  We present a new layer in which dynamic (i.e.,input-dependent) Infinite
Impulse Response (IIR) filters of order two are used to process the input
sequence prior to applying conventional attention. The input is split into
chunks, and the coefficients of these filters are determined based on previous
chunks to maintain causality. Despite their relatively low order, the causal
adaptive filters are shown to focus attention on the relevant sequence
elements. The new layer is grounded in control theory, and is shown to
generalize diagonal state-space layers. The layer performs on-par with
state-of-the-art networks, with a fraction of their parameters and with time
complexity that is sub-quadratic with input size. The obtained layer is
favorable to layers such as Heyna, GPT2, and Mega, both with respect to the
number of parameters and the obtained level of performance on multiple
long-range sequence problems.


  Quantum states are the key mathematical objects in quantum theory. It is
therefore surprising that physicists have been unable to agree on what a
quantum state truly represents. One possibility is that a pure quantum state
corresponds directly to reality. However, there is a long history of
suggestions that a quantum state (even a pure state) represents only knowledge
or information about some aspect of reality. Here we show that any model in
which a quantum state represents mere information about an underlying physical
state of the system, and in which systems that are prepared independently have
independent physical states, must make predictions which contradict those of
quantum theory.


  Density-wave fronts in a vibrofluidized wet granular layer undergoing a
gas-liquid-like transition are investigated experimentally. The threshold of
the instability is governed by the amplitude of the vertical vibrations.
Fronts, which are curved into a spiral shape, propagate coherently along the
circular rim of the container with leading edges. They are stable beyond a
critical distance from the container center. Based on an analysis of the
emerging time and length scales, we propose a model for the pattern formation
by considering the competition between the time scale for the condensation of
wet granular particles from a gas-like state and that of the energy injection
resisting this process.


  Crystallographic alignment between two-dimensional crystals in van der Waals
heterostructures brought a number of profound physical phenomena, including
observation of Hofstadter butterfly and topological currents, and promising
novel applications, such as resonant tunnelling transistors. Here, by probing
the electronic density of states in graphene using graphene-hexagonal boron
nitride tunnelling transistors, we demonstrate a structural transition of
bilayer graphene from incommensurate twisted stacking state into a commensurate
AB stacking due to a macroscopic graphene self-rotation. This structural
transition is accompanied by a topological transition in the reciprocal space
and by pseudospin texturing. The stacking transition is driven by van der Waals
interaction energy of the two graphene layers and is thermally activated by
unpinning the microscopic chemical adsorbents which are then removed by the
self-cleaning of graphene.


  The excited leptons that share the quantum numbers with the Standard Model
leptons but have larger masses are widespread in many promising new physics
theories. A subclass of excited leptons that at low energies interact with the
SM fermions dominantly through the effective coupling to lepton and
fermion-antifermion pair can be referred as leptomesons. I introduce possible
generation of the baryon asymmetry of the universe using these new particles.
The discussed baryogenesis mechanisms do not contradict to the small neutrino
masses and the proton stability, and can be interesting for the collider
experiments.


  Purpose: To evaluate an iterative learning approach for enhanced performance
of Robust Artificial-neural-networks for K-space Interpolation (RAKI), when
only a limited amount of training data (auto-calibration signals, ACS) are
available for accelerated standard 2D imaging. Methods: In a first step, the
RAKI model was optimized for the case of strongly limited training data amount.
In the iterative learning approach (termed iterative RAKI), the optimized RAKI
model is initially trained using original and augmented ACS obtained from a
linear parallel imaging reconstruction. Subsequently, the RAKI convolution
filters are refined iteratively using original and augmented ACS extracted from
the previous RAKI reconstruction. Evaluation was carried out on 200
retrospectively undersampled in-vivo datasets from the fastMRI neuro database
with different contrast settings. Results: For limited training data (18 and 22
ACS lines for R=4 and R=5, respectively), iterative RAKI outperforms standard
RAKI by reducing residual artefacts and yields strong noise suppression when
compared to standard parallel imaging, underlined by quantitative
reconstruction quality metrics. In combination with a phase constraint, further
reconstruction improvements can be achieved. Additionally, iterative RAKI shows
better performance than both GRAPPA and RAKI in case of pre-scan calibration
with varying contrast between training- and undersampled data. Conclusion: The
iterative learning approach with RAKI benefits from standard RAKIs well known
noise suppression feature but requires less original training data for the
accurate reconstruction of standard 2D images thereby improving net
acceleration.


  We consider a four-parameter family of point interactions in one dimension.
This family is a generalization of the usual $\delta$-function potential. We
examine a system consisting of many particles of equal masses that are
interacting pairwise through such a generalized point interaction. We follow
McGuire who obtained exact solutions for the system when the interaction is the
$\delta$-function potential. We find exact bound states with the four-parameter
family. For the scattering problem, however, we have not been so successful.
This is because, as we point out, the condition of no diffraction that is
crucial in McGuire's method is not satisfied except when the four-parameter
family is essentially reduced to the $\delta$-function potential.


  We define an analogue of the Dirac operator for the degenerate affine
Hecke-Clifford algebra. A main result is to relate the central characters of
the degenerate affine Hecke-Clifford algebra with the central characters of the
Sergeev algebra via Dirac cohomology. The action of the Dirac operator on
certain modules is also computed. Results in this paper could be viewed as a
projective version of the Dirac cohomology of the degenerate affine Hecke
algebra.


  In this short review, we provide an overview of recent progress in deploying
advanced characterization techniques to understand the effects of local
inhomogeneities in moir\'e heterostructures over multiple length scales.
Particular emphasis is placed on correlating the impact of twist angle
misalignment, nano-scale disorder, and atomic relaxation on the moir\'e
potential and its collective excitations, particularly moir\'e excitons.
Finally, we discuss future technological applications leveraging based on
moi\'e excitons.


  We extend our earlier results for the quark helicity evolution at small $x$
to derive the small-$x$ asymptotics of the flavor singlet and flavor
non-singlet quark helicity TMDs and PDFs and of the $g_1$ structure function.
In the flavor singlet case we re-derive the evolution equations obtained in our
previous paper on the subject, performing additional cross-checks of our
results. In the flavor non-singlet case we construct new small-$x$ evolution
equations by employing the large-$N_c$ limit. All evolution equations resum
double-logarithmic powers of $\alpha_s \, \ln^2 (1/x)$ in the
polarization-dependent evolution along with the single-logarithmic powers of
$\alpha_s \, \ln (1/x)$ in the unpolarized evolution which includes saturation
effects. We solve the linearized flavor non-singlet equation analytically,
obtaining an intercept which agrees with the one calculated earlier by Bartels,
Ermolaev and Ryskin using the infra-red evolution equations. Our numerical
solution of the linearized large-$N_c$ evolution equations for the flavor
singlet case is presented in the accompanying Letter and is further discussed
here.


  In this article we consider an N-brane description of an (N+3)-dimensional
black hole horizon. First of all, we start by reviewing a previous work where a
string theory is used as describing the dynamics of the event horizon of a four
dimensional black hole. Then we consider a particle model defined on one
dimensional Euclidean line in a three dimensional black hole as a target
spacetime metric. By solving the field equations we find a ``world line
instanton'' which connects the past event horizon with the future one. This
solution gives us the exact value of the Hawking temperature and to leading
order the Bekenstein-Hawking formula of black hole entropy. We also show that
this formalism is extensible to an arbitrary spacetime dimension. Finally we
make a comment of one-loop quantum correction to the black hole entropy .


  Laser cooling of solids currently has a temperature floor of 50 - 100 K. We
propose a method that could overcome this using defects, such as diamond color
centers, with narrow electronic manifolds and bright optical transitions. It
exploits the dressed states formed in strong fields which extend the set of
phonon transitions and have tunable energies. This allows an enhancement of the
cooling power and diminishes the effect of inhomogeneous broadening. We
demonstrate these effects theoretically for the silicon-vacancy and the
germanium-vacancy, and discuss the role of background absorption,
phonon-assisted emission, and non-radiative decay.


  The production cross-section of B+ mesons is measured as a function of
transverse momentum pT and rapidity y in proton--proton collisions at
center-of-mass energy sqrt(s) = 7 TeV, using 2.4 fb-1 of data recorded with the
ATLAS detector at the Large Hadron Collider. The differential production
cross-sections, determined in the range 9<pT<120 GeV and y<2.25, are compared
to next-to-leading-order theoretical predictions.


  Run 5 of the HL-LHC era (and beyond) may provide new opportunities to search
for physics beyond the standard model (BSM) at interaction point 2 (IP2). In
particular, taking advantage of the existing ALICE detector and infrastructure
provides an opportunity to search for displaced decays of beyond standard model
long-lived particles (LLPs). While this proposal may well be preempted by
ongoing ALICE physics goals, examination of its potential new physics reach
provides a compelling comparison with respect to other LLP proposals. In
particular, full event reconstruction and particle identification could be
possible by making use of the existing L3 magnet and ALICE time projection
chamber. For several well-motivated portals, the reach competes with or exceeds
the sensitivity of MATHUSLA and SHiP, provided that a total integrated
luminosity of approximately $100\, \text{fb}^{-1}$ could be delivered to IP2.


  We have carried out temperature-dependent inelastic neutron scattering
measurements of YMnO3 over the temperature range 50 - 1303 K, covering both the
antiferromagnetic to paramagnetic transition (70 K), as well as the
ferroelectric to paraelectric transition (1258 K). Measurements are accompanied
by first principles calculations of phonon spectra for the sake of
interpretation and analysis of the measured phonon spectra in the room
temperature ferroelectric (P63cm) and high temperature paraelectric (P63/mmc)
hexagonal phases of YMnO3. The comparison of the experimental and
first-principles calculated phonon spectra highlight unambiguously a
spin-phonon coupling character in YMnO3. This is further supported by the
pronounced differences in the magnetic and non-magnetic phonon calculations.
The calculated atomistic partial phonon contributions of the Y and Mn atoms are
not affected by inclusion of magnetic interactions, whereas the dynamical
contribution of the O atoms is found tochange. This highlights the role of the
super-exchange interactions between the magnetic Mn cations, mediated by O
bridges. Phonon dispersion relations have also been calculated, in the entire
Brillouin zone, for both the hexagonal phases. In the high-temperature phase,
unstable phonon mode at the K point is highlighted. The displacement pattern at
the K-point indicates that the freezing of this mode along with the stable mode
at the {\Gamma}-point may lead to a stabilization of the low-temperature
(P63cm) phase, and inducing ferroelectricity. Further, we have also estimated
the mode Gr\"uneisen parameter and volume thermal expansion behavior. The
latter is found to agree with the available experimental data.


  We present results from high signal-to-noise optical spectropolarimetric
observations of the Seyfert 1 galaxies NGC783 and Mrk509 in the 3500-7000 A
range. We find complex structure in the polarized emission for both objects. In
particular, Position Angle (PA) changes across the Balmer lines show a
distinctive 'M'-shaped profile that had not been observed in this detail
before, but could represent a common trait in Seyfert 1 galaxies. In fact,
while this shape is observed in all Balmer lines in NGC3783, Mrk509 transitions
into a 'M'-shaped PA profile for higher transitions lines. We have modeled the
observed profiles using the STOKES radiative transfer code and assuming that
the scattering region is co-spatial with the BLR and outflowing. The results
give compelling new evidence for the presence of nuclear winds in these two
Seyfert 1 galaxies.


  We have used V- and I- band images from the Hubble Space Telescope (HST) to
identify compact stellar clusters within the tidal tails of twelve different
interacting galaxies. The seventeen tails within our sample span a physical
parameter space of HI/stellar masses, tail pressure and density through their
diversity of tail lengths, optical brightnesses, mass ratios, HI column
densities, stage on the Toomre sequence, and tail kinematics. Our preliminary
findings in this study indicate that star cluster demographics of the tidal
tail environment are compatible with the current understanding of star cluster
formation in quiescent systems, possibly only needing changes in certain
parameters or normalization of the Schechter cluster initial mass function
(CIMF) to replicate what we observe in color-magnitude diagrams and a brightest
absolute magnitude -- log N plot.


  The electronic structure, lattice dynamics, and electron-phonon coupling of
the boron-doped diamond are investigated using the density functional supercell
method. Our results indicate the boron-doped diamond is a phonon mediated
superconductor, con rming previous theoretical conclusions deduced from the
calculations employing the virtual crystal approximation. We show that the
optical phonon modes involving B vibrations play an important role in the
electron-phonon coupling. Di erent from previous theoretical results, our
calculated electron-phonon coupling constant is 0.39 and the estimated
superconducting transition temperature Tc is 4.4 K for the boron doped diamond
with 2.78% boron content using the Coulomb pseudopotential \mu*= 0.10, in
excellent agreement with the experimental result.


  This paper describes EMBER: a labeled benchmark dataset for training machine
learning models to statically detect malicious Windows portable executable
files. The dataset includes features extracted from 1.1M binary files: 900K
training samples (300K malicious, 300K benign, 300K unlabeled) and 200K test
samples (100K malicious, 100K benign). To accompany the dataset, we also
release open source code for extracting features from additional binaries so
that additional sample features can be appended to the dataset. This dataset
fills a void in the information security machine learning community: a
benign/malicious dataset that is large, open and general enough to cover
several interesting use cases. We enumerate several use cases that we
considered when structuring the dataset. Additionally, we demonstrate one use
case wherein we compare a baseline gradient boosted decision tree model trained
using LightGBM with default settings to MalConv, a recently published
end-to-end (featureless) deep learning model for malware detection. Results
show that even without hyper-parameter optimization, the baseline EMBER model
outperforms MalConv. The authors hope that the dataset, code and baseline model
provided by EMBER will help invigorate machine learning research for malware
detection, in much the same way that benchmark datasets have advanced computer
vision research.


  We study theoretically the effects of heating on the magnetic flux moving in
superconductors with a periodic array of pinning sites (PAPS). The
voltage-current characteristic (VI-curve) of superconductors with a PAPS
includes a region with negative differential resistivity (NDR) of S-type (i.e.,
S-shaped VI-curve), while the heating of the superconductor by moving flux
lines produces NDR of N-type (i.e., with an N-shaped VI-curve). We analyze the
instability of the uniform flux flow corresponding to different parts of the
VI-curve with NDR. Especially, we focus on the appearance of the filamentary
instability that corresponds to an S-type NDR, which is extremely unusual for
superconductors. We argue that the simultaneous existence of NDR of both N- and
S-type gives rise to the appearance of self-organized two-dimensional dynamical
structures in the flux flow mode. We study the effect of the pinning site
positional disorder on the NDR and show that moderate disorder does not change
the predicted results, while strong disorder completely suppresses the S-type
NDR.


  The voter model consists of a set of agents whose opinion is a binary
variable. At each time step, an agent along with a social neighbor is selected
and the agent imitates the social neighbor at the next time step. In this
paper, we study a variant of the voter model: an imitation model based on the
majority. The agent imitates the selected social neighbor if and only if the
number of neighbors following the opinion of the selected social neighbor is
more than that following the agent. We study circumstances under which a
consensus can not be achieved and the probability of consensus on a finite
connected social graph.


  A single advertisement often benefits many parties, for example, an ad for a
Samsung laptop benefits Microsoft. We study this phenomenon in search
advertising auctions and show that standard solutions, including the status quo
ignorance of mutual benefit and a benefit-aware Vickrey-Clarke-Groves
mechanism, perform poorly. In contrast, we show that an appropriate first-price
auction has nice equilibria in a single-slot ad auction --- all equilibria that
satisfy a natural cooperative envy-freeness condition select the
welfare-maximizing ad and satisfy an intuitive lower-bound on revenue.


  Analogies between the price dynamics in the foreign exchange market and
3-dimensional fully developed turbulence were recently presented in Nature vol.
381, 767-769 (1996). Independently, we have carried out a study comparing the
parallel of the dynamical properties of the S&P 500 index and of the time
evolution of a 3-dimensional fully turbulent fluid, but our study arrives at
rather different conclusions. Specifically, we find while intermittency -- i.e.
abrupt changes of activity in the time evolution of the variance of price
changes and of the mean energy dissipation -- and non-Gaussian behavior (for
short times) in the probability distribution of price and velocity changes
characterize both systems, the stochastic nature of the two processes is
quantitatively quite different.


  Let $Q=(q_n)_{n=1}^\infty$ be a sequence of bases with $q_i\ge 2$. In the
case when the $q_i$ are slowly growing and satisfy some additional weak
conditions, we provide a construction of a number whose $Q$-Cantor series
expansion is both $Q$-normal and $Q$-distribution normal. Moreover, this
construction will result in a computable number provided we have some
additional conditions on the computability of $Q$, and from this construction
we can provide computable constructions of numbers with atypical normality
properties.


  In object detection, post-processing methods like Non-maximum Suppression
(NMS) are widely used. NMS can substantially reduce the number of false
positive detections but may still keep some detections with low objectness
scores. In order to find the exact number of objects and their labels in the
image, we propose a post processing method called Detection Selection Algorithm
(DSA) which is used after NMS or related methods. DSA greedily selects a subset
of detected bounding boxes, together with full object reconstructions that give
the interpretation of the whole image with highest likelihood, taking into
account object occlusions. The algorithm consists of four components. First, we
add an occlusion branch to Faster R-CNN to obtain occlusion relationships
between objects. Second, we develop a single reconstruction algorithm which can
reconstruct the whole appearance of an object given its visible part, based on
the optimization of latent variables of a trained generative network which we
call the decoder. Third, we propose a whole reconstruction algorithm which
generates the joint reconstruction of all objects in a hypothesized
interpretation, taking into account occlusion ordering. Finally we propose a
greedy algorithm that incrementally adds or removes detections from a list to
maximize the likelihood of the corresponding interpretation. DSA with NMS or
Soft-NMS can achieve better results than NMS or Soft-NMS themselves, as is
illustrated in our experiments on synthetic images with mutiple 3d objects.


  We prove that in any fixed dimension, K-semistable log Fano cone
singularities whose volumes are bounded from below by a fixed positive number
form a bounded set. As a consequence, we show that the set of local volumes of
klt singularities of a fixed dimension has zero as the only accumulation point.


  We investigate thick brane solutions in the Horndeski gravity. In this setup,
we found analytical solutions, applying the first-order formalism to two scalar
fields where the first field comes from the non-minimal scalar-tensor coupling
and the second is due to the matter contribution sector. With these analytical
solutions, we evaluate the symmetric thick brane solutions in Horndeski gravity
with four-dimensional geometry. In such a setup, we evaluate the gravity
fluctuations to find ``almost massless modes'', for any values of the Horndeski
parameters. These modes were used to compute the corrections to the Newtonian
potential and evaluate the limit four-dimensional gravity.


  We study the scaling properties of Higgs-Yukawa models. Using the technique
of Finite-Size Scaling, we are able to derive scaling functions that describe
the observables of the model in the vicinity of a Gaussian fixed point. A
feasibility study of our strategy is performed for the pure scalar theory in
the weak-coupling regime. Choosing the on-shell renormalisation scheme gives us
an advantage to fit the scaling functions against lattice data with only a
small number of fit parameters. These formulae can be used to determine the
universality of the observed phase transitions, and thus play an essential role
in future investigations of Higgs-Yukawa models, in particular in the strong
Yukawa coupling region.


  We introduce RNA-FrameFlow, the first generative model for 3D RNA backbone
design. We build upon SE(3) flow matching for protein backbone generation and
establish protocols for data preparation and evaluation to address unique
challenges posed by RNA modeling. We formulate RNA structures as a set of
rigid-body frames and associated loss functions which account for larger, more
conformationally flexible RNA backbones (13 atoms per nucleotide) vs. proteins
(4 atoms per residue). Toward tackling the lack of diversity in 3D RNA
datasets, we explore training with structural clustering and cropping
augmentations. Additionally, we define a suite of evaluation metrics to measure
whether the generated RNA structures are globally self-consistent (via inverse
folding followed by forward folding) and locally recover RNA-specific
structural descriptors. The most performant version of RNA-FrameFlow generates
locally realistic RNA backbones of 40-150 nucleotides, over 40% of which pass
our validity criteria as measured by a self-consistency TM-score >= 0.45, at
which two RNAs have the same global fold. Open-source code:
https://github.com/rish-16/rna-backbone-design


  We show that a Hilbert scheme of conics on a Fano fourfold double cover of
$\mathbb{P}^2\times\mathbb{P}^2$ ramified along a divisor of bidegree $(2,2)$
admits a $\mathbb{P}^1$-fibration with base being a hyper-K\"{a}hler fourfold.
We investigate the geometry of such fourfolds relating them with degenerated
EPW cubes, with elements in the Brauer groups of $K3$ surfaces of degree $2$,
and with Verra threefolds studied in [Ver04]. These hyper-K\"{a}hler fourfolds
admit natural involutions and complete the classification of geometric
realizations of anti-symplectic involutions on hyper-K\"{a}hler $4$-folds of
type $K3^{[2]}$.
  As a consequence we present also three constructions of quartic Kummer
surfaces in $\mathbb{P}^3$: as Lagrangian and symmetric degeneracy loci and as
the base of a fibration of conics in certain threefold quadric bundles over
$\mathbb{P}^1$.


  Anti-phase boundaries (APBs) are structural defects which have been shown to
be responsible for the anomalous magnetic behaviour observed in different
nanostructures. Understanding their properties is crucial in order to use them
to tune the properties of magnetic materials by growing APBs in a controlled
way since their density strongly depends on the synthesis method. With this
aim, in this work we investigate their influence on magnetite (Fe$_3$O$_4$)
thin films by considering an atomistic spin model, focusing our study on the
role that the exchange interactions play across the APB interface. We conclude
that the main atypical features reported experimentally in this material are
well described by the model we propose here, confirming the new exchange
interactions created in the APB as the responsible for this deviation from bulk
properties.


  We have performed a theoretical analysis of the operational characteristics
of a carbon-nanotube-based three-terminal nanorelay. We show that short range
and van der Waals forces have a significant impact on the characteristics of
the relay and introduce design constraints. We also investigate the effects of
dissipation due to phonon excitation in the drain contact, which changes the
switching time scales of the system, decreasing the longest time scale by two
orders of magnitude. We show that the nanorelay can be used as a memory element
and investigate the dynamics and properties of such a device.


  The transmission of Lyman-{\alpha} (Ly{\alpha}) in the spectra of distant
quasars depends on the density, temperature, and ionization state of the
intergalactic medium (IGM). Therefore, high-redshift (z > 5) Ly{\alpha} forests
could be invaluable in studying the late stages of the epoch of reionization
(EoR), as well as properties of the sources that drive it. Indeed, high-quality
quasar spectra have now firmly established the existence of large-scale opacity
fluctuations at z > 5, whose physical origins are still debated. Here we
introduce a Bayesian framework capable of constraining the EoR and galaxy
properties by forward-modelling the high-z Ly{\alpha} forest. Using priors from
galaxy and CMB observations, we demonstrate that the final overlap stages of
the EoR (when >95% of the volume was ionized) should occur at z < 5.6, in order
to reproduce the large-scale opacity fluctuations seen in forest spectra.
However, it is the combination of patchy reionization and the inhomogeneous UV
background that produces the longest Gunn-Peterson troughs. Ly{\alpha} forest
observations tighten existing constraints on the characteristic ionizing escape
fraction of galaxies, with the combined observations suggesting f_{\rm esc}
\approx 7^4_3%, and disfavoring a strong evolution with the galaxy's halo (or
stellar) mass.


  Let $f$ be a real-valued function of a single variable such that it is
positive over the primes. In this article, we construct a factorial, $n!_f$,
associated to $f$, called the associated Legendre formula, or $f$-factorial,
and show, subject to certain criteria, that $n!_f$ satisfies a weak Stirling
approximation. As an application, we will give weak approximations to the
Bhargava factorial over the set of primes and to a less well-known Legendre
formula.


  Generative Adversarial Networks (GANs) have gained significant attention in
several computer vision tasks for generating high-quality synthetic data.
Various medical applications including diagnostic imaging and radiation therapy
can benefit greatly from synthetic data generation due to data scarcity in the
domain. However, medical image data is typically kept in 3D space, and
generative models suffer from the curse of dimensionality issues in generating
such synthetic data. In this paper, we investigate the potential of GANs for
generating connected 3D volumes. We propose an improved version of 3D
$\alpha$-GAN by incorporating various architectural enhancements. On a
synthetic dataset of connected 3D spheres and ellipsoids, our model can
generate fully connected 3D shapes with similar geometrical characteristics to
that of training data. We also show that our 3D GAN model can successfully
generate high-quality 3D tumor volumes and associated treatment specifications
(e.g., isocenter locations). Similar moment invariants to the training data as
well as fully connected 3D shapes confirm that improved 3D $\alpha$-GAN
implicitly learns the training data distribution, and generates
realistic-looking samples. The capability of improved 3D $\alpha$-GAN makes it
a valuable source for generating synthetic medical image data that can help
future research in this domain.


  Decision support systems for classification tasks are predominantly designed
to predict the value of the ground truth labels. However, since their
predictions are not perfect, these systems also need to make human experts
understand when and how to use these predictions to update their own
predictions. Unfortunately, this has been proven challenging. In this context,
it has been recently argued that an alternative type of decision support
systems may circumvent this challenge. Rather than providing a single label
prediction, these systems provide a set of label prediction values constructed
using a conformal predictor, namely a prediction set, and forcefully ask
experts to predict a label value from the prediction set. However, the design
and evaluation of these systems have so far relied on stylized expert models,
questioning their promise. In this paper, we revisit the design of this type of
systems from the perspective of online learning and develop a methodology that
does not require, nor assumes, an expert model. Our methodology leverages the
nested structure of the prediction sets provided by any conformal predictor and
a natural counterfactual monotonicity assumption to achieve an exponential
improvement in regret in comparison to vanilla bandit algorithms. We conduct a
large-scale human subject study ($n = 2{,}751$) to compare our methodology to
several competitive baselines. The results show that, for decision support
systems based on prediction sets, limiting experts' level of agency leads to
greater performance than allowing experts to always exercise their own agency.
We have made available the data gathered in our human subject study as well as
an open source implementation of our system at
https://github.com/Networks-Learning/counterfactual-prediction-sets.


  It has been known that an effective smooth $({\Bbb Z}_2)^k$-action on a
smooth connected closed manifold $M^n$ fixing a finite set can be associated to
a $({\Bbb Z}_2)^k$-colored regular graph. In this paper, we consider abstract
graphs $(\Gamma,\alpha)$ of $({\Bbb Z}_2)^k$-actions, called abstract
1-skeletons. We study when an abstract 1-skeleton is a colored graph of some
$({\Bbb Z}_2)^k$-action. We also study the existence of faces of an abstract
1-skeleton (note that faces often have certain geometric meanings if an
abstract 1-skeleton is a colored graph of some $({\Bbb Z}_2)^k$-action).


  We propose a semi-supervised text classifier based on self-training using one
positive and one negative property of neural networks. One of the weaknesses of
self-training is the semantic drift problem, where noisy pseudo-labels
accumulate over iterations and consequently the error rate soars. In order to
tackle this challenge, we reshape the role of pseudo-labels and create a
hierarchical order of information. In addition, a crucial step in self-training
is to use the classifier confidence prediction to select the best candidate
pseudo-labels. This step cannot be efficiently done by neural networks, because
it is known that their output is poorly calibrated. To overcome this challenge,
we propose a hybrid metric to replace the plain confidence measurement. Our
metric takes into account the prediction uncertainty via a subsampling
technique. We evaluate our model in a set of five standard benchmarks, and show
that it significantly outperforms a set of ten diverse baseline models.
Furthermore, we show that the improvement achieved by our model is additive to
language model pretraining, which is a widely used technique for using
unlabeled documents. Our code is available at
https://github.com/p-karisani/RST.


  Quantum search is a quantum mechanical technique for searching N
possibilities in only sqrt(N) steps. This has been proved to be the best
possible algorithm for the exhuastive search problem in the sense the number of
queries it requires cannot be reduced. However, as this paper shows, the number
of non-query operations, and thus the total number of operations, can be
reduced. The number of non-query unitary operations can be reduced by a factor
of log N/alpha*log(log N) while increasing the number of queries by a factor of
only (1+(log N)^{-alpha}). Various choices of alpha yield different variants of
the algorithm. For example, by choosing alpha to be O(log N/log(log N)), the
number of non-query unitary operations can be reduced by 40% while increasing
the number of queries by just two.


  Building Integrated Photovoltaics (BIPV) is a promising technology to
decarbonize urban energy systems via harnessing solar energy available on
building envelopes. While methods to assess solar irradiation, especially on
rooftops, are well established, the assessment on building facades usually
involves a higher effort due to more complex urban features and obstructions.
The drawback of existing physics-based simulation programs is that they require
significant manual modelling effort and computing time for generating time
resolved deterministic results. Yet, solar irradiation is highly intermittent
and representing its inherent uncertainty may be required for designing robust
BIPV energy systems. Targeting on these drawbacks, this paper proposes a
data-driven model based on Deep Generative Networks (DGN) to efficiently
generate high-fidelity stochastic ensembles of annual hourly solar irradiance
time series on building facades with uncompromised spatiotemporal resolution at
the urban scale. The only input required is easily obtainable, simple fisheye
images as categorical shading masks captured from 3D models. In principle, even
actual photographs of urban contexts can be utilized, given they are
semantically segmented. Our validations exemplify the high fidelity of the
generated time series when compared to the physics-based simulator. To
demonstrate the model's relevance for urban energy planning, we showcase its
potential for generative design by parametrically altering characteristic
features of the urban environment and producing corresponding time series on
building facades under different climatic contexts in real-time.


  We propose a model-based machine-learning approach for
polarization-multiplexed systems by parameterizing the split-step method for
the Manakov-PMD equation. This approach performs hardware-friendly DBP and
distributed PMD compensation with performance close to the PMD-free case.


  In universal extra-dimensional models a conserved Z_2 parity stabilizes the
lightest Kaluza-Klein particle, a dark-matter candidate. Boundary-localized
kinetic terms, in general, do not preserve this symmetry. We examine, in the
presence of such terms, the single production of Kaluza-Klein excitations of
the neutral electroweak gauge bosons and their decay to zero-mode
fermion-antifermion pairs. We explore how experiments at the Large Hadron
Collider constrain the boundary-localized kinetic terms for different
compactification radii.


  We review the modern view of fluid dynamics as an effective low energy, long
wavelength theory of many body systems at finite temperature. We introduce the
concept of a nearly perfect fluid, defined by a ratio $\eta/s$ of shear
viscosity to entropy density of order $\hbar/k_B$ or less. Nearly perfect
fluids exhibit hydrodynamic behavior at all distances down to the microscopic
length scale of the fluid. We summarize arguments that suggest that there is
fundamental limit to fluidity, and we review the current experimental situation
of measurements of $\eta/s$ in strongly coupled quantum fluids.


  We calculate the hydrogen Hugoniot using ab initio path integral Monte Carlo.
We introduce an efficient finite-temperature fixed-node approximation for
handling fermions, which includes an optimized mixture of free particle states
and atomic orbitals. The calculated Hugoniot confirms previous fixed-node path
integral calculations at temperatures around T=30,000 K and above, while
approaching smoothly the low temperature gas gun results. The ability to
optimize the free energy within the path integral opens many new possibilities
for developing nodal density matrices for path integral simulations of other
chemical systems.


  In this paper we count the number of paths and cycles in complete graphs by
using the number $e$. Also, we compute the number of derangements in same way.
Connection by $e$ yields some nice formulas for the number of derangements,
such as $D_n=\lfloor\frac{n!+1}{e}\rfloor$ and
$D_n=\lfloor(e+e^{-1})n!\rfloor-\lfloor en!\rfloor$, and using these relations
allow us to compute some incomplete gamma functions and hypergeometric
summations; these connections are hidden in the heart of a nice polynomial that
we call it derangement function and a simple ordinary differential equation
concerning it.


  Soft Collinear Effective Theory (SCET) is an effective field theory of
Quantum Chromodynamics (QCD) for processes where there are energetic, nearly
lightlike degrees of freedom interacting with one another via soft radiation.
SCET has found many applications in high-energy and nuclear physics, especially
in recent years the physics of hadronic jets in $e^+e^-$, lepton-hadron,
hadron-hadron, and heavy-ion collisions. SCET can be used to factorize
multi-scale cross sections in these processes into single-scale hard,
collinear, and soft functions, and to evolve these through the renormalization
group to resum large logarithms of ratios of the scales that appear in the QCD
perturbative expansion, as well as to study properties of nonperturbative
effects. We overview the elementary concepts of SCET and describe how they can
be applied in high-energy and nuclear physics.


  We introduce a family of commuting-projector Hamiltonians whose degrees of
freedom involve $\mathbb{Z}_{3}$ parafermion zero modes residing in a parent
fractional-quantum-Hall fluid. The two simplest models in this family emerge
from dressing Ising-paramagnet and toric-code spin models with parafermions; we
study their edge properties, anyonic excitations, and ground-state degeneracy.
We show that the first model realizes a symmetry-enriched topological phase
(SET) for which $\mathbb{Z}_2$ spin-flip symmetry from the Ising paramagnet
permutes the anyons. Interestingly, the interface between this SET and the
parent quantum-Hall phase realizes symmetry-enforced $\mathbb{Z}_3$ parafermion
criticality with no fine-tuning required. The second model exhibits a
non-Abelian phase that is consistent with $\text{SU}(2)_{4}$ topological order,
and can be accessed by gauging the $\mathbb{Z}_{2}$ symmetry in the SET.
Employing Levin-Wen string-net models with $\mathbb{Z}_{2}$-graded structure,
we generalize this picture to construct a large class of commuting-projector
models for $\mathbb{Z}_{2}$ SETs and non-Abelian topological orders exhibiting
the same relation. Our construction provides the first
commuting-projector-Hamiltonian realization of chiral bosonic non-Abelian
topological order.


  Minkowski Functionals are a powerful tool for analyzing large scale
structure, in particular if the distribution of matter is highly non-Gaussian,
as it is in models in which cosmic strings contribute to structure formation.
Here we apply Minkowski functionals to 21cm maps which arise if structure is
seeded by a scaling distribution of cosmic strings embeddded in background
fluctuations, and then test for the statistical significance of the cosmic
string signals using the Fisher combined probability test. We find that this
method allows for detection of cosmic strings with $G \mu > 5 \times 10^{-8}$,
which would be improvement over current limits by a factor of about 3.


  The recent appreciation of the importance of event-by-event fluctuations in
relativistic heavy-ion collisions has lead to a large amount of diverse
theoretical and experimental activity. In particular, there is significant
interest in understanding the fluctuations in the initial stage of a collision,
how exactly these fluctuations are propagated through the system evolution, and
how they are manifested in correlations between measured particles. In order to
address these questions a workshop was organized on "Initial State Fluctuations
and Final State Correlations", held at ECT* in Trento, Italy during the week of
2--6 July, 2012. The goal was to collect recent work in order to provide a
coherent picture of the current status of our understanding, to identify
important questions that remain open, and to set a course for future research.
Here we report the outcome of the presentations and discussions, focusing on
the most important conclusions.


  Multi-View Stereo (MVS) is a core task in 3D computer vision. With the surge
of novel deep learning methods, learned MVS has surpassed the accuracy of
classical approaches, but still relies on building a memory intensive dense
cost volume. Novel View Synthesis (NVS) is a parallel line of research and has
recently seen an increase in popularity with Neural Radiance Field (NeRF)
models, which optimize a per scene radiance field. However, NeRF methods do not
generalize to novel scenes and are slow to train and test. We propose to bridge
the gap between these two methodologies with a novel network that can recover
3D scene geometry as a distance function, together with high-resolution color
images. Our method uses only a sparse set of images as input and can generalize
well to novel scenes. Additionally, we propose a coarse-to-fine sphere tracing
approach in order to significantly increase speed. We show on various datasets
that our method reaches comparable accuracy to per-scene optimized methods
while being able to generalize and running significantly faster. We provide the
source code at https://github.com/AIS-Bonn/neural_mvs


  A Lattice is a partially ordered set where both least upper bound and
greatest lower bound of any pair of elements are unique and exist within the
set. K\"{o}tter and Kschischang proved that codes in the linear lattice can be
used for error and erasure-correction in random networks. Codes in the linear
lattice have previously been shown to be special cases of codes in modular
lattices. Two well known classifications of semimodular lattices are geometric
and distributive lattices. Most of the frequently used coding spaces are
examples of either or both. We have identified the unique criterion which makes
a geometric lattice distributive, thus characterizing all finite geometric
distributive lattices. Our characterization helps to prove a conjecture
regarding the maximum size of a distributive sublattice of a finite geometric
lattice and identify the maximal case. The Whitney numbers of the class of
geometric distributive lattices are also calculated. We present a few other
applications of this unique characterization to derive certain results
regarding linearity and complements in the linear lattice.


  When a planet inspirals into its host star, it releases gravitational energy
which is converted into an expanding bubble of hot plasma. We study the
radiation from the bubble and show that it includes prompt optical-infrared
emission and a subsequent radio afterglow. The prompt emission from M31 and
Large Magellanic Cloud is detectable by optical-near infrared transient surveys
with a large field of view. The subsequent radio afterglows are detectable for
$10^{3-4}$~years. The event rate depends on uncertain parameters in the
formation and dynamics of giant planets. Future observation of the rate will
constrain related theoretical models. If the event rate is high (> a few events
per year), the circumstellar disk must typically be massive as suggested by
recent numerical simulations.


  In decentralized optimization, several nodes connected by a network
collaboratively minimize some objective function. For minimization of Lipschitz
functions lower bounds are known along with optimal algorithms. We study a
specific class of problems: linear models with nonsmooth loss functions. Our
algorithm combines regularization and dual reformulation to get an effective
optimization method with complexity better than the lower bounds.


  The electromagnetic field phasors in an isotropic chiral material (ICM) are
superpositions of two Beltrami fields of different handedness. Application of
the Bruggeman homogenization formalism to two-component composite materials
delivers ICMs wherein Beltrami fields of one handedness attenuate whereas
Beltrami waves of the other handedness amplify. One component material is a
dissipative ICM, the other an active dielectric material.


  We consider the time to equilibrium for the Langevin dynamics of the
spherical $p$-spin glass model of system size $N$. We show that the log-Sobolev
constant and spectral gap are order $1$ in $N$ at sufficiently high temperature
whereas the spectral gap decays exponentially in $N$ at sufficiently low
temperatures. These verify the existence of a dynamical high temperature phase
and a dynamical glass phase at the level of the spectral gap. Key to these
results are the understanding of the extremal process and restricted free
energy of Subag--Zeitouni and Subag.


  The increasing demand for autonomous machines in construction environments
necessitates the development of robust object detection algorithms that can
perform effectively across various weather and environmental conditions. This
paper introduces a new semantic segmentation dataset specifically tailored for
construction sites, taking into account the diverse challenges posed by adverse
weather and environmental conditions. The dataset is designed to enhance the
training and evaluation of object detection models, fostering their
adaptability and reliability in real-world construction applications. Our
dataset comprises annotated images captured under a wide range of different
weather conditions, including but not limited to sunny days, rainy periods,
foggy atmospheres, and low-light situations. Additionally, environmental
factors such as the existence of dirt/mud on the camera lens are integrated
into the dataset through actual captures and synthetic generation to simulate
the complex conditions prevalent in construction sites. We also generate
synthetic images of the annotations including precise semantic segmentation
masks for various objects commonly found in construction environments, such as
wheel loader machines, personnel, cars, and structural elements. To demonstrate
the dataset's utility, we evaluate state-of-the-art object detection algorithms
on our proposed benchmark. The results highlight the dataset's success in
adversarial training models across diverse conditions, showcasing its efficacy
compared to existing datasets that lack such environmental variability.


  This is a book chapter that covers general considerations of the
electrostatic stability of oxide surfaces and interfaces.


  Exploiting the recent lattice results for the infrared gluon propagator with
light dynamical quarks, we solve the gap equation for the quark propagator. We
thus model the chiral symmetry breaking mechanism with increasing number of
flavours and study confinement (intimately tied with the analytic properties of
QCD Schwinger functions) order parameters. We obtain, with this approach, clear
signals of chiral symmetry restoration and deconfinement when the number of
light quark flavors exceeds a critical value of $N_f^c \approx 8 \pm 1$, in
agreement with the state-of-the-art direct lattice analysis of chiral symmetry
restoration in QCD.


  Due to its relevance in intelligent transportation systems, anomaly detection
in traffic videos has recently received much interest. It remains a difficult
problem due to a variety of factors influencing the video quality of a
real-time traffic feed, such as temperature, perspective, lighting conditions,
and so on. Even though state-of-the-art methods perform well on the available
benchmark datasets, they need a large amount of external training data as well
as substantial computational resources. In this paper, we propose an efficient
approach for a video anomaly detection system which is capable of running at
the edge devices, e.g., on a roadside camera. The proposed approach comprises a
pre-processing module that detects changes in the scene and removes the
corrupted frames, a two-stage background modelling module and a two-stage
object detector. Finally, a backtracking anomaly detection algorithm computes a
similarity statistic and decides on the onset time of the anomaly. We also
propose a sequential change detection algorithm that can quickly adapt to a new
scene and detect changes in the similarity statistic. Experimental results on
the Track 4 test set of the 2021 AI City Challenge show the efficacy of the
proposed framework as we achieve an F1-score of 0.9157 along with 8.4027 root
mean square error (RMSE) and are ranked fourth in the competition.


  The asymmetry in the conductance peaks shown in recent PCT and STM
measurements may be a signature of the d-wave gap. This is based on our model
for the tunneling density of states (DOS) which incorporates the group
velocity, tunneling directionality, and a realistic band structure. We also
make the comparison with the isotropic s-wave and extended s-wave gap
symmetries. Whereas the asymmetry of the conductance peaks for the isotropic
s-wave tends to reflect the normal state DOS, the asymmetry for the extended
s-wave is opposite to that of the d-wave gap. The model shows that a change in
the gap symmetry from d-wave to extended s-wave may be accompanied by a
switching in the conductance peaks asymmetry.


  In the Greenberger-Horne-Zeilinger-Mermin (GHZM) proof of Bell's theorem, a
source periodically emits an entangled state of three particles whose
properties are analyzed by three distant observers and used to prove Bell's
nonlocality theorem. This paper analyzes a somewhat different gedanken
experiment involving only two observers that nevertheless makes indirect use of
the GHZ states to prove Bell's theorem. The relationship of the GHZM proof to
the present one is discussed, and it is pointed out that the latter provides an
interesting new view of the connection between the "two theorems of John Bell".


  Fully kinetic analytic calculations of an initially Maxwellian distribution
with arbitrary density and temperature gradients exhibit the development of
temperature anisotropies and magnetic field growth associated with the Biermann
battery. The calculation, performed by taking a small order expansion of the
ratio of the Debye length to the gradient scale, predicts anisotropies and
magnetic fields as a function of space given an arbitrary temperature and
density profile. These predictions are shown to qualitatively match the values
measured from particle-in-cell simulations, where the development of the Weibel
instability occurs at the same location and with a wavenumber aligned with the
predicted temperature anisotropy.


  In the $B^+\to D^+ D^-K^+$ decay, LHCb has reported the observation of the
open-charm exotic states $X_{0,1}^0\equiv X_{0,1}(2900)^0$ with four different
quark flavors~$(ud\bar s \bar c)$, where the subscripts (0,1) denote the spins.
To confirm the discovery, we propose $\Lambda_b\to \Sigma_c^{0(++)}
X_{0,1}^{\prime\,0(--)}$ in the final state interaction, where
$X_{0,1}^{\prime\,0(--)}$ with $s u\bar d \bar c$ ($ds\bar u \bar c$) are the
new $X_{0,1}$-like exotic states. More specifically, $\Lambda_c^+D_s^-$ in
$\Lambda_b\to \Lambda_c^+D_s^-$ are transformed as $\Sigma_c^{0(++)}
X_{0,1}^{\prime\,0(--)}$, by exchanging $\pi^{+(-)}$. As the order of magnitude
estimates, we calculate ${\cal B}(\Lambda_b\to \Sigma_c^{0(++)}
X_{0,1}^{\prime\,0(--)}) =(2.3\pm 0.6,4.3\pm 0.8^{+3.3}_{-2.5})\times 10^{-4}$.
In addition, we estimate other $b$-baryon decays with the $X_{0,1}$-like
states, such as ${\cal B}(\Xi_{b}^{0(-)}\to
\Xi_{c}^{0(+)}(2645)X_{0}^{\prime\,0(--)}, \Lambda_{b}\to
\Xi_{c}^{\prime\,0}X_{0,1}^{0})\sim 10^{-5}$. While one needs $\Lambda_b\to
\Sigma_c^{0(++)}M_c M$ to observe $X_{0,1}^{\prime\,0(--)}\to M_c M$ with $M_c
M=D_s^-\pi^+$, $\bar D^0 \bar K^0$ $(D_s^-\pi^-$, $D^-K^-)$, ${\cal
B}(\Lambda_b\to \Sigma_c^{0(++)} X_{0,1}^{\prime\,0(--)},
X_{0,1}^{\prime\,0(--)}\to M_c M)\sim 10^{-4}$ are accessible to the LHCb
experiment.


  For every finite abelian group $A$ and $n\geq 3$, we construct a finitely
presented group defined by explicit generators and relations, such that its
center is $\pi_n(\Sigma K(A,1))$.


  We give a general procedure for constructing metric spaces from systems of
partitions. This generalises and provides analogues of Sageev's construction of
dual CAT(0) cube complexes for the settings of hyperbolic and injective metric
spaces.
  As applications, we produce a ``universal'' hyperbolic action for groups with
strongly contracting elements, and show that many groups with ``coarsely
cubical'' features admit geometric actions on injective metric spaces. In an
appendix with Davide Spriano, we show that a large class of groups have an
infinite-dimensional space of quasimorphisms.


  This paper presents a clustering approach that allows for rigorous
statistical error control similar to a statistical test. We develop estimators
for both the unknown number of clusters and the clusters themselves. The
estimators depend on a tuning parameter alpha which is similar to the
significance level of a statistical hypothesis test. By choosing alpha, one can
control the probability of overestimating the true number of clusters, while
the probability of underestimation is asymptotically negligible. In addition,
the probability that the estimated clusters differ from the true ones is
controlled. In the theoretical part of the paper, formal versions of these
statements on statistical error control are derived in a standard model setting
with convex clusters. A simulation study and two applications to temperature
and gene expression microarray data complement the theoretical analysis.


  We derive a new class of correlation Bell-type inequalities. The inequalities
are valid for any number of outcomes of two observables per each of n parties,
including continuous and unbounded observables. We show that there are no
first-moment correlation Bell inequalities for that scenario, but such
inequalities can be found if one considers at least second moments. The
derivation stems from a simple variance inequality by setting local commutators
to zero. We show that above a constant detector efficiency threshold, the
continuous variable Bell violation can survive even in the macroscopic limit of
large n. This method can be used to derive other well-known Bell inequalities,
shedding new light on the importance of non-commutativity for violations of
local realism.


  In this paper, we review recent work published over the last 3 years under
the umbrella of Neuromorphic engineering to analyze what are the common
features among such systems. We see that there is no clear consensus but each
system has one or more of the following features:(1) Analog computing (2) Non
vonNeumann Architecture and low-precision digital processing (3) Spiking Neural
Networks (SNN) with components closely related to biology. We compare recent
machine learning accelerator chips to show that indeed analog processing and
reduced bit precision architectures have best throughput, energy and area
efficiencies. However, pure digital architectures can also achieve quite high
efficiencies by just adopting a non von-Neumann architecture. Given the design
automation tools for digital hardware design, it raises a question on the
likelihood of adoption of analog processing in the near future for industrial
designs. Next, we argue about the importance of defining standards and choosing
proper benchmarks for the progress of neuromorphic system designs and propose
some desired characteristics of such benchmarks. Finally, we show brain-machine
interfaces as a potential task that fulfils all the criteria of such
benchmarks.


  We demonstrate the excitation of ions to the Rydberg state $22F$ by vacuum
ultraviolet radiation at a wavelength of $123\,\text{nm}$ combined with the
coherent manipulation of the optical qubit transition in $^{40}\text{Ca}^+$.
With a tightly focused beam at $729\,\text{nm}$ wavelength we coherently excite
a single ion from a linear string into the metastable $3D_{5/2}$ state before a
VUV pulse excites it to the Rydberg state. In combination with ion shuttling in
the trap, we extend this approach to the addressed excitation of multiple ions.
The coherent initialization as well as the addressed Rydberg excitation are key
prerequisites for more complex applications of Rydberg ions in quantum
simulation or quantum information processing.


  We calculate $p_T$ spectra and elliptic flow for tip-tip and body-body
configurations of full overlap uranium-uranium (U+U) collisions using a
hydrodynamic model with smooth initial density distribution and compare the
results with those obtained from Au+Au collisions at RHIC. Production of
thermal photons is seen to be significantly larger for tip-tip collisions
compared to body-body collisions of uranium nuclei in the region $p_T >$ 1 GeV.
The thermal photon spectrum from body-body collisions is found to be close to
the spectrum from most central Au+Au collisions at RHIC. The elliptic flow
parameter calculated for body-body collisions is found to be large and
comparable to the $v_2(p_T)$ for mid-central collisions of Au nuclei. On the
other hand, as expected, the $v_2(p_T)$ is close to zero for tip-tip
collisions. The qualitative nature of the photon spectra and elliptic flow for
the two different orientations of uranium nuclei is found to be independent of
the initial parameters of the model calculation. We show that the photon
results from fully overlapping U+U collisions are complementary to the results
from Au+Au collisions at RHIC.


  We study a class of two-point functions in a conformal field theory near a
wedge. This is a set-up with two boundaries intersecting at an angle $\theta$.
We compute it as a solution to the Dyson-Schwinger equation of motion for a
quartic interaction in the $d=4-\epsilon$ bulk and in the $d=3-\epsilon$
boundary, up to order $\mathcal{O}(\epsilon)$. We have extracted the anomalous
dimensions from such correlators and we have complemented them with Feynman
diagrams computations.


  We study the capability of future measurements of the galaxy clustering power
spectrum to probe departures from a power-law spectrum for primordial
fluctuations. On considering the information from the galaxy clustering power
spectrum up to quasi-linear scales, i.e. $k<0.1$ h Mpc$^{-1}$, we present
forecasts for DESI, Euclid and SPHEREx in combination with CMB measurements. As
examples of departures in the primordial power spectrum from a simple
power-law, we consider four $Planck$ 2015 best-fits motivated by inflationary
models with different breaking of the slow-roll approximation. These four
representative models provide an improved fit to CMB temperature anisotropies,
although not at statistical significant level. As for other extensions in the
matter content of the simplest $\Lambda$CDM model, the complementarity of the
information in the resulting matter power spectrum expected from these galaxy
surveys and in the primordial power spectrum from CMB anisotropies can be
effective in constraining cosmological models. We find that the three galaxy
surveys can add significant information to CMB to better constrain the extra
parameters of the four models considered.


  A manifestly covariant formulation of quantum field Maslov complex-WKB theory
(semiclassical field theory) is investigated for the case of scalar field. The
main object of the theory is "semiclassical bundle". Its base is the set of all
classical states, fibers are Hilbert spaces of quantum states in the external
field. Semiclassical Maslov states may be viewed as points or surfaces on the
semiclassical bundle. Semiclassical analogs of QFT axioms are formulated. A
relationship between covariant semiclassical field theory and Hamiltonian
formulation is discussed. The constructions of axiomatic field theory
(Schwinger sources, Bogoliubov $S$-matrix, Lehmann-Symanzik-Zimmermann
$R$-functions) are used in constructing the covariant semiclassical theory. A
new covariant formulation of classical field theory and semiclassical
quantization proposal are discussed.


  In this work, we present a new method to estimate cosmological parameters
accurately based on the artificial neural network (ANN), and a code called
ECoPANN (Estimating Cosmological Parameters with ANN) is developed to achieve
parameter inference. We test the ANN method by estimating the basic parameters
of the concordance cosmological model using the simulated temperature power
spectrum of the cosmic microwave background (CMB). The results show that the
ANN performs excellently on best-fit values and errors of parameters, as well
as correlations between parameters when compared with that of the Markov Chain
Monte Carlo (MCMC) method. Besides, for a well-trained ANN model, it is capable
of estimating parameters for multiple experiments that have different
precisions, which can greatly reduce the consumption of time and computing
resources for parameter inference. Furthermore, we extend the ANN to a
multibranch network to achieve a joint constraint on parameters. We test the
multibranch network using the simulated temperature and polarization power
spectra of the CMB, Type Ia supernovae, and baryon acoustic oscillations, and
almost obtain the same results as the MCMC method. Therefore, we propose that
the ANN can provide an alternative way to accurately and quickly estimate
cosmological parameters, and ECoPANN can be applied to the research of
cosmology and even other broader scientific fields.


  Methods connecting dynamical systems and graph theory have attracted
increasing interest in the past few years, with applications ranging from a
detailed comparison of different kinds of dynamics to the characterisation of
empirical data. Here we investigate the effects of the (multi)fractal
properties of a time signal, common in sequences arising from chaotic or
strange attractors, on the topology of a suitably projected network. Relying on
the box counting formalism, we map boxes into the nodes of a network and
establish analytic expressions connecting the natural measure of a box with its
degree in the graph representation. We single out the conditions yielding to
the emergence of a scale-free topology, and validate our findings with
extensive numerical simulations.


  This paper introduces a class of k-nearest neighbor ($k$-NN) estimators
called bipartite plug-in (BPI) estimators for estimating integrals of
non-linear functions of a probability density, such as Shannon entropy and
R\'enyi entropy. The density is assumed to be smooth, have bounded support, and
be uniformly bounded from below on this set. Unlike previous $k$-NN estimators
of non-linear density functionals, the proposed estimator uses data-splitting
and boundary correction to achieve lower mean square error. Specifically, we
assume that $T$ i.i.d. samples ${X}_i \in \mathbb{R}^d$ from the density are
split into two pieces of cardinality $M$ and $N$ respectively, with $M$ samples
used for computing a k-nearest-neighbor density estimate and the remaining $N$
samples used for empirical estimation of the integral of the density
functional. By studying the statistical properties of k-NN balls, explicit
rates for the bias and variance of the BPI estimator are derived in terms of
the sample size, the dimension of the samples and the underlying probability
distribution. Based on these results, it is possible to specify optimal choice
of tuning parameters $M/T$, $k$ for maximizing the rate of decrease of the mean
square error (MSE). The resultant optimized BPI estimator converges faster and
achieves lower mean squared error than previous $k$-NN entropy estimators. In
addition, a central limit theorem is established for the BPI estimator that
allows us to specify tight asymptotic confidence intervals.


  We present the full one-loop calculation for $gg \to ZZ$ in the Minimal
Supersymmetric Model (MSSM) including nonresonant contributions from the squark
loop diagrams and provide analytical expressions for the helicity amplitudes.
The one-loop process $gg \to ZZ$ via quark loops has been calculated in the
Standard Model. In supersymmetric models, additional contributions arise from
squark loops. In some regions of the MSSM parameter space, the top and bottom
squark loops can make important contributions to the diagrams involving Higgs
bosons. The heavy Higgs scalar (H) might be detected at the Large Hadron
Collider via $gg \to H \to ZZ$ for $\tan\beta \alt 5$.


  We study the environment assisted local transitionless dynamics in closed
spin systems driven through quantum critical points. In general shortcut to
adaiabaticity (STA) in quantum critical systems requires highly non-local
control Hamiltonians. In this work we develop an approach to achieve local
shortcuts to adiabaticity (LSTA) in spin chains, using local control fields
which scale polynomially with the system size, following universal critical
exponents. We relate the control fields to reduced fidelity susceptibility and
use transverse Ising model in one dimension to exemplify our generic results.
We also extend our analysis to achieve LSTA in central spin models.


  In recent years, recommender system has become an indispensable function in
all e-commerce platforms. The review rating data for a recommender system
typically comes from open platforms, which may attract a group of malicious
users to deliberately insert fake feedback in an attempt to bias the
recommender system to their favour. The presence of such attacks may violate
modeling assumptions that high-quality data is always available and these data
truly reflect users' interests and preferences. Therefore, it is of great
practical significance to construct a robust recommender system that is able to
generate stable recommendations even in the presence of shilling attacks. In
this paper, we propose GraphRfi - a GCN-based user representation learning
framework to perform robust recommendation and fraudster detection in a unified
way. In its end-to-end learning process, the probability of a user being
identified as a fraudster in the fraudster detection component automatically
determines the contribution of this user's rating data in the recommendation
component; while the prediction error outputted in the recommendation component
acts as an important feature in the fraudster detection component. Thus, these
two components can mutually enhance each other. Extensive experiments have been
conducted and the experimental results show the superiority of our GraphRfi in
the two tasks - robust rating prediction and fraudster detection. Furthermore,
the proposed GraphRfi is validated to be more robust to the various types of
shilling attacks over the state-of-the-art recommender systems.


  We study the electromagnetic form factors of the lowest-lying singly heavy
baryons in a pion mean-field approach, which is also known as the SU(3) chiral
quark-soliton model. In the limit of the heavy-quark mass, the dynamics inside
a singly heavy baryon is governed by the $N_c-1$ valence quarks, while the
heavy quark remains as a mere static color source. In this framework, a singly
heavy baryon is described by combining the colored soliton with the singly
heavy quark. In the infinitely heavy-quark mass limit, we can compute the
electric quadrupole form factors of the baryon sextet with spin 3/2 with the
rotational $1/N_c$ and linear corrections of the explicit flavor SU(3) symmetry
breaking taken into account. We find that the sea-quark contributions or the
Dirac-sea level contributions dominate over the valence-quark contributions in
lower $Q^2$ region. We examined the effects of explicit flavor SU(3) symmetry
breaking in detail. The numerical results are also compared with the recent
data from the lattice calculation with the unphysical value of the pion mass
considered, which was used in the lattice calculation.


  A device model of biological molecule sensors based on semiconductor
nanowires has been developed. This model of a bioFET is based on the concept of
the electrolytic absolute electrode potential. From that starting point a
semiconductor device model of the nanowire solution biomolecule system was
derived. The model includes the Gouy-Chapman-Stern model of the salt solution
double layer, site binding charges on the electrode surface, and biological
molecules in the form of a membrane layer. A simple method of solving this
model is presented using the finite element method. Some examples showing the
general properties of the model are given.


  Owing to their statistical properties, non-convex sparse regularizers have
attracted much interest for estimating a sparse linear model from high
dimensional data. Given that the solution is sparse, for accelerating
convergence, a working set strategy addresses the optimization problem through
an iterative algorithm by incre-menting the number of variables to optimize
until the identification of the solution support. While those methods have been
well-studied and theoretically supported for convex regularizers, this paper
proposes a working set algorithm for non-convex sparse regularizers with
convergence guarantees. The algorithm, named FireWorks, is based on a
non-convex reformulation of a recent primal-dual approach and leverages on the
geometry of the residuals. Our theoretical guarantees derive from a lower bound
of the objective function decrease between two inner solver iterations and
shows the convergence to a stationary point of the full problem. More
importantly, we also show that convergence is preserved even when the inner
solver is inexact, under sufficient decay of the error across iterations. Our
experimental results demonstrate high computational gain when using our working
set strategy compared to the full problem solver for both block-coordinate
descent or a proximal gradient solver.


  Artery and vein (AV) classification of retinal images is a key to necessary
tasks, such as automated measurement of arteriolar-to-venular diameter ratio
(AVR). This paper comprehensively reviews the state-of-the art in AV
classification methods. To improve on previous methods, a new Local Bi- nary
Pattern-based method (LBP) is proposed. Beside its simplicity, LBP is robust
against low contrast and low quality fundus images; and it helps the process by
including additional AV texture and shape information. Experimental results
compare the performance of the new method with the state-of-the art; and also
methods with different feature extraction and classification schemas.


  We study the insulator-to-superfluid transition in a two-dimensional
disordered boson Hubbard model at zero temperature for intermediate strength of
disorder at commensurate density. Via Monte Carlo calculations of the
correlation functions in the integer current representation of the model, we
obtain the dynamical critical exponent $z=1.5 \pm 0.1$, supporting the
multi-critical behavior separating the strong and weak disorder regimes.
Investigating the density profile, we suggest that the density fluctuations due
to the particle-hole excitations drive the transition in the weak disorder
regime.


  Motivated by numerical schemes for large scale geophysical flow, we consider
the rotating shallow water and Boussinesq equations on the whole space with
horizontal kinetic energy backscatter source terms built from negative
viscosity and stabilising hyperviscosity with constant parameters. We study the
impact of this energy input through various explicit flows, which are
simultaneously solving the nonlinear equations and the linear equations that
arise upon dropping the transport nonlinearity, i.e. the linearisation in the
zero state. These include barotropic, parallel and Kolmogorov flows as well as
monochromatic inertia gravity waves. With focus on stable stratification we
find that the backscatter generates numerous solutions of this type that grow
exponentially and unboundedly, also with vertical structure. This signifies the
possibility of undesired energy concentration into specific modes due to the
backscatter. Families of steady state flows of this type arise as well and
superposition principles in the nonlinear equations provide explicit sufficient
conditions for instability of some of these. For certain steady barotropic
flows of this type we provide numerical evidence of eigenmodes whose growth
rates are proportional to the amplitude factor of the flow. For all other
arising steady solutions we prove this is not possible.


  We derive supersymmetry/supergravity models with constrained orthogonal
nilpotent superfields from the linear models in the formal limit where the
masses of the sgoldstino, inflatino and sinflaton tend to infinity. The case
where the sinflaton mass remains finite leads to a model with a `relaxed'
constraint, where the sinflaton remains an independent field. Our procedure is
equivalent to a requirement that some of the components of the curvature of the
moduli space tend to infinity.


  In this paper we consider the notion of commutation for a pair of continuous
and convex Hamiltonians, given in terms of commutation of their Lax- Oleinik
semigroups. This is equivalent to the solvability of an associated multi- time
Hamilton-Jacobi equation. We examine the weak KAM theoretic aspects of the
commutation property and show that the two Hamiltonians have the same weak KAM
solutions and the same Aubry set, thus generalizing a result recently obtained
by the second author for Tonelli Hamiltonians. We make a further step by
proving that the Hamiltonians admit a common critical subsolution, strict
outside their Aubry set. This subsolution can be taken of class C^{1,1} in the
Tonelli case. To prove our main results in full generality, it is crucial to
establish suitable differentiability properties of the critical subsolutions on
the Aubry set. These latter results are new in the purely continuous case and
of independent interest.


  The Laser Interferometer Gravitational Wave Observatory (LIGO) has recently
discovered gravitational waves (GWs) emitted by merging black hole binaries. We
examine whether future GW detections may identify triple companions of merging
binaries. Such a triple companion causes variations in the GW signal due to (1)
the varying path length along the line of sight during the orbit around the
center of mass, (2) relativistic beaming, Doppler, and gravitational redshift,
(3) the variation of the "light"-travel time in the gravitational field of the
triple companion, and (4) secular variations of the orbital elements. We find
that the prospects for detecting the triple companion are the highest for
low-mass compact object binaries which spend the longest time in the LIGO
frequency band. In particular, for merging neutron star binaries, LIGO may
detect a white dwarf or M-dwarf perturber at signal to noise ratio of 8, if it
is within 0.4 solar radius distance from the binary and the system is within a
distance of 100 Mpc. Stellar mass (supermassive) black hole perturbers may be
detected at a factor 5x (1000x) larger separations. Such pertubers in orbit
around the merging binary emit GWs at frequencies above 1 mHz detectable by the
Laser Interferometer Space Antenna (LISA) in coincidence.


  A computational approach that is based on interface finite elements with
eMbedded Profiles for Joint Roughness (MPJR) is exploited in order to study the
viscoelastic contact problems with any complex shape of the indenting profiles.
The MPJR finite elements, previously developed for partial slip contact
problems, are herein further generalized in order to deal with finite sliding
displacements. The approach is applied to a case study concerning a periodic
contact problem between a sinusoidal profile and a viscoelastic layer of finite
thickness. In particular, the effect of using three different rheological
models that are based on Prony series (with one, two, or three arms) to
approximate the viscoelastic behaviour of a real polymer is investigated. The
method allows for predicting the whole transient regime during the normal
contact problem and the subsequent sliding scenario from full stick to full
slip, and then up to gross sliding. The effects of the viscoelastic model
approximation and of the sliding velocities are carefully investigated. The
proposed approach aims at tackling a class of problems that are difficult to
address with other methods, which include the possibility of analysing
indenters of generic profile, the capability of simulating partial slip and
gross slip due to finite slidings, and, finally, the possibility of
simultaneously investigating dissipative phenomena, like viscoelastic
dissipation and energy losses due to interface friction.


  We discuss the pricing methodology for Bonus Certificates and Barrier
Reverse-Convertible Structured Products. Pricing for a European barrier
condition is straightforward for products of both types and depends on an
efficient interpolation of observed market option pricing. Pricing products We
discuss the pricing methodology for Bonus Certificates and Barrier
Reverse-Convertible Structured Products. Pricing for a European barrier
condition is straightforward for products of both types and depends on an
efficient interpolation of observed market option pricing. Pricing products
with an American barrier condition requires stochastic modelling. We show that
for typical market parameters, this stochastic pricing problem can be
systematically reduced to evaluating only one fairly simple stochastic
parameter being the asymmetry of hitting the barrier. Eventually, pricing Bonus
Certificates and Barrier Reverse Convertibles with an American barrier
condition, shows to be dependent on stochastic modelling only within a range of
$\pm\frac{2}{3}$ of accuracy - e.g. within this accuracy limitation we can
price these products without stochastic modelling. We show that the remaining
price component is weakly dependent on the stochastic models. Combining these
together, we prove to have established an almost model independent pricing
procedure for Bonus Certificates and Barrier Reverse-Convertible Structured
Products with American barrier conditions.


  In this study, we address the problem of efficient exploration in
reinforcement learning. Most common exploration approaches depend on random
action selection, however these approaches do not work well in environments
with sparse or no rewards. We propose Generative Adversarial Network-based
Intrinsic Reward Module that learns the distribution of the observed states and
sends an intrinsic reward that is computed as high for states that are out of
distribution, in order to lead agent to unexplored states. We evaluate our
approach in Super Mario Bros for a no reward setting and in Montezuma's Revenge
for a sparse reward setting and show that our approach is indeed capable of
exploring efficiently. We discuss a few weaknesses and conclude by discussing
future works.


  As two crucial tools characterizing regularity properties of stochastic
systems, the log-Harnack inequality and Bismut formula have been intensively
studied for distribution dependent (McKean-Vlasov) SDEs. However, due to
technical difficulties, existing results mainly focus on the case with
distribution free noise. In this paper, we introduce a noise decomposition
argument to establish the log-Harnack inequality and Bismut formula for
  SDEs with distribution dependent noise, in both non-degenerate and degenerate
situations. As application, the exponential ergodicity in entropy is
investigated.


  In the present paper, we prove the existence of solutions $(\lambda, u)\in
\R\times H^1(\R^N)$ to the following elliptic equations with potential
$\displaystyle -\Delta u+(V(x)+\lambda)u=g(u)\;\hbox{in}\;\R^N, $ satisfying
the normalization constraint $\displaystyle \int_{\R^N}u^2=a>0,$ which is
deduced by searching for solitary wave solution to the time-dependent nonlinear
Schr\"odinger equations. Besides the importance in the applications, not
negligible reasons of our interest for such problems with potential $V(x)$ are
their stimulating and challenging mathematical difficulties. We develop an
interesting way basing on iteration and give a new proof of the so-called
"sub-additive inequality", which can simply the standard process in the
traditional sense. Under some very relax assumption on the potential $V(x)$ and
some other suitable assumptions on $g$, we can obtain the existence of ground
state solution for prescribed $a>0$.


  It is shown that for a higher weak isospin symmetry, $SU(P)_L$ with $P\geq
3$, the baryon minus lepton charge $B-L$ neither commutes nor closes
algebraically with $SU(P)_L$ similar to the electric charge $Q$, which all lead
to a $SU(3)_C\otimes SU(P)_L\otimes U(1)_X\otimes U(1)_N$ gauge completion,
where $X$ and $N$ determine $Q$ and $B-L$, respectively. As a direct result,
the neutrinos obtain appropriate masses via a canonical seesaw. While the
version with $P=3$ supplies the schemes of single-component dark matter well
established in the literature, we prove in this work that the models with
$P\geq 4$ provide the novel scenarios of multicomponent dark matter, which
contain simultaneously at least $P-2$ stable candidates, respectively. In this
setup, the multicomponet dark matter is nontrivially unified with normal matter
by gauge multiplets, and their stability is ensured by a residual gauge
symmetry which is a remnant of the gauge symmetry after spontaneous symmetry
breaking. The three versions with $P=4$ according to the new lepton electric
charges are detailedly investigated. The mass spectrum of the scalar sector is
diagonalized when the scale of the $U(1)_N$ breaking is much higher than that
of the usual 3-4-1 symmetry breaking. All the interactions of gauge bosons with
fermions and scalars are obtained. We figure out viable parameter regimes given
that the multicomponent dark matter satisfies the Planck and (in)direct
detection experiments.


  Neural networks have shown promising potential in accelerating the numerical
simulation of systems governed by partial differential equations (PDEs).
Different from many existing neural network surrogates operating on
high-dimensional discretized fields, we propose to learn the dynamics of the
system in the latent space with much coarser discretizations. In our proposed
framework - Latent Neural PDE Solver (LNS), a non-linear autoencoder is first
trained to project the full-order representation of the system onto the
mesh-reduced space, then a temporal model is trained to predict the future
state in this mesh-reduced space. This reduction process simplifies the
training of the temporal model by greatly reducing the computational cost
accompanying a fine discretization. We study the capability of the proposed
framework and several other popular neural PDE solvers on various types of
systems including single-phase and multi-phase flows along with varying system
parameters. We showcase that it has competitive accuracy and efficiency
compared to the neural PDE solver that operates on full-order space.


  The most general scalar potential of two real scalar fields and a Higgs boson
is a quartic homogeneous polynomial about 3 variables, which defines a 4th
order 3 dimensional symmetric tensor. Hence, the boundedness from below of such
a scalar potential involves the positive (semi-)definiteness of the
corresponding tensor. So, we mainly discuss analytical expressions of positive
(semi-)definiteness for such a special 4th order 3-dimension symmetric tensor
in this paper. Firstly, an analytically necessary and sufficient condition is
given to test the positive (semi-)definiteness of a 4th order 2 dimensional
symmetric tensor. Furthermore, by means of such a result, the necessary and
sufficient conditions of the boundedness from below are obtained for a general
scalar potential of two real scalar fields and the Higgs boson.


  Brain iron deposition, in particular deep gray matter nuclei, increases with
advancing age. Hereditary Hemochromatosis (HH) is the most common inherited
disorder of systemic iron excess in Europeans and recent studies claimed high
brain iron accumulation in patient with Hemochromatosis. In this study, we
focus on Artificial Intelligence (AI)-based differential diagnosis of brain
iron deposition in HH via Quantitative Susceptibility Mapping (QSM), which is
an established Magnetic Resonance Imaging (MRI) technique to study the
distribution of iron in the brain. Our main objective is investigating
potentials of AI-driven frameworks to accurately and efficiently differentiate
individuals with Hemochromatosis from those of the healthy control group. More
specifically, we developed the Q-Net framework, which is a data-driven model
that processes information on iron deposition in the brain obtained from
multi-echo gradient echo imaging data and anatomical information on T1-Weighted
images of the brain. We illustrate that the Q-Net framework can assist in
differentiating between someone with HH and Healthy control (HC) of the same
age, something that is not possible by just visualizing images. The study is
performed based on a unique dataset that was collected from 52 subjects with HH
and 47 HC. The Q-Net provides a differential diagnosis accuracy of 83.16% and
80.37% in the scan-level and image-level classification, respectively.


  We construct a family of odd, finitely summable Fredholm modules over the
crossed product C*-algebra $C(\bd \G)\rtimes \G$ associated to the action of a
non-elementary hyperbolic group $\G$ on its Gromov boundary $\bd \G$. These
Fredholm modules all represent the same, distinguished class in K-homology,
namely that of the `boundary extension' of $C(\bd \G)\rtimes \G$ associated to
the Gromov compactification of $\G$, and is typically nonzero. Their
summability is closely related to the Hausdorff dimension of the boundary. We
use these results to compute the Connes-Chern character of the boundary
extension in cyclic cohomology.


  The charm production is sensitive to early dynamics of the created system in
RHIC heavy ion collisions. Dominant process of charm quarks production at RHIC
is believed to be initial gluon fusion which can be calculated in the
perturbative QCD. Understanding both the charm production total cross section
and the fragmentation in p+p collisions is a baseline to further exploring the
QCD medium via open charm and charmonium in heavy ion collisions. In this paper
we present the first reconstruction of open charm meson $D^{0}$ via the weak
decay to K and $\pi$ mesons in the p+p collisions at $\sqrt{s}$ = 200 GeV. The
analysis is based on the large p+p minimum bias sample collected in RHIC year
2009 by the STAR detector. The $D^0$ decay daughter identification was improved
by using the data of the newly installed Time-Of-Flight detector with 72% of
its designed coverage.


  We study the diffusion of massive particles in the space time of an Abelian
Higgs string. The particles in the early universe plasma execute Brownian
motion. This motion of the particles is modeled as a two dimensional random
walk in the plane of the Abelian Higgs string. The particles move randomly in
the space time of the string according to their geodesic equations. We observe
that for certain values of their energy and angular momentum, an overdensity of
particles is observed close to the string. We find that the string parameters
determine the distribution of the particles. We make an estimate of the density
fluctuation generated around the string as a function of the deficit angle.
Though the thickness of the string is small, the length is large and the
overdensity close to the string may have cosmological consequences in the early
universe.


  A necessary condition for a Cayley digraph Cay$(R,S)$ to be a regular
representation is that there are no non-trivial group automorphisms of $R$ that
fix $S$ setwise. A group is DRR-detecting or GRR-detecting if this condition is
also sufficient for all Cayley digraphs or graphs on the group, respectively.
In this paper, we determine precisely which groups of squarefree order are
DRR-detecting, and which are GRR-detecting.


  We show that the isomorphy type of every finitely generated field $K$ with
$\chr(K)\neq2$ is encoded by a \textit{\textbf{single\ha3explicit\ha3axiom}}
$\istp K\!$ \textit{\textbf{in\ha3the\ha3language\ha3of\ha3fields}}, i.e., for
all finitely generated fields $L$ one has: $\istp K$ holds in $L$ if and only
if $K\cong L$ as fields. This extends earlier results by \nmnm{\footnotesize\sc
Julia Robinson, Rumely, Poonen, Scanlon}, the author, and others.


  We consider in the plane the problem of reconstructing a domain from the
normal derivative of its Green's function with pole at a fixed point in the
domain. By means of the theory of conformal mappings, we obtain existence,
uniqueness, (non-spherical) symmetry results, and a formula relating the
curvature of the boundary of the domain to the normal derivative of its Green's
function.


  We improve the Cauchy radius of both scalar and matrix polynomials, which is
an upper bound on the moduli of the zeros and eigenvalues, respectively, by
using appropriate polynomial multipliers.


  LoRaWAN is one of the most promising standards for IoT applications.
Nevertheless, the high density of end-devices expected for each gateway, the
absence of an effective synchronization scheme between gateway and end-devices,
challenge the scalability of these networks. In this article, we propose to
regulate the communication of LoRaWAN networks using a Slotted-ALOHA (S-ALOHA)
instead of the classic ALOHA approach used by LoRa. The implementation is an
overlay on top of the standard LoRaWAN; thus no modification in pre-existing
LoRaWAN firmware and libraries is necessary. Our method is based on a novel
distributed synchronization service that is suitable for low-cost IoT
end-nodes. S-ALOHA supported by our synchronization service significantly
improves the performance of traditional LoRaWAN networks regarding packet loss
rate and network throughput.


  We report on the implementation of a quantum analog to the classical lock-in
amplifier. All the lock-in operations: modulation, detection and mixing, are
performed via the application of non-commuting quantum operators on the
electronic spin state of a single trapped Sr+ ion. We significantly increase
its sensitivity to external fields while extending phase coherence by three
orders of magnitude, to more than one second. With this technique we measure
magnetic fields with sensitivity of 25 pT/sqrt(Hz) and light shifts with an
uncertainty below 140 mHz after 1320 seconds of averaging. These sensitivities
are limited by quantum projection noise and, to our knowledge, are more than
two orders of magnitude better than with other single-spin probe technologies.
In fact, our reported sensitivity is sufficient for the measurement of parity
non-conservation, as well as the detection of the magnetic field of a single
electronic-spin one micrometer from an ion-detector with nanometer resolution.
As a first application we perform light shift spectroscopy of a narrow optical
quadruple transition. Finally, we emphasize that the quantum lock-in technique
is generic and can potentially enhance the sensitivity of any quantum sensor.


  We performed a detailed analysis of the use of [C/N] measured in red giant
branch stars between the completion of the first dredge up and the red giant
branch bump ([C/N]_{FDU}) as age indicator. [C/N]_{FDU} cannot give accurate
ages for individual stars, but may provide a general chronology for the
formation of composite populations and add constraints to analyses of red
giants from surface gravity-effective temperature diagrams. We provide a
theoretical calibration of [C/N]_{FDU} in terms of total metallicity [M/H] and
age, for ages greater than 1 Gyr, which we tested against variations in the
initial heavy element distribution (scaled-solar vs alpha-enhanced), efficiency
of overshooting from MS convective cores and from the convective envelopes,
variations in the initial He abundance and in the mixing length parameter. Our
calibration is compared with a small sample of available measurements of
[C/N]_{FDU} in star clusters and halo field stars, which at least qualitatively
confirm the overall trend of the predicted [C/N]_{FDU} with age and [M/H]. The
use of [C/N]_{FDU}-[M/H]-age relations obtained from independent sets of
stellar evolution calculations cause age differences (for a given [C/N]_{FDU}
and [M/H] pair) up to about 2~Gyr. More accurate spectroscopic measurements of
[C/N]_{FDU} in star clusters with well-established ages and metallicities are
required to better test theoretical calibrations of this age indicator.


  Extremely large-scale multiple-input multipleoutput (XL-MIMO) is believed to
be a cornerstone of sixth-generation (6G) wireless networks. XL-MIMO uses more
antennas to both achieve unprecedented spatial degrees of freedom (DoFs) and
exploit new electromagnetic (EM) phenomena occurring in the radiative
near-field. The near-field effects provide the XL-MIMO array with depth
perception, enabling precise localization and spatially multiplexing jointly in
the angle and distance domains. This article delineates the distinctions
between near-field and far-field propagation, highlighting the unique EM
characteristics introduced by having large antenna arrays. It thoroughly
examines the challenges these new near-field characteristics pose for user
localization and channel estimation and provides a comprehensive review of new
algorithms developed to address them. The article concludes by identifying
critical future research directions.


  We consider the split extended (N=2) supersymmetry scenario recently proposed
by Antoniadis et al. [hep-ph/0507192] as a realistic low energy framework
arising from intersecting brane models. While all scalar superpartners and
charged gauginos are naturally at a heavy scale, the model low energy spectrum
contains a Higgsino-like chargino and a neutralino sector made out of two
Higgsino and two Bino states. We show that the lightest neutralino is a viable
dark matter candidate, finding regions in the parameter space where its thermal
relic abundance matches the latest determination of the density of matter in
the
  Universe by WMAP. We also discuss dark matter detection strategies within
this model: we point out that current data on cosmic-ray antimatter already
place significant constraints on the model, while direct detection is the most
promising technique for the future. Analogies and differences with respect to
the standard split
  SUSY scenario based on the MSSM are illustrated.


  In this paper, we propose a multiple input multiple output (MIMO) Full-Duplex
Integrated Sensing and Communication System consisting of multiple targets, a
single downlink, and a single uplink user. We employed signal-to-interference
plus noise ratio (SINR) as the performance metric for radar, downlink, and
uplink communication. We use a communication-centric approach in which
communication waveform is used for both communication and sensing of the
environment. We develop a sensing algorithm capable of estimating the direction
of arrival (DoA), range, and velocity of each target. We also propose a joint
optimization framework for designing A/D transmit and receive beamformers to
improve radar, downlink, and uplink SINRs while minimizing self-interference
(SI) leakage. We also propose a null space projection (NSP) based approach to
improve the uplink rate. Our simulation results, considering orthogonal
frequency division multiplexing (OFDM) waveform, show accurate radar parameter
estimation with improved downlink and uplink rate.