  Opioid related aberrant behaviors (ORABs) present novel risk factors for
opioid overdose. This paper introduces a novel biomedical natural language
processing benchmark dataset named ODD, for ORAB Detection Dataset. ODD is an
expert-annotated dataset designed to identify ORABs from patients' EHR notes
and classify them into nine categories; 1) Confirmed Aberrant Behavior, 2)
Suggested Aberrant Behavior, 3) Opioids, 4) Indication, 5) Diagnosed opioid
dependency, 6) Benzodiazepines, 7) Medication Changes, 8) Central Nervous
System-related, and 9) Social Determinants of Health. We explored two
state-of-the-art natural language processing models (fine-tuning and
prompt-tuning approaches) to identify ORAB. Experimental results show that the
prompt-tuning models outperformed the fine-tuning models in most categories and
the gains were especially higher among uncommon categories (Suggested Aberrant
Behavior, Confirmed Aberrant Behaviors, Diagnosed Opioid Dependence, and
Medication Change). Although the best model achieved the highest 88.17% on
macro average area under precision recall curve, uncommon classes still have a
large room for performance improvement. ODD is publicly available.


  The stellar populations of galaxies hold vital clues to their formation
histories. In this paper we present results based on modeling stacked spectra
of early-type galaxies drawn from the Sloan Digital Sky Survey (SDSS) as a
function of velocity dispersion, sigma, from 90 km/s to 300 km/s. The spectra
are of extremely high quality, with typical S/N of 1000/A, and a wavelength
coverage of 4000A-8800A. Our population synthesis model includes variation in
16 elements from C to Ba, the shift in effective temperature, Delta(Teff), of
the stars with respect to a solar metallicity isochrone, amongst other
parameters. In our approach we fit the full optical spectra rather than a
select number of spectral indices and are able to, for the first time, measure
the abundances of the elements V, Cr, Mn, Co, and Ni from the integrated light
of distant galaxies. Our main results are as follows: 1) light-weighted stellar
ages range from 6-12 Gyr from low to high sigma; 2) [Fe/H] varies by less than
0.1 dex across the entire sample; 3) Mg closely tracks O, and both increase
from ~0.0 at low sigma to ~0.25 at high sigma; Si and Ti show a shallower rise
with sigma, and Ca tracks Fe rather than O; 4) the iron peak elements V, Cr,
Mn, and Ni track Fe, while Co tracks O, suggesting that Co forms primarily in
massive stars; 5) C and N track O over the full sample and [C/Fe] and [N/Fe]
exceed 0.2 at high sigma; and 6) the variation in Delta(Teff) with total
metallicity follows theoretical predictions based on stellar evolution theory.
Our derived [Mg/Fe] and [O/Fe] abundance ratios are 0.05-0.1 dex lower than
most previous determinations. Under the conventional interpretation that the
variation in these ratios is due to star formation timescale variations, our
results suggest longer star formation timescales for massive early-type
galaxies than previous studies. (ABRIDGED)


  Motivated by the emergence of materials with mean free paths on the order of
microns, we propose a novel class of solid state radiation sources based on
reimplementing classical vacuum tube designs in semiconductors. Using materials
with small effective masses, these devices should be able to access the
terahertz range. We analyze the DC and AC operation of the simplest such
device, the cylindrical diode magnetron, using effective quantum models. By
treating the magnetron as an open quantum system, we show that it continues to
operate as a radiation source even if its diameter is only a few tens of
magnetic lengths.


  The unprecedented development of non-terrestrial networks (NTN) utilizes the
low-altitude airspace for commercial and social flying activities. The
integration of NTN and terrestrial networks leads to the emergence of
low-altitude economy (LAE). A series of LAE application scenarios are enabled
by the sensing, communication, and transportation functionalities of the
aircrafts. The prerequisite technologies supporting LAE are introduced in this
paper, including the network coverage and aircrafts detection. The LAE
functionalities assisted by aircrafts with respect to sensing and communication
are then summarized, including the terrestrial and non-terrestrial targets
sensing, ubiquitous coverage, relaying, and traffic offloading. Finally,
several future directions are identified, including aircrafts collaboration,
energy efficiency, and artificial intelligence enabled LAE.


  The electronic structure and the magnetic properties of iron-based
superconductors are highly sensitive to the pnictogen height. Coherent
excitation of the $A_{1g}$ phonon by femtosecond laser directly modulates the
pnictogen height, which has been used to control the physical properties of
iron-based superconductors. Previous studies show that the driven $A_{1g}$
phonon resulted in a transient increase of the pnictogen height in
BaFe$_2$As$_2$, favoring an enhanced Fe magnetic moment. Here, we use
time-resolved broadband terahertz spectroscopy to investigate the dynamics of
BaFe$_2$As$_2$ in the $A_{1g}$ phonon driven state. Below the spin-density wave
(SDW) transition temperature, we observe a transient gap generation at early
time delays. A similar transient feature is observed in the normal state up to
room temperature.


  We prove that the field equations of supergravity for purely time-dependent
backgrounds, which reduce to those of a one--dimensional sigma model, admit a
Lax pair representation and are fully integrable. In the case where the
effective sigma model is on a maximally split non--compact coset U/H (maximal
supergravity or subsectors of lower supersymmetry supergravities) we are also
able to construct a completely explicit analytic integration algorithm,
adapting a method introduced by Kodama et al in a recent paper. The properties
of the general integral are particularly suggestive. Initial data are
represented by a pair C_0, h_0 where C_0 is in the CSA of the Lie algebra of U
and h_0 in H/W is in the compact subgroup H modded by the Weyl group of U. At
asymptotically early and asymptotically late times the Lax operator is always
in the Cartan subalgebra and due to the iso-spectral property the two limits
differ only by the action of some element of the Weyl group. Hence the entire
cosmic evolution can be seen as a billiard scattering with quantized angles
defined by the Weyl group. The solution algorithm realizes a map from H}/W into
W.


  Static program analysis tools are often required to work with only a small
part of a program's source code, either due to the unavailability of the entire
program or the lack of need to analyze the complete code. This makes it
challenging to use static analysis tools that require a complete and typed
intermediate representation (IR). We present JCoffee, a tool that leverages
compiler feedback to convert partial Java programs into their compilable
counterparts by simulating the presence of missing surrounding code. It works
with any well-typed code snippet (class, function, or even an unenclosed group
of statements) while making minimal changes to the input code fragment. A demo
of the tool is available here: https://youtu.be/O4h2g_n2Qls


  We investigate the influence of an external magnetic field on spin phase
relaxation of single electrons in semiconductor quantum dots induced by the
hyperfine interaction. The basic decay mechanism is attributed to the
dispersion of local effective nuclear fields over the ensemble of quantum dots.
The characteristics of electron spin dephasing is analyzed by taking an average
over the nuclear spin distribution. We find that the dephasing rate can be
estimated as a spin precession frequency caused primarily by the mean value of
the local nuclear magnetic field. Furthermore, it is shown that the hyperfine
interaction does not fully depolarize electron spin. The loss of initial spin
polarization during the dephasing process depends strongly on the external
magnetic field, leading to the possibility of effective suppression of this
mechanism.


  Necessary and sufficient conditions for a space-time to be conformal to an
Einstein space-time are interpreted in terms of curvature restrictions for the
corresponding Cartan conformal connection.


  The theory of graphons comes with a natural sampling procedure, which results
in an inhomogeneous variant of the Erd\H{o}s--R\'enyi random graph, called
$W$-random graphs. We prove, via the method of moments, a limit theorem for the
number of $r$-cliques in such random graphs. We show that, whereas in the case
of dense Erd\H{o}s--R\'enyi random graphs the fluctuations are normal of order
$n^{r-1}$, the fluctuations in the setting of $W$-random graphs may be of order
$0, n^{r-1}$, or $n^{r-0.5}$. Furthermore, when the fluctuations are of order
$n^{r-0.5}$ they are normal, while when the fluctuations are of order $n^{r-1}$
they exhibit either normal or a particular type of chi-square behavior whose
parameters relate to spectral properties of $W$.
  These results can also be deduced from a general setting [Janson and Nowicki,
PTRF 1991], based on the projection method. In addition to providing
alternative proofs, our approach makes direct links to the theory of graphons.


  The rate of the emission of the high energy low-mass dileptons from the QGP
is found in the first nonvanishing order with respect to strong coupling. We
base on the real-time kinetic approach [2] without an explicit assumption about
a complete thermal equilibrium in the emitting system. For the class of the
partons distributions which may simulate that of the "hot glue scenario"[1] the
rate of emission is found analytically .
  ( Figures can be obtained from the author )


  There has been extensive work on data depth-based methods for robust
multivariate data analysis. Recent developments have moved to
infinite-dimensional objects such as functional data. In this work, we propose
a new notion of depth, the total variation depth, for functional data. As a
measure of depth, its properties are studied theoretically, and the associated
outlier detection performance is investigated through simulations. Compared to
magnitude outliers, shape outliers are often masked among the rest of samples
and harder to identify. We show that the proposed total variation depth has
many desirable features and is well suited for outlier detection. In
particular, we propose to decompose the total variation depth into two
components that are associated with shape and magnitude outlyingness,
respectively. This decomposition allows us to develop an effective procedure
for outlier detection and useful visualization tools, while naturally
accounting for the correlation in functional data. Finally, the proposed
methodology is demonstrated using real datasets of curves, images, and video
frames.


  $f(P)$ gravity is a novel extension of ECG in which the Ricci scalar in the
action is replaced by a function of the curvature invariant $P$ which
represents the contractions of the Riemann tensor at the cubic order \cite{p}.
The present work is concentrated on bounding some $f(P)$ gravity models using
the concept of energy conditions where the functional forms of $f(P)$ are
represented as \textbf{a)} $f(P) = \alpha \sqrt{P}$, and \textbf{b)} $f(P) =
\alpha \exp (P)$, where $\alpha$ is the sole model parameter. Energy conditions
are interesting linear relationships between pressure and density and have been
extensively employed to derive interesting results in Einstein's gravity, and
are also an excellent tool to impose constraints on any cosmological model. To
place the bounds, we ensured that the energy density must remain positive, the
pressure must remain negative, and the EoS parameter must attain a value close
to $-1$ to make sure that the bounds respect the accelerated expansion of the
Universe and are also in harmony with the latest observational data. We report
that for both the models, suitable parameter spaces exist which satisfy the
aforementioned conditions and therefore posit the $f(P)$ theory of gravity to
be a promising modified theory of gravitation.


  For $1<p<\infty$, we consider the following problem $$ -\Delta_p u=f(u),\quad
u>0\text{ in }\Omega,\quad\partial_\nu u=0\text{ on }\partial\Omega, $$ where
$\Omega\subset\mathbb R^N$ is either a ball or an annulus. The nonlinearity $f$
is possibly supercritical in the sense of Sobolev embeddings; in particular our
assumptions allow to include the prototype nonlinearity $f(s)=-s^{p-1}+s^{q-1}$
for every $q>p$. We use the shooting method to get existence and multiplicity
of non-constant radial solutions. With the same technique, we also detect the
oscillatory behavior of the solutions around the constant solution $u\equiv1$.
In particular, we prove a conjecture proposed in [D. Bonheure, B. Noris, T.
Weth, {\it Ann. Inst. H. Poincar\'e Anal. Non Lin\'aire} vol. 29, pp. 573-588
(2012)], that is to say, if $p=2$ and $f'(1)>\lambda_{k+1}^{rad}$, there exists
a radial solution of the problem having exactly $k$ intersections with
$u\equiv1$ for a large class of nonlinearities.


  We consider a dissipative flow network that obeys the standard linear nodal
flow conservation, and where flows on edges are driven by potential difference
between adjacent nodes. We show that in the case when the flow is a
monotonically increasing function of the potential difference, solution of the
network flow equations is unique and can be equivalently recast as the solution
of a strictly convex optimization problem. We also analyze the maximum
throughput problem on such networks seeking to maximize the amount of flow that
can be delivered to the loads while satisfying bounds on the node potentials.
When the dissipation function is differentiable we develop a representation of
the maximum throughput problem in the form of a twice differentiable biconvex
optimization problem exploiting the variational representation of the network
flow equations. In the process we prove a special case of a certain
monotonicity property of dissipative flow networks. When the dissipation
function follows a power law with exponent greater than one, we suggest a mixed
integer convex relaxation of the maximum throughput problem. Finally, we
illustrate application of these general results to balanced, i.e. steady,
natural gas networks also validating the theory results through simulations on
a test case.


  We obtain equations of motion for the boost-invariant expansion of a system
of chiral particles. Our analysis is based on the Boltzmann equation for left-
and right-handed massless particles in the relaxation time approximation. We
assume Bjorken symmetry, but allow for parity breaking. We generalize the
relaxation time approximation to take into account the so-called side-jump
effect, but we show that the ensuing correction happens to vanish for Bjorken
symmetry. After expressing the conserved currents in terms of chiral moments,
we derive equations of motion for these moments from the Boltzmann equation.
After a suitable truncation, these equations allow us to study the transition
from the early-time collisionless regime to the hydrodynamic regime at late
time, where the parity-violating chiral moments decay exponentially. The
truncation that we use for the parity-violating moments is shown to be
identical to Israel-Stewart's 14-moment approximation. Our final set of
equations can be used to calculate the energy-momentum tensor, vector-, and
axial-vector currents with chiral degrees of freedom for possible applications
in heavy-ion collisions.


  Anomaly detection (AD) in a surveillance scenario is an emerging and
challenging field of research. For autonomous vehicles like drones or cars, it
is immensely important to distinguish between normal and abnormal states in
real-time. Additionally, we also need to detect any device malfunction. But the
nature and degree of abnormality may vary depending upon the actual environment
and adversary. As a result, it is impractical to model all cases a-priori and
use supervised methods to classify. Also, an autonomous vehicle provides
various data types like images and other analog or digital sensor data, all of
which can be useful in anomaly detection if leveraged fruitfully. To that
effect, in this paper, a heterogeneous system is proposed which estimates the
degree of abnormality of an unmanned surveillance drone, analyzing real-time
image and IMU (Inertial Measurement Unit) sensor data in an unsupervised
manner. Here, we have demonstrated a Convolutional Neural Network (CNN)
architecture, named AngleNet to estimate the angle between a normal image and
another image under consideration, which provides us with a measure of anomaly
of the device. Moreover, the IMU data are used in autoencoder to predict
abnormality. Finally, the results from these two algorithms are ensembled to
estimate the final degree of abnormality. The proposed method performs
satisfactorily on the IEEE SP Cup-2020 dataset with an accuracy of 97.3%.
Additionally, we have also tested this approach on an in-house dataset to
validate its robustness.


  The effects of the anomalous contact terms are taken into account in tau to
eta(eta')2pi nu decay. tau to 3pi nu is computed. Theoretical result agrees
with data. It is the first time the anomalous vertex eta aa is tested. An a_{1}
resonance is predicted in the final state. The decay rate of tau to eta'3pi nu
is predicted.


  Deep neural networks have demonstrated their ability to automatically extract
meaningful features from data. However, in supervised learning, information
specific to the dataset used for training, but irrelevant to the task at hand,
may remain encoded in the extracted representations. This remaining information
introduces a domain-specific bias, weakening the generalization performance. In
this work, we propose splitting the information into a task-related
representation and its complementary context representation. We propose an
original method, combining adversarial feature predictors and cyclic
reconstruction, to disentangle these two representations in the single-domain
supervised case. We then adapt this method to the unsupervised domain
adaptation problem, consisting of training a model capable of performing on
both a source and a target domain. In particular, our method promotes
disentanglement in the target domain, despite the absence of training labels.
This enables the isolation of task-specific information from both domains and a
projection into a common representation. The task-specific representation
allows efficient transfer of knowledge acquired from the source domain to the
target domain. In the single-domain case, we demonstrate the quality of our
representations on information retrieval tasks and the generalization benefits
induced by sharpened task-specific representations. We then validate the
proposed method on several classical domain adaptation benchmarks and
illustrate the benefits of disentanglement for domain adaptation.


  Etching experiments were performed that reveal the vertical distribution of
optically active nitrogen-vacancy (NV) centers in diamond created in close
proximity to a surface through ion implantation and annealing. The NV
distribution depends strongly on the native nitrogen concentration, and
spectral measurements of the neutral and negatively-charged NV peaks give
evidence for electron depletion effects in lower-nitrogen material. The results
are important for potential quantum information and magnetometer devices where
NV centers must be created in close proximity to a surface for coupling to
optical structures.


  We discuss the problem of estimating the characteristic length scale
$\xi_{\rm s}$, and hence the initial density, of a system of cosmic strings
formed at a continuous, second-order phase transition in the early universe. In
particular, we examine the roles of the correlation length (or string width)
$\xi_{\rm c}$ and the Ginzburg length $\xi_{\rm G}$ which defines the
``fuzziness'' of long strings. We argue that strings acquire a clear identity
only once $\xi_{\rm s}$ exceeds both $\xi_{\rm c}$ and $\xi_{\rm G}$, and
estimate its magnitude at that time.


  This paper is to explore the de Haas-van Alphen effect $(dHvA)$ of graphene
in the presence of an in-plane uniform electric field. Three major findings are
yielded. First of all, the electric field is found to modulate the de Haas-van
Alphen magnetization and magnetic susceptibility through the dimensionless
parameter $(\beta =\frac{E}{\upsilon_{F}B})$. As the parameter $\beta$
increases, the values of magnetization and magnetic susceptibility increase to
positive infinity or decrease to negative infinity at the exotic point
$\beta_{c}=1$. Besides, the $dHvA$ oscillation amplitude rises abruptly to
infinity for zero temperature at $\beta_{c}=1$, but eventually collapses at a
finite temperature thereby leading to the vanishing of de Haas-van Alphen
effect. In addition, the magnetic susceptibility depends on the electric and
magnetic fields, suggesting that the graphene should be a non-linear magnetic
medium in the presence of the external field. These results, which are
different from those obtained in the standard nonrelativistic 2D electron gas,
are attributed to its anomalous Landau level spectrum in graphene.


  In many societal resource allocation domains, machine learning methods are
increasingly used to either score or rank agents in order to decide which ones
should receive either resources (e.g., homeless services) or scrutiny (e.g.,
child welfare investigations) from social services agencies. An agency's
scoring function typically operates on a feature vector that contains a
combination of self-reported features and information available to the agency
about individuals or households.This can create incentives for agents to
misrepresent their self-reported features in order to receive resources or
avoid scrutiny, but agencies may be able to selectively audit agents to verify
the veracity of their reports.
  We study the problem of optimal auditing of agents in such settings. When
decisions are made using a threshold on an agent's score, the optimal audit
policy has a surprisingly simple structure, uniformly auditing all agents who
could benefit from lying. While this policy can, in general, be hard to compute
because of the difficulty of identifying the set of agents who could benefit
from lying given a complete set of reported types, we also present necessary
and sufficient conditions under which it is tractable. We show that the scarce
resource setting is more difficult, and exhibit an approximately optimal audit
policy in this case. In addition, we show that in either setting verifying
whether it is possible to incentivize exact truthfulness is hard even to
approximate. However, we also exhibit sufficient conditions for solving this
problem optimally, and for obtaining good approximations.


  We want to approximate general multivariate probability density functions by
deterministic sample sets. For optimal sampling, the closeness to the given
continuous density has to be assessed. This is a difficult challenge in
multivariate settings. Simple solutions are restricted to the one-dimensional
case. In this paper, we propose to employ one-dimensional density projections.
These are the Radon transforms of the densities. For every projection, we
compute their cumulative distribution function. These Projected Cumulative
Distributions (PCDs) are compared for all possible projections (or a discrete
set thereof). This leads to a tractable distance measure in multivariate space.
The proposed approximation method is efficient as calculating the distance
measure mainly entails sorting in one dimension. It is also surprisingly simple
to implement.


  Electrical space thrusters show important advantages for applications in
outer space compared to chemical thrusters, as they allow a longer mission
lifetime with lower weight and propellant consumption. Mature technologies on
the market today accelerate positive ions to generate thrust. The ion beam is
neutralized by electrons downstream, and this need for an additional
neutralization system has some drawbacks related to stability, lifetime and
total weight and power consumption. Many new concepts, to get rid of the
neutralizer, have been proposed, and the PEGASES ion-ion thruster is one of
them. This new thruster concept aims at accelerating both positive and negative
ions to generate thrust, such that additional neutralization is redundant. This
chapter gives an overview of the concept of electric propulsion and the state
of the development of this new ion-ion thruster.


  (English) This monograph aims at presenting the core weak convergence theory
for sequences of random vectors with values in $\mathbb{R}^k$. In some places,
a more general formulation in metric spaces is provided. It lays out the
necessary foundation that paves the way to applications in particular subfields
of the theory. In particular, the needs of Asymptotic Statistics are addressed.
A whole chapter is devoted to weak convergence in $\mathbb{R}$ where specific
tools, for example for handling weak convergence of sequences using independent
and indentically distributed random variables such that the Renyi's
representations by means of standard uniform or exponential random variables,
are stated. The function empirical process is presented as a powerful tool for
solving a considerable number of asymptotic problems in Statistics. The text is
written in a self-contained approach whith the proofs of all used results at
the exception of the general Skorohod-Wichura Theorem.


  Cloud detection plays a very important role in the process of remote sensing
images. This paper designs a super-pixel level cloud detection method based on
convolutional neural network (CNN) and deep forest. Firstly, remote sensing
images are segmented into super-pixels through the combination of SLIC and
SEEDS. Structured forests is carried out to compute edge probability of each
pixel, based on which super-pixels are segmented more precisely. Segmented
super-pixels compose a super-pixel level remote sensing database. Though cloud
detection is essentially a binary classification problem, our database is
labeled into four categories: thick cloud, cirrus cloud, building and other
culture, to improve the generalization ability of our proposed models.
Secondly, super-pixel level database is used to train our cloud detection
models based on CNN and deep forest. Considering super-pixel level remote
sensing images contain less semantic information compared with general object
classification database, we propose a Hierarchical Fusion CNN (HFCNN). It takes
full advantage of low-level features like color and texture information and is
more applicable to cloud detection task. In test phase, every super-pixel in
remote sensing images is classified by our proposed models and then combined to
recover final binary mask by our proposed distance metric, which is used to
determine ambiguous super-pixels. Experimental results show that, compared with
conventional methods, HFCNN can achieve better precision and recall.


  We study the low energy effective field theory of fermions with electric and
magnetic dipole moments at finite temperature. We find that at one loop there
is an interaction term of the Chern-Simon form ${\cal L_I}=m_\mu\>A_\nu {\tilde
F}^{\mu\nu}$. The four vector $m_\mu \simeq d_i \mu_i m_i^2
~{\partial_\mu}\>(ln T)$ is interpreted as a Chern- Simon type mass of photons,
which is determined by the electric (magnetic) dipole moments $d_i$ ($\mu_i$)
of the fermions in the vacuum polarisation loop diagram. The physical
consequence of such a photon mass is that, photons of opposite circular
polarisations, propagating through a hot medium, have different group
velocities. We estimate that the time lag between the arrival times of the left
and right circularly polarised light signals from pulsars. If the light
propagates through a hot plasma (where the temperature in some regions is $T
\sim 100 MeV$) then the time lag between the two circularly polarised signals
of frequency $\omega$ will be $\Delta t(\omega) \simeq 10^{-6} /\omega$. It may
be possible to observe this effect in pulsar signals which propagate through
nebula at high temperatures.


  We report our results on the nature of the lightest scalar resonances, where
we show that a quark-antiquark or glueball interpretation of the scalars
f_0(600) and K^*_0(800) requires a very unnatural fine tuning to satisfy
1/N_c-expansion predictions for quark-antiquark or glueball states, which is
not needed in the case of the lightest vector mesons \rho(770) and K^*(892).
For this we consider scattering observables whose value is fixed to 1 for
quark-antiquark and glueball states up to corrections suppressed by more than
one power of 1/N_c, thus enhancing contributions of other nature. This allows
us to evaluate these observables and check the 1/N_c predictions at N_c=3
without the need to extrapolate to unphysical N_c values. This is done using
recent and very precise dispersive \pi\pi and \pi K scattering data analyses.


  Cloud applications are increasingly shifting to interactive and
loosely-coupled microservices. Despite their advantages, microservices
complicate resource management, due to inter-tier dependencies.
  We present Sinan, a cluster manager for interactive microservices that
leverages easily-obtainable tracing data instead of empirical decisions, to
infer the impact of a resource allocation on on end-to-end performance, and
allocate appropriate resources to each tier. In a preliminary evaluation of
Sinan with an end-to-end social network built with microservices, we show that
Sinan's data-driven approach, allows the service to always meet its QoS without
sacrificing resource efficiency.


  In this article we prove the existence of Kahler-Ricci solitons on
smoothable, K-stable Q-Fano varieties. We also investigate the behavior of
twisted Kahler-Ricci solitons in the Gromov-Hausdorff topology under this
smoothing family.


  The Hessian discretisation method (HDM) for fourth order linear elliptic
equations provides a unified convergence analysis framework based on three
properties namely coercivity, consistency, and limit-conformity. Some examples
that fit in this approach include conforming and nonconforming finite element
methods, finite volume methods and methods based on gradient recovery
operators. A generic error estimate has been established in $L^2$, $H^1$ and
$H^2$-like norms in literature. In this paper, we establish improved $L^2$ and
$H^1$ error estimates in the framework of HDM and illustrate it on various
schemes. Since an improved $L^2$ estimate is not expected in general for finite
volume method (FVM), a modified FVM is designed by changing the quadrature of
the source term and a superconvergence result is proved for this modified FVM.
In addition to the Adini nonconforming finite element method (ncFEM), in this
paper, we show that the Morley ncFEM is an example of HDM. Numerical results
that justify the theoretical results are also presented.


  In general relativity, the causal structure of space-time may sometimes be
depicted by conformal Carter-Penrose diagrams or a recent extension of these -
the projection diagrams. The introduction of conformal diagrams in the sixties
was one of the progenitors of the golden age of relativity. They are the key
ingredient of many scientific papers. Unfortunately, drawing them in the form
suitable for LaTeX documents is time-consuming and not easy. We present below a
library that allows one to draw an arbitrary conformal diagram in a few simple
steps.


  Inspired by a quantum mechanical formalism to model concepts and their
disjunctions and conjunctions, we put forward in this paper a specific
hypothesis. Namely that within human thought two superposed layers can be
distinguished: (i) a layer given form by an underlying classical deterministic
process, incorporating essentially logical thought and its indeterministic
version modeled by classical probability theory; (ii) a layer given form under
influence of the totality of the surrounding conceptual landscape, where the
different concepts figure as individual entities rather than (logical)
combinations of others, with measurable quantities such as 'typicality',
'membership', 'representativeness', 'similarity', 'applicability', 'preference'
or 'utility' carrying the influences. We call the process in this second layer
'quantum conceptual thought', which is indeterministic in essence, and contains
holistic aspects, but is equally well, although very differently, organized
than logical thought. A substantial part of the 'quantum conceptual thought
process' can be modeled by quantum mechanical probabilistic and mathematical
structures. We consider examples of three specific domains of research where
the effects of the presence of quantum conceptual thought and its deviations
from classical logical thought have been noticed and studied, i.e. economics,
decision theory, and concept theories and which provide experimental evidence
for our hypothesis.


  Cardiac re-entry regime of self-organised abnormal synchronisation underlie
dangerous arrhythmias and fatal fibrillation. Recent advances in the theory of
dissipative vortices, experimental studies, and anatomically realistic computer
simulations, elucidated the role of cardiac re-entry interaction with fine
anatomical features in the heart, and anatomy induced drift. The fact that
anatomy and structural anisotropy of the heart is consistent within a species
suggested its possible functional effect on spontaneous drift of cardiac
re-entry. A comparative study of the anatomy induced drift could be used in
order to predict evolution of atrial arrhythmia, and improve low-voltage
defibrillation protocols and ablation strategies. Here, in micro-CT based model
of rat pulmonary vein wall, and in sheep atria models based on high resolution
serial histological sections, we demonstrate effects of heart geometry and
anisotropy on cardiac re-entry anatomy induced drift, its pinning to
fluctuations of thickness in the layer. The data sets of sheep atria and rat
pulmonary vein wall are incorporated into the BeatBox High Performance
Computing simulation environment. Re-entry is initiated at prescribed locations
in the spatially homogeneous mono-domain models of cardiac tissue. Excitation
is described by FitzHugh-Nagumo kinetics. In the in-silico models, isotropic
and anisotropic conduction show specific anatomy effects and the interplay
between anatomy and anisotropy of the heart. The main objectives are to
demonstrate the functional role of the species hearts geometry and anisotropy
on cardiac re-entry anatomy induced drift. In case of the rat pulmonary vein
wall with ~90 degree transmural fibre rotation, it is shown that the joint
effect of the PV wall geometry and anisotropy turns a plane excitation wave
into a re-entry pinned to a small fluctuation of thickness in the wall.


  Extensive quantum error correction is necessary in order to perform a useful
computation on a noisy quantum computer. Moreover, quantum error correction
must be implemented based on imperfect parity check measurements that may
return incorrect outcomes or inject additional faults into the qubits. To
achieve fault-tolerant error correction, Shor proposed to repeat the sequence
of parity check measurements until the same outcome is observed sufficiently
many times. Then, one can use this information to perform error correction. A
basic implementation of this fault tolerance strategy requires $\Omega(r d^2)$
parity check measurements for a distance-d code defined by r parity checks. For
some specific highly structured quantum codes, Bombin has shown that
single-shot fault-tolerant quantum error correction is possible using only r
measurements. In this work, we demonstrate that fault-tolerant quantum error
correction can be achieved using $O(d \log(d))$ measurements for any code with
distance $d \geq \Omega(n^\alpha)$ for some constant $\alpha > 0$. Moreover, we
prove the existence of a sub-single-shot fault-tolerant quantum error
correction scheme using fewer than r measurements. In some cases, the number of
parity check measurements required for fault-tolerant quantum error correction
is exponentially smaller than the number of parity checks defining the code.


  We discuss the correspondence between metric $f(R)$ gravity and $\omega=0$
Brans-Dicke theory with a potential, by working out an example that reconfirms
this equivalence.


  The importance of counterparty credit risk to the derivative contracts was
demonstrated consistently throughout the financial crisis of 2008. Accurate
valuation of Credit value adjustment (CVA) is essential to reflect the economic
values of these risks. In the present article, we reviewed several different
approaches for calculating CVA, and compared the advantage and disadvantage for
each method. We also introduced an more efficient and scalable computational
framework for this calculation.


  We have perfomed studies of the KL to PI+PI-GAMMA Direct Emission (DE) and
Inner Bremsstrahlung (IB) vertices, based on data collected by KTeV during the
1996 Fermilab fixed target run. We find a1/a2 = -0.737 +- 0.034 GeV^2 for the
DE form-factor parameter in the rho-propagator parametrization, and report on
fits of the form-factor to linear and quadratic functions as well. We
concurrently measure B.R.(KL to PI+PI-GAMMA, E_gamma >20 MeV)/(KL to PI+PI-) =
(20.8 +- 0.3)*10^-3, and a KL to PI+PI-GAMMA DE/(DE + IB) branching ratio of
0.683 +- 0.011.


  Divide and Conquer (DC) is conceptually well suited to high-dimensional
optimization by decomposing a problem into multiple small-scale sub-problems.
However, appealing performance can be seldom observed when the sub-problems are
interdependent. This paper suggests that the major difficulty of tackling
interdependent sub-problems lies in the precise evaluation of a partial
solution (to a sub-problem), which can be overwhelmingly costly and thus makes
sub-problems non-trivial to conquer. Thus, we propose an approximation
approach, named Divide and Approximate Conquer (DAC), which reduces the cost of
partial solution evaluation from exponential time to polynomial time.
Meanwhile, the convergence to the global optimum (of the original problem) is
still guaranteed. The effectiveness of DAC is demonstrated empirically on two
sets of non-separable high-dimensional problems.


  This Letter explores the potential role of primordial black holes (PBHs) to
address cosmological tensions as the presence of more massive than expected
galaxies at high redshifts, as indicated by recent James Webb Space Telescope
observations. Motivated by inflation models that enhance the power at scales
beyond the observable range that produce PBHs with Schechter-like mass
functions, we aim to explain the excess of high redshift galaxies via a
modification of the $\Lambda$ cold dark matter power spectrum that consists in
adding (i) a blue spectral index $n_b$ at $k_{\text{piv}}=10/$Mpc and (ii)
Poisson and isocurvature contributions from massive PBHs that only make up
$0.5\%$ of the dark matter.We simulated these models using the SWIFT code and
find an increased abundance of high redshift galaxies in simulations that
include PBHs. We compared these models to estimates from James Webb Space
Telescope observations.Unlike the $\Lambda$ cold dark matter model, the
inclusion of PBHs allowed us to reproduce the the observations with reasonable
values for the star formation efficiency. Furthermore, the power spectra we
adopted potentially produce PBHs that can serve as seeds for supermassive black
holes with masses $7.57 \times 10^4 M_{\odot}$.


  We consider the flat moduli space of parahoric level structures on a local
shtuka and show that it is representable over a smooth $p$-adic formal scheme
by establishing the dilatation theory of $v$-sheaves. We apply this result to
the canonical integral model of a Shimura variety at hyperspecial level and
identify the flat moduli space of parahoric level structures with the canonical
integral model at the corresponding parahoric level. We prove a similar result
for integral models of local Shimura varieties.


  In viscoelastic materials, individually short-lived bonds collectively result
in a mechanical resistance which is long-lived but finite, as ultimately cracks
appear. Here we provide a microscopic mechanism by which cracks emerge from the
nonlinear local bond dynamics. This mechanism is different from crack
initiation in solids, which is governed by a competition between elastic and
adhesion energy. We provide and numerically verify analytical equations for the
dependence of the critical crack length on the bond kinetics and applied
stress.


  Superconductivity in the cuprate oxide is studied by Kondo-lattice theory
based on the t-J model with the el-ph interaction arising from the modulation
of the superexchange interaction by phonons. The self-energy of electrons is
decomposed into the single-site and multisite ones. It is proved by using the
mapping of the single-site one in the t-J model to its corresponding one in the
Anderson model that the single-site self-energy is that of a normal Fermi
liquid, even if a superconducting (SC) order parameter appears or the multisite
one is anomalous. The electron liquid characterized by the single-site
self-energy is a normal Fermi liquid. The Fermi liquid is further stabilized by
the RVB mechanism. The stabilized Fermi liquid is a relevant unperturbed state
that can be used to study superconductivity and anomalous Fermi-liquid
behaviors. The so-called spin-fluctuation-mediated exchange interaction, which
includes the superexchange interaction as a part, is the attractive interaction
that binds d-wave Cooper pairs. An analysis of the spin susceptibility implies
that, because of the el-ph interaction, the imaginary part of the exchange
interaction has a sharp peak or dip at \pm\omega^*, where \omega^*\simeq
\omega_ph in the normal state and \epsilon_G/2 \lessim \omega^* \lessim
\epsilon_G /2+ \omega_ph in the SC state, where \omega_ph is the energy of
relevant phonons and \epsilon_G is the SC gap. If the imaginary part has a
sharp peak or dip at \pm\omega^*, the dispersion relation of quasi-particles
has kink structures near \pm\omega^* above and below the chemical potential,
the density of states has dip-and-hump structures near \pm \omega^* outside the
coherence peaks in the SC state, and the anisotropy of the gap deviates from
the simple d-wave anisotropy.


  We study the network dismantling problem, which consists in determining a
minimal set of vertices whose removal leaves the network broken into connected
components of sub-extensive size. For a large class of random graphs, this
problem is tightly connected to the decycling problem (the removal of vertices
leaving the graph acyclic). Exploiting this connection and recent works on
epidemic spreading we present precise predictions for the minimal size of a
dismantling set in a large random graph with a prescribed (light-tailed) degree
distribution. Building on the statistical mechanics perspective we propose a
three-stage Min-Sum algorithm for efficiently dismantling networks, including
heavy-tailed ones for which the dismantling and decycling problems are not
equivalent. We also provide further insights into the dismantling problem
concluding that it is an intrinsically collective problem and that optimal
dismantling sets cannot be viewed as a collection of individually well
performing nodes.


  Motivated by applications to wireless communications, this paper addresses
the propagation of waves transmitted by ambient noise sources and interacting
with metamaterials. We discuss a generalized Helmholtz-Kirchhoff identity that
is valid in dispersive media and we characterize the statistical properties of
the empirical cross spectral density of the wave field. We can then introduce
and analyze an original communication scheme between two passive arrays that
uses only ambient noise illumination. The passive transmitter array does not
transmit anything but it is a tunable metamaterial surface that can modulate
its dispersive properties and encode a message in the modulation. The passive
receiver array made of two receivers that are half-a-wavelength apart from each
other can decode the message from the empirical cross spectral density of the
wave field.


  In this talk I discuss properties of the two Symanzik polynomials which
characterise the integrand of an arbitrary multi-loop integral in its Feynman
parametric form. Based on the construction from spanning forests and Laplacian
matrices, Dodgson's relation is applied to derive factorisation identities
involving both polynomials. An application of Whitney's 2-isomorphism theorem
on matroids is discussed.


  Recent advances in data collection have facilitated the access to
time-resolved human proximity data that can conveniently be represented as
temporal networks of contacts between individuals. While this type of data is
fundamental to investigate how information or diseases propagate in a
population, it often suffers from incompleteness, which possibly leads to
biased conclusions. A major challenge is thus to estimate the outcome of
spreading processes occurring on temporal networks built from partial
information. To cope with this problem, we devise an approach based on
Non-negative Tensor Factorization (NTF) -- a dimensionality reduction technique
from multi-linear algebra. The key idea is to learn a low-dimensional
representation of the temporal network built from partial information, to adapt
it to take into account temporal and structural heterogeneity properties known
to be crucial for spreading processes occurring on networks, and to construct
in this way a surrogate network similar to the complete original network. To
test our method, we consider several human-proximity networks, on which we
simulate a loss of data. Using our approach on the resulting partial networks,
we build a surrogate version of the complete network for each. We then compare
the outcome of a spreading process on the complete networks (non altered by a
loss of data) and on the surrogate networks. We observe that the epidemic sizes
obtained using the surrogate networks are in good agreement with those measured
on the complete networks. Finally, we propose an extension of our framework
when additional data sources are available to cope with the missing data
problem.


  We derive number of relations between quadrupole energy, elastic pressure,
and shear force distributions in baryons using the large $N_c$ picture of
baryons as chiral solitons. The obtained large $N_c$ relations are independent
of particular dynamics and should hold in any picture in which the baryon is
the chiral soliton.
  One of remarkable qualitative predictions of the soliton picture is the
nullification of the tangential forces acting on the radial area element for
any tensor polarisation of the baryon. The derived relations provide a powerful
tool to check the hypothesis that the baryons are chiral solitons, say using
lattice QCD.


  We present a comparison between two recently developed colour reconnection
models, the new colour reconnection model in PYTHIA and the DIPSY rope
hadronization model. Specifically we investigate ratios of identified hadron
yields as a function of the final-state activity, as measured by the charged
multiplicity. Since both models have a nontrivial dependence on the final-state
activity, the above observables serve as excellent probes to test the effect of
these models. Both models show a clear baryon enhancement with increasing
multiplicity, while only the DIPSY rope model leads to a strangeness
enhancement. Flow-like patterns, previously found to be connected to colour
reconnection models, are investigated for the new models. Only PYTHIA shows a
$p_\perp$-dependent enhancement of the $\Lambda/K$ ratio as the final-state
activity increases, with the enhancement being largest in the mid-$p_\perp$
region.


  We study the Glauber dynamics of a two dimensional Blume-Capel model (or
dilute Ising model) with Kac potential parametrized by $(\beta,\theta)$ - the
"inverse temperature" and the "chemical potential". We prove that the locally
averaged spin field rescales to the solution of the dynamical $\Phi^4$ equation
near a curve in the $(\beta,\theta)$ plane and to the solution of the dynamical
$\Phi^6$ equation near one point on this curve. Our proof relies on a discrete
implementation of Da Prato-Debussche method as in a result by Mourrat-Weber but
an additional coupling argument is needed to show convergence of the linearized
dynamics.


  Over an infinite field $K$, we investigate the minimal free resolution of
some configurations of lines. We explicitly describe the minimal free
resolution of "complete grids of lines" and obtain an analogous result about
the so-called "complete pseudo-grids". Moreover, we characterize the total
Betti numbers of configurations that are obtained posing a multiplicity
condition on the lines of either a complete grid or a complete pseudo-grid.
Finally, we analyze when a complete pseudo-grid is seminormal, differently from
a complete grid. The main tools that have been involved in our study are the
mapping cone procedure and properties of liftings, of pseudo-liftings and of
weighted ideals.
  Although complete grids and pseudo-grids are hypersurface configurations and
many results about such type of configurations have already been stated in
literature, we give new contributions, in particular about the maps of the
resolution.


  We study the current driven by an applied voltage as a function of time
through the Sachdev-Ye-Kitaev model when coupled to two normal or
superconducting reservoirs. For normal leads, in the strong coupling limit and
for small bias, the current through the Sachdev-Ye-Kitaev model, described by a
quartic interaction term, reaches monotonically the stationarity, in contrast
to the case of a disordered quadratic interaction where the current has a peak
before reaching the stationary phase. For superconducting leads the currents
have oscillations whose frequencies are determined by the gap and the voltage,
and are suppressed in the strong coupling limit. Moreover, due to different
short time scales between the normal and the oscillating part of the
superconducting current, a peak appears before reaching the stationarity.


  We intend to continue our previous papers (\cite{MSz17} and \cite{MSz18}, as
indicated there) on dense ball packing hyperbolic space $\HYP$ by equal balls,
but here with centres belonging to different orbits of the fundamental group
$Cw(2z, 3 \le z \in \bN$, odd number), of our new series of {\it tube or cobweb
manifolds} $Cw = \HYP/\BCw$ with $z$-rotational symmetry. As we know, $\BCw$ is
a fixed-point-free isometry group, acting on $\HYP$ discontinuously with
appropriate tricky fundamental domain $Cw$, so that every point has a ball-like
neighbourhood in the usual factor-topology.
  Our every $Cw(2z)$ is minimal, i.e. does not cover regularly a smaller
manifold. It can be derived by its general symmetry group $\BW(u, v, w = u)$
that is a complete Coxeter orthoscheme reflection group, extended by the
half-turn $\Bh$ $(0 \leftrightarrow 3, 1 \leftrightarrow 2)$ of the complete
orthoscheme $A_0A_1A_2A_3 \sim b_0b_1b_2b_3$ (Fig.~1). The vertices $A_0$ and
$A_3$ are outer points of the $\HYP$, as $1/u + 1/v \le 1/2$ is required, $3
\le u = w, v$ for the above orthoscheme parameters.
  The situation is described first in Figure 1 of the half trunc-orthoscheme
$W$ and its usual extended Coxeter diagram, moreover, by the scalar product
matrix $(b^{ij}) = (\langle \Bb^i, \Bb^j \rangle)$ in formula (1.1) and its
inverse $(A_{jk}) = (\langle \BA_j, \BA_k \rangle)$ in (1.3). These will
describe the hyperbolic angle and distance metric of the half trunc-orthoscheme
$W$, then its ball packings, densities, then those of the manifolds $Cw(2z)$.
  As first results we concentrate only on particular constructions by computer
for probable material model realizations, atoms or molekules by equal balls,
for general $W(u;v;w=u)$ as well, summarized at the end of our paper.


  We consider the ABC dynamics, with equal density of the three species, on the
discrete ring with $N$ sites. In this case, the process is reversible with
respect to a Gibbs measure with a mean field interaction that undergoes a
second order phase transition. We analyze the relaxation time of the dynamics
and show that at high temperature it grows at most as $N^2$ while it grows at
least as $N^3$ at low temperature.


  We analyse the vacuum structure of isotropic Z_2 x Z_2 flux
compactifications, allowing for a single set of sources. Combining algebraic
geometry with supergravity techniques, we are able to classify all vacua for
both type IIA and IIB backgrounds with arbitrary gauge and geometric fluxes.
Surprisingly, geometric IIA compactifications lead to a unique theory with four
different vacua. In this case we also perform the general analysis allowing for
sources compatible with minimal supersymmetry. Moreover, some relevant examples
of type IIB non-geometric compactifications are studied. The computation of the
full N=4 mass spectrum reveals the presence of a number of non-supersymmetric
and nevertheless stable AdS_4 vacua. In addition we find a novel dS_4 solution
based on a non-semisimple gauging.


  Hierarchical federated learning (HFL) has demonstrated promising scalability
advantages over the traditional "star-topology" architecture-based federated
learning (FL). However, HFL still imposes significant computation,
communication, and storage burdens on the edge, especially when training a
large-scale model over resource-constrained Internet of Things (IoT) devices.
In this paper, we propose hierarchical independent submodel training (HIST), a
new FL methodology that aims to address these issues in hierarchical settings.
The key idea behind HIST is a hierarchical version of model partitioning, where
we partition the global model into disjoint submodels in each round, and
distribute them across different cells, so that each cell is responsible for
training only one partition of the full model. This enables each client to save
computation/storage costs while alleviating the communication loads throughout
the hierarchy. We characterize the convergence behavior of HIST for non-convex
loss functions under mild assumptions, showing the impact of several attributes
(e.g., number of cells, local and global aggregation frequency) on the
performance-efficiency tradeoff. Finally, through numerical experiments, we
verify that HIST is able to save communication costs by a wide margin while
achieving the same target testing accuracy.


  Transversality is a simple and effective method for implementing quantum
computation fault-tolerantly. However, no quantum error-correcting code (QECC)
can transversally implement a quantum universal gate set (Eastin and Knill,
Phys. Rev. Lett., 102, 110502). Since reversible classical computation is often
a dominating part of useful quantum computation, whether or not it can be
implemented transversally is an important open problem. We show that, other
than a small set of non-additive codes that we cannot rule out, no binary QECC
can transversally implement a classical reversible universal gate set. In
particular, no such QECC can implement the Toffoli gate transversally.
  We prove our result by constructing an information theoretically secure (but
inefficient) quantum homomorphic encryption (ITS-QHE) scheme inspired by Ouyang
et al. (arXiv:1508.00938). Homomorphic encryption allows the implementation of
certain functions directly on encrypted data, i.e. homomorphically. Our scheme
builds on almost any QECC, and implements that code's transversal gate set
homomorphically. We observe a restriction imposed by Nayak's bound (FOCS 1999)
on ITS-QHE, implying that any ITS quantum fully homomorphic scheme (ITS-QFHE)
implementing the full set of classical reversible functions must be highly
inefficient. While our scheme incurs exponential overhead, any such QECC
implementing Toffoli transversally would still violate this lower bound through
our scheme.


  In materials sciences, a large amount of research data is generated through a
broad spectrum of different experiments. As of today, experimental research
data including meta-data in materials science is often stored decentralized by
the researcher(s) conducting the experiments without generally accepted
standards on what and how to store data. The conducted research and experiments
often involve a considerable investment from public funding agencies that
desire the results to be made available in order to increase their impact. In
order to achieve the goal of citable and (openly) accessible materials science
experimental research data in the future, not only an adequate infrastructure
needs to be established but the question of how to measure the quality of the
experimental research data also to be addressed. In this publication, the
authors identify requirements and challenges towards a systematic methodology
to measure experimental research data quality prior to publication and derive
different approaches on that basis. These methods are critically discussed and
assessed by their contribution and limitations towards the set goals.
Concluding, a combination of selected methods is presented as a systematic,
functional and practical quality measurement and assurance approach for
experimental research data in materials science with the goal of supporting the
accessibility and dissemination of existing data sets.


  The goal of Machine Learning to automatically learn from data, extract
knowledge and to make decisions without any human intervention. Such automatic
(aML) approaches show impressive success. Recent results even demonstrate
intriguingly that deep learning applied for automatic classification of skin
lesions is on par with the performance of dermatologists, yet outperforms the
average. As human perception is inherently limited, such approaches can
discover patterns, e.g. that two objects are similar, in arbitrarily
high-dimensional spaces what no human is able to do. Humans can deal only with
limited amounts of data, whilst big data is beneficial for aML; however, in
health informatics, we are often confronted with a small number of data sets,
where aML suffer of insufficient training samples and many problems are
computationally hard. Here, interactive machine learning (iML) may be of help,
where a human-in-the-loop contributes to reduce the complexity of NP-hard
problems. A further motivation for iML is that standard black-box approaches
lack transparency, hence do not foster trust and acceptance of ML among
end-users. Rising legal and privacy aspects, e.g. with the new European General
Data Protection Regulations, make black-box approaches difficult to use,
because they often are not able to explain why a decision has been made. In
this paper, we present some experiments to demonstrate the effectiveness of the
human-in-the-loop approach, particularly in opening the black-box to a
glass-box and thus enabling a human directly to interact with an learning
algorithm. We selected the Ant Colony Optimization framework, and applied it on
the Traveling Salesman Problem, which is a good example, due to its relevance
for health informatics, e.g. for the study of protein folding. From studies of
how humans extract so much from so little data, fundamental ML-research also
may benefit.


  In this paper we consider a family of optimal control problems for economic
models whose state variables are driven by Delay Differential Equations
(DDE's). We consider two main examples: an AK model with vintage capital and an
advertising model with delay effect. These problems are very difficult to treat
for three main reasons: the presence of the DDE's, that makes them infinite
dimensional; the presence of state constraints; the presence of delay in the
control. Our main goal is to develop, at a first stage, the Dynamic Programming
approach for this family of problems. The Dynamic Programming approach has been
already used for similar problems in cases when it is possible to write
explicitly the value function V. Here we deal with cases when the explicit form
of V cannot be found, as most often occurs. We carefully describe the basic
setting and give some first results on the solution of the
Hamilton-Jacobi-Bellman (HJB) equation as a first step to find optimal
strategies in closed loop form.


  Neutrino astronomy saw its birth with the discovery by IceCube of a diffuse
flux at energies above 60 TeV with intensity comparable to a predicted upper
limit to the flux from extra-galactic sources of ultra-high energy cosmic rays
(UHECRs). While such an upper limit corresponds to the case of calorimetric
sources, in which cosmic rays lose all their energy into photo-pion production,
the first statistically significant coincident observation between neutrinos
and gamma rays was observed from a blazar of intriguing nature. A
very-high-energy muon event, of most probable neutrino energy of 290 TeV for an
$E^{-2.13}$ spectrum, alerted other observatories triggering a large number of
investigations in many bands of the electromagnetic (EM) spectrum. A high
gamma-ray state from the blazar was revealed soon after the event and in a
follow-up to about 40 days. A posteriori observations also in the optical and
radio bands indicated a rise of the flux from the TXS 0506+056 blazar. A
previous excess of events of the duration of more than 100~d was observed by
IceCube with higher significance than the alert itself. These observations
triggered more complex modeling than simple one-zone proton synchrotron models
for proton acceleration in jets of active galactic nuclei (AGNs) and more
observations across the EM spectrum. A second piece of evidence was a steady
excess of about 50 neutrino events with reconstructed soft spectrum in a sample
of lower energy well-reconstructed muon events than the alert event. A hot spot
was identified in a catalog of 110 gamma-ray intense emitters and starburst
galaxies in a direction compatible with NGC 1068 with a significance of
$2.9\sigma$. NGC 1068 hosts a mildly relativistic jet in a starburst galaxy,
seen not from the jet direction but rather through the torus. This Seyfert II
galaxy is at only 14.4~Mpc from the Earth. We discuss these observations.


  Recurrent Neural Networks (RNNs) with Long Short-Term Memory units (LSTM) are
widely used because they are expressive and are easy to train. Our interest
lies in empirically evaluating the expressiveness and the learnability of LSTMs
in the sequence-to-sequence regime by training them to evaluate short computer
programs, a domain that has traditionally been seen as too complex for neural
networks. We consider a simple class of programs that can be evaluated with a
single left-to-right pass using constant memory. Our main result is that LSTMs
can learn to map the character-level representations of such programs to their
correct outputs. Notably, it was necessary to use curriculum learning, and
while conventional curriculum learning proved ineffective, we developed a new
variant of curriculum learning that improved our networks' performance in all
experimental conditions. The improved curriculum had a dramatic impact on an
addition problem, making it possible to train an LSTM to add two 9-digit
numbers with 99% accuracy.


  A cosmological model where a fermion field is non-minimally coupled with the
gravitational field is studied. By applying Noether symmetry the possible
functions for the potential density of the fermion field and for the coupling
are determined. Cosmological solutions are found showing that the non-minimally
coupled fermion field behaves as an inflaton describing an accelerated
inflationary scenario, whereas the minimally coupled fermion field describes a
decelerated period being identified as dark matter.


  One promising approach to address the supply-demand mismatch between the
terrestrial infrastructure and the temporary and/or unexpected traffic demands
is to leverage the unmanned aerial vehicle (UAV) technologies. Motivated by the
recent advancement of UAV technologies and retromodulator based free space
optical communication, we propose a novel edge-computing empowered radio access
network architecture where the fronthaul and backhaul links are mounted on the
UAVs for rapid event response and flexible deployment. The implementation of
hardware and networking technologies for the proposed architecture are
investigated. Due to the limited payload and endurance as well as the high
mobility of UAVs, research challenges related to the communication resource
management and recent research progress are reported.


  What are the mechanisms leading to the shape relaxation of three dimensional
crystallites ? Kinetic Monte Carlo simulations of fcc clusters show that the
usual theories of equilibration, via atomic surface diffusion driven by
curvature, are verified only at high temperatures. Below the roughening
temperature, the relaxation is much slower, kinetics being governed by the
nucleation of a critical germ on a facet. We show that the energy barrier for
this step linearly increases with the size of the crystallite, leading to an
exponential dependence of the relaxation time.


  We study charging effects and tunneling in the single electron box. Tunneling
mixes different charge states and in the nonperturbative regime the charge in
the island may be strongly screened. When charge states are nearly degenerate
the screening of the charge is strong even in the weak tunneling regime.
Virtual tunneling processes reduce both the level splitting and the tunneling
strength. The charge on the island and the decay rates are calculated. In the
strong tunneling regime also nondegenerate states are affected by tunneling.
Strong-coupling scaling renormalizes the effective capacitance, a result which
we confirm by Monte Carlo simulations. The tunneling strength scales to smaller
values into the regime where the weak-coupling scaling applies. We propose a
two stage scaling procedure providing the unified picture for the problem. The
scaling analysis is also extended to superconducting tunnel junctions with
finite subgap conductance. (9 figures upon request)


  Concept Bottleneck Models (CBM) map the input image to a high-level
human-understandable concept space and then make class predictions based on
these concepts. Recent approaches automate the construction of CBM by prompting
Large Language Models (LLM) to generate text concepts and then use Vision
Language Models (VLM) to obtain concept scores to train a CBM. However, it is
desired to build CBMs with concepts defined by human experts instead of LLM
generated concepts to make them more trustworthy. In this work, we take a
closer inspection on the faithfulness of VLM concept scores for such
expert-defined concepts in domains like fine-grain bird species classification
and animal classification. Our investigations reveal that frozen VLMs, like
CLIP, struggle to correctly associate a concept to the corresponding visual
input despite achieving a high classification performance. To address this, we
propose a novel Contrastive Semi-Supervised (CSS) learning method which uses a
few labeled concept examples to improve concept alignment (activate truthful
visual concepts) in CLIP model. Extensive experiments on three benchmark
datasets show that our approach substantially increases the concept accuracy
and classification accuracy, yet requires only a fraction of the
human-annotated concept labels. To further improve the classification
performance, we also introduce a new class-level intervention procedure for
fine-grain classification problems that identifies the confounding classes and
intervenes their concept space to reduce errors.


  In this article we consider the approximation of expectations w.r.t.
probability distributions associated to the solution of partial differential
equations (PDEs); this scenario appears routinely in Bayesian inverse problems.
In practice, one often has to solve the associated PDE numerically, using, for
instance finite element methods and leading to a discretisation bias, with the
step-size level $h_L$. In addition, the expectation cannot be computed
analytically and one often resorts to Monte Carlo methods. In the context of
this problem, it is known that the introduction of the multilevel Monte Carlo
(MLMC) method can reduce the amount of computational effort to estimate
expectations, for a given level of error. This is achieved via a telescoping
identity associated to a Monte Carlo approximation of a sequence of probability
distributions with discretisation levels $\infty>h_0>h_1\cdots>h_L$. In many
practical problems of interest, one cannot achieve an i.i.d. sampling of the
associated sequence of probability distributions. A sequential Monte Carlo
(SMC) version of the MLMC method is introduced to deal with this problem. It is
shown that under appropriate assumptions, the attractive property of a
reduction of the amount of computational effort to estimate expectations, for a
given level of error, can be maintained within the SMC context.


  Federated Transfer Learning (FTL) is the most general variation of Federated
Learning. According to this distributed paradigm, a feature learning pre-step
is commonly carried out by only one party, typically the server, on publicly
shared data. After that, the Federated Learning phase takes place to train a
classifier collaboratively using the learned feature extractor. Each involved
client contributes by locally training only the classification layers on a
private training set. The peculiarity of an FTL scenario makes it hard to
understand whether poisoning attacks can be developed to craft an effective
backdoor. State-of-the-art attack strategies assume the possibility of shifting
the model attention toward relevant features introduced by a forged trigger
injected in the input data by some untrusted clients. Of course, this is not
feasible in FTL, as the learned features are fixed once the server performs the
pre-training step. Consequently, in this paper, we investigate this intriguing
Federated Learning scenario to identify and exploit a vulnerability obtained by
combining eXplainable AI (XAI) and dataset distillation. In particular, the
proposed attack can be carried out by one of the clients during the Federated
Learning phase of FTL by identifying the optimal local for the trigger through
XAI and encapsulating compressed information of the backdoor class. Due to its
behavior, we refer to our approach as a focused backdoor approach (FB-FTL for
short) and test its performance by explicitly referencing an image
classification scenario. With an average 80% attack success rate, obtained
results show the effectiveness of our attack also against existing defenses for
Federated Learning.


  Anti-unification refers to the process of generalizing two (or more) goals
into a single, more general, goal that captures some of the structure that is
common to all initial goals. In general one is typically interested in
computing what is often called a most specific generalization, that is a
generalization that captures a maximal amount of shared structure. In this work
we address the problem of anti-unification in CLP, where goals can be seen as
unordered sets of atoms and/or constraints. We show that while the concept of a
most specific generalization can easily be defined in this context, computing
it becomes an NP-complete problem. We subsequently introduce a generalization
algorithm that computes a well-defined abstraction whose computation can be
bound to a polynomial execution time. Initial experiments show that even a
naive implementation of our algorithm produces acceptable generalizations in an
efficient way. Under consideration for acceptance in TPLP.


  Galaxies can grow through their mutual gravitational attraction and
subsequent union. While orbiting a regular high-surface-brightness galaxy, the
body of a low-mass galaxy can be stripped away. However, the stellar heart of
the infalling galaxy, if represented by a tightly-bound nuclear star cluster,
is more resilient. From archival Hubble Space Telescope images, we have
discovered a red, tidally-stretched star cluster positioned ~5 arcseconds (~400
pc in projection) from, and pointing toward the center of, the post-merger
spiral galaxy NGC 4424. The star cluster, which we refer to as `Nikhuli', has a
near-infrared luminosity of (6.88+/-1.85)x10^6 L_{solar,F160W} and likely
represents the nucleus of a captured/wedded galaxy. Moreover, from our Chandra
X-ray Observatory image, Nikhuli is seen to contain a high-energy X-ray point
source, with L_{0.5-8 keV} = 6.31^{+7.50}_{-3.77}x10^{38} erg/s (90%
confidence). We argue that this is more likely to be an active massive black
hole than an X-ray binary. Lacking an outward-pointing comet-like appearance,
the stellar structure of Nikhuli favors infall rather than the ejection from a
gravitational-wave recoil event. A minor merger with a low-mass early-type
galaxy may have sown a massive black hole, aided an X-shaped pseudobulge, and
be sewing a small bulge. The stellar mass and the velocity dispersion of NGC
4424 predict a central black hole of (0.6-1.0)x10^5 M_solar, similar to the
expected intermediate-mass black hole in Nikhuli, and suggestive of a black
hole supply mechanism for bulgeless late-type galaxies. We may potentially be
witnessing black hole seeding by capture and sinking, with a nuclear star
cluster the delivery vehicle.


  The fuzzy linear regression (FLR) modeling was first proposed making use of
linear programming and then followed by many improvements in a variety of ways.
In almost all approaches changing the meters, objective function, and
restrictions caused to improve the fuzzy measure of efficiencies (FMEs). In
this paper, from a totally different viewpoint, we apply shrinkage estimation
strategy to improve FMEs in the FLR modeling. By several illustrative examples,
we demonstrate the superiority of the proposed estimation method. In this
respect, we show fuzzy shrinkage estimates improve FMEs estimation dramatically
compared to the existing methods.


  We investigate pfaffian trial wave functions with singlet and triplet pair
orbitals by quantum Monte Carlo methods. We present mathematical identities and
the key algebraic properties necessary for efficient evaluation of pfaffians.
Following upon our previous study \cite{pfaffianprl}, we explore the
possibilities of expanding the wave function in linear combinations of
pfaffians. We observe that molecular systems require much larger expansions
than atomic systems and linear combinations of a few pfaffians lead to rather
small gains in correlation energy. We also test the wave function based on
fully-antisymmetrized product of independent pair orbitals. Despite its
seemingly large variational potential, we do not observe additional gains in
correlation energy. We find that pfaffians lead to substantial improvements in
fermion nodes when compared to Hartree-Fock wave functions and exhibit the
minimal number of two nodal domains in agreement with recent results on fermion
nodes topology. We analyze the nodal structure differences of Hartree-Fock,
pfaffian and essentially exact large-scale configuration interaction wave
functions. Finally, we combine the recently proposed form of backflow
correlations \cite{drummond_bf,rios_bf} with both determinantal and pfaffian
based wave functions.


  GeneVis is a web-based tool to visualize complementary data sets of different
disciplines within the field of genetics. It overlays gene-cluster information,
gene-interaction data and gene-disease association data by means of web-based
interactive graph visualizations. This allows an intuitive and quick assessment
of possible relations between the different datasets. By starting from a
high-level graph abstraction based on gene clusters, which can be selected for
detailed inspection at the gene-interaction level in a separate window, GeneVis
circumvents the common visual clutter problem when using gene datasets with a
high number of gene entries.


  We study the problem of the energetic cost of information erasure by looking
at it through the lens of the Jarzynski equality. We observe that the Landauer
bound, $\langle W \rangle \geq kT \ln 2$, on average dissipated work $\langle W
\rangle$ associated to an erasure process, literally emerges from the
underlying second law bound as formulated by Kelvin, $\langle W \rangle \geq
0$, as consequence of a spontaneous breaking of the Crooks-Tasaki
fluctuation-symmetry, that accompanies logical irreversibility. We illustrate
and corroborate this insight with numerical simulations of the process of
information erasure performed on a 2D Ising ferromagnet.


  We seek for a pair of a well and barrier potentials such that the real parts
of the complex energies of the resonances transmitted through the barrier equal
the energies of the states bound within the well and find the hyperbolic
Poeschl-Teller barrier, ~sech^2\rho, and the trigonometric Scarf well, ~
\sec^2\chi. The potentials are shown to be conformally symmetric by the aid of
the de Sitter space time, dS4, related to flat conformal space time by a
conformal map. Namely, we transform the quantum mechanical wave equations with
the above potentials to free quantum motions on the respective open time like
hyperbolic and the closed space like hyper spherical, S3, geodesics of dS4, the
former by itself is related to Minkowski space time by a conformal map.We
formulate a conformal symmetry respecting classification scheme for mesons seen
either as resonances in scattering, or as states bound within a potential,
according to trajectories in which the total spin of the meson, l-depends
linearly on the first power of the invariant mass, M, and not as in the
canonical Regge formalism on the squared mass.We analyze 71 reported mesons in
this scheme and in finding good agreement with data predict the masses of 12
missing mesons. We observe that on S3 the color quantum number of mesons is
limited to neutral. To the amount physics is independent of the choice of the
set of coordinates, this property remains valid in the flat geometry too. We
conclude on the usefulness of conformal maps of flat to curved space times as
tools for modelling and interpreting the color neutrality of hadrons required
by the confinement phenomenon and on the relevance of trigonometric and
hyperbolic potentials for constituent quark models. Finally, all involved
potentials have been motivated by Wilson loops with cusps.


  We present results for mesonic propagators in temporal and spatial direction
and for topological properties at T below and above the deconfining transition
in quenched QCD. We use anisotropic lattices and Wilson fermions.


  Visual reasoning tasks such as visual question answering (VQA) require an
interplay of visual perception with reasoning about the question semantics
grounded in perception. However, recent advances in this area are still
primarily driven by perception improvements (e.g. scene graph generation)
rather than reasoning. Neuro-symbolic models such as Neural Module Networks
bring the benefits of compositional reasoning to VQA, but they are still
entangled with visual representation learning, and thus neural reasoning is
hard to improve and assess on its own. To address this, we propose (1) a
framework to isolate and evaluate the reasoning aspect of VQA separately from
its perception, and (2) a novel top-down calibration technique that allows the
model to answer reasoning questions even with imperfect perception. To this
end, we introduce a differentiable first-order logic formalism for VQA that
explicitly decouples question answering from visual perception. On the
challenging GQA dataset, this framework is used to perform in-depth,
disentangled comparisons between well-known VQA models leading to informative
insights regarding the participating models as well as the task.


  The finite difference time domain (FDTD) method has been successfully applied
to obtain energies and wave functions for two electrons in a quantum dot
modeled by a three dimensional harmonic potential. The FDTD method uses the
time-dependent Schr\"odinger equation (TDSE) in imaginary time. The TDSE is
numerically solved with an initial random wave function and after enough
simulation time, the wave function converges to the ground state wave function.
The excited states are determined by using the same procedure for the ground
state with additional constraints that the wave function must be orthogonal
with all lower energy wave functions. The numerical results for energies and
wave functions for different parameters of confinement potentials are given and
compared with published results using other numerical methods. It is shown that
the FDTD method gives accurate energies and wave functions.


  Bose-Chaudhuri-Hocquenghem (BCH) codes have been intensively investigated.
Even so, there is only a little known about primitive BCH codes, let alone
non-primitive ones. In this paper, let $q>2$ be a prime power, the dimension of
a family of non-primitive BCH codes of length $n=q^{m}+1$ (also called
antiprimitive) is studied. These codes are also linear codes with complementary
duals (called LCD codes). Through some approaches such as iterative algorithm,
partition and scaling, all coset leaders of $C_{x}$ modulo $n$ with $q^{\lceil
\frac{m}{2}\rceil}<x\leq 2q^{\lceil\frac{m}{2} \rceil}+2$ are given for $m\geq
4$. And for odd $m$ the first several largest coset leaders modulo $n$ are
determined. Furthermore, a new kind of sequences is introduced to determine the
second largest coset leader modulo $n$ with $m$ even and $q$ odd. Also, for
even $m$ some conjectures about the first several coset leaders modulo $n$ are
proposed, whose complete verification would wipe out the difficult problem to
determine the first several coset leaders of antiprimitive BCH codes. After
deriving the cardinalities of the coset leaders, we shall calculate exact
dimensions of many antiprimitive LCD BCH codes.


  Hardware Trojans can inflict harm on wireless networks by exploiting the link
margins inherent in communication systems. We investigate a setting in which,
alongside a legitimate communication link, a hardware Trojan embedded in the
legitimate transmitter attempts to establish communication with its intended
rogue receiver. To illustrate the susceptibility of wireless networks against
pilot attacks, we examine a two-phased scenario. In the channel estimation
phase, the Trojan carries out a covert pilot scaling attack to corrupt the
channel estimation of the legitimate receiver. Subsequently, in the
communication phase, the Trojan exploits the ensuing imperfect channel
estimation to covertly communicate with its receiver. By analyzing the
corresponding hypothesis tests conducted by the legitimate receiver in both
phases, we establish that the pilot scaling attack allows the Trojan to operate
in the so-called "linear regime" i.e., covertly and reliably transmitting at a
positive rate to the rogue receiver. Our results highlight the vulnerability of
the channel estimation process in wireless communication systems against
hardware Trojans.


  We derive exact and asymptotic results for the number of star and watermelon
configurations of vicious walkers confined to lie between two impenetrable
walls, as well as for the analogous problem for $\infty$-friendly walkers. Our
proofs make use of results from symmetric function theory and the theory of
basic hypergeometric series.


  The "1st Workshop of Astronomy and Astrophysics for Students" was organized
with the purpose of bringing togheter scientific Institutions involved in
Astrophysical researches, of the Campania region and beyond. In this context
the expression "Astronomy and Astrophysics" was used in its broadest meaning,
i.e. including all topics related to the study of the Universe: the Sun and the
Solar system, Stars, Galaxies, Cosmology, Astroparticles and Astronomical
Technologies, with the belief that all scientific areas would benefit from
interaction with related, if not strictly connected, research environments. The
goal of the Workshop was to offer Students (undergraduate, graduate, PhD and
Post-Docs) the opportunity to share their expertise with colleagues studying
and working in other Universities and/or Research Institutes involved in
research programs of astrophysical interests. At the same time the Workshop was
intended to be a place were the partecipants could, possibly for the first
time, promote the work done at any level of their educational and/or
professional growth. The full Conference Proceedings are available in
electronic form at http://www.na.astro.it/astrows06


  Kato and Fukaya conjectured existence of certain elements in certain
($p$-adic) Iwasawa algebras which are related to Deligne-Langland's ($l$-adic)
local epsilon constants. We prove the existence of these elements in the $l
\neq p$ case.


  3D dense captioning aims to describe individual objects by natural language
in 3D scenes, where 3D scenes are usually represented as RGB-D scans or point
clouds. However, only exploiting single modal information, e.g., point cloud,
previous approaches fail to produce faithful descriptions. Though aggregating
2D features into point clouds may be beneficial, it introduces an extra
computational burden, especially in inference phases. In this study, we
investigate a cross-modal knowledge transfer using Transformer for 3D dense
captioning, X-Trans2Cap, to effectively boost the performance of single-modal
3D caption through knowledge distillation using a teacher-student framework. In
practice, during the training phase, the teacher network exploits auxiliary 2D
modality and guides the student network that only takes point clouds as input
through the feature consistency constraints. Owing to the well-designed
cross-modal feature fusion module and the feature alignment in the training
phase, X-Trans2Cap acquires rich appearance information embedded in 2D images
with ease. Thus, a more faithful caption can be generated only using point
clouds during the inference. Qualitative and quantitative results confirm that
X-Trans2Cap outperforms previous state-of-the-art by a large margin, i.e.,
about +21 and about +16 absolute CIDEr score on ScanRefer and Nr3D datasets,
respectively.


  Variations in the grain size distribution are to be expected in the
interstellar medium (ISM) due to grain growth and destruction. In this work, we
present a dust collision model to be implemented inside a magnetohydrodynamical
(MHD) code that takes into account grain growth and shattering of charged dust
grains of a given composition (silicate or graphite). We integrate this model
in the MHD code Athena, and builds on a previous implementation of the dynamics
of charged dust grains in the same code. To demonstrate the performance of this
coagulation model, we study the variations in the grain size distribution of a
single-sized population of dust with radius 0.05 $\mu$m inside several dust
filaments formed during a 2D MHD simulation. We also consider a realistic dust
distribution with sizes ranging from 50 \AA~to 0.25 $\mu$m and analyze both the
variations in the size distribution for graphite and silicates, as well as of
the far ultraviolet extinction curve. From the obtained results, we conclude
that the methodology here presented, based on the MHD evolution of the equation
of motion for a charged particle, is optimal for studying the coagulation of
charged dust grains in a diffuse regime such as a molecular cloud envelope.
Observationally, these variations in the dust size distribution are translated
into variations in the far ultraviolet extinction curve, and they are mainly
caused by small graphite dust grains.


  As one of the possible signals for the whereabouts of the critical point on
the QCD phase diagram, recently, the multiplicity fluctuations in heavy-ion
collisions have aroused much attention. It is a crucial observable of the Beam
Energy Scan program of the Relativistic Heavy Ion Collider. In this work, we
investigate the centrality dependence of the multiplicity fluctuations
regarding the recent measurements from STAR Collaboration. By employing a
hydrodynamical approach, the present study is dedicated to the noncritical
aspects of the phenomenon. To be specific, in addition to the thermal
fluctuations, finite volume corrections, and resonance decay at the freeze-out
surface, the model is focused on the properties of the hydrodynamic expansion
of the system and the event-by-event initial fluctuations. It is understood
that the real signal of the critical point can only be obtained after
appropriately subtracting the background, the latter is investigated in the
present work. Besides the experimental data, our results are also compared to
those of the hadronic resonance gas, as well as transport models.


  The Heliospheric Imagers (HI) on board the two spacecraft of the Solar
Terrestrial Relations Observatory (STEREO) provided white-light images of
transients in the solar wind from dual perspectives from 2007 to 2014. In this
paper, we develop a new method to identify and locate the transients
automatically from simultaneous images from the two inner telescopes, known as
HI-1, based on a correlation analysis. Correlation coefficient (cc) maps along
the Sun-Earth line are constructed for the period from 1 Jan 2010 to 28 Feb
2011. From the maps, transients propagating along the Sun-Earth line are
identified, and a 27-day periodic pattern is revealed, especially for
small-scale transients. Such a periodicity in the transient pattern is
consistent with the rotation of the Sun's global magnetic structure and the
periodic crossing of the streamer structures and slow solar wind across the
Sun-Earth line, and this substantiates the reliability of our method and the
high degree of association between the small-scale transients of the slow solar
wind and the coronal streamers. Besides, it is suggested by the cc map that
small-scale transients along the Sun-Earth line are more frequent than
large-scale transients by a factor of at least 2, and that they quickly
diffused into background solar wind within about 40 Rs in terms of the
signal-to-noise ratio of white-light emissions. The method provides a new tool
to reconstruct inhomogeneous structures in the heliosphere from multiple
perspectives.


  Evaluation and ranking of large language models (LLMs) has become an
important problem with the proliferation of these models and their impact.
Evaluation methods either require human responses which are expensive to
acquire or use pairs of LLMs to evaluate each other which can be unreliable. In
this paper, we provide a novel perspective where, given a dataset of prompts
(viz. questions, instructions, etc.) and a set of LLMs, we rank them without
access to any ground truth or reference responses. Inspired by real life where
both an expert and a knowledgeable person can identify a novice our main idea
is to consider triplets of models, where each one of them evaluates the other
two, correctly identifying the worst model in the triplet with high
probability. We also analyze our idea and provide sufficient conditions for it
to succeed. Applying this idea repeatedly, we propose two methods to rank LLMs.
In experiments on different generative tasks (summarization, multiple-choice,
and dialog), our methods reliably recover close to true rankings without
reference data. This points to a viable low-resource mechanism for practical
use.


  We consider an array of XX spin-1/2 chains coupled to acoustic phonons and
placed in a magnetic field. Treating the phonons in the mean field
approximation, we show that this system presents a first order transition as a
function of the magnetic field between a partially magnetized distorted state
and the fully polarized undistorted state at low temperature. This behavior
results from the magnetostriction of the coupled chain system. A dip in the
elastic constant of the material near the saturation field along with an
anomaly in the magnetic susceptibility is predicted. We also predict the
contraction of the material as the magnetic field is reduced (positive
magnetostriction) and the reciprocal effect i.e. a decrease of magnetization
under applied pressure. At higher temperature, the first order transition is
replaced by a crossover. However, the anomalies in the susceptibilities in the
system near the saturation field are still present. We discuss the relevance of
our analysis in relation to recent experiments on spin-1/2 chain and ladder
materials in strong magnetic fields.


  It is easy to see that if $\cG$ is a non-abelian group of unitary matrices,
then for no members $A$ and $B$ of $\cG$ can the rank of $AB-BA$ be one. We
examine the consequences of the assumption that this rank is at most two for a
general semigroup $\cS$ of linear operators. Our conclusion is that under
obviously necessary, but trivial, size conditions, $\cS$ is reducible. In the
case of a unitary group satisfying the hypothesis, we show that it is contained
in the direct sum $\cG_1\oplus\cG_2$ where $\cG_1$ is at most $3\times 3$ and
$\cG_2$ is abelian.


  NA61/SHINE is a fixed target experiment at the CERN Super-Proton-Synchrotron.
The main goals of the experiment are to discover the critical point of strongly
interacting matter and study the properties of the onset of deconfinement. In
order to reach these goals, a study of hadron production properties is
performed in nucleus-nucleus, proton-proton and proton-nucleus interactions as
a function of collision energy and size of the colliding nuclei. In this talk,
recent results on particle production in p+p interactions, as well as Be+Be and
Ar+Sc collisions in the SPS energy range are reviewed. Transverse momentum,
transverse mass and rapidity spectra obtained with various analysis methods are
presented. Surprises in studies of signatures of onset of deconfinement are
discussed. The results are compared with available world data.


  A magnetic bimeron is a pair of two merons and can be understood as the
in-plane magnetized version of a skyrmion. Here we theoretically predict the
existence of single magnetic bimerons as well as bimeron crystals, and compare
the emergent electrodynamics of bimerons with their skyrmion analogues. We show
that bimeron crystals can be stabilized in frustrated magnets and analyze what
crystal structure can stabilize bimerons or bimeron crystals via the
Dzyaloshinskii-Moriya interaction. We point out that bimeron crystals, in
contrast to skyrmion crystals, allow for the detection of a pure topological
Hall effect. By means of micromagnetic simulations, we show that bimerons can
be used as bits of information in in-plane magnetized racetrack devices, where
they allow for current-driven motion for torque orientations that leave
skyrmions in out-of-plane magnets stationary.


  We study Born-Infeld gravity coupled to an anisotropic fluid in a static,
spherically symmetric background. The free function characterizing the fluid is
selected on the following grounds: i) recovery of the Reissner-Nordstr\"om
solution of GR at large distances, ii) fulfillment of classical energy
conditions and iii) inclusion of models of nonlinear electrodynamics as
particular examples. Four branches of solutions are obtained, depending on the
signs of two parameters on the gravity and matter sectors. On each branch, we
discuss in detail the modifications on the innermost region of the
corresponding solutions, which provides a plethora of configurations, including
nonsingular black holes and naked objects, wormholes and de Sitter cores. The
regular character of these configurations is discussed according to the
completeness of geodesics and the behaviour of curvature scalars.


  We calculate the vacuum polarization tensor for pion masses from 480 MeV to
270 MeV using dynamical twisted mass fermions at a lattice spacing of 0.086 fm.
We analyze the form of the polarization tensor on the lattice using the
symmetries of twisted QCD and we study both finite size effects and lattice
artifacts at a pion mass of 310 MeV. Results for the lowest order hadronic
contribution to g-2 are presented and the impact of systematic errors is
discussed.


  Why should moral philosophers, moral psychologists, and machine ethicists
care about computational complexity? Debates on whether artificial intelligence
(AI) can or should be used to solve problems in ethical domains have mainly
been driven by what AI can or cannot do in terms of human capacities. In this
paper, we tackle the problem from the other end by exploring what kind of moral
machines are possible based on what computational systems can or cannot do. To
do so, we analyze normative ethics through the lens of computational
complexity. First, we introduce computational complexity for the uninitiated
reader and discuss how the complexity of ethical problems can be framed within
Marr's three levels of analysis. We then study a range of ethical problems
based on consequentialism, deontology, and virtue ethics, with the aim of
elucidating the complexity associated with the problems themselves (e.g., due
to combinatorics, uncertainty, strategic dynamics), the computational methods
employed (e.g., probability, logic, learning), and the available resources
(e.g., time, knowledge, learning). The results indicate that most problems the
normative frameworks pose lead to tractability issues in every category
analyzed. Our investigation also provides several insights about the
computational nature of normative ethics, including the differences between
rule- and outcome-based moral strategies, and the implementation-variance with
regard to moral resources. We then discuss the consequences complexity results
have for the prospect of moral machines in virtue of the trade-off between
optimality and efficiency. Finally, we elucidate how computational complexity
can be used to inform both philosophical and cognitive-psychological research
on human morality by advancing the Moral Tractability Thesis (MTT).


  We present a study of ${\cal O}(2000)$ (quenched) lattice configurations from
the APE collaboration, for $6.0\le\beta\le 6.4$ using both the Wilson and the
SW-Clover fermion action. We determine the light hadronic spectrum and meson
decay constants. We extract the inverse lattice spacing using data at the
simulated values of the quark mass. We find an agreement with the experimental
data of $\sim 5%$ for mesonic masses and $\sim 10%-15%$ for baryonic masses and
pseudoscalar decay constants. A larger deviation is present for the vector
decay constants.