active clogged state emerges in which the mobility is
strongly reduced. For driving along non-symmetry or incommensurate directions,
there are two different clogging behaviors consisting of a drive dependent
clogged state in the low activity thermal limit and a drive independent clogged
state at high activity. These regimes are separated by a uniform flowing liquid
at intermediate activity. There is a critical activity level above which the
thermal clogged state does not occur, as well as an optimal activity level that
maximizes the disk mobility. Thermal clogged states are dependent on the
driving direction while active clogged states are not. In the low activity
regime, diluting the obstacles produces a monotonic increase in the mobility;
however, for large activities, the mobility is more robust against obstacle
dilution. We also examine the velocity-force curves for driving along
non-symmetry directions, and find that they are linear when the activity is low
or intermediate, but become nonlinear at high activity and show behavior
similar to that found for the plastic depinning of solids. At higher drives the
active clustering is lost. For low activity we also find a reentrant fluid
phase, where the system transitions from a high mobility fluid at low drives to
a clogged state at higher drives and then back into another fluid phase at very
high drives. We map the regions in which the thermally clogged, partially
clogged, active uniform fluid, clustered fluid, active clogged, and
directionally locked states occur as a function of disk density, drift force,
and activity.


Abstract:
  We investigate in atomic scale the electronic phase separation occurring in
the well known quasi 1D charge-density wave (CDW) phase of the In atomic wire
array on a Si(111) surface. The characteristic atomic scale defects, originated
from excess In atoms, are found to be actively involved in the formation of the
phase boundary between the metallic and the CDW phases by extensive analysis of
scanning tunneling microscopy images at various temperatures. These particular
defects flip the phase of the quasi 1D CDW to impose strong local constraints
in the CDW correlation. We show that such local constraints and the substantial
interwire CDW interaction induce local condensates of CDW and the phase
separation between the metallic and the CDW phases. This work unveils the
atomistic origin of the electronic phase separation, highlighting the
importance of atomic scale structures of defects and their collective
interaction in electronically inhomogeneous materials.