  The frequency dependence of dielectric constant for composites of polyaniline
(PANI) and multi-walled carbon nanotube (MWCNT) with different degree of
functionalization is studied at low temperature (down to 4.2 K) and magnetic
field (up to 3 Tesla) applied both in parallel and perpendicular direction of
ac electric field. A relaxation phenomenon is observed in all the MWCNT/PANI
composites by applying magnetic field in both the directions, below 10$^3$ Hz.
However, PANI does not show any relaxation peak with applied magnetic field in
either direction. The relaxation peak frequency does not depend on the strength
of magnetic field but it varies with temperature and degree of
functionalization of MWCNT in composites. This relaxation phenomenon occurs due
to the inhomogeneity of the medium of two highly mismatched conductive
materials at low temperatures. We have tried to explain our results in the
light of Parish and Littlewood theory about magnetocapacitance in nonmagnetic
composite.


  A correspondence exists between affine tropical varieties and algebraic
objects, following the classical Zariski correspondence between irreducible
affine varieties and the prime spectrum of the coordinate algebra in affine
algebraic geometry. Although in this context the natural analog of the
polynomial ring over a field is the polynomial semiring over a semifield
(without a zero element), one obtains homomorphic images of coordinate algebras
via congruences rather than ideals, which complicates the algebraic theory
considerably.
  In this paper, we pass to the semifield $F(\lambda_1, \dots, \lambda_n)$ of
fractions of the polynomial semiring, for which there already exists a well
developed theory of kernels, which are normal convex subgroups; this approach
enables us to switch the structural roles of addition and multiplication and
makes available much of the extensive theory of chains of homomorphisms of
groups, including the Jordan-Holder theory. The parallel of the zero set now is
the 1-set.
  These notions are refined in the language of supertropical algebra to
$\nu$-kernels and $1^\nu$-sets, lending more precision to the theory. In
analogy to Hilbert's celebrated Nullstellensatz which provides a correspondence
between radical ideals and zero sets, we develop a correspondence between
$1^\nu$-sets and a well-studied class of $\nu$-kernels of the rational
semifield called polars, originating from the theory of lattice-ordered groups.
This correspondence becomes simpler and more applicable when restricted to a
special kind of kernel, called principal, intersected with the kernel generated
by $F$. We utilize this theory to study tropical roots in tropical geometry. As
an application, we develop composition series and convexity degree, leading to
a tropical version of the Jordan-H\"{o}lder theorem.


  A sequence of $n$-particle approximations for the system of Schwinger-Dyson
equations is investigated in the model of a complex scalar field $\phi$ and a
real scalar field $\chi$ with the interaction $g\phi^*\phi\chi$.
  In the first non-trivial two-particle approximation, the system is reduced to
a system of two nonlinear integral equations for propagators.
  The study of this system shows that for equal masses a critical coupling
constant $g^2_c$ exists, which separates the weak- and strong-coupling regions
with the different asymptotic behavior for deep Euclidean momenta. In the
weak-coupling region ($g^2<g^2_c$), the propagators are asymptotically free,
which corresponds to the wide-spread opinion about the dominance of
perturbation theory for this model. At the critical point the asymptotics of
propagators are $\sim 1/p$.
  In the strong coupling region ($g^2>g^2_c$), the propagators are
asymptotically constant, which corresponds to the ultra-local limit. For
unequal masses, the critical point transforms into a segment of values, in
which there are no solutions with a self-consistent ultraviolet behavior
without Landau singularities.


  In recent years, there has been a rapid increase in the number of service
robots deployed for aiding people in their daily activities. Unfortunately,
most of these robots require human input for training in order to do tasks in
indoor environments. Successful domestic navigation often requires access to
semantic information about the environment, which can be learned without human
guidance. In this paper, we propose a set of DEDUCE - Diverse scEne Detection
methods in Unseen Challenging Environments algorithms which incorporate deep
fusion models derived from scene recognition systems and object detectors. The
five methods described here have been evaluated on several popular recent image
datasets, as well as real-world videos acquired through multiple mobile
platforms. The final results show an improvement over the existing
state-of-the-art visual place recognition systems.


  We present a novel approach to the classification of conformally equivariant
differential operators on spinors in the case of homogeneous conformal
geometry. It is based on the classification of solutions for a vector-valued
system of partial differential equations, associated to $\mathcal{D}$-modules
for the homogeneous conformal structure and controlled by the spin Howe duality
for the orthogonal Lie algebras.


  We investigate the evolution in colour and morphology of the progenitors of
red-sequence galaxies in the EAGLE cosmological hydrodynamical simulation. We
quantify colours with $u^{*}-r^{*}$ intrinsic magnitudes and morphologies with
a measure of the stellar kinematics. The time when galaxies moved onto the red
sequence depends on their morphology. Disc-type galaxies tend to have become
red during the last 3 Gyr, while elliptical-type galaxies joined the red
sequence earlier, with half the sample already being red 5 Gyr ago. The
time-scale, $\tau_{\rm{Green}}$, of colour transition through the `green
valley' depends weakly on the galaxy's morphological type. Elliptical-type
galaxies cross the green valley slightly faster ($\tau_{\rm{Green}}{\approx}1$
Gyr) than disc-type galaxies ($\tau_{\rm{Green}}{\approx}$1.5 Gyr). While
$\tau_{\rm{Green}}$ is similar for central and satellite galaxies, for
satellites $\tau_{\rm{Green}}$ decreases with increasing stellar mass to
host-halo mass ratio. Coupled with our finding that galaxies tend to become
green after becoming satellites, this indicates that satellite-specific
processes are important for quenching red-sequence galaxies. The last time
central, elliptical-type red-sequence galaxies left the blue cloud is strongly
correlated with the time the luminosity of the central black hole peaked, but
this is not the case for discs. This suggests that AGN feedback is important
for quenching ellipticals, particularly centrals, but not for discs. We find
only a weak connection between transformations in colour and morphology.


  Different mechanisms operate in various regions of the MSSM parameter space
to bring the relic density of the lightest neutralino, neutralino_1, assumed
here to be the LSP and thus the Dark Matter (DM) particle, into the range
allowed by astrophysics and cosmology. These mechanisms include coannihilation
with some nearly-degenerate next-to-lightest supersymmetric particle (NLSP)
such as the lighter stau (stau_1), stop (stop_1) or chargino (chargino_1),
resonant annihilation via direct-channel heavy Higgs bosons H/A, the light
Higgs boson h or the Z boson, and enhanced annihilation via a larger Higgsino
component of the LSP in the focus-point region. These mechanisms typically
select lower-dimensional subspaces in MSSM scenarios such as the CMSSM, NUHM1,
NUHM2 and pMSSM10. We analyze how future LHC and direct DM searches can
complement each other in the exploration of the different DM mechanisms within
these scenarios. We find that the stau_1 coannihilation regions of the CMSSM,
NUHM1, NUHM2 can largely be explored at the LHC via searches for missing E_T
events and long-lived charged particles, whereas their H/A funnel, focus-point
and chargino_1 coannihilation regions can largely be explored by the LZ and
Darwin DM direct detection experiments. We find that the dominant DM mechanism
in our pMSSM10 analysis is chargino_1 coannihilation: {parts of its parameter
space can be explored by the LHC, and a larger portion by future direct DM
searches.


  The phase transition occurring in a square 2-D spin lattice governed by an
anisotropic Heisenberg Hamiltonian has been studied according to two recently
proposed methods. The first one, the Dressed Cluster Method, provides excellent
evaluations of the cohesive energy, the discontinuity of its derivative around
the critical (isotropic) value of the anisotropy parameter confirms the
first-order character of the phase transition. Nevertheless the method
introduces two distinct reference functions (either N\'eel or XY) which may in
principle force the discontinuity. The Real Space Renormalization Group with
Effective Interactions does not reach the same numerical accuracy but it does
not introduce a reference function and the phase transition appears
qualitatively as due to the existence of two domains, with specific fixed
points. The method confirms the dependence of the spin gap on the anisotropy
parameter occurring in the Heisenberg-Ising domain.


  I present a complete list of hypersurface homogeneous space-times admitting a
non-null valence two Killing spinor, including a new class admitting only
exceptional Killing tensors. A connection is established with the
classification of locally rotationally symmetric or boost symmetric
space-times.


  We study the number of dimer-monomers $M_d(n)$ on the Tower of Hanoi graphs
$TH_d(n)$ at stage $n$ with dimension $d$ equal to 3 and 4. The entropy per
site is defined as $z_{TH_d}=\lim_{v \to \infty} \ln M_d(n)/v$, where $v$ is
the number of vertices on $TH_d(n)$. We obtain the lower and upper bounds of
the entropy per site, and the convergence of these bounds approaches to zero
rapidly when the calculated stage increases. The numerical value of $z_{TH_d}$
is evaluated to more than a hundred digits correct. Using the results with $d$
less than or equal to 4, we predict the general form of the lower and upper
bounds for $z_{TH_d}$ with arbitrary $d$.


  We use molecular line data from ALMA, SMA, JCMT, and NANTEN2 to study the
multi-scale ($\sim$15-0.005 pc) velocity statistics in the massive star
formation region NGC 6334. We find that the non-thermal motions revealed by the
velocity dispersion function (VDF) stay supersonic over scales of several
orders of magnitudes. The multi-scale non-thermal motions revealed by different
instruments do not follow the same continuous power-law, which is because the
massive star formation activities near central young stellar objects have
increased the non-thermal motions in small-scale and high-density regions. The
magnitudes of VDFs vary in different gas materials at the same scale, where the
infrared dark clump N6334S in an early evolutionary stage shows a lower level
of non-thermal motions than other more evolved clumps due to its more quiescent
star formation activity. We find possible signs of small-scale-driven (e.g., by
gravitational accretion or outflows) supersonic turbulence in clump N6334IV
with a three-point VDF analysis. Our results clearly show that the scaling
relation of velocity fields in NGC 6334 deviates from a continuous and
universal turbulence cascade due to massive star formation activities.


  We extract pixel-level masks of extreme weather patterns using variants of
Tiramisu and DeepLabv3+ neural networks. We describe improvements to the
software frameworks, input pipeline, and the network training algorithms
necessary to efficiently scale deep learning on the Piz Daint and Summit
systems. The Tiramisu network scales to 5300 P100 GPUs with a sustained
throughput of 21.0 PF/s and parallel efficiency of 79.0%. DeepLabv3+ scales up
to 27360 V100 GPUs with a sustained throughput of 325.8 PF/s and a parallel
efficiency of 90.7% in single precision. By taking advantage of the FP16 Tensor
Cores, a half-precision version of the DeepLabv3+ network achieves a peak and
sustained throughput of 1.13 EF/s and 999.0 PF/s respectively.


  In the context of studying periodic processes, this paper investigates first
under which conditions switching affine systems in the plane generate stable
limit cycles. Based on these conditions, a design methodology is proposed by
which the phase portraits of the switching systems are determined to obtain
globally stable limit cycles from simple specifications, such as given
amplitudes and frequencies of desired oscillations. As an application, the
paper finally shows that an oscillator model can be derived with a small effort
from data measured for an unknown oscillating system.


  We consider projections of planar self-similar sets, and show that one can
create nonempty interior in the projections by applying arbitrary small
perturbations, if the self-similar set satisfies the open set condition and has
Hausdorff dimension greater than 1.


  We define a faithful linear monoidal functor from the partition category, and
hence from Deligne's category $\mathrm{\underline{Re}p}(S_t)$, to the
Heisenberg category. We show that the induced map on Grothendieck rings is
injective and corresponds to the Kronecker coproduct on symmetric functions.


  We apply the general Ansatz in geometric flows on homogeneous spaces proposed
by Jorge Lauret for the Laplacian co-flow of invariant $G_2$-structures on a
Lie group, finding an explicit soliton on a particular almost Abelian
$7$-manifold.


  We use the idea of the Wigner probability distribution (WPD) in a reduced
scattering phase space (RPS) for the elastic scattering cross-section, with the
help of a Tight-Binding (TB) numerical procedure allowing us to consider the
anisotropic quantum effects, to phenomenologically predict several phases in
these two novel unconventional superconductors. Unlike our previous works with
pieces of evidence that these two compounds are in the unitary strong
scattering regime and that superconductivity is suppressed by the atoms of
strontium in both materials, several phases are built. In the case of the
strontium-substituted lanthanum cuprate, it was found three phases from one
family of Wigner probabilistic distributions, one corresponding to the
antiferromagnetic compound La2CuO4 another one which consists of a coalescing
metallic phase for very lightly doped La2-xSrxCuO4, and finally a strong
self-consistent dependent strange metal phase with optimal levels of doping. In
the case of a triplet superconductor strontium ruthenate, three phases can be
differentiated from two families of Wigner distribution probabilities, one
family of WDP with point nodes where Cooper pairs and dressed scattered normal
quasiparticles are mixed for the whole range of frequencies and which
correspond to an FS $\gamma$ flat-sheet in the ground metallic state, and two
phases from another WPD family, where, in one of then, the Miyake-Narikiyo
quasinodal tiny gap model allows the unique presence of Cooper pairs in a tiny
interval of frequencies near the superconducting transition TC, the other phase
corresponds to the mixed-phase with Cooper pairs and dressed by stoichiometric
strontium non-magnetic atoms, where strong self-consistent effects are
noticeable. This approach allows comparing experimental results for samples in
both compounds with numerical analysis studies.


  We discuss the feasibility of finding extra-galactic MACHOs by monitoring
quasars behind the Virgo cluster of galaxies. We show that with only a modest
observing programme one could detect several MACHOs in the mass range 1 X
10^{-5} to 2 X 10^{-2} solar masses if they make a significant contribution to
the mass of Virgo. The contamination by events from cosmologically distributed
MACHOs is estimated and is negligible if either the MACHO mass is greater than
about 10^{-4} solar masses or the quasar radius is greater than about 3 X
10^{15} cm.


  The process of painting fosters creativity and rational planning. However,
existing generative AI mostly focuses on producing visually pleasant artworks,
without emphasizing the painting process. We introduce a novel task,
Collaborative Neural Painting (CNP), to facilitate collaborative art painting
generation between humans and machines. Given any number of user-input
brushstrokes as the context or just the desired object class, CNP should
produce a sequence of strokes supporting the completion of a coherent painting.
Importantly, the process can be gradual and iterative, so allowing users'
modifications at any phase until the completion. Moreover, we propose to solve
this task using a painting representation based on a sequence of parametrized
strokes, which makes it easy both editing and composition operations. These
parametrized strokes are processed by a Transformer-based architecture with a
novel attention mechanism to model the relationship between the input strokes
and the strokes to complete. We also propose a new masking scheme to reflect
the interactive nature of CNP and adopt diffusion models as the basic learning
process for its effectiveness and diversity in the generative field. Finally,
to develop and validate methods on the novel task, we introduce a new dataset
of painted objects and an evaluation protocol to benchmark CNP both
quantitatively and qualitatively. We demonstrate the effectiveness of our
approach and the potential of the CNP task as a promising avenue for future
research.


  An improved U(1) slave-boson approach is applied to study the optical
conductivity of the two dimensional systems of antiferromagnetically correlated
electrons over a wide range of hole doping and temperature. Interplay between
the spin and charge degrees of freedom is discussed to explain the origin of
the peak-dip-hump structure in the in-plane conductivity of high $T_C$
cuprates. The role of spin fluctuations of short range order(spin singlet pair)
is investigated. It is shown that the spin fluctuations of the short range
order can cause the mid-infrared hump, by exhibiting a linear increase of the
hump frequency with the antiferromagnetic Heisenberg coupling strength.


  Here we will use results of Cox, Durrett, and Perkins for voter model
perturbations to study spatial evolutionary games on $Z^d$, $d\ge 3$ when the
interaction kernel is finite range, symmetric, and has covariance matrix
$\sigma^2I$. The games we consider have payoff matrices of the form ${\bf 1} +
wG$ where ${\bf 1}$ is matrix of all 1's and $w$ is small and positive. Since
our population size $N=\infty$, we call our selection small rather than weak
which usually means $w =O(1/N)$.
  The key to studying these games is the fact that when the dynamics are
suitably rescaled in space and time they convergence to solutions of a reaction
diffusion equation (RDE). Inspired by work of Ohtsuki and Nowak and Tarnita et
al we show that the reaction term is the replicator equation for a modified
game matrix and the modifications of the game matrix depend on the interaction
kernel only through the values of two or three simple probabilities for an
associated coalescing random walk.
  Two strategy games lead to an RDE with a cubic nonlinearity, so we can
describe the phase diagram completely. Three strategy games lead to a pair of
coupled RDE, but using an idea from our earlier work, we are able to show that
if there is a repelling function for the replicator equation for the modified
game, then there is coexistence in the spatial game when selection is small.
This enables us to prove coexistence in the spatial model in a wide variety of
examples where the replicator equation of the modified game has an attracting
equilibrium with all components positive. Using this result we are able to
analyze the behavior of four evolutionary games that have recently been used in
cancer modeling.


  Semi-supervised variational autoencoders (VAEs) have obtained strong results,
but have also encountered the challenge that good ELBO values do not always
imply accurate inference results. In this paper, we investigate and propose two
causes of this problem: (1) The ELBO objective cannot utilize the label
information directly. (2) A bottleneck value exists and continuing to optimize
ELBO after this value will not improve inference accuracy. On the basis of the
experiment results, we propose SHOT-VAE to address these problems without
introducing additional prior knowledge. The SHOT-VAE offers two contributions:
(1) A new ELBO approximation named smooth-ELBO that integrates the label
predictive loss into ELBO. (2) An approximation based on optimal interpolation
that breaks the ELBO value bottleneck by reducing the margin between ELBO and
the data likelihood. The SHOT-VAE achieves good performance with a 25.30% error
rate on CIFAR-100 with 10k labels and reduces the error rate to 6.11% on
CIFAR-10 with 4k labels.


  NLP has a rich history of representing our prior understanding of language in
the form of graphs. Recent work on analyzing contextualized text
representations has focused on hand-designed probe models to understand how and
to what extent do these representations encode a particular linguistic
phenomenon. However, due to the inter-dependence of various phenomena and
randomness of training probe models, detecting how these representations encode
the rich information in these linguistic graphs remains a challenging problem.
In this paper, we propose a new information-theoretic probe, Bird's Eye, which
is a fairly simple probe method for detecting if and how these representations
encode the information in these linguistic graphs. Instead of using classifier
performance, our probe takes an information-theoretic view of probing and
estimates the mutual information between the linguistic graph embedded in a
continuous space and the contextualized word representations. Furthermore, we
also propose an approach to use our probe to investigate localized linguistic
information in the linguistic graphs using perturbation analysis. We call this
probing setup Worm's Eye. Using these probes, we analyze BERT models on their
ability to encode a syntactic and a semantic graph structure, and find that
these models encode to some degree both syntactic as well as semantic
information; albeit syntactic information to a greater extent.


  We heal the cosmological constant problem by means of a \emph{cancellation
mechanism} that adopts a phase transition during which quantum fluctuations are
eliminated. To this purpose, we propose that a generalized scalar (dark) matter
field with a non-vanishing pressure term can remove the vacuum energy
contribution, if its corresponding thermodynamics is written in terms of a
\emph{quasi-quintessence} representation. In such a picture, pressure differs
from quintessence as it shows a zero kinetic contribution. Using this field, we
investigate a metastable transition phase, in which the universe naturally
passes through an inflationary phase. To reach this target, we single out a
double exponential potential, describing the metastable inflationary dynamics
by considering suitable boundaries and thermodynamic conditions. We analyze
stability investigating saddle, stable and unstable points and we thus predict
a chaotic inflation that mimics the Starobinsky exponential potential.
Consequently, the role of the proposed dark matter field is investigated
throughout the overall universe evolution. To do so, we provide a physical
explanation on unifying the dark sector with inflation by healing the
cosmological constant problem.


  Federated learning (FL) is a rapidly growing privacy-preserving collaborative
machine learning paradigm. In practical FL applications, local data from each
data silo reflect local usage patterns. Therefore, there exists heterogeneity
of data distributions among data owners (a.k.a. FL clients). If not handled
properly, this can lead to model performance degradation. This challenge has
inspired the research field of heterogeneous federated learning, which
currently remains open. In this paper, we propose a data heterogeneity-robust
FL approach, FedGSP, to address this challenge by leveraging on a novel concept
of dynamic Sequential-to-Parallel (STP) collaborative training. FedGSP assigns
FL clients to homogeneous groups to minimize the overall distribution
divergence among groups, and increases the degree of parallelism by reassigning
more groups in each round. It is also incorporated with a novel Inter-Cluster
Grouping (ICG) algorithm to assist in group assignment, which uses the centroid
equivalence theorem to simplify the NP-hard grouping problem to make it
solvable. Extensive experiments have been conducted on the non-i.i.d. FEMNIST
dataset. The results show that FedGSP improves the accuracy by 3.7% on average
compared with seven state-of-the-art approaches, and reduces the training time
and communication overhead by more than 90%.


  In this paper, the third in a series, we continue to study the generalized
Lam\'{e} equation H$(n_0,n_1,n_2,n_3;B)$ with the Darboux-Treibich-Verdier
potential \begin{equation*} y^{\prime \prime }(z)=\bigg[
\sum_{k=0}^{3}n_{k}(n_{k}+1)\wp(z+\tfrac{ \omega_{k}}{2}|\tau)+B\bigg]
y(z),\quad n_{k}\in \mathbb{Z}_{\geq0} \end{equation*} and a related linear ODE
with additional singularities $\pm p$ from the monodromy aspect.We establish
the uniqueness of these ODEs with respect to the global monodromy data.
Surprisingly, our result shows that the Riemann-Hilbert correspondence from the
set \[\{\text{H}(n_0,n_1,n_2,n_3;B)|B\in\mathbb{C}\}\cup
\{\text{H}(n_0+2,n_1,n_2,n_3;B) | B\in\mathbb{C}\}\] to the set of group
representations $\rho:\pi_1(E_{\tau})\to SL(2,\mathbb{C})$ is one-to-one. We
emphasize that this result is not trivial at all. There is an example that for
$\tau=\frac12+i\frac{\sqrt{3}}{2}$, there are $B_1,B_2$ such that the monodromy
representations of H$(1,0,0,0;B_1)$ and H$(4,0,0,0;B_2)$ are {\bf the same},
namely the Riemann-Hilbert correspondence from the set
\[\{\text{H}(n_0,n_1,n_2,n_3;B)|B\in\mathbb{C}\}\cup
\{\text{H}(n_0+3,n_1,n_2,n_3;B) | B\in\mathbb{C}\}\] to the set of group
representations is {\bf not} necessarily one-to-one. This example shows that
our result is completely different from the classical one concerning linear
ODEs defined on $\mathbb{CP}^1$ with finite singularities.


  This paper proposes a straightforward 2-D method to spatially calibrate the
visual field of a camera with the auditory field of an array microphone by
generating and overlaying an acoustic image over an optical image. Using a
low-cost microphone array and an off-the-shelf camera, we show that polynomial
regression can deal efficiently with non-linear camera distortion, and that a
recently proposed sound source localization method for real-time processing,
SVD-PHAT, can be adapted for this task.


  We examine the question of whether a collection of random walks on a graph
can be coupled so that they never collide. In particular, we show that on the
complete graph on n vertices, with or without loops, there is a Markovian
coupling keeping apart Omega(n/log n) random walks, taking turns to move in
discrete time.


  Excitons, which represent a type of quasi-particles consisting of
electron-hole pairs bound by the mutual Coulomb interaction, were often
observed in lowly-doped semiconductors or insulators. However, realizing
excitons in the semiconductors or insulators with high charge carrier densities
is a challenging task. Here, we perform infrared spectroscopy, electrical
transport, ab initio calculation, and angle-resolved-photoemission spectroscopy
studies of a van der Waals degenerate-semiconductor Bi4O4SeCl2. A peak-like
feature (i.e., alpha peak) is present around ~ 125 meV in the optical
conductivity spectra at low temperature T = 8 K and room temperature. After
being excluded from the optical excitations of free carriers, interband
transitions, localized states and polarons, the alpha peak is assigned as the
exciton absorption. Moreover, assuming the existence of weakly-bound
excitons--Wannier-type excitons in this material violates the
Lyddane-Sachs-Teller relation. Besides, the exciton binding energy of ~ 375
meV, which is about an order of magnitude larger than those of conventional
semiconductors, and the charge-carrier concentration of ~ 1.25 * 10^19 cm^-3,
which is higher than the Mott density, further indicate that the excitons in
this highly-doped system should be tightly bound. Our results pave the way for
developing the optoelectronic devices based on the tightly-bound and
room-temperature-stable excitons in highly-doped van der Waals degenerate
semiconductors.


  Elliptic current algebras E_{q,p}(\hat{g}) for arbitrary simply laced finite
dimensional Lie algebra g are defined and their co-algebraic structures are
studied. It is shown that under the Drinfeld like comultiplications, the
algebra E_{q,p}(\hat{g}) is not co-closed for any g. However putting the
algebras E_{q,p}(\hat{g}) with different deformation parameters together, we
can establish a structure of infinite Hopf family of algebras. The level 1
bosonic realization for the algebra E_{q,p}(\hat{g}) is also established.


  Language is often considered a key aspect of human thinking, providing us
with exceptional abilities to generalize, explore, plan, replan, and adapt to
new situations. However, Reinforcement Learning (RL) agents are far from
human-level performance in any of these abilities. We hypothesize one reason
for such cognitive deficiencies is that they lack the benefits of thinking in
language and that we can improve AI agents by training them to think like
humans do. We introduce a novel Imitation Learning framework, Thought Cloning,
where the idea is to not just clone the behaviors of human demonstrators, but
also the thoughts humans have as they perform these behaviors. While we expect
Thought Cloning to truly shine at scale on internet-sized datasets of humans
thinking out loud while acting (e.g. online videos with transcripts), here we
conduct experiments in a domain where the thinking and action data are
synthetically generated. Results reveal that Thought Cloning learns much faster
than Behavioral Cloning and its performance advantage grows the further out of
distribution test tasks are, highlighting its ability to better handle novel
situations. Thought Cloning also provides important benefits for AI Safety and
Interpretability, and makes it easier to debug and improve AI. Because we can
observe the agent's thoughts, we can (1) more easily diagnose why things are
going wrong, making it easier to fix the problem, (2) steer the agent by
correcting its thinking, or (3) prevent it from doing unsafe things it plans to
do. Overall, by training agents how to think as well as behave, Thought Cloning
creates safer, more powerful agents.


  Explicit stabilized methods are an efficient alternative to implicit schemes
for the time integration of stiff systems of differential equations in large
dimension. In this paper, we derive explicit stabilized integrators of orders
one and two for the optimal control of stiff systems. We analyze their
favorable stability properties based on the continuous optimality conditions.
Furthermore, we study their order of convergence taking advantage of the
symplecticity of the corresponding partitioned Runge-Kutta method involved for
the adjoint equations. Numerical experiments including the optimal control of a
nonlinear diffusion-advection PDE illustrate the efficiency of the new
approach.


  Since 2005, measurements of Collins and Sivers asymmetries from the HERMES
and COMPASS experiments have allowed to assess that the transversity and the
Sivers PDFs are different from zero and measurable in semi-inclusive DIS on
transversely polarised targets. Most of the data were collected on proton
targets, only small event samples were collected in the early phase of the
COMPASS experiment on a deuteron ($^6$LiD) target and more recently at JLab, on
$^3$He, so that the $d$-quark and the sea-quarks PDFs are much more poorly
known than the $u$-quark PDFs. This constitutes an important limitation to the
knowledge of the transverse spin structure of the nucleon. For this reason the
COMPASS Collaboration has proposed to measure semi-inclusive DIS on
transversely polarised deuterons with good accuracy, comparable with that of
the existing transverse spin asymmetry data on protons. The proposal has been
accepted by CERN and the experiment will run in 2021, as soon as the Long
Shut-down 2 is over, providing measurements which will stay unique for many
years to come. Projections will be given for the extraction of the transversity
PDFs, and for the evaluation of the isovector tensor charge of the nucleon.


  We consider the Hall effect in a system of weakly coupled one-dimensional
chains with Luttinger interaction within each chain. We construct a
perturbation theory in the inter-chain hopping term and find that there is a
power law dependence of the Hall conductivity on the magnetic field with an
exponent depending on the interaction constant. We show that this perturbation
theory becomes valid if the magnetic field is sufficiently large.


  As the nearest accessible massive early-type galaxy, NGC 5128 presents an
exceptional opportunity to measure dark matter halo parameters for a
representative elliptical galaxy. Here we take advantage of rich new
observational datasets of large-radius tracers to perform dynamical modeling of
NGC 5128, using a discrete axisymmetric anisotropic Jeans approach with a total
tracer population of nearly 1800 planetary nebulae, globular clusters, and
dwarf satellite galaxies extending to a projected distance of $\sim250$ kpc
from the galaxy center. We find that a standard NFW halo provides an excellent
fit to nearly all the data, excepting a subset of the planetary nebulae that
appear to be out of virial equilibrium. The best-fit dark matter halo has a
virial mass of ${\rm M}_{vir}=4.4^{+2.4}_{-1.4}\times10^{12} {\rm M}_{\odot}$,
and NGC 5128 appears to sit below the mean stellar mass--halo mass and globular
cluster mass--halo mass relations, which both predict a halo virial mass closer
to ${\rm M}_{vir} \sim 10^{13} {\rm M}_{\odot}$. The inferred NFW virial
concentration is $c_{vir}=5.6^{+2.4}_{-1.6}$, nominally lower than $c_{vir}
\sim 9$ predicted from published $c_{vir}$--${\rm M}_{vir}$ relations, but
within the $\sim 30\%$ scatter found in simulations. The best-fit dark matter
halo constitutes only $\sim10\%$ of the total mass at 1 effective radius but
$\sim50\%$ at 5 effective radii. The derived halo parameters are relatively
insensitive to reasonable variations in the tracer population considered,
tracer anisotropies, and system inclination. Our analysis highlights the value
of comprehensive dynamical modeling of nearby galaxies, and the importance of
using multiple tracers to allow cross-checks for model robustness.


  We show that $$\sum_{\substack{p_n \in [x, 2x] \\ p_{n+1} - p_n \ge x^{1/2}}}
(p_{n+1} - p_n) \ll x^{0.57+\epsilon}$$ and $$\sum_{\substack{p_n \in [x, 2x]
\\ p_{n+1} - p_n \ge x^{0.45}}} (p_{n+1} - p_n) \ll x^{0.63+\epsilon},$$ where
$p_n$ is the $n$th prime number. The proof combines Heath-Brown's recent work
with Harman's sieve, improving and extending his results. We give applications
of the results to prime-representing functions, binary digits of primes and
approximation of reals by multiplicative functions.


  We discuss the effect of dissipation on heating which occurs in periodically
driven quantum many body systems. We especially focus on a periodically driven
Bose-Hubbard model coupled to an energy and particle reservoir. Without
dissipation, this model is known to undergo parametric instabilities which can
be considered as an initial stage of heating. By taking the weak on-site
interaction limit as well as the weak system-reservoir coupling limit, we find
that parametric instabilities are suppressed if the dissipation is stronger
than the on-site interaction strength and stable steady states appear. Our
results demonstrate that periodically-driven systems can emit energy, which is
absorbed from external drivings, to the reservoir so that they can avoid
heating.


  We present a review of recent work on the mathematical aspects of the BCS gap
equation, covering our results of [arXiv:0801.4159] as well our recent joint
work with Hamza and Solovej [arXiv:math-ph/0703086] and with Frank and Naboko
[arXiv:0704.3564], respectively. In addition, we mention some related new
results.


  In this work, we propose a novel cross-talk rejection framework for a
multi-channel multi-talker setup for a live multiparty interactive show. Our
far-field audio setup is required to be hands-free during live interaction and
comprises four adjacent talkers with directional microphones in the same space.
Such setups often introduce heavy cross-talk between channels, resulting in
reduced automatic speech recognition (ASR) and natural language understanding
(NLU) performance. To address this problem, we propose voice activity detection
(VAD) model for all talkers using multichannel information, which is then used
to filter audio for downstream tasks. We adopt a synthetic training data
generation approach through playback and re-recording for such scenarios,
simulating challenging speech overlap conditions. We train our models on this
synthetic data and demonstrate that our approach outperforms single-channel VAD
models and energy-based multi-channel VAD algorithm in various acoustic
environments. In addition to VAD results, we also present multiparty ASR
evaluation results to highlight the impact of using our VAD model for filtering
audio in downstream tasks by significantly reducing the insertion error.


  Given a domain $\Omega$ in $\mathbb{C}^n$ and a collection of test functions
$\Psi$ on $\Omega$, we consider the complex-valued $\Psi$-Schur-Agler class
associated to the pair $(\Omega,\,\Psi)$. In this article, we characterize
interpolating sequences for the associated Banach algebra of which the
$\Psi$-Schur-Agler class is the closed unit ball. When $\Omega$ is the unit
disc $\mathbb{D}$ in the complex plane $\mathbb{C}$ and the class of test
function includes only the identity function on $\mathbb{D}$, the
aforementioned algebra is the algebra of bounded holomorphic functions on
$\mathbb{D}$ and in this case, our characterization reduces to the well known
result by Carleson. Furthermore, we present several other cases of the pair
$(\Omega,\,\Psi)$, where our main result could be applied to characterize
interpolating sequences which also show the efficacy of our main result.


  We consider path integration of a fermionic oscillator with a one-parameter
family of boundary conditions with respect to the time coordinate. The
dependence of the fermion determinant on these boundary conditions is derived
in a closed form with the help of the self-adjoint extension of differential
operators. The result reveals its crucial dependence on them, contrary to the
conventional understanding that this dependence becomes negligible over
sufficiently long time evolution. An example in which such dependence plays a
significant role is discussed in a model of supersymmetric quantum mechanics.


  In this work we establish rigorously a measurement uncertainty relation (MUR)
for three unbiased qubit observables, which was previously shown to hold true
under some presumptions. The triplet MUR states that the uncertainty, which is
quantified by the total statistic distance between the target observables and
the jointly implemented observables, is lower bounded by an incompatibility
measure that reflects the joint measurement conditions. We derive a necessary
and sufficient condition for the triplet MUR to be saturated and the
corresponding optimal measurement. To facilitate experimental tests of MURs we
propose a straightforward implementation of the optimal joint measurements. The
exact values of incompatibility measure are analytically calculated for some
symmetric triplets when the corresponding triplet MURs are not saturated. We
anticipate that our work may enrich the understanding of quantum
incompatibility in terms of MURs and inspire further applications in quantum
information science. This work presents a complete theory relevant to a
parallel work [Y.-L. Mao, et al., Testing Heisenberg's measurement uncertainty
relation of three observables, arXiv:2211.09389] on experimental tests.


  The celebrated Wiedemann-Franz (WF) law is believed to be robust in metals as
long as interactions between electrons preserve their fermion-quasiparticle
character. We study thermal transport and the fate of the WF law close to a
continuous metal-insulator transition (MIT) in the Falicov-Kimball model (FKM)
using cluster-dynamical mean-field theory (CDMFT). Surprisingly, as for
electrical transport, we find robust and novel quantum critical scaling in
thermal transport across the MIT. We unearth the deeper reasons for these novel
findings in terms of (i) the specific structure of energy-current correlations
for the FKM and (ii) the microscopic electronic processes which facil- itate
energy transport while simultaneously blocking charge transport close to the
MIT. However, within (C)DMFT, we also find that the WF law survives at T=0 in
the incoherent metal right up to the MIT, even in absence of Landau
quasiparticles.


  Two-dimensional (2D) Rashba systems have been intensively studied in the last
decade due to their unconventional physics, tunability capabilities, and
potential for spin-charge interconversion when compared to conventional heavy
metals. With the advent of a new generation of spin-based logic and memory
devices, the search for Rashba systems with more robust and larger conversion
efficiencies is expanding. Conventionally, demanding techniques such as angle-
and spin-resolved photoemission spectroscopy are required to determine the
Rashba parameter $\alpha_{R}$ that characterizes these systems. Here, we
introduce a simple method that allows a quantitative extraction of
$\alpha_{R}$, through the analysis of the bilinear response of angle-dependent
magnetotransport experiments. This method is based on the modulation of the
Rashba-split bands under a rotating in-plane magnetic field. We show that our
method is able to correctly yield the value of $\alpha_{R}$ for a wide range of
Fermi energies in the 2D electron gas at the LaAlO$_{3}$/SrTiO$_{3}$ interface.
By applying a gate voltage, we observe a maximum $\alpha_{R}$ in the region of
the band structure where interband effects maximize the Rashba effect,
consistently with theoretical predictions.


  This document describes BinCodex, a common format for the output of binary
population synthesis (BPS) codes agreed upon by the members of the LISA
Synthetic UCB Catalogue Group. The goal of the format is to provide a common
reference framework to describe the evolution of a single, isolated binary
system or a population of isolated binaries.


  Focusing inversion of potential field data for the recovery of sparse
subsurface structures from surface measurement data on a uniform grid is
discussed. For the uniform grid the model sensitivity matrices exhibit block
Toeplitz Toeplitz block structure, by blocks for each depth layer of the
subsurface. Then, through embedding in circulant matrices, all forward
operations with the sensitivity matrix, or its transpose, are realized using
the fast two dimensional Fourier transform. Simulations demonstrate that this
fast inversion algorithm can be implemented on standard desktop computers with
sufficient memory for storage of volumes up to size $n \approx 1M$. The linear
systems of equations arising in the focusing inversion algorithm are solved
using either Golub Kahan bidiagonalization or randomized singular value
decomposition algorithms in which all matrix operations with the sensitivity
matrix are implemented using the fast Fourier transform. These two algorithms
are contrasted for efficiency for large-scale problems with respect to the
sizes of the projected subspaces adopted for the solutions of the linear
systems. The presented results confirm earlier studies that the randomized
algorithms are to be preferred for the inversion of gravity data, and that it
is sufficient to use projected spaces of size approximately $m/8$, for data
sets of size $m$. In contrast, the Golub Kahan bidiagonalization leads to more
efficient implementations for the inversion of magnetic data sets, and it is
again sufficient to use projected spaces of size approximately $m/8$. Moreover,
it is sufficient to use projected spaces of size $m/20$ when $m$ is large, $m
\approx 50000$, to reconstruct volumes with $n \approx 1M$. Simulations support
the presented conclusions and are verified on the inversion of a practical
magnetic data set that is obtained over the Wuskwatim Lake region in Manitoba,
Canada.


  We present galaxy circular velocity functions (GCVFs) for 34 low redshift (z
< 0.15) clusters identified in the Sloan Digital Sky Survey (SDSS), for fifteen
clusters drawn from dark matter simulations of hierarchical structure growth in
a LambdaCDM cosmology, and for ~29,000 SDSS field galaxies. We find that the
observed and simulated cluster GCVFs take the form of a power law. The
cumulative GCVFs of the simulated clusters are very similar across a wide range
of cluster masses, provided individual subhalo circular velocities are scaled
by the circular velocities of the parent cluster. Once all sources of error are
accounted for, the intrinsic scatter in the cumulative, scaled observed cluster
GCVF is consistent with the simulations. The slope of the observed cluster GCVF
is -2.5, independent of cluster velocity dispersion. The average slope of the
simulated GCVFs is somewhat steeper, although formally consistent given the
errors. Using our highest resolution cluster, we find that the effects of
baryons on galaxy rotation curves is to flatten the simulated cluster GCVF into
better agreement with observations. Finally, we find that the field GCVF
deviates significantly from a power law, being flatter than the cluster GCVF at
circular velocities less than 200 km/s, and steeper at circular velocities
greater than 200 km/s.


  We examine AdS Galileon Lagrangians using the method of non-linear
realization. By contractions 1) flat curvature limit and 2) non-relativistic
brane algebra limit and 3) (1)+(2) limits we obtain DBI, Newton-Hoock and
Galilean Galileons respectively. We make clear how these Lagrangians appear as
invariant 4-forms and/or pseudo-invariant Wess-Zumino terms using Maurer-Cartan
equations on the coset $G/SO(3,1)$. We show the equations of motion are written
in terms of the MC forms only and explain why the inverse Higgs condition is
obtained as the equation of motion for all cases.
  The supersymmetric extension is also examined using SU(2,2|1)/(SO(3,1)x U(1))
supercoset and five WZ forms are constructed. They are reduced to the
corresponding five Galileon WZ forms in the bosonic limit and are candidates of
for supersymmetric Galileon.


  This paper reports the 3D planetary geostrophic viscous model has the
exponential ergodicity and global attractor if this model is driven by an
additive random noise, which results in the support of the integration of
invariant measure for the dynamic is exactly a minimal global attractor. It's
worth mentioning that this global attractor has finite Hausdorff dimension.


  We study the lifetime of single-particle excitations in a dilute homogeneous
Bose-Einstein condensate at zero temperature based on a self-consistent
perturbation expansion of satisfying Goldstone's theorem and conservation laws
simultaneously.It is shown that every excitation for each momentum ${\bf p}$
should have a finite lifetime proportional to the inverse $a^{-1}$ of the
$s$-wave scattering length $a$, instead of $a^{-2}$ for the normal state, due
to a new class of Feynman diagrams for the self-energy that emerges upon
condensation. We calculate the lifetime as a function of $|{\bf p}|$
approximately.


  A number of recent applications of jet substructure, in particular searches
for light new particles, require substructure observables that are decorrelated
with the jet mass. In this paper we introduce the Convolved SubStructure (CSS)
approach, which uses a theoretical understanding of the observable to
decorrelate the complete shape of its distribution. This decorrelation is
performed by convolution with a shape function whose parameters and mass
dependence are derived analytically. We consider in detail the case of the
$D_2$ observable and perform an illustrative case study using a search for a
light hadronically decaying $Z'$. We find that the CSS approach completely
decorrelates the $D_2$ observable over a wide range of masses. Our approach
highlights the importance of improving the theoretical understanding of jet
substructure observables to exploit increasingly subtle features for
performance.


  We study the problem of detecting zeros of continuous functions that are
known only up to an error bound, extending the earlier theoretical work with
explicit algorithms and experiments with an implementation. More formally, the
robustness of zero of a continuous map $f: X\to \mathbb{R}^n$ is the maximal
$r>0$ such that each $g:X\to\mathbb{R}^n$ with $\|f-g\|_\infty\le r$ has a
zero. We develop and implement an efficient algorithm approximating the
robustness of zero. Further, we show how to use the algorithm for approximating
worst-case optima in optimization problems in which the feasible domain is
defined by equations that are only known approximately.
  An important ingredient is an algorithm for deciding the topological
extension problem based on computing cohomological obstructions to
extendability and their persistence. We describe an explicit algorithm for the
primary and secondary obstruction, two stages of a sequence of algorithms with
increasing complexity. We provide experimental evidence that for random
Gaussian fields, the primary obstruction---a much less computationally
demanding test than the secondary obstruction---is typically sufficient for
approximating robustness of zero.


  In this paper, we point out that the method used in [Acta Arith. 128(2007)
245-279] can be modified slightly to obtain the following result. Let
$\varepsilon \in (0,\frac 14)$ and $c>0$, and let $p$ be a sufficiently large
prime depending on $\varepsilon$ and $c$. Then every zero-sumfree sequence $S$
over $C_p\oplus C_p$ of length $|S|\geq 2p-c\sqrt{p}$ contains some element at
least $\lfloor p^{\frac14-\varepsilon}\rfloor$ times.


  Here, we develop an audiovisual deep residual network for multimodal apparent
personality trait recognition. The network is trained end-to-end for predicting
the Big Five personality traits of people from their videos. That is, the
network does not require any feature engineering or visual analysis such as
face detection, face landmark alignment or facial expression recognition.
Recently, the network won the third place in the ChaLearn First Impressions
Challenge with a test accuracy of 0.9109.


  We give a proof of Mukai's Theorem on the existence of certain exceptional
vector bundles on prime Fano threefolds. To our knowledge this is the first
complete proof in the literature. The result is essential for Mukai's biregular
classification of prime Fano threefolds, and for the existence of
semiorthogonal decompositions in their derived categories.
  Our approach is based on Lazarsfeld's construction that produces vector
bundles on a variety from globally generated line bundles on a divisor, on
Mukai's theory of stable vector bundles on K3 surfaces, and on Brill--Noether
properties of curves and (in the sense of Mukai) of K3 surfaces.


  Countries, companies, and universities are increasingly competing over
top-tier artificial intelligence (AI) researchers. Where are these researchers
likely to immigrate and what affects their immigration decisions? We conducted
a survey $(n = 524)$ of the immigration preferences and motivations of
researchers that had papers accepted at one of two prestigious AI conferences:
the Conference on Neural Information Processing Systems (NeurIPS) and the
International Conference on Machine Learning (ICML). We find that the U.S. is
the most popular destination for AI researchers, followed by the U.K., Canada,
Switzerland, and France. A country's professional opportunities stood out as
the most common factor that influences immigration decisions of AI researchers,
followed by lifestyle and culture, the political climate, and personal
relations. The destination country's immigration policies were important to
just under half of the researchers surveyed, while around a quarter noted
current immigration difficulties to be a deciding factor. Visa and immigration
difficulties were perceived to be a particular impediment to conducting AI
research in the U.S., the U.K., and Canada. Implications of the findings for
the future of AI talent policies and governance are discussed.


  Conditional evolution is crucial for generating non-Gaussian resources for
quantum information tasks in the continuous variable scenario. However, tools
are lacking for a convenient representation of heralded process in terms of
quantum maps for continuous variable states, in the same way as Wigner
functions are able to give a compact description of the quantum state. Here we
propose and study such a representation, based on the introduction of a
suitable transfer function to describe the action of a quantum operation on the
Wigner function. We also reconstruct the maps of two relevant examples of
conditional process, that is, noiseless amplification and photon addition, by
combining experimental data and a detailed physical model. This analysis allows
to fully characterize the effect of experimental imperfections in their
implementations.


  Let p > 2 be prime. We use purely local methods to determine the possible
reductions of certain two-dimensional crystalline representations, which we
call "pseudo-Barsotti-Tate representations", over arbitrary finite extensions
of the p-adics. As a consequence, we establish (under the usual Taylor-Wiles
hypothesis) the weight part of Serre's conjecture for GL(2) over arbitrary
totally real fields.


  We consider the solution of the subset sum problem based on a parallel
computer consisting of self-propelled biological agents moving in a
nanostructured network that encodes the NP-complete task in its geometry. We
develop an approximate analytical method to analyze the effects of small errors
in the nonideal junctions composing the computing network by using a Gaussian
confidence interval approximation of the multinomial distribution. We
concretely evaluate the probability distribution for error-induced paths and
determine the minimal number of agents required to obtain a proper solution. We
finally validate our theoretical results with exact numerical simulations of
the subset sum problem for different set sizes and error probabilities.


  We show that the conductance ($G$) and transition voltage ($V_t$)
spectroscopy data for standard vacuum nanogaps reported in the preceding paper
by Sotthewes \emph{et al} cannot be understood within the framework of the
existing theories/models whatever realistic effects are incorporates. However,
if we include an additional ("ghost") current, the trends of the dependencies
of $G$ and $V_t$ on the nanogap size $d$ can be explained. The ghost current is
very small. Therefore, effects related to the ghost current can only be
revealed at larger $d$'s, where the ghost current becomes comparable to or
overcomes the tunneling current. Although we are not able to unravel the origin
of this ghost contribution, we can and do refer to experimental $G$- and
$V_t$-data reported for molecular junctions, which exhibit similarities to the
presently considered vacuum nanojunctions. Analyzing notable difficulties
related to the regime assigned as transport via hopping, we speculate that a
ghost contribution may also exist in molecular junctions.


  We present resistivity, magnetization, and zero field muon spin relaxation
($\mu$SR) data for the pyrochlore iridate materials
Nd$_{2-x}$Ca$_{x}$Ir$_{2}$O$_{7}$ ($x = 0, 0.06$, and $0.10$) and
Sm$_2$Ir$_2$O$_7$. While Nd$_{2}$Ir$_{2}$O$_{7}$ (Nd227) is weakly conducting,
Sm$_{2}$Ir$_{2}$O$_{7}$ (Sm227) has slowly diverging resistivity at low
temperature. Nd227 and Sm227 exhibit magnetic anomalies at $T_{M} = 105 K$ and
$137 K$, respectively. However, zero-field $\mu$SR measurements show that
long-range magnetic order of the Ir$^{4+}$ sublattice sets in at much lower
temperatures ($T_{LRO} \sim 8 K$ for Nd227 and $70 K$ for Sm227); both
materials show heavily damped muon precession with a characteristic frequency
near 9 MHz. The magnetic anomaly at $T_{M}$ in Nd227 is not significantly
affected by the introduction of hole carriers by Ca-substitution in the
conducting Nd$_{2-x}$Ca$_{x}$Ir$_{2}$O$_{7}$ samples, but the muon precession
is fully suppressed for both.


  We consider the problem of deducing the duality relation from the extended
double shuffle relation for multiple zeta values. Especially we prove that the
duality relation for double zeta values and that for the sum of multiple zeta
values whose first components are 2's are deduced from the derivation relation,
which is known as a subclass of the extended double shuffle relation.


  The signal registered by a plane photodetector placed behind an optically
inhomogeneous object irradiated by two coherent Gaussian beams intersecting
inside the object at small angle to each other is calculated in the
single-scattering approximation. In the considered arrangement, only one of the
beams hits the detector and serves as local oscillator for heterodyning the
field scattered by the other beam (not hitting the detector). The results of
analytical calculation show that the signal detected in this way is contributed
only by the region of the inhomogeneous object where the two beams overlap. By
moving the scatterer with respect to the overlap region and monitoring the
heterodyned signal, with the aid of the derived expression, one can reconstruct
the refractive-index relief of the scatterer. We also propose a simple method
of spatial mapping of the sample that allows one to estimate the magnitude and
characteristic dimensions of the inhomogeneities.


  In enterprise data pipelines, data insertions occur periodically and may
impact downstream services if data quality issues are not addressed. Typically,
such problems can be investigated and fixed by on-call engineers, but locating
the cause of such problems and fixing errors are often time-consuming.
Therefore, automatic data validation is a better solution to defend the system
and downstream services by enabling early detection of errors and providing
detailed error messages for quick resolution. This paper proposes a
self-validate data management system with automatic pattern discovery
techniques to verify the correctness of semi-structural string data in
enterprise data pipelines. Our solution extracts patterns from historical data
and detects erroneous incoming data in a top-down fashion. High-level
information of historical data is analyzed to discover the format skeleton of
correct values. Fine-grained semantic patterns are then extracted to strike a
balance between generalization and specification of the discovered pattern,
thus covering as many correct values as possible while avoiding over-fitting.
To tackle cold start and rapid data growth, we propose an incremental update
strategy and example generalization strategy. Experiments on large-scale
industrial and public datasets demonstrate the effectiveness and efficiency of
our method compared to alternative solutions. Furthermore, a case study on an
industrial platform (Ant Group Inc.) with thousands of applications shows that
our system captures meaningful data patterns in daily operations and helps
engineers quickly identify errors.


  We study the direct enstrophy cascade in a two-dimensional flow generated in
an electromagnetically driven thin layer of fluid. Due to the presence of
bottom friction, the energy spectrum deviates from the classical Kraichnan
prediction $k^{-3}$. We find that the correction to the spectral slope depends
on the thickness on the layer, in agreement with a theoretical prediction based
on the analogy with passive scalar statistics.


  We introduce the notion of a weighted lift zonoid and show that, for properly
chosen weights v, the ordering condition on a measure \mu, formulated in terms
of the weighted lift zonoids of this measure, leads to certain functional
inequalities for this measure, such as non-linear extensions of Bobkov's shift
inequality and weighted inverse log-Sobolev inequality. The choice of the
weight K, involved in our version of the inverse log-Sobolev inequality,
differs substantially from those available in the literature, and requires the
weight v, involved into the definition of the weighted lift zonoid, to equal
the divergence of the weight K w.r.t. initial measure \mu. We observe that such
a choice may be useful for proving direct log-Sobolev inequality, as well,
either in its weighted or classical forms.


  Here we systematically explore the use of pulsed laser deposition technique
(PLD) to grow three basic oxides that have rocksalt structure but different
chemical stability in the ambient atmosphere: NiO (stable), MnO (metastable)
and EuO (unstable). By tuning laser fluence, an epitaxial single-phase nickel
oxide thin-film growth can be achieved in a wide range of temperatures from 10
to 750 {\deg}C. At the lowest growth temperature, the out-of-plane strain
raises to 1.5%, which is five times bigger than that in a NiO film grown at 750
{\deg}C. MnO thin films that had long-range ordered were successfully deposited
on the MgO substrates after appropriate tuning of deposition parameters. The
growth of MnO phase was strongly influenced by substrate temperature and laser
fluence. EuO films with satisfactory quality were deposited by PLD after oxygen
availability had been minimized. Synthesis of EuO thin films at rather low
growth temperature prevented thermally-driven lattice relaxation and allowed
growth of strained films. Overall, PLD was a quick and reliable method to grow
binary oxides with rocksalt structure in high quality that can satisfy
requirements for applications and for basic research.


  The quasi-particle tunneling phenomena in the paired fractional quantum Hall
states are studied. A single point-contact system is first considered. Because
of relevancy of the quasi-particle tunneling term, the strong tunneling regime
should be investigated.
  Using the instanton method it is shown that the strong quasi-particle
tunneling regime is described as the weak electron tunneling regime
effectively.
  Expanding to the network model the paired quantum Hall liquid to insulator
transition is discussed.


  The detection of microlensing events from stars in the Large Magellanic Cloud
and in the Galactic bulge raise important constraints on the distribution of
dark matter and on galactic structure, although some events may be due to a new
type of intrinsic variability. When lenses are relatively close to the sources,
we predict that chromatic and spectroscopic effects are likely to appear for a
significant fraction of the microlensing events. These effects are due to the
differential amplification of the limb and the centre of the stellar disc, and
present a systematic dependence with wavelength and time that provide an
unambiguous signature of a microlensing event (as opposed to a new type of
intrinsic stellar variability). In addition, their measure would provide a
direct constraint on stellar atmospheres, allowing a 3-dimensional
reconstruction or imaging of its structure, a unique tool to test the current
models of stellar atmospheres.


  Recently there is huge interest in graph theory and intensive study on
computing integer powers of matrices. In this paper, we investigate
relationships between one type of graph and well-known Fibonacci sequence. In
this content, we consider the adjacency matrix of one type of graph with 2k
(k=1,2,...) vertices. It is also known that for any positive integer r, the
(i,j)th entry of A^{r} (A is the adjacency matrix of the graph) is just the
number of walks from vertex i to vertex j, that use exactly k edges.


  We analyze the emission of single GaN nanowires with (In,Ga)N insertions
using both micro-photoluminescence and cathodoluminescence spectroscopy. The
emission spectra are dominated by a green luminescence band that is strongly
blueshifted with increasing excitation density. In conjunction with
finite-element simulations of the structure to obtain the piezoelectric
polarization, these results demonstrate that our (In,Ga)N/GaN nanowire
heterostructures are subject to the quantum-confined Stark effect. Additional
sharp peaks in the spectra, which do not shift with excitation density, are
attributed to emission from localized states created by compositional
fluctuations in the ternary (In,Ga)N alloy.


  A beautiful theorem of Zeckendorf states that every integer can be written
uniquely as a sum of non-consecutive Fibonacci numbers
$\{F_n\}_{n=1}^{\infty}$. Lekkerkerker proved that the average number of
summands for integers in $[F_n, F_{n+1})$ is $n/(\phi^2 + 1)$, with $\phi$ the
golden mean. This has been generalized to the following: given nonnegative
integers $c_1,c_2,...,c_L$ with $c_1,c_L>0$ and recursive sequence
$\{H_n\}_{n=1}^{\infty}$ with $H_1=1$, $H_{n+1}
=c_1H_n+c_2H_{n-1}+...+c_nH_1+1$ $(1\le n< L)$ and
$H_{n+1}=c_1H_n+c_2H_{n-1}+...+c_LH_{n+1-L}$ $(n\geq L)$, every positive
integer can be written uniquely as $\sum a_iH_i$ under natural constraints on
the $a_i$'s, the mean and the variance of the numbers of summands for integers
in $[H_{n}, H_{n+1})$ are of size $n$, and the distribution of the numbers of
summands converges to a Gaussian as $n$ goes to the infinity. Previous
approaches used number theory or ergodic theory. We convert the problem to a
combinatorial one. In addition to re-deriving these results, our method
generalizes to a multitude of other problems (in the sequel paper \cite{BM} we
show how this perspective allows us to determine the distribution of gaps
between summands in decompositions). For example, it is known that every
integer can be written uniquely as a sum of the $\pm F_n$'s, such that every
two terms of the same (opposite) sign differ in index by at least 4 (3). The
presence of negative summands introduces complications and features not seen in
previous problems. We prove that the distribution of the numbers of positive
and negative summands converges to a bivariate normal with computable, negative
correlation, namely $-(21-2\phi)/(29+2\phi) \approx -0.551058$.


  Classical geostatistical methods face serious computational challenges if
they are confronted with large sets of spatially distributed data. We present a
simplified stochastic local interaction (SLI) model for computationally
efficient spatial prediction that can handle large data. The SLI method
constructs a spatial interaction matrix (precision matrix) that accounts for
the data values, their locations, and the sampling density variations without
user input. We show that this precision matrix is strictly positive definite.
The SLI approach does not require matrix inversion for parameter estimation,
spatial prediction, and uncertainty estimation, leading to computational
procedures that are significantly less intensive computationally than kriging.
The precision matrix involves compact kernel functions (spherical, quadratic,
etc.) which enable the application of sparse matrix methods, thus improving
computational time and memory requirements. We investigate the proposed SLI
method with a data set that includes approximately 11500 drill-hole data of
coal thickness from Campbell County (Wyoming, USA). We also compare SLI with
ordinary kriging (OK) in terms of estimation performance, using cross
validation analysis, and computational time. According to the validation
measures used, SLI performs slightly better in estimating seam thickness than
OK in terms of cross-validation measures. In terms of computation time, SLI
prediction is 3 to 25 times (depending on the size of the kriging neighborhood)
faster than OK for the same grid size.


  As scientific applications extend to the simulation of more and more complex
systems, they involve an increasing number of abstraction levels, at each of
which errors can emerge and across which they can propagate; tools for
correctness evaluation and enforcement at every level (from the code level to
the application level) are therefore necessary. Whilst code-level debugging
tools are already a well established standard, application-level tools are
lagging behind, possibly due to their stronger dependence on the application's
details. In this paper, we describe the programming model introduced by the
Cactus framework, review the High Performance Computing (HPC) challenges that
Cactus is designed to address, and illustrate the correctness strategies that
are currently available in Cactus at the code, component, and application
level.


  Deep neural networks are susceptible to adversarial attacks, which can
compromise their performance and accuracy. Adversarial Training (AT) has
emerged as a popular approach for protecting neural networks against such
attacks. However, a key challenge of AT is robust overfitting, where the
network's robust performance on test data deteriorates with further training,
thus hindering generalization. Motivated by the concept of active forgetting in
the brain, we introduce a novel learning paradigm called "Forget to Mitigate
Overfitting (FOMO)". FOMO alternates between the forgetting phase, which
randomly forgets a subset of weights and regulates the model's information
through weight reinitialization, and the relearning phase, which emphasizes
learning generalizable features. Our experiments on benchmark datasets and
adversarial attacks show that FOMO alleviates robust overfitting by
significantly reducing the gap between the best and last robust test accuracy
while improving the state-of-the-art robustness. Furthermore, FOMO provides a
better trade-off between standard and robust accuracy, outperforming baseline
adversarial methods. Finally, our framework is robust to AutoAttacks and
increases generalization in many real-world scenarios.


  Inelastic neutron scattering provides a probe for studying the spin and
momentum structure of the superconducting gap. Here, using a two-orbital model
for the Fe-pnicitide superconductors and an RPA-BCS approximation for the
dynamic spin susceptibility, we explore the scattering response for various
gaps that have been proposed.


  This paper describes the design principles behind jsRealB (Version 4.0), a
surface realizer written JavaScript for English or French sentences from a
specification inspired by the constituent syntax formalism but for which a
dependency-based input notation is also available. jsRealB can be used either
within a web page or as a node.js module. We show that the seemingly simple
process of text realization involves many interesting implementation challenges
in order to take into account the specifics of each language. jsRealB has a
large coverage of English and French and has been used to develop realistic
data-to-text applications and to reproduce existing literary texts and
sentences from Universal Dependency annotations. Its source code and that of
its applications are available on GitHub. The port of this approach to Python
(pyrealb) is also presented.


  We show that weak solutions of degenerate Navier-Stokes equations converge to
the strong solutions of the pressureless Euler system with linear drag term,
Newtonian repulsion and quadratic confinement. The proof is based on the
relative entropy method using the artificial velocity formulation for the
one-dimensional Navier-Stokes system.


  Human motion is fundamental to understanding behavior. Despite progress on
single-image 3D pose and shape estimation, existing video-based
state-of-the-art methods fail to produce accurate and natural motion sequences
due to a lack of ground-truth 3D motion data for training. To address this
problem, we propose Video Inference for Body Pose and Shape Estimation (VIBE),
which makes use of an existing large-scale motion capture dataset (AMASS)
together with unpaired, in-the-wild, 2D keypoint annotations. Our key novelty
is an adversarial learning framework that leverages AMASS to discriminate
between real human motions and those produced by our temporal pose and shape
regression networks. We define a temporal network architecture and show that
adversarial training, at the sequence level, produces kinematically plausible
motion sequences without in-the-wild ground-truth 3D labels. We perform
extensive experimentation to analyze the importance of motion and demonstrate
the effectiveness of VIBE on challenging 3D pose estimation datasets, achieving
state-of-the-art performance. Code and pretrained models are available at
https://github.com/mkocabas/VIBE.


  We present a Pfaffian formula to calculate matrix elements of three-body
operators in symmetry-restoration beyond-mean-field methods, including the case
of multiple quasi-particle configurations. Detailed derivation based on
[Mizusaki et al., Phys. Lett. B 715, 219 (2012)] and [Hu et al., Phys. Lett. B
734, 162 (2014)] is provided, and potential applications in generator
coordinate method with chiral interactions, as well as in study of nuclear
matrix elements in neutrinoless double beta decay are discussed.


  Explanations are crucial for improving users' transparency, persuasiveness,
engagement, and trust in Recommender Systems (RSs). However, evaluating the
effectiveness of explanation algorithms regarding those goals remains
challenging due to existing offline metrics' limitations. This paper introduces
new metrics for the evaluation and validation of explanation algorithms based
on the items and properties used to form the sentence of an explanation.
Towards validating the metrics, the results of three state-of-the-art post-hoc
explanation algorithms were evaluated for six RSs, comparing the offline
metrics results with those of an online user study. The findings show the
proposed offline metrics can effectively measure the performance of explanation
algorithms and highlight a trade-off between the goals of transparency and
trust, which are related to popular properties, and the goals of engagement and
persuasiveness, which are associated with the diversification of properties
displayed to users. Furthermore, the study contributes to the development of
more robust evaluation methods for explanation algorithms in RSs.


  Particle physics or High Energy Physics (HEP) studies the elementary
constituents of matter and their interactions with each other. Machine Learning
(ML) has played an important role in HEP analysis and has proven extremely
successful in this area. Usually, the ML algorithms are trained on numerical
simulations of the experimental setup and then applied to the real experimental
data. However, any discrepancy between the simulation and real data may lead to
dramatic consequences concerning the performances of the algorithm on real
data. In this paper, we present an application of domain adaptation using a
Domain Adversarial Neural Network trained on public HEP data. We demonstrate
the success of this approach to achieve sim-to-real transfer and ensure the
consistency of the ML algorithms performances on real and simulated HEP
datasets.


  This fluid dynamics video presents the gravity-driven jets induced by the
relaxation of large prolate bubbles or large holes at a free liquid surface. It
shows that the jet developing inside a bubble can be strong enough to give rise
to liquid projections shooting out way above the free surface.


  In this manuscript, recent theoretical investigations by the authors in the
area of oxide multilayers are briefly reviewed. The calculations were carried
out using model Hamiltonians and a variety of non-perturbative techniques.
Moreover, new results are also included here. They correspond to the generation
of a metallic state by mixing insulators in a multilayer geometry, using the
Hubbard and Double Exchange models. For the latter, the resulting metallic
state is also ferromagnetic. This illustrates how electron or hole doping via
transfer of charge in multilayers can lead to the study of phase diagrams of
transition metal oxides in the clean limit. Currently, these phase diagrams are
much affected by the disordering standard chemical doping procedure, which
introduces quenched disorder in the material.


  This paper proposes a knowledge-based legal document assembly method that
uses a machine-readable representation of knowledge of legal professionals.
This knowledgebase has two components - the formal knowledge of legal norms
represented as a rule-base and the tacit knowledge represented by a document
template. A document assembly system is developed as a proof of concept. It
collects input data in the form of an interactive interview, performs legal
reasoning over input data, and generates the output document. The system also
creates an argument graph as an explanation of the reasoning process providing
the user with an interpretation of how the input data and the rule-base
influence the content of the output document. The system also semantically
marks up data in the output document, facilitating its further processing and
providing support to the interoperability of information systems in the legal
domain.


  This paper is devoted to the specific class of pseudoconformal mappings of
quaternion and octonion variables. Normal families of functions are defined and
investigated. Four criteria of a family being normal are proven. Then groups of
pseudoconformal diffeomorphisms of quaternion and octonion manifolds are
investigated. It is proven, that they are finite dimensional Lie groups for
compact manifolds. Their examples are given. Many charactersitic features are
found in comparison with commutative geometry over $\bf R$ or $\bf C$.


  Despite intensive research, the mechanisms underlying how neurons encode
external inputs remain poorly understood. Recent work has focused on the
response of a single neuron to a weak, subthreshold periodic signal. By
simulating the FitzHugh-Nagumo stochastic model and then using a symbolic
method to analyze the firing activity of the neuron, preferred and infrequent
spike patterns (defined by the relative timing of the spikes) were detected,
whose probabilities encode information about the signal. As not individual
neurons in isolation but neuronal populations are responsible for the emergence
of complex behaviors, a relevant question is whether this coding mechanism is
robust when the neuron is not isolated. We study how a second neuron, which
does not perceive the subthreshold signal, affects the detection and the
encoding of the signal, done by the first neuron. Through simulations of two
coupled FitzHugh-Nagumo neurons we show that the coding mechanism is indeed
robust, as the neuron that perceives the signal fires a spike train that has
symbolic patterns whose probabilities depend on the features of the signal.
Moreover, we show that the second neuron facilitates the detection of the
signal, by lowering the firing threshold of the first neuron. This in turn
decreases the internal noise level need to fire the spikes that encode the
signal. We also show that the probabilities of the symbolic patterns achieve
maximum or minimum values when the period of the external signal is close to
(or is half of) the mean firing period of the neuron.


  Infrared and visible image fusion plays a vital role in the field of computer
vision. Previous approaches make efforts to design various fusion rules in the
loss functions. However, these experimental designed fusion rules make the
methods more and more complex. Besides, most of them only focus on boosting the
visual effects, thus showing unsatisfactory performance for the follow-up
high-level vision tasks. To address these challenges, in this letter, we
develop a semantic-level fusion network to sufficiently utilize the semantic
guidance, emancipating the experimental designed fusion rules. In addition, to
achieve a better semantic understanding of the feature fusion process, a fusion
block based on the transformer is presented in a multi-scale manner. Moreover,
we devise a regularization loss function, together with a training strategy, to
fully use semantic guidance from the high-level vision tasks. Compared with
state-of-the-art methods, our method does not depend on the hand-crafted fusion
loss function. Still, it achieves superior performance on visual quality along
with the follow-up high-level vision tasks.


  The dephasing mechanisms of p-shell and s-shell excitons in an InAs
self-assembled quantum dot ensemble are examined using two-dimensional coherent
spectroscopy (2DCS). 2DCS provides a comprehensive picture of how the energy
level structure of dots affects the exciton dephasing rates and recombination
lifetimes. We find that at low temperatures, dephasing of s-shell excitons is
lifetime limited, whereas p-shell excitons exhibit significant pure dephasing
due to scattering between degenerate spin states. At elevated temperatures,
quadratic exciton-phonon coupling plays an important role in both s-shell and
p-shell exciton dephasing. We show that multiple p-shell states are also
responsible for stronger phonon dephasing for these transitions


  Let $\varphi(\tau)=\eta((\tau+1)/2)^2/\sqrt{2\pi}e^\frac{\pi
i}{4}\eta(\tau+1)$ where $\eta(\tau)$ is the Dedekind eta-function. We show
that if $\tau_0$ is an imaginary quadratic number with $\mathrm{Im}(\tau_0)>0$
and $m$ is an odd integer, then $\sqrt{m}\varphi(m\tau_0)/\varphi(\tau_0)$ is
an algebraic integer dividing $\sqrt{m}$. This is a generalization of Theorem
4.4 given in [B. C. Berndt, H. H. Chan and L. C. Zhang, Ramanujan's remarkable
product of theta-functions, Proc. Edinburgh Math. Soc. (2) 40 (1997), no. 3,
583-612]. On the other hand, let $K$ be an imaginary quadratic field and
$\theta_K$ be an element of $K$ with $\mathrm{Im}(\theta_K)>0$ which generators
the ring of integers of $K$ over $\mathbb{Z}$. We develop a sufficient
condition of $m$ for $\sqrt{m}\varphi(m\theta_K)/\varphi(\theta_K)$ to become a
unit.


  Refractory producers face many challenges in terms of producing
MgO-containing castables due to the high likelihood of magnesia to hydrate in
contact with water, resulting in Mg(OH)2 generation. The expansive feature of
this transformation affects the performance of such refractories, as (i) if
this hydrated phase is not accommodated in the formed microstructure, ceramic
linings with cracks and low green mechanical strength will be obtained; and
(ii) if crack-free pieces are prepared, they should present low porosity and
reduced permeability, which require special attention when heating these
materials. This work investigated the ability of various additives in the
optimization of the drying behavior of Al2O3-MgO castables. Vibratable
compositions were tested after incorporating polymeric fibers (PF), an organic
salt (OAS), SiO2-based additive (SM) or permeability enhancing active compound
(MP) into the dry-mixtures. Various experimental measurements were performed to
infer the role of the drying agents to prevent the samples explosion and
whether they would also influence other properties of the castables. As
observed, OAS and MP helped to inhibit the MgO-bonded samples explosion even
under severe heating conditions (2-20C/min) and increased their green
mechanical strength and slag infiltration resistance when compared to the
additive-free composition. On the other hand, the addition of polymeric fibers
(PF) or silica-based compound (SM) to the formulations was not able to prevent
the castables explosion when using a high heating rate and other side effects
could also be observed when testing these materials. Thus, the selection of
suitable drying agents is a key issue, as they may allow the development of
MgO-bonded castables with enhanced properties and lower spalling risk during
their first thermal treatment.


  In this paper, we extend past work done on the application of the mathematics
of category theory to quantum information science. Specifically, we present a
realization of a dagger-compact category that can model finite-dimensional
quantum systems and explicitly allows for the interaction of systems of
arbitrary, possibly unequal, dimensions. Hence our framework can handle generic
tensor network states, including matrix product states. Our categorical model
subsumes the traditional quantum circuit model while remaining directly and
easily applicable to problems stated in the language of quantum information
science. The circuit diagrams themselves now become morphisms in a category,
making quantum circuits a special case of a much more general mathematical
framework. We introduce the key algebraic properties of our tensor calculus
diagrammatically and show how they can be applied to solve problems in the
field of quantum information.


  The first evidence of transverse oscillations of a multistranded loop with
growing amplitudes and internal coupling observed by the Atomspheric Imaging
Assembly (AIA) onboard the Solar Dynamics Observatory (SDO) is presented. The
loop oscillation event occurred on 2011 March 8, triggered by a CME. The
multiwavelength analysis reveals the presence of multithermal strands in the
oscillating loop, whose dynamic behaviors are temperature-dependent, showing
differences in their oscillation amplitudes, phases and emission evolution. The
physical parameters of growing oscillations of two strands in 171 A are
measured and the 3-D loop geometry is determined using STEREO-A/EUVI data.
These strands have very similar frequencies, and between two 193 A strands a
quarter-period phase delay sets up. These features suggest the coupling between
kink oscillations of neighboring strands and the interpretation by the
collective kink mode as predicted by some models. However, the temperature
dependence of the multistarnded loop oscillations was not studied previously
and needs further investigation. The transverse loop oscillations are
associated with intensity and loop width variations. We suggest that the
amplitude-growing kink oscillations may be a result of continuous non-periodic
driving by magnetic deformation of the CME, which deposits energy into the loop
system at a rate faster than its loss.


  We study differential calculus on h-deformed bosonic and fermionic quantum
space. It is shown that the fermionic quantum space involves a parafermionic
variable as well as a classical fermionic one. Further we construct the
classical $su(2)$ algebra on the fermionic quantum space and discuss a mapping
between the classical $su(2)$ and the h-deformed $su(2)$ algebras.


  In this paper, we revisit the problem of the determination of the frequency
of occurrence of galactic nova outbursts which involve an oxygen-neon (ONe)
white dwarf. The improvement with respect to previous work on the subject
derives from the fact that we use the results that our evolutionary
calculations provide for the final mass and for the chemical profiles of
intermediate-to-massive primary components of close binary systems. In
particular, the final evolutionary stages, such as the carbon burning phase,
have been carefully followed for the whole range of masses of interest. The
chemical profiles obtained with our evolutionary code are of interest in
determining the chemical composition of the ejecta after being processed
through the thermonuclear runaway, although such other factors as the
efficiency of the mixing between the accreted material and that of the
underlying white dwarf must also be considered. In our calculations of the
frequency of occurrence of nova outbursts involving an ONe white dwarf, we also
take into account the observational selection effects introduced by the
different recurrence times of the outbursts and by the spatial distribution of
novae. In spite of the very different evolutionary sequences, we find that
approximately 1/3 of the novae observed in outburst should involve an
oxygen-neon white dwarf, in agreement with previous theoretical estimates.


  The phenomenon of residential segregation was captured by Schelling's famous
segregation model where two types of agents are placed on a grid and an agent
is content with her location if the fraction of her neighbors which have the
same type as her is at least $\tau$, for some $0<\tau<1$. Discontent agents
simply swap their location with a randomly chosen other discontent agent or
jump to a random empty cell.
  We analyze a generalized game-theoretic model of Schelling segregation which
allows more than two agent types and more general underlying graphs modeling
the residential area. For this we show that both aspects heavily influence the
dynamic properties and the tractability of finding an optimal placement. We map
the boundary of when improving response dynamics (IRD), i.e., the natural
approach for finding equilibrium states, are guaranteed to converge. For this
we prove several sharp threshold results where guaranteed IRD convergence
suddenly turns into the strongest possible non-convergence result: a violation
of weak acyclicity. In particular, we show such threshold results also for
Schelling's original model, which is in contrast to the standard assumption in
many empirical papers. Furthermore, we show that in case of convergence, IRD
find an equilibrium in $\mathcal{O}(m)$ steps, where $m$ is the number of edges
in the underlying graph and show that this bound is met in empirical
simulations starting from random initial agent placements.


  Given $d\geq 2$, we show that the number of approximates
$\frac{1}{q}\mathbf{p}\in \mathbb{Q}^d$ of $\mathbf{x}\in\mathbb{R}^d$
satisfying $|q\mathbf{x}-\mathbf{p}|\leq cq^{-\frac{1}{d}}$ with denominator
$1\leq q < T$ decays to the asymptotic term $c\text{vol}_d(B_d(0,1))\log T$
with error of order $\left(\log T\right)^{-\frac{1}{2}}\left(\log \log
T\right)^\frac{3}{2}\left(\log\log\log T\right)^{\frac{1}{2}+\epsilon}$ for
almost all $\mathbf{x}\in\mathbb{R}^d$ and for any $\epsilon >0$. Results with
the same order are proven for primitive lattice approximates for all $d\geq 1$
and also for the case of linear forms and affine lattices. These results,
especially in the primitive case for $d=1$, are an improvement to the results
of Schmidt.


  In this note, we prove that the one-loop pfaffian of the non-perturbative
superpotential generated by Euclidean D-branes in type II compactifications on
orientifolds of Calabi-Yau threefolds is determined by the moduli integral of
the new supersymmetric index defined by Cecotti, Fendley, Intriligator, and
Vafa. As this quantity can be computed via topological string theory,
Chern-Simons theory, matrix models, or by solving the holomorphic anomaly
equation, this result provides a method to directly compute the one-loop
pfaffian of the non-perturbative superpotential. The relation between the
one-loop pfaffian, threshold corrections to the gauge coupling, and the
one-loop partition function of open topological string theory is discussed.


  We study the synchronization of a superconducting qubit to an external
optical field via a mechanical resonator in a hybrid optoelectromechanical
system. The quantum trajectory method is employed to investigate
synchronization. The bistability in one of the qubit polarization vectors,
where the qubit rotates about the polarization vector, is observed for a single
quantum trajectory run. The rotation in one of the stable states is synced with
the external optical drive. When the number of trajectories is significantly
increased, the qubit no longer displays bistability. However, synchronization
with less quantum fluctuations is still observed. The scheme could be used to
transfer the phase of the microwave qubit's rotation to a long-lived optical
photon through synchronization, which may find applications in long-distance
quantum communication. Also, this hybrid system can be used to study quantum
synchronization.


  Maximum pseudolikelihood method has been among the most important methods for
learning parameters of statistical physics models, such as Ising models. In
this paper, we study how pseudolikelihood can be derived for learning
parameters of a mixture of Ising models. The performance of the proposed
approach is demonstrated for Ising and Potts models on both synthetic and real
data.


  We present a novel selection algorithm for N-2 contingency analysis problem.
The algorithm is based on the iterative bounding of line outage distribution
factors and successive pruning of the set of contingency pair candidates. The
selection procedure is non-heuristic, and is certified to identify all events
that lead to thermal constraints violations in DC approximation. The complexity
of the algorithm is O(N^2) comparable to the complexity of N-1 contingency
problem. We validate and test the algorithm on the Polish grid network with
around 3000 lines. For this test case two iterations of the pruning procedure
reduce the total number of candidate pairs by a factor of almost 1000 from 5
millions line pairs to only 6128.


  Gravitational lensing effect is one of most significant observational probes
to investigate compact dark matter/objects over a wide mass range. In this
work, we first propose to derive the population information and the abundance
of supermassive compact dark matter in the mass range $\sim10^5-10^7~M_{\odot}$
from 6 millilensed gamma-ray burst (GRB) candidates in 3000 Fermi GRB events
using the hierarchical Bayesian inference method. We obtain that, for the mass
range $\sim10^5-10^7~M_{\odot}$, the abundance of supermassive compact dark
matter is $f_{\rm CO}=10^{-1.60}$ in the log-normal mass distribution scenario.
This result is in obvious tension with some other observational constraints,
e.g. ultra-faint dwarfs and dynamical friction. However, it also was argued
that there is only one system in these 6 candidates has been identified as
lensed GRB event with fairly high confidence. In this case, the tension would
be significantly alleviated. Therefore, it would be an interesting clue for
both the millilensed GRB identification and the formation mechanism of
supermassive compact dark matter.


  We show that there is a low metallicity tidal stream that runs along l=143
deg. in the South Galactic Cap, about 34 kpc from the Sun, discovered from
SEGUE stellar velocities. Since the most concentrated detections are in the
Cetus constellation, and the orbital path is nearly polar, we name it the Cetus
Polar Stream (CPS). Although it is spatially coincident with the Sgr dwarf
trailing tidal tail at b=-70 deg., the metallicities ([Fe/H] = -2.1), ratio of
blue straggler to blue horizontal branch stars, and velocities of the CPS stars
differ from Sgr. Some CPS stars may contaminate previous samples of Sgr dwarf
tidal debris. The unusual globular cluster NGC 5824 is located along an orbit
fit to the CPS, with the correct radial velocity.


  In this work, we proposed new shape nanoparticles in the name of supershape
nanoparticles by manipulation in the morphology of a disk nanoparticle. The
electric field distribution of supershape nanoparticles were investigated at
resonance wavelength of particle plasmons. The effects of dispersed medium and
different sensory materials on particle plasmons were reported. The obtained
results showed that there exist the multiple plasmonics modes in supershape
nanoparticles. We found that the high sensitivity factor is available in the
supershape nanoparticles. This study can be a base for the characterization of
multiple particle plasmons in plasmonic devices for sensing applications.


  The ideas of Sensible Quantum Mechanics are expressed in lay terms for
philosophers of consciousness and others. A framework is proposed and explained
for the `psycho-physical-parallelism' between conscious experiences and the
mathematical structures of quantum physics (e.g., a set of quantum operators
obeying some algebra, and a quantum state giving the expectation value of each
operator). In particular, it is proposed that each set of possible conscious
experiences has a measure given by the expectation value of a corresponding
operator (a positive-operator-valued measure). Then one has a generalization of
the Weak Anthropic Principle named the Conditional Aesthemic Principle: given
that we are conscious beings, our conscious experiences are likely to be
typical experiences in the set of all conscious experiences with its measure.


  Safety-critical chemical processes are the backbone of multi-billion-dollar
industries, thus society deserves the strongest possible guarantees that they
are safe. To that end, models of chemical processes are well-studied in the
formal methods literature, including hybrid systems models which combine
discrete and continuous dynamics. This paper is the first to use the KeYmaera X
theorem-prover to verify chemical models with differential dynamic logic. Our
case studies are novel in combining the following: we provide strong
general-case correctness theorems, use particularly rich hybrid dynamics, and
have particularly rigorous proofs. This novel combination is made possible by
KeYmaera X.
  Simultaneously, we tell a general story about KeYmaera X: recent advances in
automated reasoning about safety and liveness for differential equations have
enabled elegant proofs about reaction dynamics.


  The realm of computer vision has witnessed a paradigm shift with the advent
of foundational models, mirroring the transformative influence of large
language models in the domain of natural language processing. This paper delves
into the exploration of open-world segmentation, presenting a novel approach
called Image Prompt Segmentation (IPSeg) that harnesses the power of vision
foundational models. IPSeg lies the principle of a training-free paradigm,
which capitalizes on image prompt techniques. Specifically, IPSeg utilizes a
single image containing a subjective visual concept as a flexible prompt to
query vision foundation models like DINOv2 and Stable Diffusion. Our approach
extracts robust features for the prompt image and input image, then matches the
input representations to the prompt representations via a novel feature
interaction module to generate point prompts highlighting target objects in the
input image. The generated point prompts are further utilized to guide the
Segment Anything Model to segment the target object in the input image. The
proposed method stands out by eliminating the need for exhaustive training
sessions, thereby offering a more efficient and scalable solution. Experiments
on COCO, PASCAL VOC, and other datasets demonstrate IPSeg's efficacy for
flexible open-world segmentation using intuitive image prompts. This work
pioneers tapping foundation models for open-world understanding through visual
concepts conveyed in images.