  Given a compact Kaehler manifold X, it is shown that pairs of the form (E,
D), where E is a trivial holomorphic vector bundle on X, and D is an integrable
holomorphic connection on $E$, produce a neutral Tannakian category. The
corresponding pro-algebraic affine group scheme is studied. In particular, it
is shown that this pro-algebraic affine group scheme for a compact Riemann
surface determines uniquely the isomorphism class of the Riemann surface.


  Successful network deployment of the Internet of Things (IoT) requires many
critical system design considerations. This paper highlights how an LTE system
supporting Cat-M devices can be engineered to deal with the numerous
constraints the 3GPP standard imposes for this new device type. Fundamental
changes to the control channels, control and data timing relationships, the
need to support half-duplexing, and variable repetition lengths pose
non-trivial challenges, particularly when attempting to satisfy the critical
coverage KPI for Cat-M devices while at the same time preserving the capacity
KPI for legacy LTE devices. In addition, the nature of IoT traffic is
fundamentally different than legacy LTE data, requiring changes to existing
system parameters and MAC algorithms. Finally, we will touch upon the topic of
supporting voice over IP traffic on Cat-M devices and the challenges therein.


  Understanding protein interactions and pathway knowledge is crucial for
unraveling the complexities of living systems and investigating the underlying
mechanisms of biological functions and complex diseases. While existing
databases provide curated biological data from literature and other sources,
they are often incomplete and their maintenance is labor-intensive,
necessitating alternative approaches. In this study, we propose to harness the
capabilities of large language models to address these issues by automatically
extracting such knowledge from the relevant scientific literature. Toward this
goal, in this work, we investigate the effectiveness of different large
language models in tasks that involve recognizing protein interactions,
identifying genes associated with pathways affected by low-dose radiation, and
gene regulatory relations. We thoroughly evaluate the performance of various
models, highlight the significant findings, and discuss both the future
opportunities and the remaining challenges associated with this approach. The
code and data are available at: https://github.com/boxorange/BioIE-LLM


  We discuss the possible role of reconnection in electro-magnetically
dominated cores of relativistic AGN jets. We suggest that reconnection may
proceed in a two-fold fashion: initial explosive collapse on the Alfven
time-scale of a current-carrying jet (which is of the order of the light
crossing time) and subsequent slow quasi-steady reconnection. Sites of
explosive collapse are associated with bright knots, while steady-state
reconnection re-energizes particles in the ``bridges'' between the knots.
  Ohmic dissipation in reconnection layers leads to particle acceleration
either by inductive electric fields or by stochastic particle acceleration in
the ensuing electro-magnetic turbulence.


  Let $X$ be a finite aspherical CW-complex whose fundamental group $\pi_1(X)$
possesses a subnormal series $\pi_1(X) \rhd G_m \rhd ... \rhd G_0$ with a
non-trivial elementary amenable group $G_0$. We investigate the
$L^2$-invariants of the universal covering of such a CW-complex $X$. We show
that the Novikov-Shubin invariants $\alpha_n({\tilde X})$ are positive. We
further prove that the $L^2$-torsion $\rho^{(2)}({\tilde X})$ vanishes if
$\pi_1(X)$ has semi-integral determinant.


  In dilute astrophysical plasmas, thermal conduction is primarily along
magnetic field lines, and therefore highly anisotropic. As a result, the usual
convective stability criterion is modified from a condition on entropy to a
condition on temperature. For small magnetic fields or small wavenumbers,
instability occurs in any atmosphere where the temperature and pressure
gradients point in the same direction. We refer to the resulting convective
instability as the magnetothermal instability (MTI). We present fully
three-dimensional simulations of the MTI and show that saturation results in an
atmosphere with different vertical structure, dependent upon the boundary
conditions. When the temperature at the boundary of the unstable layer is
allowed to vary, the temperature gradient relaxes until the unstable region is
almost isothermal. When the temperature at the boundary of the unstable region
is fixed, the magnetic field is reoriented to an almost vertical geometry as a
result of buoyant motions. This case exhibits more vigorous turbulence. In both
cases the resulting saturated heat flux is almost one-half of the value
expected if the conduction were purely isotropic. The action of the MTI results
in dynamical processes that lead to significant transport perpendicular to the
initial direction of the magnetic field. The resulting magnetoconvection in
both cases amplifies the magnetic field until it is almost in equipartition
with sustained subsonic turbulence. These results are relevant to understanding
measurements of the temperature profiles of the intracluster medium of clusters
of galaxies as well as the structure of radiatively inefficient accretion
flows.


  We prove that the universal enveloping algebra of a Poisson-Ore extension is
a length two iterated Ore extension of the original universal enveloping
algebra. As consequences, we observe certain ring-theoretic invariants of the
universal enveloping algebras that are preserved under iterated Poisson-Ore
extensions. We apply our results to iterated quadratic Poisson algebras arising
from semiclassical limits of quantized coordinate rings and a family of graded
Poisson algebras of Poisson structures of rank at most two.


  The equation of state of nuclear matter is strongly affected by the presence
of a magnetic field. Here we study the equilibrium configuration of asymmetric
nuclear matter for a wide range of densities, isospin composition, temperatures
and magnetic fields. Special attention is paid to the low density and low
temperature domain, where a thermodynamical instability exists. Neglecting
fluctuations of the Coulomb force, a coexistence of phases is found under such
conditions, even for extreme magnetic intensities. We describe the nuclear
interaction by using the non--relativistic Skyrme potential model within a
Hartree--Fock approach. We found that the coexistence of phases modifies the
equilibrium configuration, masking most of the manifestations of the spin
polarized matter. However, the compressibility and the magnetic susceptibility
show clear signals of this fact. Thermal effects are significative for both
quantities, mainly out of the coexistence region.


  Commutative four dimensional supersymmetric Yang-Mills (SYM) is known to be
renormalizable for ${\mathcal N} = 1, 2$, and finite for ${\mathcal N} = 4$.
However, in the noncommutative version of the model the UV/IR mechanism gives
rise to infrared divergences which may spoil the perturbative expansion. In
this work we pursue the study of the consistency of the ${\cal N} = 1, 2, 4$
noncommutative supersymmetric Yang-Mills theory with gauge group U(N) (NCSYM).
We employ the covariant superfield framework to compute the one-loop
corrections to the two- and three-point functions of the gauge superfield $V$.
It is found that the cancellation of the harmful UV/IR infrared divergences
only takes place in the fundamental representation of the gauge group. We argue
that this is in agreement with the low energy limit of the open superstring in
the presence of an external magnetic field. As expected, the planar sector of
the two-point function of the $V$ superfield exhibits UV divergences. They are
found to cancel, in the Feynman gauge, for the maximally extended ${\cal N} =
4$ supersymmetric theory. This gives support to the belief that the ${\cal N} =
4$ NCSYM theory is UV finite.


  Elastic tunneling is used as a powerful direct tool to determine the upper
critical field $H_{c2}(T)$ in the high-$T_c$ oxide Ba$_{1-x}$K$_x$BiO$_3$. The
temperature dependence of $H_{c2}$ inferred from the tunneling follows the
Werthamer-Helfand-Hohenberg prediction for type-II superconductors. A
comparison will be made with resistively determined critical field data.


  After sketching a context in which to seek observable signals of
spatio-temporal discreteness, I briefly review the status of the causal set
program for quantum gravity, concluding with a simple model for the field
produced by a moving charge in a background causal set.


  We study hypercyclicity, Devaney chaos, topological mixing properties and
strong mixing in the measure-theoretic sense for operators on topological
vector spaces with invariant sets. More precisely, our purpose is to establish
links between the fact of satisfying any of these properties on certain
invariant sets, and the analogous property on the closed span of the invariant
set. We also give examples that illustrate these results.


  A 4D-Var data assimilation technique is applied to the rectangular-box
configuration of the NEMO in order to identify the optimal parametrization of
boundary conditions at lateral boundaries. The case of the staircase-shaped
coastlines is studied by rotating the model grid around the center of the box.
It is shown that, in some cases, the formulation of the boundary conditions at
the exact boundary leads to appearance of exponentially growing modes while
optimal boundary conditions allow to correct the errors induced by the
staircase-like appriximation of the coastline.


  Boolean automata networks (aka Boolean networks) are space-time discrete
dynamical systems, studied as a model of computation and as a representative
model of natural phenomena. A collection of simple entities (the automata)
update their 0-1 states according to local rules. The dynamics of the network
is highly sensitive to update modes, i.e., to the schedule according to which
the automata apply their local rule. A new family of update modes appeared
recently, called block-parallel, which is dual to the well studied
block-sequential. Although basic, it embeds the rich feature of update
repetitions among a temporal updating period, allowing for atypical asymptotic
behaviors. In this paper, we prove that it is able to breed complex
computations, squashing almost all decision problems on the dynamics to the
traditionally highest (for reachability questions) class PSPACE. Despite
obtaining these complexity bounds for a broad set of local and global
properties, we also highlight a surprising gap: bijectivity is still coNP.


  The 7.6-eV-isomer of Thorium-229 offers the opportunity to perform high
resolution laser spectroscopy of a nuclear transition. We give a brief review
of the investigations of this isomer. The nuclear resonance connecting ground
state and isomer may be used as the reference of an optical clock of very high
accuracy using trapped and laser-cooled thorium ions, or in a compact
solid-state optical frequency standard of high stability.


  This article studies several properties of the half-space last passage
percolation, in particular the two-time covariance. We show that, when the two
end-points are at small macroscopic distance, then the first order correction
to the covariance for the point-to-point model is the same as the one of the
stationary model. In order to obtain the result, we first derive comparison
inequalities of the last passage increments for different models. This is used
to prove tightness of the point-to-point process as well as localization of the
geodesics. Unlike for the full-space case, for half-space we have to overcome
the difficulty that the point-to-point model in half-space with generic start
and end points is not known.


  What has happened in machine learning lately, and what does it mean for the
future of medical image analysis? Machine learning has witnessed a tremendous
amount of attention over the last few years. The current boom started around
2009 when so-called deep artificial neural networks began outperforming other
established models on a number of important benchmarks. Deep neural networks
are now the state-of-the-art machine learning models across a variety of areas,
from image analysis to natural language processing, and widely deployed in
academia and industry. These developments have a huge potential for medical
imaging technology, medical data analysis, medical diagnostics and healthcare
in general, slowly being realized. We provide a short overview of recent
advances and some associated challenges in machine learning applied to medical
image processing and image analysis. As this has become a very broad and fast
expanding field we will not survey the entire landscape of applications, but
put particular focus on deep learning in MRI.
  Our aim is threefold: (i) give a brief introduction to deep learning with
pointers to core references; (ii) indicate how deep learning has been applied
to the entire MRI processing chain, from acquisition to image retrieval, from
segmentation to disease prediction; (iii) provide a starting point for people
interested in experimenting and perhaps contributing to the field of machine
learning for medical imaging by pointing out good educational resources,
state-of-the-art open-source code, and interesting sources of data and problems
related medical imaging.


  We study the tunneling through an arbitrary number of finite rectangular
opaque barriers and generalize earlier results by showing that the total
tunneling phase time depends neither on the barrier thickness nor on the
inter-barrier separation. We also predict two novel peculiar features of the
system considered, namely the independence of the transit time (for non
resonant tunneling) and the resonant frequency on the number of barriers
crossed, which can be directly tested in photonic experiments. A thorough
analysis of the role played by inter-barrier multiple reflections and a
physical interpretation of the results obtained is reported, showing that
multibarrier tunneling is a highly non-local phenomenon.


  Probabilistic numerical methods (PNMs) solve numerical problems via
probabilistic inference. They have been developed for linear algebra,
optimization, integration and differential equation simulation. PNMs naturally
incorporate prior information about a problem and quantify uncertainty due to
finite computational resources as well as stochastic input. In this paper, we
present ProbNum: a Python library providing state-of-the-art probabilistic
numerical solvers. ProbNum enables custom composition of PNMs for specific
problem classes via a modular design as well as wrappers for off-the-shelf use.
Tutorials, documentation, developer guides and benchmarks are available online
at www.probnum.org.


  In this paper we state a full classification for Coxeter polytopes in
$\mathbb{H}^{n}$ with $n+3$ facets which are non-compact and have precisely one
non-simple vertex.


  The family of Wilder continua in cubes of dimension > 2 and its two
subfamilies-of continuum-wise Wilder continua and of hereditarily arcwise
connected continua-are recognized as coanalytic absorbers in the hyperspace of
subcontinua of the cubes. In particular, each of them is homeomorphic to the
set of all nonempty countable closed subsets of the unit interval.


  We investigate the prospects of supersymmetry searches at the LHC with 7 TeV
energy. A new set of selection cuts is proposed based on event shapes to
control backgrounds. Our preliminary studies show that it is possible to
minimize backgrounds to a significantly low level and conservative estimate
suggests that the mass reach can be extended to $\sim$ 1.1 TeV for luminosity
1/fb with a reasonable signal-to-background ratio.


  Domain generalization (DG) has attracted much attention in person
re-identification (ReID) recently. It aims to make a model trained on multiple
source domains generalize to an unseen target domain. Although achieving
promising progress, existing methods usually need the source domains to be
labeled, which could be a significant burden for practical ReID tasks. In this
paper, we turn to investigate unsupervised domain generalization for ReID, by
assuming that no label is available for any source domains.
  To address this challenging setting, we propose a simple and efficient
domain-specific adaptive framework, and realize it with an adaptive
normalization module designed upon the batch and instance normalization
techniques. In doing so, we successfully yield reliable pseudo-labels to
implement training and also enhance the domain generalization capability of the
model as required. In addition, we show that our framework can even be applied
to improve person ReID under the settings of supervised domain generalization
and unsupervised domain adaptation, demonstrating competitive performance with
respect to relevant methods. Extensive experimental study on benchmark datasets
is conducted to validate the proposed framework. A significance of our work
lies in that it shows the potential of unsupervised domain generalization for
person ReID and sets a strong baseline for the further research on this topic.


  A search for proton decay into three charged leptons has been performed by
using 0.37$\,$Mton$\cdot$years of data collected in Super-Kamiokande. All
possible combinations of electrons, muons and their anti-particles consistent
with charge conservation were considered as decay modes. No significant excess
of events has been found over the background, and lower limits on the proton
lifetime divided by the branching ratio have been obtained. The limits range
between $9.2\times10^{33}$ to $3.4\times10^{34}$ years at 90$\,$% confidence
level, improving by more than an order of magnitude upon limits from previous
experiments. A first limit has been set for the $p\rightarrow\mu^-e^+e^+$ mode.


  We propose an electrically driven spin injector into normal metals and
semiconductors, which is based on a magnetic tunnel junction (MTJ) subjected to
a microwave voltage. Efficient functioning of such an injector is provided by
electrically induced magnetization precession in the "free" layer of MTJ, which
generates the spin pumping into a metallic or semiconducting overlayer. We
theoretically describe the spin and charge dynamics in the
CoFeB/MgO/CoFeB/Au(GaAs) heterostructures. First, the magnedynamics in the free
CoFeB layer is quantified with the account of a spin-transfer torque and a
voltage-controlled magnetic anisotropy. By numerically solving the
magnetodynamics equation, we determine dependences of the precession amplitude
on the frequency $f$ and magnitude $V_\mathrm{max}$ of the ac voltage applied
to the MTJ. It is found that the frequency dependence changes drastically above
the threshold amplitude $V_\mathrm{max} \approx 200$mV, exhibiting a break at
the resonance frequency $f_\mathrm{res}$ due to nonlinear effects. The results
obtained for the magnetization dynamics are used to describe the spin injection
and pumping into the Au and GaAs overlayers. Since the generated spin current
creates additional charge current owing to the inverse spin Hall effect, we
also calculate distribution of the electric potential in the thick Au
overlayer. The calculations show that the arising transverse voltage becomes
experimentally measurable at $f = f_\mathrm{res}$. Finally, we evaluate the
spin accumulation in a long n$^+$-GaAs bar coupled to the MTJ and determine its
temporal variation and spatial distribution along the bar. It is found that the
spin accumulation under resonant excitation is large enough for experimental
detection even at micrometer distances from the MTJ. This result demonstrates
high efficiency of the described nanoscale spin injector.


  The extra stiffness that the magnetic field adds to the ISM changes the way
it reacts to the presence of a spiral perturbation. At intermediate to high z,
the gas shoots up before the arm, flows over, and falls behind it, as it
approaches the next arm. This generates a multicell circulation pattern, within
each of which the net radial mass flux is positive near the midplane and
negative at higher z. The flow distorts the magnetic field lines. In the arm
region, the gas flows nearly parallel to the arm, and therefore, the magnetic
field adopts a similar pitch angle. Between the arms, the gas flows out in
radius, generating a negative pitch angle in the magnetic field. The intensity
and direction of the field yield synthetic synchrotron maps that reproduce some
features of the synchrotron maps of external galaxies, like the islands of
emission and the displacement between the gaseous and synchrotron arms. When
comparing the magnitude of the field with the local gas density, two
distinctive relations appear, depending on whether the magnetic pressure is
dominant. Above the plane, the density structure develops a shape resembling a
breaking wave. This structure collapses and rises again with a period of about
60 Myr, similar to that of a vertical oscillation mode. The falling gas plays
an important part in the overall hydrostatics, since its deceleration
compresses the low z gas, raising the average midplane pressure in the interarm
region above that provided by the weight of the material above.


  Thermodynamics of type II superconductors in electromagnetic field based on
the Ginzburg - Landau theory is presented. The Abrikosov flux lattice solution
is derived using an expansion in a parameter characterizing the "distance" to
the superconductor - normal phase transition line. The expansion allows a
systematic improvement of the solution. The phase diagram of the vortex matter
in magnetic field is determined in detail. In the presence of significant
thermal fluctuations on the mesoscopic scale (for example in high $T_{c}$
materials) the vortex crystal melts into a vortex liquid. A quantitative theory
of thermal fluctuations using the lowest Landau level approximation is given.
It allows to determine the melting line and discontinuities at melt, as well as
important characteristics of the vortex liquid state. In the presence of
quenched disorder (pinning) the vortex matter acquires certain "glassy"
properties. The irreversibility line and static properties of the vortex glass
state are studied using the "replica" method. Most of the analytical methods
are introduced and presented in some detail. Various quantitative and
qualitative features are compared to experiments in type II superconductors,
although the use of a rather universal Ginzburg - Landau theory is not
restricted to superconductivity and can be applied with certain adjustments to
other physical systems, for example rotating Bose - Einstein condensate.


  We explain how we computed equations for all genus 4 curves defined of the
field with 2 elements, up-to-isomorphism, and some of the data we obtained. We
give descriptions also of nice models for genus 4 curves over characteristic 2
fields, in both the hyperelliptic case and the trigonal case.


  In the present work, initially a mixed-three-spin (1/2,1,1/2) cell of a
mixed-N-spin chain with Ising-XY model is introduced, for which pair spins
(1,1/2) have Ising-type interaction and pair spins (1/2,1/2) have both XY-type
and Dzyaloshinskii-Moriya(DM) interactions together. An external homogeneous
magnetic field B is considered for the system in thermal equilibrium.
Integer-spins have a single-ion anisotropy property with coefficient {\zeta}.
Then, we investigate the quantum entanglement between half-spins (1/2,1/2), by
means of the concurrence. Classical correlation(CC) for this pair of spins is
investigated as well as the concurrence and some interesting the temperature,
the magnetic field and the DM interaction properties are expressed. Moreover,
single-ion anisotropy effects on the correlation between half-spins is
verified. According to the verifications based on the communication channels
category by D. Rossini, V. Giovannetti and R. Fazio 63, we theoretically
consider such tripartite spin model as an ideal quantum channel, then calculate
its information transmission rate and express some differences in behaviour
between this suggested model and introduced simple models in the previous
works(chains without spin integer and DM interaction) from information
transferring protocol point of view.


  LiDAR point cloud analysis is a core task for 3D computer vision, especially
for autonomous driving. However, due to the severe sparsity and noise
interference in the single sweep LiDAR point cloud, the accurate semantic
segmentation is non-trivial to achieve. In this paper, we propose a novel
sparse LiDAR point cloud semantic segmentation framework assisted by learned
contextual shape priors. In practice, an initial semantic segmentation (SS) of
a single sweep point cloud can be achieved by any appealing network and then
flows into the semantic scene completion (SSC) module as the input. By merging
multiple frames in the LiDAR sequence as supervision, the optimized SSC module
has learned the contextual shape priors from sequential LiDAR data, completing
the sparse single sweep point cloud to the dense one. Thus, it inherently
improves SS optimization through fully end-to-end training. Besides, a
Point-Voxel Interaction (PVI) module is proposed to further enhance the
knowledge fusion between SS and SSC tasks, i.e., promoting the interaction of
incomplete local geometry of point cloud and complete voxel-wise global
structure. Furthermore, the auxiliary SSC and PVI modules can be discarded
during inference without extra burden for SS. Extensive experiments confirm
that our JS3C-Net achieves superior performance on both SemanticKITTI and
SemanticPOSS benchmarks, i.e., 4% and 3% improvement correspondingly.


  The long-range properties of the random flux model (lattice fermions hopping
under the influence of maximally random link disorder) are shown to be
described by a supersymmetric field theory of non-linear sigma model type,
where the group GL(n|n) is the global invariant manifold. An extension to
non-abelian generalizations of this model identifies connections to lattice
QCD, Dirac fermions in a random gauge potential, and stochastic non-Hermitian
operators.


  The mid-IR detection rate of water lines in disks around Herbig stars disks
is about 5\%, while it is around 50\% for disks around TTauri stars. The reason
for this is still unclear. In this study, we want to find an explanation for
the different detection rates between low mass and high mass pre-main-sequence
stars (PMSs) in the mid-IR regime. We run disk models with stellar parameters
adjusted to spectral types B9 through M2, using the radiation thermo-chemical
disk modeling code ProDiMo. We produce convolved spectra at the resolution of
Spitzer IRS, JWST MIRI and VLT VISIR spectrographs. We apply random noise
derived from typical Spitzer spectra for a direct comparison with observations.
The strength of the mid-IR water lines correlates directly with the luminosity
of the central star. We explored a small parameter space around a standard disk
model, considering dust-to-gas mass ratio, disk gas mass, mixing coefficient
for dust settling, flaring index, dust maximum size and size power law
distribution index. The models show that it is possible to suppress the water
emission, however, current observations are not sensitive enough to detect
mid-IR lines in disks for most of the explored parameters. The presence of
noise in the spectra, combined with the high continuum flux (noise level is
proportional to the continuum flux), is the most likely explanation for the non
detections towards Herbig stars. Mid-IR spectra with resolution higher than
20000 are needed to investigate water in protoplanetary disks. Intrinsic
differences in disk structure, e.g. inner gaps, gas-to-dust ratio, dust size
and distribution, and inner disk scale height, between Herbig and TTauri star
disks are able to explain a lower water detection rate in disks around Herbig
stars.


  In this letter we consider the consequences for the LHC of light vector-like
exotica with fractional electric charge. It is shown that such states are found
in orbifold constructions of the heterotic string. Moreover, these exotica are
consistent with gauge coupling unification at one loop, even though they do not
come in complete multiplets of SU(5).


  In this paper, we develop symplectic Hodge theory on transversely symplectic
foliations. In particular, we establish the symplectic $d\delta$-lemma for any
such foliations with the (transverse) $s$-Lefschetz property. As transversely
symplectic foliations include many geometric structures, such as contact
manifolds, co-symplectic manifolds, symplectic orbifolds, and symplectic
quasi-folds as special examples, our work provides a unifying treatment of
symplectic Hodge theory in these geometries.
  As an application, we show that on compact $K$-contact manifolds, the
$s$-Lefschetz property implies a general result on the vanishing of cup
products, and that the cup length of a $2n+1$ dimensional compact $K$-contact
manifold with the (transverse) $s$-Lefschetz property is at most $2n-s$. For
any even integer $s\geq 2$, we also apply our main result to produce examples
of $K$-contact manifolds that are $s$-Lefschetz but not $(s+1)$-Lefschetz.


  The Penrose limit is generalized to show that, any leading order solution of
the low-energy field equations in any one of the five string theories has a
plane wave solution as a limit. This limiting procedure takes into account all
the massless fields that may arise and commutes with the T-duality so that any
dual solution has again a plane wave limit. The scaling rules used in the limit
are unique and stem from the scaling property of the D=11 supergravity action.
Although the leading order solutions need not be exact or supersymmetric, their
plane wave limits always preserve some portion of the Poincare supersymmetry
and solve the relevant field equations in all powers of the string tension
parameter. Further properties of the limiting procedure are discussed.


  This paper analyzes the reconstruction of diffusion and absorption parameters
in an elliptic equation from knowledge of internal data. In the application of
photo-acoustics, the internal data are the amount of thermal energy deposited
by high frequency radiation propagating inside a domain of interest. These data
are obtained by solving an inverse wave equation, which is well-studied in the
literature. We show that knowledge of two internal data based on well-chosen
boundary conditions uniquely determines two constitutive parameters in
diffusion and Schroedinger equations. Stability of the reconstruction is
guaranteed under additional geometric constraints of strict convexity. No
geometric constraints are necessary when $2n$ internal data for well-chosen
boundary conditions are available, where $n$ is spatial dimension. The set of
well-chosen boundary conditions is characterized in terms of appropriate
complex geometrical optics (CGO) solutions.


  We have obtained FUSE and HST/STIS time-resolved spectroscopy of the polar AM
Herculis during a deep low state. The spectra are entirely dominated by the
emission of the white dwarf. Both the far-ultraviolet (FUV) flux as well as the
spectral shape vary substantially over the orbital period, with maximum flux
occurring at the same phase as during the high state. The variations are due to
the presence of a hot spot on the white dwarf, which we model quantitatively.
The white dwarf parameters can be determined from a spectral fit to the faint
phase data, when the hot spot is self-eclipsed. Adopting the distance of
79+8-6pc determined by Thorstensen, we find an effective temperature of
19800+-700K and a mass of Mwd=0.78+0.12-0.17Msun. The hot spot has a lower
temperature than during the high state, ~34000-40000K, but covers a similar
area, ~10% of the white dwarf surface. Low state FUSE and STIS spectra taken
during four different epochs in 2002/3 show no variation of the FUV flux level
or spectral shape, implying that the white dwarf temperature and the hot spot
temperature, size, and location do not depend on the amount of time the system
has spent in the low state. Possible explanations are ongoing accretion at a
low level, or deep heating, both alternatives have some weaknesses that we
discuss. No photospheric metal absorption lines are detected in the FUSE and
STIS spectra, suggesting that the average metal abundances in the white dwarf
atmosphere are lower than 1e-3 times their solar values.


  Using an updated simulation tool, we examine molecular junctions comprised of
benzene-1,4-dithiolate bonded between gold nanotips, focusing on the importance
of environmental factors and inter-electrode distance on the formation and
structure of bridged molecules. We investigate the complex relationship between
monolayer density and tip separation, finding that the formation of
multi-molecule junctions is favored at low monolayer density, while
single-molecule junctions are favored at high density. We demonstrate that tip
geometry and monolayer interactions, two factors that are often neglected in
simulation, affect the bonding geometry and tilt angle of bridged molecules. We
further show that the structures of bridged molecules at 298 and 77 K are
similar.


  This work developed a kernel-based residual learning framework for
quadrupedal robotic locomotion. Initially, a kernel neural network is trained
with data collected from an MPC controller. Alongside a frozen kernel network,
a residual controller network is trained via reinforcement learning to acquire
generalized locomotion skills and resilience against external perturbations.
With this proposed framework, a robust quadrupedal locomotion controller is
learned with high sample efficiency and controllability, providing
omnidirectional locomotion at continuous velocities. Its versatility and
robustness are validated on unseen terrains that the expert MPC controller
fails to traverse. Furthermore, the learned kernel can produce a range of
functional locomotion behaviors and can generalize to unseen gaits.


  A nest representation of a strongly maximal TAF algebra $A$ is a
representation $\pi$ for which $\operatorname{Lat} \pi(A) is totally ordered.
We prove that if the spectrum of $A$ is totally ordered, or if
$\operatorname{Lat} \pi(A)$ contains an atom, then $\operatorname{ker} \pi$ is
a meet irreducible ideal.


  The fourth catalog of active galactic nuclei (AGNs) detected by the Fermi
Gamma-ray Space Telescope Large Area Telescope (4LAC) between 2008 August 4 and
2016 August 2 contains 2863 objects located at high Galactic latitudes
(|b|>10{\deg}). It includes 85% more sources than the previous 3LAC catalog
based on 4 years of data. AGNs represent at least 79% of the high-latitude
sources in the fourth Fermi-Large Area Telescope Source Catalog (4FGL), which
covers the energy range from 50 MeV to 1 TeV. In addition, 344 gamma-ray AGNs
are found at low Galactic latitudes. Most of the 4LAC AGNs are blazars (98%),
while the remainder are other types of AGNs. The blazar population consists of
24% Flat Spectrum Radio Quasars (FSRQs), 38% BL Lac-type objects (BL Lacs), and
38% blazar candidates of unknown types (BCUs). On average, FSRQs display softer
spectra and stronger variability in the gamma-ray band than BL Lacs do,
confirming previous findings. All AGNs detected by ground-based atmospheric
Cherenkov telescopes are also found in the 4LAC.


  Recently, the problems of evaluating performances of switches and routers
have been formulated as online problems, and a great amount of results have
been presented. In this paper, we focus on managing outgoing packets (called
{\em egress traffic}) on switches that support Quality of Service (QoS), and
analyze the performance of one of the most fundamental scheduling policies {\em
Priority Queuing} ($PQ$) using competitive analysis. We formulate the problem
of managing egress queues as follows: An output interface is equipped with $m$
queues, each of which has a buffer of size $B$. The size of a packet is unit,
and each buffer can store up to $B$ packets simultaneously. Each packet is
associated with one of $m$ priority values $\alpha_{j}$ ($1 \leq j \leq m$),
where $\alpha_{1} \leq \alpha_{2} \leq \cdots \leq \alpha_{m}$, $\alpha_{1} =
1$, and $\alpha_{m} = \alpha$ and the task of an online algorithm is to select
one of $m$ queues at each scheduling step. The purpose of this problem is to
maximize the sum of the values of the scheduled packets. For any $B$ and any
$m$, we show that the competitive ratio of $PQ$ is exactly $2 - \min_{x \in [1,
m-1] } \{ \frac{ \alpha_{x+1} }{ \sum_{j = 1}^{x+1} \alpha_{j} } \}$. That is,
we conduct a complete analysis of the performance of $PQ$ using worst case
analysis. Moreover, we show that no deterministic online algorithm can have a
competitive ratio smaller than $1 + \frac{ \alpha^3 + \alpha^2 + \alpha }{
\alpha^4 + 4 \alpha^3 + 3 \alpha^2 + 4 \alpha + 1 }$.


  There has been tremendous progress in multimodal Large Language Models
(LLMs). Recent works have extended these models to video input with promising
instruction following capabilities. However, an important missing piece is
temporal localization. These models cannot accurately answer the "When?"
questions. We identify three key aspects that limit their temporal localization
capabilities: (i) time representation, (ii) architecture, and (iii) data. We
address these shortcomings by proposing Language Instructed
Temporal-Localization Assistant (LITA) with the following features: (1) We
introduce time tokens that encode timestamps relative to the video length to
better represent time in videos. (2) We introduce SlowFast tokens in the
architecture to capture temporal information at fine temporal resolution. (3)
We emphasize temporal localization data for LITA. In addition to leveraging
existing video datasets with timestamps, we propose a new task, Reasoning
Temporal Localization (RTL), along with the dataset, ActivityNet-RTL, for
learning and evaluating this task. Reasoning temporal localization requires
both the reasoning and temporal localization of Video LLMs. LITA demonstrates
strong performance on this challenging task, nearly doubling the temporal mean
intersection-over-union (mIoU) of baselines. In addition, we show that our
emphasis on temporal localization also substantially improves video-based text
generation compared to existing Video LLMs, including a 36% relative
improvement of Temporal Understanding. Code is available at:
https://github.com/NVlabs/LITA


  Asymmetric multicore processors (AMPs) couple high-performance big cores and
low-power small cores with the same instruction-set architecture but different
features, such as clock frequency or microarchitecture. Previous work has shown
that asymmetric designs may deliver higher energy efficiency than symmetric
multicores for diverse workloads. Despite their benefits, AMPs pose significant
challenges to runtime systems of parallel programming models. While previous
work has mainly explored how to efficiently execute task-based parallel
applications on AMPs, via enhancements in the runtime system, improving the
performance of unmodified data-parallel applications on these architectures is
still a big challenge. In this work we analyze the particular case of
loop-based OpenMP applications, which are widely used today in scientific and
engineering domains, and constitute the dominant application type in many
parallel benchmark suites used for performance evaluation on multicore systems.
We observed that conventional loop-scheduling OpenMP approaches are unable to
efficiently cope with the load imbalance that naturally stems from the
different performance delivered by big and small cores.
  To address this shortcoming, we propose \textit{Asymmetric Iteration
Distribution} (AID), a set of novel loop-scheduling methods for AMPs that
distribute iterations unevenly across worker threads to efficiently deal with
performance asymmetry. We implemented AID in \textit{libgomp} --the GNU OpenMP
runtime system--, and evaluated it on two different asymmetric multicore
platforms. Our analysis reveals that the AID methods constitute effective
replacements of the \texttt{static} and \texttt{dynamic} methods on AMPs, and
are capable of improving performance over these conventional strategies by up
to 56\% and 16.8\%, respectively.


  We consider statistical learning problems, when the distribution $P'$ of the
training observations $Z'_1,\; \ldots,\; Z'_n$ differs from the distribution
$P$ involved in the risk one seeks to minimize (referred to as the test
distribution) but is still defined on the same measurable space as $P$ and
dominates it. In the unrealistic case where the likelihood ratio
$\Phi(z)=dP/dP'(z)$ is known, one may straightforwardly extends the Empirical
Risk Minimization (ERM) approach to this specific transfer learning setup using
the same idea as that behind Importance Sampling, by minimizing a weighted
version of the empirical risk functional computed from the 'biased' training
data $Z'_i$ with weights $\Phi(Z'_i)$. Although the importance function
$\Phi(z)$ is generally unknown in practice, we show that, in various situations
frequently encountered in practice, it takes a simple form and can be directly
estimated from the $Z'_i$'s and some auxiliary information on the statistical
population $P$. By means of linearization techniques, we then prove that the
generalization capacity of the approach aforementioned is preserved when
plugging the resulting estimates of the $\Phi(Z'_i)$'s into the weighted
empirical risk. Beyond these theoretical guarantees, numerical results provide
strong empirical evidence of the relevance of the approach promoted in this
article.


  We introduce and study the problem \mpd, which asks for two planar graphs
$G_1$ and $G_2$ whether $G_1$ can be embedded such that its dual is isomorphic
to $G_2$. Our algorithmic main result is an NP-completeness proof for the
general case and a linear-time algorithm for biconnected graphs. To shed light
onto the combinatorial structure of the duals of a planar graph, we consider
the \emph{common dual relation} $\sim$, where $G_1 \sim G_2$ if and only if
they have a common dual. While $\sim$ is generally not transitive, we show that
the restriction to biconnected graphs is an equivalence relation. In this case,
being dual to each other carries over to the equivalence classes, i.e., two
graphs are dual to each other if and only if any two elements of their
respective equivalence classes are dual to each other.
  To achieve the efficient testing algorithm for \mpd on biconnected graphs, we
devise a succinct representation of the equivalence class of a biconnected
planar graph. It is similar to SPQR-trees and represents exactly the graphs
that are contained in the equivalence class. The testing algorithm then works
by testing in linear time whether two such representations are isomorphic. We
note that a special case of \mpd is testing whether a graph $G$ is self-dual.
Our algorithm handles the case where $G$ is biconnected and our NP-hardness
proof extends to testing self-duality of general planar graphs and also to
testing map self-duality, where a graph $G$ is map self-dual if it admits a
planar embedding $\mathcal G$ such that $G^\star$ is isomorphic to $G$, and
additionally the embedding induced by $\mathcal G$ on $G^\star$ is $\mathcal
G$.


  The investigations of the spectral and dynamical delocalization-localization
(DL) transition have revealed intriguing features in a wide range of
non-Hermitian systems. The present study aims at exploring the spectral and
dynamical properties in a non-Hermitian quasiperiodic system with asymmetric
hopping in the presence of Rashba Spin-Orbit (RSO) interaction. In particular,
in such systems, we have identified that the DL transition is associated with a
concurrent change in the energy spectrum, where the eigenstates always break
the time-reversal symmetry for all strenghts of the quasiperiodic potential,
contrary to the systems without RSO interaction. Remarkably, we find that the
reality of energy spectrum under the open boundary condition that is frequently
symbolised as a hallmark of the skin-effect, is a system-size dependent
phenomena, and appears even when the associated energies are indeed complex. In
addition, it is demonstrated that the spin-flip term in the RSO interaction in
fact possesses a tendency to diminish the directionality of the skin-effect. On
scrutinizing the dynamical attributes in our non-Hermitian system, we unveil
that in spite of the fact that the spectral DL transition accords with the
dynamical phase transition, interestingly, the system comes across
hyper-diffusive and negative diffusion dynamical regimes depending upon the
strength of the RSO interaction, in the spectrally localized regime.


  In this paper, we describe the relation between the Turaev--Viro TQFT and the
string-net space introduced in the papers of Levin and Wen. In particular, the
case of surfaces with boundary is considered in detail.


  Effects of nanostructured defects of copper solid surface on the bubble
growth in liquid argon have been investigated through a hybrid
atomistic-continuum method. The same solid surfaces with five different
nanostructures, namely, wedge defect, deep rectangular defect (R-I), shallow
rectangular defect (R-II), small rectangular defect (R-III) and no defect, have
been modeled at molecular level. The liquid argon is placed on top of the hot
solid copper with superheat of 30 K after equilibration is achieved with CFD-MD
coupled simulation. Phase change of argon on five nanostructures has been
observed and analyzed accordingly. The results showed that the solid surface
with wedge defect tends to induce a nano-bubble relatively more easily than the
others, and the larger the size of the defect is the easier the bubble
generate.


  A geometric description of the first Poisson cohomology groups is given in
the semilocal context, around (possibly singular) symplectic leaves. This
result is based on the splitting theorems for infinitesimal automorphisms of
coupling Poisson structures which describe the interaction between the
tangential and transversal data of the characteristic distributions. As a
consequence, we derive some criteria of vanishing of the first Poisson
cohomology groups and apply the general splitting formulas to some particular
classes of Poisson structures associated with singular symplectic foliations.


  The lack of an engaging pedagogy and the highly competitive atmosphere in
introductory science courses tend to discourage students from pursuing science,
technology, engineering, and mathematics (STEM) majors. Once in a STEM field,
academic and social integration has been long thought to be important for
students' persistence. Yet, it is rarely investigated. In particular, the
relative impact of in-class and out-of-class interactions remains an open
issue. Here, we demonstrate that, surprisingly, for students whose grades fall
in the "middle of the pack," the out-of-class network is the most significant
predictor of persistence. To do so, we use logistic regression combined with
Akaike's information criterion to assess in- and out-of-class networks, grades,
and other factors. For students with grades at the very top (and bottom), final
grade, unsurprisingly, is the best predictor of persistence---these students
are likely already committed (or simply restricted from continuing) so they
persist (or drop out). For intermediate grades, though, only out-of-class
closeness---a measure of one's immersion in the network---helps predict
persistence. This does not negate the need for in-class ties. However, it
suggests that, in this cohort, only students that get past the convenient
in-class interactions and start forming strong bonds outside of class are or
become committed to their studies. Since many students are lost through
attrition, our results suggest practical routes for increasing students'
persistence in STEM majors.


  CDM simulations predict that there are hundreds of lumps of with masses
greater than 10e7 solar masses in the Milky Way halo. However, we know of only
a dozen dwarf satellites close to this mass. Are these lumps simply lacking in
stars or is there a fundamental flaw in our most popular cosmology? By studying
the tidal debris of known satellites we can potentially address this question.
In this paper, we quantify the the effects of the dark matter lumps on tidal
tails. The lumps scatter stars in the tidal tails from their original orbits
producing a distinctive signature. We simulate debris evolution in smooth and
lumpy halos potentials and use our simulations to motivate and test a
statistical measure of the degree of scattering apparent in the angular
position and radial velocity measurement of debris stars - the ``scattering
index''. We find that the scattering index can in general distinguish between
the levels of substructure predicted by CDM cosmologies and smooth Milky Way
models, but that the sensitivity of the debris depends on the orientation of
the parent satellite's orbit relative to the largest lumps orbits.
  We apply our results to the carbon star stream associated with the
Sagittarius dwarf galaxy (Sgr) and find that these stars appear to be more
scattered than we expect for debris orbiting in a smooth halo. However, the
degree of scattering is entirely consistent with that expected due to the
influence of the Large Magellanic Cloud, which is on an orbit that intersects
Sgr's own. We conclude that the current data is unable to constrain CDM models.
Nevertheless, our study suggests that future data sets of debris stars
associated with other Milky Way satellites could provide strong constraints on
CDM models.


  In collaborative planning activities, since the agents are autonomous and
heterogeneous, it is inevitable that conflicts arise in their beliefs during
the planning process. In cases where such conflicts are relevant to the task at
hand, the agents should engage in collaborative negotiation as an attempt to
square away the discrepancies in their beliefs. This paper presents a
computational strategy for detecting conflicts regarding proposed beliefs and
for engaging in collaborative negotiation to resolve the conflicts that warrant
resolution. Our model is capable of selecting the most effective aspect to
address in its pursuit of conflict resolution in cases where multiple conflicts
arise, and of selecting appropriate evidence to justify the need for such
modification. Furthermore, by capturing the negotiation process in a recursive
Propose-Evaluate-Modify cycle of actions, our model can successfully handle
embedded negotiation subdialogues.


  We analyze the Kondo effect of a magnetic impurity attached to an ultrasmall
metallic wire using the density matrix renormalization group. The spatial spin
correlation function and the impurity spectral density are computed for system
sizes of up to L=511 sites, covering the crossover from $L<\ell_K$ to $L >
\ell_K$, with $\ell_K$ the spin screening length. %Strong mesoscopic variations
of the Kondo temperature $T_K$ %and of the spectral features override, to some
extent, the %even/odd effect predicted earlier for averaged quantities. We
establish a proportionality between the weight of the Kondo resonance and
$\ell_K$ as function of $L$. This suggests a spectroscopic way of detecting the
Kondo cloud.


  Stuttering, also called stammering, is a communication disorder that breaks
the continuity of the speech. This program of work is an attempt to develop
automatic recognition procedures to assess stuttered dysfluencies and use these
assessments to filter out speech therapies for an individual. Stuttering may be
in the form of repetitions, prolongations or abnormal stoppages of sounds and
syllables. Our system aims to help stutterers by diagnosing the severity and
type of stutter and also by suggesting appropriate therapies for practice by
learning the correlation between stutter descriptors and the effectiveness of
speech therapies on them. This paper focuses on the implementation of a stutter
diagnosis agent using Gated Recurrent CNN on MFCC audio features and therapy
recommendation agent using SVM. It also presents the results obtained and
various key findings of the system developed.


  A conceptually simple physical interpretation of a conserved Hamiltonian
$\mathcal{H}$ for a mechanical system with a time-dependent constraint is
given. For the case of a bead on a vertical hoop forced to rotate with constant
angular velocity $\omega$, $\mathcal{H}$ is nothing but the total energy of the
system plus the external actuator keeping $\omega$ fixed. In an analogy with
thermodynamics, the Hamiltonian is introduced as a thermodynamic potential
obtained from a Legendre transformation of the energy, in a very instructive
way. The ideas can be made extensive to different problems with time-dependent
constraints.


  Hovering insects are limited by their physiology and need to rotate their
wings at the end of each back and forth motion to keep the wing's leading edge
ahead of its trailing edge. The wing rotation at the end of each half-stroke
pushes the leading edge vortex away from the wing which leads to a loss in the
lift. Unlike biological fliers, human-engineered flapping wing micro air
vehicles have different design limitations. They can be designed to avoid the
end of stroke wing rotation and use so-called water-treading flapping
kinematics. Flapping wings using conventional flapping kinematics have a
designated leading and trailing edge. In the water-treading mode, the role of
the leading and trailing edges are continuously alternated throughout the
stroke. Here, we compare velocity field and force measurements for a
rectangular flapping wing conducting normal hovering and water-treading
kinematics to study the difference in fluid dynamic performance between the two
types of flapping kinematics. We show that for similar power consumption, the
water-treading mode produces more lift than the conventional hovering mode and
is 50% more efficient for symmetric pitching kinematics. In the water-treading
mode, the leading edge vortex from the previous stroke is not pushed away but
is captured and keeps the newly formed leading edge vortex closer to the wing,
leading to a more rapid increase of the lift coefficient which is sustained for
longer. This makes the water-treading mode a promising alternative for
human-engineered flapping wing vehicles.


  QCD lattice simulations yield hadron masses as functions of the quark masses.
From the gradients of the hadron masses the sigma terms can then be determined.
We consider here dynamical 2+1 flavour simulations, in which we start from a
point of the flavour symmetric line and then keep the singlet or average quark
mass fixed as we approach the physical point. This leads to highly constrained
fits for hadron masses in a multiplet. The gradient of this path for a hadron
mass then gives a relation between the light and strange sigma terms. A further
relation can be found from the change in the singlet quark mass along the
flavour symmetric line. This enables light and strange sigma terms to be
estimated for the baryon octet.


  The Ophiuchus clouds, in particular L~1688, are an excellent region to study
the embedded phases of star formation, due to the relatively large number of
protostars. However, the standard method of finding and characterizing embedded
young stellar objects (YSOs) through just their infrared spectral slope does
not yield a reliable sample. This may affect the age determinations, often
derived from the statistics on the total number of embedded YSOs and pre-main
sequence stars within a cloud.Our aim is to characterize the structure of
protostellar envelopes on an individual basis and to correctly identify the
embedded YSO population of L1688. Spectral maps of the HCO+ J=4--3 and C18O
J=3--2 lines using the HARP-B array on the James Clerk Maxwell Telescope and
SCUBA 850 micron dust maps are obtained of all sources in the L1688 region with
infrared spectral slopes consistent with, or close to, that of embedded YSOs.
Selected 350 micron maps obtained with the Caltech Submillimeter Observatory
are presented as well. The properties, extent and variation of dense gas,
column density and dust on scalesup to 1' are probed at 15" resolution. Using
the spatial variation of the gas and dust, together with the intensity of the
HCO+ J=4--3 line, we are able to accurately identify the truly embedded YSOs
and determine their properties. RESULTS The protostellar envelopes range from
0.05 to 0.5 Msun in mass. The concentration of HCO+ emission (~0.5 to 0.9) is
generally higher than that of the dust concentration. Combined with absolute
intensities, HCO+ proves to be a better tracer of protostellar envelopes than
dust, which can contain disk and cloud contributions. Our total sample of 45
sources, including all previously classified Class I sources, several
flat-spectrum sources and some known disks, was re-classified using the ....


  We present an experimental and theoretical study of row switching in
two-dimensional Josephson junction arrays. We have observed novel dynamic
states with peculiar percolative patterns of the voltage drop inside the
arrays. These states were directly visualized using laser scanning microscopy
and manifested by fine branching in the current-voltage characteristics of the
arrays. Numerical simulations show that such percolative patterns have an
intrinsic origin and occur independently of positional disorder. We argue that
the appearance of these dynamic states is due to the presence of various
metastable superconducting states in arrays.


  Figure-eight solutions are solutions to planar equal mass three-body problem
under homogeneous or inhomogeneous potentials. They are known to be invariant
under the transformation group $D_6$: the dihedral group of regular hexagons.
Numerical investigation shows that each figure-eight solution has some
bifurcation points. Six bifurcation patterns are known with respect to the
symmetry of the bifurcated solution.
  In this paper we will show the followings. The variational principle of
action and group theory show that the bifurcations of every figure-eight
solution are determined by the irreducible representations of $D_6$. Each
irreducible representation has one to one correspondence to each bifurcation.
This explains numerically observed six bifurcation patterns. In general, in
Lagrangian mechanics, bifurcations of a periodic solution is determined by
irreducible representations of the transformation group that leaves this
solution invariant.


  We give an introductory account of two recent approaches towards an effective
proof of the Mordell conjecture, due to Lawrence--Venkatesh and Kim. The latter
method, which is usually called the method of Chabauty--Kim or non-abelian
Chabauty in the literature, has the advantage that in some cases it has been
turned into an effective method to determine the set of rational points on a
curve, and we illustrate this by presenting three new examples of modular
curves where this set can be determined.


  Search for new physics events at the LHC mostly rely on the assumption that
the events are characterized in terms of standard-reconstructed objects such as
isolated photons, leptons, and jets initiated by QCD-partons. While such
strategy works for a vast majority of physics beyond the standard model
scenarios, there are examples aplenty where new physics give rise to anomalous
objects (such as collimated and equally energetic particles, decays due to long
lived particles etc.) in the detectors, which can not be classified as any of
the standard-objects. Varied methods and search strategies have been proposed,
each of which is trained and optimized for specific models, topologies, and
model parameters. Further, as LHC keeps excluding all expected candidates for
new physics, the need for a generic method/tool that is capable of finding the
unexpected can not be understated. In this paper, we propose one such method
that relies on the philosophy that all anomalous objects are $\it{not}$
standard-objects. The anomaly finder, we suggest, simply is a collection of
vetoes that eliminate all standard-objects up to a pre-determined acceptance
rate. Any event containing at least one anomalous object (that passes all these
vetoes), can be identified as a candidate for new physics. Subsequent offline
analyses can determine the nature of the anomalous object as well as of the
event, paving a robust way to search for these new physics scenarios in a
model-independent fashion. Further, since the method relies on learning only
the standard-objects, for which control samples are readily available from
data, one can build the analysis in an entirely data-driven way.


  Quantum-mechanical close-coupling calculations for state-to-state cross
sections and thermal rates are reported for H2+H2 collisions. Two recently
developed potential energy surfaces (PES) for the H2-H2 system are applied,
namely, the global potential surface from the work of A.I. Boothroyd, P.G.
Martin, W.J. Keogh, M.J. Peterson, J. Chem. Phys., 116 (2002) 666, and a
restricted, model surface from the works of P. Diep, J.K. Johnson, J. Chem.
Phys., 113 (2000) 3480; ibid. 112, 4465. The low temperature limit is
investigated. We found significant differences in cross sections and
corresponding thermal rates calculated with these two PESs.


  Diffusion Weighted Imaging (DWI) is an advanced imaging technique commonly
used in neuroscience and neurological clinical research through a Diffusion
Tensor Imaging (DTI) model. Volumetric scalar metrics including fractional
anisotropy, mean diffusivity, and axial diffusivity can be derived from the DTI
model to summarise water diffusivity and other quantitative microstructural
information for clinical studies. However, clinical practice constraints can
lead to sub-optimal DWI acquisitions with missing slices (either due to a
limited field of view or the acquisition of disrupted slices). To avoid
discarding valuable subjects for group-wise studies, we propose a novel 3D
Tensor-Wise Brain-Aware Gate network (TW-BAG) for inpainting disrupted DTIs.
The proposed method is tailored to the problem with a dynamic gate mechanism
and independent tensor-wise decoders. We evaluated the proposed method on the
publicly available Human Connectome Project (HCP) dataset using common image
similarity metrics derived from the predicted tensors and scalar DTI metrics.
Our experimental results show that the proposed approach can reconstruct the
original brain DTI volume and recover relevant clinical imaging information.


  We study open domain response generation with limited message-response pairs.
The problem exists in real-world applications but is less explored by the
existing work. Since the paired data now is no longer enough to train a neural
generation model, we consider leveraging the large scale of unpaired data that
are much easier to obtain, and propose response generation with both paired and
unpaired data. The generation model is defined by an encoder-decoder
architecture with templates as prior, where the templates are estimated from
the unpaired data as a neural hidden semi-markov model. By this means, response
generation learned from the small paired data can be aided by the semantic and
syntactic knowledge in the large unpaired data. To balance the effect of the
prior and the input message to response generation, we propose learning the
whole generation model with an adversarial approach. Empirical studies on
question response generation and sentiment response generation indicate that
when only a few pairs are available, our model can significantly outperform
several state-of-the-art response generation models in terms of both automatic
and human evaluation.


  I review the linear and second-order perturbation theory in dark energy
models with explicit interaction to matter in view of applications to N-body
simulations and non-linear phenomena. Several new or generalized results are
obtained: the general equations for the linear perturbation growth; an
analytical expression for the bias induced by a species-dependent interaction;
the Yukawa correction to the gravitational potential due to dark energy
interaction; the second-order perturbation equations in coupled dark energy and
their Newtonian limit. I also show that a density-dependent effective dark
energy mass arises if the dark energy coupling is varying.


  We study ancient solutions of polynomial growth to heat equations on graphs,
and extend Colding and Minicozzi's theorem [CM19] on manifolds to graphs: For a
graph of polynomial volume growth, the dimension of the space of ancient
solutions of polynomial growth is bounded by the product of the growth degree
and the dimension of harmonic functions with the same growth.


  Monte Carlo methods are essential tools for Bayesian inference. Gibbs
sampling is a well-known Markov chain Monte Carlo (MCMC) algorithm, extensively
used in signal processing, machine learning, and statistics, employed to draw
samples from complicated high-dimensional posterior distributions. The key
point for the successful application of the Gibbs sampler is the ability to
draw efficiently samples from the full-conditional probability density
functions. Since in the general case this is not possible, in order to speed up
the convergence of the chain, it is required to generate auxiliary samples
whose information is eventually disregarded. In this work, we show that these
auxiliary samples can be recycled within the Gibbs estimators, improving their
efficiency with no extra cost. This novel scheme arises naturally after
pointing out the relationship between the standard Gibbs sampler and the chain
rule used for sampling purposes. Numerical simulations involving simple and
real inference problems confirm the excellent performance of the proposed
scheme in terms of accuracy and computational efficiency. In particular we give
empirical evidence of performance in a toy example, inference of Gaussian
processes hyperparameters, and learning dependence graphs through regression.


  This paper describes our approach to the EmotionX-2019, the shared task of
SocialNLP 2019. To detect emotion for each utterance of two datasets from the
TV show Friends and Facebook chat log EmotionPush, we propose two-step deep
learning based methodology: (i) encode each of the utterance into a sequence of
vectors that represent its meaning; and (ii) use a simply softmax classifier to
predict one of the emotions amongst four candidates that an utterance may
carry. Notice that the source of labeled utterances is not rich, we utilise a
well-trained model, known as BERT, to transfer part of the knowledge learned
from a large amount of corpus to our model. We then focus on fine-tuning our
model until it well fits to the in-domain data. The performance of the proposed
model is evaluated by micro-F1 scores, i.e., 79.1% and 86.2% for the testsets
of Friends and EmotionPush, respectively. Our model ranks 3rd among 11
submissions.


  We study intersecting noncommutative (NC) M5-branes from \kappa-invariance of
an open supermembrane action with constant three-form fluxes. The
$\kappa$-invariance gives rise to possible D-brane configurations for which
projection operators can be determined. We construct projection operators for
two types of 1/4 BPS intersecting NC M5-branes. The one is an intersection of
two NC M5-branes: NC M5\bot NC M5 (3). The other is that of a NC M5-brane and a
commutative (C) M5-brane: NC M5\bot C M5 (1). A NC M5-brane can be viewed as a
bound state of M5 and M2, and the configurations M2\bot M5 (1) and M2\bot M2
(0) are realized on the intersecting M5-branes. Taking a commutative limit the
allowed intersecting M5-branes are surely reproduced: M5\bot M5 (3) and M5\bot
M5 (1).


  We present a concept and test results of an eddy-current flowmeter for liquid
metals. The flow rate is determined by applying a weak ac magnetic field to a
liquid metal flow and measuring the flow-induced phase disturbance in the
external electromagnetic field. The phase disturbance is found to be more
robust than that of the amplitude used in conventional eddy-current flowmeters.
The basic characteristics of this type of flowmeter are analysed using simple
theoretical models, where the flow is approximated by a solid body motion.
Design of such a flowmeter is presented and its test results reported.


  We show that the desirable weak mixing angle sin^2 theta_W = 0.2312 at m_Z
scale can be generated naturally in the SU(3)_C times SU(3) model on M^4 times
S^1 / (Z_2 times Z_2') where the gauge symmetry SU(3) is broken down to SU(2)_L
times U(1)_Y by orbifold projection. For a supersymmetric model with a TeV
scale extra dimension, the SU(3) unification scale is about hundreds of TeVs at
which the gauge couplings for SU(3)_C and SU(3) can also be equal in the mean
time. For the non-supersymmetric model, SU(2)_L times U(1)_Y are unified at
order of 10 TeV. These models may serve as good candidates for physics beyond
the SM or MSSM.


  The article is dedicated to thye memory of a distinguished mathematician
Professor Misha Shubin


  In this paper, we study alternating links in thickened surfaces in terms of
the lattices of integer flows on their Tait graphs. We use this approach to
give a short proof of the first two generalised Tait conjectures. We also prove
that the flow lattice is an invariant of alternating links in thickened
surfaces and is further invariant under disc mutation. For classical links, the
flow lattice and $d$-invariants are complete invariants of the mutation class
of an alternating link. For links in thickened surfaces, we show that this is
no longer the case by finding a stronger mutation invariant, namely the
Gordon-Litherland linking form. In particular, we find alternating knots in
thickened surfaces which have isometric flow lattices but with non-isomorphic
linking forms.


  Hexagonal boron nitride (hBN) is a van der Waals semiconductor with a wide
bandgap of ~ 5.96 eV. Despite the indirect bandgap characteristics of hBN,
charge carriers excited by high energy electrons or photons efficiently emit
luminescence at deep-ultraviolet (DUV) frequencies via strong electron-phonon
interaction, suggesting potential DUV light emitting device applications.
However, electroluminescence from hBN has not been demonstrated at DUV
frequencies so far. In this study, we report DUV electroluminescence and
photocurrent generation in graphene/hBN/graphene heterostructures at room
temperature. Tunneling carrier injection from graphene electrodes into the band
edges of hBN enables prominent electroluminescence at DUV frequencies. On the
other hand, under DUV laser illumination and external bias voltage, graphene
electrodes efficiently collect photo-excited carriers in hBN, which generates
high photocurrent. Laser excitation micro-spectroscopy shows that the radiative
recombination and photocarrier excitation processes in the heterostructures
mainly originate from the pristine structure and the stacking faults in hBN.
Our work provides a pathway toward efficient DUV light emitting and detection
devices based on hBN.


  The logistic equation has been extensively used to model biological phenomena
across a variety of disciplines and has provided valuable insight into how our
universe operates. Incorporating time-dependent parameters into the logistic
equation allows the modeling of more complex behavior than its autonomous
analog, such as a tumor's varying growth rate under treatment, or the expansion
of bacterial colonies under varying resource conditions. Some of the most
commonly used numerical solvers produce vastly different approximations for a
non-autonomous logistic model with a periodically-varying growth rate changing
signum. Incorrect, inconsistent, or even unstable approximate solutions for
this non-autonomous problem can occur from some of the most frequently used
numerical methods, including the lsoda, implicit backwards difference, and
Runge-Kutta methods, all of which employ a black-box framework. Meanwhile, a
simple, manually-programmed Runge-Kutta method is robust enough to accurately
capture the analytical solution for biologically reasonable parameters and
consistently produce reliable simulations. Consistency and reliability of
numerical methods are fundamental for simulating non-autonomous differential
equations and dynamical systems, particularly when applications are physically
or biologically informed.


  In a seminal work, Chiba and Nishizeki [SIAM J. Comput. `85] developed
subgraph listing algorithms for triangles, 4-cycle and $k$-cliques, where $k
\geq 3.$ The runtimes of their algorithms are parameterized by the number of
edges $m$ and the arboricity $\alpha$ of a graph. The arboricity $\alpha$ of a
graph is the minimum number of spanning forests required to cover it. Their
work introduces: * A triangle listing algorithm that runs in $O(m\alpha)$ time.
* An output-sensitive 4-Cycle-Listing algorithm that lists all 4-cycles in
$O(m\alpha + t)$ time, where $t$ is the number of 4-cycles in the graph. * A
k-Clique-Listing algorithm that runs in $O(m\alpha^{k-2})$ time, for $k \geq
4.$
  Despite the widespread use of these algorithms in practice, no improvements
have been made over them in the past few decades. Therefore, recent work has
gone into studying lower bounds for subgraph listing problems. The works of
Kopelowitz, Pettie and Porat [SODA `16] and Vassilevska W. and Xu [FOCS `20]
showed that the triangle-listing algorithm of Chiba and Nishizeki is optimal
under the $\mathsf{3SUM}$ and $\mathsf{APSP}$ hypotheses respectively. However,
it remained open whether the remaining algorithms were optimal.
  In this note, we show that in fact all the above algorithms are optimal under
popular hardness conjectures. First, we show that the
$\mathsf{4}\text{-}\mathsf{Cycle}\text{-}\mathsf{Listing}$ algorithm is tight
under the $\mathsf{3SUM}$ hypothesis following the techniques of Jin and Xu
[STOC `23], and Abboud, Bringmann and Fishcher [STOC `23] . Additionally, we
show that the $k\text{-}\mathsf{Clique}\text{-}\mathsf{Listing}$ algorithm is
essentially tight under the exact $k$-clique hypothesis by following the
techniques of Dalirooyfard, Mathialagan, Vassilevska W. and Xu [STOC `24].
These hardness results hold even when the number of 4-cycles or $k$-cliques in
the graph is small.


  Robots are increasingly operating in indoor environments designed for and
shared with people. However, robots working safely and autonomously in uneven
and unstructured environments still face great challenges. Many modern indoor
environments are designed with wheelchair accessibility in mind. This presents
an opportunity for wheeled robots to navigate through sloped areas while
avoiding staircases. In this paper, we present an integrated software and
hardware system for autonomous mobile robot navigation in uneven and
unstructured indoor environments. This modular and reusable software framework
incorporates capabilities of perception and navigation. Our robot first builds
a 3D OctoMap representation for the uneven environment with the 3D mapping
using wheel odometry, 2D laser and RGB-D data. Then we project multilayer 2D
occupancy maps from OctoMap to generate the the traversable map based on layer
differences. The safe traversable map serves as the input for efficient
autonomous navigation. Furthermore, we employ a variable step size Rapidly
Exploring Random Trees that could adjust the step size automatically,
eliminating tuning step sizes according to environments. We conduct extensive
experiments in simulation and real-world, demonstrating the efficacy and
efficiency of our system.


  We address the problem of segmenting and retrieving word images in
collections of historical manuscripts given a text query. This is commonly
referred to as "word spotting". To this end, we first propose an end-to-end
trainable model based on deep neural networks that we dub Ctrl-F-Net. The model
simultaneously generates region proposals and embeds them into a word embedding
space, wherein a search is performed. We further introduce a simplified version
called Ctrl-F-Mini. It is faster with similar performance, though it is limited
to more easily segmented manuscripts. We evaluate both models on common
benchmark datasets and surpass the previous state of the art. Finally, in
collaboration with historians, we employ the Ctrl-F-Net to search within a
large manuscript collection of over 100 thousand pages, written across two
centuries. With only 11 training pages, we enable large scale data collection
in manuscript-based historical research. This results in a speed up of data
collection and the number of manuscripts processed by orders of magnitude.
Given the time consuming manual work required to study old manuscripts in the
humanities, quick and robust tools for word spotting has the potential to
revolutionise domains like history, religion and language.


  The goal of the paper is to study specific properties of nonbinary
low-density parity-check (NB LDPC) codes when used in coded modulation systems.
The paper is focused on the practically important NB LDPC codes over extensions
of the Galois field GF$(2^m)$ with $m \le 6$ used with QAM signaling.
Performance of NB QC LDPC coded transmission strongly depends on mapping of
nonbinary symbols to signal constellation points. We obtain a random coding
bound on the maximum-likelihood decoding error probability for an ensemble of
random irregular NB LDPC codes used with QAM signaling for specific
symbol-to-signal point mappings. This bound is based on the ensemble average
Euclidean distance spectra derived for these mappings. The simulation results
for the belief-propagation decoding in the coded modulation schemes with the NB
quasi-cyclic (QC)-LDPC codes under different mappings are given. Comparisons
with the optimized binary QC-LDPC codes in the WiFi and 5G standards, as well
as with the new bound, are performed.


  We wish to investigate whether there is an extension to the base $\Lambda$CDM
cosmology that can resolve the tension between the Planck observation of the
cosmic microwave background anisotropies and the local measurement of the
Hubble constant. We consider various plausible extended models in this work,
and we use the Planck 2015 observation, combined with the baryon acoustic
oscillation data, the JLA type Ia supernovae data, and the local measurement of
the Hubble constant (by Riess et al. in 2016), to make an analysis. We find
that the holographic dark energy plus sterile neutrino model can reduce the
tension to be at the 1.11$\sigma$ level, but this model is obviously not
favored by the current observations. Among these extended models, the
$\Lambda$CDM+$N_{\rm eff}$ model is most favored by the current observations,
and this model can reduce the tension to be at the 1.87$\sigma$ level. By a
careful test, we conclude that none of these extended models can convincingly
resolve the $H_0$ tension.


  We show how to prove the examples of a paper by Chu and Zhang using the
WZ-algorithm.


  The quasi-classical model in a gauge theory with the Yang-Mills (YM) field is
developed. On a basis of the exact solution of the Dirac equation in the SU(N)
gauge field, which is in the eikonal approximation, the Yang-Mills (YM)
equations containing the external fermion current are solved. The derived
solutions are quantized in the quasi-classical approach. The developed model
proves to have the self-consistent solutions of the Dirac and Yang-Mills
equations at $N\geq 3$. Thereat the solutions take place provided that the
fermion and gauge fields exist simultaneously, so that the fermion current
completely compensates the current generated by the gauge field due to it
self-interaction. The obtained solution are considered in the context of QCD.


  By applying generalized dimensional reduction on the type IIB supersymmetry
variations, we derive the supersymmetry variations for the massive
9-dimensional supergravity. We use these variations and the ones for massive
type IIA to derive the supersymmetry transformation of the gravitino for the
proposed massive 11-dimensional supergravity.


  Recurrent Neural Networks (RNNs) have revolutionized many areas of machine
learning, particularly in natural language and data sequence processing. Long
Short-Term Memory (LSTM) has demonstrated its ability to capture long-term
dependencies in sequential data. Inspired by the Kolmogorov-Arnold Networks
(KANs) a promising alternatives to Multi-Layer Perceptrons (MLPs), we proposed
a new neural networks architecture inspired by KAN and the LSTM, the Temporal
Kolomogorov-Arnold Networks (TKANs). TKANs combined the strenght of both
networks, it is composed of Recurring Kolmogorov-Arnold Networks (RKANs) Layers
embedding memory management. This innovation enables us to perform multi-step
time series forecasting with enhanced accuracy and efficiency. By addressing
the limitations of traditional models in handling complex sequential patterns,
the TKAN architecture offers significant potential for advancements in fields
requiring more than one step ahead forecasting.


  Mirrors are everywhere in our daily lives. Existing computer vision systems
do not consider mirrors, and hence may get confused by the reflected content
inside a mirror, resulting in a severe performance degradation. However,
separating the real content outside a mirror from the reflected content inside
it is non-trivial. The key challenge is that mirrors typically reflect contents
similar to their surroundings, making it very difficult to differentiate the
two. In this paper, we present a novel method to segment mirrors from an input
image. To the best of our knowledge, this is the first work to address the
mirror segmentation problem with a computational approach. We make the
following contributions. First, we construct a large-scale mirror dataset that
contains mirror images with corresponding manually annotated masks. This
dataset covers a variety of daily life scenes, and will be made publicly
available for future research. Second, we propose a novel network, called
MirrorNet, for mirror segmentation, by modeling both semantical and low-level
color/texture discontinuities between the contents inside and outside of the
mirrors. Third, we conduct extensive experiments to evaluate the proposed
method, and show that it outperforms the carefully chosen baselines from the
state-of-the-art detection and segmentation methods.


  Al/ maleic anhydride (MA) /p-Si organic Schottky devices were fabricated on a
p-Si semiconductor wafer by spin coating. The frequency and voltage dependent
dielectric constant of Al/MA/p-Si have been investigated. Dielectric properties
and electrical conductivity of contact structures have been investigated in
detail by using spectroscopic technique in a wide range of frequencies and
applied bias voltages at room temperature. The values of dielectric constant,
dielectric loss, dielectric loss tangent, real and imaginary parts of the
electrical modulus and ac electrical conductivity were found considerably
sensitive to frequency and applied bias voltage especially in depletion and
accumulation regions. Experimental results indicate that the values of
dielectric constant show a steep decrease with increasing frequency for each
voltage. The values of dielectric loss as a function of voltage show a jump,
and dielectric loss decreases with decreasing voltage and increasing frequency.
The weak increasing of the ac electrical conductivity on frequency is observed.
The real part of electric modulus increases with increasing frequency. Also,
the imaginary part of electric modulus shows a peak and the peak position
shifts to higher frequency with increasing applied voltage. It can be concluded
that the interfacial polarization can be more easily occurred at low
frequencies and the majority of interface states at metal semiconductor
interface contributes to deviation of dielectric properties of Al/MA/p-Si
structures.


  We present new trial wave-functions which include 3-body correlations into
the backflow coordinates and a 4-body symmetric potential. We show that our
wavefunctions lower the energy enough to stabilize the ground state energies of
normal liquid $^3$He in the unpolarized state at all pressures in agreement
with experiment; however, quantitative discrepancies remain. Further, we
include strong spin coupling into the Fermi liquid by adapting pairing wave
functions. We demonstrate a new, numerically stable method to evaluate pairing
functions which is also useful for Path Integrals calculations at low, but
non-zero temperatures.


  In P\"otscher and Preinerstorfer (2022) and in the abridged version
P\"otscher and Preinerstorfer (2024, published in Econometrica) we have tried
to clear up the confusion introduced in Hansen (2022a) and in the earlier
versions Hansen (2021a,b). Unfortunatelly, Hansen's (2024) reply to P\"otscher
and Preinerstorfer (2024) further adds to the confusion. While we are already
somewhat tired of the matter, for the sake of the econometrics community we
feel compelled to provide clarification. We also add a comment on Portnoy
(2023), a "correction" to Portnoy (2022), as well as on Lei and Wooldridge
(2022).


  First-order model counting emerged recently as a novel reasoning task, at the
core of efficient algorithms for probabilistic logics. We present a
Skolemization algorithm for model counting problems that eliminates existential
quantifiers from a first-order logic theory without changing its weighted model
count. For certain subsets of first-order logic, lifted model counters were
shown to run in time polynomial in the number of objects in the domain of
discourse, where propositional model counters require exponential time.
However, these guarantees apply only to Skolem normal form theories (i.e., no
existential quantifiers) as the presence of existential quantifiers reduces
lifted model counters to propositional ones. Since textbook Skolemization is
not sound for model counting, these restrictions precluded efficient model
counting for directed models, such as probabilistic logic programs, which rely
on existential quantification. Our Skolemization procedure extends the
applicability of first-order model counters to these representations. Moreover,
it simplifies the design of lifted model counting algorithms.


  We study a new statistics of wave functions in several chaotic and disordered
systems: the random matrix model, band random matrix model, the Lipkin model,
chaotic quantum billiard and the 1D tight-binding model. Both numerical and
analytical results show that the distribution function of a generalized Riccati
variable, defined as the ratio of components of eigenfunctions on basis states
coupled by perturbation, is universal, and has the form of Lorentzian
distribution.


  This work deals with decay bounds for Green matrices and generalized
eigenvectors of block Jacobi matrices when the real part of the spectral
parameter lies in an infinite gap of the operator's essential spectrum. We
consider the cases of commutative and noncommutative matrix entries separately.
An example of a block Jacobi operator with noncommutative entries and
nonnegative essential spectrum is given to illustrate the results.


  We studied optical response of microcavity non-equilibrium exciton-polariton
Bose-Einstein condensate with saturable nonlinearity under simultaneous
resonant and non-resonant pumping. We demonstrated the emergence of
multistabile behavior due to the satutration of the excitonic absorbtion.
Stable periodic Rabi- type oscillations of the excitonic and photonic
condensate components in the regime of the stationary pump and their transition
to the chaotic dynamics through the cascade of Hopf bifurcations by tuning of
the electrical pump are revealed.


  This paper presents GoPose, a 3D skeleton-based human pose estimation system
that uses WiFi devices at home. Our system leverages the WiFi signals reflected
off the human body for 3D pose estimation. In contrast to prior systems that
need specialized hardware or dedicated sensors, our system does not require a
user to wear or carry any sensors and can reuse the WiFi devices that already
exist in a home environment for mass adoption. To realize such a system, we
leverage the 2D AoA spectrum of the signals reflected from the human body and
the deep learning techniques. In particular, the 2D AoA spectrum is proposed to
locate different parts of the human body as well as to enable
environment-independent pose estimation. Deep learning is incorporated to model
the complex relationship between the 2D AoA spectrums and the 3D skeletons of
the human body for pose tracking. Our evaluation results show GoPose achieves
around 4.7cm of accuracy under various scenarios including tracking unseen
activities and under NLoS scenarios.


  The interaction of cobalt atoms with silicon (111) surface has been
investigated by means of scanning tunneling microscopy (STM) and low-energy
electron diffraction (LEED). Besides the Co silicide islands, we have
successfully distinguished two inequivalent Co-induced
$\sqrt{13}\times\sqrt{13}$ reconstructions on Si (111) surface. Our
high-resolution STM images provide some structural properties of the two
different $\sqrt{13}\times\sqrt{13}$ derived phases. Both of the two phases
seem to form islands with single domain. The new findings will help us to
understand the early stage of Co silicide formations.


  The operation of organic optoelectronic devices relies notably on the bulk
properties of compound molecular species, but even more so on the influence of
interfaces thereof. The identification and characterization of elemental
processes in these critical sections of a device thereby requires well-defined
interfaces with low defect density. In this context titanyl phthalocyanine
(TiOPc) arises as an excellent candidate that reveals the formation of a stable
bilayer structure with a characteristic "up-down" molecular arrangement that
optimizes the dipole-dipole interaction within the bilayer. In our experimental
study, long-range ordered TiOPc bilayers have been grown on Ag(111) surfaces
and analyzed using infrared absorption spectroscopy and scanning tunneling
microscopy. By monitoring the prominent Ti=O stretching mode in IRAS and
identifying local configurations in STM, a microscopic model for the growth of
TiOPc bilayers on Ag(111) is suggested. We demonstrate that defect structures
within these bilayers lead to characteristic vibrational signatures which react
sensitively to the local environment of the molecules. Thermal desorption
spectroscopy reveals a high thermal stability of the TiOPc bilayer up to 500 K,
which is attributed to hydrogen bonds between oxygen of the titanyl unit and
the hydrogen rim of phthalocyanines in the second layer, in addition to
contributions arising from the oppositely oriented axial dipole moments and the
ubiquitous van der Waals interactions.


  Slow light waveguides are advantageous for implementing high-performance
single-photon sources required for scalable operation of integrated quantum
photonic circuits (IQPCs), though such waveguides are known to suffer from
propagation loss due to backscattering. A way to overcome the drawback is to
use topological photonics, in which robust waveguiding in
topologically-protected optical modes has recently been demonstrated. Here, we
report single-photon sources using single quantum dots (QDs) embedded in
topological slow light waveguides based on valley photonic crystals. We observe
Purcell-enhanced single-photon emission from a QD into a topological slow light
mode with a group index over 20 and its robust propagation even under the
presence of sharp bends. These results pave the way for the realization of
robust and high-performance single-photon sources indispensable for IQPCs.


  We propose a low complexity antenna selection algorithm for low target rate
users in cloud radio access networks. The algorithm consists of two phases: In
the first phase, each remote radio head (RRH) determines whether to be included
in a candidate set by using a predefined selection threshold. In the second
phase, RRHs are randomly selected within the candidate set made in the first
phase. To analyze the performance of the proposed algorithm, we model RRHs and
users locations by a homogeneous Poisson point process, whereby the
signal-to-interference ratio (SIR) complementary cumulative distribution
function is derived. By approximating the derived expression, an approximate
optimum selection threshold that maximizes the SIR coverage probability is
obtained. Using the obtained threshold, we characterize the performance of the
algorithm in an asymptotic regime where the RRH density goes to infinity. The
obtained threshold is then modified depending on various algorithm options. A
distinguishable feature of the proposed algorithm is that the algorithm
complexity keeps constant independent to the RRH density, so that a user is
able to connect to a network without heavy computation at baseband units.


  A multilayer surface detector for ultracold neutrons (UCNs) is described. The
top $^{10}$B layer is exposed to the vacuum chamber and directly captures UCNs.
The ZnS:Ag layer beneath the $^{10}$B layer is a few microns thick, which is
sufficient to detect the charged particles from the $^{10}$B(n,$\alpha$)$^7$Li
neutron-capture reaction, while thin enough so that ample light due to $\alpha$
and $^7$Li escapes for detection by photomultiplier tubes. One-hundred-nm thick
$^{10}$B layer gives high UCN detection efficiency, as determined by the mean
UCN kinetic energy, detector materials and others. Low background, including
negligible sensitivity to ambient neutrons, has also been verified through
pulse-shape analysis and comparisons with other existing $^3$He and $^{10}$B
detectors. This type of detector has been configured in different ways for UCN
flux monitoring, development of UCN guides and neutron lifetime research.


  We have examined the production of J/$\psi$ mesons in high energy
proton-proton and proton-nucleus collisions at beam energies in the range from
158 GeV to 920 GeV, available from different fixed target experiments. In the
employed model J/$\psi$ production in hadronic collisions is assumed to be a
factorisable two step process: (i) production of a $c\bar{c}$ pair which can be
reliably described by perturbative QCD, and (ii) formation of J/$\psi$
resonance from the $c\bar{c}$ pair, which can be conveniently parameterized
incorporating different existing physical mechanisms of color neutralization.
We show that, for lower collision energies, J/$\psi$ production through
quark-anti-quark annihilation gives larger contribution at higher $x_F$, while
gluon-gluon fusion dominates the production at smaller $x_F$. For
proton-nucleus collisions the model takes into account both the initial state
modification of parton distributions in nuclei and the final state interaction
of the produced $c\bar{c}$ pairs with the target nucleons. The model is found
to give reasonable description of data on J/$\psi$ production in protonic and
proton-nucleus collisions, for different existing fixed target experiments. In
case of proton-nucleus collisions, our calculations show a non-negligible
dependence of the final state nuclear dissociation of J/$\psi$ mesons on the
energy of the incident proton beam. The model has been applied to predict the
J/$\psi$ production and suppression expected in proton-nucleus collisions at
energies relevant to FAIR, the upcoming accelerator facility at Darmstadt,
Germany. The amount of suppressions, for different mechanisms of J/$\psi$
hadronization has been found to be distinguishably different which might help
an experimental settlement of the much controversial issue of color
neutralization.


  A search for the pair production of heavy leptons as predicted by the
type-III seesaw mechanism is presented. The search uses proton-proton collision
data at a centre-of-mass energy of 13 TeV, corresponding to 139 fb$^{-1}$ of
integrated luminosity recorded by the ATLAS detector during Run 2 of the Large
Hadron Collider. The analysis focuses on the final state with two light leptons
(electrons or muons) of different flavour and charge combinations, with at
least two jets and large missing transverse momentum. No significant excess
over the Standard Model expectation is observed. The results are translated
into exclusion limits on heavy-lepton masses, and the observed lower limit on
the mass of the type-III seesaw heavy leptons is 790 GeV at 95% confidence
level.


  The composition of the surface of the Galilean icy moons has been debated
since the Galileo mission. Several chemistries have been proposed to explain
the composition of the non-icy component of the moon's surfaces, notably,
sulphuric acid hydrates and magnesium and sodium sulphates. More recently,
magnesium and sodium chlorides have been proposed to explain features observed
in ground-based observations. We have considered four salts (NaCl, Na2SO4,
MgSO4 and MgCl2) with various concentrations, to produce salty ice analogues.
Granular particles were produced by a flash-freezing procedure. Additionally,
compact slabs of salty ices were produced by a slow crystallisation of salty
liquid solution. These two methods mimic the end-members (plumes and slow ice
block formation) for producing hydrated salty ices on the surface of icy moons
such as Europa and Ganymede. We have monitored the near-infrared (NIR)
evolution of our salty ices during sublimation, revealing differences between
the granular particles and the slabs. The slabs formed a higher amount of
hydrates and the most highly hydrated compounds. Granular ices must be formed
from a more concentrated salty solution to increase the amount of hydrates
within the ice particles. The sublimation of salty ices removed all excess
water ice efficiently, but the dehydration of the salts was not observed. The
final spectra of the slabs were most flattened around 1.5 and 2.0 {\mu}m,
especially for the Na2SO4, MgCl2 and MgSO4, suggesting the presence of stable,
highly hydrated compounds. We find that Na2SO4, MgCl2 and MgSO4 are most
compatible with the non-icy component at the surface of the icy moons as
observed by the NIMS instrument on Galileo and by ground-based observations.