  A technical note is given regarding our previous laboratory
plasma-astrophysical studies [C.-S. Jao et al., High Energy Density Physics 32,
31-43 (2019) and Y. Chen et al., Nucl. Instrum. Methods Phys. Res., Sect. A
903, 119 (2018)]. In this note, an upgraded accelerator beamline design is
proposed based on a feasible experimental setup in a realistic laboratory
environment. The improved design aims to provide milliampere (mA)
mega-electron-volt (MeV) quasi-continuous (cw) electron beams for
plasma-astrophysical applications. Such a design utilizes a so-called
mixed-guiding-field magnetic system right after the cut disk structure (CDS)
booster cavity to provide a periodic longitudinal focusing field. The
transportation of the produced cw beam with large energy spread to the plasma
cell location is improved. The magnetic field serves as well as a seeding field
in the plasma environment for the growth of electromagnetic instabilities. In
conjunction with the appliance of a circular collimator at the exit of the CDS,
the new design allows production of quasi-cw beams with a three orders higher
number density at the entrance of the plasma cell compared to the previous
design for a seeding magnetic field of about 50 mT while the locally enhanced
electric field at the cathode is up to 8 GV/m. The associated beam dynamics
simulation results are presented. As proof of principle studies, the produced
electron beams are applied in nonlinear plasma-astrophysical simulations for
exploring the growth of the instabilities. The extracted parameters and/or
distributions from the generated electron beams in the laboratory environment
are used in these particle-in-cell simulations. The obtained results are
presented and discussed.


  We address Newton-type problems of minimal resistance from an optimal control
perspective. It is proven that for Newton-type problems the Pontryagin maximum
principle is a necessary and sufficient condition. Solutions are then computed
for concrete situations, including the new case when the flux of particles is
non-parallel.


  The Right Frontier Constraint (RFC), as a constraint on the attachment of new
constituents to an existing discourse structure, has important implications for
the interpretation of anaphoric elements in discourse and for Machine Learning
(ML) approaches to learning discourse structures. In this paper we provide
strong empirical support for SDRT's version of RFC. The analysis of about 100
doubly annotated documents by five different naive annotators shows that SDRT's
RFC is respected about 95% of the time. The qualitative analysis of presumed
violations that we have performed shows that they are either click-errors or
structural misconceptions.


  Nonlinear distortion of infrasonic waves through atmospheres up to
thermospheric altitudes govern large-range ground-level observations of
explosive noise sources, causing large differences between the near and far
field. Propagation modelling in this scenario to include realistic nonlinear
effects has thus far been limited to high-fidelity, numerically intensive
Direct Numerical Simulations of the Navier-Stokes equations, or nonlinear
parabolic equations with restrictions on the mean flow Mach number. For the
accurate modelling of nonlinear waveform synthesis through realistic
atmospheric winds up to thermospheric altitudes, this work presents nonlinear
wave equation analysis which results in in a Westervelt equation for weakly
stratified, arbitrary Mach number atmospheres. This is intended to be used as a
benchmark for model development and numerical analyses such that alternative
low-fidelity numerical calculations to Direct Numerical Simulations can be
sought.


  We present an estimate of perturbative QCD corrections to the decay $Z \to
Wu\bar d$. A simple approximate approach is described in detail. The difference
of masses of $M_Z$ and $M_W$ is used as an expansion parameter. A complete
analytical formula for a part of the corrections is also presented.


  We have observed six high-mass star-forming regions in the 2 Pi 3/2, J = 7/2
lines of OH using the GBT in order to investigate whether the magnetic field,
and hence the density, measured in absorption differs from that implied by
maser Zeeman splitting. We detect absorption in both the 13441 and 13434 MHz
main lines in all six sources. Zeeman splitting in the F = 3-3 absorption line
in W3(OH) implies a line-of-sight magnetic field strength of 3.0 +/- 0.3 mG.
This is significantly less than full magnetic field strengths detected from OH
maser Zeeman splitting, suggesting that OH maser regions may be denser than the
non-masing OH material by a factor of several. Zeeman splitting is not detected
in other sources, but we are able to place upper limits on B_parallel of 1.2 mG
in G10.624-0.385 and 2.9 mG in K3-50. These results are consistent with a
density enhancement of the masers, but other explanations for the lower
magnetic field in absorption compared to maser emission are possible for these
two sources. Absorption in one or both of the 13442 and 13433 MHz satellite
lines is also seen in four sources. This is the very first detection of the 2
Pi 3/2, J = 7/2 satellite lines. Ratios of satellite-line to main-line
absorption suggest enhancement of the satellite lines from local thermodynamic
equilibrium values. Masers are seen in the F = 4-4 and 3-3 transitions of
W3(OH) and the 4-4 transition of ON 1. A previously undetected 4-4 maser is
seen near -44.85 km/s in W3(OH).


  We analyse the optical spectral properties of the complete sample of Very
Luminous Infrared Galaxies presented by Wu et al. (1998a,b) and we find a high
fraction (~50 %) of spectra showing both a strong H_delta line in absorption
and relatively modest [OII] emission (e(a) spectra). The e(a) signature has
been proposed as an efficient method to identify dusty starburst galaxies and
we study the star formation activity and the nature of these galaxies, as well
as the effects of dust on their observed properties. We examine their emission
line characteristics, in particular their [OII]/H_alpha ratio, and we find this
to be greatly affected by reddening. A search for AGN spectral signatures
reveals that the e(a)'s are typically HII/LINER galaxies. We compare the star
formation rates derived from the FIR luminosities with the estimates based on
the H_alpha line and find that the values obtained from the optical emission
lines are a factor of 10-70 (H_alpha) and 20-140 ([OII]) lower than the FIR
estimates (50-300 M_sun yr^-1). We then study the morphological properties of
the e(a) galaxies, looking for a near companion or signs of a
merger/interaction. In order to explore the evolution of the e(a) population,
we present an overview of the available observations of e(a)'s in different
environments both at low and high redshift. Finally, we discuss the role of
dust in determining the e(a) spectral properties and we propose a scenario of
selective obscuration in which the extinction decreases with the stellar age.


  We consider nonconforming methods for symmetric elliptic problems and
characterize their quasi-optimality in terms of suitable notions of stability
and consistency. The quasi-optimality constant is determined and the possible
impact of nonconformity on its size is quantified by means of two alternative
consistency measures. Identifying the structure of quasi-optimal methods, we
show that their construction reduces to the choice of suitable linear operators
mapping discrete functions to conforming ones. Such smoothing operators are
devised in the forthcoming parts of this work for various finite element
spaces.


  The aim of this note is to prove a representation theorem for left--invariant
functionals in Carnot groups. As a direct consequence, we can also provide a
$\Gamma$-convergence result for a smaller class of functionals.


  In previous work by two of the present authors, twistors were re-interpreted
as 4-d spinors with a position dependence within the formalism of geometric
(Clifford) algebra. Here we extend that approach and justify the nature of the
position dependence. We deduce the spinor representation of the restricted
conformal group in geometric algebra, and use it to show that the position
dependence is the result of the action of the translation operator in the
conformal space on the 4-d spinor. We obtain the geometrical description of
twistors through the conformal geometric algebra, and derive the Robinson
congruence. This verifies our formalism. Furthermore, we show that this novel
approach brings considerable simplifications to the twistor formalism, and new
advantages. We map the twistor to the 6-d conformal space, and derive the
simplest geometrical description of the twistor as an observable of a
relativistic quantum system. The new 6-d twistor takes the r\^ole of the state
for that system. In our new interpretation of twistors as 4-d spinors, we
therefore only need to apply the machinery already known from quantum mechanics
in the geometric algebra formalism, in order to recover the physical and
geometrical properties of 1-valence twistors.


  The transition to a renewable energy system poses challenges for power grid
operation and stability. Secondary control is key in restoring the power system
to its reference following a disturbance. Underestimating the necessary control
capacity may require emergency measures, such as load shedding. Hence, a solid
understanding of the emerging risks and the driving factors of control is
needed. In this contribution, we establish an explainable machine learning
model for the activation of secondary control power in Germany. Training
gradient boosted trees, we obtain an accurate description of control
activation. Using SHapely Additive exPlanation (SHAP) values, we investigate
the dependency between control activation and external features such as the
generation mix, forecasting errors, and electricity market data. Thereby, our
analysis reveals drivers that lead to high reserve requirements in the German
power system. Our transparent approach, utilizing open data and making machine
learning models interpretable, opens new scientific discovery avenues.


  Benchmark performance of deep learning classifiers alone is not a reliable
predictor for the performance of a deployed model. In particular, if the image
classifier has picked up spurious features in the training data, its
predictions can fail in unexpected ways. In this paper, we develop a framework
that allows us to systematically identify spurious features in large datasets
like ImageNet. It is based on our neural PCA components and their
visualization. Previous work on spurious features often operates in toy
settings or requires costly pixel-wise annotations. In contrast, we work with
ImageNet and validate our results by showing that presence of the harmful
spurious feature of a class alone is sufficient to trigger the prediction of
that class. We introduce the novel dataset "Spurious ImageNet" which allows to
measure the reliance of any ImageNet classifier on harmful spurious features.
Moreover, we introduce SpuFix as a simple mitigation method to reduce the
dependence of any ImageNet classifier on previously identified harmful spurious
features without requiring additional labels or retraining of the model. We
provide code and data at https://github.com/YanNeu/spurious_imagenet .


  Gaussian Processes (GPs) have been widely used in machine learning to model
distributions over functions, with applications including multi-modal
regression, time-series prediction, and few-shot learning. GPs are particularly
useful in the last application since they rely on Normal distributions and
enable closed-form computation of the posterior probability function.
Unfortunately, because the resulting posterior is not flexible enough to
capture complex distributions, GPs assume high similarity between subsequent
tasks - a requirement rarely met in real-world conditions. In this work, we
address this limitation by leveraging the flexibility of Normalizing Flows to
modulate the posterior predictive distribution of the GP. This makes the GP
posterior locally non-Gaussian, therefore we name our method Non-Gaussian
Gaussian Processes (NGGPs). More precisely, we propose an invertible ODE-based
mapping that operates on each component of the random variable vectors and
shares the parameters across all of them. We empirically tested the flexibility
of NGGPs on various few-shot learning regression datasets, showing that the
mapping can incorporate context embedding information to model different noise
levels for periodic functions. As a result, our method shares the structure of
the problem between subsequent tasks, but the contextualization allows for
adaptation to dissimilarities. NGGPs outperform the competing state-of-the-art
approaches on a diversified set of benchmarks and applications.


  The Umbral Moonshine Conjectures assert that there are infinite-dimensional
graded modules, for prescribed finite groups, whose McKay-Thompson series are
certain distinguished mock modular forms. Gannon has proved this for the
special case involving the largest sporadic simple Mathieu group. Here we
establish the existence of the umbral moonshine modules in the remaining 22
cases.


  For any finite dimensional Hilbert space, we construct explicitly five
orthonormal bases such that the corresponding measurements allow for efficient
tomography of an arbitrary pure quantum state. This means that such
measurements can be used to distinguish an arbitrary pure state from any other
state, pure or mixed, and the pure state can be reconstructed from the outcome
distribution in a feasible way. The set of measurements we construct is
independent of the unknown state, and therefore our results provide a fixed
scheme for pure state tomography, as opposed to the adaptive (state dependent)
scheme proposed by Goyeneche et al. in [Phys. Rev. Lett. 115, 090401 (2015)].
We show that our scheme is robust with respect to noise in the sense that any
measurement scheme which approximates these measurements well enough is equally
suitable for pure state tomography. Finally, we present two convex programs
which can be used to reconstruct the unknown pure state from the measurement
outcome distributions.


  This paper presents Telebrain, a browser-based performatization platform
invented for organizing real-time telematic performances. Performatization is
the human performance of algorithms. When computers and humans performatize
cooperatively, the human-computer interaction (HCI) becomes the location of
computation. Novel modes of machine-human communication are necessary for
organizing performatizations. Telebrain is designed to facilitate machine-human
languages. Capitalizing on the ubiquity and cross-platform compatibility of the
Internet, Telebrain is an open-source web application supporting PerPL
(Performer Programming Language), a human-interpreted configurable language of
multi-media instructions used to program performers. Telebrain facilitates a
variety of performance disciplines such as music, theater, dance, computational
performance, networked scoring (image and audio), prompted improvisation,
real-space multi-player gaming, collaborative transdisciplinary karaoke and
quantum square-dancing. (http://telebrain.org)


  We report on the prototyping of a plastic scintillator hodoscope with
wavelength-shifting fibre read-out by a multi-anode photomultiplier as part of
the development of a detector for cosmic ray muons to be carried aboard an
aircraft. Light yield and light attenuation measurements on single- and
double-clad wavelength-shifting fibres were performed. Low power,
low-threshold, discriminators were designed. A prototype 16-channel hodoscope
with two planes was built and tested with cosmic rays. After correcting for
geometrical factors a global intrinsic efficiency of $\epsilon > 98$ % was
obtained in both planes. The overall performance of the hodoscope proved it to
be well suited for the {\sc Adler} experiment to measure the high altitude muon
flux.


  We theoretically study the orbital destructive effect against
superconductivity in a parallel magnetic field in the
Fulde-Ferrell-Larkin-Ovchinnikov (FFLO or LOFF) phase at zero temperature in a
quasi-two-dimensional (Q2D) conductor. We demonstrate that at zero temperature
a special parameter, $\lambda = l_{\perp}(H)/d$, is responsible for strength of
the orbital effect, where $l_{\perp}(H)$ is a typical "size" of the
quasi-classical electron orbit in a magnetic field and $d$ is the inter-plane
distance. We discuss applications of our results to the existing experiments on
the FFLO phase in the organic Q2D conductors $\kappa$-(ET)$_2$Cu(NCS)$_2$ and
$\kappa$-(ET)$_2$Cu[N(CN)$_2$]Cl.


  We construct U(2) noncommutative multi-instanton solutions by extending
Witten's ansatz [1] which reduces the problem of cylindrical symmetry in four
dimensions to that of a set of Bogomol'nyi equations for an Abelian Higgsmodel
in two dimensional curved space. Using the Fock space approach, we give
explicit vortex solutions to the Bogomol'nyi equations and, from them, we
present multi-instanton solutions.


  The Product Conjecture for the homological Bieri-Neumann-Strebel-Renz
invariants is proved over a field. Under certain hypotheses the Product
Conjecture is shown to also hold over Z, even though D. Schuetz has recently
shown that the Conjecture is false in general over Z. Our version over Z is
applied in a joint paper with D. Kochloukova to derive new information about
subgroups of Thompson's group F, namely that F has subgroups F_m which are not
of type F_{m+1}.


  A novel method is presented which can pin down the time the accretion disk
re-established itself in the RS Oph system after it experienced a nova
disruption. The method is based on the re-ionisation of the ejecta by
photoionisation from the radiation released in the boundary layer from
accretion.


  Nowadays it is very important to maintain a high level security to ensure
safe and trusted communication of information between various organizations.
But secured data communication over internet and any other network is always
under threat of intrusions and misuses. So Intrusion Detection Systems have
become a needful component in terms of computer and network security. There are
various approaches being utilized in intrusion detections, but unfortunately
any of the systems so far is not completely flawless. So, the quest of
betterment continues. In this progression, here we present an Intrusion
Detection System (IDS), by applying genetic algorithm (GA) to efficiently
detect various types of network intrusions. Parameters and evolution processes
for GA are discussed in details and implemented. This approach uses evolution
theory to information evolution in order to filter the traffic data and thus
reduce the complexity. To implement and measure the performance of our system
we used the KDD99 benchmark dataset and obtained reasonable detection rate.


  We report on measurements of an elementary surface mode in an ultracold,
strongly interacting Fermi gas of 6Li atoms. The radial quadrupole mode allows
us to probe hydrodynamic behavior in the BEC-BCS crossover without being
influenced by changes in the equation of state. We examine frequency and
damping of this mode, along with its expansion dynamics. In the unitarity limit
and on the BEC side of the resonance, the observed frequencies agree with
standard hydrodynamic theory. However, on the BCS side of the crossover, a
striking down shift of the oscillation frequency is observed in the
hydrodynamic regime as a precursor to an abrupt transition to collisionless
behavior; this indicates coupling of the oscillation to fermionic pairs.


  This work presents MLIR, a novel approach to building reusable and extensible
compiler infrastructure. MLIR aims to address software fragmentation, improve
compilation for heterogeneous hardware, significantly reduce the cost of
building domain specific compilers, and aid in connecting existing compilers
together. MLIR facilitates the design and implementation of code generators,
translators and optimizers at different levels of abstraction and also across
application domains, hardware targets and execution environments. The
contribution of this work includes (1) discussion of MLIR as a research
artifact, built for extension and evolution, and identifying the challenges and
opportunities posed by this novel design point in design, semantics,
optimization specification, system, and engineering. (2) evaluation of MLIR as
a generalized infrastructure that reduces the cost of building
compilers-describing diverse use-cases to show research and educational
opportunities for future programming languages, compilers, execution
environments, and computer architecture. The paper also presents the rationale
for MLIR, its original design principles, structures and semantics.


  We propose a geographic and spatio-temporal information based distributed
cooperative positioning (GSTICP) algorithm for wireless networks that require
three-dimensional (3D) coordinates and operate in the line-of-sight (LOS) and
nonline-of-sight (NLOS) mixed environments. First, a factor graph (FG) is
created by factorizing the a posteriori distribution of the position-vector
estimates and mapping the spatial-domain and temporal-domain operations of
nodes onto the FG. Then, we exploit a geographic information based NLOS
identification scheme to reduce the performance degradation caused by NLOS
measurements. Furthermore, we utilize a finite symmetric sampling based scaled
unscented transform (SUT) method to approximate the nonlinear terms of the
messages passing on the FG with high precision, despite using only a small
number of samples. Finally, we propose an enhanced anchor upgrading (EAU)
mechanism to avoid redundant iterations. Our GSTICP algorithm supports any type
of ranging measurement that can determine the distance between nodes.
Simulation results and analysis demonstrate that our GSTICP has a lower
computational complexity than the state-of-the-art belief propagation (BP)
based localizers, while achieving an even more competitive positioning
performance.


  We present an optical setup that can be used to characterize the thicknesses
of thin NbN films to screen samples for fabrication and to better model the
performance of the resulting superconducting nanowire single photon detectors.
The infrared transmissometer reported here is easy to use, gives results within
minutes and is non-destructive. Thus, the thickness measurement can be easily
integrated into the workflow of deposition and characterization. Comparison to
a similar visible-wavelength transmissometer is provided.


  Ion acceleration resulting from the interaction of 11 fs laser pulses of ~35
mJ energy with ultrahigh contrast (<10^-10), and 10^19 W/cm^2 peak intensity
with foil targets made of various materials and thicknesses at normal
(0-degree) and 45-degree laser incidence is investigated. The maximum energy of
the protons accelerated from both the rear and front sides of the target was
above 1 MeV. A conversion efficiency from laser pulse energy to proton beam is
estimated to be as high as ~1.4 % at 45-degree laser incidence using a 51
nm-thick Al target. The excellent laser contrast indicates the predominance of
vacuum heating via the Brunels effect as an absorption mechanism involving a
tiny pre-plasma of natural origin due to the Gaussian temporal laser pulse
shape. Experimental results are in reasonable agreement with theoretical
estimates where proton acceleration from the target rear into the forward
direction is well explained by a TNSA-like mechanism, while proton acceleration
from the target front into the backward direction can be explained by the
formation of a charged cavity in a tiny pre-plasma. The exploding Coulomb field
from the charged cavity also serves as a source for forward-accelerated ions at
thick targets.


  We consider the problem of nonparametric estimation of the drift of a
continuously observed one-dimensional diffusion with periodic drift. Motivated
by computational considerations, van der Meulen e.a. (2014) defined a prior on
the drift as a randomly truncated and randomly scaled Faber-Schauder series
expansion with Gaussian coefficients. We study the behaviour of the posterior
obtained from the prior from a frequentist asymptotic point of view. If the
true data generating drift is smooth, it is proved that the posterior is
adaptive with posterior contraction rates for the $L_2$-norm that are optimal
up to a log factor. Moreover, contraction rates in $L_p$-norms with $p\in
(2,\infty]$ are derived as well.


  Recent image generation models such as Stable Diffusion have exhibited an
impressive ability to generate fairly realistic images starting from a simple
text prompt. Could such models render real images obsolete for training image
prediction models? In this paper, we answer part of this provocative question
by investigating the need for real images when training models for ImageNet
classification. Provided only with the class names that have been used to build
the dataset, we explore the ability of Stable Diffusion to generate synthetic
clones of ImageNet and measure how useful these are for training classification
models from scratch. We show that with minimal and class-agnostic prompt
engineering, ImageNet clones are able to close a large part of the gap between
models produced by synthetic images and models trained with real images, for
the several standard classification benchmarks that we consider in this study.
More importantly, we show that models trained on synthetic images exhibit
strong generalization properties and perform on par with models trained on real
data for transfer. Project page: https://europe.naverlabs.com/imagenet-sd/


  Given an infinite group G, we consider the finitely additive measure defined
on finite unions of cosets of finite index subgroups. We show that this shares
many properties with the size of subsets of a finite group, for instance we can
obtain equivalent results on Ruzsa distance and product free sets. In
particular if G has infinitely many finite index subgroups then it has subsets
S of measure arbitrarily close to 1/2 with the square of S having measure less
than 1.


  Gaining access to the cell interior is fundamental for many applications,
such as electrical recording, drug and biomolecular delivery. A very promising
technique consists of culturing cells on nano/micro pillars. The tight adhesion
and high local deformation of cells in contact with nanostructures can promote
the permeabilization of lipids at the plasma membrane, providing access to the
internal compartment. However, there is still much experimental controversy
regarding when and how the intracellular environment is targeted and the role
of the geometry and interactions with surfaces. Consequently, we investigated,
by coarse-grained molecular dynamics simulations of the cell membrane, the
mechanical properties of the lipid bilayer under high strain and bending
conditions. We found out that a high curvature of the lipid bilayer
dramatically lowers the traction force necessary to achieve membrane rupture.
Afterwards, we experimentally studied the permeabilization rate of cell
membrane by pillars with comparable aspect ratios but different sharpness
values at the edges. The experimental data support the simulation results: even
pillars with diameters in the micron range may cause local membrane disruption
when their edges are sufficiently sharp. Therefore, the permeabilization
likelihood is connected to the local geometric features of the pillars rather
than diameter or aspect ratio. The present study can also provide significant
contributions to the design of 3D biointerfaces for tissue engineering and
cellular growth.


  Massive stars and supernovae (SNe) have a huge impact on their environment.
Despite their importance, a comprehensive knowledge of which massive stars
produce which SNe is hitherto lacking. We use a Monte Carlo method to predict
the mass-loss rates of massive stars in the Hertzsprung-Russell Diagram (HRD)
covering all phases from the OB main sequence, the unstable Luminous Blue
Variable (LBV) stage, to the final Wolf-Rayet (WR) phase. Although WR produce
their own metals, a strong dependence of the mass-loss rate on the initial iron
abundance is found at sub-solar metallicities (1/10 -- 1/100 solar). This may
present a viable mechanism to prevent the loss of angular momentum by stellar
winds, which could inhibit GRBs occurring at solar metallicities -- providing a
significant boost to the collapsar model. Furthermore, we discuss recently
reported quasi-sinusoidal modulations in the radio lightcurves of SNe 2001ig
and 2003bg. We show that both the sinusoidal behaviour and the recurrence
timescale of these modulations are consistent with the predicted mass-loss
behaviour of LBVs. We discuss potential ramifications for the ``Conti''
scenario for massive star evolution.


  Spectral line emissivities have usually been calculated for a Maxwellian
electron distribution. But many theoretical works on galaxy groups and clusters
and on the solar corona suggest to consider modified Maxwellian electron
distribution functions to fit observed X-ray spectra. Here we examine the
influence of high energy electron populations on measurements of metal
abundances. A generalized approach which was proposed in the paper by Prokhorov
et al. (2009) is used to calculate the line emissivities for a modified
Maxwellian distribution. We study metal abundances in galaxy groups and
clusters where hard X-ray excess emission was observed. We found that for
modified Maxwellian distributions the argon abundance decreases for the HCG 62
group, the iron abundance decreases for the Centaurus cluster and the oxygen
abundance decreases for the solar corona with respect to the case of a
Maxwellian distribution. Therefore, metal abundance measurements are a
promising tool to test the presence of high energy electron populations.


  The large difference between the Planck scale and the electroweak scale,
known as the hierarchy problem, has been addressed in some models through the
existence of extra spatial dimensions. A search for evidence of extra spatial
dimensions has been performed, through an analysis of the diphoton final state
in data recorded in 2011 with the ATLAS detector at the CERN Large Hadron
Collider. The analysis uses a dataset of 2.12 fb-1 of proton-proton collisions
at sqrts=7 TeV. The diphoton invariant mass spectrum is observed to be in good
agreement with the expected Standard Model (SM) background. We set 95 CL lower
limits on the fundamental Planck scale in the context of the Arkani-Hamed,
Dimopoulos, Dvali model (ADD) and on the lightest Kaluza Klein (KK) excitation
mass in the context of the Randall-Sundrum model (RS).


  An SU(Nf)xSU(Nf) Yang-Mills theory on an extra-dimensional interval is
considered, with appropriate symmetry-breaking boundary conditions on the IR
brane. UV-brane to UV-brane correlators at high energies are compared with the
OPE of two-point functions of QCD quark currents. Condensates correspond to
departure from AdS of the (different) metrics felt by vector and axial
combinations, away from the UV brane. Their effect on hadronic observables is
studied: the extracted condensates agree with the signs and orders of magnitude
expected from QCD.


  Bayesian experimental design involves the optimal allocation of resources in
an experiment, with the aim of optimising cost and performance. For implicit
models, where the likelihood is intractable but sampling from the model is
possible, this task is particularly difficult and therefore largely unexplored.
This is mainly due to technical difficulties associated with approximating
posterior distributions and utility functions. We devise a novel experimental
design framework for implicit models that improves upon previous work in two
ways. First, we use the mutual information between parameters and data as the
utility function, which has previously not been feasible. We achieve this by
utilising Likelihood-Free Inference by Ratio Estimation (LFIRE) to approximate
posterior distributions, instead of the traditional approximate Bayesian
computation or synthetic likelihood methods. Secondly, we use Bayesian
optimisation in order to solve the optimal design problem, as opposed to the
typically used grid search or sampling-based methods. We find that this
increases efficiency and allows us to consider higher design dimensions.


  Using the density functional theory (DFT) formulated within the framework of
the plane-wave basis projector augmented wave (PAW) method, the
temperature-dependent elastic properties of MgRE (RE=Y, Dy, Pr, Sc, Tb)
intermetallics with B2-type structure are presented from first-principles. Our
calculations are based on the fact that the elastic moduli as a function of
temperature mainly results from thermal expansion. The comparison between the
predicted results and the available experimental data for a benchmark material
NiAl provides good agreements. At $T=0K$, our calculated values of lattice
parameter and elastic moduli for MgRE intermetallics show excellent agreement
with previous theoretical results and experimental data. While temperature
increases, we find that the elastic constants decrease and approach linearity
at higher temperature and zero slope around zero temperature.


  The process of matching startup founders with venture capital investors is a
necessary first step for many modern technology companies, yet there have been
few attempts to study the characteristics of the two parties and their
interactions. Surprisingly little has been shown quantitatively about the
process, and many of the common assumptions are based on anecdotal evidence. In
this thesis, we aim to learn more about the matching component of the startup
fundraising process. We begin with a tool (VCWiz), created from the current set
of best-practices to help inexperienced founders navigate the founder-investor
matching process. The goal of this tool is to increase efficiency and
equitability, while collecting data to inform further studies. We use this
data, combined with public data on venture investments in the USA, to draw
conclusions about the characteristics of venture financing rounds. Finally, we
explore the communication data contributed to the tool by founders who are
actively fundraising, and use it to learn which social attributes are most
beneficial for individuals to possess when soliciting investments.


  Purpose: In recent years Monte-Carlo sampling methods, such as Monte Carlo
tree search, have achieved tremendous success in model free reinforcement
learning. A combination of the so called upper confidence bounds policy to
preserve the "exploration vs. exploitation" balance to select actions for
sample evaluations together with massive computing power to store and to update
dynamically a rather large pre-evaluated game tree lead to the development of
software that has beaten the top human player in the game of Go on a 9 by 9
board. Much effort in the current research is devoted to widening the range of
applicability of the Monte-Carlo sampling methodology to partially observable
Markov decision processes with non-immediate payoffs. The main challenge
introduced by randomness and incomplete information is to deal with the action
evaluation at the chance nodes due to drastic differences in the possible
payoffs the same action could lead to. The aim of this article is to establish
a version of a theorem that originated from population genetics and has been
later adopted in evolutionary computation theory that will lead to novel
Monte-Carlo sampling algorithms that provably increase the AI potential. Due to
space limitations the actual algorithms themselves will be presented in the
sequel papers, however, the current paper provides a solid mathematical
foundation for the development of such algorithms and explains why they are so
promising.


  We compute the Lens space index for 4d supersymmetric gauge theories
involving symplectic gauge groups. This index can distinguish between different
gauge groups from a given algebra and it matches across theories related by
supersymmetric dualities. We provide explicit calculations for $\mathcal{N}=4$
SYM and for classes of $\mathcal{N}=2$ and $\mathcal{N}=1$ Lagrangian quivers
related by S-duality. In these cases the index matches across the S-dual
phases, while models in different S-duality orbits have a different Lens index.
We provide analogous computations for a 4d $\mathcal{N}=1$ toric quiver gauge
theory corresponding to a $\mathbb{Z}_7$ orbifold of $\mathbb{C}^3$. This
$SU(n)^7$ gauge theory becomes interesting in the case of $n=2$ because it is
conformally dual to other two models, with symplectic and unitary gauge groups,
bifundamentals and antisymmetric tensors. We explicitly check this triality at
the level of the Lens space index.


  Electronic properties of V$_2$OPO$_4$ have been investigated by means of hard
x-ray photoemission spectroscopy (HAXPES) and subsequent theoretical
calculations. The V 1$s$ and 2$p$ HAXPES spectra are consistent with the charge
ordering of V$^{2+}$ and V$^{3+}$. The binding energy difference between the
V$^{2+}$ and V$^{3+}$ components is unexpectedly large indicating large
bonding-antibonding splitting between them in the final states of core level
photoemission. The V 1$s$ HAXPES spectrum exhibits a charge transfer satellite
which can be analyzed by configuration interaction calculations on a V$_2$O$_9$
cluster. The V 3$d$ spectral weight near the Fermi level is assigned to the
3$d$ $t_{2g}$ orbitals of the V$^{2+}$ site. The broad V 3$d$ spectral
distribution is consistent with the strong hybridization between V$^{2+}$ and
V$^{3+}$ in the ground state. The core level and valence band HAXPES results
indicate substantial charge transfer from the V$^{2+}$ site to the V$^{3+}$
site.7 figure


  In this paper, we study the associations between human faces and voices.
Audiovisual integration, specifically the integration of facial and vocal
information is a well-researched area in neuroscience. It is shown that the
overlapping information between the two modalities plays a significant role in
perceptual tasks such as speaker identification. Through an online study on a
new dataset we created, we confirm previous findings that people can associate
unseen faces with corresponding voices and vice versa with greater than chance
accuracy. We computationally model the overlapping information between faces
and voices and show that the learned cross-modal representation contains enough
information to identify matching faces and voices with performance similar to
that of humans. Our representation exhibits correlations to certain demographic
attributes and features obtained from either visual or aural modality alone. We
release our dataset of audiovisual recordings and demographic annotations of
people reading out short text used in our studies.


  We formulate a modular approach to the design and analysis of a particular
class of mutual exclusion algorithms for shared memory multiprocessor systems.
Specifically, we consider algorithms that organize waiting processes into a
queue. Such algorithms can achieve O(1) remote memory reference (RMR)
complexity, which minimizes (asymptotically) the amount of traffic through the
processor-memory interconnect. We first describe a generic mutual exclusion
algorithm that relies on a linearizable implementation of a particular
queue-like data structure that we call MutexQueue. Next, we show two
implementations of MutexQueue using O(1) RMRs per operation based on
synchronization primitives commonly available in multiprocessors. These
implementations follow closely the queuing code embedded in previously
published mutual exclusion algorithms. We provide rigorous correctness proofs
and RMR complexity analyses of the algorithms we present.


  Quantum metrology offers superior measurement precision compared to classical
counterparts. Traditionally, achieving this enhancement involves employing
multi-particle entangled quantum states, necessitating state preparation and
detection schemes that are practically challenging. In this article, we present
a versatile and on-demand protocol for deterministic parameter estimation that
leverages two state-transfer operations on a single bosonic mode. Specifically,
we demonstrate this protocol in the context of phase and amplitude estimation
using the superposition of coherent states in the bosonic circuit quantum
electrodynamics (cQED) platform. With low photon numbers of up to 1.76, we
achieve quantum-enhanced precision approaching the Heisenberg scaling, reaching
a metrological gain of 7.5(6) dB and 9.3(5) dB respectively for phase and
amplitude estimation. We show that the gain or sensitivity range can be further
enhanced on the fly by tailoring the input states based on specific system
constraints. Our protocol can be adapted to further optimize the desired
figures of merit using tailored quantum states or operations, not only for
bosonic cQED hardware but also readily extensible to other continuous-variable
platforms. The realisation of this versatile and effective scheme affords a
promising path towards practical quantum-enhanced sensing.


  In this paper we consider a non-local (in time) heat equation on a
time-increasing parabolic set whose boundary is determined by a suitable curve.
We provide a notion of solution for this equation and we study well-posedness
under Dirichlet conditions outside the domain. A maximum principle is proved
and used to derive uniqueness and continuity with respect to the initial datum
of the solutions of the Dirichlet problem. Existence is proved by showing a
stochastic representation based on the delayed Brownian motion killed on the
boundary. Related properties on crossing probabilities of the delayed Brownian
motion are obtained. The asymptotic behaviour of the mean square displacement
of the process is determined, showing that the diffusive behaviour is
anomalous.


  Knowledge tracing and dropout prediction are crucial for online education to
estimate students' knowledge states or to prevent dropout rates. While
traditional systems interacting with students suffered from data sparsity and
overfitting, recent sample-level contrastive learning helps to alleviate this
issue. One major limitation of sample-level approaches is that they regard
students' behavior interaction sequences as a bundle, so they often fail to
encode temporal contexts and track their dynamic changes, making it hard to
find optimal representations for knowledge tracing and dropout prediction. To
apply temporal context within the sequence, this study introduces a novel
student modeling framework, SAICL: \textbf{s}tudent modeling with
\textbf{a}uxiliary \textbf{i}nteraction-level \textbf{c}ontrastive
\textbf{l}earning. In detail, SAICL can utilize both proposed
self-supervised/supervised interaction-level contrastive objectives: MilCPC
(\textbf{M}ulti-\textbf{I}nteraction-\textbf{L}evel \textbf{C}ontrastive
\textbf{P}redictive \textbf{C}oding) and SupCPC (\textbf{Sup}ervised
\textbf{C}ontrastive \textbf{P}redictive \textbf{C}oding). While previous
sample-level contrastive methods for student modeling are highly dependent on
data augmentation methods, the SAICL is free of data augmentation while showing
better performance in both self-supervised and supervised settings. By
combining cross-entropy with contrastive objectives, the proposed SAICL
achieved comparable knowledge tracing and dropout prediction performance with
other state-of-art models without compromising inference costs.


  Quantitative reasoning is a critical skill to analyze data, yet the
assessment of such ability remains limited. To address this gap, we introduce
the Quantitative Reasoning with Data (QRData) benchmark, aiming to evaluate
Large Language Models' capability in statistical and causal reasoning with
real-world data. The benchmark comprises a carefully constructed dataset of 411
questions accompanied by data sheets from textbooks, online learning materials,
and academic papers. To compare models' quantitative reasoning abilities on
data and text, we enrich the benchmark with an auxiliary set of 290 text-only
questions, namely QRText. We evaluate natural language reasoning, program-based
reasoning, and agent reasoning methods including Chain-of-Thought,
Program-of-Thoughts, ReAct, and code interpreter assistants on diverse models.
The strongest model GPT-4 achieves an accuracy of 58%, which has much room for
improvement. Among open-source models, Deepseek-coder-instruct, a code LLM
pretrained on 2T tokens, gets the highest accuracy of 37%. Analysis reveals
that models encounter difficulties in data analysis and causal reasoning, and
struggle in using causal knowledge and provided data simultaneously. Code and
data are in https://github.com/xxxiaol/QRData.


  We investigate sequence machine learning techniques on raw radio signal
time-series data. By applying deep recurrent neural networks we learn to
discriminate between several application layer traffic types on top of a
constant envelope modulation without using an expert demodulation algorithm. We
show that complex protocol sequences can be learned and used for both
classification and generation tasks using this approach.


  Efficient manipulation of magnetic order with electric current pulses is
desirable for achieving fast spintronic devices. The Rashba-Edelstein effect,
wherein a spin polarization is electrically induced in noncentrosymmetric
systems, provides a mean to achieve current-induced staggered spin-orbit
torques. Initially predicted for spin, the orbital counterpart of this effect
has been disregarded up to now. Here, we present a generalized Rashba-Edelstein
effect, which generates not only spin polarization but also orbital
polarization, which we find to be far from being negligible and could play a
crucial role in the magnetization dynamics. We show that the orbital
Rashba-Edelstein effect does not require spin-orbit coupling to exist. We
present first-principles calculations of the frequency-dependent spin and
orbital Rashba-Edelstein susceptibility tensors for the noncentrosymmetric
antiferromagnets CuMnAs and Mn2Au. We show that the electrically induced local
magnetization has both staggered in-plane components and non-staggered
out-of-plane components, and can exhibit Rashba-like or Dresselhaus-like
symmetries, depending on the magnetic configuration. Furthermore, there is an
induced local magnetization on the nonmagnetic atoms as well, that is smaller
in Mn2Au than in CuMnAs. We compute sizable induced magnetizations at optical
frequencies, which suggest that electric-field driven switching could be
achieved at much higher frequencies.


  Grossberg and VanDieren have started a program to develop a stability theory
for tame classes. We prove, for instance, that for tame abstract elementary
classes satisfying the amlagamation property and for large enough cardinals
kappa, stability in kappa implies stability in kappa^{+n} for each natural
number n.


  We demonstrate direct focused ion beam (FIB) writing as an enabling
technology for realizing spin-wave-optics devices. It is shown that ion-beam
irradiation changes the characteristics of YIG films on a submicron scale in a
highly controlled way, allowing to engineer the magnonic index of refraction
adapted to desired applications. This technique does not physically remove
material, and allows rapid fabrication of high-quality architectures of
modified magnetization in magnonic media with minimal edge damage (compared to
more common techniques such as etching or milling). By experimentally showing
magnonic versions of a number of optical devices (lenses, gratings,
Fourier-domain processors) we envision this technology as the gateway to
building magnonic computing devices that rival their optical counterparts in
their complexity and computational power.


  Radical pair recombination reactions are known to be sensitive to the
application of both low and high magnetic fields. The application of a weak
magnetic field reduces the singlet yield of a singlet-born radical pair,
whereas the application of a strong magnetic field increases the singlet yield.
The high field effect arises from energy conservation: when the magnetic field
is stronger than the sum of the hyperfine fields in the two radicals, ${\rm
S}\to {\rm T}_{\pm}$ transitions become energetically forbidden, thereby
reducing the number of pathways for singlet to triplet interconversion. The low
field effect arises from symmetry breaking: the application of a weak magnetic
field lifts degeneracies among the zero field eigenstates and increases the
number of pathways for singlet to triplet interconversion. However, the details
of this effect are more subtle, and have not previously been properly
explained. Here we present a complete analysis of the low field effect in a
radical pair containing a single proton, and in a radical pair in which one of
the radicals contains a large number of hyperfine-coupled nuclear spins. We
find that the new transitions that occur when the field is switched on are
between ${\rm S}$ and ${\rm T}_0$ in both cases, and not between ${\rm S}$ and
${\rm T}_{\pm}$ as has previously been claimed. We then illustrate this result
by using it in conjunction with semiclassical spin dynamics simulations to
account for the observation of a biphasic--triphasic--biphasic transition with
increasing magnetic field strength in the magnetic field effect on the
time-dependent survival probability of a photoexcited
carotenoid-porphyrin-fullerene radical pair.


  We present rigid supersymmetric backgrounds for three-dimensional N=2
supersymmetric gauge theories, comprising a two-parameter U(1)xU(1)-invariant
deformed three-sphere, and their gravity duals. These are described by
supersymmetric solutions of four-dimensional N=2 gauged supergravity with a
self-dual metric on the ball and different instantons for the graviphoton
field. We find two types of solutions, distinguished by their holographic free
energies. In one type the holographic free energy is constant, whereas in
another type it depends in a simple way on the parameters and is generically
complex. This leads to a conjecture for the localized partition function of a
class of N=2 supersymmetric gauge theories on these backgrounds.


  We study the number of rational points of smooth projective curves over
finite fields in some relative situations in the spirit of a previous paper
from an euclidean point of vue. We prove some kinds of relative Weil bounds,
derived from Schwarz inequality for some "relative parts" of the diagonal and
of the graph of the Frobenius on some euclidean sub-spaces of the numerical
space of the squared curve endowed with the opposite of the intersection
product.


  More predictable words are easier to process - they are read faster and
elicit smaller neural signals associated with processing difficulty, most
notably, the N400 component of the event-related brain potential. Thus, it has
been argued that prediction of upcoming words is a key component of language
comprehension, and that studying the amplitude of the N400 is a valuable way to
investigate the predictions we make. In this study, we investigate whether the
linguistic predictions of computational language models or humans better
reflect the way in which natural language stimuli modulate the amplitude of the
N400. One important difference in the linguistic predictions of humans versus
computational language models is that while language models base their
predictions exclusively on the preceding linguistic context, humans may rely on
other factors. We find that the predictions of three top-of-the-line
contemporary language models - GPT-3, RoBERTa, and ALBERT - match the N400 more
closely than human predictions. This suggests that the predictive processes
underlying the N400 may be more sensitive to the surface-level statistics of
language than previously thought.


  In an era when the performance of a single compute device plateaus, software
must be designed to scale on massively parallel systems for better runtime
performance. However, in the context of training deep learning models, the
popular back-propagation (BP) algorithm imposes a strong sequential dependency
in the process of gradient computation. Under model parallelism, BP takes
$\Theta (n)$ steps to complete which hinders its scalability on parallel
systems ($n$ represents the number of compute devices into which a model is
partitioned).
  In this work, in order to improve the scalability of BP, we reformulate BP
into a scan operation which is a primitive that performs an in-order
aggregation on a sequence of values and returns the partial result at each
step. We can then scale such reformulation of BP on parallel systems by our
modified version of the Blelloch scan algorithm which theoretically takes
$\Theta (\log n)$ steps. We evaluate our approach on a vanilla Recurrent Neural
Network (RNN) training with synthetic datasets and a RNN with Gated Recurrent
Units (GRU) training with the IRMAS dataset, and demonstrate up to $2.75\times$
speedup on the overall training time and $108\times$ speedup on the backward
pass. We also demonstrate that the retraining of pruned networks can be a
practical use case of our method.


  Spherical matrix arrays arguably represent an advantageous tomographic
detection geometry for non-invasive deep tissue mapping of vascular networks
and oxygenation with volumetric optoacoustic tomography (VOT). Hybridization of
VOT with ultrasound (US) imaging remains difficult with this configuration due
to the relatively large inter-element pitch of spherical arrays. We suggest a
new approach for combining VOT and US contrast-enhanced imaging employing
injection of clinically-approved microbubbles. Power Doppler (PD) and US
localization imaging were enabled with a sparse US acquisition sequence and
model-based inversion based on infimal convolution of total variation (ICTV)
regularization. Experiments in tissue-mimicking phantoms and in vivo in mice
demonstrate the powerful capabilities of the new dual-mode imaging system for
blood velocity mapping and anatomical imaging with enhanced resolution and
contrast.


  Given a simplicial complex with $n$ simplices, we consider the Connected
Subsurface Recognition (c-SR) problem of finding a subcomplex that is
homeomorphic to a given connected surface with a fixed boundary. We also study
the related Sum-of-Genus Subsurface Recognition (SoG) problem, where we instead
search for a surface whose boundary, number of connected components, and total
genus are given.
  For both of these problems, we give parameterized algorithms with respect to
the treewidth $k$ of the Hasse diagram that run in $2^{O(k \log k)}n^{O(1)}$
time. For the SoG problem, we also prove that our algorithm is optimal assuming
the exponential-time hypothesis. In fact, we prove the stronger result that our
algorithm is ETH-tight even without restriction on the total genus.


  At $e^+e^-$ and $ep$ colliders, the large fluxes of almost on-shell photons
accompanying the lepton beams lead to the photoproduction of jets. As the
centre-of-mass energy is increased, regions of smaller $x$ in the parton
densities are explored and these are regions of high parton density. As a
result, the probability for more than one hard partonic scattering occurring in
a single $\gamma \gamma$ or $\gamma p$ collision can become significant. This
effect has been simulated using an eikonal prescription combined with the
HERWIG Monte Carlo program. The possible effects of multiple hard interactions
on event shapes and jet cross sections have been studied in this framework at a
range of energies relevant to HERA and LEPII. The results indicate that the
effects could be significant.


  A State Space Model (SSM) is a foundation model in time series analysis,
which has recently been shown as an alternative to transformers in sequence
modeling. In this paper, we theoretically study the generalization of SSMs and
propose improvements to training algorithms based on the generalization
results. Specifically, we give a \textit{data-dependent} generalization bound
for SSMs, showing an interplay between the SSM parameters and the temporal
dependencies of the training sequences. Leveraging the generalization bound, we
(1) set up a scaling rule for model initialization based on the proposed
generalization measure, which significantly improves the robustness of the
output value scales on SSMs to different temporal patterns in the sequence
data; (2) introduce a new regularization method for training SSMs to enhance
the generalization performance. Numerical results are conducted to validate our
results.


  Can cost-reducing technical change lead to a fall in the long run rate of
profit if class struggle manages to keep the rate of exploitation constant? In
a general circulating capital model, we derive sufficient conditions for
cost-reducing technical change to both keep the rate of exploitation constant
and lead to a fall in the equilibrium rate of profit. Further, if the real wage
bundle is such that the maximum price-value ratio is larger than 1 plus the
rate of exploitation, then starting from any configuration of technology and
real wage, we can always find a viable, CU-LS technical change that satisfies
the sufficient conditions for the previous result. Taken together, these
results vindicate Marx's claim in Volume III of Capital, that if the rate of
exploitation remains unchanged then viable, CU-LS technical change in
capitalist economies can lead to a fall in the long run rate of profit.


  We prove that the answer to the question of the title is `as many times as
you want.' More precisely, given any constant $c>0$, we construct two oblique
triangular bipyramids, $P$ and $Q$, such that $P$ is convex, $Q$ is nonconvex
and intrinsically isometric to $P$, and $\textrm{vol} Q>c\cdot \textrm{vol}
P>0$.


  Breakup of small solid aggregates in homogeneous and isotropic turbulence is
studied theoretically and by using Direct Numerical Simulations at high
Reynolds number, Re_{\lambda} \simeq 400. We show that turbulent fluctuations
of the hydrodynamic stress along the aggregate trajectory play a key role in
determining the aggregate mass distribution function. Differences between
turbulent and laminar flows are discussed. A novel definition of the
fragmentation rate is proposed in terms of the typical frequency at which the
hydrodynamic stress becomes sufficiently high to cause breakup along each
Lagrangian path. We also define an Eulerian proxy of the real fragmentation
rate, based on the joint statistics of the stress and its time derivative,
which should be easier to measure in any experimental set-up. Both our Eulerian
and Lagrangian formulations define a clear procedure for the computation of the
mass distribution function due to fragmentation. Contrary, previous estimates
based only on single point statistics of the hydrodynamic stress exhibit some
deficiencies. These are discussed by investigating the evolution of an ensemble
of aggregates undergoing breakup and aggregation.


  On a general Fano hypersurface in projective space, we determine for
infinitely many $k$ the minimal degree $e$ of a rational curve through a
general collection of $k$ points. In the case of a hypersurface of index 1, our
results hold for all $k\geq 1$. In an appendix, M.C. Chang proves an
arithmetical result which implies that in the case of index $>1$, the density
of the set of curve degrees $e$ covered by our method is approximately
$\frac{(n-d)(d-\frac{5}{2})}{(n-2)d}$.


  An interesting aspect in the research of complex (dusty) plasmas is the
experimental study of the interaction of micro-particles with the surrounding
plasma for diagnostic purposes. Local electric fields can be determined from
the behaviour of particles in the plasma, e.g. particles may serve as
electrostatic probes. Since in many cases of applications in plasma technology
it is of great interest to describe the electric field conditions in front of
floating or biased surfaces, the confinement and behaviour of test particles is
studied in front of floating walls inserted into a plasma as well as in front
of additionally biased surfaces. For the latter case, the behaviour of
particles in front of an adaptive electrode, which allows for an efficient
confinement and manipulation of the grains, has been experimentally studied in
dependence on the discharge parameters and on different bias conditions of the
electrode. The effect of the partially biased surface (dc, rf) on the charged
micro-particles has been investigated by particle falling experiments. In
addition to the experiments we also investigate the particle behaviour
numerically by molecular dynamics, in combination with a fluid and
particle-in-cell description of the plasma.


  The phase diagram of the two-dimensional Nambu--Jona-Lasinio model with
isospin is explored in the large Nc limit with semiclassical methods. We
consider finite temperature and include chemical potentials for all conserved
charges. In the chiral limit, a full analytical solution is presented,
expressed in terms of known results for the single-flavor Gross-Neveu and
Nambu--Jona-Lasinio models. A novel crystalline structure appears and is shown
explicitly to be thermodynamically more stable than the homogeneous phase at
zero temperature. If we include a bare fermion mass, the problem reduces again
to solved problems in one-flavor models provided that either the fermionic or
the isospin chemical potentials vanish. In the general case, a stability
analysis is used to construct the perturbative phase boundary between
homogeneous and inhomogeneous phases. This is sufficient to get a good overview
of the complete phase diagram. Missing non-perturbative phase boundaries
requiring a full numerical Hartree-Fock calculation will be presented in future
work.


  Current benchmarks for optical flow algorithms evaluate the estimation either
directly by comparing the predicted flow fields with the ground truth or
indirectly by using the predicted flow fields for frame interpolation and then
comparing the interpolated frames with the actual frames. In the latter case,
objective quality measures such as the mean squared error are typically
employed. However, it is well known that for image quality assessment, the
actual quality experienced by the user cannot be fully deduced from such simple
measures. Hence, we conducted a subjective quality assessment crowdscouring
study for the interpolated frames provided by one of the optical flow
benchmarks, the Middlebury benchmark. We collected forced-choice paired
comparisons between interpolated images and corresponding ground truth. To
increase the sensitivity of observers when judging minute difference in paired
comparisons we introduced a new method to the field of full-reference quality
assessment, called artefact amplification. From the crowdsourcing data, we
reconstructed absolute quality scale values according to Thurstone's model. As
a result, we obtained a re-ranking of the 155 participating algorithms w.r.t.
the visual quality of the interpolated frames. This re-ranking not only shows
the necessity of visual quality assessment as another evaluation metric for
optical flow and frame interpolation benchmarks, the results also provide the
ground truth for designing novel image quality assessment (IQA) methods
dedicated to perceptual quality of interpolated images. As a first step, we
proposed such a new full-reference method, called WAE-IQA. By weighing the
local differences between an interpolated image and its ground truth WAE-IQA
performed slightly better than the currently best FR-IQA approach from the
literature.


  We establish a Chung-type law of the iterated logarithm for the solutions of
a class of stochastic heat equations driven by a multiplicative noise whose
coefficient depends on the solution, and this dependence takes us away from
Gaussian setting. Based on the literature on small ball probabilities and the
technique of freezing coefficients, the limiting constant in Chung's law of the
iterated logarithm can be evaluated almost surely.


  In this paper, we discuss the definition of Q factor for nonlinear
oscillators. While available definitions of Q are often limited to linear
resonators or oscillators with specific topologies, our definition is
applicable to any oscillator as a figure of merit for its amplitude stability.
It can be formulated rigorously and computed numerically from oscillator
equations. With this definition, we calculate and analyze the Q factors of
several oscillators of different types. The results confirm that the proposed Q
formulation is a useful addition to the characterization techniques for
oscillators.


  In this paper the old problem of determining the discrete spectrum of a
multi-particle Hamiltonian is reconsidered. The aim is to bring a fermionic
Hamiltonian for large numbers N of particles by analytical means into a shape
such that modern numerical methods can successfully be applied. For this
purpose the Cook-Schroeck Formalism is taken as starting point. This includes
the use of the occupation-number representation. It is shown that the
N-particle Hamiltonian is determined in a canonical way by a \fictional
2-particle Hamiltonian. A special approximation of this 2-particle operator
delivers an approximation of the N-particle Hamiltonian, which is the
orthogonal sum of finite dimensional operators. A complete classification of
the matrices of these operators is given. Finally the method presented here is
formulated as a work program for practical applications. The connection with
other methods for solving the same problem is discussed.


  To find out the role of the wiring cost in the organization of the neural
network of the nematode \textit{Caenorhapditis elegans} (\textit{C. elegans}),
we build the neuronal map of \textit{C. elegans} based on geometrical positions
of neurons and define the cost as inter-neuronal Euclidean distance \textit{d}.
We show that the wiring probability decays exponentially as a function of
\textit{d}. Using the edge exchanging method and the component placement
optimization scheme, we show that positions of neurons are not randomly
distributed but organized to reduce the total wiring cost. Furthermore, we
numerically study the trade-off between the wiring cost and the performance of
the Hopfield model on the neural network.


  In recent years, the synchrosqueezing transform (SST) has gained popularity
as a method for the analysis of signals that can be broken down into multiple
components determined by instantaneous amplitudes and phases. One such version
of SST, based on the short-time Fourier transform (STFT), enables the
sharpening of instantaneous frequency (IF) information derived from the STFT,
as well as the separation of amplitude-phase components corresponding to
distinct IF curves. However, this SST is limited by the time-frequency
resolution of the underlying window function, and may not resolve signals
exhibiting diverse time-frequency behaviors with sufficient accuracy. In this
work, we develop a framework for an SST based on a "quilted" short-time Fourier
transform (SST-QSTFT), which allows adaptation to signal behavior in separate
time-frequency regions through the use of multiple windows. This motivates us
to introduce a discrete reassignment frequency formula based on a finite
difference of the phase spectrum, ensuring computational accuracy for a wider
variety of windows. We develop a theoretical framework for the SST-QSTFT in
both the continuous and the discrete settings, and describe an algorithm for
the automatic selection of optimal windows depending on the region of interest.
Using synthetic data, we demonstrate the superior numerical performance of
SST-QSTFT relative to other SST methods in a noisy context. Finally, we apply
SST-QSTFT to audio recordings of animal calls to demonstrate the potential of
our method for the analysis of real bioacoustic signals.


  The Wasserstein metric is introduced as a probabilistic method to enable
quantitative evaluations of LES combustion models. The Wasserstein metric can
directly be evaluated from scatter data or statistical results using
probabilistic reconstruction against experimental data. The method is derived
and generalized for turbulent reacting flows, and applied to validation tests
involving the Sydney piloted jet flame. It is shown that the Wasserstein metric
is an effective validation tool that extends to multiple scalar quantities,
providing an objective and quantitative evaluation of model deficiencies and
boundary conditions on the simulation accuracy. Several test cases are
considered, beginning with a comparison of mixture-fraction results, and the
subsequent extension to reactive scalars, including temperature and species
mass fractions of \ce{CO} and \ce{CO2}. To demonstrate the versatility of the
proposed method in application to multiple datasets, the Wasserstein metric is
applied to a series of different simulations that were contributed to the
TNF-workshop. Analysis of the results allowed to identify competing
contributions to model deviations, arising from uncertainties in the boundary
conditions and model deficiencies. These applications demonstrate that the
Wasserstein metric constitutes an easily applicable mathematical tool that
reduce multiscalar combustion data and large datasets into a scalar-valued
quantitative measure.


  A simple extension of the Standard Model (SM), based on the gauge group
$SU(3)_C\otimes SU(3)_L\otimes U(1)_Y$ with $Y$ being the hypercharge, is
considered. We show that, by imposing an approximate global $SU(2)_L\times
SU(2)_R$ custodial symmetry at the SM energy scale, the $Z-Z'$ mixing is absent
at tree level and the value of the $\rho$ parameter can be kept close to one.
Tree-level flavor-changing neutral currents are also reduced to three
particles, namely $Z'$, a CP-odd Higgs and a CP-even Higgs. The model predicts
new leptons with electric charges of $\pm 1/2e$ and new quarks with $\pm 1/6e$
charges as well as new gauge and scalar bosons with $\pm 1/2e$ charges.
Electric charge conservation requires that one of them must be stable. Their
masses are unfortunately free parameters.


  Large Language Models (LLMs) are capable of reasoning over diverse input data
modalities through pre-trained encoders. However, the growing diversity of
input data modalities prevents incorporating all modalities into LLMs,
especially when LLMs are deployed on resource-constrained edge devices for
embodied AI applications. Instead, a better option is to adaptively involve
only the useful modalities at runtime, depending on the current environmental
contexts and task requirements. For such modality adaptation, existing work
adopts fixed connections between encoders and the LLM's input layer, leading to
high training cost at runtime and ineffective cross-modal interaction. In this
paper, we address these limitations by presenting mPnP-LLM, a new technique
that allows fully elastic, automated and prompt runtime modality adaptation, by
connecting unimodal encoders to a flexible set of last LLM blocks and making
such latent connections fully trainable at runtime. Experiments over the
nuScenes-QA dataset show that mPnP-LLM can achieve up to 3.7x FLOPs reduction
and 30% GPU memory usage reduction, while retaining on-par accuracy with the
existing schemes. Under the same compute budget, mPnP-LLM improves the task
accuracy by up to 4% compared to the best existing scheme.


  Simulations of the two-dimensional Ising and 3-state Potts models at their
critical points are performed using the invaded cluster (IC) algorithm. It is
argued that observables measured on a sub-lattice of size l should exhibit a
crossover to Swendsen-Wang (SW) behavior for l sufficiently less than the
lattice size L, and a scaling form is proposed to describe the crossover
phenomenon. It is found that the energy autocorrelation time tau(l,L) for an
l*l sub-lattice attains a maximum in the crossover region, and a dynamic
exponent z for the IC algorithm is defined according to tau_max ~ L^z.
Simulation results for the 3-state model yield z=.346(.002) which is smaller
than values of the dynamic exponent found for the SW and Wolff algorithms and
also less than the Li-Sokal bound. The results are less conclusive for the
Ising model, but it appears that z<.21 and possibly that tau_max ~ log L so
that z=0 -- similar to previous results for the SW and Wolff algorithms.


  Let I be the defining ideal of a smooth irreducible complete intersection
space curve C with defining equations of degrees a and b. We use the partial
elimination ideals introduced by Mark Green to show that the lexicographic
generic initial ideal of I has Castelnuovo-Mumford regularity 1+ab(a-1)(b-1)/2
with the exception of the case a=b=2, where the regularity is 4. Note that
ab(a-1)(b-1)/2 is exactly the number of singular points of a general projection
of C to the plane. Additionally, we show that for any term ordering tau, the
generic initial ideal of a generic set of points in P^r is a tau-segment ideal.


  Curved textures of ferroelectric smectic C* liquid crystals produce space
charge when they involve divergence of the spontaneous polarization field.
Impurity ions can partially screen this space charge, reducing long range
interactions to local ones. Through studies of the textures of islands on very
thin free-standing smectic films, we see evidence of this effect, in which
materials with a large spontaneous polarization have static structures
described by a large effective bend elastic constant. To address this issue, we
calculated the electrostatic free energy of a free standing film of
ferroelectric liquid crystal, showing how the screened coulomb interaction
contributes a term to the effective bend elastic constant, in the static long
wavelength limit. We report experiments which support the main features of this
model.


  Recent work has shown the potential of transformers for computer vision
applications. An image is first partitioned into patches, which are then used
as input tokens for the attention mechanism. Due to the expensive quadratic
cost of the attention mechanism, either a large patch size is used, resulting
in coarse-grained global interactions, or alternatively, attention is applied
only on a local region of the image, at the expense of long-range interactions.
In this work, we propose an approach that allows for both coarse global
interactions and fine-grained local interactions already at early layers of a
vision transformer.
  At the core of our method is the application of local and global attention
layers. In the local attention layer, we apply attention to each patch and its
local shifts, resulting in virtually located local patches, which are not bound
to a single, specific location. These virtually located patches are then used
in a global attention layer. The separation of the attention layer into local
and global counterparts allows for a low computational cost in the number of
patches, while still supporting data-dependent localization already at the
first layer, as opposed to the static positioning in other visual transformers.
Our method is shown to be superior to both convolutional and transformer-based
methods for image classification on CIFAR10, CIFAR100, and ImageNet. Code is
available at: https://github.com/shellysheynin/Locally-SAG-Transformer.


  Automatic segmentation of organs-at-risk (OARs) in CT scans using
convolutional neural networks (CNNs) is being introduced into the radiotherapy
workflow. However, these segmentations still require manual editing and
approval by clinicians prior to clinical use, which can be time consuming. The
aim of this work was to develop a tool to automatically identify errors in 3D
OAR segmentations without a ground truth. Our tool uses a novel architecture
combining a CNN and graph neural network (GNN) to leverage the segmentation's
appearance and shape. The proposed model is trained using self-supervised
learning using a synthetically-generated dataset of segmentations of the
parotid and with realistic contouring errors. The effectiveness of our model is
assessed with ablation tests, evaluating the efficacy of different portions of
the architecture as well as the use of transfer learning from an unsupervised
pretext task. Our best performing model predicted errors on the parotid gland
with a precision of 85.0% & 89.7% for internal and external errors
respectively, and recall of 66.5% & 68.6%. This offline QA tool could be used
in the clinical pathway, potentially decreasing the time clinicians spend
correcting contours by detecting regions which require their attention. All our
code is publicly available at
https://github.com/rrr-uom-projects/contour_auto_QATool.


  We demonstrate that the steep decay and long plateau in the early phases of
gamma ray burst (GRB) X-ray afterglows are naturally produced in the collapsar
model, by a means ultimately related to the dynamics of relativistic jet
propagation through a massive star. We present two-dimensional axisymmetric
hydrodynamical simulations which start from a collapsar engine and evolve all
the way through the late afterglow phase. The resultant outflow includes a jet
core which is highly relativistic after breaking out of the star, but becomes
baryon-loaded after colliding with a massive outer shell, corresponding to mass
from the stellar atmosphere of the progenitor star which became trapped in
front of the jet core at breakout. The prompt emission produced before or
during this collision would then have the signature of a high Lorentz factor
jet, but the afterglow is produced by the amalgamated post-collision ejecta
which has more inertia than the original highly relativistic jet core and thus
has a delayed deceleration. This naturally explains the early light curve
behavior discovered by Swift, including a steep decay and a long plateau,
without invoking late-time energy injection from the central engine. The
numerical simulation is performed continuously from engine to afterglow,
covering a dynamic range of over ten orders of magnitude in radius. Light
curves calculated from the numerical output demonstrate that this mechanism
reproduces basic features seen in early afterglow data. Initial steep decays
are produced by internal shocks, and the plateau corresponds to the coasting
phase of the outflow.


  We study the dilaton stabilization in models with anomalous U(1) symmetry by
adding specific string-motivated, non-perturbative corrections to the
tree-level dilaton K\"{a}hler potential. We find that the non-perturbative
effects can stabilize the dilaton at a desirably large value. We also observe
that the size of Fayet-Iliopoulos term is reduced at the stabilized point.


  In this paper, we propose a novel approach for developing
Sense-Compute-Control (SCC) applications for the Internet of Things and
Services (IoTS) following the Model-Driven Software Engineering (MDSE)
methodology. We review the recent approaches to MDSE and argue that Domain
Specific Modeling (DSM) suites our needs very well. However, in line with the
recent trends in cloud computing and the emer-gence of the IoTS, we believe
that both DSM creation tools and DSM solutions that are created via those tools
should also be provided to their respective users in a service-oriented fashion
through the cloud in the IoTS. In this work, we concentrate on the latter,
i.e., DSM solu-tions that are created via a DSM creation tool. We argue that it
makes sense for the owners of a DSM solution in a domain to provide their DSM
solution as a service, following the well known Software as a Ser-vice (SaaS)
model, to the interested customers through the IoTS. Our proposed approach
concentrates on such a DSM solution for developing SCC applications in the
IoTS. However, the idea could be applied to DSM solutions in other domains as
well.


  State space models (SSMs) have recently shown promising results on
small-scale sequence and language modelling tasks, rivalling and outperforming
many attention-based approaches. In this paper, we propose a multi-head state
space (MH-SSM) architecture equipped with special gating mechanisms, where
parallel heads are taught to learn local and global temporal dynamics on
sequence data. As a drop-in replacement for multi-head attention in transformer
encoders, this new model significantly outperforms the transformer transducer
on the LibriSpeech speech recognition corpus. Furthermore, we augment the
transformer block with MH-SSMs layers, referred to as the Stateformer,
achieving state-of-the-art performance on the LibriSpeech task, with word error
rates of 1.76\%/4.37\% on the development and 1.91\%/4.36\% on the test sets
without using an external language model.


  We derive non-perturbative relations between the expectation value of the
invariant element in a homogeneous and isotropic state and the quantum
gravitationally induced pressure and energy density. By exploiting previously
obtained bounds for the maximum possible growth of perturbative corrections to
a locally de Sitter background we show that the two loop result dominates all
higher orders. We also show that the quantum gravitational slowing of inflation
becomes non-perturbatively strong earlier than previously expected.


  Current state-of-the-art (SOTA) codec-based audio synthesis systems can mimic
anyone's voice with just a 3-second sample from that specific unseen speaker.
Unfortunately, malicious attackers may exploit these technologies, causing
misuse and security issues. Anti-spoofing models have been developed to detect
fake speech. However, the open question of whether current SOTA anti-spoofing
models can effectively counter deepfake audios from codec-based speech
synthesis systems remains unanswered. In this paper, we curate an extensive
collection of contemporary SOTA codec models, employing them to re-create
synthesized speech. This endeavor leads to the creation of CodecFake, the first
codec-based deepfake audio dataset. Additionally, we verify that anti-spoofing
models trained on commonly used datasets cannot detect synthesized speech from
current codec-based speech generation systems. The proposed CodecFake dataset
empowers these models to counter this challenge effectively.


  Dipole moments are a simple, global measure of the accuracy of the electron
density of a polar molecule. Dipole moments also affect the interactions of a
molecule with other molecules as well as electric fields. To directly assess
the accuracy of modern density functionals for calculating dipole moments, we
have developed a database of 200 benchmark dipole moments, using coupled
cluster theory through triple excitations, extrapolated to the complete basis
set limit. This new database is used to assess the performance of 88 popular or
recently developed density functionals. The results suggest that double hybrid
functionals perform the best, yielding dipole moments within about 3.6-4.5%
regularized RMS error versus the reference values---which is not very different
from the 4% regularized RMS error produced by coupled cluster singles and
doubles. Many hybrid functionals also perform quite well, generating
regularized RMS errors in the 5-6% range. Some functionals however exhibit
large outliers and local functionals in general perform less well than hybrids
or double hybrids.


  The aim of this work is to study the dual and the algebraic dual of an
evaluation code using standard monomials and indicator functions. We show that
the dual of an evaluation code is the evaluation code of the algebraic dual. We
develop an algorithm for computing a basis for the algebraic dual. Let $C_1$
and $C_2$ be linear codes spanned by standard monomials. We give a
combinatorial condition for the monomial equivalence of $C_1$ and the dual
$C_2^\perp$. Moreover, we give an explicit description of a generator matrix of
$C_2^\perp$ in terms of that of $C_1$ and coefficients of indicator functions.
For Reed--Muller-type codes we give a duality criterion in terms of the
v-number and the Hilbert function of a vanishing ideal. As an application, we
provide an explicit duality for Reed--Muller-type codes corresponding to
Gorenstein ideals. In addition, when the evaluation code is monomial and the
set of evaluation points is a degenerate affine space, we classify when the
dual is a monomial code.


  We deal with one dimensional $p$-Laplace equation of the form $$ u_t =
(|u_x|^{p-2} u_x )_x + f(x,u), \ x\in (0,l), \ t>0, $$ under Dirichlet boundary
condition, where $p>2$ and $f\colon [0,l]\times \mathbb{R}\to \mathbb{R}$ is a
continuous function with $f(x,0)=0$. We will prove that if there is at least
one eigenvalue of the $p$-Laplace operator between $\lim_{u\to 0}
f(x,u)/|u|^{p-2}u$ and $\lim_{|u|\to +\infty} f(x,u)/|u|^{p-2}u$, then there
exists a nontrivial stationary solution. Moreover we show the existence of a
connecting orbit between stationary solutions. The results are obtained by use
of Conley type homotopy index and continuation along $p$ techniques. We obtain
stronger results than by use of fixed point index and additionally get the
existence of a connecting orbit.


  This paper presents existence theories for several families of axisymmetric
solitary waves on the surface of an otherwise cylindrical ferrofluid jet
surrounding a stationary metal rod. The ferrofluid, which is governed by a
general (nonlinear) magnetisation law, is subject to an azimuthal magnetic
field generated by an electric current flowing along the rod.
  The ferrohydrodynamic problem for axisymmetric travelling waves is formulated
as an infinite-dimensional Hamiltonian system in which the axial direction is
the time-like variable. A centre-manifold reduction technique is employed to
reduce the system to a locally equivalent Hamiltonian system with a finite
number of degrees of freedom, and homoclinic solutions to the reduced system,
which correspond to solitary waves, are detected by dynamical-systems methods.


  Generative Artificial Intelligence (AI) is integrated into everyday
technology, including news, education, and social media. AI has further
pervaded private conversations as conversational partners, auto-completion, and
response suggestions. As social media becomes young people's main method of
peer support exchange, we need to understand when and how AI can facilitate and
assist in such exchanges in a beneficial, safe, and socially appropriate way.
We asked 622 young people to complete an online survey and evaluate blinded
human- and AI-generated responses to help-seeking messages. We found that
participants preferred the AI-generated response to situations about
relationships, self-expression, and physical health. However, when addressing a
sensitive topic, like suicidal thoughts, young people preferred the human
response. We also discuss the role of training in online peer support exchange
and its implications for supporting young people's well-being. Disclaimer: This
paper includes sensitive topics, including suicide ideation. Reader discretion
is advised.


  Recent studies have uncovered that language model distillation is less
effective when facing a large capacity gap between the teacher and the student,
and introduced teacher assistant-based distillation to bridge the gap. As a
connection, the scale and the performance of the teacher assistant is of vital
importance to bring the knowledge from the teacher to the student. However,
existing teacher assistant-based methods require maximally many trials before
scheduling an optimal teacher assistant. To this end, we propose a minimal
distillation schedule (MiniDisc) for scheduling the optimal teacher assistant
in minimally one trial. In particular, motivated by the finding that the
performance of the student is positively correlated to the scale-performance
tradeoff of the teacher assistant, MiniDisc is designed with a
$\lambda$-tradeoff to measure the optimality of the teacher assistant without
trial distillation to the student. MiniDisc then can schedule the optimal
teacher assistant with the best $\lambda$-tradeoff in a sandwich framework.
MiniDisc is evaluated with an extensive set of experiments on GLUE.
Experimental results demonstrate the improved efficiency our MiniDisc compared
to several state-of-the-art baselines. We further apply MiniDisc to a language
model with billions of parameters and show its scalability.


  We present a geometric way of describing the irrationality of a number using
the area of a circular sector $A(r)$. We establish a connection between this
and the continued fraction expansion of the number, and prove bounds for $A(r)$
as $r\to\infty$ by describing the asymptotic behavior of the ratios of the
denominators of the convergents.


  Image classification plays a pivotal role across diverse applications, yet
challenges persist when models are deployed in real-world scenarios. Notably,
these models falter in detecting unfamiliar classes that were not incorporated
during classifier training, a formidable hurdle for safe and effective
real-world model deployment, commonly known as out-of-distribution (OOD)
detection. While existing techniques, like max logits, aim to leverage logits
for OOD identification, they often disregard the intricate interclass
relationships that underlie effective detection. This paper presents an
innovative class relevance learning method tailored for OOD detection. Our
method establishes a comprehensive class relevance learning framework,
strategically harnessing interclass relationships within the OOD pipeline. This
framework significantly augments OOD detection capabilities. Extensive
experimentation on diverse datasets, encompassing generic image classification
datasets (Near OOD and Far OOD datasets), demonstrates the superiority of our
method over state-of-the-art alternatives for OOD detection.


  We report a detailed electronic structure calculation for Vanadium (V) using
DFT, DFT+$U$, $G_0W_0$, $GW_0$ and DFT+DMFT methods. The calculated values of
$W$, $U$ and $J$ by cRPA method are $\sim$1.1, $\sim$3.4 and $\sim$0.52 eV,
respectively. The comparison between calculated spectra (CS) and experimental
spectra (ES) suggests that $W$ ($U$) is more accurate for DFT+$U$ (DFT+DMFT)
method. The CS, obtained by these methods, give fairly good agreement with ES
for peaks' positions except $GW_0$. The shallowness of the dips lying $\sim$
-1.5 eV and $\sim$1.0 eV in ES are properly explained by DFT+DMFT method only,
due to the presence of incoherent $t_{2g}$ states. This work suggests that for
the proper explanation of ES, sophisticated many-body theory is needed even for
the simple metal.


  In an optical configuration consisting of a flat plate of vacuum between
upper and lower spaces of uniform dielectric regions of n>1, we have calculated
two output light intensities for two input lights from the Maxwell's equations
as functions of the incision angle, a light intensity ratio, a phase difference
of the two input lights, and a thickness of the vacuum layer, where the two
input lights come from upper and lower dielectric regions with the same
incision angles, and one of the output light goes into upper dielectric and the
other goes into lower dielectric. We have found that, when evanescent lights
exist at the upper and lower boundary and interfere each other, there is one
set of incision angles and phase differences for any combination of an input
light ratio and a thickness of the vacuum layer where one of output lights
becomes zero. This finding will possibly lead to an innovative optical switch
with which an optical output light can be switched on and off with a control
light with an intensity much lower than that of the output light.


  We apply to locally finite partially ordered sets a construction which
associates a complete lattice to a given poset; the elements of the lattice are
the closed subsets of a closure operator, defined starting from the concurrency
relation. We show that, if the partially ordered set satisfies a property of
local density, i.e.: N-density, then the associated lattice is also
orthomodular. We then consider occurrence nets, introduced by C.A. Petri as
models of concurrent computations, and define a family of subsets of the
elements of an occurrence net; we call those subsets "causally closed" because
they can be seen as subprocesses of the whole net which are, intuitively,
closed with respect to the forward and backward local state changes. We show
that, when the net is K-dense, the causally closed sets coincide with the
closed sets induced by the closure operator defined starting from the
concurrency relation. K-density is a property of partially ordered sets
introduced by Petri, on the basis of former axiomatizations of special
relativity theory.


  CMS has recently reported a moderate excess in the $\mu\nu jj$ final state in
a second generation Leptoquark search, but they have disregarded it because the
excess is not present in the $\mu\mu jj$ final state and because they do not
observe the expected resonant peak in the distributions. As a proof of concept
we show that a simple Leptoquark model including second and third generation
couplings with non-negligible single- and non-resonant production in addition
to usual pair production could explain the data: excess ($\mu\nu jj$), lack of
excess ($\mu\mu jj$) and missing peak in the distributions; while being in
agreement with collider constraints. We take this result and analysis as a
starting point of a reconsideration of the ATLAS and CMS second generation
Leptoquark searches. We also discuss which would be the consequences and
modifications that should be performed in the searches to test if this
deviation would correspond to a New Physics signal. We observe that low-energy
flavor constraints can be avoided by adding heavier particles to the model.


  Single domain generalization is a challenging case of model generalization,
where the models are trained on a single domain and tested on other unseen
domains. A promising solution is to learn cross-domain invariant
representations by expanding the coverage of the training domain. These methods
have limited generalization performance gains in practical applications due to
the lack of appropriate safety and effectiveness constraints. In this paper, we
propose a novel learning framework called progressive domain expansion network
(PDEN) for single domain generalization. The domain expansion subnetwork and
representation learning subnetwork in PDEN mutually benefit from each other by
joint learning. For the domain expansion subnetwork, multiple domains are
progressively generated in order to simulate various photometric and geometric
transforms in unseen domains. A series of strategies are introduced to
guarantee the safety and effectiveness of the expanded domains. For the domain
invariant representation learning subnetwork, contrastive learning is
introduced to learn the domain invariant representation in which each class is
well clustered so that a better decision boundary can be learned to improve
it's generalization. Extensive experiments on classification and segmentation
have shown that PDEN can achieve up to 15.28% improvement compared with the
state-of-the-art single-domain generalization methods.


  Quantum optimal control can be used to shape the control pulses for realizing
unitary and non-unitary transformations of quantum states. These control pulses
provide the fundamental interface between the quantum compiler and the quantum
hardware. Most current software for quantum optimal control (e.g. Qutip or
Krotov) is restricted to run on shared memory platforms, limiting their
applicability to smaller quantum systems, in particular if interactions with
the environment are taken into account. This paper gives an overview of the
open-source code Quandary, which is designed to solve quantum control problems
in larger open quantum systems modelled by Lindblad's master equation.
Implemented in C++, Quandary uses the message passing paradigm for distributed
memory computers that enables scalability to large numbers of compute cores.
Accompanied by numerical examples, this paper presents an overview on existing
theoretical developments for open optimal quantum control realizing
state-to-state transfer, unitary gate optimization as well as
state-preparation, and presents the numerical tools and implementation aspect
as realized in Quandary, for deployment on modern high-performance computing
platforms.


  Within the $t$-$J$ model, we study the doping dependence of
antiferromagnetism in the copper oxide materials by considering quantum
fluctuations of spinons in the random-phase-approximation. The staggered
magnetization vanishes around doping $\delta=5%$ for a reasonable parameter
value $t/J=5$, which is in agreement with the experiments on copper oxide
materials.


  In this paper we discuss two different existing algorithms for computing
topological entropy and we perform one of them in order to compute the
isentropes for cubic polynomials.


  A recently developed three-dimensional approach (without partial-wave
decomposition) is considered to investigate solutions of Faddeev-Yakubovsky
integral equations in momentum space for three- and four-body bound states,
with the inclusion of three-body forces. In the calculations of the binding
energies, spin-dependent nucleon-nucleon (NN) potential models (named, S$_{3}$,
MT-I/III, YS-type and P$_{5.5}$GL) are considered along with the scalar
two-meson exchange three-body potential. Good agreement of the presently
reported results with the ones obtained by other techniques are obtained,
demonstrating the advantage of an approach in which the formalism is much more
simplified and easy to manage for direct computation.


  We study the quantization of the 6d Seiberg-Witten curve for D-type minimal
conformal matter theories compactified on a two-torus. The quantized 6d curve
turns out to be a difference equation established via introducing codimension
two and four surface defects. We show that, in the Nekrasov-Shatashvili limit,
the 6d partition function with insertions of codimension two and four defects
serve as the eigenfunction and eigenvalues of the difference equation,
respectively. We further identify the quantum curve of D-type minimal conformal
matters with an elliptic Garnier system recently studied in the integrability
community. At last, as a concrete consequence of our elliptic quantum curve, we
study its RG flows to obtain various quantum curves of 5d ${\rm Sp}(N)+N_f
\mathsf{F},N_f\leq 2N+5$ theories.