  During the past decade, several studies have been conducted to estimate the
false positive error rate (FPR) associated with latent fingerprint examination.
The so-called Black-box study by Ulery et al. is regularly used to support the
claim that the FPR in fingerprint examination is reasonably low (0.1%). The
Ulery et al.'s estimate of the FPR is supported by the results of the extensive
study of the overall fingerprint examination process by Langenburg. In 2014,
the Miami Dade Police Department (MDPD) Forensic Services Bureau conducted
research to study the false positive error rate associated with latent
fingerprint examination. They report that approximately 3.0% of latent
fingerprint examinations result in a false positive conclusion. Their estimate
of the FPR becomes as high as 4.2% when inconclusive decisions are excluded
from the calculation. In their 2016 report, the President's Council of Advisors
on Science and Technology (PCAST) proposes that the MDPD FPR estimate be used
to inform jurors that errors occur at a detectable rate in fingerprint
examination; more specifically, they declare that false positives may occur as
often as 1 in 18 cases. The large discrepancy between the FPR estimates
reported by Ulery et al. and Langenburg on the one hand, and the MDPD on the
other hand, causes a great deal of controversy. In this paper, we review the
MDPD study and the various error rate calculations that have been proposed to
interpret its data. To assess the appropriateness of the different proposed
estimates, we develop a model that re-creates the MDPD study. This model allows
us to estimate the expected number of false positive conclusions that should be
obtained with any proposed FPR and compare this number to the actual number of
erroneous identifications observed by MDPD.


  We derive the equations of motion for Palatini F(R) gravity by applying an
entropy balance law T dS= \delta Q+\delta N to the local Rindler wedge that can
be constructed at each point of spacetime. Unlike previous results for metric
F(R), there is no bulk viscosity term in the irreversible flux \delta N. Both
theories are equivalent to particular cases of Brans-Dicke scalar-tensor
gravity. We show that the thermodynamical approach can be used ab initio also
for this class of gravitational theories and it is able to provide both the
metric and scalar equations of motion. In this case, the presence of an
additional scalar degree of freedom and the requirement for it to be dynamical
naturally imply a separate contribution from the scalar field to the heat flux
\delta Q. Therefore, the gravitational flux previously associated to a bulk
viscosity term in metric F(R) turns out to be actually part of the reversible
thermodynamics. Hence we conjecture that only the shear viscosity associated
with Hartle-Hawking dissipation should be associated with irreversible
thermodynamics.


  The Perseus molecular cloud complex is a ~30pc long chain of molecular clouds
most well-known for the two star-forming clusters NGC1333 and IC348 and the
well-studied outflow source in B5. However, when studied at mid- to
far-infrared wavelengths the region is dominated by a ~10pc diameter shell of
warm dust, likely generated by an HII region caused by the early B-star
HD278942. Using a revised calibration technique the COMPLETE team has produced
high-sensitivity temperature and column-density maps of the Perseus region from
IRAS Sky Survey Atlas (ISSA) 60 and 100um data. In this paper, we combine the
ISSA based dust-emission maps with other observations collected as part of the
COMPLETE Survey, along with archival H-alpha and MSX observations. Molecular
line observations from FCRAO and extinction maps constructed by applying the
NICER method to the 2MASS catalog provide independent estimates of the ``true''
column-density of the shell. H-alpha emission in the region of the shell
confirms that it is most likely an HII region located behind the cloud complex,
and 8um data from MSX indicates that the shell may be interacting with the
cloud. Finally, the two polarisation components previously seen towards
background stars in the region can be explained by the association of the
stronger component with the shell. If confirmed, this would be the first
observation of a parsec-scale swept-up magnetic field.


  When a superconducting tunnel junction at a finite voltage is irradiated with
microwaves, the interplay between the alternating Josephson current and the ac
field gives rise to steps in the dc current known as Shapiro steps. In this
work we predict that in a mesoscopic structure connected to several
superconducting terminals one can induce Shapiro-like steps in the absence of
any external radiation simply by tuning the voltages of the leads. To
illustrate this effect we make quantitative predictions for a three-terminal
structure which comprises a diffusive superconductor-normal
metal-superconductor junction and a tunneling probe, a set-up which can be
realized experimentally.


  We show that offline actor-critic reinforcement learning can scale to large
models - such as transformers - and follows similar scaling laws as supervised
learning. We find that offline actor-critic algorithms can outperform strong,
supervised, behavioral cloning baselines for multi-task training on a large
dataset containing both sub-optimal and expert behavior on 132 continuous
control tasks. We introduce a Perceiver-based actor-critic model and elucidate
the key model features needed to make offline RL work with self- and
cross-attention modules. Overall, we find that: i) simple offline actor critic
algorithms are a natural choice for gradually moving away from the currently
predominant paradigm of behavioral cloning, and ii) via offline RL it is
possible to learn multi-task policies that master many domains simultaneously,
including real robotics tasks, from sub-optimal demonstrations or
self-generated data.


  This paper develops an innovative method that enables neural networks to
generate and utilize knowledge graphs, which describe their concept-level
knowledge and optimize network parameters through alignment with human-provided
knowledge. This research addresses a gap where traditionally, network-generated
knowledge has been limited to applications in downstream symbolic analysis or
enhancing network transparency. By integrating a novel autoencoder design with
the Vector Symbolic Architecture (VSA), we have introduced auxiliary tasks that
support end-to-end training. Our approach eschews traditional dependencies on
ontologies or word embedding models, mining concepts from neural networks and
directly aligning them with human knowledge. Experiments show that our method
consistently captures network-generated concepts that align closely with human
knowledge and can even uncover new, useful concepts not previously identified
by humans. This plug-and-play strategy not only enhances the interpretability
of neural networks but also facilitates the integration of symbolic logical
reasoning within these systems.


  This paper explores various classes of invariant subspaces of the classical
Ces\`{a}ro operator $C$ on the Hardy space $H^2$. We provide a new
characterization of the finite co-dimensional $C$-invariant subspaces, based on
earlier work of the first two authors, and determine exactly which model spaces
are $C$-invariant subspaces. We also describe the $C$-invariant subspaces
contained in model spaces and establish that they are all cyclic. Along the
way, we re-examine an associated Hilbert space of analytic functions on the
unit disk developed by Kriete and Trutt. We also make a connection between the
adjoint of the Ces\`{a}ro operator and certain composition operators on $H^2$
which have universal translates in the sense of Rota.


  Transformers have recently emerged as powerful neural networks for graph
learning, showcasing state-of-the-art performance on several graph property
prediction tasks. However, these results have been limited to small-scale
graphs, where the computational feasibility of the global attention mechanism
is possible. The next goal is to scale up these architectures to handle very
large graphs on the scale of millions or even billions of nodes. With
large-scale graphs, global attention learning is proven impractical due to its
quadratic complexity w.r.t. the number of nodes. On the other hand,
neighborhood sampling techniques become essential to manage large graph sizes,
yet finding the optimal trade-off between speed and accuracy with sampling
techniques remains challenging. This work advances representation learning on
single large-scale graphs with a focus on identifying model characteristics and
critical design constraints for developing scalable graph transformer (GT)
architectures. We argue such GT requires layers that can adeptly learn both
local and global graph representations while swiftly sampling the graph
topology. As such, a key innovation of this work lies in the creation of a fast
neighborhood sampling technique coupled with a local attention mechanism that
encompasses a 4-hop reception field, but achieved through just 2-hop
operations. This local node embedding is then integrated with a global node
embedding, acquired via another self-attention layer with an approximate global
codebook, before finally sent through a downstream layer for node predictions.
The proposed GT framework, named LargeGT, overcomes previous computational
bottlenecks and is validated on three large-scale node classification
benchmarks. We report a 3x speedup and 16.8% performance gain on ogbn-products
and snap-patents, while we also scale LargeGT on ogbn-papers100M with a 5.9%
performance improvement.


  Self-propelled particles have been experimentally shown to orbit spherical
obstacles and move along surfaces. Here, we theoretically and numerically
investigate this behavior for a hydrodynamic squirmer interacting with
spherical objects and flat walls using three different methods of approximately
solving the Stokes equations: The method of reflections, which is accurate in
the far field; lubrication theory, which describes the close-to-contact
behavior; and a lattice Boltzmann solver that accurately accounts for
near-field flows. The method of reflections predicts three distinct behaviors:
orbiting/sliding, scattering, and hovering, with orbiting being favored for
lower curvature as in the literature. Surprisingly, it also shows backward
orbiting/sliding for sufficiently strong pushers, caused by fluid recirculation
in the gap between the squirmer and the obstacle leading to strong forces
opposing forward motion. Lubrication theory instead suggests that only hovering
is a stable point for the dynamics. We therefore employ lattice Boltzmann to
resolve this discrepancy and we qualitatively reproduce the richer far-field
predictions. Our results thus provide insight into a possible mechanism of
mobility reversal mediated solely through hydrodynamic interactions with a
surface.


  Let $\mathcal{A}$ and $\mathcal{B}$ be two unital complex $\ast $-algebras
such that $\mathcal{A}$ has a nontrivial projection. In this paper, we study
the structure of bijective mappings $\Phi :\mathcal{A}\rightarrow \mathcal{B}$
preserving sum of triple products $\alpha_{1} ab^{*}c+\alpha_{2}
acb^{*}+\alpha_{3} b^{*}ac +\alpha_{4} cab^{*}+\alpha_{5} b^{*}ca+\alpha_{6}
cb^{*}a,$ where the scalars $\{\alpha_{k}\}_{k=1}^{6}$ are complex numbers
satisfying some conditions. Applications of obtained results are given.


  The spin-dependent transport properties of armchair graphene nanoribbons in
the presence of extrinsic spin-orbit coupling induced by a random distribution
of Nickel adatoms is studied. By combining a recursive Green's function
formalism with density functional theory, we explore the influence of ribbon
length and metal adatom concentration on the conductance. At a given length, we
observed a significant enhancement of the spin-flip channel around resonances
and at energies right above the Fermi level. We also estimate the
spin-relaxation length, finding values on the order of tens of micrometers at
low Ni adatom concentrations. This study is conducted at singular ribbon
lengths entirely from fully ab-initio methods, providing indirectly evidence
that the Dyakonov-Perel spin relaxation mechanism might be the dominant at low
concentrations as well as the observation of oscillations in the
spin-polarization.


  A draft memory model (DM) for neural networks with spike propagation delay
(SNNwD) is described. Novelty in this approach are that the DM learns
immediately, with stimuli presented once, without synaptic weight changes, and
without external learning algorithm. Basal on this model is to trap spikes
within neural loops. In order to construct the DM we developed two functional
blocks, also described herein. The decoder block receives input from a single
spikes source and connect it to one among many outputs. The selector block
operates in the opposite direction, receiving many spikes sources and
connecting one of them to a single output. We realized conceptual proofs by
testing the DM in the prime numbers classifying task. This activation-based
memory can be used as immediate and short-term memory.


  The COVID-19 pandemic, caused by the severe acute respiratory syndrome
coronavirus 2, emerged into a world being rapidly transformed by artificial
intelligence (AI) based on big data, computational power and neural networks.
The gaze of these networks has in recent years turned increasingly towards
applications in healthcare. It was perhaps inevitable that COVID-19, a global
disease propagating health and economic devastation, should capture the
attention and resources of the world's computer scientists in academia and
industry. The potential for AI to support the response to the pandemic has been
proposed across a wide range of clinical and societal challenges, including
disease forecasting, surveillance and antiviral drug discovery. This is likely
to continue as the impact of the pandemic unfolds on the world's people,
industries and economy but a surprising observation on the current pandemic has
been the limited impact AI has had to date in the management of COVID-19. This
correspondence focuses on exploring potential reasons behind the lack of
successful adoption of AI models developed for COVID-19 diagnosis and
prognosis, in front-line healthcare services. We highlight the moving clinical
needs that models have had to address at different stages of the epidemic, and
explain the importance of translating models to reflect local healthcare
environments. We argue that both basic and applied research are essential to
accelerate the potential of AI models, and this is particularly so during a
rapidly evolving pandemic. This perspective on the response to COVID-19, may
provide a glimpse into how the global scientific community should react to
combat future disease outbreaks more effectively.


  With healthcare being critical aspect, health insurance has become an
important scheme in minimizing medical expenses. Following this, the healthcare
industry has seen a significant increase in fraudulent activities owing to
increased insurance, and fraud has become a significant contributor to rising
medical care expenses, although its impact can be mitigated using fraud
detection techniques. To detect fraud, machine learning techniques are used.
The Centers for Medicaid and Medicare Services (CMS) of the United States
federal government released "Medicare Part D" insurance claims is utilized in
this study to develop fraud detection system. Employing machine learning
algorithms on a class-imbalanced and high dimensional medicare dataset is a
challenging task. To compact such challenges, the present work aims to perform
feature extraction following data sampling, afterward applying various
classification algorithms, to get better performance. Feature extraction is a
dimensionality reduction approach that converts attributes into linear or
non-linear combinations of the actual attributes, generating a smaller and more
diversified set of attributes and thus reducing the dimensions. Data sampling
is commonlya used to address the class imbalance either by expanding the
frequency of minority class or reducing the frequency of majority class to
obtain approximately equal numbers of occurrences for both classes. The
proposed approach is evaluated through standard performance metrics. Thus, to
detect fraud efficiently, this study applies autoencoder as a feature
extraction technique, synthetic minority oversampling technique (SMOTE) as a
data sampling technique, and various gradient boosted decision tree-based
classifiers as a classification algorithm. The experimental results show the
combination of autoencoders followed by SMOTE on the LightGBM classifier
achieved best results.


  We study the problem of coalitional manipulation in elections using the
unweighted Borda rule. We provide empirical evidence of the manipulability of
Borda elections in the form of two new greedy manipulation algorithms based on
intuitions from the bin-packing and multiprocessor scheduling domains. Although
we have not been able to show that these algorithms beat existing methods in
the worst-case, our empirical evaluation shows that they significantly
outperform the existing method and are able to find optimal manipulations in
the vast majority of the randomly generated elections that we tested. These
empirical results provide further evidence that the Borda rule provides little
defense against coalitional manipulation.


  The harmonic oscillator is one of the most studied systems in Physics with a
myriad of applications. One of the first problems solved in a Quantum Mechanics
course is calculating the energy spectrum of the simple harmonic oscillator
with analytic and algebraic approaches. In the algebraic solution, creation and
annihilation operators are introduced to factorize the Hamiltonian. This work
presents an algebraic solution for the simple harmonic oscillator in the
context of Classical Mechanics, exploring the Hamiltonian formalism. In this
solution, similarities between the canonical coordinates in a convenient basis
for the classical problem and the corresponding operators in Quantum Mechanics
are highlighted. Moreover, the presented algebraic solution provides a
straightforward procedure for the quantization of the classical harmonic
oscillator, motivating and justifying some operator definitions commonly used
to solve the correspondent problem in Quantum Mechanics.


  The Gottesman-Kitaev-Preskill (GKP) code may be used to overcome noise in
continuous variable quantum systems. However, preparing GKP states remains
experimentally challenging. We propose a method for preparing GKP states by
engineering a time-periodic Hamiltonian whose Floquet states are GKP states.
This Hamiltonian may be realized in a superconducting circuit comprising a
SQUID shunted by a superinductor and a capacitor, with a characteristic
impedance twice the resistance quantum. The GKP Floquet states can be prepared
by adiabatically tuning the frequency of the external magnetic flux drive. We
predict that highly squeezed $>11.9$ dB ($10.8$ dB) GKP magic states can be
prepared on a microsecond timescale, given a quality factor of $10^6$ ($10^5$)
and flux noise at typical rates.


  Riemannian Manifolds may be $C^{1,1}$ and the geometry of these manifolds is
investigated in \cite{Groah1}. Here, a similar analysis is given for
pseudohermitian, torsion-free manifolds whereby, instead of assuming that the
metric is parallel, it is assumed that the metric is pseudohermitian, a
condition adopted by Einstein and elaborated upon in \cite{Hlavaty}. At the
level of regularity assumed here, Einstein's formulation of the pseudohermitian
condition is not tensorial and so a reformulation of this condition is given
here. It is shown that a $C^{1,1}$ manifold is pseudohermitian and torsion-free
if and only if it is Riemannian.


  We explored cultural biases-individualism vs. collectivism-in ChatGPT across
three Western languages (i.e., English, German, and French) and three Eastern
languages (i.e., Chinese, Japanese, and Korean). When ChatGPT adopted an
individualistic persona in Western languages, its collectivism scores (i.e.,
out-group values) exhibited a more negative trend, surpassing their positive
orientation towards individualism (i.e., in-group values). Conversely, when a
collectivistic persona was assigned to ChatGPT in Eastern languages, a similar
pattern emerged with more negative responses toward individualism (i.e.,
out-group values) as compared to collectivism (i.e., in-group values). The
results indicate that when imbued with a particular social identity, ChatGPT
discerns in-group and out-group, embracing in-group values while eschewing
out-group values. Notably, the negativity towards the out-group, from which
prejudices and discrimination arise, exceeded the positivity towards the
in-group. The experiment was replicated in the political domain, and the
results remained consistent. Furthermore, this replication unveiled an
intrinsic Democratic bias in Large Language Models (LLMs), aligning with
earlier findings and providing integral insights into mitigating such bias
through prompt engineering. Extensive robustness checks were performed using
varying hyperparameter and persona setup methods, with or without social
identity labels, across other popular language models.


  BBP-type formulas are usually discovered experimentally, through computer
searches. In this paper, however, starting with two simple generators, and
hence without doing any computer searches, we derive a wide range of BBP-type
formulas in general bases. Many previously discovered BBP-type formulas turn
out to be particular cases of the formulas derived here.


  We continue the enumeration of plane lattice walks with small steps avoiding
the negative quadrant, initiated by the first author in 2016. We solve in
detail a new case, namely the king model where all eight nearest neighbour
steps are allowed. The associated generating function is proved to be the sum
of a simple, explicit D-finite series (related to the number of walks confined
to the first quadrant), and an algebraic one. This was already the case for the
two models solved by the first author in 2016. The principle of the approach is
also the same, but challenging theoretical and computational difficulties arise
as we now handle algebraic series of larger degree.
  We expect a similar algebraicity phenomenon to hold for the seven Weyl step
sets, which are those for which walks confined to the first quadrant can be
counted using the reflection principle. With this paper, this is now proved for
three of them. For the remaining four, we predict the D-finite part of the
solution, and in three of the four cases, give evidence for the algebraicity of
the remaining part.


  We describe an Arabic-Hebrew parallel corpus of TED talks built upon WIT3,
the Web inventory that repurposes the original content of the TED website in a
way which is more convenient for MT researchers. The benchmark consists of
about 2,000 talks, whose subtitles in Arabic and Hebrew have been accurately
aligned and rearranged in sentences, for a total of about 3.5M tokens per
language. Talks have been partitioned in train, development and test sets
similarly in all respects to the MT tasks of the IWSLT 2016 evaluation
campaign. In addition to describing the benchmark, we list the problems
encountered in preparing it and the novel methods designed to solve them.
Baseline MT results and some measures on sentence length are provided as an
extrinsic evaluation of the quality of the benchmark.


  Generative Adversarial Imitation Learning (GAIL) can learn policies without
explicitly defining the reward function from demonstrations. GAIL has the
potential to learn policies with high-dimensional observations as input, e.g.,
images. By applying GAIL to a real robot, perhaps robot policies can be
obtained for daily activities like washing, folding clothes, cooking, and
cleaning. However, human demonstration data are often imperfect due to
mistakes, which degrade the performance of the resulting policies. We address
this issue by focusing on the following features: 1) many robotic tasks are
goal-reaching tasks, and 2) labeling such goal states in demonstration data is
relatively easy. With these in mind, this paper proposes Goal-Aware Generative
Adversarial Imitation Learning (GA-GAIL), which trains a policy by introducing
a second discriminator to distinguish the goal state in parallel with the first
discriminator that indicates the demonstration data. This extends a standard
GAIL framework to more robustly learn desirable policies even from imperfect
demonstrations through a goal-state discriminator that promotes achieving the
goal state. Furthermore, GA-GAIL employs the Entropy-maximizing Deep P-Network
(EDPN) as a generator, which considers both the smoothness and causal entropy
in the policy update, to achieve stable policy learning from two
discriminators. Our proposed method was successfully applied to two
real-robotic cloth-manipulation tasks: turning a handkerchief over and folding
clothes. We confirmed that it learns cloth-manipulation policies without
task-specific reward function design. Video of the real experiments are
available at https://youtu.be/h_nII2ooUrE.


  We study a mixture of strongly interacting bosons and spinless fermions with
on-site repulsion in a three-dimensional optical lattice. For this purpose we
develop and apply a generalized DMFT scheme, which is exact in infinite
dimensions and reliably describes the full range from weak to strong coupling.
We restrict ourselves to half filling. For weak Bose-Fermi repulsion a
supersolid forms, in which bosonic superfluidity coexists with charge-density
wave order. For stronger interspecies repulsion the bosons become localized
while the charge density wave order persists. The system is unstable against
phase separation for weak repulsion among the bosons.


  The calculation of the partial widths of the Z boson in the effective scheme
is discussed. Simple formulae for Sin^2 theta_eff^lept, M_W, and Gamma_l in the
on-shell, MS, and effective renormalization schemes are presented. Their domain
of validity extends to low M_H values probed by current experiments, and to the
range of Delta alpha_hadr results from recent calculations. Physical
applications of these formulae are illustrated. A comparative analysis of the
above mentioned schemes is presented. The paper includes a discussion of the
discrepancy of the Sin^2 theta_eff^lept values derived from the leptonic and
hadronic sectors and of the shortcomings of theoretical error estimates of
Sin^2 theta_eff^lept based solely on those affecting M_W.


  The local to global principle for densities is a very convenient tool
proposed by Poonen and Stoll to compute the density of a given subset of the
integers. In this paper we provide an effective criterion to find all higher
moments of the density (e.g. the mean, the variance) of a subset of a finite
dimensional free module over the ring of algebraic integers of a number field.
More precisely, we provide a local to global principle that allows the
computation of all higher moments corresponding to the density, over a general
number field $K$. This work advances the understanding of local to global
principles for density computations in two ways: on one hand, it extends a
result of Bright, Browning and Loughran, where they provide the local to global
principle for densities over number fields; on the other hand, it extends the
recent result on a local to global principle for expected values over the
integers to both the ring of algebraic integers and to moments higher than the
expected value. To show how effective and applicable our method is, we compute
the density, mean and variance of Eisenstein polynomials and shifted Eisenstein
polynomials over number fields. This extends (and fully covers) results in the
literature that were obtained with ad-hoc methods.


  Precisely knowing an interaction Hamiltonian is crucial to realize quantum
information tasks, especially to experimentally demonstrate a quantum computer
and a quantum memory. We propose a scheme to experimentally evaluate the
spin-spin interaction for a two-qubit system by the weak measurement technique
initiated by Yakir Aharonov and his colleagues. Furthermore, we numerically
confirm our proposed scheme in a specific system of a nitrogen vacancy center
in diamond. This means that the weak measurement can also be taken as a
concrete example of the quantum process tomography.


  Finite distributive lattices whose join-meet ideals are of K\"onig type will
be classified. Furthermore, a class of polyominoes whose polyomino ideals are
of K\"onig type will be studied.


  The dependence of the Er3+ cathodoluminescence and selective emission on the
power of the YAG:Er3+ and Er2O3 excitation by an electron beam is
spectroscopically studied for applications in high-intensity radiation sources
of the visible and near-IR spectral ranges.


  We show how the scattering phase shift, the s-wave scattering length and the
p-wave scattering volume can be obtained from Riccati equations derived in
variable phase theory. We find general expressions that provide upper and lower
bounds for the scattering length and the scattering volume. We show how, in the
framework of the variable phase method, Levinson's theorem yields the number of
bound states supported by a potential. We report new results from a study of
the heteronuclear alkali dimers NaRb and LiRb. We consider $ab$ $initio$
molecular potentials for the X${}^1\Sigma^+$ and $a{}^3\Sigma^+$ states of both
dimers and compare and discuss results obtained from experimentally based
X${}^1\Sigma^+$ and $a{}^3\Sigma^+$ potentials of NaRb. We explore the mass
dependence of the scattering data by considering all isotopomers and we
calculate the numbers of bound states supported by the molecular potentials for
each isotopomer.


  We study the B ring's complex optical depth structure. The source of this
structure may be the complex dynamics of the Keplerian shear and the
self-gravity of the ring particles. The outcome of these dynamic effects
depends sensitively on the collisional and physical properties of the
particles. Two mechanisms can emerge that dominate the macroscopic physical
structure of the ring: self-gravity wakes and viscous overstability. Here we
study the interplay between these two mechanisms by using our recently
developed particle collision method that allows us to better model the
inter-particle contact physics. We find that for a constant ring surface
density and particle internal density, particles with rough surfaces tend to
produce axisymmetric ring features associated with the viscous overstability,
while particles with smoother surfaces produce self-gravity wakes.


  For a non-associative algebra $A$ with a derivation $d$, its derived algebra
$A^{(d)}$ is the same space equipped with new operations $a\succ b = d(a)b$,
$a\prec b = ad(b)$, $a,b\in A$. Given a variety ${\rm Var}$ of algebras, its
derived variety is generated by all derived algebras $A^{(d)}$ for all $A$ in
${\rm Var}$ and for all derivations $d$ of $A$. The same terminology is applied
to binary operads governing varieties of non-associative algebras. For example,
the operad of Novikov algebras is the derived one for the operad of
(associative) commutative algebras. We state a sufficient condition for every
algebra from a derived variety to be embeddable into an appropriate
differential algebra of the corresponding variety. We also find that for ${\rm
Var} = {\rm Zinb}$, the variety of Zinbiel algebras, there exist algebras from
the derived variety (which coincides with the class of pre-Novikov algebras)
that cannot be embedded into a Zinbiel algebra with a derivation.


  We consider Hodgkin-Huxley-type model that is a stiff ODE system with two
fast and one slow variables. For the parameter ranges under consideration the
original version of the model has unstable fixed point and the oscillating
attractor that demonstrates bifurcation from bursting to spiking dynamics. Also
a modified version is considered where the bistability occurs such that an area
in the parameter space appears where the fixed point becomes stable and
coexists with the bursting attractor. For these two systems we create
artificial neural networks that are able to reproduce their dynamics. The
created networks operate as recurrent maps and are trained on trajectory cuts
sampled at random parameter values within a certain range. Although the
networks are trained only on oscillatory trajectory cuts, it also discover the
fixed point of the considered systems. The position and even the eigenvalues
coincide very well with the fixed point of the initial ODEs. For the bistable
model it means that the network being trained only on one brunch of the
solutions recovers another brunch without seeing it during the training. These
results, as we see it, are able to trigger the development of new approaches to
complex dynamics reconstruction and discovering. From the practical point of
view reproducing dynamics with the neural network can be considered as a sort
of alternative method of numerical modeling intended for use with contemporary
parallel hard- and software.


  We investigate a stationary pair production cascade in the outer
magnetosphere of a spinning neutron star. The charge depletion due to global
flows of charged particles, causes a large electric field along the magnetic
field lines. Migratory electrons and/or positrons are accelerated by this field
to radiate curvature gamma-rays, some of which collide with the X-rays to
materialize as pairs in the gap. The replenished charges partially screen the
electric field, which is self-consistently solved, together with the
distribution functions of particles and gamma-rays. By solving the Vlasov
equations describing this pair production cascade, we demonstrate the existence
of a stationary gap in the outer magnetosphere of PSR B1055-52 for a wide range
of current densities flowing in the accelerator: From sub to super
Goldreich-Julian values. However, we find that the expected GeV spectrum
becomes very soft if the current density exceeds the Goldreich-Julian value. We
also demonstrate that the GeV spectrum softens with decreasing magnetic
inclination and with increasing distance to this pulsar. We thus conclude that
a sub-Goldreich-Julian current, a large magnetic inclination, and a small
distance (500 pc, say) are plausible to account for EGRET observations.
Furthermore, it is found that the TeV flux due to inverse Compton scatterings
of infrared photons whose spectrum is inferred from the Rayleigh-Jeans side of
the soft blackbody spectrum is much less than the observational upper limit.


  In this paper, the problem of classifying synthetic and natural texture
images is addressed. To tackle this problem, an innovative method is proposed
that combines concepts from corrosion modeling and cellular automata to
generate a texture descriptor. The core processes of metal (pitting) corrosion
are identified and applied to texture images by incorporating the basic
mechanisms of corrosion in the transition function of the cellular automaton.
The surface morphology of the image is analyzed before and during the
application of the transition function of the cellular automaton. In each
iteration the cumulative mass of corroded product is obtained to construct each
of the attributes of the texture descriptor. In a final step, this texture
descriptor is used for image classification by applying Linear Discriminant
Analysis. The method was tested on the well-known Brodatz and Vistex databases.
In addition, in order to verify the robustness of the method, its invariance to
noise and rotation were tested. To that end, different variants of the original
two databases were obtained through addition of noise to and rotation of the
images. The results showed that the method is effective for texture
classification according to the high success rates obtained in all cases. This
indicates the potential of employing methods inspired on natural phenomena in
other fields.


  We study the renormalization group flow of the Euclidean
Engle-Pereira-Rovelli-Livine and Freidel-Krasnov (EPRL-FK) spin foam model in
the large-$j$-limit. The vertex amplitude is deformed to include a cosmological
constant term. The state sum is reduced to describe a foliated spacetime whose
spatial slices are flat, isotropic and homogeneous. The model admits a
non-vanishing extrinsic curvature whereas the scale factor can expand or
contract at successive time steps.
  The reduction of degrees of freedom allows a numerical evaluation of certain
geometric observables on coarser and finer discretizations. Their comparison
defines the renormalization group (RG) flow of the model in the parameters
$(\alpha,\Lambda,G)$. We first consider the projection of the RG flow along the
$\alpha$ direction, which shows a UV-attractive fixed point. Then, we extend
our analysis to two- and three-dimensional parameter spaces. Most notably, we
find the indications of a fixed point in the $(\alpha,\Lambda,G)$ space showing
one repulsive and two attractive directions.


  Quantum machine learning with parametrised quantum circuits has attracted
significant attention over the past years as an early application for the era
of noisy quantum processors. However, the possibility of achieving concrete
advantages over classical counterparts in practical learning tasks is yet to be
demonstrated. A promising avenue to explore potential advantages is the
learning of data generated by quantum mechanical systems and presented in an
inherently quantum mechanical form. In this article, we explore the
applicability of quantum-data learning to practical problems in high-energy
physics, aiming to identify domain specific use-cases where quantum models can
be employed. We consider quantum states governed by one-dimensional lattice
gauge theories and a phenomenological quantum field theory in particle physics,
generated by digital quantum simulations or variational methods to approximate
target states. We make use of an ansatz based on quantum convolutional neural
networks and numerically show that it is capable of recognizing quantum phases
of ground states in the Schwinger model, (de)confinement phases from
time-evolved states in the $\mathbb{Z}_2$ gauge theory, and that it can extract
fermion flavor/coupling constants in a quantum simulation of parton shower. The
observation of non-trivial learning properties demonstrated in these benchmarks
will motivate further exploration of the quantum-data learning architecture in
high-energy physics.


  The wide and rapid adoption of deep learning by practitioners brought
unintended consequences in many situations such as in the infamous case of
Google Photos' racist image recognition algorithm; thus, necessitated the
utilization of the quantified uncertainty for each prediction. There have been
recent efforts towards quantifying uncertainty in conventional deep learning
methods (e.g., dropout as Bayesian approximation); however, their optimal use
in decision making is often overlooked and understudied. In this study, we
propose a mixed-integer programming framework for classification with reject
option (also known as selective classification), that investigates and combines
model uncertainty and predictive mean to identify optimal classification and
rejection regions. Our results indicate superior performance of our framework
both in non-rejected accuracy and rejection quality on several publicly
available datasets. Moreover, we extend our framework to cost-sensitive
settings and show that our approach outperforms industry standard methods
significantly for online fraud management in real-world settings.


  The $4f^6$ $^{7}F_0$ $\to 4f^6$ $^{5}D_0$ intra-configuration transition in
Sm:SrF$_2$ is forbidden for Sm$^{2+}$ ions in the octahedrally symmetric
substitution sites in SrF$_2$. We report the direct observation of this
transition using laser-induced fluorescence at cryogenic temperatures, and
measurements of the excited state lifetime and the excitation cross section. To
the best of our knowledge, this optical transition has the longest lived
excited state ever observed in a solid.


  In this work, we exploit the power of \emph{unambiguity} for the
complementation problem of B\"uchi automata by utilizing reduced run directed
acyclic graphs (DAGs) over infinite words, in which each vertex has at most one
predecessor. We then show how to use this type of reduced run DAGs as a
\emph{unified tool} to optimize \emph{both} rank-based and slice-based
complementation constructions for B\"uchi automata with a finite degree of
ambiguity. As a result, given a B\"uchi automaton with $n$ states and a finite
degree of ambiguity, the number of states in the complementary B\"uchi
automaton constructed by the classical rank-based and slice-based
complementation constructions can be improved, respectively, to $2^{O(n)}$ from
$2^{O(n\log n)}$ and to $O(4^n)$ from $O((3n)^n)$.


  We show that, for any prime p, a knot K in the 3-sphere is determined by its
p-fold cyclic unbranched covering. We also investigate when the m-fold cyclic
unbranched covering of a knot coincides with the n-fold cyclic unbranched
covering of another knot, for different coprime integers m and n.


  The model of the position-dependent noncommutativety in quantum mechanics is
proposed. We start with a given commutation relations between the operators of
coordinates [x^{i},x^{j}]=\omega^{ij}(x), and construct the complete algebra of
commutation relations, including the operators of momenta. The constructed
algebra is a deformation of a standard Heisenberg algebra and obey the Jacobi
identity. The key point of our construction is a proposed first-order
Lagrangian, which after quantization reproduces the desired commutation
relations. Also we study the possibility to localize the noncommutativety.


  Consider the principal $U(n)$ bundles over Grassmann manifolds
$U(n)\rightarrow U(n+m)/U(m) \stackrel{\pi}\rightarrow G_{n,m}$. Given $X \in
U_{m,n}(\mathbb{C})$ and a 2-dimensional subspace $\mathfrak{m}' \subset
\mathfrak{m} $ $ \subset \mathfrak{u}(m+n), $ assume either $\mathfrak{m}'$ is
induced by $X,Y \in U_{m,n}(\mathbb{C})$ with $X^{*}Y = \mu I_n$ for some $\mu
\in \mathbb{R}$ or by $X,iX \in U_{m,n}(\mathbb{C})$. Then $\mathfrak{m}'$
gives rise to a complete totally geodesic surface $S$ in the base space.
Furthermore, let $\gamma$ be a piecewise smooth, simple closed curve on $S$
parametrized by $0\leq t\leq 1$, and $\widetilde{\gamma}$ its horizontal lift
on the bundle $U(n) \rightarrow \pi^{-1}(S) \stackrel{\pi}{\rightarrow} S,$
which is immersed in $U(n) \rightarrow U(n+m)/U(m) \stackrel{\pi}\rightarrow
G_{n,m} $. Then $$ \widetilde{\gamma}(1)= \widetilde{\gamma}(0) \cdot ( e^{i
\theta} I_n) \text{\quad or \quad } \widetilde{\gamma}(1)=
\widetilde{\gamma}(0), $$ depending on whether the immersed bundle is flat or
not, where $A(\gamma)$ is the area of the region on the surface $S$ surrounded
by $\gamma$ and $\theta= 2 \cdot \tfrac{n+m}{2n} A(\gamma).$


  We present results from a set of experiments in this pilot study to
investigate the causal influence of user activity on various environmental
parameters monitored by occupant carried multi-purpose sensors. Hypotheses with
respect to each type of measurements are verified, including temperature,
humidity, and light level collected during eight typical activities: sitting in
lab / cubicle, indoor walking / running, resting after physical activity,
climbing stairs, taking elevators, and outdoor walking. Our main contribution
is the development of features for activity and location recognition based on
environmental measurements, which exploit location- and activity-specific
characteristics and capture the trends resulted from the underlying
physiological process. The features are statistically shown to have good
separability and are also information-rich. Fusing environmental sensing
together with acceleration is shown to achieve classification accuracy as high
as 99.13%. For building applications, this study motivates a sensor fusion
paradigm for learning individualized activity, location, and environmental
preferences for energy management and user comfort.


  We show that the cobordism groups of negative codimensional folds maps
contain direct sums of stable homotopy groups of Thom spaces of vector bundles
like the circle and the infinite dimensional projective space. We give
geometrical invariants which detect these direct summands.


  In this work we use the theory of Teleparallelism Equivalent to General
Relativity based in non-commutative space-time coordinates. In this context, we
write the corrections of the Schwarzschild solution. As a important result, we
find the corrections of the gravitational energy in the realm of teleparallel
gravity due to the non-commutativity of space-time. Then we interpret such
corrections as a manifestation of quantum theory in gravitational field.


  While in-context Learning (ICL) has proven to be an effective technique to
improve the performance of Large Language Models (LLMs) in a variety of complex
tasks, notably in translating natural language questions into Structured Query
Language (NL2SQL), the question of how to select the most beneficial
demonstration examples remains an open research problem. While prior works
often adapted off-the-shelf encoders to retrieve examples dynamically, an
inherent discrepancy exists in the representational capacities between the
external retrievers and the LLMs. Further, optimizing the selection of examples
is a non-trivial task, since there are no straightforward methods to assess the
relative benefits of examples without performing pairwise inference. To address
these shortcomings, we propose DeTriever, a novel demonstration retrieval
framework that learns a weighted combination of LLM hidden states, where rich
semantic information is encoded. To train the model, we propose a proxy score
that estimates the relative benefits of examples based on the similarities
between output queries. Experiments on two popular NL2SQL benchmarks
demonstrate that our method significantly outperforms the state-of-the-art
baselines on one-shot NL2SQL tasks.


  Depinning of nanoscale magnetic textures, such as domain walls, vortices and
skyrmions, is of paramount importance for magnetic storage and information
processing. We measure time-resolved magnetic switching statistics of an
individual, non-single-domain Fe$_3$O$_4$ nanoparticle using a micrometer-scale
superconducting quantum interference device. Surprisingly, a strong narrowing
of the waiting-time distributions before reaching the final state is observed
as compared to the exponential distribution expected for a single barrier. The
magnetization reversal across the nanostructure is thus shown to result from
multiple serial barriers in the minimum energy pathway.


  The main objective of the present project is to explore the viability of an
adaptive optics control system based exclusively on Field Programmable Gate
Arrays (FPGAs), making strong use of their parallel processing capability. In
an Adaptive Optics (AO) system, the generation of the Deformable Mirror (DM)
control voltages from the Wavefront Sensor (WFS) measurements is usually
through the multiplication of the wavefront slopes with a predetermined
reconstructor matrix. The ability to access several hundred hard multipliers
and memories concurrently in an FPGA allows performance far beyond that of a
modern CPU or GPU for tasks with a well defined structure such as Adaptive
Optics control. The target of the current project is to generate a signal for a
real time wavefront correction, from the signals coming from a Wavefront
Sensor, wherein the system would be flexible to accommodate all the current
Wavefront Sensing techniques and also the different methods which are used for
wavefront compensation. The system should also accommodate for different data
transmission protocols (like Ethernet, USB, IEEE 1394 etc.) for transmitting
data to and from the FPGA device, thus providing a more flexible platform for
Adaptive Optics control. Preliminary simulation results for the formulation of
the platform, and a design of a fully scalable slope computer is presented.


  Nanophotonic engineering of light-matter interaction at subwavelength scale
allows thermal radiation that is fundamentally different from that of
traditional thermal emitters and provides exciting opportunities for various
thermal-photonic applications. We propose a new kind of integrated and
electrically controlled thermal emitter that exploits layered metamaterials
with lithography-free and dielectric/metallic nanolayers. We demonstrate both
theoretically and experimentally that the proposed concept can create a strong
photonic bandgap in the visible regime and allow small impedance mismatch at
the infrared wavelengths, which gives rise to optical features of significantly
enhanced emissivity at the broad infrared wavelengths of 1.4-14 um as well as
effectively suppressed emissivity in the visible region. The electrically
driven metamaterial devices are optically and thermally stable at temperature
up to ~800 K with electro-optical conversion efficiency reaching ~30%. We
believe that the proposed high efficiency thermal emitters will pave the way
towards integrated infrared light source platforms for various thermal-photonic
applications and particularly provide a novel alternative for cost-effective,
compact, low glare, and energy-efficient infrared heating.


  We obtain a new extension of Rogers-Shephard inequality providing an upper
bound for the volume of the sum of two convex bodies $K$ and $L$. We also give
lower bounds for the volume of the $k$-th limiting convolution body of two
convex bodies $K$ and $L$. Special attention is paid to the $(n-1)$-th limiting
convolution body, for which a sharp inequality, which is equality only when
$K=-L$ is a simplex, is given. Since the $n$-th limiting convolution body of
$K$ and $-K$ is the polar projection body of $K$, these inequalities can be
viewed as an extension of Zhang's inequality.


  We present updated predictions for the total cross section for Higgs boson
production by gluon--gluon fusion in hadron collisions. Our calculation
includes the most advanced theoretical information available at present for
this observable: soft-gluon resummation up to next-to-next-to-leading
logarithmic accuracy, the exact treatment of the bottom-quark contribution up
to next-to-leading order, and two-loop electroweak effects. We adopt the most
recent parametrization of parton distribution functions at
next-to-next-to-leading order, and we evaluate the corresponding uncertainties.
In comparison with our previous central predictions, at the Tevatron the
difference ranges from +9% for m_H=115 GeV to -9% for m_H=200 GeV. At the LHC
the cross section is instead significantly increased. The effect goes from +30%
for m_H=115 GeV to +9% for m_H=300 GeV, and is mostly due to the new parton
distribution functions. We also provide new predictions for the LHC at
sqrt{s}=10 TeV.


  We present results on the X(3872), produced in $B^+ \to X(3872) K^+$ and $B^0
\to X(3872) K^0_S$ decays where $X(3872) \to J/\psi \pi^+ \pi^-$. We report the
first statistically significant observation of $B^0 \to X(3872) K^0_S$ and
measure the ratio of branching fractions to be $\frac{{\cal B}(B^0 \to
X(3872)K^0)} {{\cal B}(B^+ \to X(3872)K^+)} = 0.82 \pm 0.22 \pm 0.05$,
consistent with unity. The mass difference between the X(3872) states produced
in $B^+$ and $B^0$ decay is found to be $\delta M \equiv M_{XK^+} - M_{XK^0} =
(+0.18 \pm 0.89 \pm 0.26)$ MeV/$c^2$, consistent with zero. In addition, we
search for the X(3872) in the decay $B^0 \to X(3872)K^+\pi^-$, $X(3872) \to
J/\psi \pi^+ \pi^-$. We measure ${\cal B}(B^0 \to X(3872) (K^+ \pi^-)_{NR})
\times {\cal B}(X(3872) \to J/\psi \pi^+ \pi^-) = (8.1 \pm 2.0
^{+1.1}_{-1.4})\times 10^{-6}$ and we set the 90% C.L. limit, ${\cal B}(B^0 \to
X(3872)K^{*}(892)^0) \times {\cal B}(X(3872) \to J/\psi \pi^+ \pi^-) < 3.4
\times 10^{-6}$. The analysis is based on a 605 fb$^{-1}$ data sample collected
at the $\Upsilon(4S)$ with the Belle detector at the KEKB collider.


  The purpose of this paper is to present a method for automatic classification
of dialogue utterances and the results of applying that method to a corpus.
Superficial features of a set of training utterances (which we will call cues)
are taken as the basis for finding relevant utterance classes and for
extracting rules for assigning these classes to new utterances. Each cue is
assumed to partially contribute to the communicative function of an utterance.
Instead of relying on subjective judgments for the tasks of finding classes and
rules, we opt for using machine learning techniques to guarantee objectivity.


  Prospective registration of study protocols in clinical trial registries is a
useful way to minimize the risk of publication bias in meta-analysis, and
several clinical trial registries are available nowadays. However, they are
mainly used as a tool for searching studies and information submitted to the
registries has not been utilized as efficiently as it could. In addressing
publication bias in meta-analyses, sensitivity analysis with the Copas
selection model is a more objective alternative to widely-used graphical
methods such as the funnel-plot and the trim-and-fill method. Despite its
ability to quantify the potential impact of publication bias, a drawback of the
model is that some parameters not to be specified. This may result in some
difficulty in interpreting the results of the sensitivity analysis. In this
paper, we propose an alternative inference procedure for the Copas selection
model by utilizing information from clinical trial registries. Our method
provides a simple and accurate way to estimate all unknown parameters in the
Copas selection model. A simulation study revealed that our proposed method
resulted in smaller biases and more accurate confidence intervals than existing
methods. Furthermore, two published meta-analyses had been re-analysed to
demonstrate how to implement the proposed method in practice.


  We investigate the dynamical behaviour of the Ising model under a zero
temperature quench with the initial fraction of up spins $0\leq x\leq 1$. In
one dimension, the known results for persistence probability are verified; it
shows algebraic decay for both up and down spins asymptotically with different
exponents. It is found that the conventional finite size scaling is valid here.
In two dimensions however, the persistence probabilities are no longer
algebraic; in particular for $x\leq 0.5$, persistence for the up (minority)
spins shows the behaviour $P_{min}(t) \sim t^{-\gamma}\exp(-(t/\tau)^{\delta})$
with time $t$, while for the down (majority) spins, $P_{maj}(t)$ approaches a
finite value. We find that the timescale $\tau$ diverges as $(x_c-x)^{-
\lambda}$, where $x_c=0.5$ and $\lambda\simeq2.31$. The exponent $\gamma$
varies as $\theta_{2d}+c_0(x_c-x)^{\beta}$ where $\theta_{2d}\simeq0.215$ is
very close to the persistence exponent in two dimensions; $\beta\simeq1$. The
results in two dimensions can be understood qualitatively by studying the exit
probability, which for different system size is found to have the form $E(x) =
f\big[(\frac{x-x_c}{x_c})L^{1/\nu}\big]$, with $\nu \approx 1.47$. This result
suggests that $\tau \sim L^{\tilde{z}}$, where $\tilde{z} = \frac{\lambda}{\nu}
= 1.57 \pm 0.11$ is an exponent not explored earlier.


  Users have renewed interest in protecting their private data in the digital
space. When they don't believe that their privacy is sufficiently covered by
one platform, they will readily switch to another. Such an increasing level of
privacy awareness has made privacy preservation an essential research topic.
Nevertheless, new privacy attacks are emerging day by day. Therefore, a
holistic survey to compare the discovered techniques on attacks over privacy
preservation and their mitigation schemes is essential in the literature. We
develop a study to fill this gap by assessing the resilience of
privacy-preserving methods to various attacks and conducting a comprehensive
review of countermeasures from a broader perspective. First, we introduce the
fundamental concepts and critical components of privacy attacks. Second, we
comprehensively cover major privacy attacks targeted at anonymous data,
statistical aggregate data, and privacy-preserving models. We also summarize
popular countermeasures to mitigate these attacks. Finally, some promising
future research directions and related issues in the privacy community are
envisaged. We believe this survey will successfully shed some light on privacy
research and encourage researchers to entirely understand the resilience of
different existing privacy-preserving approaches.


  Finding optical setups producing measurement results with a targeted
probability distribution is hard as a priori the number of possible
experimental implementations grows exponentially with the number of modes and
the number of devices. To tackle this complexity, we introduce a method
combining reinforcement learning and simulated annealing enabling the automated
design of optical experiments producing results with the desired probability
distributions. We illustrate the relevance of our method by applying it to a
probability distribution favouring high violations of the Bell-CHSH inequality.
As a result, we propose new unintuitive experiments leading to higher Bell-CHSH
inequality violations than the best currently known setups. Our method might
positively impact the usefulness of photonic experiments for device-independent
quantum information processing.


  We consider the problem of the Winterbottom's construction and Young's
equation in the presence of a rough substate and establish their microscopic
validity within a 1+1-dimensional SOS type model. We then present the low
temperature expansion of the wall tension leading to the Wenzel's law for the
wall tension and its corrections. Finally, for a fix roughness, we compare the
influence of different geometries of the substrate on wetting properties. We
show that there is an optimal geometry with a given roughness for a certain
class of simple substrates. Our results are in agreement and explain recent
numerical simulations.


  Analogous to the bowl soliton of mean curvature flow, we construct
rotationally symmetric translating solutions to a very large class of extrinsic
curvature flows, namely those whose speeds are $\alpha$-homogeneous
($\alpha>0$), elliptic and symmetric with respect to the principal curvatures.
We show that these solutions are necessarily convex, and give precise criteria
for the speed functions which determine whether these translators are defined
on all of $\mathbb{R}^{n}$ or contained in a cylinder. For speeds that are
nonzero when at least one of the principal curvatures is nonzero, we are also
able to describe the asymptotics of the translator at infinity.


  The design of safe-critical control algorithms for systems under
Denial-of-Service (DoS) attacks on the system output is studied in this work.
We aim to address scenarios where attack-mitigation approaches are not
feasible, and the system needs to maintain safety under adversarial attacks. We
propose an attack-recovery strategy by designing a switching observer and
characterizing bounds in the error of a state estimation scheme by specifying
tolerable limits on the time length of attacks. Then, we propose a switching
control algorithm that renders forward invariant a set for the observer. Thus,
by satisfying the error bounds of the state estimation, we guarantee that the
safe set is rendered conditionally invariant with respect to a set of initial
conditions. A numerical example illustrates the efficacy of the approach.


  Using the diagonal entropy, we analyze the dynamical signatures of the
Lipkin-Meshkov-Glick (LMG) model excited-state quantum phase transition
(ESQPT). We first show that the time evolution of the diagonal entropy behaves
as an efficient indicator of the presence of an ESQPT. We further consider the
diagonal entropy as a random variable over a certain time interval and we find
that its associated probability distribution provides a clear distinction
between the different phases of ESQPT. We observe that the probability
distribution of the diagonal entropy at the ESQPT critical point has an
universal form, well described by a beta distribution, and we demonstrate that
a reliable detection of the ESQPT can be obtained from the diagonal entropy
central moments.


  We explore the electronic and related properties of
Cu$_{2}$MnAl$_{1-x}$Ga$_{x}$ with a first-principles, relativistic
multiscattering Green function approach. We discuss our results in relation to
existing experimental data and show that the electron-core hole interaction is
essential for the description of the optical spectra especially in describing
the X-ray absorption and magnetic circular dichroism spectra at the L$_{2,3}$
edges of Cu and Mn.


  Retrieval of text information from natural scene images and video frames is a
challenging task due to its inherent problems like complex character shapes,
low resolution, background noise, etc. Available OCR systems often fail to
retrieve such information in scene/video frames. Keyword spotting, an
alternative way to retrieve information, performs efficient text searching in
such scenarios. However, current word spotting techniques in scene/video images
are script-specific and they are mainly developed for Latin script. This paper
presents a novel word spotting framework using dynamic shape coding for text
retrieval in natural scene image and video frames. The framework is designed to
search query keyword from multiple scripts with the help of on-the-fly
script-wise keyword generation for the corresponding script. We have used a
two-stage word spotting approach using Hidden Markov Model (HMM) to detect the
translated keyword in a given text line by identifying the script of the line.
A novel unsupervised dynamic shape coding based scheme has been used to group
similar shape characters to avoid confusion and to improve text alignment.
Next, the hypotheses locations are verified to improve retrieval performance.
To evaluate the proposed system for searching keyword from natural scene image
and video frames, we have considered two popular Indic scripts such as Bangla
(Bengali) and Devanagari along with English. Inspired by the zone-wise
recognition approach in Indic scripts[1], zone-wise text information has been
used to improve the traditional word spotting performance in Indic scripts. For
our experiment, a dataset consisting of images of different scenes and video
frames of English, Bangla and Devanagari scripts were considered. The results
obtained showed the effectiveness of our proposed word spotting approach.


  Multiferroics possess two or more switchable states such as polarization,
magnetization, etc. Phonon excitations in multiferroic phase are strongly
modified by magnetoelectric coupling, spin-phonon coupling, and anharmonic
phonon-phonon interactions. Here, we have investigated the correlation between
phonons and multiferroic order parameters in hexagonal BaMO3: M = Ti1-x(Mn/Fe)x
systems using powder x-ray diffraction (PXRD), Raman spectroscopic, and
magnetic measurements. The structural transformation from a polar tetragonal to
a non-polar 6H-type hexagonal phase is observed as a function of doping
(Mn/Fe). Magnetic measurements reveal that the BaTi1-xMnxO3 is paramagnetic
while BaTi1-xFexO3 exhibits composition-dependent ferromagnetic order.
Importantly, Anomalous temperature-dependence is observed for two phonons (E1g
at ~ 152 cm-1 and A1g at ~ 636 cm-1) in both the systems exhibiting similar
trend with the doping (Mn/Fe) irrespective of the differences in their magnetic
ground state. Hence, we attribute the phonon anomalies in both the (Fe/Mn
doped) systems to strong anharmonic phonon-phonon interactions arising from
large atomic displacements involved in the vibrations. In addition, we have
also observed signatures of correlation of phonons with ferroelectric phase as
well as magnetically ordered state suggesting the presence of a strain-induced
magnetoelectric coupling in the doped compounds.


  We study the six-dimensional dilaton gravity Yang black holes of
hep-th/0607193, which carry (1,-1) charge in SU(2)xSU(2) gauge group. We find
what values of the asymptotic parameters (mass and scalar charge) lead to a
regular horizon, and show that there are no regular solutions with an extremal
horizon.


  Structure of $^{13}$Be was investigated with antisymmetrized molecular
dynamics. The variation after spin and parity projections was performed. An
unnatural parity $1/2^-$ state was suggested to be lower than $5/2^+$ state
indicating that vanishing of the N=8 magic number occurs in $^{13}$Be. A
low-lying $3/2^+$ state with a $2\hbar\omega$ configuration was also suggested.
Developed cluster structures were found in the intruder states. Lowering
mechanism of the intruder states was discussed in terms of molecular orbitals
around a $2\alpha$ core.


  We study X-ray and soft gamma-ray spectra from the hard state of the
accreting black-hole binary MAXI J1820+070. We perform analysis of two joint
spectra from NuSTAR and INTEGRAL, covering the range of 3--650 keV, and of an
average joint spectrum over the rise of the hard state, covering the 3--2200
keV range. The spectra are well modelled by Comptonization of soft seed
photons. However, the distributions of the scattering electrons are not purely
thermal; we find they have substantial high-energy tails, well modelled as
power laws. The photon tail in the average spectrum is detected well beyond the
threshold for electron-positron pair production, 511 keV. This allows us to
calculate the rate of the electron-positron pair production and put a lower
limit on the size of the source from pair equilibrium. At the fitted Thomson
optical depth of the Comptonizing plasma, the limit is about 4 gravitational
radii. If we adopt the sizes estimated by us from the reflection spectroscopy
of $>$20 gravitational radii, the fractional pair abundance becomes much less
than unity. The low pair abundance is confirmed by the lack of both an
annihilation feature and of a pair absorption cutoff above 511 keV in the
average spectrum.


  A useful and universal formula for the expectation value of the radial
operator in the presence of the Aharonov-Bohm flux and the Coulomb Field is
established. We find that the expectation value $< r^{\lambda}>$ $(-\infty \leq
\lambda \leq \infty)$ is greatly affected due to the non-local effect of the
magnetic flux although the Aharonov-Bohm flux does not have any dynamical
significance in classical mechanics. In particular, the quantum fluctuation
increases in the presence of the magnetic flux due to the Aharonov-Bohm effect.
In addition, the Virial theory in quantum mechanics is also constructed for the
spherically symmetric system under the Aharonov-Bohm effect.


  We proved by computer enumeration that the Jones polynomial distinguishes the
unknot for knots up to 22 crossings. Following an approach of Yamada, we
generated knot diagrams by inserting algebraic tangles into Conway polyhedra,
computed their Jones polynomials by a divide-and-conquer method, and tested
those with trivial Jones polynomials for unknottedness with the computer
program SnapPy. We employed numerous novel strategies for reducing the
computation time per knot diagram and the number of knot diagrams to be
considered. That made computations up to 21 crossings possible on a single
processor desktop computer. We explain these strategies in this paper. We also
provide total numbers of algebraic tangles up to 18 crossings and of Conway
polyhedra up to 22 vertices. We encountered new unknot diagrams with no
crossing-reducing pass moves in our search. We report one such diagram in this
paper.


  Many forms of locomotion, both natural and artificial, are dominated by
viscous friction in the sense that without power expenditure they quickly come
to a standstill. From geometric mechanics, it is known that for swimming at the
"Stokesian" (viscous; zero Reynolds number) limit, the motion is governed by a
reduced order "connection" model that describes how body shape change produces
motion for the body frame with respect to the world. In the "perturbed Stokes
regime" where inertial forces are still dominated by viscosity, but are not
negligible (low Reynolds number), we show that motion is still governed by a
functional relationship between shape velocity and body velocity, but this
function is no longer linear in shape change rate. We derive this model using
results from singular perturbation theory, and the theory of noncompact
normally hyperbolic invariant manifolds (NHIMs).
  Using the theoretical properties of this reduced-order model, we develop an
algorithm that estimates an approximation to the dynamics near a cyclic body
shape change (a "gait") directly from observational data of shape and body
motion. This extends our previous work which assumed kinematic "connection"
models. To compare the old and new algorithms, we analyze simulated swimmers
over a range of inertia to damping ratios. Our new class of models performs
well on the Stokesian regime, and over several orders of magnitude outside it
into the perturbed Stokes regime, where it gives significantly improved
prediction accuracy compared to previous work.
  In addition to algorithmic improvements, we thereby present a new class of
models that is of independent interest. Their application to data-driven
modeling improves our ability to study the optimality of animal gaits, and our
ability to use hardware-in-the-loop optimization to produce gaits for robots.


  The properties of a helix-like shaped QCD string are studied in the context
of the Lund fragmentation model, where the concept of a 3-dimensional structure
of the gluon field offers an alternative aproach to the modelling of the
transverse momentum of hadrons.
  The paper is focused on the phenomenology of the model, which introduces
correlations between transverse and longitudinal components of hadrons, as well
as azimuthal ordering of hadrons along the string. The similarities between
2-particle correlations stemming from the helix-string structure and those
commonly attributed to the Bose-Einstein interference are pointed out. It is
shown that the charged assymetry observed in the production of close hadron
pairs can be associated with fluctuations in the space-time history of the
string breakup, and with the presence of resonant hadronic states.


  In this article we define the algebra of differential modular forms and we
prove that it is generated by Eisenstein series of weight $2,4$ and 6. We
define Hecke operators on them, find some analytic relations between these
Eisenstein series and obtain them in a natural way as coefficients of a family
of elliptic curves. Then we describe the relation between the dynamics of a
foliation in $\C^3$ induced by the Ramanujan relations, with vanishing of
elliptic integrals. The fact that a complex manifold over the Moduli of
Polarized Hodge Structures in the case $h^{10}=h^{01}=1$ has an algebraic
structure with an action of an algebraic group plays a basic role in all of the
proofs.


  We demonstrate electrical spin injection into multilayer graphene (MLG) in a
lateral spin valve device from a highly spin-polarized Co2FeSi (CFS) Huesler
electrode. Exfoliated MLG was transferred onto pre-patterned epitaxial CFS
wires grown on an Si(111) substrate by a polymer-based transfer method. This
method enabled us to fabricate multiple single-crystal CFS electrodes in
contact with MLG. Electrical spin injection from CFS to MLG was detected
through non-local magnetoresistance (MR) measurement. A non-local spin signal
of 430 ohm was observed; this is the largest value among all reported non-local
MR values in graphene-based devices.


  Due to the brittle feature of carbon fiber reinforced plastic laminates,
mechanical multi-joint within these composite components show uneven load
distribution for each bolt, which weaken the strength advantage of composite
laminates. In order to reduce this defect and achieve the goal of even load
distribution in mechanical joints, we propose a machine learning-based
framework as an optimization method. Since that the friction effect has been
proven to be a significant factor in determining bolt load distribution, our
framework aims at providing optimal parameters including bolt-hole clearances
and tightening torques for a minimum unevenness of bolt load. A novel circuit
model is established to generate data samples for the training of artificial
networks at a relatively low computational cost. A database for all the
possible inputs in the design space is built through the machine learning
model. The optimal dataset of clearances and torques provided by the database
is validated by both the finite element method, circuit model, and an
experimental measurement based on the linear superposition principle, which
shows the effectiveness of this general framework for the optimization problem.
Then, our machine learning model is further compared and worked in
collaboration with commonly used optimization algorithms, which shows the
potential of greatly increasing computational efficiency for the inverse design
problem.


  In this paper we report the occurrence of chimera patterns in a network of
neuronal oscillators, which are coupled through {\it local}, synaptic {\it
gradient} coupling. We discover a new chimera pattern, namely the {\it
imperfect traveling chimera} where the incoherent traveling domain spreads into
the coherent domain of the network. Remarkably, we also find that chimera
states arise even for {\it one-way} local coupling, which is in contrast to the
earlier belief that only nonlocal, global or nearest neighbor local coupling
can give rise to chimera; this find further relaxes the essential connectivity
requirement of getting a chimera state. We choose a network of identical
bursting Hindmarsh-Rose neuronal oscillators and show that depending upon the
relative strength of the synaptic and gradient coupling several chimera
patterns emerge. We map all the spatiotemporal behaviors in parameter space and
identify the transitions among several chimera patterns, in-phase synchronized
state and global amplitude death state.


  Causal confusion is a phenomenon where an agent learns a policy that reflects
imperfect spurious correlations in the data. Such a policy may falsely appear
to be optimal during training if most of the training data contain such
spurious correlations. This phenomenon is particularly pronounced in domains
such as robotics, with potentially large gaps between the open- and closed-loop
performance of an agent. In such settings, causally confused models may appear
to perform well according to open-loop metrics during training but fail
catastrophically when deployed in the real world. In this paper, we study
causal confusion in offline reinforcement learning. We investigate whether
selectively sampling appropriate points from a dataset of demonstrations may
enable offline reinforcement learning agents to disambiguate the underlying
causal mechanisms of the environment, alleviate causal confusion in offline
reinforcement learning, and produce a safer model for deployment. To answer
this question, we consider a set of tailored offline reinforcement learning
datasets that exhibit causal ambiguity and assess the ability of active
sampling techniques to reduce causal confusion at evaluation. We provide
empirical evidence that uniform and active sampling techniques are able to
consistently reduce causal confusion as training progresses and that active
sampling is able to do so significantly more efficiently than uniform sampling.


  A liquid droplet is placed on a rotating helical fiber. We find that the
droplet may slide down, attach or climb up the fiber. We inspect experimentally
the domain of existence of these three behaviors as a function of the
geometrical characteristics of the fiber, its angle relatively to the
horizontal, the wetting properties of the fluid and the rotating speed of the
helix. A theoretical model is proposed in order to capture the boundaries of
the experimental phase diagram.


  We are concerned with the question of when a homeomorphism between
$C_p(X\times \tau)$ and $C_p(Y\times \tau)$ implies the existence of a
homeomorphism between $C_p(X\times \tau')$ and $C_p(Y\times \tau')$, where
$\tau$ and $\tau'$ are spaces of ordinals with some additional desired
properties.


  In this paper, we study a simple iterative method for finding the Dantzig
selector, which was designed for linear regression problems. The method
consists of two main stages. The first stage is to approximate the Dantzig
selector through a fixed-point formulation of solutions to the Dantzig selector
problem. The second stage is to construct a new estimator by regressing data
onto the support of the approximated Dantzig selector. We compare our method to
an alternating direction method, and present the results of numerical
simulations using both the proposed method and the alternating direction method
on synthetic and real data sets. The numerical simulations demonstrate that the
two methods produce results of similar quality, however the proposed method
tends to be significantly faster.


  We propose a program for finding the cyclicity of period annuli of quadratic
systems with centers of genus one. As a first step, we classify all such
systems and determine the essential one-parameter quadratic perturbations which
produce the maximal number of limit cycles. We compute the associated
Poincare-Pontryagin-Melnikov functions whose zeros control the number of limit
cycles. To illustrate our approach, we determine the cyclicity of the annuli of
two particular reversible systems.


  The experimentally observed amazing dependence of a critical magnetic field
Bc of a full field-induced spin polarization and a spin susceptibility of a
two-dimensional electron gas on the electron density can be explained by
screening of the Coulomb potential. The possibility of spontaneous full spin
polarization expected at lower electron densities also crucially depends on
screening.


  In this article we study Figalli and Gigli's formulation of optimal transport
between non-negative Radon measures in the setting of metric pairs. We carry
over classical characterisations of optimal plans to this setting and prove
that the resulting spaces of measures, $\mathcal{M}_p(X,A)$, are complete,
separable and geodesic whenever the underlying space, $X$, is so. We also prove
that, for $p>1$, $\mathcal{M}_p(X,A)$ preserves the property of being
non-branching, and for $p=2$ it preserves non-negative curvature in the
Alexandrov sense. Finally, we prove isometric embeddings of generalised spaces
of persistence diagrams $\mathcal{D}_p(X,A)$ into the corresponding spaces
$\mathcal{M}_p(X,A)$, generalising a result by Divol and Lacombe. As an
application of this framework, we show that several known geometric properties
of spaces of persistence diagrams follow from those of $\mathcal{M}_p(X,A)$,
including the fact that $\mathcal{D}_2(X,A)$ is an Alexandrov space of
non-negative curvature whenever $X$ is a proper non-negatively curved
Alexandrov space.


  Modern CNN-based object detectors rely on bounding box regression and
non-maximum suppression to localize objects. While the probabilities for class
labels naturally reflect classification confidence, localization confidence is
absent. This makes properly localized bounding boxes degenerate during
iterative regression or even suppressed during NMS. In the paper we propose
IoU-Net learning to predict the IoU between each detected bounding box and the
matched ground-truth. The network acquires this confidence of localization,
which improves the NMS procedure by preserving accurately localized bounding
boxes. Furthermore, an optimization-based bounding box refinement method is
proposed, where the predicted IoU is formulated as the objective. Extensive
experiments on the MS-COCO dataset show the effectiveness of IoU-Net, as well
as its compatibility with and adaptivity to several state-of-the-art object
detectors.


  If double neutron star mergers leave behind a massive magnetar rather than a
black hole, a bright early afterglow can follow the gravitational wave burst
(GWB) even if there is no short gamma-ray burst (SGRB) - GWB association or
there is an association but the SGRB does not beam towards earth. Besides
directly dissipating the proto-magnetar wind as suggested by Zhang, we here
suggest that the magnetar wind could push the ejecta launched during the merger
process, and under certain conditions, would reach a relativistic speed. Such a
magnetar-powered ejecta, when interacting with the ambient medium, would
develop a bright broad-band afterglow due to synchrotron radiation. We study
this physical scenario in detail, and present the predicted X-ray, optical and
radio light curves for a range of magnetar and ejecta parameters. We show that
the X-ray and optical lightcurves usually peak around the magnetar spindown
time scale (10^3-10^5s), reaching brightness readily detectable by wide-field
X-ray and optical telescopes, and remain detectable for an extended period. The
radio afterglow peaks later, but is much brighter than the case without a
magnetar energy injection. Therefore, such bright broad-band afterglows, if
detected and combined with GWBs in the future, would be a probe of massive
millisecond magnetars and stiff equation-of-state for nuclear matter.


  We investigate the relationship between social media, Twitter in particular,
and stock market. We provide an in-depth analysis of the Twitter volume and
sentiment about the 30 companies in the Dow Jones Industrial Average index,
over a period of three years. We focus on Earnings Announcements and show that
there is a considerable difference with respect to when the announcements are
made: before the market opens or after the market closes. The two different
timings of the Earnings Announcements were already investigated in the
financial literature, but not yet in the social media. We analyze the
differences in terms of the Twitter volumes, cumulative abnormal returns, trade
returns, and earnings surprises.
  We report mixed results. On the one hand, we show that the Twitter sentiment
(the collective opinion of the users) on the day of the announcement very well
reflects the stock moves on the same day. We demonstrate this by applying the
event study methodology, where the polarity of the Earnings Announcements is
computed from the Twitter sentiment. Cumulative abnormal returns are high
(2--4\%) and statistically significant. On the other hand, we find only weak
predictive power of the Twitter sentiment one day in advance. It turns out that
it is important how to account for the announcements made after the market
closes. These after-hours announcements draw high Twitter activity immediately,
but volume and price changes in trading are observed only on the next day. On
the day before the announcements, the Twitter volume is low, and the sentiment
has very weak predictive power. A useful lesson learned is the importance of
the proper alignment between the announcements, trading and Twitter data.


  The entropy of Boltzmann-Gibbs, as proved by Shannon and Khinchin, is based
on four axioms, where the fourth one concerns additivity. The group theoretic
entropies make use of formal group theory to replace this axiom with a more
general composability axiom. As has been pointed out before, generalised
entropies crucially depend on the number of allowed number degrees of freedom
$N$. The functional form of group entropies is restricted (though not uniquely
determined) by assuming extensivity on the equal probability ensemble, which
leads to classes of functionals corresponding to sub-exponential, exponential
or super-exponential dependence of the phase space volume $W$ on $N$. We review
the ensuing entropies, discuss the composability axiom, relate to the Gibbs'
paradox discussion and explain why group entropies may be particularly relevant
from an information theoretic perspective.


  Powder bed fusion additive manufacturing (PBFAM) of metals has the potential
to enable new paradigms of product design, manufacturing and supply chains
while accelerating the realization of new technologies in the medical,
aerospace, and other industries. Currently, wider adoption of PBFAM is held
back by difficulty in part qualification, high production costs and low
production rates, as extensive process tuning, post-processing, and inspection
are required before a final part can be produced and deployed. Physics-based
modeling and predictive simulation of PBFAM offers the potential to advance
fundamental understanding of physical mechanisms that initiate process
instabilities and cause defects. In turn, these insights can help link process
and feedstock parameters with resulting part and material properties, thereby
predicting optimal processing conditions and inspiring the development of
improved processing hardware, strategies and materials. This work presents
recent developments of our research team in the modeling of metal PBFAM
processes spanning length scales, namely mesoscale powder modeling, mesoscale
melt pool modeling, macroscale thermo-solid-mechanical modeling and
microstructure modeling. Ongoing work in experimental validation of these
models is also summarized. In conclusion, we discuss the interplay of these
individual submodels within an integrated overall modeling approach, along with
future research directions.


  We describe a method of solving quantum field theories using operator
techniques based on the expansion of interacting fields in terms of asymptotic
fields. For bound states, we introduce an asymptotic field for each (stable)
bound state. We choose the nonrelativistic hydrogen atom as an example to
illustrate the method. Future work will apply this N-quantum approach to
relativistic theories that include bound states in motion.


  We introduce a pipeline that leverages Large Language Models (LLMs) to
transform single-turn psychotherapy counseling sessions into multi-turn
interactions. While AI-supported online counseling services for individuals
with mental disorders exist, they are often constrained by the limited
availability of multi-turn training datasets and frequently fail to fully
utilize therapists' expertise. Our proposed pipeline effectively addresses
these limitations. The pipeline comprises two main steps: 1) Information
Extraction and 2) Multi-turn Counseling Generation. Each step is meticulously
designed to extract and generate comprehensive multi-turn counseling
conversations from the available datasets. Experimental results from both
zero-shot and few-shot generation scenarios demonstrate that our approach
significantly enhances the ability of LLMs to produce higher quality multi-turn
dialogues in the context of mental health counseling. Our pipeline and dataset
are publicly available
https://github.com/jwkim-chat/A-Data-Augmentation-Pipeline-Leveraging-Large-Language-Models-for-Counseling-Conversations.


  While most research on controllable text generation has focused on steering
base Language Models, the emerging instruction-tuning and prompting paradigm
offers an alternate approach to controllability. We compile and release
ConGenBench, a testbed of 17 different controllable generation tasks, using a
subset of it to benchmark the performance of 9 different baselines and methods
on Instruction-tuned Language Models. To our surprise, we find that
prompting-based approaches outperform controllable text generation methods on
most datasets and tasks, highlighting a need for research on controllable text
generation with Instruction-tuned Language Models in specific. Prompt-based
approaches match human performance on most stylistic tasks while lagging on
structural tasks, foregrounding a need to study more varied constraints and
more challenging stylistic tasks. To facilitate such research, we provide an
algorithm that uses only a task dataset and a Large Language Model with
in-context capabilities to automatically generate a constraint dataset. This
method eliminates the fields dependence on pre-curated constraint datasets,
hence vastly expanding the range of constraints that can be studied in the
future.


  Observational study of galactic magnetic fields is limited by projected
observables. Comparison with numerical simulations is helpful to understand the
real structures, and observational visualization of numerical data is an
important task. Machida et al. (2018) have reported Faraday depth maps obtained
from numerical simulations. They showed that the relation between azimuthal
angle and Faraday depth depends on the inclination angle. In this paper, we
investigate 100MHz to 10GHz radio synchrotron emission from spiral galaxies,
using the data of global three-dimensional magneto-hydrodynamic simulations. We
model internal and external Faraday depolarization at small scales and assume a
frequency independent depolarization. It is found that the internal and
external Faraday depolarization becomes comparable inside the disk and the
dispersion of Faraday depth becomes about 4rad/m^{2} for face-on view and
40rad/m2 for edge-on view, respectively. The internal depolarization becomes
ineffective in the halo. Because of the magnetic turbulence inside the disk,
frequency independent depolarization works well and the polarization degree
becomes 0.3 at high frequency. When the observed frequency is in the 100 MHz
band, polarized intensity vanishes in the disk, while that from the halo can be
observed. Because the remaining component of polarized intensity is weak in the
halo and the polarization degree is about a few %, it may be difficult to
observe that component. These results indicate that the structures of global
magnetic fields in spiral galaxies could be elucidated, if broadband
polarimetry such as that with the Square Kilometre Array is achieved.


  We report the first empirical demonstration that resonant inelastic x-ray
scattering (RIXS) is sensitive to \emph{collective} magnetic excitations in
$S=1$ systems by probing the Ni $L_3$-edge of La$_{2-x}$Sr$_x$NiO$_4$ ($x = 0,
0.33, 0.45$). The magnetic excitation peak is asymmetric, indicating the
presence of single and multi spin-flip excitations. As the hole doping level is
increased, the zone boundary magnon energy is suppressed at a much larger rate
than that in hole doped cuprates. Based on the analysis of the orbital and
charge excitations observed by RIXS, we argue that this difference is related
to the orbital character of the doped holes in these two families. This work
establishes RIXS as a probe of fundamental magnetic interactions in nickelates
opening the way towards studies of heterostructures and ultra-fast pump-probe
experiments.


  The entropy power inequality (EPI) and the Brascamp-Lieb inequality (BLI) are
fundamental inequalities concerning the differential entropies of linear
transformations of random vectors. The EPI provides lower bounds for the
differential entropy of linear transformations of random vectors with
independent components. The BLI, on the other hand, provides upper bounds on
the differential entropy of a random vector in terms of the differential
entropies of some of its linear transformations. In this paper, we define a
family of entropy functionals, which we show are subadditive. We then establish
that Gaussians are extremal for these functionals by mimicking the idea in Geng
and Nair (2014). As a consequence, we obtain a new entropy inequality that
generalizes both the BLI and EPI. By considering a variety of independence
relations among the components of the random vectors appearing in these
functionals, we also obtain families of inequalities that lie between the EPI
and the BLI.


  We consider a random walker in a dynamic random environment given by a system
of independent simple symmetric random walks. We obtain ballisticity results
under two types of perturbations: low particle density, and strong local drift
on particles. Surprisingly, the random walker may behave very differently
depending on whether the underlying environment particles perform lazy or
non-lazy random walks, which is related to a notion of permeability of the
system. We also provide a strong law of large numbers, a functional central
limit theorem and large deviation bounds under an ellipticity condition.


  The ESR model proposes a new theoretical perspective which incorporates the
mathematical formalism of standard (Hilbert space) quantum mechanics (QM) in a
noncontextual framework, reinterpreting quantum probabilities as conditional on
detection instead of absolute. We have provided in some previous papers
mathematical representations of the physical entities introduced by the ESR
model, namely observables, properties, pure states, proper and improper
mixtures, together with rules for calculating conditional and overall
probabilities, and for describing transformations of states induced by
measurements. We study in this paper the relevant physical case of the quantum
harmonic oscillator in our mathematical formalism. We reinterpret the standard
quantum rules for probabilities, provide new expressions for absolute
probabilities, and show how the standard state transformations must be modified
according to the ESR model.


  Estimating output changes by input changes is the main task in causal
analysis. In previous work, input and output Self-Organizing Maps (SOMs) were
associated for causal analysis of multivariate and nonlinear data. Based on the
association, a weight distribution of the output conditional on a given input
was obtained over the output map space. Such a weighted SOM pattern of the
output changes when the input changes. In order to analyze the change, it is
important to measure the difference of the patterns. Many methods have been
proposed for the dissimilarity measure of patterns. However, it remains a major
challenge when attempting to measure how the patterns change. In this paper, we
propose a visualization approach that simplifies the comparison of the
difference in terms of the pattern property. Using this approach, the change
can be analyzed by integrating colors and star glyph shapes representing the
property dissimilarity. Ecological data is used to demonstrate the usefulness
of our approach and the experimental results show that our approach provides
the change information effectively.


  Gaussian Boson Sampling is a model of photonic quantum computing where
single-mode squeezed states are sent through linear-optical interferometers and
measured using single-photon detectors. In this work, we employ a recent exact
sampling algorithm for GBS with threshold detectors to perform classical
simulations on the Titan supercomputer. We determine the time and memory
resources as well as the amount of computational nodes required to produce
samples for different numbers of modes and detector clicks. It is possible to
simulate a system with 800 optical modes postselected on outputs with 20
detector clicks, producing a single sample in roughly two hours using $40\%$ of
the available nodes of Titan. Additionally, we benchmark the performance of GBS
when applied to dense subgraph identification, even in the presence of photon
loss. We perform sampling for several graphs containing as many as 200
vertices. Our findings indicate that large losses can be tolerated and that the
use of threshold detectors is preferable over using photon-number-resolving
detectors postselected on collision-free outputs.


  Combining Reinforcement Learning (RL) with a prior controller can yield the
best out of two worlds: RL can solve complex nonlinear problems, while the
control prior ensures safer exploration and speeds up training. Prior work
largely blends both components with a fixed weight, neglecting that the RL
agent's performance varies with the training progress and across regions in the
state space. Therefore, we advocate for an adaptive strategy that dynamically
adjusts the weighting based on the RL agent's current capabilities. We propose
a new adaptive hybrid RL algorithm, Contextualized Hybrid Ensemble Q-learning
(CHEQ). CHEQ combines three key ingredients: (i) a time-invariant formulation
of the adaptive hybrid RL problem treating the adaptive weight as a context
variable, (ii) a weight adaption mechanism based on the parametric uncertainty
of a critic ensemble, and (iii) ensemble-based acceleration for data-efficient
RL. Evaluating CHEQ on a car racing task reveals substantially stronger data
efficiency, exploration safety, and transferability to unknown scenarios than
state-of-the-art adaptive hybrid RL methods.


  Wireless communications literature is very rich with empirical studies and
measurement campaigns that study the nature of the wireless propagation
channel. However, despite their undoubted usefulness, many of these studies
have omitted a fundamental yet key feature of the physical signal propagation,
that is, wireless propagation asymmetry. This feature does not agree with the
electromagnetic reciprocity theorem, and the many research papers that adopt
wireless channel symmetry, and hence rendering their modeling, unexpectedly,
inaccurate. Besides, asymmetry is unquestionably an important characteristic of
wireless channels, which needs to be accurately characterized for
vehicular/mobile communications, 5G networks, and associated applications such
as indoor/outdoor localization. This paper presents a modest and a preliminary
study that reports potential causes of propagation asymmetry. Measurements
conducted on Khalifa University campus in UAE show that wireless channels are
symmetric in the absence of symmetry impairments. Therefore, care should be
taken when considering some practical wireless propagation scenarios. Key
conclusions and recommendation are summarized. We believe that this study will
be inspiring for the academic community and will trigger further investigations
within wireless propagation assumptions.