  Considering the large amount of content created online by the minute,
slang-aware automatic tools are critically needed to promote social good, and
assist policymakers and moderators in restricting the spread of offensive
language, abuse, and hate speech. Despite the success of large language models
and the spontaneous emergence of slang dictionaries, it is unclear how far
their combination goes in terms of slang understanding for downstream social
good tasks. In this paper, we provide a framework to study different
combinations of representation learning models and knowledge resources for a
variety of downstream tasks that rely on slang understanding. Our experiments
show the superiority of models that have been pre-trained on social media data,
while the impact of dictionaries is positive only for static word embeddings.
Our error analysis identifies core challenges for slang representation
learning, including out-of-vocabulary words, polysemy, variance, and annotation
disagreements, which can be traced to characteristics of slang as a quickly
evolving and highly subjective language.


  The flow characterized by a linear longitudinal velocity field
$u_x(x,t)=a(t)x$, where $a(t)={a_0}/({1+a_0t})$, a uniform density $n(t)\propto
a(t)$, and a uniform temperature $T(t)$ is analyzed for dilute granular gases
by means of a BGK-like model kinetic equation in $d$ dimensions. For a given
value of the coefficient of normal restitution $\alpha$, the relevant control
parameter of the problem is the reduced deformation rate $a^*(t)=a(t)/\nu(t)$
(which plays the role of the Knudsen number), where $\nu(t)\propto
n(t)\sqrt{T(t)}$ is an effective collision frequency. The relevant response
parameter is a nonlinear viscosity function $\eta^*(a^*)$ defined from the
difference between the normal stress $P_{xx}(t)$ and the hydrostatic pressure
$p(t)=n(t)T(t)$. The main results of the paper are: (a) an exact first-order
ordinary differential equation for $\eta^*(a^*)$ is derived from the kinetic
model; (b) a recursion relation for the coefficients of the Chapman--Enskog
expansion of $\eta^*(a^*)$ in powers of $a^*$ is obtained; (c) the
Chapman--Enskog expansion is shown to diverge for elastic collisions
($\alpha=1$) and converge for inelastic collisions ($\alpha<1$), in the latter
case with a radius of convergence that increases with inelasticity; (d) a
simple approximate analytical solution for $\eta^*(a^*)$, hardly
distinguishable from the numerical solution of the differential equation, is
constructed.


  In this paper we develop non-asymptotic Gaussian approximation results for
the sampling distribution of suprema of empirical processes when the indexing
function class $\mathcal{F}_n$ varies with the sample size $n$ and may not be
Donsker. Prior approximations of this type required upper bounds on the metric
entropy of $\mathcal{F}_n$ and uniform lower bounds on the variance of $f \in
\mathcal{F}_n$ which, both, limited their applicability to high-dimensional
inference problems. In contrast, the results in this paper hold under simpler
conditions on boundedness, continuity, and the strong variance of the
approximating Gaussian process. The results are broadly applicable and yield a
novel procedure for bootstrapping the distribution of empirical process suprema
based on the truncated Karhunen-Lo{\`e}ve decomposition of the approximating
Gaussian process. We demonstrate the flexibility of this new bootstrap
procedure by applying it to three fundamental problems in high-dimensional
statistics: simultaneous inference on parameter vectors, inference on the
spectral norm of covariance matrices, and construction of simultaneous
confidence bands for functions in reproducing kernel Hilbert spaces.


  In this short note we describe an alternative global version of the twisting
procedure used by Dolgushev to prove formality theorems. This allows us to
describe the maps of Fedosov resolutions, which are key factors of the
formality morphisms, in terms of a twist of the fiberwise quasi-isomorphisms
induced by the local formality theorems proved by Kontsevich and Shoikhet. The
key point consists in considering $L_\infty$-resolutions of the Fedosov
resolutions obtained by Dolgushev and an adapted notion of Maurer-Cartan
element. This allows us to perform the twisting of the quasi-isomorphism
intertwining them in a global manner.


  In mathematical physics, the gradient operator with nonconstant coefficients
encompasses various models, including Fourier's law for heat propagation and
Fick's first law, that relates the diffusive flux to the gradient of the
concentration. Specifically, consider $n\geq 3$ orthogonal unit vectors
$e_1,\dots,e_n\in\mathbb{R}^n$, and let $\Omega\subseteq\mathbb{R}^n$ be some
(in general unbounded) Lipschitz domain. This paper investigates the spectral
properties of the gradient operator
$T=\sum_{i=1}^ne_ia_i(x)\frac{\partial}{\partial x_i}$ with nonconstant
positive coefficients $a_i:\overline{\Omega}\to(0,\infty)$. Under certain
regularity and growth conditions on the $a_i$, we identify bisectorial or
strip-type regions that belong to the $S$-resolvent set of $T$. Moreover, we
obtain suitable estimates of the associated resolvent operator. Our focus lies
in the spectral theory on the $S$-spectrum, designed to study the operators
acting in Clifford modules $V$ over the Clifford algebra $\mathbb{R}_n$, with
vector operators being a specific crucial subclass. The spectral properties
related to the $S$-spectrum of $T$ are linked to the inversion of the operator
$Q_s(T):=T^2-2s_0T+|s|^2$, where $s\in\mathbb{R}^{n+1}$ is a paravector, i.e.,
it is of the form $s=s_0+s_1e_1+\dots+s_ne_n$. This spectral problem is
substantially different from the complex one, since it allows to associate
general boundary conditions to $Q_s(T)$, i.e., to the squared operator $T^2$.


  Most action recognition datasets and algorithms assume a closed world, where
all test samples are instances of the known classes. In open set problems, test
samples may be drawn from either known or unknown classes. Existing open set
action recognition methods are typically based on extending closed set methods
by adding post hoc analysis of classification scores or feature distances and
do not capture the relations among all the video clip elements. Our approach
uses the reconstruction error to determine the novelty of the video since
unknown classes are harder to put back together and thus have a higher
reconstruction error than videos from known classes. We refer to our solution
to the open set action recognition problem as "Humpty Dumpty", due to its
reconstruction abilities. Humpty Dumpty is a novel graph-based autoencoder that
accounts for contextual and semantic relations among the clip pieces for
improved reconstruction. A larger reconstruction error leads to an increased
likelihood that the action can not be reconstructed, i.e., can not put Humpty
Dumpty back together again, indicating that the action has never been seen
before and is novel/unknown. Extensive experiments are performed on two
publicly available action recognition datasets including HMDB-51 and UCF-101,
showing the state-of-the-art performance for open set action recognition.


  In solids, external stress induces the Peach-Koehler force, which drives
dislocations to move. Similarly, in liquid crystals, an external angular stress
creates an analogous force, which drives disclinations to move. In this work,
we develop a method to calculate the relevant angular stress either
analytically or numerically, and hence to determine the force on a
disclination. We demonstrate this method by applying the Peach-Koehler force
theory to four problems: (a) Single disclination in a liquid crystal cell
between two uniform in-plane alignments perpendicular to each other. (b) Array
of disclinations in a liquid crystal cell with patterned substrates. (c) Pair
of disclinations in a long capillary tube with homeotropic anchoring. (d)
Radial hedgehog or disclination loop inside a sphere with homeotropic
anchoring, and its response to an applied magnetic field. In all of these
problems, the Peach-Koehler force theory predicts the equilibrium defect
structure, and the predictions are consistent with the results of minimizing
the total free energy.


  We present a new spectroscopic technique based in part on targeting the
upward fluctuations of the surface brightness for studying the internal stellar
kinematics and metallicities of low surface brightness galaxies and streams
beyond the Local Group. The distance to these systems makes them unsuitable for
targeting individual red giant branch (RGB) stars (tip of RGB at
$I\gtrsim24$~mag) and their surface brightness is too low ($\mu_r\gtrsim
25$~mag~arcsec$^{-2}$) for integrated light spectroscopic measurements. This
technique overcomes these two problems by targeting individual objects that are
brighter than the tip of the RGB. We apply this technique to the star-forming
dwarf galaxy NGC 4449 and its stellar stream. We use Keck/DEIMOS data to
measure the line-of-sight radial velocity out to $\sim7$~kpc in the East side
of the galaxy and $\sim8$~kpc along the stream. We find that the two systems
are likely gravitationally bound to each other and have heliocentric radial
velocities of $227.3\pm10.7$~km/s and $225.8\pm16.0$~km/s, respectively.
Neither the stream nor the near half of the galaxy shows a significant velocity
gradient. We estimate the stellar metallicity of the stream based on the
equivalent width of its Calcium triplet lines and find [Fe/H]~$=-1.37\pm0.41$,
which is consistent with the metallicity-luminosity relation for Local Group
dwarf galaxies. Whether the stream's progenitor was moderately or severely
stripped cannot be constrained with this metallicity uncertainty. We
demonstrate that this new technique can be used to measure the kinematics and
(possibly) the metallicity of the numerous faint satellites and stellar streams
in the halos of nearby ($\sim 4$~Mpc) galaxies.


  Desalination that produces clean freshwater from seawater holds the promise
to solve the global water shortage for drinking, agriculture and industry.
However, conventional desalination technologies such as reverse osmosis and
thermal distillation involve large amounts of energy consumption, and the
semipermeable membranes widely used in reverse osmosis face the challenge to
provide a high throughput at high salt rejection. Here we find by comprehensive
molecular dynamics simulations and first principles modeling that, pristine
graphyne, one of the graphene like one-atom-thick carbon allotropes, can
achieve 100% rejection of nearly all ions in seawater including Na+, Cl-, Mg2+,
K+ and Ca2+, at an exceptionally high water permeability about two orders of
magnitude higher than those for commercial state-of-the-art reverse osmosis
membranes at a salt rejection of ~98.5%. This complete ion rejection by
graphyne, independent of the salt concentration and the operating pressure, is
revealed to be originated from the significantly higher energy barriers for
ions than that for water. This intrinsic specialty of graphyne should provide a
new possibility for the efforts to alleviate the global shortage of freshwater
and other environmental problems.


  Node-link diagrams are widely used to facilitate network explorations.
However, when using a graph drawing technique to visualize networks, users
often need to tune different algorithm-specific parameters iteratively by
comparing the corresponding drawing results in order to achieve a desired
visual effect. This trial and error process is often tedious and
time-consuming, especially for non-expert users. Inspired by the powerful data
modelling and prediction capabilities of deep learning techniques, we explore
the possibility of applying deep learning techniques to graph drawing.
Specifically, we propose using a graph-LSTM-based approach to directly map
network structures to graph drawings. Given a set of layout examples as the
training dataset, we train the proposed graph-LSTM-based model to capture their
layout characteristics. Then, the trained model is used to generate graph
drawings in a similar style for new networks. We evaluated the proposed
approach on two special types of layouts (i.e., grid layouts and star layouts)
and two general types of layouts (i.e., ForceAtlas2 and PivotMDS) in both
qualitative and quantitative ways. The results provide support for the
effectiveness of our approach. We also conducted a time cost assessment on the
drawings of small graphs with 20 to 50 nodes. We further report the lessons we
learned and discuss the limitations and future work.


  We put forward a possible new interpretation and explanatory framework for
quantum theory. The basic hypothesis underlying this new framework is that
quantum particles are conceptual entities. More concretely, we propose that
quantum particles interact with ordinary matter, nuclei, atoms, molecules,
macroscopic material entities, measuring apparatuses, ..., in a similar way to
how human concepts interact with memory structures, human minds or artificial
memories. We analyze the most characteristic aspects of quantum theory, i.e.
entanglement and non-locality, interference and superposition, identity and
individuality in the light of this new interpretation, and we put forward a
specific explanation and understanding of these aspects. The basic hypothesis
of our framework gives rise in a natural way to a Heisenberg uncertainty
principle which introduces an understanding of the general situation of 'the
one and the many' in quantum physics. A specific view on macro and micro
different from the common one follows from the basic hypothesis and leads to an
analysis of Schrodinger's Cat paradox and the measurement problem different
from the existing ones. We reflect about the influence of this new quantum
interpretation and explanatory framework on the global nature and evolutionary
aspects of the world and human worldviews, and point out potential explanations
for specific situations, such as the generation problem in particle physics,
the confinement of quarks and the existence of dark matter.


  We present a multi-wavelenght study of the recently discovered Ultraluminous
X-ray transient XMMUJ004243.6+412519 (ULX2 hereafter) in M31, based on Swift
data and the 1.8-m Copernico Telescope in Asiago (Italy). Undetected until
January 2012, the source suddenly showed a powerful X-ray emission with a
luminosity of 1e38 erg/s (assuming a distance of 780 kpc). In the following
weeks, its luminosity overcame 1e39 erg/s, remaining fairly constant for at
least 40 days and fading below 1e38 erg/s in the next 200 days. The spectrum
can be well described by a single multi-color disk blackbody model which
progressively softened during the decay (from kT=0.9 keV to 0.4 keV). No
emission from ULX2 was detected down to 22 mag in the optical band and to 23-24
mag in the near ultraviolet. We compare its properties with those of other
known ULXs and Galactic black hole transients, finding more similarities with
the latter.


  Scattering transform is a well known powerful tool for quantisation of field
theories in (1+1) dimensions. Conventionally only those models whose classical
counterparts admit a Lax pair (origin of which is always mysterious) have been
quantised in this way. In relativistic quantum field theories we show that the
scattering transforms can be constructed ab initio from its invariance under
Lorentz transformation (both proper and improper), irreducible transformation
nature of scalar and Dirac fields, the existence of a momentum scale associated
with asymptotic nature of the scattering transform and the closure of short
distance operator product algebra. For single fields it turns out that theories
quantisable by scattering transforms are restricted to sine-Gordon type for
spin-0 and Massive Thirring type for spin-1/2 if the target space of the
scattering transform matrix is assumed to be parity invariant. There are
interesting unexplored extensions if the target space is given chirality.


  It is a very old and interesting open problem to characterize those
collections of embedded topological types of local plane curve singularities
which may appear as singularities of a projective plane curve C of degree d.
The goal of the present article is to give a complete (topological)
classification of those cases when C is rational and it has a unique
singularity which is locally irreducible (i.e. C is unicuspidal) with one
Puiseux pair.


  These lectures introduce some of the basic methods of perturbative QCD and
their applications to phenomenology at high energy. Emphasis is given to
techniques that are used to study QCD and related field theories to all orders
in perturbation theory, with introductions to infrared safety, factorization
and evolution in high energy hard scattering.


  In the rate-distortion function and the Maximum Entropy (ME) method, Minimum
Mutual In-formation (MMI) distributions and ME distributions are expressed by
Bayes-like formulas, in-cluding Negative Exponential Functions (NEFs) and
partition functions. Why do these non-probability functions exist in Bayes-like
formulas? On the other hand, the rate-distortion function has three
disadvantages: (1) the distortion function is subjectively defined; (2) the
defi-nition of the distortion function between instances and labels is often
difficult; (3) it cannot be used for data compression according to the labels'
semantic meanings. The author has proposed using the semantic information G
measure with both statistical probability and logical probability before. We
can now explain NEFs as truth functions, partition functions as logical
probabilities, Bayes-like formulas as semantic Bayes' formulas, MMI as Semantic
Mutual Information (SMI), and ME as extreme ME minus SMI. In overcoming the
above disadvantages, this paper sets up the relationship between truth
functions and distortion functions, obtains truth functions from samples by
machine learning, and constructs constraint conditions with truth functions to
extend rate-distortion functions. Two examples are used to help readers
understand the MMI iteration and to support the theoretical results. Using
truth functions and the semantic information G measure, we can combine machine
learning and data compression, including semantic com-pression. We need further
studies to explore general data compression and recovery, according to the
semantic meaning.


  We introduce the two-dimensional Gross-Pitaevskii/nonlinear-Schrodinger
(GP/NLS) equation with the self-focusing nonlinearity confined to two identical
circles, separated or overlapped. The model can be realized in terms of
Bose-Einstein condensates (BECs) and photonic-crystal fibers. Following the
recent analysis of the spontaneous symmetry breaking (SSB) of localized modes
trapped in 1D and 2D double-well nonlinear potentials (also known as
pseudopotentials), we aim to find 2D solitons in the two-circle setting, using
numerical methods and the variational approximation (VA). Well-separated
circles support stable symmetric and antisymmetric solitons. The decrease of
separation L between the circles leads to destabilization of the solitons. The
symmetric modes undergo two SSB transitions. First, they are transformed into
weakly asymmetric breathers, which is followed by a transition to single-peak
modes trapped in one circle. The antisymmetric solitons perform a direct
transition to the single-peak mode. The symmetric solitons are described
reasonably well by the VA. For touching (L=0) and overlapping (L<0) circles,
single-peak solitons are found-asymmetric ones, trapped in either circle, and
symmetric solitons centered at the midpoint of the bi-circle configuration. If
the overlap is weak, the symmetric soliton is unstable. It may spontaneously
leap into either circle and perform shuttle motion in it. A region of stability
of the symmetric solitons appears with the increase of the overlap degree. In
the case of a moderately strong overlap, another SSB effect is found, in the
form of a pair of symmetry-breaking and restoring bifurcations which link
families of the symmetric and asymmetric solitons.


  A production representation of partial-wave $S$ matrix is utilized to
construct low-energy elastic pion-nucleon scattering amplitudes from cuts and
poles on complex Riemann sheets. Among them, the contribution of left-hand cuts
is estimated using the $\mathcal{O}(p^3)$ results obtained in covariant baryon
chiral perturbation theory within the extended-on-nass-shell scheme. By fitting
to data on partial-wave phase shifts, it is indicated that the existences of
hidden poles in $S_{11}$ and $P_{11}$ channels, as conjectured in our previous
paper~\citep{Wang:2017agd}, are firmly established. Specifically, the pole mass
of the $S_{11}$ hidden resonance is determined to be $(895\pm81)-(164\pm23)i$
MeV, whereas, the virtual pole in the $P_{11}$ channel locates at $(966\pm18)$
MeV. It is found that analyses at the $\mathcal{O}(p^3)$ level improves
significantly the fit quality, comparing with the previous $\mathcal{O}(p^2)$
one. Quantitative studies with cautious physical discussions are also conducted
for the other $S$- and $P$-wave channels.


  Using the Einstein-Cartan-Dirac theory, we study the effect of torsion on
neutrino oscillation. We see that torsion cannot induce neutrino oscillation,
but affects it whenever oscillation exists for other reasons. We show that the
torsion effect on neutrino oscillation is as important as the neutrino mass
effect, whenever the ratio of neutrino number density to neutrino energy is
$\sim 10^{69}$ cm$^{-3}$ /eV, or the number density of the matter is $\sim
10^{69}$ cm$^{-3}$.


  Imaging the atmosphere using ground-based sky cameras is a popular approach
to study various atmospheric phenomena. However, it usually focuses on the
daytime. Nighttime sky/cloud images are darker and noisier, and thus harder to
analyze. An accurate segmentation of sky/cloud images is already challenging
because of the clouds' non-rigid structure and size, and the lower and less
stable illumination of the night sky increases the difficulty. Nonetheless,
nighttime cloud imaging is essential in certain applications, such as
continuous weather analysis and satellite communication.
  In this paper, we propose a superpixel-based method to segment nighttime
sky/cloud images. We also release the first nighttime sky/cloud image
segmentation database to the research community. The experimental results show
the efficacy of our proposed algorithm for nighttime images.


  The discovery of a population of close-orbiting giant planets ($\le$ 1 au)
has raised a number of questions about their origins and dynamical histories.
These issues have still not yet been fully resolved, despite over 20 years of
exoplanet detections and a large number of discovered exoplanets. In
particular, it is unclear whether warm Jupiters (WJs) form in situ, or whether
they migrate from further outside and are even currently migrating to form hot
Jupiters (HJs). Here, we report the possible discovery and characterization of
the planets in a highly mutually-inclined ($I_{\rm mut}\simeq 45^\circ$),
compact two-planet system (KOI-984), in which the newly discovered warm Jupiter
KOI-984$c$ is on a 21.5-day, moderately eccentric ($e\simeq 0.4$) orbit, in
addition to a previously known 4.3-day planet candidate KOI-984$b$. Meanwhile,
the orbital configuration of a moderately inclined ($I_{\rm mut}\simeq
15^\circ$), low-mass ($m_{c}\simeq 24 M_{\oplus}$;$P_b\simeq 8.6$ days)
perturbing planet near 1:2 mean motion resonace with KOI-984$b$ could also well
reproduce observed transit timing variations and transit duration variations of
KOI-984$b$. Such an eccentric WJ with a close-in sibling would pose a challenge
to the proposed formation and migration mechanisms of WJs, if the first
scenario is supported with more evidences in near future; this system with
several other well-measured inclined WJ systems (e.g., Kepler-419 and
Kepler-108) may provide additional clues for the origin and dynamical histories
of WJs.


  Copper bromide vapor laser is the most successful sealed-off version of a
plenty of copper vapor lasers launched, studied and produced for the last half
of century. Being first reported in 1975, it became the leading and
long-lasting R&D&T project followed by the Laboratory of metal vapor lasers in
Institute of solid state physics of the Bulgarian Academy of Sciences ever
since. This manuscript gathers major research & development results of the last
studies unfolding unexplored and unknown optical, electric and thermal
properties of CuBr laser held in the Laboratory of metal vapor lasers during
foregoing 20+ years. Some of these new aftermaths are record-high power,
efficiency and lifetime of CuBr laser; comprehensive studies of energy
dissipation and dynamics of the electric field of CuBr laser; achievement of
high-brightness diffraction-limited throughout-pulse emission of
master-oscillator power-amplifier (MOPA) CuBr laser and high focusability of
CuBr laser emission low-affected by beam profile intensity variations.


  We focus on mean-variance hedging problem for models whose asset price
follows an exponential additive process. Some representations of mean-variance
hedging strategies for jump type models have already been suggested, but none
is suited to develop numerical methods of the values of strategies for any
given time up to the maturity. In this paper, we aim to derive a new explicit
closed-form representation, which enables us to develop an efficient numerical
method using the fast Fourier transforms. Note that our representation is
described in terms of Malliavin derivatives. In addition, we illustrate
numerical results for exponential L\'evy models.


  We consider the optical dipole potential energy, which arises from the
interaction of a two-level atom with a circularly polarized Laguerre-Gaussian
laser beam of small waist. The beam is characterized by the existence of a
longitudinal electric field component which is responsible for the appearance
of a chiral term in the optical dipole potential energy. This term reverses
sign if either the winding number or the wave polarization of the beam reverses
sign. We propose a scheme of a bi-chromatic vortex interaction with the
two-level atom in which the resulting optical dipole potential is fully chiral.


  We demonstrate a novel strong law of large numbers for branching processes,
with a simple proof via measure-theoretic manipulations and spine theory.
Roughly speaking, any sequence of events that eventually occurs almost surely
for the spine entails the almost sure convergence of a certain sum over
particles in the population.


  Disordered hyperuniform (DHU) states are recently discovered exotic states of
condensed matter. DHU systems are similar to liquids or glasses in that they
are statistically isotropic and lack conventional long-range translational and
orientational order. On the other hand, they completely suppress normalized
infinite-wavelength density fluctuations like crystals, and in this sense
possess a hidden long-range order. Very recently, there are several exciting
discoveries of disordered hyperuniformity in solid-state materials, including
amorphous carbon nanotubes, amorphous 2D silica, amorphous graphene, defected
transition metal dichalcogenides, defected pentagonal 2D materials, and
medium/high-entropy alloys. It has been found the DHU states of these materials
often possess a significantly lower energy than other disorder models, and can
lead to unique electronic and thermal transport properties, which resulted from
mechanisms distinct from those identified for their crystalline counterparts.
For example, DHU states can enhance electronic transport in 2D amorphous
silica; DHU medium/high-entropy alloys realize the Vegard's law, and possess
enhanced electronic band gaps and thermal transport at low temperatures. These
unique properties open up many promising potential device applications in
optoelectronics and thermoelectrics. Here, we provide a focused review on these
important new developments of hyperuniformity in solid-state materials, taking
an applied and ``materials'' perspective, which complements the existing
reviews on hyperuniformity in physical systems and photonic materials. Future
directions and outlook are also provided, with a focus on the design and
discovery of DHU quantum materials for quantum information science and
engineering.


  From particle identification to the discovery of the Higgs boson, deep
learning algorithms have become an increasingly important tool for data
analysis at the Large Hadron Collider (LHC). We present an innovative
end-to-end deep learning approach for jet identification at the Compact Muon
Solenoid (CMS) experiment at the LHC. The method combines deep neural networks
with low-level detector information, such as calorimeter energy deposits and
tracking information, to build a discriminator to identify different particle
species. Using two physics examples as references: electron vs. photon
discrimination and quark vs. gluon discrimination, we demonstrate the
performance of the end-to-end approach on simulated events with full detector
geometry as available in the CMS Open Data. We also offer insights into the
importance of the information extracted from various sub-detectors and describe
how end-to-end techniques can be extended to event-level classification using
information from the whole CMS detector.


  We study a variational Ginzburg-Landau type model depending on a small
parameter $\epsilon>0$ for (tangent) vector fields on a $2$-dimensional
Riemannian surface. As $\epsilon\to 0$, the vector fields tend to be of unit
length and will have singular points of a (non-zero) index, called vortices.
Our main result determines the interaction energy between these vortices as a
$\Gamma$-limit (at the second order) as $\epsilon\to 0$.


  Based on a sample of 500 million $e^+e^- \to c\bar c$ events recorded by the
BaBar detector at c.m. energies of close to 10.6 GeV, we report on a study of
the decay $D^0 \to \pi^- e^+ \nu_e$. We measure the ratio of branching
fractions $R_D = {\cal B}(D^0 \to \pi^- e^+ \nu_e)/{\cal B}(D^0 \to K^- \pi^+)
= 0.0713 \pm 0.0017_{stat.} \pm 0.0024_{syst.}$, and use the present world
average for ${\cal B}(D^0 \to K^- \pi^+)$ to obtain ${\cal B}(D^0 \to \pi^- e^+
\nu_e) = (2.770 \pm 0.068_{{\rm stat.}} \pm 0.092_{{\rm syst.}} \pm 0.037_{{\rm
ext.}})\times 10^{-3}$ where the third error accounts for the uncertainty on
the branching fraction for the reference channel. The measured dependence of
the differential branching fraction on $q^2$, the four-momentum transfer
squared between the $D$ and the $\pi$ meson, is compared to various theoretical
predictions for the hadronic form factor, $f_{+,D}^{\pi}(q^2)$, and the
normalization $|V_{cd}| \times f_{+,D}^{\pi}(q^2=0)=0.1374\pm0.0038_{{\rm
stat.}} \pm0.0022_{{\rm syst.}} \pm0.0009_{{\rm ext.}}$ is extracted from a fit
to data. Using the most recent LQCD prediction of $f_{+,D}^{\pi}(q^2=0)=0.666
\pm 0.029$, we obtain $|V_{cd}|=0.206 \pm 0.007_{\rm exp.} \pm 0.009_{\rm
LQCD}$. Assuming instead, $|V_{cd}|=|V_{us}|=0.2252 \pm 0.0009$, we obtain
$f_{+,D}^{\pi}(q^2=0)=0.610 \pm 0.020_{\rm exp.} \pm 0.005_{\rm ext.}$. The
$q^2$ dependence of $f_{+,D}^{\pi}(q^2)$ is compared to a variety of multi-pole
parameterizations. This information is applied to $B^0 \to \pi^- e^+ \nu_e$
decays and, combined with an earlier $B^0 \to \pi^- e^+ \nu_e$ measurement by
BaBar, is used to derive estimates of $|V_{ub}|$.


  The FAMU experiment aims to accurately measure the hyperfine splitting of the
ground state of the muonic hydrogen atom. A measurement of the transfer rate of
muons from hydrogen to heavier gases is necessary for this purpose. In June
2014, within a preliminary experiment, a pressurized gas-target was exposed to
the pulsed low-energy muon beam at the RIKEN RAL muon facility (Rutherford
Appleton Laboratory, UK). The main goal of the test was the characterization of
both the noise induced by the pulsed beam and the X-ray detectors. The
apparatus, to some extent rudimental, has served admirably to this task.
Technical results have been published that prove the validity of the choices
made and pave the way for the next steps. This paper presents the results of
physical relevance of measurements of the muon transfer rate to carbon dioxide,
oxygen, and argon from non-thermalized excited mu-p atoms. The analysis
methodology and the approach to the systematics errors are useful for the
subsequent study of the transfer rate as function of the kinetic energy of the
mu-p currently under way.


  The growing gap between processor and memory speeds results in complex memory
hierarchies as processors evolve to mitigate such divergence by taking
advantage of the locality of reference. In this direction, the BSC performance
analysis tools have been recently extended to provide insight relative to the
application memory accesses depicting their temporal and spatial
characteristics, correlating with the source-code and the achieved performance
simultaneously. These extensions rely on the Precise Event-Based Sampling
(PEBS) mechanism available in recent Intel processors to capture information
regarding the application memory accesses. The sampled information is later
combined with the Folding technique to represent a detailed temporal evolution
of the memory accesses and in conjunction with the achieved performance and the
source-code counterpart. The results obtained from the combination of these
tools help not only application developers but also processor architects to
understand better how the application behaves and how the system performs. In
this paper, we describe a tighter integration of the sampling mechanism into
the monitoring package. We also demonstrate the value of the complete workflow
by exploring already optimized state--of--the--art benchmarks, providing
detailed insight of their memory access behavior. We have taken advantage of
this insight to apply small modifications that improve the applications'
performance.


  This is an extended version of my Nobel Lecture, delivered on December
$8^{th}$ 2021. I will recall the genesis of the concept of multiple equilibria
in natural sciences. I will then describe my contribution to the development of
this concept in the framework of statistical mechanics. Finally, I will briefly
mention the cornucopia of applications of these ideas both in physics and in
other disciplines.


  Identifying parallel sides of a collection of Euclidean polygons yields a
flat surface with cone points of angles multiples of 2 pi, naturally a compact
Riemann surface but also an algebraic curve, and a hyperbolic surface. In
general two different metrics on a surface have no geodesic arcs in common, but
in special cases the surface is decomposed into polygons geodesic for both the
flat and the hyperbolic metric. This is the case for certain surfaces which are
translation and half-turn tiled by an Euclidean rectangle. We explore them in
this paper. Their multi-geodesic tessellation provides a mechanical way to
reconstruct a Fuchsian group for them; allows to describe their Teichmueller
disk in terms of Fenchel-Nielsen coordinates; allows for an interpretation in
terms of fractional Dehn twists of the natural PSL_2(Z) action on the
PSL_2(R)-orbit of such surfaces. In many cases the tiling by rectangles allows
to recover an equation for the corresponding algebraic curve, providing a
bridge between the algebraic equation and the hyperbolic structure deduced from
the multi-geodesic tessellation; in other words solving the uniformization
problem for such curves. In fact it also gives a scheme to do uniformization
for infinitely many families of curves. We also discuss some number theoretic
aspects.


  We study the eigenvalues of Dirac operators in QCD with two mass degenerate
dynamical fermions. The gauge configurations have been obtained with HMC and
the so-called Chirally Improved fermionic action. We compare eigenvalues
obtained for the overlap Dirac operator on these configurations with those for
the Chirally Improved (CI) operator (studied earlier). Results of Random Matrix
Theory allow us to determine the chiral condensate.


  A large lepton asymmetry in the Universe is still a viable possibility and
leads to many interesting phenomena such as gauge symmetry nonrestoration at
high temperature. We show that a large lepton asymmetry changes the predicted
cosmic microwave background (CMB) anisotropy and that any degeneracy in the
relic neutrino sea will be measured to a precision of 1% or better when the CMB
anisotropy is measured at the accuracy expected to result from the planned
satellite missions MAP and Planck. In fact, the current measurements already
put an upper limit on the lepton asymmetry of the Universe which is stronger
than the one coming from considerations of primordial nucleosynthesis and
structure formation.


  Possibility to detect DCC fluctuations is discussed. It is shown that
interactions with quark background and dissipative effects due to interactions
in the chiral field may result in damping of fluctuations. Since the magnitude
of fluctuations depends strongly on the initial state and speed of chiral phase
transition accurate evaluation of all modifying processes is required to
predict observability of DCCs.


  In this article, we study the optimal design of High Occupancy Toll (HOT)
lanes. The traffic authority determines the road capacity allocation between
HOT lanes and ordinary lanes, as well as the toll price charged for travelers
using HOT lanes who do not meet the high-occupancy eligibility criteria. We
develop a game-theoretic model to analyze the decisions of travelers with
heterogeneous preference parameters in values of time and carpool disutilities.
These travelers choose between paying or forming carpools to use the HOT lanes,
or taking the ordinary lanes. Travelers' welfare depends on the congestion cost
of the lane they use, the toll payment, and the carpool disutilities. For
highways with a single entrance and exit node, we provide a complete
characterization of equilibrium strategies and a comparative statics analysis
of how the equilibrium vehicle flow and travel time change with HOT capacity
and toll price. We then extend the single segment model to highways with
multiple entrance and exit nodes. We extend the equilibrium concept and propose
various design objectives considering traffic congestion, toll revenue, and
social welfare. Using the data collected from the HOT lane of the California
Interstate Highway 880 (I-880), we formulate a convex program to estimate the
travel demand and approximate the distribution of travelers' preference
parameters. We then compute the optimal toll design of five segments for I-880
for achieve each one of the four objectives, and compare the optimal solution
with the current toll pricing.


  We enumerate lattice paths in the planar integer lattice consisting of
positively directed unit vertical and horizontal steps with respect to a
specific elliptic weight function. The elliptic generating function of paths
from a given starting point to a given end point evaluates to an elliptic
generalization of the binomial coefficient. Convolution gives an identity
equivalent to Frenkel and Turaev's 10-V-9 summation. This appears to be the
first combinatorial proof of the latter, and at the same time of some important
degenerate cases including Jackson's 8-phi-7 and Dougall's 7-F-6 summation. By
considering nonintersecting lattice paths we are led to a multivariate
extension of the 10-V-9 summation which turns out to be a special case of an
identity originally conjectured by Warnaar, later proved by Rosengren. We
conclude with discussing some future perspectives.


  With $\{X_i\}$ independent $N \times N$ standard Gaussian random matrices,
the probability $p_{N,N}^{P_m}$ that all eigenvalues are real for the matrix
product $P_m = X_m X_{m-1} \cdots X_1$ is expressed in terms of an $N/2 \times
N/2$ ($N$ even) and $(N+1)/2 \times (N+1)/2$ ($N$ odd) determinant. The entries
of the determinant are certain Meijer $G$-functions. In the case $m=2$ high
precision computation indicates that the entries are rational multiples of
$\pi^2$, with the denominator a power of 2, and that to leading order in $N$
$p_{N,N}^{P_m}$ decays as $(\pi/4)^{N^2/2}$. We are able to show that for
general $m$ and large $N$, $p_{N,N}^{P_m} \sim b_m^{N^2}$ with an explicit
$b_m$. An analytic demonstration that $p_{N,N}^{P_m} \to 1$ as $m \to \infty$
is given.


  Phase reference calibration is a necessary procedure in practical
continuous-variable measurement-device-independent quantum key distribution
(CV-MDI-QKD) for the need of Bell-State Measurement (BSM). However, the phase
reference calibration may become imperfect in practical applications. We
explored the practical security of CV-MDI-QKD with imperfect phase reference
calibration under realistic conditions of lossy and noisy quantum channel.
Specifically, a comprehensive framework is developed to model and characterize
the imperfection of practical phase reference calibration operation, which is
mainly caused by the non-synchronization of two remote lasers in senders.
Security analysis shows that the imperfect phase reference calibration has
significant side effects on the performance and security of the CV-MDI-QKD
protocol. A tight security bound to excess noise introduced by imperfect phase
reference calibration is derived for reverse reconciliation against arbitrary
collective attacks in the asymptotic limit, and the tolerance of the CV-MDI-QKD
protocol to this excess noise is also obtained. This security analysis
framework can eliminate the security hazards caused by imperfect phase
reference calibration without changing the existing CV-MDI-QKD system
structure.


  We classify 3-braid knots whose topological 4-genus coincides with their
Seifert genus, using McCoy's twisting method and the Xu normal form. In
addition, we give upper bounds for the topological 4-genus of positive and
strongly quasipositive 3-braid knots.


  We have investigated thermal conductivity of graphene laminate films
deposited on polyethylene terephthalate substrates. Two types of graphene
laminate were studied - as deposited and compressed - in order to determine the
physical parameters affecting the heat conduction the most. The measurements
were performed using the optothermal Raman technique and a set of suspended
samples with the graphene laminate thickness from 9 to 44 micrometers. The
thermal conductivity of graphene laminate was found to be in the range from 40
W/mK to 90 W/mK at room temperature. It was found unexpectedly that the average
size and the alignment of graphene flakes are more important parameters
defining the heat conduction than the mass density of the graphene laminate.
The thermal conductivity scales up linearly with the average graphene flake
size in both uncompressed and compressed laminates. The compressed laminates
have higher thermal conductivity for the same average flake size owing to
better flake alignment. The possibility of up to 600X enhancement of the
thermal conductivity of plastic materials by coating them with the thin
graphene laminate films has important practical implications.


  The quality of fetal MRI is significantly affected by unpredictable and
substantial fetal motion, leading to the introduction of artifacts even when
fast acquisition sequences are employed. The development of 3D real-time fetal
pose estimation approaches on volumetric EPI fetal MRI opens up a promising
avenue for fetal motion monitoring and prediction. Challenges arise in fetal
pose estimation due to limited number of real scanned fetal MR training images,
hindering model generalization when the acquired fetal MRI lacks adequate pose.
  In this study, we introduce FetalDiffusion, a novel approach utilizing a
conditional diffusion model to generate 3D synthetic fetal MRI with
controllable pose. Additionally, an auxiliary pose-level loss is adopted to
enhance model performance. Our work demonstrates the success of this proposed
model by producing high-quality synthetic fetal MRI images with accurate and
recognizable fetal poses, comparing favorably with in-vivo real fetal MRI.
Furthermore, we show that the integration of synthetic fetal MR images enhances
the fetal pose estimation model's performance, particularly when the number of
available real scanned data is limited resulting in 15.4% increase in PCK and
50.2% reduced in mean error. All experiments are done on a single 32GB V100
GPU. Our method holds promise for improving real-time tracking models, thereby
addressing fetal motion issues more effectively.


  In reconfigurable intelligent surface (RIS)-assisted wireless communication
systems, the pointing accuracy and intensity of reflections depend crucially on
the 'profile,' representing the amplitude/phase state information of all
elements in a RIS array. The superposition of multiple single-reflection
profiles enables multi-reflection for distributed users. However, the
optimization challenges from periodic element arrangements in single-reflection
and multi-reflection profiles are understudied. The combination of periodical
single-reflection profiles leads to amplitude/phase counteractions, affecting
the performance of each reflection beam. This paper focuses on a
dual-reflection optimization scenario and investigates the far-field
performance deterioration caused by the misalignment of overlapped profiles. To
address this issue, we introduce a novel deep reinforcement learning
(DRL)-based optimization method. Comparative experiments against random and
exhaustive searches demonstrate that our proposed DRL method outperforms both
alternatives, achieving the shortest optimization time. Remarkably, our
approach achieves a 1.2 dB gain in the reflection peak gain and a broader beam
without any hardware modifications.


  An analogue of the total variation prior for the normal vector field along
the boundary of smooth shapes in 3D is introduced. The analysis of the total
variation of the normal vector field is based on a differential geometric
setting in which the unit normal vector is viewed as an element of the
two-dimensional sphere manifold. It is shown that spheres are stationary points
when the total variation of the normal is minimized under an area constraint.
Shape calculus is used to characterize the relevant derivatives. Since the
total variation functional is non-differentiable whenever the boundary contains
flat regions, an extension of the split Bregman method to manifold valued
functions is proposed.


  We consider the problem of imaging sparse scenes from a few noisy data using
an $l_1$-minimization approach. This problem can be cast as a linear system of
the form $A \, \rho =b$, where $A$ is an $N\times K$ measurement matrix. We
assume that the dimension of the unknown sparse vector $\rho \in
{\mathbb{C}}^K$ is much larger than the dimension of the data vector $b \in
{\mathbb{C}}^N$, i.e, $K \gg N$. We provide a theoretical framework that allows
us to examine under what conditions the $\ell_1$-minimization problem admits a
solution that is close to the exact one in the presence of noise. Our analysis
shows that $l_1$-minimization is not robust for imaging with noisy data when
high resolution is required. To improve the performance of $l_1$-minimization
we propose to solve instead the augmented linear system $ [A \, | \, C] \rho
=b$, where the $N \times \Sigma$ matrix $C$ is a noise collector. It is
constructed so as its column vectors provide a frame on which the noise of the
data, a vector of dimension $N$, can be well approximated. Theoretically, the
dimension $\Sigma$ of the noise collector should be $e^N$ which would make its
use not practical. However, our numerical results illustrate that robust
results in the presence of noise can be obtained with a large enough number of
columns $\Sigma \approx 10 K$.


  We compute the equivariant $KO$-homology of the classifying space for proper
actions of $\textrm{SL}_3(\mathbb{Z})$ and $\textrm{GL}_3(\mathbb{Z})$. We also
compute the Bredon homology and equivariant $K$-homology of the classifying
spaces for proper actions of $\textrm{PSL}_2(\mathbb{Z}[\frac{1}{p}])$ and
$\textrm{SL}_2(\mathbb{Z}[\frac{1}{p}])$ for each prime $p$. Finally, we prove
the Unstable Gromov-Lawson-Rosenberg Conjecture for a large class of groups
whose maximal finite subgroups are odd order and have periodic cohomology.


  The moduli space of spatial polygons is known as a symplectic manifold
equipped with both K\"ahler and real polarizations. In this paper, associated
to the K\"ahler and real polarizations, morphisms of operads
$\mathsf{f}_{\mathsf{K}\ddot{\mathsf{a}}\mathsf{h}}$ and
$\mathsf{f}_{\mathsf{re}}$ are constructed by using the quantum Hilbert spaces
$\mathscr{H}_{\mathrm{K}\ddot{\mathrm{a}}\mathrm{h}}$ and
$\mathscr{H}_\mathrm{re}$, respectively. Moreover, the relationship between the
two morphisms of operads $\mathsf{f}_{\mathsf{K}\ddot{\mathsf{a}}\mathsf{h}}$
and $\mathsf{f}_{\mathsf{re}}$ is studied and then the equality $\dim
\mathscr{H} _{\mathrm{K}\ddot{\mathrm{a}}\mathrm{h}}=\dim
\mathscr{H}_\mathrm{re}$ is proved in general setting. This operadic framework
is regarded as a development of the recurrence relation method by Kamiyama for
proving $\dim \mathscr{H}_{\mathrm{K}\ddot{\mathrm{a}}\mathrm{h}}=\dim
\mathscr{H}_\mathrm{re}$ in a special case.


  We consider the problem of designing experiments for investigating particle
in-flight properties in thermal spraying. Observations are available on an
extensive design for an initial day and thereafter in limited number for any
particular day. Generalized linear models including additional day effects are
used for analyzing the process, where the models vary with respect to different
responses. We construct robust D-optimal designs to collect additional data on
any current day, which are efficient for the estimation of the parameters in
all models under consideration. These designs improve a reference fractional
factorial design substantially. We also investigate designs, which maximize the
power of the test for an additional day effect. The results are used to design
additional experiments of the thermal spraying process and a comparison of the
statistical analysis based on a reference design as well as on a selected
D-optimal design is performed.


  We explore the unusual non-reciprocal and diffraction-less properties of
surface plasmon polaritons propagating in drift-biased graphene-based
metasurfaces. We show that applying a drift-current on a graphene sheet leads
to extremely asymmetric in-plane modal dispersions from terahertz to infrared
frequencies, associated with plasmons with low-loss (high-loss and ultra-high
confinement) traveling along (against) the bias. Strikingly, truly
unidirectional wave propagation is prevented by the intrinsic nonlocal response
of a graphene, a mechanism that shapes the energy flow over the surface. We
also show that highly-directive hyperbolic plasmons completely immune to
backscattering propagate obliquely along the drift in nanostructured graphene.
Finally, we discuss how spin-orbit interactions can be exploited in this
platform to efficiently launch collimated plasmons along a single direction
while maintaining giant non-reciprocal responses. Our findings open a new
paradigm to excite, collimate, steer, and process surface plasmons over a broad
frequency band.


  We discuss how the statistical properties of the area and radius of gyration
of single self-avoiding loops, and of Ising and percolation cluster boundaries,
may be calculated using ideas of two-dimensional field theory. For cluster
boundaries, we show that almost all loops have area $C\ln L+O(1)$, where $L$ is
the size of the system, and $C$ is a calculable constant. We also compute the
universal ratios $\langle A\rangle_\ell/\langle R^2\rangle_\ell$ of the area to
the squared radius of gyration of loops of a given large perimeter $\ell$.


  Garland introduced a vanishing criterion for a characteristic zero cohomology
group of a locally finite and locally connected simplicial complex. The
criterion is based on the spectral gaps of the graph Laplacians of the links of
faces and has turned out to be effective in a wide range of examples. In this
note we extend the approach to include a range of non-simplicial (co)chain
complexes associated to combinatorial structures we call Garland posets and
elaborate further on the case of cubical complexes.


  Let $\mu_0$ and $\mu_1$ be measures supported on an unbounded interval and
$S_{n,\lambda_n}$ the extremal varying Sobolev polynomial which minimizes
\begin{equation*} < P, P >_{\lambda_n}=\int P^2 d\mu_0 + \lambda_n \int P'^2
d\mu_1, \quad \lambda_n >0 \end{equation*} \noindent in the class of all monic
polynomials of degree $n$. The goal of this paper is twofold. On one hand, we
discuss how to balance both terms of this inner product, that is, how to choose
a sequence $(\lambda_n)$ such that both measures $\mu_0$ and $\mu_1$ play a
role in the asymptotics of $(S_{n, \lambda_n}).$ On the other, we apply such
ideas to the case when both $\mu_0$ and $\mu_1$ are Freud weights. Asymptotics
for the corresponding $S_{n, \lambda_n}$ are computed, illustrating the
accuracy of the choice of $\lambda_n .$


  Generative Large Language Models (LLMs) have become the mainstream choice for
fewshot and zeroshot learning thanks to the universality of text generation.
Many users, however, do not need the broad capabilities of generative LLMs when
they only want to automate a classification task. Smaller BERT-like models can
also learn universal tasks, which allow them to do any text classification task
without requiring fine-tuning (zeroshot classification) or to learn new tasks
with only a few examples (fewshot), while being significantly more efficient
than generative LLMs. This paper (1) explains how Natural Language Inference
(NLI) can be used as a universal classification task that follows similar
principles as instruction fine-tuning of generative LLMs, (2) provides a
step-by-step guide with reusable Jupyter notebooks for building a universal
classifier, and (3) shares the resulting universal classifier that is trained
on 33 datasets with 389 diverse classes. Parts of the code we share has been
used to train our older zeroshot classifiers that have been downloaded more
than 55 million times via the Hugging Face Hub as of December 2023. Our new
classifier improves zeroshot performance by 9.4%.


  A theory determining the evolution of general Rayleigh-Taylor mixing fronts
is established to reproduce firstly all of the documented experiments conducted
for diverse acceleration histories and all density ratios. The theory is
established in terms of the fundamental conservation and symmetry principles,
with special consideration given to the symmetry breaking of the density fields
occurring in actual flows. The results reveal the sensitivity/insensitivity of
the evolution of a mixing front neighbouring light/heavy fluid to the degree of
symmetry breaking, and also explain the distinct evolutions in two experiments
with the same configurations.


  In this work we define a class of ordered adversaries causing distractions
according to some partial order fixed by the adversary before the execution,
and study how they affect performance of algorithms. We focus on the Do-All
problem of performing t tasks on a shared channel consisting of p crash-prone
stations. The channel restricts communication: no message is delivered to the
alive stations if more than one station transmits at the same time. The
performance measure for the Do-All problem is work: the total number of
available processor steps during the whole execution. We address the question
of how the ordered adversaries controlling crashes of stations influence work
performance of Do-All algorithms. The first presented algorithm solves Do-All
with work O(t+p\sqrt{t}\log p) against the Linearly-Ordered adversary,
restricted by some pre-defined linear order of crashing stations. Another
algorithm runs against the Weakly-Adaptive adversary, restricted by some
pre-defined set of f crash-prone stations (it can be seen as restricted by the
order being an anti-chain of crashing stations). The work done by this
algorithm is O(t+p\sqrt{t}+p\min{p/(p-f),t}\log p). Both results are close to
the corresponding lower bounds from [CKL]. We generalize this result to the
class of adversaries restricted by a partial order with a maximum anti-chain of
size k and complement with the lower bound. We also consider a class of delayed
adaptive adversaries, who could see random choices with some delay. We give an
algorithm that runs against the 1-RD adversary (seeing random choices of
stations with one round delay), achieving close to optimal O(t+p\sqrt{t}\log^2
p) work complexity. This shows that restricting adversary by even 1 round delay
results in (almost) optimal work on a shared channel.


  The Uhlmann process is built on the density matrix of a mixed quantum state
and offers a way to characterize topological properties at finite temperatures.
We analyze an ideal spin-j quantum paramagnet in a magnetic field undergoing an
Uhlmann process and derive general formulae of the Uhlmann phase and Loschmidt
amplitude for arbitrary j as the system traverses a great circle in the
parameter space. A quantized jump of the Uhlmann phase signifies a topological
quantum phase transition (TQPT) of the underlying process, which is accompanied
by a zero of the Loschmidt amplitude. The exact results of j=1/2 and j=1
systems show topological regimes that only survive at finite temperatures but
not at zero temperature, and the number of TQPTs is associated with the winding
number in the parameter space. Our results pave the way for future studies on
finite-temperature topological properties, and possible experimental protocols
and implications for atomic simulators and digital simulations are discussed.


  We present the recently derived Wolf et al. (2009) mass estimator, which is
applicable for spherical pressure-supported stellar systems spanning over ten
orders of magnitude in luminosity, as a tool to test galaxy formation theories.
We show that all of the Milky Way dwarf spheroidal galaxies (MW dSphs) are
consistent with having formed within a halo of mass approximately 3 x 10^9 Msun
in LCDM cosmology. The faintest MW dSphs seem to have formed in dark matter
halos that are at least as massive as those of the brightest MW dSphs, despite
the almost five orders of magnitude spread in luminosity. We expand our
analysis to the full range of observed pressure-supported stellar systems and
examine their half-light I-band mass-to-light ratios. The M/L vs. half-light
mass M_1/2 relation for pressure-supported galaxies follows a U-shape, with a
broad minimum near M/L ~ 3 that spans dwarf elliptical galaxies to normal
ellipticals, a steep rise to M/L ~ 3,200 for ultra-faint dSphs, and a more
shallow rise to M/L ~ 800 for galaxy cluster spheroids.


  We treat three types of measures of the quantum walk (QW) with the spatial
perturbation at the origin, which was introduced by [1]: time averaged limit
measure, weak limit measure, and stationary measure. From the first two
measures, we see a coexistence of the ballistic and localized behaviors in the
walk as a sequential result following [1,2]. We propose a universality class of
QWs with respect to weak limit measure. It is shown that typical spatial
homogeneous QWs with ballistic spreading belong to the universality class. We
find that the walk treated here with one defect also belongs to the class. We
mainly consider the walk starting from the origin. However when we remove this
restriction, we obtain a stationary measure of the walk. As a consequence, by
choosing parameters in the stationary measure, we get the uniform measure as a
stationary measure of the Hadamard walk and a time averaged limit measure of
the walk with one defect respectively.


  The properties of galaxies in the local universe have been shown to depend
upon their environment. Future large scale photometric surveys such as DES and
Euclid will be vital to gain insight into the evolution of galaxy properties
and the role of environment. Large samples come at the cost of redshift
precision and this affects the measurement of environment. We study this by
measuring environments using SDSS spectroscopic and photometric redshifts and
also simulated photometric redshifts with a range of uncertainties. We consider
the Nth nearest neighbour and fixed aperture methods and evaluate the impact of
the aperture parameters and the redshift uncertainty. We find that photometric
environments have a smaller dynamic range than spectroscopic measurements
because uncertain redshifts scatter galaxies from dense environments into less
dense environments. At the expected redshift uncertainty of DES, 0.1, there is
Spearman rank correlation coefficient of 0.4 between the measurements using the
optimal parameters. We examine the galaxy red fraction as a function of mass
and environment using photometric redshifts and find that the bivariate
dependence is still present in the SDSS photometric measurements. We show that
photometric samples with a redshift uncertainty of 0.1 must be approximately
6-16 times larger than spectroscopic samples to detect environment correlations
with equivalent fractional errors.


  Stanley introduced the chromatic symmetric function of a simple graph, which
is a generalization of a chromatic polynomial. This is expressed in terms of
the integer points of the complements of the corresponding graphic arrangement.
Stanley proved a combinatorial reciprocity theorem for chromatic functions.
This is considered as an Ehrhart-type reciprocity theorem for the graphic
arrangement.
  We introduce the chromatic signed-symmetric function of a signed graph, an
analogue of the chromatic symmetric function, by the integer points of the
complements of the corresponding signed-graphic arrangement and prove a
generalization of Stanley's reciprocity theorem.
  Stanley has conjectured that the chromatic symmetric function distinguishes
trees. This conjecture is also generalized for signed trees. We verify the
conjecture for certain classes of signed paths.


  Superradiant scattering is a radiation enhancement process that takes place
in many contexts, and which has recently found exciting applications in astro
and particle physics. In the framework of curved spacetime physics, it has been
associated with the classical Penrose process for particles. Superradiance is
usually also associated with bosonic fields around geometries with ergoregions
and horizons. These notions are in clear tension however: the Penrose process
occurs for horizonless geometries, and particles are composed of fermions.
  Here, we resolve the tension in its different aspects, by showing that (i)
superradiance occurs for self-interacting fermions on flat spacetime; (ii)
superradiance occurs also for horizonless geometries, where it leads to an
ergoregion instability. Ultracompact, horizonless geometries will usually
respond with echoes of growing amplitude, until rotational (or electrostatic)
energy is extracted from the object; (iii) the Fourier-domain analysis leads to
absence of superradiance when horizons are not present. We elucidate why this
analysis fails to give meaningful results; (iv) finally, we show that
superradiant, ergoregion instabilities have a particle analog of similar growth
timescales and which can power the formation of a structure outside a compact,
rotating star.


  We report the detection in space of a new molecular species which has been
characterized spectroscopically and fully identified from astrophysical data.
The observations were carried out with the 30m IRAM telescope. The molecule is
ubiquitous as its $J$=2$\rightarrow$1 transition has been found in cold
molecular clouds, prestellar cores, and shocks. However, it is not found in the
hot cores of Orion-KL and in the carbon-rich evolved star IRC+10216. Three
rotational transitions in perfect harmonic relation J'=2/3/5 have been
identified in the prestellar core B1b. The molecule has a 1Sigma electronic
ground state and its J=2-1 transition presents the hyperfine structure
characteristic of a molecule containing a nucleus with spin 1. A careful
analysis of possible carriers shows that the best candidate is NS+. The derived
rotational constant agrees within 0.3-0.7 % with ab initio calculations. NS+
was also produced in the laboratory to unambiguously validate the astrophysical
assignment. The observed rotational frequencies and determined molecular
constants confirm the discovery of the nitrogen sulfide cation in space. The
chemistry of NS+ and related nitrogen-bearing species has been analyzed by
means of a time-dependent gas phase model. The model reproduces well the
observed NS/NS+ abundance ratio, in the range 30-50, and indicates that NS+ is
formed by reactions of the neutral atoms N and S with the cations SH+ and NH+,
respectively.


  Biological neural systems encode and transmit information as patterns of
activity tracing complex trajectories in high-dimensional state-spaces,
inspiring alternative paradigms of information processing. Heteroclinic
networks, naturally emerging in artificial neural systems, are networks of
saddles in state-space that provide a transparent approach to generate complex
trajectories via controlled switches among interconnected saddles. External
signals induce specific switching sequences, thus dynamically encoding inputs
as trajectories. Recent works have focused either on computational aspects of
heteroclinic networks, i.e. Heteroclinic Computing, or their stochastic
properties under noise. Yet, how well such systems may transmit information
remains an open question. Here we investigate the information transmission
properties of heteroclinic networks, studying them as communication channels.
Choosing a tractable but representative system exhibiting a heteroclinic
network, we investigate the mutual information rate (MIR) between input signals
and the resulting sequences of states as the level of noise varies.
Intriguingly, MIR does not decrease monotonically with increasing noise.
Intermediate noise levels indeed maximize the information transmission capacity
by promoting an increased yet controlled exploration of the underlying network
of states. Complementing standard stochastic resonance, these results highlight
the constructive effect of stochastic facilitation (i.e. noise-enhanced
information transfer) on heteroclinic communication channels and possibly on
more general dynamical systems exhibiting complex trajectories in state-space.


  Shock waves propagate in falling coupled harmonic oscillators. The bottom end
of coupled harmonic oscillators does not fall downwards until a shock wave
reaches the bottom end. The exact solution can be expressed by the Fourier
series expansion, and an approximate solution can be expressed by the integral
of the Airy function. The width of the shock wave increases slowly in
accordance with a power law.


  Entanglement is defined as presence of quantum correlations beyond those
achieved by local action and classical communication. To identify its presence
in a generic state, one can, for example, check for existence of a
decomposition of separable states. A natural extension is a genuine
multipartite entanglement (GME), understood as nonexistenence of a
decomposition into biseparable states (later called biseparable decomposition,
BD). In this contribution we revisit activation of GME. We discuss few examples
of states, which are decomposable into a mixture of biproduct states. However,
after merging two copies of these states, we certify nonexistence of BD with
witness operators. This seems to challenge our understanding of GME as a
separate resource. It turns out that it requires a careful consideration of the
physical context. We stress that activation of GME from multiple copies of
GME-free states necessarily involves entangling operations.


  We prove a non-archimedean analogue of J{\o}rgensen's inequality, and use it
to deduce several algebraic convergence results. As an application we show that
every dense subgroup of $\mathrm{SL}(\mathbb{Q}_p)$ contains two elements which
generate a dense subgroup of $\mathrm{SL}(\mathbb{Q}_p)$, which is a special
case of a result by Breuillard and Gelander. We also list several other related
results, which are well-known to experts, but not easy to locate in the
literature; for example, we show that a non-elementary subgroup of
$\mathrm{SL}(K)$ over a non-archimedean local field $K$ is discrete if and only
if each of its two-generator subgroups is discrete.


  In this study, we apply quantum master equations beyond secular
approximation, and investigate the nonequilibrium thermodynamic cost of
enhanced quantum metrology and quantum correlations. We find that the
nonequilibrium conditions enhance quantum Fisher information (QFI) and quantum
correlations predominantly for weak tunneling scenarios. The enhancement is
assisted by a corresponding increase of the thermodynamic cost characterized by
the entropy production rate (EPR). For the strong tunneling regimes, the QFI
and quantum correlations can not be unceasingly boosted by higher thermodynamic
costs and decay once the system is overburdened with extremely large energy
currents. The result indicates that for open systems with weak tunneling rates,
thermodynamic cost can be potentially exploited to improve the quantum
metrology and quantum correlations.


  We propose a mechanism for the generation of pure superconducting
spin-current carried by equal-spin triplet Cooper pairs in a superconductor (S)
sandwiched between a ferromagnet (F) and a normal metal (N$_{\rm so}$) with
intrinsic spin-orbit coupling. We show that in the presence of Landau
Fermi-liquid interactions the superconducting proximity effect can induce
non-locally a ferromagnetic exchange field in the normal layer, which
disappears above the superconducting transition temperature of the structure.
The internal Landau Fermi-liquid exchange field leads to the onset of a spin
supercurrent associated with the generation of long-range spin-triplet
superconducting correlations in the trilayer. We demonstrate that the magnitude
of the spin supercurrent as well as the induced magnetic order in the N$_{\rm
so}$ layer depends critically on the superconducting proximity effect between
the S layer and the F and N$_{\rm so}$ layers and the magnitude of the relevant
Landau Fermi-liquid interaction parameter. We investigate the effect of spin
flip processes on this mechanism. Our results demonstrate the crucial role of
Landau Fermi-liquid interaction in combination with spin-orbit coupling for the
creation of spin supercurrent in superconducting spintronics, and give a
possible explanation of a recent experiment utilizing spin-pumping via
ferromagnetic resonance [Jeon $\it{ et}$ $\it{al.}$, Nat. Mat. ${\bf 17}$, 499
(2018)].


  Let $K$ be an algebraically closed field of characteristic $p \geq 0$. A
generalized Fermat curve of type $(k,n)$, where $k,n \geq 2$ are integers (for
$p \neq 0$ we also assume that $k$ is relatively prime to $p$), is a
non-singular irreducible projective algebraic curve $F_{k,n}$ defined over $K$
admitting a group of automorphisms $H \cong {\mathbb Z}_{k}^{n}$ so that
$F_{k,n}/H$ is the projective line with exactly $(n+1)$ cone points, each one
of order $k$. Such a group $H$ is called a generalized Fermat group of type
$(k,n)$. If $(n-1)(k-1)>2$, then $F_{k,n}$ has genus $g_{n,k}>1$ and it is
known to be non-hyperelliptic. In this paper, we prove that every generalized
Fermat curve of type $(k,n)$ has a unique generalized Fermat group of type
$(k,n)$ if $(k-1)(n-1)>2$ (for $p>0$ we also assume that $k-1$ is not a power
of $p$).
  Generalized Fermat curves of type $(k,n)$ can be described as a suitable
fiber product of $(n-1)$ classical Fermat curves of degree $k$. We prove that,
for $(k-1)(n-1)>2$ (for $p>0$ we also assume that $k-1$ is not a power of $p$),
each automorphism of such a fiber product curve can be extended to an
automorphism of the ambient projective space. In the case that $p>0$ and $k-1$
is a power of $p$, we use tools from the theory of complete projective
intersections in order to prove that, for $k$ and $n+1$ relatively prime, every
automorphism of the fiber product curve can also be extended to an automorphism
of the ambient projective space.
  In this article we also prove that the set of fixed points of the non-trivial
elements of the generalized Fermat group coincide with the hyper-osculating
points of the fiber product model under the assumption that the characteristic
$p$ is either zero or $p>k^{n-1}$.


  We show an attempt to establish the zeroth law of thermodynamics within the
framework of nonextensive statistical mechanics based on the classic
normalization $\texttt{Tr}\hat{\rho}=1$ and the unnormalized expectation
$x=\texttt{Tr}\hat{\rho}^q\hat{x}$. The first law of thermodynamics and the
definition of heat and work in this formalism are discussed.


  Over the last decade, Artificial Intelligence (AI) has become increasingly
popular, especially with the use of chatbots such as ChatGPT, Gemini, and
DALL-E. With this rise, large language models (LLMs) and Generative AI (GenAI)
have also become more prevalent in everyday use. These advancements strengthen
cybersecurity's defensive posture and open up new attack avenues for
adversaries as well. This paper provides a comprehensive overview of the
current state-of-the-art deployments of GenAI, covering assaults, jailbreaking,
and applications of prompt injection and reverse psychology. This paper also
provides the various applications of GenAI in cybercrimes, such as automated
hacking, phishing emails, social engineering, reverse cryptography, creating
attack payloads, and creating malware. GenAI can significantly improve the
automation of defensive cyber security processes through strategies such as
dataset construction, safe code development, threat intelligence, defensive
measures, reporting, and cyberattack detection. In this study, we suggest that
future research should focus on developing robust ethical norms and innovative
defense mechanisms to address the current issues that GenAI creates and to also
further encourage an impartial approach to its future application in
cybersecurity. Moreover, we underscore the importance of interdisciplinary
approaches further to bridge the gap between scientific developments and
ethical considerations.


  We present a quantitative analysis of the effect of microlensing caused by
random motion of individual stars in the galaxy which is lensing a background
quasar. We calculate a large number of magnification patterns for positions of
the stars slightly offset from one frame to the next, and in this way obtain
light curves for fixed quasar and galaxy positions, only due to the change in
the relative star positions. These light curves are analyzed to identify micro-
lensing events, which are classified according to height, duration, and slope.
We find that microlensing events produced by random motion of stars are
shorter, steeper, and more frequent than bulk motion events. This difference is
caused by the fact that in the case of random motion, caustics can move with
arbitrarily high velocity, producing very short events. An accompanying video
illustrates these results. For three different values of the surface mass
density kappa, it shows time sequences of 1000 magnification patterns for
slowly changing lens positions, together with the positions and velocity
vectors of the microlensing stars. Short sequences of the video are available
as MPEG movie by anonymous ftp at
ftp://astro.princeton.edu/jkw/microlensing/moving_stars .


  We propose an informative path planning (IPP) algorithm for active
classification using an unmanned aerial vehicle (UAV), focusing on weed
detection in precision agriculture. We model the presence of weeds on farmland
using an occupancy grid and generate plans according to information-theoretic
objectives, enabling the UAV to gather data efficiently. We use a combination
of global viewpoint selection and evolutionary optimization to refine the UAV's
trajectory in continuous space while satisfying dynamic constraints. We
validate our approach in simulation by comparing against standard "lawnmower"
coverage, and study the effects of varying objectives and optimization
strategies. We plan to evaluate our algorithm on a real platform in the
immediate future.


  We report on a search for dark matter axion-like particles (ALPs) using a
Ramsey-type apparatus for cold neutrons. A hypothetical ALP-gluon-coupling
would manifest in a neutron electric dipole moment signal oscillating in time.
Twenty-four hours of data have been analyzed in a frequency range from 23
$\mu$Hz to 1 kHz, and no significant oscillating signal has been found. The
usage of present dark-matter models allows to constrain the coupling of ALPs to
gluons in the mass range from $10^{-19}$ to $4 \times 10^{-12}$ eV. The best
limit of $C_G$/$f_a m_a = 2.7 \times 10^{13}$ GeV$^{-2}$ (95% C.L.) is reached
in the mass range from $2 \times 10^{-17}$ to $2 \times 10^{-14}$ eV.


  On a given manifold M, the Nijenhuis bracket makes the superspace of
vector-valued differential forms into a Lie superalgebra that can be
interpreted as the centralizer of the exterior differential considered as a
vector field on the supermanifold associated with the de Rham bundle on M. A
similar bracket introduces structures of DG Lie superalgebra in the superspaces
of cochains and cohomology with coefficients in the adjoint module for any Lie
superalgebra. We use a Mathematica--based package SuperLie (already proven
useful in various problems) to explicitly describe these Lie superalgebras for
some simple finite dimensional Lie superalgebras and their ``relatives'' (the
nontrivial central extensions or derivation algebras of the considered simple
ones).


  We give a presentation for the cohomology algebra of the Spaltenstein variety
of all partial flags annihilated by a fixed nilpotent matrix, generalizing the
description of the cohomology algebra of the Springer fiber found by De
Concini, Procesi and Tanisaki.


  We discuss modifications to the Hawking spectrum that arise when the
asymptotic states are supertranslated or superrotated. For supertranslations we
find nontrivial off-diagonal phases in the two-point correlator although the
emission spectrum is eventually left unchanged, as previously pointed out in
the literature. In contrast, superrotations give rise to modifications which
manifest themselves in the emission spectrum and depend nontrivially on the
associated conformal factor at future null infinity. We study Lorentz boosts
and a class of superrotations whose conformal factors do not depend on the
azimuthal angle on the celestial sphere and whose singularities at the north
and south poles have been associated to the presence of a cosmic string. In
spite of such singularities, superrotations still lead to finite spectral
emission rates of particles and energy which display a distinctive power-law
behavior at high frequencies for each angular momentum state. The integrated
particle emission rate and emitted power, on the contrary, while finite for
boosts, do exhibit ultraviolet divergences for superrotations, between
logarithmic and quadratic. Such divergences can be ascribed to modes with
support along the cosmic string. In the logarithimic case, corresponding to a
superrotation which covers the sphere twice, the total power emitted still
presents the Stefan-Boltzmann form but with an effective area which diverges
logarithmically in the ultraviolet.


  This paper explores an iterative coupling approach to solve linear
thermo-poroelasticity problems, with its application as a high-fidelity
discretization utilizing finite elements during the training of
projection-based reduced order models. One of the main challenges in addressing
coupled multi-physics problems is the complexity and computational expenses
involved. In this study, we introduce a decoupled iterative solution approach,
integrated with reduced order modeling, aimed at augmenting the efficiency of
the computational algorithm. The iterative coupling technique we employ builds
upon the established fixed-stress splitting scheme that has been extensively
investigated for Biot's poroelasticity. By leveraging solutions derived from
this coupled iterative scheme, the reduced order model employs an additional
Galerkin projection onto a reduced basis space formed by a small number of
modes obtained through proper orthogonal decomposition. The effectiveness of
the proposed algorithm is demonstrated through numerical experiments,
showcasing its computational prowess.


  Matter effects may strongly enhance the Jarlskog factor J in T and
CP-violating three-neutrino oscillation probabilities. However, we show that
when J is enhanced, the same matter effects suppress the oscillating factors
and increase the oscillation length. The net result is that there is no large
enhancement in measurable probabilities for earth-bound experiments using
neutrino parameters suggested by current experiments. We show that by an
appropriate choice of the experimental parameters, neutrino energy and travel
length, the T-violating probability can be enhanced by matter effects over
their vacuum values by 50%. Our approach is analytical, allowing considerable
insight into the underlying physics.


  For a recently derived pairwise model of network epidemics with non-Markovian
recovery, we prove that under some mild technical conditions on the
distribution of the infectious periods, smaller variance in the recovery time
leads to higher reproduction number, and consequently to a larger epidemic
outbreak, when the mean infectious period is fixed. We discuss how this result
is related to various stochastic orderings of the distributions of infectious
periods. The results are illustrated by a number of explicit stochastic
simulations, suggesting that their validity goes beyond regular networks.


  The engineering and control of devices at the quantum-mechanical level--such
as those consisting of small numbers of atoms and photons--is a delicate
business. The fundamental uncertainty that is inherently present at this scale
manifests itself in the unavoidable presence of noise, making this a novel
field of application for stochastic estimation and control theory. In this
expository paper we demonstrate estimation and feedback control of quantum
mechanical systems in what is essentially a noncommutative version of the
binomial model that is popular in mathematical finance. The model is extremely
rich and allows a full development of the theory, while remaining completely
within the setting of finite-dimensional Hilbert spaces (thus avoiding the
technical complications of the continuous theory). We introduce discretized
models of an atom in interaction with the electromagnetic field, obtain
filtering equations for photon counting and homodyne detection, and solve a
stochastic control problem using dynamic programming and Lyapunov function
methods.


  We present large deviations principles for the moments of the empirical
spectral measure of Wigner matrices and empirical measure of $\beta$-ensembles
in three cases : the case of Wigner matrices without Gaussian tails, that is
Wigner matrices whose entries have tail distributions decreasing as
$e^{-ct^{\alpha}}$, for some constant $c>0$ and with $\alpha \in (0,2)$, the
case of Gaussian Wigner matrices, and the case of $\beta$-ensembles associated
with a convex potential with polynomial growth.


  We present new data on the Bjorken sum $\overline \Gamma_1^{p-n}(Q^2)$ at
4-momentum transfer $ 0.021 \leq Q^2 \leq 0.496$ GeV$^2$. The data were
obtained in two experiments performed at Jefferson Lab: EG4 on polarized
protons and deuterons, and E97110 on polarized $^3$He from which neutron data
were extracted. The data cover the domain where chiral effective field theory
($\chi$EFT), the leading effective theory of the Strong Force at large
distances, is expected to be applicable. We find that our data and the
predictions from $\chi$EFT are only in marginal agreement. This is somewhat
surprising as the contribution from the $\Delta(1232)$ resonance is suppressed
in this observable, which should make it more reliably predicted by $\chi$EFT
than quantities in which the $\Delta$ contribution is important. The data are
also compared to a number of phenomenological models with various degrees of
agreement.


  Bipartite networks provide an effective resource for representing,
characterizing, and modeling several abstract and real-world systems and
structures involving binary relations, which include food webs, social
interactions, and customer-product relationships. Of particular interest is the
problem of, given a specific bipartite network, to identify possible respective
groups or clusters characterized by similar interconnecting patterns. The
present work approaches this issue by extending and complementing a previously
described coincidence similarity methodology (Bioarxiv,
doi.org/10.1101/2022.07.16.500294) in several manners, including the
consideration of direct and non-directed bipartite networks, the
characterization of groups in those networks, as well as considering synthetic
bipartite networks presenting groups as a resource for studying the performance
of the described methodology. Several interesting results are described and
discussed, including the corroboration of the potential of the coincidence
similarity methodology for achieving enhanced separation between the groups in
bipartite networks.


  We study the general theory of Frobenius algebras with group actions. These
structures arise when one is studying the algebraic structures associated to a
geometry stemming from a physical theory with a global finite gauge group, i.e.
orbifold theories. In this context, we introduce and axiomatize these algebras.
Furthermore, we define a geometric cobordism categories whose functors to the
category of vector spaces are parameterized by these algebras. The theory is
also extended to the graded and super-graded cases. As an application, we
consider Frobenius algebras having some additional properties making them more
tractable. These properties are present in Frobenius algebras arising as
quotients of Jacobian ideal, such as those having their origin in
quasi-homogeneous singularities and their symmetries.


  We introduce and characterize two different measures which quantify the level
of synchronization of interacting continuous variable quantum systems. The two
measures allow to extend to the quantum domain the notions of complete and
phase synchronization. The Heisenberg principle sets a universal bound to
complete synchronization. The measure of phase synchronization is in principle
unbounded, however in the absence of quantum resources (e.g. squeezing) the
synchronization level is bounded below a certain threshold. We elucidate some
interesting connections between entanglement and synchronization and, finally,
discuss an application based on quantum opto-mechanical systems.


  Answering complex logical queries over large-scale knowledge graphs (KGs)
represents an important artificial intelligence task, entailing a range of
applications. Recently, knowledge representation learning (KRL) has emerged as
the state-of-the-art approach, wherein KG entities and the query are embedded
into a latent space such that entities that answer the query are embedded close
to the query. Yet, despite its surging popularity, the potential security risks
of KRL are largely unexplored, which is concerning, given the increasing use of
such capabilities in security-critical domains (e.g., cyber-security and
healthcare).
  This work represents a solid initial step towards bridging this gap. We
systematize the potential security threats to KRL according to the underlying
attack vectors (e.g., knowledge poisoning and query perturbation) and the
adversary's background knowledge. More importantly, we present ROAR(Reasoning
Over Adversarial Representations), a new class of attacks that instantiate a
variety of such threats. We demonstrate the practicality of ROAR in two
representative use cases (i.e., cyber-threat hunting and drug repurposing). For
instance, ROAR attains over 99% attack success rate in misleading the threat
intelligence engine to give pre-defined answers for target queries, yet without
any impact on non-target ones. Further, we discuss potential countermeasures
against ROAR, including filtering of poisoning facts and robust training with
adversarial queries, which leads to several promising research directions.


  (see paper for full abstract)
  Given a vertex-weighted directed graph $G=(V,E)$ and a set $T=\{t_1, t_2,
\ldots t_k\}$ of $k$ terminals, the objective of the SCSS problem is to find a
vertex set $H\subseteq V$ of minimum weight such that $G[H]$ contains a
$t_{i}\rightarrow t_j$ path for each $i\neq j$. The problem is NP-hard, but
Feldman and Ruhl [FOCS '99; SICOMP '06] gave a novel $n^{O(k)}$ algorithm for
the SCSS problem, where $n$ is the number of vertices in the graph and $k$ is
the number of terminals. We explore how much easier the problem becomes on
planar directed graphs:
  - Our main algorithmic result is a $2^{O(k)}\cdot n^{O(\sqrt{k})}$ algorithm
for planar SCSS, which is an improvement of a factor of $O(\sqrt{k})$ in the
exponent over the algorithm of Feldman and Ruhl.
  - Our main hardness result is a matching lower bound for our algorithm: we
show that planar SCSS does not have an $f(k)\cdot n^{o(\sqrt{k})}$ algorithm
for any computable function $f$, unless the Exponential Time Hypothesis (ETH)
fails.
  The following additional results put our upper and lower bounds in context:
  - In general graphs, we cannot hope for such a dramatic improvement over the
$n^{O(k)}$ algorithm of Feldman and Ruhl: assuming ETH, SCSS in general graphs
does not have an $f(k)\cdot n^{o(k/\log k)}$ algorithm for any computable
function $f$.
  - Feldman and Ruhl generalized their $n^{O(k)}$ algorithm to the more general
Directed Steiner Network (DSN) problem; here the task is to find a subgraph of
minimum weight such that for every source $s_i$ there is a path to the
corresponding terminal $t_i$. We show that, assuming ETH, there is no
$f(k)\cdot n^{o(k)}$ time algorithm for DSN on acyclic planar graphs.


  We introduce a simplified technique for incorporating diffusive phenomena
into lattice-gas molecular dynamics models. In this method, spatial
interactions take place one dimension at a time, with a separate fractional
timestep devoted to each dimension, and with all dimensions treated
identically. We show that the model resulting from this technique is equivalent
to the macroscopic diffusion equation in the appropriate limit.
  This technique saves computational resources and reduces the complexity of
model design, programming, debugging, simulation and analysis. For example, a
reaction-diffusion simulation can be designed and tested as a one-dimensional
system, and then directly extended to two or more dimensions. We illustrate the
use of this approach in constructing a microscopically reversible model of
diffusion limited aggregation as well as in a model of growth of biological
films.


  This paper relates skein spaces based on the Kauffman bracket and spin
structures. A spin structure on an oriented 3-manifold provides an isomorphism
between the skein space for parameter A and the skein space for parameter -A.
  There is an application to Penrose's binor calculus, which is related to the
tensor calculus of representations of SU(2). The perspective developed here is
that this tensor calculus is actually a calculus of spinors on the plane, and
the matrices a re determined by a type of spinor transport which generalises to
links in any 3-manifold.
  A second application shows that there is a skein space which is the algebra
of functions on the set of spin structures for the 3-manifold.


  Decoherence induced by the laser frequency noise is one of the most important
obstacles in the quantum information processing. In order to suppress this
decoherence, the noise power spectral density needs to be accurately
characterized. In particular, the noise spectrum measurement based on the
coherence characteristics of qubits would be a meaningful and still challenging
method. Here, we theoretically analyze and experimentally obtain the spectrum
of laser frequency noise based on the continuous dynamical decoupling
technique. We first estimate the mixture-noise (including laser and magnetic
noises) spectrum up to $(2\pi)$530 kHz by monitoring the transverse relaxation
from an initial state $+X$, followed by a gradient descent data process
protocol. Then the contribution from the laser noise is extracted by enconding
the qubits on different Zeeman sublevels. We also investigate two sufficiently
strong noise components by making an analogy between these noises and driving
lasers whose linewidth assumed to be negligible. This method is verified
experimentally and finally helps to characterize the noise.


  This article presents an analysis of the stabilization of a multidimensional
partial differential wave equation under a well designed event-triggering
mechanism that samples the boundary control input. The wave equation is set in
a bounded domain and the control is performed through a boundary classical
damping term, where the Neumann boundary condition is made proportional to the
velocity. First of all, existence and regularity of the solution to the
closed-loop system under the event-triggering mechanism of the control are
proven. Then, sufficient conditions based on the use of a specific Lyapunov
functional are proposed in order to ensure that the solutions converge into a
compact set containing the origin, that can be tuned by the designer.
Furthermore, as expected, any Zeno behavior of the closed-loop system is
avoided.


  We propose an improved quark coalescence model for spin alignment of vector
mesons by spin density matrix with phase space dependence. Within this model we
propose an understanding of spin alignments of vector mesons $\phi$ and
$K^{*0}$ in the static limit: a large positive deviation of $\rho_{00}$ for
$\phi$ mesons from $1/3$ may come from the electric part of the vector $\phi$
field, while a negative deviation of $\rho_{00}$ for $K^{*0}$ mesons may come
from the electric part of vorticity fields. In the low-$p_T$ region,
$\rho_{00}$ for $K^{*0}$ mesons is proportional to $p_T^2$, which is
qualitatively agree with experimental results.


  We describe the quantization of 2-plectic manifolds as they arise in the
context of the quantum geometry of M-branes and non-geometric flux
compactifications of closed string theory. We review the groupoid approach to
quantizing Poisson manifolds in detail, and then extend it to the loop spaces
of 2-plectic manifolds, which are naturally symplectic manifolds. In
particular, we discuss the groupoid quantization of the loop spaces of R^3, T^3
and S^3, and derive some interesting implications which match physical
expectations from string theory and M-theory.


  Several areas have been improved with Deep Learning during the past years.
Implementing Deep Neural Networks (DNN) for non-safety related applications
have shown remarkable achievements over the past years; however, for using DNNs
in safety critical applications, we are missing approaches for verifying the
robustness of such models. A common challenge for DNNs occurs when exposed to
out-of-distribution samples that are outside of the scope of a DNN, but which
result in high confidence outputs despite no prior knowledge of such input.
  In this paper, we analyze three methods that separate between in- and
out-of-distribution data, called supervisors, on four well-known DNN
architectures. We find that the outlier detection performance improves with the
quality of the model. We also analyse the performance of the particular
supervisors during the training procedure by applying the supervisor at a
predefined interval to investigate its performance as the training proceeds. We
observe that understanding the relationship between training results and
supervisor performance is crucial to improve the model's robustness and to
indicate, what input samples require further measures to improve the robustness
of a DNN. In addition, our work paves the road towards an instrument for safety
argumentation for safety critical applications. This paper is an extended
version of our previous work presented at 2019 SEAA (cf. [1]); here, we
elaborate on the used metrics, add an additional supervisor and test them on
two additional datasets.


  The missing mass spectroscopy of $\Xi^{-}$ hypernuclei with the
$(K^{-},K^{+})$ reaction is planned to be performed at the J-PARC K1.8 beam
line by using a new magnetic spectrometer, Strangeness $-2$ Spectrometer
(S-2S). A $\v{C}$cerenkov detector with a radiation medium of pure water
(refractive index of 1.33) is designed to be used for on-line proton rejection
for a momentum range of 1.2 to 1.6 GeV/$c$ in S-2S. Prototype water
$\v{C}$erenkov detectors were developed and tested with positron beams and
cosmic rays to estimate their proton-rejection capability. We achieved an
average number of photoelectrons of greater than 200 with the latest prototype
for cosmic rays, which was stable during an expected beam time of one month.
The performance of the prototype in the cosmic-ray test was well reproduced
with a Monte Carlo simulation in which some input parameters were adjusted.
Based on the Monte Carlo simulation, we expect to achieve $>90\%$
proton-rejection efficiency while maintaining $>95\%$ $K^{+}$ survival ratio in
the whole S-2S acceptance. The performance satisfies the requirements to
conduct the spectroscopic study of $\Xi^{-}$ hypernuclei at J-PARC.


  We define a new basis of cubic splines such that the coordinates of a natural
cubic spline are sparse. We use it to analyse and to extend the classical
Schoenberg and Reinsch result and to estimate a noisy cubic spline. We also
discuss the choice of the smoothing parameter. All our results are illustrated
graphically.