  We study renormalized quenched strong-coupling QED in four dimensions in
arbitrary covariant gauge, in the Dyson-Schwinger equation formalism. Above the
chiral critical coupling, we show that there is no finite chiral limit. This
behaviour is found to be independent of the detailed choice of proper vertex,
provided that the vertex is consistent with the Ward-Takahashi identity and
multiplicative renormalizability. The finite solutions previously reported lie
in an unphysical regime of the theory with multiple solutions and oscillating
mass functions. This study is consistent with the assertion that strong
coupling QED$_4$ does not have a continuum limit in the conventional sense.


  A nonperturbative calculation of elastic electroproduction of the J/psi meson
is presented and compared to the experimental data.
  Our model describes well the observed dependences of the cross sections on
the photon virtuality Q2 and on the energy, and the measured ratio R of
longitudinal to transverse cross sections.


  A tri-objective optimal Micro Phasor Measurement Units ({\mu}-PMUs) Placement
method is presented, with a focus on minimizing the following three parameters:
i) the total number of {\mu}-PMU channels, (ii) the maximum state estimation
uncertainty, and (iii) the sensitivity of state estimation to line parameter
tolerances. The suggested formulation takes single-line and {\mu}-PMU failures
into consideration while guaranteeing the complete observability of the system
in the presence and absence of contingencies. It also takes into account the
impact of zero injection nodes and the quantity of {\mu}-PMU channels carried
out at every node. The suggested placement issue is addressed using a
customized version of the nondominated sorting genetic algorithm II (NSGA-II).
According to the results achieved utilizing three test systems of varying
sizes, {\mu}-PMU channels beyond predetermined thresholds only result in higher
costs and negligible further decreases in state estimation uncertainty and
sensitivity to line parameter tolerances. Additionally, we may omit to
instrument between 30 and 40% of buses if {\mu}-PMUs with only two three-phase
channels are utilized, with only a modest negative effect on state estimate
performance even in the event of contingencies.


  Near-zero-index (NZI) materials, i.e. materials having a phase refractive
index close to zero, are known to enhance or inhibit light-matter interactions.
Most theoretical derivations of fundamental radiative processes rely on
energetic considerations and detailed balance equations, but not on momentum
considerations. Because momentum exchange should also be incorporated into
theoretical models, we investigate momentum inside the three categories of NZI
materials, i.e. inside epsilon-and-mu near-zero (EMNZ), epsilon-near-zero (ENZ)
and mu-near-zero (MNZ) materials. In the context of Abraham-Minkowski debate in
dispersive materials, we show that Minkowski-canonical momentum of light is
zero inside all categories of NZI materials while Abraham-kinetic momentum of
light is zero in ENZ and MNZ materials but nonzero inside EMNZ materials. We
theoretically demonstrate that momentum recoil, transfer momentum from the
field to the atom and Doppler shift are inhibited in NZI materials. Fundamental
radiative processes inhibition is also explained due to those momentum
considerations inside three-dimensional NZI materials. Lastly, absence of
diffraction pattern in slits experiments is seen as a consequence of zero
Minkowski momentum. Those findings are appealing for a better understanding of
fundamental light-matter interactions at the nanoscale as well as for lasing
applications.


  Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) with
hybrid precoding is a promising technique for the future 5G wireless
communications. Due to a large number of antennas but a much smaller number of
radio frequency (RF) chains, estimating the high-dimensional mmWave massive
MIMO channel will bring the large pilot overhead. To overcome this challenge,
this paper proposes a super-resolution channel estimation scheme based on
two-dimensional (2D) u- nitary ESPRIT algorithm. By exploiting the angular
sparsity of mmWave channels, the continuously distributed angle of
arrivals/departures (AoAs/AoDs) can be jointly estimated with high accuracy.
Specifically, by designing the uplink training signals at both base station
(BS) and mobile station (MS), we first use low pilot overhead to estimate a
low-dimensional effective channel, which has the same shift-invariance of array
response as the high-dimensional mmWave MIMO channel to be estimated. From the
low-dimensional effective channel, the super- resolution estimates of AoAs and
AoDs can be jointly obtained by exploiting the 2D unitary ESPRIT channel
estimation algorithm. Furthermore, the associated path gains can be acquired
based on the least squares (LS) criterion. Finally, we can reconstruct the
high-dimensional mmWave MIMO channel according to the obtained AoAs, AoDs, and
path gains. Simulation results have confirmed that the proposed scheme is
superior to conventional schemes with a much lower pilot overhead.


  Highly non-linear vehicle dynamics plays an important role in autonomous
driving systems, especially in congested traffic situations at very low speeds.
Due to this fact, accurate controllers are needed in order to ensure safety
during navigation. In this paper, based on a previous fractional order speed
control, an improved fractional order control is presented to control a
commercial Citro\"en C3 prototype -which has automatic driving capabilities- at
low speeds, which considers a hybrid model of the vehicle. Specifically, two
different fractional order PI{\alpha} controllers are designed to act over the
throttle and brake pedals, respectively. Concerning to the uncertain dynamics
of the system during the brake action parameters are tuned to design a robust
controller. In addition, the system is modeled as hybrid fractional order
differential inclusions. Experimental and simulation results, obtained in a
real circuit, are given to demonstrate the effectiveness of the proposed
strategies for cruise control at low speeds.


  We report a preliminary measurement of the first and second moments of the
hadronic mass distributions in B --> X_c lv decays. The measurements are based
on Upsilon (4S) --> BBbar events where the hadronic decay of one of the B
mesons is fully reconstructed and a charged lepton from the decay of the other
B meson is identified. The moments are presented for threshold lepton momenta
ranging from 0.9 to 1.6 GeV. From the $<M_X^2>$ moments we determine the
non-perturbative Heavy Quark Expansion (HQE) parameters, $\bar{\Lambda}$ and
$\lambda_1$. We combine the measured moments $<M_X^2>$ with earlier BABAR
measurements of the semileptonic branching ratios and B lifetimes and perform a
simultaneous fit to the HQE for the moments obtained for different threshold
lepton momenta and the semileptonic decay width. This fit results in an
improved value for the CKM matrix element $|V_{cb}|$.


  The program of studying general nonlinear Markov processes was put forward in
V. N. Kolokoltsov "Nonlinear Markov Semigroups and Interacting L\'evy Type
Processes" (Journ. Stat. Physics 126:3 (2007), 585-642), and was developed by
the author in monograph "Nonlinear Markov processes and kinetic equations".
Cambridge University Press, 2010, where, in particular, nonlinear L\'evy
processes were introduced.
  The present paper is an invitation to the rapidly developing topic of
noninear Markov processes. We provide a quick (and at the same time more
abstract) introduction to the basic analytical aspects of the theory developed
in Part II of the above mentioned book.


  Photovoltaic (PV) systems have received much attention in recent years due to
their ability of efficiently converting solar power into electricity, which
offers important benefits to the environment. PV systems in regions with high
solar irradiation can produce a higher output but the temperature affects their
performance. This paper presents a study on the effect of cold climate at high
altitude on the PV system output. We report a comparative case study, which
presents measurement results at two distinct sites, one at a height of 612
meters and another one at a mountain site at a height of 1764 meters. This case
study applies the maximum power point tracking (MPPT) technique in order to
determine maximum power from the PV panel at different azimuth and altitude
angles. We used an Arduino system to measure and display the attributes of the
PV system. The measurement results indicate an increased efficiency of 42% for
PV systems at higher altitude.


  We study a one-dimensional elliptic problem with highly oscillatory random
diffusion coefficient. We derive a homogenized solution and a so-called
Gaussian corrector. We also prove a "pointwise" large deviation principle (LDP)
for the full solution and approximate this LDP with a more tractable form.
These results allow one to access the limits of Gaussian correctors. In
general, the corrector does not capture the large deviation behavior.
Applications to uncertainty quantification are considered.


  We discuss projective equivalence of ideals in Noetherian rings and the
existence or failure of existence of projectively full ideals. We describe
connections with the Rees valuations and Rees integers of an ideal, and
consider the question of whether improvements can be made by passing to an
integral extension ring.


  We introduce two new stochastic conjugate frameworks for a class of nonconvex
and possibly also nonsmooth optimization problems. These frameworks are built
upon Stochastic Recursive Gradient Algorithm (SARAH) and we thus refer to them
as Acc-Prox-CG-SARAH and Acc-Prox-CG-SARAH-RS, respectively. They are
efficiently accelerated, easy to implement, tune free and can be smoothly
extended and modified. We devise a deterministic restart scheme for stochastic
optimization and apply it in our second stochastic conjugate framework, which
serves the key difference between the two approaches. In addition, we apply the
ProbAbilistic Gradient Estimator (PAGE) and further develop a practical
variant, denoted as Acc-Prox-CG-SARAH-ST, in order to reduce potential
computational overhead. We provide comprehensive and rigorous convergence
analysis for all three approaches and establish linear convergence rates for
unconstrained minimization problem with nonconvex and nonsmooth objective
functions. Experiments have demonstrated that Acc-Prox-CG-SARAH and
Acc-Prox-CG-SARAH-RS both outperform state-of-art methods consistently and
Acc-Prox-CG-SARAH-ST can as well achieve comparable convergence speed. In terms
of theory and experiments, we verify the strong computational efficiency of the
deterministic restart scheme in stochastic optimization methods.


  We consider weighted Bergman spaces $A_\mu^1$ on the unit disc as well as the
corresponding spaces of entire functions, defined using non-atomic Borel
measures with radial symmetry. By extending the techniques from the case of
reflexive Bergman spaces we characterize the solid core of $A_\mu^1$. Also, as
a consequence of a characterization of solid $A_\mu^1$-spaces we show that, in
the case of entire functions, there indeed exist solid $A_\mu^1$-spaces. The
second part of the paper is restricted to the case of the unit disc and it
contains a characterization of the solid hull of $A_\mu^1$, when $\mu $ equals
the weighted Lebesgue measure with weight $v$. The results are based on a
duality relation of weighted $A^1$- and $H^\infty$-spaces, the validity of
which requires the assumption that $- \log v$ belongs to the class
$\mathcal{W}_0$, studied in a number of publications; moreover, $v$ has to
satisfy condition $(b)$, introduced by the authors. The exponentially
decreasing weight $v(z) = \exp( -1 /(1-|z|)$ provides an example satisfying
both assumptions.


  A novel algorithm for tunable compression to within the precision of
reproduction targets, or storage, is proposed. The new algorithm is termed the
`Perceptron Algorithm', which utilises simple existing concepts in a novel way,
has multiple immediate commercial application aspects as well as it opens up a
multitude of fronts in computational science and technology. The aims of this
paper are to present the concepts underlying the algorithm, observations by its
application to some example cases, and the identification of a multitude of
potential areas of applications such as: image compression by orders of
magnitude, signal compression including sound as well, image analysis in a
multilayered detailed analysis, pattern recognition and matching and rapid
database searching (e.g. face recognition), motion analysis, biomedical
applications e.g. in MRI and CAT scan image analysis and compression, as well
as hints on the link of these ideas to the way how biological memory might work
leading to new points of view in neural computation. Commercial applications of
immediate interest are the compression of images at the source (e.g.
photographic equipment, scanners, satellite imaging systems), DVD film
compression, pay-per-view downloads acceleration and many others identified in
the present paper at its conclusion and future work section.


  The surface Fermi arc states in $Z_2$ Weyl semimetals $A_3 Bi$ (A=Na, K, Rb)
are studied by employing a continuum low-energy effective model. It is shown
that the surface Fermi arc states can be classified with respect to the
ud-parity symmetry. Because of the symmetry, the arcs come in mirror symmetric
pairs. The effects of symmetry breaking terms on the structure of the Fermi
arcs are also studied. Among other results, we find at least two qualitatively
different types of the surface Fermi arcs. The arcs of the first type link
disconnected sheets of the bulk Fermi surface, while arcs of the second type
link different points of the same bulk Fermi surface sheet.


  The Crossing or Not-Crossing (C/NC) problem is important to autonomous
vehicles (AVs) for safe vehicle/pedestrian interactions. However, this problem
setup often ignores pedestrians walking along the direction of the vehicles'
movement (LONG). To enhance the AVs' awareness of pedestrians behavior, we make
the first step towards extending the C/NC to the C/NC/LONG problem and
recognize them based on single body pose. In contrast, previous C/NC state
classifiers depend on multiple poses or contextual information. Our proposed
shallow neural network classifier aims to recognize these three states swiftly.
We tested it on the JAAD dataset and reported an average 81.23% accuracy.
Furthermore, this model can be integrated with different sensors and algorithms
that provide 2D pedestrian body pose so that it is able to function across
multiple light and weather conditions.


  We reveal that an isotropic homogeneous subwavelength particle with a high
refractive index can produce ultra-weak total scattering due to vanishing
contribution of the electric dipole moment. This effect can be explained with
the help of the Fano resonance and scattering efficiency associated with the
excitation of an anapole mode. The latter is a nonradiative mode emerging from
destructive interference of electric and toroidal dipole moments, and it can be
employed for a design of highly transparent optical materials.


  From a consistent expression for the quadriforce describing the interaction
between a coloured particle and gauge fields, we investigate the relativistic
motion of a particle with isospin interacting with a BPS monopole and with a
Julia-Zee dyon. The analysis of such systems reveals the existence of
unidimensional unbounded motion and asymptotic trajectories restricted to
conical surfaces, which resembles the equivalent case of Electromagnetism.


  In this paper, we investigate how it is possible to define a new class of
lattice gauge models based on a dualization procedure of a previous
generalization of the Kitaev Quantum Double Models. In the case of this
previous generalization that will be used as a basis, it was defined by adding
new qudits (which were denoted as matter fields in reference to some works) to
the lattice vertices with the intention of, for instance, interpreting its
models as Kitaev Quantum Double Models coupled with Potts ones. Now, with
regard to the generalization that we investigate here, which we want to define
as the dual of this previous one, these new qudits were added to the lattice
faces. And as the coupling between gauge and matter qudits of the previous
generalization was performed by a gauge group action, we show that the dual
behaviour of these two generalizations was achieved by coupling these same
qudits in the second one through a gauge group co-action homomorphism. One of
the most striking dual aspects of these two generalizations is that, in both,
part of the quasiparticles that were inherited from the Kitaev Quantum Double
Models become confined when these action and co-action are non-trivial. But the
big news here is that, in addition to the group homomorphism (that defines this
gauge group co-action) allows us to classify all the different models of this
second generalization, this same group homomorphism also suggests that all
these models can be interpreted as two-dimensional restrictions of the $ 2
$-lattice gauge theories.


  We construct the Atiyah-Hirzebruch spectral sequence (AHSS) for twisted
differential generalized cohomology theories. This generalizes to the twisted
setting the authors' corresponding earlier construction for differential
cohomology theories, as well as to the differential setting the AHSS for
twisted generalized cohomology theories, including that of twisted K-theory by
Rosenberg and Atiyah-Segal. In describing twisted differential spectra we build
on the work of Bunke-Nikolaus, but we find it useful for our purposes to take
an approach that highlights direct analogies with classical bundles and that is
at the same time amenable for calculations. We will, in particular, establish
that twisted differential spectra are bundles of spectra equipped with a flat
connection. Our prominent case will be twisted differential K-theory, for which
we work out the differentials in detail. This involves differential refinements
of primary and secondary cohomology operations the authors developed in earlier
papers. We illustrate our constructions and computational tools with examples.


  We develop techniques to probe the dynamics of quantum information, and
implement them experimentally on an IBM superconducting quantum processor. Our
protocols adapt shadow tomography for the study of time evolution channels
rather than of quantum states, and rely only on single-qubit operations and
measurements. We identify two unambiguous signatures of quantum information
scrambling, neither of which can be mimicked by dissipative processes, and
relate these to many-body teleportation. By realizing quantum chaotic dynamics
in experiment, we measure both signatures, and support our results with
numerical simulations of the quantum system. We additionally investigate
operator growth under this dynamics, and observe behaviour characteristic of
quantum chaos. As our methods require only a single quantum state at a time,
they can be readily applied on a wide variety of quantum simulators.


  Subvacuum effects arise in quantum field theory when a classically positive
quantity, such as the local energy density, acquires a negative renormalized
expectation value. Here we investigate the case of states of the quantized
electromagnetic field with negative mean-squared electric field, and their
effects on the propagation of light pulses in a nonlinear dielectric material
with a nonzero third-order susceptibility. We identify two distinct signatures
of the subvacuum effect in this situation. The first is an increase in the
speed of the pulse, which is analogous to the superluminal light propagation in
gravity which can arise from negative energy density. This increase in speed
leads to a phase shift which might be large enough to observe. The second
effect is a change in the frequency and power spectra of the pulse. We identify
a specific measure of the modified spectra which can signal the presence of a
negative mean squared electric field. These ideas are implemented in the
particular example of a wave guide filled with a nonlinear dielectric material.


  We introduce the concept of pattern graphs--directed acyclic graphs
representing how response patterns are associated. A pattern graph represents
an identifying restriction that is nonparametrically identified/saturated and
is often a missing not at random restriction. We introduce a selection model
and a pattern mixture model formulations using the pattern graphs and show that
they are equivalent. A pattern graph leads to an inverse probability weighting
estimator as well as an imputation-based estimator. We also study the
semi-parametric efficiency theory and derive a multiply-robust estimator using
pattern graphs.


  The current transition in the European energy sector towards climate
neutrality requires detailed and reliable energy system modeling. The quality
and relevance of the energy system modeling highly depend on the availability
and quality of model input datasets. However, detailed and reliable datasets,
especially for energy infrastructure, are still missing. In this contribution,
we present our approach to developing an open-source and open-data model of the
gas transport network in Europe. Various freely available data sources were
used to collect gas transport datasets and their attributes. The resulting
datasets of the various data sources were processed, and unique elements were
merged together. Statistical and heuristic methods were used to generate
missing network element attributes.
  As a result, we successfully created a gas transport network model only using
open-source data. The SciGRID_gas model contains 237.000 km of pipeline data
which is a very good approximation to know length values. In addition, datasets
of compressor stations, LNG terminals, storage, production sides, gas power
plants, border points, and demand time series are provided. Finally, we have
discussed data gaps and how they can potentially be closed.


  The optimal traverse of irregular terrains made by ground mobile robots
heavily depends on the adequacy of the cost models used to plan the path they
follow. The criteria to define optimality may be based on minimizing energy
consumption and/or preserving the robot stability. This entails the proper
assessment of anisotropy to account for the robot driving on top of slopes with
different directions. To fulfill this demand, this paper presents the
Continuous Anisotropic Model for Inclined Surfaces, a cost model compatible
with anisotropic path planners like the bi-directional Ordered Upwind Method.
This model acknowledges how the orientation of the robot with respect to any
slope determines its energetic cost, considering the action of gravity and
terramechanic effects such as the slippage. Moreover, the proposed model can be
tuned to define a trade-off between energy minimization and Roll angle
reduction. The results from two simulation tests demonstrate how, to find the
optimal path in scenarios containing slopes, in certain situations the use of
this model can be more advantageous than relying on isotropic cost functions.
Finally, the outcome of a field experiment involving a skid-steering robot that
drives on top of a real slope is also discussed.


  The goal of this paper is to give a group-theoretic proof of the congruence
subgroup property for $Aut(F_2)$, the group of automorphisms of a free group on
two generators. This result was first proved by Asada using techniques from
anabelian geometry, and our proof is, to a large extent, a translation of
Asada's proof into group-theoretic language. This translation enables us to
simplify many parts of Asada's original argument and prove a quantitative
version of the congruence subgroup property for $Aut(F_2)$.


  We describe an approach for identifying groups of dynamically similar
locations in spatial time-series data based on a simple Markov transition
model. We give maximum-likelihood, empirical Bayes, and fully Bayesian
formulations of the model, and describe exhaustive, greedy, and MCMC-based
inference methods. The approach has been employed successfully in several
studies to reveal meaningful relationships between environmental patterns and
disease dynamics.


  In classes on cosmology, students are often told that photons stretch as
space expands, but just how physical is this picture? Does space really expand?
In this article, we explore the notion of the redshift of light with Einstein's
general theory of relativity, showing that the core underpinning principles
reveal that redshifts are both simpler and more complex than you might naively
think. This has significant implications for the observed redshifting of
photons as they travel across the universe, often refereed to as the
cosmological redshift, and for the idea of expanding space.


  The energy of a graph is defined as the sum of the absolute values of the
eigenvalues of the graph. In this paper, we present a new method to compare the
energies of two $k$-subdivision bipartite graphs on some cut edges. As the
applications of this new method, we determine the first
$\lfloor\frac{n-7}{2}\rfloor$ largest energy trees of order $n$ for $n\ge 31$,
and we also give a simplified proof of the conjecture on the fourth maximal
energy tree.


  Classifying a weapon based on its muzzle blast is a challenging task that has
significant applications in various security and military fields. Most of the
existing works rely on ad-hoc deployment of spatially diverse microphone
sensors to capture multiple replicas of the same gunshot, which enables
accurate detection and identification of the acoustic source. However,
carefully controlled setups are difficult to obtain in scenarios such as crime
scene forensics, making the aforementioned techniques inapplicable and
impractical. We introduce a novel technique that requires zero knowledge about
the recording setup and is completely agnostic to the relative positions of
both the microphone and shooter. Our solution can identify the category,
caliber, and model of the gun, reaching over 90% accuracy on a dataset composed
of 3655 samples that are extracted from YouTube videos. Our results demonstrate
the effectiveness and efficiency of applying Convolutional Neural Network (CNN)
in gunshot classification eliminating the need for an ad-hoc setup while
significantly improving the classification performance.


  The enhancement of charged-particle pairs with large pseudorapidity
difference and small azimuthal angle difference, often referred to as the
``ridge signal'', is a phenomenon widely observed in high multiplicity
proton-proton, proton-ion and deutron-ion collisions, which is not yet fully
understood. In heavy-ion collisions, the hydrodynamic expansion of the
Quark-Gluon Plasma is one of the possible explanations of the origin of the
ridge signal. Measurements in the $e^+e^-$ collision system, without the
complexities introduced by hadron structure in the initial state, can serve as
a complementary probe to examine the formation of a ridge signal. The first
measurement of two-particle angular correlation functions in high multiplicity
$e^+e^-$ collisions at $\sqrt{s}=10.52$ GeV is reported. The hadronic $e^+e^-$
annihilation data collected by the Belle detector at KEKB are used in this
study. Two-particle angular correlation functions are measured over the full
azimuth and large pseudorapidity intervals which are defined by either the
electron beam axis or the event thrust as a function of charged particle
multiplicity. The measurement in the event thrust analysis, with mostly
outgoing quark pairs determining the reference axis, is sensitive to the region
of additional soft gluon emissions. No significant ridge signal is observed
with either coordinates analyses. Near side jet correlations appear to be
absent in the thrust axis analysis. The measurements are compared to
predictions from various event generators and expected to provide new
constraints to the phenomenological models in the low energy regime.


  Emerging devices such as a neuromorphic device and a qubit can use the Mott
transition phenomenon, but in particular, the diverging mechanism of the
phenomenon remains to be clarified. The diverging-effective mass near Mott
insulators was measured in strongly correlated Mott systems such as a fermion
$^3$He and a Si metal-oxide-semiconductor-field-effect transistor, and is
closely fitted by the effective mass obtained by the extension of the
Brinkman-Rice(BR) picture,
$m^*/m=1/[1-(U/U_c)^2]=1/(1-{\kappa}^2_{BR}{\rho}^4)$ when
${\kappa}^2_{BR}{\approx}1({\neq}$1), where $0<U/U_c={\kappa}_{BR}{\rho}^2<1$,
correlation strength is ${\kappa}_{BR}$, band-filling is ${\rho}$. Its
identification is a percolation of a constant mass in the Brinkman-Rice
picture. Over ${\kappa}_{BR}{\approx}0.96$ is evaluated.


  Case-oriented physics education research - which seeks to refine and develop
theory by linking that theory to cases - incorporates distinct practices for
selecting data for analysis, generalizing results, and making causal claims.
Unanswered questions about these practices may constrain researchers more
familiar with the recurrence-oriented research paradigm - which seeks to inform
instructional predictions by discerning reproducible, representative patterns
and relationships - from participating in or critically engaging with
case-oriented research. We use results from interviews with physics education
researchers, a synthesis of the literature on research methodologies, and
published examples of case-oriented and recurrence-oriented research to answer
"hard-hitting questions" that researchers may pose. In doing so, we aim to
substantiate our position that both case-oriented and recurrence- oriented PER
are rigorous but that the rigor is of a different nature in each paradigm.


  Multi-agent interactions between Large Language Model (LLM) agents have shown
major improvements on diverse reasoning tasks. However, these involve long
generations from multiple models across several rounds, making them expensive.
Moreover, these multi-agent approaches fail to provide a final, single model
for efficient inference. To address this, we introduce MAGDi, a new method for
structured distillation of the reasoning interactions between multiple LLMs
into smaller LMs. MAGDi teaches smaller models by representing multi-agent
interactions as graphs, augmenting a base student model with a graph encoder,
and distilling knowledge using three objective functions: next-token
prediction, a contrastive loss between correct and incorrect reasoning, and a
graph-based objective to model the interaction structure. Experiments on seven
widely used commonsense and math reasoning benchmarks show that MAGDi improves
the reasoning capabilities of smaller models, outperforming several methods
that distill from a single teacher and multiple teachers. Moreover, MAGDi also
demonstrates an order of magnitude higher efficiency over its teachers. We
conduct extensive analyses to show that MAGDi (1) enhances the generalizability
to out-of-domain tasks, (2) scales positively with the size and strength of the
base student model, and (3) obtains larger improvements (via our multi-teacher
training) when applying self-consistency -- an inference technique that relies
on model diversity.


  COVID-19 pandemic and social distancing urge a reliable human face
recognition system in different abnormal situations. However, there is no
research which studies the influence of glass factor in facial recognition
system. This paper provides a comprehensive review of glass factor. The study
contains two steps: data collection and accuracy test. Data collection includes
collecting human face images through different situations, such as clear
glasses, glass with water and glass with mist. Based on the collected data, an
existing state-of-the-art face detection and recognition system built upon
MTCNN and Inception V1 deep nets is tested for further analysis. Experimental
data supports that 1) the system is robust for classification when comparing
real-time images and 2) it fails at determining if two images are of same
person by comparing real-time disturbed image with the frontal ones.


  In this study of catalytic microfluidic reactors we show that, when optimally
structured, these reactors share underlying scaling properties. The scaling is
predicted theoretically and verified numerically. Furthermore, we show how to
increase the reaction rate significantly by distributing the active porous
material within the reactor using a high-level implementation of topology
optimization.


  We relate Nakajima Quiver Varieties (or, rather, their multiplicative
version) with moduli spaces of perverse sheaves. More precisely, we consider a
generalization of the concept of perverse sheaves: microlocal sheaves on a
nodal curve X. They are defined as perverse sheaves on normalization of X with
a Fourier transform condition near each node and form an abelian category M(X).
One has a similar triangulated category DM(X) of microlocal complexes. For a
compact X we show that DM(X) is Calabi-Yau of dimension 2. In the case when all
components of X are rational, M(X) is equivalent to the category of
representations of the multiplicative pre-projective algebra associated to the
intersection graph of X. Quiver varieties in the proper sense are obtained as
moduli spaces of microlocal sheaves with a framing of vanishing cycles at
singular points. The case when components of X have higher genus, leads to
interesting generalizations of preprojective algebras and quiver varieties. We
analyze them from the point of view of pseudo-Hamiltonian reduction and
group-valued moment maps.


  The capacitance of arrays of vertical wrapped-gate InAs nanowires are
analyzed. With the help of a Poisson-Schr"odinger solver, information about the
doping density can be obtained directly. Further features in the measured
capacitance-voltage characteristics can be attributed to the presence of
surface states as well as the coexistence of electrons and holes in the wire.
For both scenarios, quantitative estimates are provided. It is furthermore
shown that the difference between the actual capacitance and the geometrical
limit is quite large, and depends strongly on the nanowire material.


  Three different hybrid Vlasov-fluid systems are derived by applying reduction
by symmetry to Hamilton's variational principle. In particular, the discussion
focuses on the Euler-Poincar\'e formulation of three major hybrid MHD models,
which are compared in the same framework. These are the current-coupling scheme
and two different variants of the pressure-coupling scheme. The Kelvin-Noether
theorem is presented explicitly for each scheme, together with the Poincar\'e
invariants for its hot particle trajectories. Extensions of Ertel's relation
for the potential vorticity and for its gradient are also found in each case,
as well as new expressions of cross helicity invariants.


  Quantum optimal control theory allows to design accurate quantum gates. We
employ it to design high-fidelity two-bit gates for Josephson charge qubits in
the presence of both leakage and noise. Our protocol considerably increases the
fidelity of the gate and, more important, it is quite robust in the disruptive
presence of 1/f noise. The improvement in the gate performances discussed in
this work (errors of the order of 10^{-3}-10^{-4} in realistic cases) allows to
cross the fault tolerance threshold.


  Conventional electronics are based invariably on the intrinsic degrees of
freedom of an electron, namely, its charge and spin. The exploration of novel
electronic degrees of freedom has important implications in both basic quantum
physics and advanced information technology. Valley as a new electronic degree
of freedom has received considerable attention in recent years. In this paper,
we develop the theory of spin and valley physics of an antiferromagnetic
honeycomb lattice. We show that by coupling the valley degree of freedom to
antiferromagnetic order, there is an emergent electronic degree of freedom
characterized by the product of spin and valley indices, which leads to
spin-valley dependent optical selection rule and Berry curvature-induced
topological quantum transport. These properties will enable optical
polarization in the spin-valley space, and electrical detection/manipulation
through the induced spin, valley and charge fluxes. The domain walls of an
antiferromagnetic honeycomb lattice harbors valley-protected edge states that
support spin-dependent transport. Finally, we employ first principles
calculations to show that the proposed optoelectronic properties can be
realized in antiferromagnetic manganese chalcogenophosphates (MnPX_3, X = S,
Se) in monolayer form.


  In robotic tasks, changes in reference frames typically do not influence the
underlying physical properties of the system, which has been known as
invariance of physical laws.These changes, which preserve distance, encompass
isometric transformations such as translations, rotations, and reflections,
collectively known as the Euclidean group. In this work, we delve into the
design of improved learning algorithms for reinforcement learning and planning
tasks that possess Euclidean group symmetry. We put forth a theory on that
unify prior work on discrete and continuous symmetry in reinforcement learning,
planning, and optimal control. Algorithm side, we further extend the 2D path
planning with value-based planning to continuous MDPs and propose a pipeline
for constructing equivariant sampling-based planning algorithms. Our work is
substantiated with empirical evidence and illustrated through examples that
explain the benefits of equivariance to Euclidean symmetry in tackling natural
control problems.


  A systematic analysis of the unitary electroweak model described by the
higher derivative Lagrangian depending on extra dimension [1] is presented.


  Active galactic nuclei (AGN) have been suggested to be sources of very high
energy neutrinos. We consider the possibility of using AGN neutrinos to test
neutrino mixings. From the atmospheric, solar and laboratory data on neutrino
oscillations we derive the flavour composition of the AGN neutrino flux in
different neutrino mixing schemes. We show that most of the schemes considered
can be distinguished from each other and the existence of a sterile neutrino
can be specially tested. AGN neutrinos can also be used to test those
four-neutrino scenarios where solar neutrinos oscillate into an arbitrary
mixture of $\nu_s$ and $\nu_\tau$.


  Let $\alpha$ be an approximately inner flow on a $C^*$ algebra $A$ with
generator $\delta$ and let $\delta_n$ denote the bounded generators of the
approximating flows $\alpha^{(n)}$. We analyze the structure of the set
  \cd=\{x\in D(\delta): \lim_{n\to\infty}\delta_n(x)=\delta(x)\} of pointwise
convergence of the generators. In particular we examine the relationship of
$\cd$ and various cores related to spectral subspaces.


  With more and more machine learning methods applied to bioinspired polarized
skylight navigation, the demand for polarized skylight navigation datasets is
more and more urgent, which can be used to train and test machine learning
methods. So, in this paper, an open polarized skylight navigation dataset is
constructed for the first time. Firstly, a polarized sky model was proposed
based on Sun position model, Berry sky model and Hosek sky model, which
contains the information of the light intensity (LI), degree of polarization
(DOP) and angle of polarization (AOP). Secondly, a polarization imaging
simulation system is constructed, which can capture not only LI, DOP and AOP
images, but also original black-and-white LI images in different polarization
directions. Black-and-white LI images are the original data collected by actual
polarization imager, so this system can completely describe the whole process
of polarization imager capturing skylight polarization patterns. Above all, a
polarized skylight navigation simulation (PSNS) dataset can be constructed. In
addition, to facilitate researchers to build their own datasets based on their
own polarized light sensors and sky models, we have disclosed the source code
of polarization imager and original LI imager on GitHub.


  The resonances of many-body Stark Hamiltonians are characterized by the
complex absorbing potential method. Namely, the resonances are shown to be the
limit points of complex discrete eigenvalues of many-body Stark Hamiltonians
with quadratic complex potential when the coefficient of the complex potential
tends to zero.


  Currently, there starts a research trend to leverage neural architecture for
recommendation systems. Though several deep recommender models are proposed,
most methods are too simple to characterize users' complex preference. In this
paper, for a fine-grain analysis, users' ratings are explained from multiple
perspectives, based on which, we propose our neural architecture. Specifically,
our model employs several sequential stages to encode the user and item into
hidden representations. In one stage, the user and item are represented from
multiple perspectives and in each perspective, the representations of user and
item put attentions to each other. Last, we metric the output representations
of final stage to approach the users' rating. Extensive experiments demonstrate
that our method achieves substantial improvements against baselines.


  The JETSCAPE framework is a modular and versatile Monte Carlo software
package for the simulation of high energy nuclear collisions. In this work we
present a new tune of JETSCAPE, called PP19, and validate it by comparison to
jet-based measurements in $p+p$ collisions, including inclusive single jet
cross sections, jet shape observables, fragmentation functions, charged hadron
cross sections, and dijet mass cross sections. These observables in $p+p$
collisions provide the baseline for their counterparts in nuclear collisions.
Quantifying the level of agreement of JETSCAPE results with $p+p$ data is thus
necessary for meaningful applications of JETSCAPE to A+A collisions. The
calculations use the JETSCAPE PP19 tune, defined in this paper, based on
version 1.0 of the JETSCAPE framework. For the observables discussed in this
work calculations using JETSCAPE PP19 agree with data over a wide range of
collision energies at a level comparable to standard Monte Carlo codes. These
results demonstrate the physics capabilities of the JETSCAPE framework and
provide benchmarks for JETSCAPE users.


  We present high-resolution (1".0) Atacama Large Millimeter/submillimeter
Array (ALMA) observations of CO (1-0) and CO (2- 1) rotational transitions
toward the nearby IR-luminous merger NGC 1614 supplemented with ALMA archival
data of CO (3-2), and CO (6-5) transitions. The CO (6-5) emission arises from
the starburst ring (central 590 pc in radius), while the lower-$J$ CO lines are
distributed over the outer disk ($\sim$ 3.3 kpc in radius). Radiative transfer
and photon dominated region (PDR) modeling reveal that the starburst ring has a
single warmer gas component with more intense far-ultraviolet radiation field
($n_{\rm{H_2}}$ $\sim$ 10$^{4.6}$ cm$^{-3}$, $T_{\rm{kin}}$ $\sim$ 42 K, and
$G_{\rm{0}}$ $\sim$ 10$^{2.7}$) relative to the outer disk ($n_{\rm{H_2}}$
$\sim$ 10$^{5.1}$ cm$^{-3}$, $T_{\rm{kin}}$ $\sim$ 22 K, and $G_{\rm{0}}$
$\sim$ 10$^{0.9}$). A two-phase molecular interstellar medium with a warm and
cold ($>$ 70 K and $\sim$ 19 K) component is also an applicable model for the
starburst ring. A possible source for heating the warm gas component is
mechanical heating due to stellar feedback rather than PDR. Furthermore, we
find evidence for non-circular motions along the north-south optical bar in the
lower-$J$ CO images, suggesting a cold gas inflow. We suggest that star
formation in the starburst ring is sustained by the bar-driven cold gas inflow,
and starburst activities radiatively and mechanically power the CO excitation.
The absence of a bright active galactic nucleus can be explained by a scenario
that cold gas accumulating on the starburst ring is exhausted as the fuel for
star formation, or is launched as an outflow before being able to feed to the
nucleus.


  Reinforcement learning (RL) trains agents to accomplish complex tasks through
environmental interaction data, but its capacity is also limited by the scope
of the available data. To obtain a knowledgeable agent, a promising approach is
to leverage the knowledge from large language models (LLMs). Despite previous
studies combining LLMs with RL, seamless integration of the two components
remains challenging due to their semantic gap. This paper introduces a novel
method, Knowledgeable Agents from Language Model Rollouts (KALM), which
extracts knowledge from LLMs in the form of imaginary rollouts that can be
easily learned by the agent through offline reinforcement learning methods. The
primary challenge of KALM lies in LLM grounding, as LLMs are inherently limited
to textual data, whereas environmental data often comprise numerical vectors
unseen to LLMs. To address this, KALM fine-tunes the LLM to perform various
tasks based on environmental data, including bidirectional translation between
natural language descriptions of skills and their corresponding rollout data.
This grounding process enhances the LLM's comprehension of environmental
dynamics, enabling it to generate diverse and meaningful imaginary rollouts
that reflect novel skills. Initial empirical evaluations on the CLEVR-Robot
environment demonstrate that KALM enables agents to complete complex
rephrasings of task goals and extend their capabilities to novel tasks
requiring unprecedented optimal behaviors. KALM achieves a success rate of 46%
in executing tasks with unseen goals, substantially surpassing the 26% success
rate achieved by baseline methods. Furthermore, KALM effectively enables the
LLM to comprehend environmental dynamics, resulting in the generation of
meaningful imaginary rollouts that reflect novel skills and demonstrate the
seamless integration of large language models and reinforcement learning.


  Experiments handling Rydberg atoms near surfaces must necessarily deal with
the high sensitivity of Rydberg atoms to (stray) electric fields that typically
emanate from adsorbates on the surface. We demonstrate a method to modify and
reduce the stray electric field by changing the adsorbates distribution. We use
one of the Rydberg excitation lasers to locally affect the adsorbed dipole
distribution. By adjusting the averaged exposure time we change the strength
(with the minimal value less than $0.2\,\textrm{V/cm}$ at $78\,\mu\textrm{m}$
from the chip) and even the sign of the perpendicular field component. This
technique is a useful tool for experiments handling Ryberg atoms near surfaces,
including atom chips.


  We discuss various analytic and numerical methods that have been used to get
option prices within a framework of the VG model. We show that some popular
methods, for instance, Carr-Madan's FFT method could blow up for certain values
of the model parameters even for an European vanilla option. Alternative
methods - one originally proposed by Lewis, and Black-Scholes-wise method are
considered that seem to work fine for any value of the VG parameters. Test
examples are given to demonstrate efficiency of these methods. Convergency of
all methods is also discussed.}


  Recently, semantic segmentation and general object detection frameworks have
been widely adopted by scene text detecting tasks. However, both of them alone
have obvious shortcomings in practice. In this paper, we propose a novel
end-to-end trainable deep neural network framework, named Pixel-Anchor, which
combines semantic segmentation and SSD in one network by feature sharing and
anchor-level attention mechanism to detect oriented scene text. To deal with
scene text which has large variances in size and aspect ratio, we combine FPN
and ASPP operation as our encoder-decoder structure in the semantic
segmentation part, and propose a novel Adaptive Predictor Layer in the SSD.
Pixel-Anchor detects scene text in a single network forward pass, no complex
post-processing other than an efficient fusion Non-Maximum Suppression is
involved. We have benchmarked the proposed Pixel-Anchor on the public datasets.
Pixel-Anchor outperforms the competing methods in terms of text localization
accuracy and run speed, more specifically, on the ICDAR 2015 dataset, the
proposed algorithm achieves an F-score of 0.8768 at 10 FPS for 960 x 1728
resolution images.


  We study the potential of the LHC accelerator, and a future 33 TeV proton
collider, to observe the production of a light top squark pair in association
with the lightest Higgs boson (${\tilde t}_1 {\tilde t}_1 h_1$), as predicted
by the Next-to-Minimal Supersymmetric Standard Model (NMSSM). We scan randomly
about ten million points of the NMSSM parameter space, allowing all possible
decays of the lightest top squark and lightest Higgs boson, with no assumptions
about their decay rates, except for known physical constraints such as
perturbative bounds, Dark matter relic density consistent with recent Planck
experiment measurements, Higgs mass bounds on the next to lightest Higgs boson,
$h_2$, assuming it is consistent with LHC measurements for the Standard Model
Higgs boson, LEP bounds for the chargino mass and Z invisible width,
experimental bounds on B meson rare decays and some LHC experimental bounds on
SUSY particle spectra different to the particles involved in our study. We find
that for low mass top-squark, the dominating decay mode is ${\tilde t}_1
\rightarrow b \tilde{\chi}_1^{\pm}$ with $\tilde{\chi}_1^{\pm} \rightarrow
W^{\pm} \tilde{\chi}_1^0 $. We use three bench mark points with the highest
cross sections, which naturally fall within the compressed spectra of the top
squark, and make a phenomenological analysis to determine the optimal event
selection that maximizes the signal significance over backgrounds. We focus on
the leptonic decays of both $W$'s and the decay of lightest Higgs boson into
b-quarks ($h_1 \rightarrow b \bar b$). Our results show that the high
luminosity LHC will have limitations to observe the studied signal and only a
proton collider with higher energy will be able to observe the SUSY scenario
studied with more than three standard deviations over background.


  Topic models have been evolving rapidly over the years, from conventional to
recent neural models. However, existing topic models generally struggle with
either effectiveness, efficiency, or stability, highly impeding their practical
applications. In this paper, we propose FASTopic, a fast, adaptive, stable, and
transferable topic model. FASTopic follows a new paradigm: Dual
Semantic-relation Reconstruction (DSR). Instead of previous conventional,
neural VAE-based or clustering-based methods, DSR discovers latent topics by
reconstruction through modeling the semantic relations among document, topic,
and word embeddings. This brings about a neat and efficient topic modeling
framework. We further propose a novel Embedding Transport Plan (ETP) method.
Rather than early straightforward approaches, ETP explicitly regularizes the
semantic relations as optimal transport plans. This addresses the relation bias
issue and thus leads to effective topic modeling. Extensive experiments on
benchmark datasets demonstrate that our FASTopic shows superior effectiveness,
efficiency, adaptivity, stability, and transferability, compared to
state-of-the-art baselines across various scenarios. Our code is available at
https://github.com/bobxwu/FASTopic .


  The Jiangmen Underground Neutrino Observatory (JUNO), a 20ktons multi-purpose
underground liquid scintillator detector, was proposed with the determination
of the neutrino mass hierarchy as a primary physics goal. Due to low background
requirement of the experiment, a multi-veto system ,which consists of a water
Cherenkov detector and a top tracker detector, is required. In order to keep
the water quality good and remove the radon in the water, a ultra-pure water
system, a radon removal system and radon concentration measurement system have
been designed. In this paper, the radon removal equipments and its radon
removal limit will be presented.


  We derive the projected surface mass distribution Sigma_M for spherically
symmetric mass distributions having an arbitrary rotation curve. For a galaxy
with a flat rotation curve and an ISM disk having a constant Toomre stability
parameter, Q, the ISM surface mass density Sigma_g as well as Sigma_M both fall
off as 1/R. We use published data on a sample of 20 well studied galaxies to
show that ISM disks do maintain a constant Q over radii usually encompassing
more than 50% of the HI mass. The power law slope in Sigma_g covers a range of
exponents and is well correlated with the slope in the epicyclic frequency.
This implies that the ISM disk is responding to the potential, and hence that
secular evolution is important for setting the structure of ISM disks. We show
that the gas to total mass ratio should be anti-correlated with the maximum
rotational velocity, and that the sample falls on the expected relationship. A
very steep fall off in Sigma_g is required at the outermost radii to keep the
mass and angular momentum content finite for typical rotation curve shapes, and
is observed. The observation that HI traces dark matter over a significant
range of radii in galaxies is thus due to the disks stabilising themselves in a
normal dark matter dominated potential. This explanation is consistent with the
cold dark matter paradigm.


  We present a timing and spectral analysis of the variable X-ray source 3A
1954+319. Our analysis is mainly based on an outburst serendipitously observed
during INTEGRAL Key Program observations of the Cygnus region in 2008 fall and
on the Swift/BAT longterm light curve. Previous observations, though sparse,
have identified the source to be one of only nine known symbiotic X-ray
binaries, i.e., systems composed of an accreting neutron star orbiting in a
highly inhomogeneous medium around an M-giant companion. The spectrum of 3A
1954+319 above 20 keV can be best described by a broken power law model. The
extremely long pulse period of ~5.3 hours is clearly visible in the
INTEGRAL/ISGRI light curve and confirmed through an epoch folding period
search. Furthermore, the light curve allows us to determine a very strong spin
up of -2x10^-4 h/h during the outburst. This spin up is confirmed by the pulse
period evolution calculated from Swift/BAT data. The Swift/BAT data also show a
long spin-down trend prior to the 2008 outburst, which is confirmed in archival
INTEGRAL/ISGRI data. We discuss possible accretion models and geometries
allowing for the transfer of such large amounts of angular momentum and
investigate the harder spectrum of this outburst compared to previously
published results.


  We explore and refine techniques for estimating the Hausdorff dimension of
exceptional sets and their diffeomorphic images. Specifically, we use a variant
of Schmidt's game to deduce the strong C^1 incompressibility of the set of
badly approximable systems of linear forms as well as of the set of vectors
which are badly approximable with respect to a fixed system of linear forms.


  Neural network (NN) approximations of model predictive control (MPC) are a
versatile approach if the online solution of the underlying optimal control
problem (OCP) is too demanding and if an exact computation of the explicit MPC
law is intractable. The drawback of such approximations is that they typically
do not preserve stability and performance guarantees of the original MPC.
However, such guarantees can be recovered if the maximum error with respect to
the optimal control law and the Lipschitz constant of that error are known. We
show in this work how to compute both values exactly when the control law is
approximated by a maxout NN. We build upon related results for ReLU NN
approximations and derive mixed-integer (MI) linear constraints that allow a
computation of the output and the local gain of a maxout NN by solving an MI
feasibility problem. Furthermore, we show theoretically and experimentally that
maxout NN exist for which the maximum error is zero.


  We compare two approaches in supersymmetric confinement, the Seiberg
formulation and the superconvergence rule. For the latter, the critical point
is $\gamma_{00}=0$ in the Landau gauge. We find $4\gamma_{00}=\beta_0$ is the
critical point for most of confining theories without a tree-level
superpotential in the Seiberg formulation, in particular, in the large $N_c$
and $N_f$ limit. We show how confining theories with a discrete symmetry and a
tree-level superpotential connect these two critical points: the large and
small discrete symmetry limits correspond to the critical points
$4\gamma_{00}=\beta_0$ and $\gamma_{00}=0$, respectively.


  A phenomenological theory of fermion masses and mixings is constructed within
the framework of a four- family symmetry. It is found that the most favored set
of relevant CKM elements are $|V_{us}|\approx 0.222$, $|V_{cb}|\approx 0.044$,
$|V_{ub}/V_{cb}|\approx 0.082$, $|V_{ud}|\approx 0.974$, $|V_{cs}|\approx
0.9736$, $|V_{cd}|\approx 0.224$ with $\hat{B}_K \approx 0.8$. The top quark
mass is predicted to be 258 GeV at 1 GeV with its physical mass approximately
equal to 153 GeV. The Majorana scale associated with the fourth neutrino is
bound from above to be 6.4 TeV.


  We explore a scenario where metal poor globular clusters (GCs) form at the
centres of their own dark matter halos in the early universe before
reionization. This hypothesis leads to predictions about the abundance,
distribution and kinematics of GCs today that we explore using cosmological
N-body simulations and analytical modelling. We find that selecting the massive
tail of collapsed objects at $z\gtrsim 9$ as GC formation sites leads to four
main predictions: i) a highly clustered population of GCs around galaxies
today, ii) a natural scaling between number of GCs and halo virial mass that
follows roughly the observed trend, iii) a very low number of free floating GCs
outside massive halos and iv) GCs should be embedded within massive and
extended dark matter (sub)halos. We find that the strongest constraint to the
model is given by the combination of (i) and (ii): a mass cut to tagged GCs
halos which accounts for the number density of metal poor GCs today predicts a
radial distribution that is too extended compared to recent observations. On
the other hand, a mass cut sufficient to match the observed half number radius
could only explain 60% of the metal poor population. In all cases, observations
favour early redshifts for GC formation ($z\geq 15$).


  We introduce and characterize inertial updating of beliefs. Under inertial
updating, a decision maker (DM) chooses a belief that minimizes the subjective
distance between their prior belief and the set of beliefs consistent with the
observed event. Importantly, by varying the subjective notion of distance,
inertial updating provides a unifying framework that nests three different
types of belief updating: (i) Bayesian updating, (ii) non-Bayesian updating
rules, and (iii) updating rules for events with zero probability, including the
conditional probability system (CPS) of Myerson (1986a,b). We demonstrate that
our model is behaviorally equivalent to the Hypothesis Testing model (HT) of
Ortoleva (2012), clarifying the connection between HT and CPS. We apply our
model to a persuasion game.


  Global sensitivity analysis is used to quantify the influence of uncertain
input parameters on the response variability of a numerical model. The common
quantitative methods are applicable to computer codes with scalar input
variables. This paper aims to illustrate different variance-based sensitivity
analysis techniques, based on the so-called Sobol indices, when some input
variables are functional, such as stochastic processes or random spatial
fields. In this work, we focus on large cpu time computer codes which need a
preliminary meta-modeling step before performing the sensitivity analysis. We
propose the use of the joint modeling approach, i.e., modeling simultaneously
the mean and the dispersion of the code outputs using two interlinked
Generalized Linear Models (GLM) or Generalized Additive Models (GAM). The
``mean'' model allows to estimate the sensitivity indices of each scalar input
variables, while the ``dispersion'' model allows to derive the total
sensitivity index of the functional input variables. The proposed approach is
compared to some classical SA methodologies on an analytical function. Lastly,
the proposed methodology is applied to a concrete industrial computer code that
simulates the nuclear fuel irradiation.


  We present a search for associated HI 21 cm absorption in a sample of 29
radio-loud active galactic nuclei (AGNs) at $0.7 < z < 1$, carried out with the
upgraded Giant Metrewave Radio Telescope. We detect HI 21 cm absorption against
none of our target AGNs, obtaining $3\sigma$ upper limits to the optical depth
of $\lesssim$ 1% per 50 km s$^{-1}$ channel. The radio luminosity of our
sources is lower than that of most AGNs searched for HI 21 cm absorption at
similar redshifts in the literature, and, for all targets except two, the UV
luminosity is below the threshold $10^{23}$ W Hz$^{-1}$, above which the HI in
the AGN environment has been suggested to be completely ionised. We stacked the
HI spectra to obtain a more stringent limit of $\approx 0.17$% per 50 km
s$^{-1}$ channel on the average HI 21 cm optical depth of the sample. The
sample is dominated by extended radio sources, 24 of which are extended on
scales of tens of kiloparsecs. Including similar extended sources at $0.7 < z <
1.0$ from the literature, and comparing with a low-$z$ sample of extended radio
sources, we find statistically significant ($\approx 3\sigma$) evidence that
the strength of HI 21 cm absorption towards extended radio sources is weaker at
$0.7<z<1.0$ than at $z < 0.25$, with a lower detection rate of HI 21 cm
absorption at $0.7 < z < 1.0$. Redshift evolution in the physical conditions of
HI is the likely cause of the weaker associated HI 21 cm absorption at high
redshifts, due to either a low HI column density or a high spin temperature in
high-$z$ AGN environments.


  Under certain circumstances, advanced neural video codecs can surpass the
most complex traditional codecs in their rate-distortion (RD) performance. One
of the main reasons for the high performance of existing neural video codecs is
the use of the entropy model, which can provide more accurate probability
distribution estimations for compressing the latents. This also implies the
rigorous requirement that entropy models running on different platforms should
use consistent distribution estimations. However, in cross-platform scenarios,
entropy models running on different platforms usually yield inconsistent
probability distribution estimations due to floating point computation errors
that are platform-dependent, which can cause the decoding side to fail in
correctly decoding the compressed bitstream sent by the encoding side. In this
paper, we propose a cross-platform video compression framework based on
codebooks, which avoids autoregressive entropy modeling and achieves video
compression by transmitting the index sequence of the codebooks. Moreover,
instead of using optical flow for context alignment, we propose to use the
conditional cross-attention module to obtain the context between frames. Due to
the absence of autoregressive modeling and optical flow alignment, we can
design an extremely minimalist framework that can greatly benefit computational
efficiency. Importantly, our framework no longer contains any distribution
estimation modules for entropy modeling, and thus computations across platforms
are not necessarily consistent. Experimental results show that our method can
outperform the traditional H.265 (medium) even without any entropy constraints,
while achieving the cross-platform property intrinsically.


  Pre-trial risk assessment tools are used to make recommendations to judges
about appropriate conditions of pre-trial supervision for people who have been
arrested. Increasingly, there is concern about whether these models are
operating fairly, including concerns about whether the models' input factors
are fair measures of one's criminal activity. In this paper, we assess the
impact of booking charges that do not result in a conviction on a popular risk
assessment tool, the Arnold Public Safety Assessment. Using data from a pilot
run of the tool in San Francisco, CA, we find that booking charges that do not
result in a conviction (i.e. charges that are dropped or end in an acquittal)
increased the recommended level of pre-trial supervision in around 27% of cases
evaluated by the tool


  We provide a constructive method designed in order to control the stability
of a given periodic orbit of a general completely integrable system. The method
consists of a specific type of perturbation, such that the resulting perturbed
system becomes a codimension-one dissipative dynamical system which also admits
that orbit as a periodic orbit, but whose stability can be a-priori prescribed.
The main results are illustrated in the case of a three dimensional dissipative
perturbation of the harmonic oscillator, and respectively Euler's equations
form the free rigid body dynamics.


  We introduce an algorithm to generate (not solve) spin-glass instances with
planted solutions of arbitrary size and structure. First, a set of small
problem patches with open boundaries is solved either exactly or with a
heuristic, and then the individual patches are stitched together to create a
large problem with a known planted solution. Because in these problems
frustration is typically smaller than in random problems, we first assess the
typical computational complexity of the individual patches using population
annealing Monte Carlo, and introduce an approach that allows one to fine-tune
the typical computational complexity of the patch-planted system. The scaling
of the typical computational complexity of these planted instances with various
numbers of patches and patch sizes is investigated and compared to random
instances.


  Recently, two different approaches were put forward to extend the
supersymmetry method in random matrix theory from Gaussian ensembles to general
rotation invariant ensembles. These approaches are the generalized
Hubbard-Stratonovich transformation and the superbosonization formula. Here, we
prove the equivalence of both approaches. To this end, we reduce integrals over
functions of supersymmetric Wishart-matrices to integrals over quadratic
supermatrices of certain symmetries.


  Robots need the capability of placing objects in arbitrary, specific poses to
rearrange the world and achieve various valuable tasks. Object reorientation
plays a crucial role in this as objects may not initially be oriented such that
the robot can grasp and then immediately place them in a specific goal pose. In
this work, we present a vision-based manipulation system, ReorientBot, which
consists of 1) visual scene understanding with pose estimation and volumetric
reconstruction using an onboard RGB-D camera; 2) learned waypoint selection for
successful and efficient motion generation for reorientation; 3) traditional
motion planning to generate a collision-free trajectory from the selected
waypoints. We evaluate our method using the YCB objects in both simulation and
the real world, achieving 93% overall success, 81% improvement in success rate,
and 22% improvement in execution time compared to a heuristic approach. We
demonstrate extended multi-object rearrangement showing the general capability
of the system.


  A new spatial IIR beamformer based direction-of-arrival (DoA) estimation
method is proposed in this paper. We propose a retransmission based spatial
feedback method for an array of transmit and receive antennas that improves the
performance parameters of a beamformer, viz. half-power beamwidth (HPBW),
side-lobe suppression, and directivity. Through quantitative comparison, we
show that our approach outperforms the previous feedback beamforming approach
with a single transmit antenna, and the conventional beamformer. We then
incorporate a retransmission based minimum variance distortionless response
(MVDR) beamformer with the feedback beamforming setup. We propose two
approaches, show that one approach is superior in terms of lower estimation
error, and use that as the DoA estimation method. We then compare this approach
with Multiple Signal Classification (MUSIC), Estimation of Parameters using
Rotation Invariant Technique (ESPRIT), robust MVDR, nested-array MVDR, and
reduced-dimension MVDR methods. The results show that at SNR levels of -60 dB
to -10 dB, the angle estiation error of the proposed method is 20 degree less
compared to that of prior methods.


  Gradient-based methods are often used for policy optimization in deep
reinforcement learning, despite being vulnerable to local optima and saddle
points. Although gradient-free methods (e.g., genetic algorithms or evolution
strategies) help mitigate these issues, poor initialization and local optima
are still concerns in highly nonconvex spaces. This paper presents a method for
policy optimization based on Monte-Carlo tree search and gradient-free
optimization. Our method, called Monte-Carlo tree search for policy
optimization (MCTSPO), provides a better exploration-exploitation trade-off
through the use of the upper confidence bound heuristic. We demonstrate
improved performance on reinforcement learning tasks with deceptive or sparse
reward functions compared to popular gradient-based and deep genetic algorithm
baselines.


  Despite the widespread availability of LLMs, there remains a substantial gap
in their capabilities and availability across diverse languages. One approach
to address these issues has been to take an existing pre-trained LLM and
continue to train it on new languages. While prior works have experimented with
language adaptation, many questions around best practices and methodology have
not been covered. In this paper, we present a comprehensive investigation into
the adaptation of LLMs to new languages. Our study covers the key components in
this process, including vocabulary extension, direct preference optimization
and the data scarcity problem for human alignment in low-resource languages. We
scale these experiments across 9 languages and 2 parameter scales (7B and 70B).
We compare our models against Llama 2, Aya-101, XGLM, BLOOM and existing
language experts, outperforming all prior published baselines. Additionally,
all evaluation code and checkpoints are made public to facilitate future
research.


  We study the nonhomogeneous Dirichlet problem for first order Hamilton-Jacobi
equations associated with Tonelli Hamiltonians on a bounded domain $\Omega$ of
$\R^n$ assuming the energy level to be supercritical. First, we show that the
viscosity (weak KAM) solution of such a problem is Lipschitz continuous and
locally semiconcave in $\Omega$. Then, we analyse the singular set of a
solution showing that singularities propagate along suitable curves, the
so-called generalized characteristics, and that such curves stay singular
unless they reach the boundary of $\Omega$. Moreover, we prove that the latter
is never the case for mechanical systems and that singular generalized
characteristics converge to a critical point of the solution in finite or
infinite time. Finally, under stronger assumptions for the domain and Dirichlet
data, we are able to conclude that solutions are globally semiconcave and
semiconvex near the boundary.


  The conversion of multilayer graphenes into sp^3-bonded carbon films on metal
surfaces (through hydrogenation or fluorination of the outer surface of the top
graphene layer) is indicated through first-principles computations. The main
driving force for this conversion is the hybridization between carbon sp^3
orbitals and metal surface dz^2 orbitals. The induced electronic gap states in
the carbon layers are confined in a region within 0.5 nm of the metal surface.
Whether the conversion occurs depend on the fraction of hydrogenated
(fluorinated) C atoms and on the number of stacked graphene layers. In the
analysis of the Eliashberg spectral functions for the sp^3 carbon films on
diamagnetic metals, the strong covalent metal-sp^3 carbon bonds induce soft
phonon modes that predominantly contribute to large electron-phonon couplings,
suggesting the possibility of phonon-mediated superconductivity. Our results
suggest a route to experimental realization of large-area ultrathin sp^3-bonded
carbon films on metal surfaces.


  In a model for rotating non-Boussinesq convection with mean flow we identify
a regime of spatio-temporal chaos that is based on a hexagonal planform and is
sustained by the {\it induced nucleation} of dislocations by penta-hepta
defects. The probability distribution function for the number of defects
deviates substantially from the usually observed Poisson-type distribution. It
implies strong correlations between the defects inthe form of density-dependent
creation and annihilation rates of defects. We extract these rates from the
distribution function and also directly from the defect dynamics.


  To make the illposedness argument more transparent the argument is rewritten
to reduce the equation to the constant dispersion case. Minor errors are
corrected. Accepted to the Proceedings of the AMS.


  We consider the question of efficient estimation in the tails of Gaussian
copulas. Our special focus is estimating expectations over multi-dimensional
constrained sets that have a small implied measure under the Gaussian copula.
We propose three estimators, all of which rely on a simple idea: identify
certain \emph{dominating} point(s) of the feasible set, and appropriately shift
and scale an exponential distribution for subsequent use within an importance
sampling measure. As we show, the efficiency of such estimators depends
crucially on the local structure of the feasible set around the dominating
points. The first of our proposed estimators $\estOpt$ is the
"full-information" estimator that actively exploits such local structure to
achieve bounded relative error in Gaussian settings. The second and third
estimators $\estExp$, $\estLap$ are "partial-information" estimators, for use
when complete information about the constraint set is not available, they do
not exhibit bounded relative error but are shown to achieve polynomial
efficiency. We provide sharp asymptotics for all three estimators. For the
NORTA setting where no ready information about the dominating points or the
feasible set structure is assumed, we construct a multinomial mixture of the
partial-information estimator $\estLap$ resulting in a fourth estimator
$\estNt$ with polynomial efficiency, and implementable through the ecoNORTA
algorithm. Numerical results on various example problems are remarkable, and
consistent with theory.


  Let $\mathcal{X}$ be a subset of $L^1$ that contains the space of simple
random variables $\mathcal{L}$ and $\rho: \mathcal{X} \rightarrow
(-\infty,\infty]$ a dilatation monotone functional with the Fatou property. In
this note, we show that $\rho$ extends uniquely to a $\sigma(L^1,\mathcal{L})$
lower semicontinuous and dilatation monotone functional $\overline{\rho}: L^1
\rightarrow (-\infty,\infty]$. Moreover, $\overline{\rho}$ preserves
monotonicity, (quasi)convexity, and cash-additivity of $\rho$. Our findings
complement recent extension results for quasiconvex law-invariant functionals
proved in [17,20]. As an application of our results, we show that transformed
norm risk measures on Orlicz hearts admit a natural extension to $L^1$ that
retains the robust representations obtained in [4,6].


  Quantum physics allows for unconditionally secure communication through
insecure communication channels. The achievable rates of quantum-secured
communication are fundamentally limited by the laws of quantum physics and in
particular by the properties of entanglement. For a lossy communication line,
this implies that the secret-key generation rate vanishes at least
exponentially with the communication distance. We show that this fundamental
limitation can be violated in a realistic scenario where the eavesdropper can
store quantum information for only a finite, yet arbitrarily long, time. We
consider communication through a lossy bononic channel (modeling linear loss in
optical fibers) and we show that it is in principle possible to achieve a
constant rate of key generation of one bit per optical mode over arbitrarily
long communication distances.


  We construct D=10 supergravity solutions corresponding to type IIB fivebranes
wrapping a two-sphere in a Calabi-Yau two-fold. These are related in the IR to
the large N limit of pure N=2 SU(N) super Yang-Mills theory. We show that the
singularities in the IR correspond to the wrapped branes being distributed on a
ring. We analyse the dynamics of a probe fivebrane and show that it
incorporates the full perturbative structure of the gauge theory. For a class
of solutions the two-dimensional moduli space is non-singular and we match the
result for the corresponding slice of the Coulomb branch of the gauge theory.


  Even though modern service-oriented and data-oriented architectures promise
to deliver loosely coupled control systems, they are inherently brittle as they
commonly depend on a priori agreed interfaces and data models. At the same
time, the Semantic Web and a whole set of accompanying standards and tools are
emerging, advocating ontologies as the basis for knowledge exchange. In this
paper we aim to identify a number of key ideas from the myriad of
knowledge-based practices that can readily be implemented by control systems
today. We demonstrate with a practical example (a three-channel imager for the
Mercator Telescope) how ontologies developed in the Web Ontology Language (OWL)
can serve as a meta-model for our instrument, covering as many engineering
aspects of the project as needed. We show how a concrete system model can be
built on top of this meta-model via a set of Domain Specific Languages (DSLs),
supporting both formal verification and the generation of software and
documentation artifacts. Finally we reason how the available semantics can be
exposed at run-time by adding a "semantic layer" that can be browsed, queried,
monitored etc. by any OPC UA-enabled client.


  The heavy ion experiments in Relativistic Heavy Ion Collider (RHIC) and Large
Hadron Collider (LHC) are going through upgrade in the next five years,
shifting their focus more on the hard processes in the new runs. One of the
main goals is to draw a finer image for the quark gluon plasma (QGP). The heavy
flavor probes , which witness the whole history of heavy ion collision are
particularly sensitive to test the properties of QGP formed in such collisions.
The lattice results for heavy flavor probes provide transport and
phenomenological models crucial inputs to describe the experimental
observations like the strong suppression of the nuclear modification factor
$R_{AA}$ and the non-zero azimuthal anisotropy at low $p_T$. In the last two
years we have seen significant advances in the lattice QCD studies of heavy
flavor probes, including the in-medium quarkonium properties, the complex
static quark-antiquark potential and the heavy quark diffusion from lattice
simulations at nonzero temperature. These achievements substantially deepen our
understanding of the fate of quarkonium, the screening/unscreening of the
complex potential and the temperature and quark mass dependence of the heavy
quark diffusion in thermal medium. In these proceedings, we review recent
results and briefly discuss possible directions in these studies.


  Spin-labeling of molecules with paramagnetic ions is an important approach
for determining molecular structure, however current ensemble techniques lack
the sensitivity to detect few isolated spins. In this Letter, we demonstrate
two-dimensional nanoscale imaging of paramagnetic gadolinium compounds using
scanning relaxometry of a single nitrogen vacancy (NV) center in diamond.
Gadopentetate dimeglumine attached to an atomic force microscope tip is
controllably interacted with and detected by the NV center, by virtue of the
fact that the NV exhibits fast relaxation in the fluctuating magnetic field
generated by electron spin flips in the gadolinium. Using this technique, we
demonstrate a reduction in the $T_1$ relaxation time of the NV center by over
two orders of magnitude, probed with a spatial resolution of 20 nm. Our result
exhibits the viability of the technique for imaging individual spins attached
to complex nanostructures or biomolecules, along with studying the magnetic
dynamics of isolated spins.


  To better exploit search logs and model users' behavior patterns, numerous
click models are proposed to extract users' implicit interaction feedback. Most
traditional click models are based on the probabilistic graphical model (PGM)
framework, which requires manually designed dependencies and may oversimplify
user behaviors. Recently, methods based on neural networks are proposed to
improve the prediction accuracy of user behaviors by enhancing the expressive
ability and allowing flexible dependencies. However, they still suffer from the
data sparsity and cold-start problems. In this paper, we propose a novel
graph-enhanced click model (GraphCM) for web search. Firstly, we regard each
query or document as a vertex, and propose novel homogeneous graph construction
methods for queries and documents respectively, to fully exploit both
intra-session and inter-session information for the sparsity and cold-start
problems. Secondly, following the examination hypothesis, we separately model
the attractiveness estimator and examination predictor to output the
attractiveness scores and examination probabilities, where graph neural
networks and neighbor interaction techniques are applied to extract the
auxiliary information encoded in the pre-constructed homogeneous graphs.
Finally, we apply combination functions to integrate examination probabilities
and attractiveness scores into click predictions. Extensive experiments
conducted on three real-world session datasets show that GraphCM not only
outperforms the state-of-art models, but also achieves superior performance in
addressing the data sparsity and cold-start problems.


  In a one-pion plus one-kaon exchange model, we calculate the kaon production
cross sections in nucleon-nucleon, nucleon-delta and delta-delta interactions.
We find that this model describes reasonably well the experimental data on kaon
production in the proton-proton interaction. Near the kaon production
threshold, the cross section obtained from this model is smaller than that from
the linear parameterization of Randrup and Ko. For kaon production cross
sections from the nucleon-delta and delta-delta interactions, the cross
sections are singular in free space, so we calculate them in a nuclear medium
by including the (complex) pion self-energy. The results are compared with the
scaling ansatz of Randrup and Ko. The theoretical cross sections are then used
in a transport model to study kaon production from Au+Au collisions at 1
GeV/nucleon.


  We prove an analogue of the Hitchin-Kobayashi correspondence for compact,
oriented, taut Riemannian foliated manifolds with transverse Hermitian
structure. In particular, our Hitchin-Kobayashi theorem holds on any compact
Sasakian manifold. We define the notion of stability for foliated Hermitian
vector bundles with transverse holomorphic structure and prove that such
bundles admit a basic Hermitian-Einstein connection if and only if they are
polystable. Our proof is obtained by adapting the proof by Uhlenbeck and Yau to
the foliated setting. We relate the transverse Hermitian-Einstein equations to
higher dimensional instanton equations and in particular we look at the
relation to higher contact instantons on Sasaki manifolds. For foliations of
complex codimension 1, we obtain a transverse Narasimhan-Seshadri theorem. We
also demonstrate that the weak Uhlenbeck compactness theorem fails in general
for basic connections on a foliated bundle. This shows that not every result in
gauge theory carries over to the foliated setting.


  Four-terminal resistance measurements have been carried out on Zn nanowires
formed using electron-beam lithography. When driven resistive by current, these
wires re-enter the superconducting state upon application of small magnetic
fields. The data are qualitatively different from those of previous experiments
on superconducting nanowires, which revealed either negative magnetoresistance
near T_{c} or magnetic field enhanced critical currents. We suggest that our
observations are associated with the damping of phase slip processes by the
enhancement of dissipation by the quasiparticle conductance channel resulting
from the application of a magnetic field.


  Skeleton-based action recognition is a central task in human-computer
interaction. However, most previous methods suffer from two issues: (i)
semantic ambiguity arising from spatial-temporal information mixture; and (ii)
overlooking the explicit exploitation of the latent data distributions (i.e.,
the intra-class variations and inter-class relations), thereby leading to
sub-optimum solutions of the skeleton encoders. To mitigate this, we propose a
spatial-temporal decoupling contrastive learning (STD-CL) framework to obtain
discriminative and semantically distinct representations from the sequences,
which can be incorporated into various previous skeleton encoders and can be
removed when testing. Specifically, we decouple the global features into
spatial-specific and temporal-specific features to reduce the spatial-temporal
coupling of features. Furthermore, to explicitly exploit the latent data
distributions, we employ the attentive features to contrastive learning, which
models the cross-sequence semantic relations by pulling together the features
from the positive pairs and pushing away the negative pairs. Extensive
experiments show that STD-CL with four various skeleton encoders (HCN, 2S-AGCN,
CTR-GCN, and Hyperformer) achieves solid improvements on NTU60, NTU120, and
NW-UCLA benchmarks. The code will be released soon.


  We present a new proof to a general result due to Kestelman. Our proof
differs completely from the other proofs we know and we hope that readers will
find it clearer. We also include a quite exhaustive bibliographical analysis on
related results and proofs.


  Model-independent methods in cosmology has become an essential tool in order
to deal with an increasing number of theoretical alternatives for explaining
the late-time acceleration of the Universe. In principle, this provides a way
of testing the Cosmological Concordance (or $\Lambda$CDM) model under different
assumptions and ruling out whole classes of competing theories. One such
model-independent method is the so-called cosmographic approach, which relies
only on the homogeneity and isotropy of the Universe on large scales. We show
that this method suffers from many shortcomings, providing biased results
depending on the auxiliary variable used in the series expansion and is unable
to rule out models or adequately reconstruct theories with higher-order
derivatives in either the gravitational or matter sector. Consequently, in its
present form, this method seems unable to provide reliable or useful results
for cosmological applications.


  We present a detailed study of the first simple mechanical system that shows
fully realistic transport behavior while still being exactly solvable at the
level of equilibrium statistical mechanics. The system under consideration is a
Lorentz gas with fixed freely-rotating circular scatterers interacting with
point particles via perfectly rough collisions. Upon imposing a temperature
and/or a chemical potential gradient, a stationary state is attained for which
local thermal equilibrium holds for low values of the imposed gradients.
Transport in this system is normal, in the sense that the transport
coefficients which characterize the flow of heat and matter are finite in the
thermodynamic limit. Moreover, the two flows are non-trivially coupled,
satisfying Onsager's reciprocity relations to within numerical accuracy as well
as the Green-Kubo relations . We further show numerically that an applied
electric field causes the same currents as the corresponding chemical potential
gradient in first order of the applied field. Puzzling discrepancies in higher
order effects (Joule heating) are also observed. Finally, the role of entropy
production in this purely Hamiltonian system is shortly discussed.


  In \cite{butman1976} the linear coding scheme is applied, $X_t
=g_t\Big(\Theta - {\bf E}\Big\{\Theta\Big|Y^{t-1}, V_0=v_0\Big\}\Big)$,
$t=2,\ldots,n$, $X_1=g_1\Theta$, with $\Theta: \Omega \to {\mathbb R}$, a
Gaussian random variable, to derive a lower bound on the feedback rate, for
additive Gaussian noise (AGN) channels, $Y_t=X_t+V_t, t=1, \ldots, n$, where
$V_t$ is a Gaussian autoregressive (AR) noise, and $\kappa \in [0,\infty)$ is
the total transmitter power. For the unit memory AR noise, with parameters $(c,
K_W)$, where $c\in [-1,1]$ is the pole and $K_W$ is the variance of the
Gaussian noise, the lower bound is $C^{L,B} =\frac{1}{2} \log \chi^2$, where
$\chi =\lim_{n\longrightarrow \infty} \chi_n$ is the positive root of
$\chi^2=1+\Big(1+ \frac{|c|}{\chi}\Big)^2 \frac{\kappa}{K_W}$, and the sequence
$\chi_n \triangleq \Big|\frac{g_n}{g_{n-1}}\Big|, n=2, 3, \ldots,$ satisfies a
certain recursion, and conjectured that $C^{L,B}$ is the feedback capacity.
  In this correspondence, it is observed that the nontrivial lower bound
$C^{L,B}=\frac{1}{2} \log \chi^2$ such that $\chi >1$, necessarily implies the
scaling coefficients of the feedback code, $g_n$, $n=1,2, \ldots$, grow
unbounded, in the sense that, $\lim_{n\longrightarrow\infty}|g_n| =+\infty$.
The unbounded behaviour of $g_n$ follows from the ratio limit theorem of a
sequence of real numbers, and it is verified by simulations. It is then
concluded that such linear codes are not practical, and fragile with respect to
a mismatch between the statistics of the mathematical model of the channel and
the real statistics of the channel. In particular, if the error is perturbed by
$\epsilon_n>0$ no matter how small, then $X_n =g_t\Big(\Theta - {\bf
E}\Big\{\Theta\Big|Y^{t-1}, V_0=v_0\Big\}\Big)+g_n \epsilon_n$, and
$|g_n|\epsilon_n \longrightarrow \infty$, as $n \longrightarrow \infty$.


  We refine a well-known theorem of Auslander and Reiten about the extension
closedness of n-th syzygies over noether algebras. Applying it, we obtain the
converse of a celebrated theorem of Evans and Griffith on Serre's condition
(S_n) and the local Gorensteiness of a commutative ring in height less than n.
This especially extends a recent result of Araya and Iima concerning a
Cohen-Macaulay local ring with canonical module to an arbitrary local ring.


  We propose a novel statistical test to assess the mutual independence of
multidimensional random vectors. Our approach is based on the $L_1$-distance
between the joint density function and the product of the marginal densities
associated with the presumed independent vectors. Under the null hypothesis, we
employ Poissonization techniques to establish the asymptotic normal
approximation of the corresponding test statistic, without imposing any
regularity assumptions on the underlying Lebesgue density function, denoted as
$f(\cdot)$. Remarkably, we observe that the limiting distribution of the
$L_1$-based statistics remains unaffected by the specific form of $f(\cdot)$.
This unexpected outcome contributes to the robustness and versatility of our
method. Moreover, our tests exhibit nontrivial local power against a subset of
local alternatives, which converge to the null hypothesis at a rate of {${\tiny
n^{\tiny -1/2}h_n^{\tiny -{d/4}}}$}, $d\geq 2$, where $n$ represents the sample
size and $h_n$ denotes the bandwidth. Finally, the theory is supported by a
comprehensive simulation study to investigate the finite-sample performance of
our proposed test. The results demonstrate that our testing procedure generally
outperforms existing approaches across various examined scenarios.


  We describe an algorithm which pattern embeds, in the sense of Woo-Yong, any
Bruhat interval of a symmetric group into an interval whose extremes lie in the
same right Kazhdan-Lusztig cell. This apparently harmless fact has applications
in finding examples of reducible associated varieties of
$\mathfrak{sl}_n$-highest weight modules, as well as in the study of $W$-graphs
for symmetric groups, and in comparing various bases of irreducible
representations of the symmetric group or its Hecke algebra. For example, we
are able to systematically produce many negative answers to a question from the
1980s of Borho-Brylinski and Joseph, which had been settled by Williamson via
computer calculations only in 2014.


  We investigate the presence of twinlike models in theories described by
several real scalar fields. We focus on the first-order formalism, and we show
how to build distinct scalar field theories that support the same extended
solution, with the same energy density and the very same linear stability. The
results are valid for two distinct classes of generalized models, that include
the standard model and cover a diversity of generalized models of current
interest in high energy physics.


  We prove the existence of positive solutions for the supercritical nonlinear
fractional Schr\"odinger equation $(-\Delta)^s u+V(x)u-u^p=0$ in $\mathbb R^n$,
with $u(x)\to 0$ as $|x|\to +\infty$, where $p>\frac{n+2s}{n-2s}$ for $s\in
(0,1), \ n>2s$. We show that if $V(x)=o(|x|^{-2s})$ as $|x|\to +\infty$, then
for $p>\frac{n+2s-1}{n-2s-1}$, this problem admits a continuum of solutions.
More generally, for $p>\frac{n+2s}{n-2s}$, conditions for solvability are also
provided. This result is the extension of the work by Davila, Del Pino, Musso
and Wei to the fractional case. Our main contributions are: the existence of a
smooth, radially symmetric, entire solution of $(-\Delta)^s w=w^p$ in $\mathbb
R^n$, and the analysis of its properties. The difficulty here is the lack of
phase-plane analysis for a nonlocal ODE; instead we use conformal geometry
methods together with Schaaf's argument as in the paper by Ao, Chan, DelaTorre,
Fontelos, Gonz\'alez and Wei on the singular fractional Yamabe problem.


  The use of high-frequency currents in neurostimulation has received increased
attention in recent years due to its varied effects on tissues and cells.
Neurons are commonly modeled as nonlinear systems, and questions such as
stability can thus be addressed with well-known averaging methods. A recent
strategy called interferential currents uses electrodes delivering sinusoidal
signals of slightly different frequencies, and thus classical averaging
(well-adapted to deal with a single frequency) cannot be directly applied. In
this paper, we consider the one-dimensional FitzHugh-Nagumo system under the
effects of a source composed of two terms that are sinusoidal in time and
quadratically decaying in space. To study this setting we develop a new
averaging strategy to prove that, when the frequencies involved are
sufficiently high, the full system can be approximated by an explicit
highly-oscillatory term plus the solution of a simpler -- albeit non-autonomous
-- system. This decomposition can be seen as a stability result around a
varying trajectory. One of the main novelties of the proofs presented here is
an extension of the contracting rectangles method to the case of parabolic
equations with space and time-depending coefficients.


  Effective scaffolds for bone tissue-engineering are those that combine
adequate mechanical and chemical performance with osseointegrative,
angiogenetic and anti-bacterial modes of bioactivity. To address these
requirements via a combined approach, we additively manufactured square strut
scaffolds by robocasting precipitation-derived strontium/copper co-substituted
diopside. Microstructure, mechanical performance, bioactivity,
biocompatibility, and antibacterial activity were examined. The results show
that the presence of strontium and copper in the diopside lattice reduces the
grain size and increases the density of the ceramics. The compressive strength,
hardness, and fracture toughness of the diopside showed improvement, attributed
to a finer microstructure and improved sintering. Scaffolds had excellent
compressive strength with a high porosity (68 to 72 %), which is attributed to
the structure of the stacked square struts. All materials showed good in vitro
bioactivity and favorable proliferation of osteogenic sarcoma cells, while
strontium and copper co-doped materials exhibited the strongest
anti-Escherichia coli activity. We show that across multiple indicators this
system offers pathways towards high-performance bone substitutes.


  Gamma-ray binaries are stellar systems containing a neutron star or black
hole with gamma-ray emission produced by an interaction between the components.
These systems are rare, even though binary evolution models predict dozens in
our Galaxy. A search for gamma-ray binaries with the Fermi Large Area Telescope
(LAT) shows that 1FGL J1018.6-5856 exhibits intensity and spectral modulation
with a 16.6 day period. We identified a variable X-ray counterpart, which shows
a sharp maximum coinciding with maximum gamma-ray emission, as well as an
O6V((f)) star optical counterpart and a radio counterpart that is also
apparently modulated on the orbital period. 1FGL J1018.6-5856 is thus a
gamma-ray binary, and its detection suggests the presence of other fainter
binaries in the Galaxy.


  Roulette-wheel selection is a frequently used method in genetic and
evolutionary algorithms or in modeling of complex networks. Existing routines
select one of N individuals using search algorithms of O(N) or O(log(N))
complexity. We present a simple roulette-wheel selection algorithm, which
typically has O(1) complexity and is based on stochastic acceptance instead of
searching. We also discuss a hybrid version, which might be suitable for highly
heterogeneous weight distributions, found, for example, in some models of
complex networks. With minor modifications, the algorithm might also be used
for sampling with fitness cut-off at a certain value or for sampling without
replacement.