  User profiling and region analysis are two tasks of significant commercial
value. However, in practical applications, modeling different features
typically involves four main steps: data preparation, data processing, model
establishment, evaluation, and optimization. This process is time-consuming and
labor-intensive. Repeating this workflow for each feature results in abundant
development time for tasks and a reduced overall volume of task development.
Indeed, human mobility data contains a wealth of information. Several
successful cases suggest that conducting in-depth analysis of population
movement data could potentially yield meaningful profiles about users and
areas. Nonetheless, most related works have not thoroughly utilized the
semantic information within human mobility data and trained on a fixed number
of the regions. To tap into the rich information within population movement,
based on the perspective that Regions Are Who walk them, we propose a large
spatiotemporal model based on trajectories (RAW). It possesses the following
characteristics: 1) Tailored for trajectory data, introducing a GPT-like
structure with a parameter count of up to 1B; 2) Introducing a spatiotemporal
fine-tuning module, interpreting trajectories as collection of users to derive
arbitrary region embedding. This framework allows rapid task development based
on the large spatiotemporal model. We conducted extensive experiments to
validate the effectiveness of our proposed large spatiotemporal model. It's
evident that our proposed method, relying solely on human mobility data without
additional features, exhibits a certain level of relevance in user profiling
and region analysis. Moreover, our model showcases promising predictive
capabilities in trajectory generation tasks based on the current state,
offering the potential for further innovative work utilizing this large
spatiotemporal model.


  A fundamental part of the study of star formation is to place young stellar
objects in an evolutionary sequence. Establishing a robust evolutionary
classification scheme allows us not only to understand how the Sun was born but
also to predict what kind of main sequence star a given protostar will become.
Traditionally, low-mass young stellar objects are classified according to the
shape of their SEDs. Such methods are however prone to misclassification due to
degeneracy and do not constrain the temporal evolution. More recently, young
stellar objects have been classified based on envelope, disk, and stellar
masses determined from resolved images of their continuum and line emission at
submillimeter wavelengths. Through detailed modeling of two Class I sources, we
aim at determining accurate velocity profiles and explore the role of
freeze-out chemistry in such objects. We present new Submillimeter Array
observations of the continuum and HCO$^+$ line emission at 1.1 mm toward two
protostars, IRS 63 and IRS 43 in the Ophiuchus star forming region. The sources
are modeled in detail using dust radiation transfer to fit the SED and
continuum images and line radiation transfer to produce synthetic
position-velocity diagrams. We use a $\chi^2$ search algorithm to find the best
model fit to the data and to estimate the errors in the model variables. Our
best fit models present disk, envelope and stellar masses as well as the
HCO$^+$ abundance and inclination of both sources. We also identify a ring
structure with a radius of about 200 AU in IRS 63. We find that freeze-out
chemistry is important in IRS 63 but not for IRS 43. We show that the velocity
field in IRS 43 is consistent with Keplerian rotation. Due to molecular
depletion, it is not possible to draw a similar conclusion for IRS 63. We
identify a ring shaped structure in IRS 63 on the same spatial scale as the
disk outer radius.


  We study linear and non-linear equations related to the fractional
Hardy--Sobolev inequality. We prove nondegeneracy of ground state solutions to
the basic equation and investigate existence and qualitative properties,
including symmetry of solutions to some perturbed equations.


  We prove that no infinite field is interpretable in the first-order theory of
nonabelian free groups.


  Hidden semi-Markov models (HSMMs) are latent variable models which allow
latent state persistence and can be viewed as a generalization of the popular
hidden Markov models (HMMs). In this paper, we introduce a novel spectral
algorithm to perform inference in HSMMs. Unlike expectation maximization (EM),
our approach correctly estimates the probability of given observation sequence
based on a set of training sequences. Our approach is based on estimating
moments from the sample, whose number of dimensions depends only
logarithmically on the maximum length of the hidden state persistence.
Moreover, the algorithm requires only a few matrix inversions and is therefore
computationally efficient. Empirical evaluations on synthetic and real data
demonstrate the advantage of the algorithm over EM in terms of speed and
accuracy, especially for large datasets.


  The rapid evolution of artificial intelligence (AI), specifically large
language models (LLMs), has opened opportunities for various educational
applications. This paper explored the feasibility of utilizing ChatGPT, one of
the most popular LLMs, for automating feedback for Java programming assignments
in an introductory computer science (CS1) class. Specifically, this study
focused on three questions: 1) To what extent do students view LLM-generated
feedback as formative? 2) How do students see the comparative affordances of
feedback prompts that include their code, vs. those that exclude it? 3) What
enhancements do students suggest for improving AI-generated feedback? To
address these questions, we generated automated feedback using the ChatGPT API
for four lab assignments in the CS1 class. The survey results revealed that
students perceived the feedback as aligning well with formative feedback
guidelines established by Shute. Additionally, students showed a clear
preference for feedback generated by including the students' code as part of
the LLM prompt, and our thematic study indicated that the preference was mainly
attributed to the specificity, clarity, and corrective nature of the feedback.
Moreover, this study found that students generally expected specific and
corrective feedback with sufficient code examples, but had diverged opinions on
the tone of the feedback. This study demonstrated that ChatGPT could generate
Java programming assignment feedback that students perceived as formative. It
also offered insights into the specific improvements that would make the
ChatGPT-generated feedback useful for students.


  Pre-trained language models (LMs) are capable of in-context learning (ICL):
they can adapt to a task with only a few examples given in the prompt without
any parameter update. However, it is unclear where this capability comes from
as there is a stark distribution shift between pre-training text and ICL
prompts. In this work, we study what patterns of the pre-training data
contribute to ICL. We find that LMs' ICL ability depends on $\textit{parallel
structures}$ in the pre-training data -- pairs of phrases following similar
templates in the same context window. Specifically, we detect parallel
structures by checking whether training on one phrase improves prediction of
the other, and conduct ablation experiments to study their effect on ICL. We
show that removing parallel structures in the pre-training data reduces LMs'
ICL accuracy by 51% (vs 2% from random ablation). This drop persists even when
excluding common patterns such as n-gram repetitions and long-range dependency,
showing the diversity and generality of parallel structures. A closer look at
the detected parallel structures indicates that they cover diverse linguistic
tasks and span long distances in the data.


  This article investigates asymmetric brane-world scenarios in the limit when
the bulk gravity is negligible. We show that, even when true self gravity is
negligible, local mass concentrations will be subject to a mutual attraction
force which simulates the effect of Newtonian gravity in the non-relativistic
limit. Cosmological and also post-Newtonian constraints are examined.


  Whilst the most dynamic solar active regions (ARs) are known to flare
frequently, predicting the occurrence of individual flares and their magnitude,
is very much a developing field with strong potentials for machine learning
applications.
  The present work is based on a method which is developed to define numerical
measures of the mixed states of ARs with opposite polarities. The method yields
compelling evidence for the assumed connection between the level of mixed
states of a given AR and the level of the solar eruptive probability of this AR
by employing two morphological parameters: (i) the separation parameter
$S_{l-f}$ and (ii) the sum of the horizontal magnetic gradient $G_{S}$.
  In this work, we study the efficiency of $S_{l-f}$ and $G_{S}$ as flare
predictors on a representative sample of ARs, based on the SOHO/MDI-Debrecen
Data (SDD) and the SDO/HMI - Debrecen Data (HMIDD) sunspot catalogues. In
particular, we investigate about 1000 ARs in order to test and validate the
joint prediction capabilities of the two morphological parameters by applying
the logistic regression machine learning method. Here, we confirm that the two
parameters with their threshold values are, when applied together, good
complementary predictors. Furthermore, the prediction probability of these
predictor parameters is given at least 70\% a day before.


  We discuss a phase-sensitive technique for remote interrogation of passive
Bragg grating Fabry-Perot resonators. It is based on Pound-Drever-Hall laser
frequency locking, using radio-frequency phase modulation sidebands to derive
an error signal from the complex optical response, near resonance, of a
Fabry-Perot interferometer. We examine how modulation frequency and resonance
bandwidth affect this error signal. Experimental results are presented that
demonstrate when the laser is locked, this method detects differential phase
shifts in the optical carrier relative to its sidebands, due to minute fiber
optical path displacements.


  Let $P\subset \mathbb R^n$ be a belt polytope, that is a polytope whose
normal fan coincides with the fan of some hyperplane arrangement $\mathcal A$.
Also, let $G:\mathbb R^n\to\mathbb R^d$ be a linear map of full rank whose
kernel is in general position with respect to the faces of $P$. We derive a
formula for the number of $j$-faces of the ``projected'' polytope $GP$ in terms
of the $j$-th level characteristic polynomial of $\mathcal A$. In particular,
we show that the face numbers of $GP$ do not depend on the linear map $G$
provided a general position assumption is satisfied. Furthermore, we derive
formulas for the sum of the conic intrinsic volumes and Grassmann angles of the
tangent cones of $P$ at all of its $j$-faces. We apply these results to
permutohedra of types $A$ and $B$, which yields closed formulas for the face
numbers of projected permutohedra and the generalized angle sums of
permutohedra in terms of Stirling numbers of both kinds and their
$B$-analogues.


  We discuss the phase structure of a lattice Higgs-Yukawa system in the
variational mean field approximation with contributions of fermionic
determinant being calculated in a ladder approximation. In particular, we
demonstrate that in this approximation the ferrimagnetic phase in the $Z_2$
model with naive fermions can appear as an artifact of a finite lattice and
that the phase diagram for this model on infinite lattice changes qualitatively
at space-time dimension $D = 4$ compared with those at $D > 4$.


  The batch size is an essential parameter to tune during the development of
new neural networks. Amongst other quality indicators, it has a large degree of
influence on the model's accuracy, generalisability, training times and
parallelisability. This fact is generally known and commonly studied. However,
during the application phase of a deep learning model, when the model is
utilised by an end-user for inference, we find that there is a disregard for
the potential benefits of introducing a batch size. In this study, we examine
the effect of input batching on the energy consumption and response times of
five fully-trained neural networks for computer vision that were considered
state-of-the-art at the time of their publication. The results suggest that
batching has a significant effect on both of these metrics. Furthermore, we
present a timeline of the energy efficiency and accuracy of neural networks
over the past decade. We find that in general, energy consumption rises at a
much steeper pace than accuracy and question the necessity of this evolution.
Additionally, we highlight one particular network, ShuffleNetV2(2018), that
achieved a competitive performance for its time while maintaining a much lower
energy consumption. Nevertheless, we highlight that the results are model
dependent.


  In this letter we study a model of inflation in which the inflationary
regimen comes from a type of holographic energy density. In particular, we
consider the Granda-Oliveros proposal for the holographic energy density, which
contains two free dimensionless parameters, $\alpha$ and $\beta$. This
holographic energy density is associated to the so-called Granda-Oliveros
infrared cutoff (G-O cutoff). Additionally, since in the inflationary regimen
the energy scales are very high, it is necessary to modify the G-O cutoff
taking into account a correction due to the ultraviolet cutoff. In this way, we
obtain an algebraic equation which implicitly includes the Hubble parameter (as
a function of e-folding number, $N$) and from this, we calculate the Hubble
slow-roll parameters and the values of the inflationary observables: the scalar
spectral index of the curvature perturbations and its running, the tensor
spectral index and the tensor-to-scalar ratio. Finally, since the values for
these inflationary observables are known (Planck 2018 observations), we present
constraints on the parameters $\alpha$ and $\beta$ to make this a viable model.


  Este art\'iculo presenta un an\'alisis de los tweets recolectados el 28 de
Octubre de 2012, en el contexto de las elecciones municipales de 2012 en Chile.
Dicho an\'alisis se realiza mediante una metodolog\'ia basada en literatura
previa, en particular en t\'ecnicas de recuperaci\'on de la informaci\'on y de
an\'alisis de espacios de informaci\'on. Como resultado, se determinan: 1)
caracter\'isticas demogr\'aficas b\'asicas de la poblaci\'on virtual chilena,
incluyendo su distribuci\'on geogr\'afica, 2) el contenido que caracteriza a
cada regi\'on, y c\'omo fluye informaci\'on entre regiones, y 3) el grado de
representatividad de la poblaci\'on virtual participante en el evento con
respecto a la poblaci\'on f\'isica. Se determina que la muestra obtenida es
representativa de la poblaci\'on en t\'erminos de distribuci\'on geogr\'afica,
que el centralismo que afecta al pa\'is se ve reflejado en Twitter, y que, a
pesar de los sesgos poblacionales, es posible identificar el contenido que
caracteriza a cada regi\'on. Se finaliza con una discusi\'on de las
implicaciones y conclusiones pr\'acticas de este trabajo, as\'i como futuras
aplicaciones.


  The energy dissipation law and the maximum bound principle (MBP) are two
important physical features of the well-known Allen-Cahn equation. While some
commonly-used first-order time stepping schemes have turned out to preserve
unconditionally both energy dissipation law and MBP for the equation,
restrictions on the time step size are still needed for existing second-order
or even higher-order schemes in order to have such simultaneous preservation.
In this paper, we develop and analyze novel first- and second-order linear
numerical schemes for a class of Allen-Cahn type gradient flows. Our schemes
combine the generalized scalar auxiliary variable (SAV) approach and the
exponential time integrator with a stabilization term, while the standard
central difference stencil is used for discretization of the spatial
differential operator. We not only prove their unconditional preservation of
the energy dissipation law and the MBP in the discrete setting, but also derive
their optimal temporal error estimates under fixed spatial mesh. Numerical
experiments are also carried out to demonstrate the properties and performance
of the proposed schemes.


  Identification using biometrics is an important yet challenging task.
Abundant research has been conducted on identifying personal identity or gender
using given signals. Various types of biometrics such as electrocardiogram
(ECG), electroencephalogram (EEG), face, fingerprint, and voice have been used
for these tasks. Most research has only focused on single modality or a single
task, while the combination of input modality or tasks is yet to be
investigated. In this paper, we propose deep identification and gender
classification using multimodal biometrics. Our model uses ECG, fingerprint,
and facial data. It then performs two tasks: gender identification and
classification. By engaging multi-modality, a single model can handle various
input domains without training each modality independently, and the correlation
between domains can increase its generalization performance on the tasks.


  Epidemic spreading and cascading failure are two important dynamical
processes over complex networks. They have been investigated separately for a
long history. But in the real world, these two dynamics sometimes may interact
with each other. In this paper, we explore a model combined with SIR epidemic
spreading model and local loads sharing cascading failure model. There exists a
critical value of tolerance parameter that whether the epidemic with high
infection probability can spread out and infect a fraction of the network in
this model. When the tolerance parameter is smaller than the critical value,
cascading failure cuts off abundant of paths and blocks the spreading of
epidemic locally. While the tolerance parameter is larger than the critical
value, epidemic spreads out and infects a fraction of the network. A method for
estimating the critical value is proposed. In simulation, we verify the
effectiveness of this method in Barab\'asi-Albert (BA) networks.


  We compute the one-loop lattice renormalization of the two-quark operators
$\bar{\psi} \Gamma \psi$, where $\Gamma$ denotes the generic Dirac matrix, for
the lattice formulation of QCD using the overlap-Dirac operator.
  We also study the renormalization of quark bilinears which are more extended
and have better chiral properties.
  Finally, we present improved estimates of these renormalization constants,
coming from cactus resummation and from mean field perturbation theory.


  Nontrivial isometric embeddings for flat metrics (i.e., those which are not
just planes in the ambient space) can serve as useful tools in the description
of gravity in the embedding gravity approach. Such embeddings can additionally
be required to have the same symmetry as the metric. On the other hand, it is
possible to require the embedding to be unfolded so that the surface in the
ambient space would occupy the subspace of the maximum possible dimension. In
the weak gravitational field limit, such a requirement together with a large
enough dimension of the ambient space makes embedding gravity equivalent to
General Relativity, while at lower dimensions it guarantees the linearizability
of the equations of motion. We discuss symmetric embeddings for the metrics of
flat Euclidean three-dimensional space and Minkowski space. We propose the
method of sequential surface deformations for the construction of unfolded
embeddings. We use it to construct such embeddings of flat Euclidean
three-dimensional space and Minkowski space, which can be used to analyze the
equations of motion of embedding gravity.


  [Abridged]We compare both the Milky Way and M31 galaxies to local external
disk galaxies within the same mass range, using their relative locations in the
planes formed by V_flat versus M_K, j_disk, and the average Fe abundance of
stars in the galaxy outskirts. We find, for all relationships, that the MW is
systematically offset by ~ 1 sigma, showing a significant deficiency in stellar
mass, in angular momentum, in disk radius and [Fe/H] in the stars in its
outskirts at a given V_flat. On the basis of their location in the M_K, V_flat,
and R_d volume, the fraction of spirals like the MW is 7+/-1%, while M31
appears to be a "typical'' spiral. Our Galaxy appears to have escaped any
significant merger over the last ~10 Gyrs which may explain why it is deficient
by a factor 2 to 3 in stellar mass, angular momentum and outskirts metallicity
and then, unrepresentative of the typical spiral. As with M31, most local
spirals show evidence for a history shaped mainly by relatively recent merging.
We conclude that the standard scenario of secular evolution is generally unable
to reproduce the properties of most (if not all) spiral galaxies. However, the
so-called "spiral rebuilding'' scenario proposed by Hammer et al. 2005 is
consistent with the properties of both distant galaxies and of their
descendants - the local spirals.


  We report the ALMA observation of the Class I binary protostellar system
L1551 NE in the 0.9-mm continuum, C18O (3-2), and 13CO (3-2) lines at a ~1.6
times higher resolution and a ~6 times higher sensitivity than those of our
previous SMA observations, which revealed a r ~300 AU-scale circumbinary disk
in Keplerian rotation. The 0.9-mm continuum shows two opposing U-shaped
brightenings in the circumbinary disk, and exhibits a depression between the
circumbinary disk and the circumstellar disk of the primary protostar. The
molecular lines trace non-axisymmetric deviations from Keplerian rotation in
the circumbinary disk at higher velocities relative to the systemic velocity,
where our previous SMA observations could not detect the lines. In addition, we
detect inward motion along the minor axis of the circumbinary disk. To explain
the newly-observed features, we performed a numerical simulation of gas orbits
in a Roche potential tailored to the inferred properties of L1551 NE. The
observed U-shaped dust features coincide with locations where gravitational
torques from the central binary system are predicted to impart angular momentum
to the circumbinary disk, producing shocks and hence density enhancements seen
as a pair of spiral arms. The observed inward gas motion coincides with
locations where angular momentum is predicted to be lowered by the
gravitational torques. The good agreement between our observation and model
indicates that gravitational torques from the binary stars constitute the
primary driver for exchanging angular momentum so as to permit infall through
the circumbinary disk of L1551 NE.


  A main goal in the field of statistical shape analysis is to define
computable and informative metrics on spaces of immersed manifolds, such as the
space of curves in a Euclidean space. The approach taken in the elastic shape
analysis framework is to define such a metric by starting with a
reparameterization-invariant Riemannian metric on the space of parameterized
shapes and inducing a metric on the quotient by the group of diffeomorphisms.
This quotient metric is computed, in practice, by finding a registration of two
shapes over the diffeomorphism group. For spaces of Euclidean curves, the
initial Riemannian metric is frequently chosen from a two-parameter family of
Sobolev metrics, called elastic metrics. Elastic metrics are especially
convenient because, for several parameter choices, they are known to be locally
isometric to Riemannian metrics for which one is able to solve the geodesic
boundary problem explictly -- well-known examples of these local isometries
include the complex square root transform of Younes, Michor, Mumford and Shah
and square root velocity (SRV) transform of Srivastava, Klassen, Joshi and
Jermyn. In this paper, we show that the SRV transform extends to elastic
metrics for all choices of parameters, for curves in any dimension, thereby
fully generalizing the work of many authors over the past two decades. We give
a unified treatment of the elastic metrics: we extend results of Trouv\'{e} and
Younes, Bruveris as well as Lahiri, Robinson and Klassen on the existence of
solutions to the registration problem, we develop algorithms for computing
distances and geodesics, and we apply these algorithms to metric learning
problems, where we learn optimal elastic metric parameters for statistical
shape analysis tasks.


  The comment to article "Coexistence of Diamagnetism and Vanishingly Small
Electrical Resistance at Ambient Temperature and Pressure in Nanostructures"
shows the role of local electron pairing and Bose-Einstein-Condensation of
pairs for superconductivity at Tc = 286 K in nanostructures.


  The language used by US courtroom actors in criminal trials has long been
studied for biases. However, systematic studies for bias in high-stakes court
trials have been difficult, due to the nuanced nature of bias and the legal
expertise required. Large language models offer the possibility to automate
annotation. But validating the computational approach requires both an
understanding of how automated methods fit in existing annotation workflows and
what they really offer. We present a case study of adding a computational model
to a complex and high-stakes problem: identifying gender-biased language in US
capital trials for women defendants. Our team of experienced death-penalty
lawyers and NLP technologists pursue a three-phase study: first annotating
manually, then training and evaluating computational models, and finally
comparing expert annotations to model predictions. Unlike many typical NLP
tasks, annotating for gender bias in months-long capital trials is complicated,
with many individual judgment calls. Contrary to standard arguments for
automation that are based on efficiency and scalability, legal experts find the
computational models most useful in providing opportunities to reflect on their
own bias in annotation and to build consensus on annotation rules. This
experience suggests that seeking to replace experts with computational models
for complex annotation is both unrealistic and undesirable. Rather,
computational models offer valuable opportunities to assist the legal experts
in annotation-based studies.


  Tree properties are introduced by Shelah, and it is well-known that a theory
has TP (the tree property) if and only if it has TP$_1$ or TP$_2$. In any
simple theory (i.e., a theory not having TP), forking supplies a good
independence notion as it satisfies symmetry, transitivity, extension, local
character, and type-amalgamation. Shelah also introduced SOP$_n$ ($n$-strong
order property). Recently it is proved that in any NSOP$_1$ theory (i.e. a
theory not having SOP$_1$) holding nonforking existence, Kim-forking also
satisfies all the mentioned independence properties except base monotonicity
(one direction of transitivity). These results are the sources of motivation
for this paper.
  Mainly, we produce type-counting criteria for SOP$_2$ (which is equivalent to
TP$_1$) and SOP$_1$. In addition, we study relationships between TP$_2$ and
Kim-forking, and obtain that a theory is supersimple iff there is no countably
infinite Kim-forking chain.


  We analyze the Basis Pursuit recovery of signals with general perturbations.
Previous studies have only considered partially perturbed observations Ax + e.
Here, x is a signal which we wish to recover, A is a full-rank matrix with more
columns than rows, and e is simple additive noise. Our model also incorporates
perturbations E to the matrix A which result in multiplicative noise. This
completely perturbed framework extends the prior work of Candes, Romberg and
Tao on stable signal recovery from incomplete and inaccurate measurements. Our
results show that, under suitable conditions, the stability of the recovered
signal is limited by the noise level in the observation. Moreover, this
accuracy is within a constant multiple of the best-case reconstruction using
the technique of least squares. In the absence of additive noise numerical
simulations essentially confirm that this error is a linear function of the
relative perturbation.


  Minimal algebraic surfaces of general type $X$ such that
$K^2_X=2\chi(\mathcal{O}_X)-6$ are called Horikawa surfaces. In this note the
group of automorphisms of Horikawa surfaces is studied. The main result states
that given an admissible pair $(K^2, \chi)$ such that $K^2=2\chi-6$, every
irreducible component of Gieseker's moduli space $\mathfrak{M}_{K^2,\chi}$
contains an open subset consisting of surfaces with group of automorphisms
isomorphic to $\mathbb{Z}_2$.


  The goal of cross-view image based geo-localization is to determine the
location of a given street view image by matching it against a collection of
geo-tagged satellite images. This task is notoriously challenging due to the
drastic viewpoint and appearance differences between the two domains. We show
that we can address this discrepancy explicitly by learning to synthesize
realistic street views from satellite inputs. Following this observation, we
propose a novel multi-task architecture in which image synthesis and retrieval
are considered jointly. The rationale behind this is that we can bias our
network to learn latent feature representations that are useful for retrieval
if we utilize them to generate images across the two input domains. To the best
of our knowledge, ours is the first approach that creates realistic street
views from satellite images and localizes the corresponding query street-view
simultaneously in an end-to-end manner. In our experiments, we obtain
state-of-the-art performance on the CVUSA and CVACT benchmarks. Finally, we
show compelling qualitative results for satellite-to-street view synthesis.


  The first order phase transition of the three-dimensional Blume Capel are
investigated using cooling algorithm which improved from Creutz Cellular
Automaton for the $D/J=2.9$ parameter value in the first order phase transition
region. The analysis of the data using the finite-size effect and the histogram
technique indicate that the magnetic susceptibility maxima and the specific
heat maxima increase with the system volume ($L^{d}$) at $% D/J=2.9$.


  We investigate the occurrence of many-body localization (MBL) on a spin-1/2
transverse-field Ising model defined on a Chimera connectivity graph with
random exchange interactions and longitudinal fields. We observe a transition
from an ergodic phase to a non-thermal phase for individual energy eigenstates
induced by a critical disorder strength for the Ising parameters. Our result
follows from the analysis of both the mean half-system block entanglement and
the energy level statistics. We identify the critical point associated with
this transition using the maximum variance of the block entanglement over the
disorder ensemble as a function of the disorder strength. The calculated energy
density phase diagram shows the existence of a mobility edge in the energy
spectrum. In terms of the energy level statistics, the system changes from the
Gaussian orthogonal ensemble for weak disorder to a Poisson distribution limit
for strong randomness, which implies localization behavior. We then realize the
time-independent disordered Ising Hamiltonian experimentally using a reverse
annealing quench-pause-quench protocol on a D-Wave 2000Q programmable quantum
annealer. We characterize the transition from the thermal to the localized
phase through magnetization measurements at the end of the annealing dynamics,
and the results are compatible with our theoretical prediction for the critical
point. However, the same behavior can be reproduced using a classical
spin-vector Monte Carlo simulation, which suggests that genuine quantum
signatures of the phase transition remain out of reach using this experimental
platform and protocol.


  We construct weak axial one-boson exchange currents for the Bethe-Salpeter
equation, starting from chiral Lagrangians of the
N-\Delta(1236)-pi-rho-a_1-omega system. The currents fulfil the Ward-Takahashi
identities and the matrix element of the full current between the two-body
solutions of the Bethe-Salpeter equation satisfies the PCAC constraint exactly.


  We report the results of the first search for $B^-$ decays to the
$\bar{\Xi}_{c}^{0} \bar{\Lambda}_{c}^{-}$ final state using 711~${\rm fb^{-1}}$
of data collected at the $\Upsilon(4S)$ resonance with the Belle detector at
the KEKB asymmetric-energy $e^+ e^-$ collider. The results are interpreted in
terms of both direct baryon-number-violating $B^-$ decay and
$\Xi_{c}^{0}-\bar{\Xi}_{c}^{0}$ oscillations which follow the Standard Model
decay $B^- \to \Xi_{c}^{0} \bar{\Lambda}_{c}^{-}$. We observe no evidence for
baryon number violation and set the 95\% confidence-level upper limits on the
ratio of baryon-number-violating and Standard Model branching fractions
${\mathcal{B}(B^- \rightarrow \bar{\Xi}_{c}^{0}
\bar{\Lambda}_{c}^{-})}/{\mathcal{B}(B^- \rightarrow \Xi_{c}^{0}
\bar{\Lambda}_{c}^{-})}$ to be $< 2.7\%$ and on the $\Xi_{c}^{0} -
\bar{\Xi}_{c}^{0}$ oscillation angular frequency $\omega$ to be $< 0.76\
\mathrm{ps}^{-1}$ (equivalent to $\tau_{\rm mix} > 1.3$~ps).


  The proximity and duration of the tidal disruption event (TDE) ASASSN-14li
led to the discovery of narrow, blue-shifted absorption lines in X-rays and UV.
The gas seen in X-ray absorption is consistent with bound material close to the
apocenter of elliptical orbital paths, or with a disk wind similar to those
seen in Seyfert-1 active galactic nuclei. We present a new analysis of the
deepest high-resolution XMM-Newton and Chandra spectra of ASASSN-14li. Driven
by the relative strengths of He-like and H-like charge states, the data require
[N/C] > 2.4, in qualitative agreement with UV spectral results. Flows of the
kind seen in the X-ray spectrum of ASASSN-14li were not clearly predicted in
simulations of TDEs; this left open the possibility that the observed
absorption might be tied to gas released in prior AGN activity. However, the
abundance pattern revealed in this analysis points to a single star rather than
a standard AGN accretion flow comprised of myriad gas contributions. The
simplest explanation of the data is likely that a moderately massive star (M ~
3 Msun) with significant CNO processing was disrupted. An alternative
explanation is that a lower mass star was disrupted that had previously been
stripped of its envelope. We discuss the strengths and limitations of our
analysis and these interpretations.


  For his work in the economics of climate change, Professor William Nordhaus
was a co-recipient of the 2018 Nobel Memorial Prize for Economic Sciences. A
core component of the work undertaken by Nordhaus is the Dynamic Integrated
model of Climate and Economy, known as the DICE model. The DICE model is a
discrete-time model with two control inputs and is primarily used in
conjunction with a particular optimal control problem in order to estimate
optimal pathways for reducing greenhouse gas emissions. In this paper, we
provide a tutorial introduction to the DICE model and we indicate challenges
and open problems of potential interest for the systems and control community.


  In-plane uniaxial pressure has been shown to strongly tune the
superconducting state of Sr2RuO4 by approaching a Lifshitz transition and
associated Van Hove singularity (VHS) in the density of states. At the VHS,
$T_c$ and the in- and out-of-plane upper critical fields are all strongly
enhanced, and the latter has changed its curvature as a function of temperature
from convex to concave. However, due to strain inhomogeneity it has not been
possible so far to determine how the upper critical fields change with strain.
Here, we show the strain dependence of both upper critical fields, which was
achieved due to an improved sample preparation. We find that the in-plane upper
critical field is mostly linear in $T_c$. On the other hand, the out-of-plane
upper critical field varies with a higher power in $T_c$, and peaks strongly at
the VHS. The strong increase in magnitude and the change in form of
$H_\mathrm{c2||c}$ occur very close to the Van Hove strain, and points to a
strong enhancement of both the density of states and the gap magnitude at the
Lifshitz transition.


  Noncommutative or quantum Riemannian geometry has been proposed as an
effective theory for aspects of quantum gravity. Here the metric is an
invertible bimodule map $\Omega^1\otimes_A\Omega^1\to A$ where $A$ is a
possibly noncommutative or `quantum' spacetime coordinate algebra and
$(\Omega^1,d)$ is a specified bimodule of 1-forms or `differential calculus'
over it. In this paper we explore the proposal of a `quantum Koszul formula'
with initial data a degree -2 bilinear map $\perp$ on the full exterior algebra
$\Omega$ obeying the 4-term relations \[ (-1)^{|\eta|}
(\omega\eta)\perp\zeta+(\omega\perp\eta)\zeta=\omega\perp(\eta\zeta)+(-1)^{|\omega|+|\eta|}\omega(\eta\perp\zeta),\quad\forall\omega,\eta,\zeta\in\Omega\]
and a compatible degree -1 `codifferential' map $\delta$. These provide a
quantum metric and interior product and a canonical bimodule connection
$\nabla$ on all degrees. The theory is also more general than classically in
that we do not assume symmetry of the metric nor that $\delta$ is obtained from
the metric. We solve and interpret the $(\delta,\perp)$ data on the
bicrossproduct model quantum spacetime $[r,t]=\lambda r$ for its two standard
choices of $\Omega$. For the $\alpha$-family calculus the construction includes
the quantum Levi-Civita connection for a general quantum symmetric metric,
while for the more standard $\beta=1$ calculus we find the quantum Levi-Civita
connection for a quantum `metric' that in the classical limit is antisymmetric.


  A covariance graph is an undirected graph associated with a multivariate
probability distribution of a given random vector where each vertex represents
each of the different components of the random vector and where the absence of
an edge between any pair of variables implies marginal independence between
these two variables. Covariance graph models have recently received much
attention in the literature and constitute a sub-family of graphical models.
Though they are conceptually simple to understand, they are considerably more
difficult to analyze. Under some suitable assumption on the probability
distribution, covariance graph models can also be used to represent more
complex conditional independence relationships between subsets of variables.
When the covariance graph captures or reflects all the conditional independence
statements present in the probability distribution the latter is said to be
faithful to its covariance graph - though no such prior guarantee exists.
Despite the increasingly widespread use of these two types of graphical models,
to date no deep probabilistic analysis of this class of models, in terms of the
faithfulness assumption, is available. Such an analysis is crucial in
understanding the ability of the graph, a discrete object, to fully capture the
salient features of the probability distribution it aims to describe. In this
paper we demonstrate that multivariate Gaussian distributions that have trees
as covariance graphs are necessarily faithful. The method of proof is original
as it uses an entirely new approach and in the process yields a technique that
is novel to the field of graphical models.


  This work introduces a new class of cross-diffusion systems for studying
overcrowding dispersal of two species. The approach, based on proximal
minimization energy through a minimum flow process, offers a potential
generalization of existing segregation models. Unlike prior methods using PDEs
or $W_2$-Wasserstein flows, it establishes a well-posed PDE framework for
capturing the interplay between diffusion and concentration gradients. This
framework has the potential to significantly improve our understanding of how
cross-diffusion shapes spatial patterns, coexistence, and overall distribution
of multiple species. Notably, for homogeneous cases, the approach definitely
leads to a well-defined PDE grounded in a new general $H^{-1}$-theory
specifically developed for overcrowding dispersal. This theory provides a
robust foundation for further analysis.


  Advertising platforms have evolved in estimating Lifetime Value (LTV) to
better align with advertisers' true performance metric. However, the sparsity
of real-world LTV data presents a significant challenge to LTV predictive
model(i.e., pLTV), severely limiting the their capabilities. Therefore, we
propose to utilize external data, in addition to the internal data of
advertising platform, to expand the size of purchase samples and enhance the
LTV prediction model of the advertising platform. To tackle the issue of data
distribution shift between internal and external platforms, we introduce an
Adaptive Difference Siamese Network (ADSNet), which employs cross-domain
transfer learning to prevent negative transfer. Specifically, ADSNet is
designed to learn information that is beneficial to the target domain. We
introduce a gain evaluation strategy to calculate information gain, aiding the
model in learning helpful information for the target domain and providing the
ability to reject noisy samples, thus avoiding negative transfer. Additionally,
we also design a Domain Adaptation Module as a bridge to connect different
domains, reduce the distribution distance between them, and enhance the
consistency of representation space distribution. We conduct extensive offline
experiments and online A/B tests on a real advertising platform. Our proposed
ADSNet method outperforms other methods, improving GINI by 2$\%$. The ablation
study highlights the importance of the gain evaluation strategy in negative
gain sample rejection and improving model performance. Additionally, ADSNet
significantly improves long-tail prediction. The online A/B tests confirm
ADSNet's efficacy, increasing online LTV by 3.47$\%$ and GMV by 3.89$\%$.


  Much more image details can be resolved by improving the system's imaging
resolution and enhancing the resolution beyond the system's Rayleigh
diffraction limit is generally called super-resolution. By combining the sparse
prior property of images with the ghost imaging method, we demonstrated
experimentally that super-resolution imaging can be nonlocally achieved in the
far field even without looking at the object. Physical explanation of
super-resolution ghost imaging via compressive sampling and its potential
applications are also discussed.


  For quantum state tomography on rank-$r$ dimension-$d$ states, we show that
$\widetilde{O}(r^{.5}d^{1.5}/\epsilon) \leq \widetilde{O}(d^2/\epsilon)$ copies
suffice for accuracy~$\epsilon$ with respect to (Bures) $\chi^2$-divergence,
and $\widetilde{O}(rd/\epsilon)$ copies suffice for accuracy~$\epsilon$ with
respect to quantum relative entropy. The best previous bound was
$\widetilde{O}(rd/\epsilon) \leq \widetilde{O}(d^2/\epsilon)$ with respect to
infidelity; our results are an improvement since infidelity is bounded above by
both the relative entropy and the $\chi^2$-divergence. For algorithms that are
required to use single-copy measurements, we show that $\widetilde{O}(r^{1.5}
d^{1.5}/\epsilon) \leq \widetilde{O}(d^3/\epsilon)$ copies suffice for
$\chi^2$-divergence, and $\widetilde{O}(r^{2} d/\epsilon)$ suffice for relative
entropy.
  Using this tomography algorithm, we show that
$\widetilde{O}(d^{2.5}/\epsilon)$ copies of a $d\times d$-dimensional bipartite
state suffice to test if it has quantum mutual information~$0$ or at
least~$\epsilon$. As a corollary, we also improve the best known sample
complexity for the \emph{classical} version of mutual information testing to
$\widetilde{O}(d/\epsilon)$.


  We prove that every finite dimensional algebra over an algebraically closed
field is either derived tame or derived wild. We also prove that any
deformation of a derived tame algebra is derived tame.


  In holographic duality an eternal AdS black hole is described by two copies
of the boundary CFT in the thermal field double state. In this paper we provide
explicit constructions in the boundary theory of infalling time evolutions
which can take bulk observers behind the horizon. The constructions also help
to illuminate the boundary emergence of the black hole horizons, the interiors,
and the associated causal structure. A key element is the emergence, in the
large $N$ limit of the boundary theory, of a type III$_1$ von Neumann algebraic
structure from the type I boundary operator algebra and the half-sided modular
translation structure associated with it.


  Filtered backprojection (FBP) is an efficient and popular class of
tomographic image reconstruction methods. In photoacoustic tomography, these
algorithms are based on theoretically exact analytic inversion formulas which
results in accurate reconstructions. However, photoacoustic measurement data
are often incomplete (limited detection view and sparse sampling), which
results in artefacts in the images reconstructed with FBP. In addition to that,
properties such as directivity of the acoustic detectors are not accounted for
in standard FBP, which affects the reconstruction quality, too. To account for
these issues, in this papers we propose to improve FBP algorithms based on
machine learning techniques. In the proposed method, we include additional
weight factors in the FBP, that are optimized on a set of incomplete data and
the corresponding ground truth photoacoustic source. Numerical tests show that
the learned FBP improves the reconstruction quality compared to the standard
FBP.


  Liquid xenon particle detectors rely on excellent light collection efficiency
for their performance. This depends on the high reflectivity of
polytetrafluoroethylene (PTFE) at the xenon scintillation wavelength of 178 nm,
but the angular dependence of this reflectivity is not well-understood. IBEX is
designed to directly measure the angular distribution of xenon scintillation
light reflected off PTFE in liquid xenon. These measurements are fully
described by a microphysical reflectivity model with few free parameters.
Dependence on PTFE type, surface finish, xenon pressure, and wavelength of
incident light is explored. Total internal reflection is observed, which
results in the dominance of specular over diffuse reflection and a reflectivity
near 100% for high angles of incidence.


  A person is given a numbered sequence of positions on a sheet of paper. The
person is asked, "Which will be the next (or the next after that) position?"
Everyone has an opinion as to how he or she would proceed. There are regular
sequences for which there is general agreement on how to continue. However,
there are less regular sequences for which this assessment is less certain.
There are sequences for which every continuation is perceived to be arbitrary.
I would like to present a mathematical model that reflects these opinions and
perceptions with the aid of a valuation function. It is necessary to apply a
rich set of invariant features of position sequences to ensure the quality of
this model. All other properties of the model are arbitrary.


  Camouflage poses challenges in distinguishing a static target, whereas any
movement of the target can break this disguise. Existing video camouflaged
object detection (VCOD) approaches take noisy motion estimation as input or
model motion implicitly, restricting detection performance in complex dynamic
scenes. In this paper, we propose a novel Explicit Motion handling and
Interactive Prompting framework for VCOD, dubbed EMIP, which handles motion
cues explicitly using a frozen pre-trained optical flow fundamental model. EMIP
is characterized by a two-stream architecture for simultaneously conducting
camouflaged segmentation and optical flow estimation. Interactions across the
dual streams are realized in an interactive prompting way that is inspired by
emerging visual prompt learning. Two learnable modules, i.e. the camouflaged
feeder and motion collector, are designed to incorporate segmentation-to-motion
and motion-to-segmentation prompts, respectively, and enhance outputs of the
both streams. The prompt fed to the motion stream is learned by supervising
optical flow in a self-supervised manner. Furthermore, we show that long-term
historical information can also be incorporated as a prompt into EMIP and
achieve more robust results with temporal consistency. Experimental results
demonstrate that our EMIP achieves new state-of-the-art records on popular VCOD
benchmarks. The code will be publicly available.


  We develop a combinatorial approach to the quantum permutation algebras, as
Hopf images of representations of type $\pi:A_s(n)\to B(H)$. We discuss several
general problems, including the commutativity and cocommutativity ones, the
existence of tensor product or free wreath product decompositions, and the
Tannakian aspects of the construction. The main motivation comes from the
quantum invariants of the complex Hadamard matrices: we show here that, under
suitable regularity assumptions, the computations can be performed up to $n=6$.


  We report a comprehensive analysis of the ground state properties of
axisymmetric toroidal crystals based on the elastic theory of defects on curved
substrates. The ground state is analyzed as a function of the aspect ratio of
the torus, which provides a non-local measure of the underlying Gaussian
curvature, and the ratio of the defect core-energy to the Young modulus.
Several structural features are discussed,including a spectacular example of
curvature-driven amorphization in the limit of the aspect ratio approaching
one. The outcome of the elastic theory is then compared with the results of a
numerical study of a system of point-like particles constrained on the surface
of a torus and interacting via a short range potential.


  Online Convex Optimization plays a key role in large scale machine learning.
Early approaches to this problem were conservative, in which the main focus was
protection against the worst case scenario. But recently several algorithms
have been developed for tightening the regret bounds in easy data instances
such as sparsity, predictable sequences, and curved losses. In this work we
unify some of these existing techniques to obtain new update rules for the
cases when these easy instances occur together. First we analyse an adaptive
and optimistic update rule which achieves tighter regret bound when the loss
sequence is sparse and predictable. Then we explain an update rule that
dynamically adapts to the curvature of the loss function and utilizes the
predictable nature of the loss sequence as well. Finally we extend these
results to composite losses.


  We prove an irreducibility criterion for polynomials of the form $h(x)=x^{2m}
+ bx^m + c_1 \in F[x]$ relating to the Dickson polynomials of the first kind
$D_p$. In the case when $F = \mathbb{Q}$, $m$ is a prime $p>3$, and $c_1=c^p$,
for $c\in\mathbb{Q}$, we explicitly determine the Galois group of $d_h= D_p(x,
c) + b$, which is $\mathrm{Aff}(\mathbb{F}_p)$ or $C_p \rtimes C_{(p - 1)/2}
\vartriangleleft \mathrm{Aff}(\mathbb{F}_p)$, and the Galois group of $h$,
which is $C_2 \times \mathrm{Aff}(\mathbb{F}_p), \mathrm{Aff}(\mathbb{F}_p)$,
or $C_2 \times (C_p \rtimes C_{(p - 1)/2}) \vartriangleleft C_2 \times
\mathrm{Aff}(\mathbb{F}_p)$.


  Pure t-motives were introduced by G. Anderson as higher dimensional
generalizations of Drinfeld modules, and as the appropriate analogs of abelian
varieties in the arithmetic of function fields. In order to construct moduli
spaces for pure t-motives the second author has previously introduced the
concept of abelian \tau-sheaf. In this article we clarify the relation between
pure t-motives and abelian \tau-sheaves. We obtain an equivalence of the
respective quasi-isogeny categories. Furthermore, we develop the elementary
theory of both structures regarding morphisms, isogenies, Tate modules, and
local shtukas. The later are the analogs of p-divisible groups.


  We introduce a deep learning framework designed to train smoothed
elastoplasticity models with interpretable components, such as a smoothed
stored elastic energy function, a yield surface, and a plastic flow that are
evolved based on a set of deep neural network predictions. By recasting the
yield function as an evolving level set, we introduce a machine learning
approach to predict the solutions of the Hamilton-Jacobi equation that governs
the hardening mechanism. This machine learning hardening law may recover
classical hardening models and discover new mechanisms that are otherwise very
difficult to anticipate and hand-craft. This treatment enables us to use
supervised machine learning to generate models that are thermodynamically
consistent, interpretable, but also exhibit excellent learning capacity. Using
a 3D FFT solver to create a polycrystal database, numerical experiments are
conducted and the implementations of each component of the models are
individually verified. Our numerical experiments reveal that this new approach
provides more robust and accurate forward predictions of cyclic stress paths
than these obtained from black-box deep neural network models such as a
recurrent GRU neural network, a 1D convolutional neural network, and a
multi-step feedforward model.


  Cross-lingual word alignment plays a crucial role in various natural language
processing tasks, particularly for low-resource languages. Recent study
proposes a BiLSTM-based encoder-decoder model that outperforms pre-trained
language models in low-resource settings. However, their model only considers
the similarity of word embedding spaces and does not explicitly model the
differences between word embeddings. To address this limitation, we propose
incorporating contrastive learning into the BiLSTM-based encoder-decoder
framework. Our approach introduces a multi-view negative sampling strategy to
learn the differences between word pairs in the shared cross-lingual embedding
space. We evaluate our model on five bilingual aligned datasets spanning four
ASEAN languages: Lao, Vietnamese, Thai, and Indonesian. Experimental results
demonstrate that integrating contrastive learning consistently improves word
alignment accuracy across all datasets, confirming the effectiveness of the
proposed method in low-resource scenarios. We will release our data set and
code to support future research on ASEAN or more low-resource word alignment.


  We consider the Dynamical Low Rank (DLR) approximation of random parabolic
equations and propose a class of fully discrete numerical schemes. Similarly to
the continuous DLR approximation, our schemes are shown to satisfy a discrete
variational formulation. By exploiting this property, we establish stability of
our schemes: we show that our explicit and semi-implicit versions are
conditionally stable under a parabolic type CFL condition which does not depend
on the smallest singular value of the DLR solution; whereas our implicit scheme
is unconditionally stable. Moreover, we show that, in certain cases, the
semi-implicit scheme can be unconditionally stable if the randomness in the
system is sufficiently small. Furthermore, we show that these schemes can be
interpreted as projector-splitting integrators and are strongly related to the
scheme proposed by Lubich et al. [BIT Num. Math., 54:171-188, 2014; SIAM J. on
Num. Anal., 53:917-941, 2015], to which our stability analysis applies as well.
The analysis is supported by numerical results showing the sharpness of the
obtained stability conditions.


  In this note, we consider the frog model on $\mathbb{Z}^d$ and a two-type
version of it with two types of particles. For the one-type model, we show that
the asymptotic shape does not depend on the initially activated set and the
configuration there. For the two-type model, we show that the possibility for
the types to coexist in that both of them activate infinitely many particles
does not depend on the choice of the initially activated sets and the
configurations there.


  Let D be the support design of the minimum weight of an extremal binary
doubly even self-dual [24m,12m,4m+4] code. In this note, we consider the case
when D becomes a t-design with t \geq 6.


  We exploit the [Mg/Mn]-[Al/Fe] chemical abundance plane to help identify
nearby halo stars in the 14th data release from the APOGEE survey that have
been accreted on to the Milky Way. Applying a Gaussian Mixture Model, we find a
`blob' of 856 likely accreted stars, with a low disc contamination rate of ~7%.
Cross-matching the sample with the second data release from Gaia gives us
access to parallaxes and apparent magnitudes, which place constraints on
distances and intrinsic luminosities. Using a Bayesian isochrone pipeline, this
enables us to estimate new ages for the accreted stars, with typical
uncertainties of ~20%. Our new catalogue is further supplemented with estimates
of orbital parameters.
  The blob stars span a metallicities between -0.5 to -2.5, and [Mg/Fe] between
-0.1 to 0.5. They constitute ~30% of the metal-poor ([Fe/H] < -0.8) halo at
metallicities of ~-1.4. Our new ages are mainly range between 8 to 13 Gyr, with
the oldest stars the metal-poorest, and with the highest [Mg/Fe] abundance. If
the blob stars are assumed to belong to a single progenitor, the ages imply
that the system merged with our Milky Way around 8 Gyr ago and that star
formation proceeded for ~5 Gyr. Dynamical arguments suggest that such a single
progenitor would have a total mass of ~1011Msun, similar to that found by other
authors using chemical evolution models and simulations. Comparing the scatter
in the [Mg/Fe]-[Fe/H] plane of the blob stars to that measured for stars
belonging to the Large Magellanic Cloud suggests that the blob does indeed
contain stars from only one progenitor.


  A high-precision charge measurement can be achieved by the area integration
of a digitized quasi-Gaussian signal after the signal passes through the shaper
and analog-to-digital converter (ADC). The charge measurement contains an error
due to the uncertainty of the first sampled point of a signal waveform. To
reduce the error, we employ a time-to-digital converter (TDC) to measure the
uncertainty precisely, and we design correction algorithms to improve the
resolution of the charge measurement. This work includes analysis and
simulations of the proposed algorithms and implementation of them in an FPGA
device. Besides, the tests are also conducted to evaluate the performance of
the correction method. Test results indicate that the resolution of the charge
measurement is successfully improved from 0.231% to 0.126% by using a signal
from the shaping circuit (with the amplitude of 2 V, and leading and trailing
edges of about 80 ns and 280 ns, respectively) digitized at the sampling rate
of 62.5 Msps.


  We studied theoretically ballistic electronic transport in a proposed
mesoscopic structure - Quantum Cable. Our results demonstrated that Qauntum
Cable is a unique structure for the study of mesoscopic transport. As a
function of Fermi energy, Ballistic conductance exhibits interesting stepwise
features. Besides the steps of one or two quantum conductance units ($2e^2/h$),
conductance plateaus of more than two quantum conductance units can also be
expected due to the accidental degeneracies (crossings) of subbands. As
structure parameters is varied, conductance width displays oscillatory
properties arising from the inhomogeneous variation of energy difference
betweeen adjoining transverse subbands. In the weak coupling limits,
conductance steps of height $2e^2/h$ becomes the first and second plateaus for
the Quantum Cable of two cylinder wires with the same width.


  Complex oxides are mesmerizing material systems to realize multiple physical
properties and functionalities by integrating different elements in a single
compound. However, owing to the chemical instability, not all the combinations
of elements can be materialized despite the intriguing potential expected from
their magnetic and electronic properties. In this study, we demonstrate an
epitaxial stabilization of orthorhombic Ru$^{3+}$ perovskite oxides: LaRuO$_3$
and NdRuO$_3$, and their magnetotransport properties that reflect the
difference between non-magnetic La$^{3+}$ and magnetic Nd$^{3+}$. Above all, an
unconventional anomalous Hall effect accompanied by an inflection point in
magnetoresistance is observed around 1.3 T below 1 K for NdRuO$_3$, which is
ascribed to topological Hall effect possibly due to a non-coplanar spin texture
on Nd$^{3+}$ sublattice. These studies not only serve a new testbed for the
interplay between spin-orbit coupling and Coulomb interaction but also open a
new avenue to explore topological emergent phenomena in well-studied perovskite
oxides.


  A recent lattice QCD (LQCD) calculation of $\Sigma$-$\Lambda$ mixing by the
QCDSF-UKQCD Collaboration [Phys. Rev. D 91, 074512 (2015)] finds a mixing angle
about half of that found from the Dalitz-von Hippel (DvH) flavor SU(3) mass
formula which relates the $\Sigma$-$\Lambda$ mixing matrix element to known
octet baryon mass differences and which has been used widely to evaluate charge
symmetry breaking effects in $\Lambda$ hypernuclei. We show that the
LQCD-calculated $\Sigma$-$\Lambda$ mixing matrix element and octet baryon
masses satisfy the DvH mass formula, concluding thereby that a good LQCD
evaluation of $\Sigma$-$\Lambda$ mixing requires an equally good reproduction
of octet baryon mass differences which is yet to be demonstrated.


  Despite great improvements in semantic segmentation, challenges persist
because of the lack of local/global contexts and the relationship between them.
In this paper, we propose Contextrast, a contrastive learning-based semantic
segmentation method that allows to capture local/global contexts and comprehend
their relationships. Our proposed method comprises two parts: a) contextual
contrastive learning (CCL) and b) boundary-aware negative (BANE) sampling.
Contextual contrastive learning obtains local/global context from multi-scale
feature aggregation and inter/intra-relationship of features for better
discrimination capabilities. Meanwhile, BANE sampling selects embedding
features along the boundaries of incorrectly predicted regions to employ them
as harder negative samples on our contrastive learning, resolving segmentation
issues along the boundary region by exploiting fine-grained details. We
demonstrate that our Contextrast substantially enhances the performance of
semantic segmentation networks, outperforming state-of-the-art contrastive
learning approaches on diverse public datasets, e.g. Cityscapes, CamVid,
PASCAL-C, COCO-Stuff, and ADE20K, without an increase in computational cost
during inference.


  Let $\mathcal{M}_{\rm KSB}$ (resp. $\mathcal{M}_{\rm KSB}'$) be the the
moduli space of $n$-dimensional K\"ahler-Einstein manifolds (resp. varieties)
$X$ with $K_X$ ample. We prove that the Weil-Petersson metric on
$\mathcal{M}_{\rm KSB}$ extends uniquely to the projective variety
$\mathcal{M}_{\rm KSB}'$, as a closed positive current with continuous local
potentials. This generalizes a theorem of Wolpert which treats the case $n=1$,
and also confirms a conjecture of Berman-Guenancia. In addition, we derive
uniform estimates for the volumes of sublevel sets of K\"ahler-Einstein
potentials


  We derive the analytic expression of the two one-loop dipole contributions to
the elastic 4-gluon amplitude in QCD. The first one corresponds to the double
QCD pomeron exchange, the other to an order alpha^2 correction to one-pomeron
exchange. Both are expressed in terms of the square of the recently derived
triple QCD pomeron vertex and involve a summation over all conformal
Eigenvectors of the BFKL kernel.


  Nowadays it is not uncommon to have to deal with dissemination on
multi-layered networks and often finding the source of said propagation can be
a crucial task. In this paper we tackle this exact problem with a maximum
likelihood approach that we extend to be operational on multi-layered graphs.
We test our method for source location estimation on synthetic networks and
outline its potential strengths and limitations. We also observe some
non-trivial and perhaps surprising phenomena where the more of the system one
observes the worse the results become whereas increased problem complexity in
the form of more layers can actually improve our performance.


  We extend the notion of Morita equivalence of Poisson manifolds to the
setting of {\em formal} Poisson structures, i.e., formal power series of
bivector fields $\pi=\pi_0 + \lambda\pi_1 +\cdots$ satisfying the Poisson
integrability condition $[\pi,\pi]=0$. Our main result gives a complete
description of Morita equivalent formal Poisson structures deforming the zero
structure ($\pi_0=0$) in terms of $B$-field transformations, relying on a
general study of formal deformations of Poisson morphisms and dual pairs.
Combined with previous work on Morita equivalence of star products, our results
link the notions of Morita equivalence in Poisson geometry and noncommutative
algebra via deformation quantization.


  We present an approach for computing long-range van der Waals (vdW)
interactions between complex molecular systems and arbitrarily shaped
macroscopic bodies, melding atomistic treatments of electronic fluctuations
based on density functional theory in the former, with continuum descriptions
of strongly shape-dependent electromagnetic fields in the latter, thus
capturing many-body and multiple scattering effects to all orders. Such a
theory is especially important when considering vdW interactions at mesoscopic
scales, i.e. between molecules and structured surfaces with features on the
scale of molecular sizes, in which case the finite sizes, complex shapes, and
resulting nonlocal electronic excitations of molecules are strongly influenced
by electromagnetic retardation and wave effects that depend crucially on the
shapes of surrounding macroscopic bodies. We show that these effects together
can modify vdW interactions by orders of magnitude compared to previous
treatments based on Casimir--Polder or non-retarded approximations, which are
valid only at macroscopically large or atomic-scale separations, respectively.


  Momentum Iterative Hessian Sketch (M-IHS) techniques, a group of solvers for
large scale regularized linear Least Squares (LS) problems, are proposed and
analyzed in detail. Proposed M-IHS techniques are obtained by incorporating the
Heavy Ball Acceleration into the Iterative Hessian Sketch algorithm and they
provide significant improvements over the randomized preconditioning
techniques. By using approximate solvers along with the iterations, the
proposed techniques are capable of avoiding all matrix decompositions and
inversions, which is one of the main advantages over the alternative solvers
such as the Blendenpik and the LSRN. Similar to the Chebyshev semi-iterations,
the M-IHS variants do not use any inner products and eliminate the
corresponding synchronization steps in hierarchical or distributed memory
systems, yet the M-IHS converges faster than the Chebyshev Semi-iteration based
solvers. Lower bounds on the required sketch size for various randomized
distributions are established through the error analyses. Unlike the previously
proposed approaches to produce a solution approximation, the proposed M-IHS
techniques can use sketch sizes that are proportional to the statistical
dimension which is always smaller than the rank of the coefficient matrix. The
relative computational saving gets more significant as the regularization
parameter or the singular value decay rate of the coefficient matrix increase.


  We study the behaviour of lattice momentum-space gluon propagators for a pure
$SU(2)$ gauge theory at finite temperature. We find out that the magnetic mass
is $0.26 g^2(T) T$; we have repeated the same calculations in three dimensions.


  If systems change as fast as possible as they pass through a phase transition
then the initial domain structure is constrained by causality. We shall show
how we can trace these causal horizons by measuring the spontaneous production
of flux in annular Josephson Tunnel Junctions as a function of the quench time
$\tau_{Q}$ into the superconducting phase. A specific test of our analysis is
that the probability P$_{1}$ to trap a single fluxon at the N-S transition
clearly follows an allometric dependence on $\tau_{Q}$ as $P_1 = a
\tau_Q^{-\sigma}$, with a scaling exponent $\sigma = 0.25$, in agreement with
the data.


  Deep neural networks are utilized in a growing number of industries. Much of
the current literature focuses on the applications of deep neural networks
without discussing the security of the network itself. One security issue
facing deep neural networks is neural trojans. Through a neural trojan, a
malicious actor may force the deep neural network to act in unintended ways.
Several potential defenses have been proposed, but they are computationally
expensive, complex, or unusable in commercial applications. We propose Merkle
trees as a novel way to detect and isolate neural trojans.


  Question answering (QA) is a critical task for speech-based retrieval from
knowledge sources, by sifting only the answers without requiring to read
supporting documents. Specifically, open-domain QA aims to answer user
questions on unrestricted knowledge sources. Ideally, adding a source should
not decrease the accuracy, but we find this property (denoted as
"monotonicity") does not hold for current state-of-the-art methods. We identify
the cause, and based on that we propose Judge-Specialist framework. Our
framework consists of (1) specialist retrievers/readers to cover individual
sources, and (2) judge, a dedicated language model to select the final answer.
Our experiments show that our framework not only ensures monotonicity, but also
outperforms state-of-the-art multi-source QA methods on Natural Questions.
Additionally, we show that our models robustly preserve the monotonicity
against noise from speech recognition. We publicly release our code and
setting.


  The dynamical evolution of protoplanetary disks is of key interest for
building a comprehensive theory of planet formation and to explain the
observational properties of these objects. Using the magnetohydrodynamics code
Athena++, with an isothermal shearing box setup, we study the boundary between
the active and dead zone, where the accretion rate changes and mass can
accumulate. We quantify how the turbulence level is affected by the presence of
a non uniform ohmic resistivity in the radial-x direction that leads to a
region of inhibited turbulence (or dead zone). Comparing the turbulent activity
to that of ideal simulations, the turbulence inhibited area shows density
fluctuations and magnetic activity at its boundaries, driven by energy
injection from the active (ideal) zone boundaries. We find magnetic dissipation
to be significantly stronger in the ideal regions, and the turbulence
penetration through the boundary of the dead zone is determined by the value of
the resistivity itself, through the ohmic dissipation process, though the
thickness of the transition does not play a significant role in changing the
dissipation. We investigate the 1D spectra along the shearing direction:
magnetic spectra appear flat at large scales both in ideal as well as resistive
simulations, though a Kolmogorov scaling over more than one decade persists in
the dead zone, suggesting the turbulent cascade is determined by the
hydrodynamics of the system: MRI dynamo action is inhibited where sufficiently
high resistivity is present.


  We present a deterministic distributed algorithm in the LOCAL model that
finds a proper $(\Delta + 1)$-edge-coloring of an $n$-vertex graph of maximum
degree $\Delta$ in $\mathrm{poly}(\Delta, \log n)$ rounds. This is the first
nontrivial distributed edge-coloring algorithm that uses only $\Delta+1$ colors
(matching the bound given by Vizing's theorem). Our approach is inspired by the
recent proof of the measurable version of Vizing's theorem due to Greb\'ik and
Pikhurko.


  A D3-D5 intersection gives rise to a defect CFT, wherein the rank of the
gauge group jumps by k units across a domain wall. The one-point functions of
local operators in this set-up map to overlaps between on-shell Bethe states in
the underlying spin chain and a boundary state representing the D5 brane.
Focussing on the k=1 case, we extend the construction to gluonic and fermionic
sectors, which was prohibitively difficult to achieve for k>1. As a byproduct,
we test an all-loop proposal for the one-point functions in the su(2) sector at
the half-wrapping order of perturbation theory.


  We aim to shed light on the physical properties and kinematics of the
molecular material in the nucleus of one of the closest type 2 active galaxies.
  To this end, we obtained high angular resolution ALMA observations of the
nucleus of the Circinus galaxy. The observations map the emission at 350GHz and
690GHz with spatial resolutions of ~3.8pc and ~2.2pc, respectively.
  The continuum emission traces cold ($T\lesssim100$K) dust in a circumnuclear
disk with spiral arms on scales of 25pc, plus a marginally resolved nuclear
emission peak. The latter is not extended in polar direction as claimed based
on earlier ALMA observations. A significant amount (of the order of 40%) of the
350GHz emission is not related to dust, but most likely free-free emission
instead. We detect CO(3-2) and CO(6-5) as well as HCO$^+$(4-3), HCN(4-3), and
CS(4-3). The CO emission is extended, showing a spiral pattern, similar to the
extended dust emission. Towards the nucleus, CO is excited to higher
transitions and its emission is self-absorbed, leading to an apparent hole in
the CO(3-2) but not the CO(6-5) emission. On the other hand, the high gas
density tracers HCO$^+$, HCN, and CS show a strong, yet unresolved
(($\lesssim4$pc) concentration of the emission at the nucleus, pointing at a
very small 'torus'. The kinematics are dominated by rotation and point at a
geometrically thin disk down to the resolution limit of our observations. In
contrast to several other AGNs, no HCN enhancement is found towards the
nucleus.
  The Circinus nucleus is therefore composed of at least two distinct
components: (1) an optically thin, warm outflow of ionised gas containing
clouds of dust; and (2) a cold molecular and dusty disk. These findings support
the most recent radiative transfer calculations of the obscuring structures in
AGNs, which find a similar two-component structure. (Abridged)


  We present a method for calculating transfer matrices for the $q$-state Potts
model partition functions $Z(G,q,v)$, for arbitrary $q$ and temperature
variable $v$, on cyclic and M\"obius strip graphs $G$ of the square (sq),
triangular (tri), and honeycomb (hc) lattices of width $L_y$ vertices and of
arbitrarily great length $L_x$ vertices. For the cyclic case we express the
partition function as $Z(\Lambda,L_y \times L_x,q,v)=\sum_{d=0}^{L_y} c^{(d)}
Tr[(T_{Z,\Lambda,L_y,d})^m]$, where $\Lambda$ denotes lattice type, $c^{(d)}$
are specified polynomials of degree $d$ in $q$, $T_{Z,\Lambda,L_y,d}$ is the
transfer matrix in the degree-$d$ subspace, and $m=L_x$ ($L_x/2$) for
$\Lambda=sq, tri (hc)$, respectively. An analogous formula is given for
M\"obius strips. We exhibit a method for calculating $T_{Z,\Lambda,L_y,d}$ for
arbitrary $L_y$. Explicit results for arbitrary $L_y$ are given for
$T_{Z,\Lambda,L_y,d}$ with $d=L_y$ and $d=L_y-1$. In particular, we find very
simple formulas the determinant $det(T_{Z,\Lambda,L_y,d})$, and trace
$Tr(T_{Z,\Lambda,L_y})$. Corresponding results are given for the equivalent
Tutte polynomials for these lattice strips and illustrative examples are
included. We also present formulas for self-dual cyclic strips of the square
lattice.


  We examine long-term evolution of a random wind wave field generated by
constant forcing, by comparing numerical simulations of the kinetic equation
and direct numerical simulations (DNS) of the dynamical equations. While
integral characteristics of spectra are in reasonably good agreement, the
spectral shapes differ considerably at large times, the DNS spectral shape
being in much better agreement with field observations. Varying the number of
resonant and approximately resonant wave interactions in the DNS numerical
scheme, we show that when the ratio of nonlinear and linear parts of the
Hamiltonian tends to zero, the DNS spectral shape approaches the shape
predicted by the kinetic equation. We attribute the discrepancies between the
kinetic equation modelling, on one side, and the DNS and observations, on the
other, to the neglect of non-gaussianity in the derivation of the kinetic
equation.


  We investigate theoretically the origins of observed variations in the
critical currents of Nb/Al-AlO$_x$/Nb Josephson junctions in terms of various
types of disorder. We consider the following disorder sources: vacancies within
the Al layer; thickness variations in the AlO$_x$ layer; and "pinholes" (i.e.,
point contacts) within the AlO$_x$ layer. The calculations are all performed by
solving the microscopic Bogoliubov-de Gennes Hamiltonian self-consistently. It
is found that a small concentration of vacancies within the Al layer is
sufficient to suppress the critical current, while the presence of a small
number of thick regions of the oxide layer induces a similar effect as well.
The pinhole scenario is found to result in anomalous behavior that resembles
neither that of a pure tunnel junction nor that of an SNS junction, but a
regime that interpolates between these two limits. We comment on the degree to
which each of the three scenarios describes the actual situation present in
these junctions.


  We present an auditory stimulus optimization and a pilot study of a two-step
input speller application combined with a spatial auditory brain-computer
interface (saBCI) for paralyzed users. The application has been developed for
45, out of 48 defining the full set, Japanese kana characters in a two-step
input procedure setting for an easy-to-use BCI-speller interface. The user
first selects the representative letter of a subset, defining the second step.
In the second step, the final choice is made. At each interfacing step, the
choices are classified based on the P300 event related potential (ERP)
responses captured in the EEG, as in the classic oddball paradigm. The BCI
online experiment and EEG responses classification results of the pilot study
confirm the effectiveness of the proposed spelling method.


  It is proposed that superconductors possess a hidden `hole core' buried deep
in the Fermi sea. The proposed hole core is a small region of the Brillouin
zone (usually at the center of the zone) where the lowest energy states in the
normal state reside. We propose that in the superconducting state these energy
states become {\it singly occupied} with electrons of a definite spin helicity.
In other words, that holes of a definite spin helicity condense from the top to
the bottom of the band in the transition to superconductivity, and electrons of
that spin helicity `float' on top of the hole core, thus becoming highly
mobile. The hole core has radius $q_0=1/2\lambda_L$, with $\lambda_L$ the
London penetration depth, and the electrons expelled from the hole core give an
excess negative charge density within a London penetration depth of the real
space surface of the superconductor. The hole core explains the development of
a spin current in the transition to superconductivity (Spin Meissner effect)
and the associated negative charge expulsion from the interior of metals in the
transition to superconductivity, effects we have proposed in earlier work to
exist in all superconductors and to be at the root of the Meissner effect.


  We evaluate analytically the elastic $\pi\pi$ scattering amplitude to two
loops in chiral perturbation theory and give numerical values for the two
$S$--wave scattering lengths and for the phase shift difference
$\delta_0^0-\delta_1^1$.


  We report on the measurement of freeze-out radii for pairs of
identical-charge pions measured in Pb--Pb collisions at $\sqrt{s_{\rm
NN}}=2.76$ TeV as a function of collision centrality and the average transverse
momentum of the pair $k_{\rm T}$. Three-dimensional sizes of the system
(femtoscopic radii), as well as direction-averaged one-dimensional radii are
extracted. The radii decrease with $k_{\rm T}$, following a power-law behavior.
This is qualitatively consistent with expectations from a collectively
expanding system, produced in hydrodynamic calculations. The radii also scale
linearly with $\left< \mathrm{d}N_{\rm ch}/\mathrm{d}\eta \right>^{1/3}$. This
behaviour is compared to world data on femtoscopic radii in heavy-ion
collisions. While the dependence is qualitatively similar to results at smaller
$\sqrt{s_{\rm NN}}$, a decrease in the $R_{\rm out}/R_{\rm side}$ ratio is
seen, which is in qualitative agreement with specific predictions from
hydrodynamic models. The results provide further evidence for the production of
a collective, strongly coupled system in heavy-ion collisions at the LHC.


  We present arguments proving that the results obtained by Hassanabadi and
coworkers in the study of the D-dimensional Schr\"odinger equation with
molecular Hua potential through the supersymmetry method in quantum mechanics
are incorrect. We identified the inconsistencies in their reasoning on the
allowed values of the parameter q and we constructed the correct energy
spectrum.


  In this paper we investigate a potential use of fluid approximation
techniques in the context of stochastic model checking of CSL formulae. We
focus on properties describing the behaviour of a single agent in a (large)
population of agents, exploiting a limit result known also as fast simulation.
In particular, we will approximate the behaviour of a single agent with a
time-inhomogeneous CTMC which depends on the environment and on the other
agents only through the solution of the fluid differential equation. We will
prove the asymptotic correctness of our approach in terms of satisfiability of
CSL formulae and of reachability probabilities. We will also present a
procedure to model check time-inhomogeneous CTMC against CSL formulae.


  In this paper, we investigate a partially observable zero sum games where the
state process is a discrete time Markov chain. We consider a general utility
function in the optimization criterion. We show the existence of value for both
finite and infinite horizon games and also establish the existence of optimal
polices. The main step involves converting the partially observable game into a
completely observable game which also keeps track of the total discounted
accumulated reward/cost.


  The idea of relating the infrared and ultraviolet cutoffs is applied to
Brans-Dicke theory of gravitation. We find that extended holographic dark
energy from the Hubble scale or the particle horizon as the infrared cutoff
will not give accelerating expansion. The dynamical cosmological constant with
the event horizon as the infrared cutoff is a viable dark energy model.


  Deep Language Models (DLMs) provide a novel computational paradigm for
understanding the mechanisms of natural language processing in the human brain.
Unlike traditional psycholinguistic models, DLMs use layered sequences of
continuous numerical vectors to represent words and context, allowing a
plethora of emerging applications such as human-like text generation. In this
paper we show evidence that the layered hierarchy of DLMs may be used to model
the temporal dynamics of language comprehension in the brain by demonstrating a
strong correlation between DLM layer depth and the time at which layers are
most predictive of the human brain. Our ability to temporally resolve
individual layers benefits from our use of electrocorticography (ECoG) data,
which has a much higher temporal resolution than noninvasive methods like fMRI.
Using ECoG, we record neural activity from participants listening to a
30-minute narrative while also feeding the same narrative to a high-performing
DLM (GPT2-XL). We then extract contextual embeddings from the different layers
of the DLM and use linear encoding models to predict neural activity. We first
focus on the Inferior Frontal Gyrus (IFG, or Broca's area) and then extend our
model to track the increasing temporal receptive window along the linguistic
processing hierarchy from auditory to syntactic and semantic areas. Our results
reveal a connection between human language processing and DLMs, with the DLM's
layer-by-layer accumulation of contextual information mirroring the timing of
neural activity in high-order language areas.


  We study the construction of the minimum cost spanning geometric graph of a
given rooted point set $P$ where each point of $P$ is connected to the root by
a path that satisfies a given property. We focus on two properties, namely the
monotonicity w.r.t. a single direction ($y$-monotonicity) and the monotonicity
w.r.t. a single pair of orthogonal directions ($xy$-monotonicity). We propose
algorithms that compute the rooted $y$-monotone ($xy$-monotone) minimum
spanning tree of $P$ in $O(|P|\log^2 |P|)$ (resp. $O(|P|\log^3 |P|)$) time when
the direction (resp. pair of orthogonal directions) of monotonicity is given,
and in $O(|P|^2\log|P|)$ time when the optimum direction (resp. pair of
orthogonal directions) has to be determined. We also give simple algorithms
which, given a rooted connected geometric graph, decide if the root is
connected to every other vertex by paths that are all monotone w.r.t. the same
direction (pair of orthogonal directions).


  Recent work has demonstrated an attractor mechanism for extremal rotating
black holes subject to the assumption of a near-horizon SO(2,1) symmetry. We
prove the existence of this symmetry for any extremal black hole with the same
number of rotational symmetries as known four and five dimensional solutions
(including black rings). The result is valid for a general two-derivative
theory of gravity coupled to abelian vectors and uncharged scalars, allowing
for a non-trivial scalar potential. We prove that it remains valid in the
presence of higher-derivative corrections. We show that SO(2,1)-symmetric
near-horizon solutions can be analytically continued to give SU(2)-symmetric
black hole solutions. For example, the near-horizon limit of an extremal 5D
Myers-Perry black hole is related by analytic continuation to a non-extremal
cohomogeneity-1 Myers-Perry solution.


  Image retrieval is a fundamental problem in computer vision. This paper
presents our 3rd place detailed solution to the Google Landmark Retrieval 2020
challenge. We focus on the exploration of data cleaning and models with metric
learning. We use a data cleaning strategy based on embedding clustering.
Besides, we employ a data augmentation method called Corner-Cutmix, which
improves the model's ability to recognize multi-scale and occluded landmark
images. We show in detail the ablation experiments and results of our method.


  In this paper we consider a slab of viscous incompressible fluid bounded
above by a free boundary, bounded below by a flat rigid interface, and acted on
by gravity. The unique equilibrium is a flat slab of quiescent fluid. It is
well-known that equilibria are asymptotically stable but that the rate of decay
to equilibrium depends heavily on whether or not surface tension forces are
accounted for at the free interface. The aim of the paper is to better
understand the decay rate by studying a generalization of the linearized
dynamics in which the surface tension operator is replaced by a more general
fractional-order differential operator, which allows us to continuously
interpolate between the case without surface tension and the case with surface
tension. We study the decay of the linearized problem in terms of the choice of
the generalized operator and in terms of the horizontal cross-section. In the
case of a periodic cross-section we identify a critical order of the
differential operator at which the decay rate transitions from almost
exponential to exponential.


  We elaborate the generalizations of the approach to gauge-invariant
deformations of the gauge theories developed in our previous work [1]. In the
given paper we construct the exact transformations defying the gauge-invariant
deformed theory on the base of initial gauge theory with irreducible open gauge
algebra. Like in [1], for the theories with open gauge algebras these
transformations are the shifts of the initial gauge fields $A \rightarrow
A+h(A)$, with the help of the arbitrary and in general non-local functions
$h(A)$. The results are applied to study the quantum aspects of the deformed
theories. We derive the exact relation between the quantum effective actions
for the above classical theories, where one is obtained from another with the
help of the deformation.


  We propose a conjecture to compute the all-order asymptotic expansion of the
colored Jones polynomial of the complement of a hyperbolic knot, J_N(q =
exp(2u/N)) when N goes to infinity. Our conjecture claims that the asymptotic
expansion of the colored Jones polynomial is a the formal wave function of an
integrable system whose semiclassical spectral curve S would be the SL_2(C)
character variety of the knot (the A-polynomial), and is formulated in the
framework of the topological recursion. It takes as starting point the proposal
made recently by Dijkgraaf, Fuji and Manabe (who kept only the perturbative
part of the wave function, and found some discrepancies), but it also contains
the non-perturbative parts, and solves the discrepancy problem. These
non-perturbative corrections are derivatives of Theta functions associated to
S, but the expansion is still in powers of 1/N due to the special properties of
A-polynomials. We provide a detailed check for the figure-eight knot and the
once-punctured torus bundle L^2R. We also present a heuristic argument inspired
from the case of torus knots, for which knot invariants can be computed from a
matrix model.


  We present 2D Monte Carlo radiative transfer simulations of flattened
prestellar cores. We argue the importance of observing prestellar cores near
the peak of their emission spectra, and we point out observable characteristic
features on isophotal maps of asymmetric cores at FIR wavelengths that are
indicative of the cores' density and temperature structure. These features are
on scales 1/5 to 1/3 of the overall core size, and so high resolution
observations are needed to observe them. Finally, we discuss the importance of
determining the temperatures of prestellar cores with high accuracy.


  We investigate the individual activity coefficients of ions in LaCl$_{3}$
using our theory that is based on the competition of ion-ion (II) and ion-water
(IW) interactions (Vincze et al., J. Chem. Phys. 133, 154507, 2010). The II
term is computed from Grand Canonical Monte Carlo simulations on the basis of
the implicit solvent model of electrolytes using hard sphere ions with Pauling
radii. The IW term is computed on the basis of Born's treatment of solvation
using experimental hydration free energies. This agreement is remarkable
considering the facts that (i) the result is the balance of two terms that are
large in absolute value (up to $20 kT$) but opposite in sign, and (ii) that our
model does not contain any adjustable parameter. All the parameters used in the
model are taken from experiments: concentration dependent dielectric constant,
hydration free energies, Pauling radii.


  We present a series of RXTE observations of the nearby obscured Seyfert
galaxies ESO103-G35, IC5063, NGC4507 and NGC7172. The period of monitoring
ranges from seven days for NGC7172 up to about seven months for ESO103-G035.
The spectra of all galaxies are well fit with a highly obscured (N_H>10^23
cm-2) power-law and an Fe line at 6.4 keV. We find strong evidence for the
presence of a reflection component in ESO103-G35 and NGC4507. The observed flux
presents strong variability on day timescales in all objects. Spectral
variability is also detected in the sense that the spectrum steepens with
increasing flux similar to the behaviour witnessed in some Seyfert-1 galaxies.


  We show that the mutual fund theorems of Merton (1971) extend to the problem
of optimal investment to minimize the probability of lifetime ruin. We obtain
two such theorems by considering a financial market both with and without a
riskless asset for random consumption. The striking result is that we obtain
two-fund theorems despite the additional source of randomness from consumption.


  POI-level geo-information of social posts is critical to many location-based
applications and services. However, the multi-modality, complexity and diverse
nature of social media data and their platforms limit the performance of
inferring such fine-grained locations and their subsequent applications. To
address this issue, we present a transformer-based general framework, which
builds upon pre-trained language models and considers non-textual data, for
social post geolocation at the POI level. To this end, inputs are categorized
to handle different social data, and an optimal combination strategy is
provided for feature representations. Moreover, a uniform representation of
hierarchy is proposed to learn temporal information, and a concatenated version
of encodings is employed to capture feature-wise positions better. Experimental
results on various social datasets demonstrate that three variants of our
proposed framework outperform multiple state-of-art baselines by a large margin
in terms of accuracy and distance error metrics.


  Here we investigate the connection between topological order and the
geometric entanglement, as measured by the logarithm of the overlap between a
given state and its closest product state of blocks. We do this for a variety
of topologically-ordered systems such as the toric code, double semion, color
code, and quantum double models. As happens for the entanglement entropy, we
find that for sufficiently large block sizes the geometric entanglement is, up
to possible sub-leading corrections, the sum of two contributions: a bulk
contribution obeying a boundary law times the number of blocks, and a
contribution quantifying the underlying pattern of long-range entanglement of
the topologically-ordered state. This topological contribution is also present
in the case of single-spin blocks in most cases, and constitutes an alternative
characterisation of topological order for these quantum states based on a
multipartite entanglement measure. In particular, we see that the topological
term for the 2D color code is twice as much as the one for the toric code, in
accordance with recent renormalization group arguments. Motivated by this, we
also derive a general formalism to obtain upper- and lower-bounds to the
geometric entanglement of states with a non-Abelian symmetry, and which we use
to analyse quantum double models. Furthermore, we also analyse the robustness
of the topological contribution using renormalization and perturbation theory
arguments, as well as a numerical estimation for small systems. Some of our
results rely on the ability to disentangle single sites from the system, which
is always possible in our framework. Aditionally, we relate our results to the
relative entropy of entanglement in topological systems, and discuss some
tensor network numerical approaches that could be used to extract the
topological contribution for large systems beyond exactly-solvable models.


  We provide the mathematical framework which elucidates the way of using a
Tully-Fisher (TF) like relation in the determination of the Hubble constant
$H_0$, as well as for distances of galaxies. The methods related to the
so-called Direct and Inverse TF Relations (herein DTF and ITF) are interpreted
as maximum likelihood statistics. We show that, as long as the same model is
used for the calibration of the TF relation and for the determination of $H_0$,
we obtain a coherent Hubble's constant. The choice of the model is motivated by
reasons of robustness of statistics, it depends on selection effects in
observation which are present in the sample. The difference on the distance
estimates when using either the ITF or the DTF model is only due to random
fluctuations. It is interesting to point out that the DTF estimate does not
depend on the luminosity distribution of sources. Both statistics show a
correction for a bias, inadequately believed to be of Malmquist type. The
repercussion of measurement errors, and additional selection effects are also
analyzed


  Common models describing magnetotransport properties of periodically
modulated two--dimensional systems often either directly start from a
semiclassical approach or give results well conceivable within the
semiclassical framework. Recently, magnetoresistance oscillations have been
found on samples with strong unilateral modulation and short period (d=15 nm)
which cannot be explained on a semiclassical level (magnetic breakdown). We use
a simple fully quantum mechanical model which gives us both magnetoresistance
data nicely comparing to the experiments and a good intuitive insight into the
effects taking place in the system.


  Traditional email encryption schemes are vulnerable to EFail attacks, which
exploit the lack of message authentication by manipulating ciphertexts and
exfiltrating plaintext via HTML backchannels. Swiss Post's IncaMail, a secure
email service for transmitting legally binding, encrypted, and verifiable
emails, counters EFail attacks using an authenticated-encryption with
associated data (AEAD) encryption scheme to ensure message privacy and
authentication between servers. IncaMail relies on a trusted infrastructure
backend and encrypts messages per user policy. This paper presents a revised
IncaMail architecture that offloads the majority of cryptographic operations to
clients, offering benefits such as reduced computational load and energy
footprint, relaxed trust assumptions, and per-message encryption key policies.
Our proof-of-concept prototype and benchmarks demonstrate the robustness of the
proposed scheme, with client-side WebAssembly-based cryptographic operations
yielding significant performance improvements (up to ~14x) over conventional
JavaScript implementations.