  Nearby metal-poor starburst dwarf galaxies present a unique opportunity to
probe the physics of high-density star formation with a detail and sensitivity
unmatched by any observation of the high-z Universe. Here we present the first
results from a chemodynamical study of the nearby, gas-rich starburst dwarf
CGCG 007-025. We use VLT/MUSE integral field spectroscopy to characterise the
properties of the star-forming (SF) gas, from its metal content to its
kinematics. The star formation rate (SFR) surface density presents a clumpy
distribution, with the brightest knot hosting a 5 Myr young, Wolf-Rayet (WR)
population (revealed by the presence of the characteristic 5808\AA~WR bump).
The ionised gas kinematics are dominated by disordered motions. A superposition
of a narrow ($\sigma \approx$ 50 km s$^{-1}$), intermediate (150 km s$^{-1}$)
and broad (1000 km s$^{-1}$) kinematic components are needed to model the
emission line profiles in the brightest SF region, suggesting the presence of
energetic outflows from massive stars. The gas-phase metallicity of the galaxy
spans 0.6 dex and displays a strong anti-correlation with SFR surface density,
dropping to 12+log(O/H) = 7.7 in the central SF knot. The spatially-resolved
BPTs indicates the gas is being ionised purely by SF processes. Finally, the
anti-correlation between the SFR and the gas metallicity points out to
accretion of metal-poor gas as the origin of the recent off-centre starburst,
in which the infalling material ignites the SF episode.


  This is a continuation of our previous paper 1502.01744. We examine a class
of non-commutative algebras A that depend on an elliptic curve and a
translation automorphism of it. They may be defined in terms of the
4-dimensional Sklyanin algebra S that is associated to the same data. The
algebra A has the same Hilbert series as the polynomial ring in 4 variables,
and there is an associated non-commutative variety, Proj(A), that is a
non-commutative analogue of P^3. The structure and representation theory of A,
and the geometric properties of Proj(A) are closely related to the geometric
properties of E sitting as a quartic curve in P^3. Our main results concern the
classification of point modules, fat point modules, line modules, and the
incidence relations between them. The line modules are parametrized by a degree
20 curve in the Grassmannian G(1,3) that is a union of 4 disjoint plane conics
and 3 disjoint quartic elliptic curves that are isomorphic to E/(t) where t
runs over the three 2-torsion points. A finite quantum group related to the
Heisenberg group of size 4^3 acts as auto-equivalences of the category of
graded A-modules and those quantum symmetries of A play a central role in our
analysis.


  Single crystal metal films on insulating substrates are attractive for
microelectronics and other applications, but they are difficult to achieve on
macroscopic length scales. The conventional approach to obtaining such films is
epitaxial growth at high temperature using slow deposition in ultrahigh vacuum
conditions. Here we describe a different approach: sputter deposition at modest
temperatures followed by annealing to induce secondary grain growth. We show
that polycrystalline as-deposited Cu on \alpha-Al2O3(0001) can be transformed
into Cu(111) with centimeter-sized grains. Employing optical microscopy, x-ray
diffraction, and electron backscatter diffraction to characterize the films
before and after annealing, we find a particular as-deposited grain structure
that promotes the growth of giant grains upon annealing. To demonstrate one
potential application of such films, we grow graphene by chemical vapor
deposition on wafers of annealed Cu and obtain epitaxial graphene grains of 0.2
mm diameter.


  The theories of brane world and multidimensional gravity are widely discussed
in the literature in connection with problems of evolution of early Universe,
including dark matter and energy. A natural physical concept is that a
distinguished surface in the space-time manifold is a topological defect
appeared as a result of a phase transition with spontaneous symmetry breaking.
The macroscopic theory of phase transitions allows considering the brane world
concept self-consistently, even without the knowledge of the nature of physical
vacuum. Gravitational properties of topological defects (cosmic strings,
monopoles,...) in extra dimensions are studied in General Relativity
considering the order parameter as a vector and a multiplet in a plane target
space of scalar fields. The common results and differences of these two
approaches are analyzed and demonstrated in detail. Among the variety of
regular solutions, there are those having brane features, including solutions
with multiple branes, as well as the ones of potential interest from the
standpoint of the dark matter and hierarchy problems. Regular configurations
have a growing gravitational potential and are able to trap the matter on the
brane. If the energy of spontaneous symmetry breaking is high, the attracting
potential can have several points of minimum. Identical in the uniform bulk
spin-less particles, being trapped within the separate points of minimum,
acquire different masses and appear to an observer on brane as different
particles with integer spins.


  This paper investigates the use of a Passive Intelligent Mirrors (PIM) to
operate a multi-user MISO downlink communication. The transmit powers and the
mirror reflection coefficients are designed for sum-rate maximization subject
to individual QoS guarantees to the mobile users. The resulting problem is
non-convex, and is tackled by combining alternating maximization with the
majorization-minimization method. Numerical results show the merits of the
proposed approach, and in particular that the use of PIM increases the system
throughput by at least $40\%$, without requiring any additional energy
consumption.


  In this work, we prove the existence of least energy nodal solution for a
class of elliptic problem in both cases, bounded and unbounded domain, when the
nonlinearity has exponential critical growth in $\mathbb{R}^2$. Moreover, we
also prove a nonexistence result of least energy nodal solution for the
autonomous case in whole $\mathbb{R}^{2}$.


  Network synthesis problem (NSP) is the problem of designing a minimum-cost
network (from the empty network) satisfying a given connectivity requirement.
Hau, Hirai, and Tsuchimura showed that if the edge-cost is a tree metric, then
a simple greedy-type algorithm solves NSP to obtain a half-integral optimal
solution. This is a generalization of the classical result by Gomory and Hu for
the uniform edge-cost. In this note, we present an integer version of Hau,
Hirai, and Tsuchimura's result for integer network synthesis problem (INSP),
where a required network must have an integer capacity. We prove that if each
connectivity requirement is at least 2 and the edge-cost is a tree metrc, then
INSP can be solved in polynomial time.


  We obtain a substantially improved lower bound for the minimum overlap
problem asked by Erd\H{o}s. Our approach uses elementary Fourier analysis to
translate the problem to a convex optimization program.


  The flip graph for a set $P$ of points in the plane has a vertex for every
triangulation of $P$, and an edge when two triangulations differ by one flip
that replaces one triangulation edge by another. The flip graph is known to
have some connectivity properties: (1) the flip graph is connected; (2)
connectivity still holds when restricted to triangulations containing some
constrained edges between the points; (3) for $P$ in general position of size
$n$, the flip graph is $\lceil \frac{n}{2} -2 \rceil$-connected, a recent
result of Wagner and Welzl (SODA 2020).
  We introduce the study of connectivity properties of the flip graph when some
edges between points are forbidden. An edge $e$ between two points is a flip
cut edge if eliminating triangulations containing $e$ results in a disconnected
flip graph. More generally, a set $X$ of edges between points of $P$ is a flip
cut set if eliminating all triangulations that contain edges of $X$ results in
a disconnected flip graph. The flip cut number of $P$ is the minimum size of a
flip cut set.
  We give a characterization of flip cut edges that leads to an $O(n \log n)$
time algorithm to test if an edge is a flip cut edge and, with that as
preprocessing, an $O(n)$ time algorithm to test if two triangulations are in
the same connected component of the flip graph. For a set of $n$ points in
convex position (whose flip graph is the 1-skeleton of the associahedron) we
prove that the flip cut number is $n-3$.


  Three dimensional (3D) magnetic field information on molecular clouds and
cores is important for revealing their kinematical stability (magnetic support)
against gravity which is fundamental for studying the initial conditions of
star formation. In the present study, the 3D magnetic field structure of the
dense starless core FeSt 1-457 is determined based on the near-infrared
polarimetric observations of the dichroic polarization of background stars and
simple 3D modeling. With an obtained angle of line-of-sight magnetic
inclination axis $\theta_{\rm inc}$ of $45^{\circ}\pm10^{\circ}$ and previously
determined plane-of-sky magnetic field strength $B_{\rm pol}$ of $23.8\pm12.1$
$\mu{\rm G}$, the total magnetic field strength for FeSt 1-457 is derived to be
$33.7\pm18.0$ $\mu{\rm G}$. The critical mass of FeSt 1-457, evaluated using
both magnetic and thermal/turbulent support is ${M}_{\rm cr} = 3.70\pm0.92$
${\rm M}_{\odot}$, which is identical to the observed core mass, $M_{\rm
core}=3.55\pm0.75$ ${\rm M}_{\odot}$. We thus conclude that the stability of
FeSt 1-457 is in a condition close to the critical state. Without infalling gas
motion and no associated young stars, the core is regarded to be in the
earliest stage of star formation, i.e., the stage just before the onset of
dynamical collapse following the attainment of a supercritical condition. These
properties would make FeSt 1-457 one of the best starless cores for future
studies of the initial conditions of star formation.


  Robustly classifying ground infrastructure such as roads and street crossings
is an essential task for mobile robots operating alongside pedestrians. While
many semantic segmentation datasets are available for autonomous vehicles,
models trained on such datasets exhibit a large domain gap when deployed on
robots operating in pedestrian spaces. Manually annotating images recorded from
pedestrian viewpoints is both expensive and time-consuming. To overcome this
challenge, we propose TrackletMapper, a framework for annotating ground surface
types such as sidewalks, roads, and street crossings from object tracklets
without requiring human-annotated data. To this end, we project the robot
ego-trajectory and the paths of other traffic participants into the ego-view
camera images, creating sparse semantic annotations for multiple types of
ground surfaces from which a ground segmentation model can be trained. We
further show that the model can be self-distilled for additional performance
benefits by aggregating a ground surface map and projecting it into the camera
images, creating a denser set of training annotations compared to the sparse
tracklet annotations. We qualitatively and quantitatively attest our findings
on a novel large-scale dataset for mobile robots operating in pedestrian areas.
Code and dataset will be made available at
http://trackletmapper.cs.uni-freiburg.de.


  Collisional avalanches are identified to be responsible for an 8-fold
increase of the initial loss rate of a large 87-Rb condensate. We show that the
collisional opacity of an ultra-cold gas exhibits a critical value. When
exceeded, losses due to inelastic collisions are substantially enhanced. Under
these circumstances, reaching the hydrodynamic regime in conventional BEC
experiments is highly questionable.


  The competition, coexistence and cooperation of various orders in
low-dimensional materials like spin, charge, topological orders and
charge-density-wave has been one of the most intriguing issues in condensed
matter physics. In particular, layered transition metal dichalcogenides provide
an ideal platform for studying such an interplay with a notable case of
1${T}$-TaS$_{2}$ featuring Mott-insulating ground state, charge-density-wave,
spin frustration and emerging superconductivity together. We investigated local
electronic states of Se-substituted 1${T}$-TaS$_{2}$ by scanning tunneling
microscopy/spectroscopy (STM/STS), where superconductivity emerges from the
unique Mott-CDW state. Spatially resolved STS measurements reveal that an
apparent V-shape pseudogap forms at the Fermi Level (E$_{F}$), with the origin
of the electronic states splitting and transformation from the Mott states, and
the CDW gaps are largely preserved. The formation of the pseudogap has little
correlation to the variation of local Se concentration, but appears to be a
global characteristics. Furthermore, the correlation length of local density of
states (LDOS) diverges at the Fermi energy and decays rapidly at high energies.
The spatial correlation shows a power-law decay close to the Fermi energy. Our
statistics analysis of the LDOS indicates that our system exhibits weak
multifractal behavior of the wave functions. These findings strongly support a
correlated metallic state induced by disorder in our system, which provides an
new insight into the novel mechanism of emerging superconductivity in the
two-dimensional correlated electronic systems.


  To a matroid M with n edges, we associate the so-called facet ideal F(M)
generated by monomials corresponding to bases of M. We show that the Betti
numbers related to an N-graded minimal free resolution of F(M) are determined
by the Betti numbers related to the blocks of M. Similarly, we show that the
higher weight hierarchy of M is determined by the weight hierarchies of the
blocks, as well. Drawing on these results, we show that when M is the cycle
matroid of a cactus graph, the Betti numbers determine the higher weight
hierarchy -- and vice versa. Finally, we demonstrate by way of counterexamples
that this fails to hold for outerplanar graphs in general.


  Two-dimensional (2D) semiconductor of MoS2 has great potential for advanced
electronics technologies beyond silicon1-9. So far, high-quality monolayer MoS2
wafers10-12 are already available and various demonstrations from individual
transistors to integrated circuits have also been shown13-15. In addition to
the monolayer, multilayers have narrower band gaps but improved carrier
mobilities and current capacities over the monolayer5,16-18. However, achieving
high-quality multilayer MoS2 wafers remains a challenge. Here we report the
growth of high quality multilayer MoS2 4-inch wafers via the layer-by-layer
epitaxy process. The epitaxy leads to well-defined stacking orders between
adjacent epitaxial layers and offers a delicate control of layer numbers up to
6. Systematic evaluations on the atomic structures and electronic properties
were carried out for achieved wafers with different layer numbers. Significant
improvements on device performances were found in thicker-layer field effect
transistors (FETs), as expected. For example, the average field-effect mobility
({\mu}FE) at room temperature (RT) can increase from ~80 cm2V-1s-1 for
monolayer to ~110/145 cm2V-1s-1 for bilayer/trilayer devices. The highest RT
{\mu}FE=234.7 cm2V-1s-1 and a record-high on-current densities of 1.704
mA{\mu}m-1 at Vds=2 V were also achieved in trilayer MoS2 FETs with a high
on/off ratio exceeding 107. Our work hence moves a step closer to practical
applications of 2D MoS2 in electronics.


  The integration of ferromagnetic and ferroelectric materials into hybrid
heterostructures yields multifunctional systems with improved or novel
functionality. We here report on the structural, electronic and magnetic
properties of the ferromagnetic double perovskite Sr2CrReO6, grown as epitaxial
thin film onto ferroelectric BaTiO3. As a function of temperature, the
crystal-structure of BaTiO3 undergoes phase transitions, which induce
qualitative changes in the magnetic anisotropy of the ferromagnet. We observe
abrupt changes in the coercive field of up to 1.2T along with resistance
changes of up to 6.5%. These results are attributed to the high sensitivity of
the double perovskites to mechanical deformation.


  We propose a kernel method to identify finite mixtures of nonparametric
product distributions. It is based on a Hilbert space embedding of the joint
distribution. The rank of the constructed tensor is equal to the number of
mixture components. We present an algorithm to recover the components by
partitioning the data points into clusters such that the variables are jointly
conditionally independent given the cluster. This method can be used to
identify finite confounders.


  We deal with generalized Melvin-like solutions associated with Lie algebras
of rank $4$ ($A_4$, $B_4$, $C_4$, $D_4$, $F_4$). Any solution has static
cylindrically-symmetric metric in $D$ dimensions in presence of four Abelian
2-forms and four scalar fields. The solution is governed by four moduli
functions $H_s(z)$ ($s = 1,...,4$) of squared radial coordinate $z=\rho^2$
obeying four differential equations of the Toda chain type. These functions are
polynomials of powers $(n_1,n_2, n_3, n_4) = (4,6,6,4), (8,14,18,10),
(7,12,15,16), (6,10,6,6), (22,42,30,16)$ for Lie algebras $A_4$, $B_4$, $C_4$,
$D_4$, $F_4$, respectively. The asymptotic behaviour for the polynomials at
large $z$ is governed by an integer-valued $4 \times 4$ matrix $\nu$ connected
in a certain way with the inverse Cartan matrix of the Lie algebra and (in
$A_4$ case) the matrix representing a generator of the $\mathbb{Z}_2$-group of
symmetry of the Dynkin diagram. The symmetry properties and duality identities
for polynomials are studied. We also present 2-form flux integrals over a
$2$-dimensional submanifold. Dilatonic black hole analogs of the obtained
Melvin-type solutions, e.g. "fantom" ones, are also considered. The phantom
black holes are described by fluxbrane polynomials under consideration.


  Quantum cloud computing is an emerging paradigm of computing that empowers
quantum applications and their deployment on quantum computing resources
without the need for a specialized environment to host and operate physical
quantum computers. This paper reviews recent advances, identifies open
problems, and proposes future directions in quantum cloud computing. It
discusses the state-of-the-art quantum cloud advances, including the various
cloud-based models, platforms, and recently developed technologies and software
use cases. Furthermore, it discusses different aspects of the quantum cloud,
including resource management, quantum serverless, security, and privacy
problems. Finally, the paper examines open problems and proposes the future
directions of quantum cloud computing, including potential opportunities and
ongoing research in this emerging field.


  Inclusive production of a quarkonium $\eta_{c,b}$ in hadron collisions at low
transverse momentum can be used to extract various
Transverse-Momentum-Dependent(TMD) gluon distributions of hadrons, provided the
TMD factorization for the process holds. The factorization involving
unpolarized TMD gluon distributions of unpolarized hadrons has been examined
with on-shell gluons at one-loop level. In this work we study the factorization
at one-loop level with diagram approach in the most general case, where all TMD
gluon distributions at leading twist are involved. We find that the
factorization holds and the perturbative effects are represented by one
perturbative coefficient. Since the initial gluons from hadrons are off-shell
in general, there exists the so-called super-leading region found recently. We
find that the contributions from this region can come from individual diagrams
at one-loop level, but they are cancelled in the sum. Our factorized result for
the differential cross-section is explicitly gauge-invariant.


  We have performed magnetization measurements at high magnetic fields of up to
53 T on single crystals of a uranium heavy-fermion compound U$_2$Zn$_{17}$
grown by the Bridgman method. In the antiferromagnetic state below the N\'{e}el
temperature $T_{\rm N}$ = 9.7 K, a metamagnetic transition is found at $H_c$
$\simeq$ 32 T for the field along the [11$\bar{2}$0] direction ($a$-axis). The
magnetic phase diagram for the field along the [11$\bar{2}$0] direction is
given. The magnetization curve shows a nonlinear increase at $H_m$ $\simeq$ 35
T in the paramagnetic state above $T_{\rm N}$ up to a characteristic
temperature $T_{{\chi}{\rm max}}$ where the magnetic susceptibility or
electrical resistivity shows a maximum value. This metamagnetic behavior of the
magnetization at $H_m$ is discussed in comparison with the metamagnetic
magnetism of the heavy-fermion superconductors UPt$_3$, URu$_2$Si$_2$, and
UPd$_2$Al$_3$. We have also carried out high-pressure resistivity measurement
on U$_2$Zn$_{17}$ using a diamond anvil cell up to 8.7 GPa. Noble gas argon was
used as a pressure-transmitting medium to ensure a good hydrostatic
environment. The N\'{e}el temperature $T_{\rm N}$ is almost
pressure-independent up to 4.7 GPa and starts to increase in the
higher-pressure region. The pressure dependences of the coefficient of the
$T^2$ term in the electrical resistivity $A$, the antiferromagnetic gap
$\Delta$, and the characteristic temperature $T_{{\rho}{\rm max}}$ are
discussed. It is found that the effect of pressure on the electronic states in
U$_2$Zn$_{17}$ is weak compared with those in the other heavy fermion
compounds.


  The paradigm of layered networks is used to describe many real-world systems
-- from biological networks, to social organizations and transportation
systems. Recently there has been much progress in understanding the general
properties of multilayer networks, our understanding of how to control such
systems remains limited. One aspect that makes this endeavor challenging is
that each layer can operate at a different timescale, thus we cannot directly
apply standard ideas from structural control theory of individual networks.
Here we address the problem of controlling multilayer and multi-timescale
networks focusing on two-layer multiplex networks with one-to-one interlayer
coupling. We investigate the case when the control signal is applied to the
nodes of one layer. We develop a theory based on disjoint path covers to
determine the minimum number of inputs ($N_\T i$) necessary for full control.
We show that if both layers operate on the same timescale then the network
structure of both layers equally affect controllability. In the presence of
timescale separation, controllability is enhanced if the controller interacts
with the faster layer: $N_\T i$ decreases as the timescale difference increases
up to a critical timescale difference, above which $N_\T i$ remains constant
and is completely determined by the faster layer. In contrast, control becomes
increasingly difficult if the controller interacts with the layer operating on
the slower timescale and increasing timescale separation leads to increased
$N_\T i$, again up to a critical value, above which $N_\T i$ still depends on
the structure of both layers. By identifying the underlying mechanisms that
connect timescale difference and controllability for a simplified model, we
provide insight into disentangling how our ability to control real interacting
complex systems is affected by a variety of sources of complexity.


  We compare different strategies aimed to prepare an ensemble with a given
density matrix $\rho$. Preparing the ensemble of eigenstates of $\rho$ with
appropriate probabilities can be treated as `generous' strategy: it provides
maximal accessible information about the state. Another extremity is the
so-called `Scrooge' ensemble, which is mostly stingy to share the information.
We introduce `lazy' ensembles which require minimal efforts to prepare the
density matrix by selecting pure states with respect to completely random
choice.
  We consider two parties, Alice and Bob, playing a kind of game. Bob wishes to
guess which pure state is prepared by Alice. His null hypothesis, based on the
lack of any information about Alice's intention, is that Alice prepares any
pure state with equal probability. Then, the average quantum state measured by
Bob turns out to be $\rho$, and he has to make a new hypothesis about Alice's
intention solely based on the information that the observed density matrix is
$\rho$. The arising `lazy' ensemble is shown to be the alternative hypothesis
which minimizes the Type I error.


  We study properties of a periodically driven system coupled to a thermal
bath. As a nontrivial example, we consider periodically driven metallic system
coupled to a superconducting bath. The effect of the superconductor on the
driven system is two-fold: it (a) modifies density of states in the metal via
the proximity effect and (b) acts as a thermal bath for light-excited
quasi-particles. Using Keldysh formalism, we calculate, nonpertubatively in the
system-bath coupling, the steady-state properties of the system and obtain
non-equilibrium distribution function. The latter allows one to calculate
observable quantities which can be spectroscopically measured in tunneling
experiments.


  Cold atoms with laser-induced spin-orbit (SO) interactions provide promising
platforms to explore novel quantum physics, in particular the exotic
topological phases, beyond natural conditions of solids. The past several years
have witnessed important progresses in both theory and experiment in the study
of SO coupling and novel quantum states for ultracold atoms. Here we review the
physics of the SO coupled quantum gases, focusing on the latest theoretical and
experimental progresses of realizing SO couplings beyond one-dimension (1D),
and the further investigation of novel topological quantum phases in such
systems, including the topological insulating phases and topological
superfluids. A pedagogical introduction to the SO coupling for ultracold atoms
and topological quantum phases is presented. We show that the so-called optical
Raman lattice schemes, which combine the creation of the conventional optical
lattice and Raman lattice with topological stability, can provide minimal
methods with high experimental feasibility to realize 1D to 3D SO couplings.
The optical Raman lattices exhibit novel intrinsic symmetries, which enable the
natural realization of topological phases belonging to different symmetry
classes, with the topology being detectable through minimal measurement
strategies. We introduce how the non-Abelian Majorana modes emerge in the SO
coupled superfluid phases which can be topologically nontrivial or trivial, for
which a few fundamental theorems are presented and discussed. The experimental
schemes for achieving non-Abelian superfluid phases are given. Finally, we
point out the future important issues in this rapidly growing research field.


  We construct a generalization of the JIMWLK Hamiltonian, going beyond the
eikonal approximation, which governs the high-energy evolution of the
scattering between a dilute projectile and a dense target with an arbitrary
longitudinal extent (a nucleus, or a slice of quark-gluon plasma). Different
physical regimes refer to the ratio $L/\tau$ between the longitudinal size $L$
of the target and the lifetime $\tau$ of the gluon fluctuations. When $L/\tau
\ll 1$, meaning that the target can be effectively treated as a shockwave, we
recover the JIMWLK Hamiltonian, as expected. When $L/\tau \gg 1$, meaning that
the fluctuations live inside the target, the new Hamiltonian governs phenomena
like the transverse momentum broadening and the radiative energy loss, which
accompany the propagation of an energetic parton through a dense QCD medium.
Using this Hamiltonian, we derive a non-linear equation for the dipole
amplitude (a generalization of the BK equation), which describes the
high-energy evolution of jet quenching. As compared to the original BK-JIMWLK
evolution, the new evolution is remarkably different: the plasma saturation
momentum evolves much faster with increasing energy (or decreasing Bjorken's
$x$) than the corresponding scale for a shockwave (nucleus). This widely opens
the transverse phase-space for the evolution and implies the existence of large
radiative corrections, enhanced by the double logarithm $\ln^2(LT)$, with $T$
the temperature of the medium. This confirms and explains from a physical
perspective a recent result by Liou, Mueller, and Wu (arXiv:1304.7677). The
dominant corrections are smooth enough to be absorbed into a renormalization of
the jet quenching parameter $\hat q$. This renormalization is controlled by a
linear equation supplemented with a saturation boundary, which emerges via
controlled approximations from the generalized BK equation alluded to above.


  We investigate dispersionless integrable systems in 3D associated with
fourfolds in the Grassmannian Gr(3,5). Such systems appear in numerous
applications in continuum mechanics, general relativity and differential
geometry, and include such well-known examples as the dispersionless
Kadomtsev-Petviashvili equation, the Boyer-Finley equation, etc. We prove the
equivalence of the four different approaches to integrability, revealing a
remarkable correspondence with Einstein-Weyl geometry and the theory of GL(2,R)
structures.


  We study the performance of a proportional weights algorithm for online
capacitated bipartite matching modeling the delivery of impression ads. The
algorithm uses predictions on the advertiser nodes to match arriving impression
nodes fractionally in proportion to the weights of its neighbors. This paper
gives a thorough empirical study of the performance of the algorithm on a
data-set of ad impressions from Yahoo! and shows its superior performance
compared to natural baselines such as a greedy water-filling algorithm and the
ranking algorithm. The proportional weights algorithm has recently received
interest in the theoretical literature where it was shown to have strong
guarantees beyond the worst-case model of algorithms augmented with
predictions. We extend these results to the case where the advertisers'
capacities are no longer stationary over time. Additionally, we show the
algorithm has near optimal performance in the random-order arrival model when
the number of impressions and the optimal matching are sufficiently large.


  We demonstrate the procedure of Scanning Probe Microscopy (SPM) conductive
probe fabrication with a single multi-walled carbon nanotube (MWNT) on a
silicon cantilever pyramid. The nanotube bundle reliably attached to the
metal-covered pyramid is formed using electrophoresis technique from the MWNT
suspension. It is shown that the dimpled aluminium sample can be used both for
shortening/modification of the nanotube bundle by applying pulse voltage
between the probe and the sample, and for controlling the probe shape via
Atomic Force Microscopy (AFM) imaging the sample. It allows to fabricate a
probe suitable for SPM imaging in the contact and modulation regimes. The
majority of such probes are conductive with conductivity not degrading within
hours of SPM imaging.


  The approach that makes it possible to explain the phenomenon of super
resolution in a virtual image using dielectric microparticles is presented. A
resolution of the order of 50 nm in the visible range is demonstrated. The
presented two-dimensional model uses the FDTD code to generate radiation and
its propagation through a microparticle-object system and the following optical
image generation using backward propagation technique.


  We present a multilinear statistical model of the human tongue that captures
anatomical and tongue pose related shape variations separately. The model is
derived from 3D magnetic resonance imaging data of 11 speakers sustaining
speech related vocal tract configurations. The extraction is performed by using
a minimally supervised method that uses as basis an image segmentation approach
and a template fitting technique. Furthermore, it uses image denoising to deal
with possibly corrupt data, palate surface information reconstruction to handle
palatal tongue contacts, and a bootstrap strategy to refine the obtained
shapes. Our evaluation concludes that limiting the degrees of freedom for the
anatomical and speech related variations to 5 and 4, respectively, produces a
model that can reliably register unknown data while avoiding overfitting
effects. Furthermore, we show that it can be used to generate a plausible
tongue animation by tracking sparse motion capture data.


  The activated surface diffusion of interacting adsorbates is described in
terms of the so-called interacting single adsorbate approximation, which is
applied to the diffusion of Na atoms on Cu(001) for coverages up to 20% in
quasielastic He atom scattering experiments. This approximation essentially
consists of solving the standard Langevin equation with two noise sources and
frictions: a Gaussian white noise accounting for the friction with the
substrate, and a white shot noise characterized by a collisional friction
simulating the adsorbate-adsorbate collisions. The broadenings undergone by the
quasielastic peak are found to be in very good agreement with the experimental
data reported at two surface temperatures 200 and 300 K.


  A generalization of a theorem of Crabb and Hubbuck concerning the embedding
of flag representations in divided powers is given, working over an arbitrary
finite field F, using the category of functors from finite-dimensional F-vector
spaces to F-vector spaces.


  In various fields of physics and astronomy, access to experimental facilities
or to telescopes is becoming more and more competitive and limited. It becomes
therefore important to optimize the type of measurements and their scheduling
to reach a given scientific objective and to increase the chances of success of
a scientific project. In this communication, extending the work of Ford (2008)
and of Loredo et al. (2012), we present an efficient adaptive scheduling tool
aimed at prioritzing measurements in order to reach a scientific goal. The
algorithm, based on the Fisher matrix, can be applied to a wide class of
measurements. We present this algorithm in detail and discuss some
practicalities such as systematic errors or measurements losses due to
contigencies (such as weather, experimental failure, ...). As an illustration,
we consider measurements of the short-period star S0-2 in our Galactic Center.
We show that the radial velocity measurements at the two turning points of the
radial velocity curve are more powerful for detecting the gravitational
redshift than measurements at the maximal relativistic signal. We also
explicitly present the methodology that was used to plan measurements in order
to detect the relativistic redshift considering systematics and possible
measurements losses. For the future, we identify the astrometric turning points
to be highly sensitive to the relativistic advance of the periastron. Finally,
we also identify measurements particularly sensitive to the distance to our
Galactic Center: the radial velocities around periastron and the astrometric
measurements just before closest approach and at the maximal right ascension
astrometric turning point.


  Let $\mathcal{A} = \{A_1, \ldots, A_m\}$ and $\mathcal{B} = \{B_1, \ldots,
B_n\}$ be a pair of dual multi-hypergraphs on the common ground set $O = \{o_1,
\ldots, o_k\}$. Note that each of them may have embedded or equal edges. An
edge is called containment minimal (or just minimal, for short) if it is not a
strict superset of another edge. Yet, equal minimal edges may exist. By
duality,
  (i) $A \cap B \neq \emptyset$ for every pair $A \in \mathcal{A}$ and $B \in
\mathcal{B}$;
  (ii) if $A$ is minimal then for every $o \in A$ there exists a $B \in
\mathcal{B}$ such that $A \cap B = \{o\}$.
  We will extend claim (ii) as follows. A linear order $\succ$ over $O$ defines
a unique lexicographic order $\succ_L$ over the $2^O$. Let $A$ be a
lexicographically maximal (lexmax) edge of $\mathcal{A}$. Then,
  (iii) $A$ is minimal and for every $o \in A$ there exists a minimal $B \in
\mathcal{B}$ such that $A \cap B = \{o\}$ and $o \succeq o'$ for each $o' \in
B$.
  This property has important applications in game theory implying
Nash-solvability of tight game forms as shown in the old (1975 and 1989) work
of the first author. Here we give a new, very short, proof of (iii). Edges $A$
and $B$ mentioned in (iii) can be found out in polynomial time. This is trivial
if $\mathcal{A}$ and $\mathcal{B}$ are given explicitly. Yet, it is true even
if only $\mathcal{A}$ is given, and not explicitly, but by a polynomial
containment oracle, which for a subset $O_A \subseteq O$ answers in polynomial
time whether $O_A$ contains an edge of $\mathcal{A}$.


  The Quantum Hall Effects in all even dimensions are uniformly constructed.
Contrary to some recent accounts in the literature, the existence of Quantum
Hall Effects does not {\it crucially} depend on the existence of division
algebras. For QHE on flat space of even dimensions, both the Hamiltonians and
the ground state wave-functions for a single particle are explicitly described.
This explicit description immediately tells us that QHE on a higher even
dimensional flat space shares the common features such as incompressibility
with QHE on plane.


  Recently, many convolutional neural networks (CNNs) for classification by
time domain data of multisignals have been developed. Although some signals are
important for correct classification, others are not. When data that do not
include important signals for classification are taken as the CNN input layer,
the calculation, memory, and data collection costs increase. Therefore,
identifying and eliminating nonimportant signals from the input layer are
important. In this study, we proposed features gradient-based signals selection
algorithm (FG-SSA), which can be used for finding and removing nonimportant
signals for classification by utilizing features gradient obtained by the
calculation process of grad-CAM. When we define N as the number of signals, the
computational complexity of the proposed algorithm is linear time O(N), that
is, it has a low calculation cost. We verified the effectiveness of the
algorithm using the OPPORTUNITY Activity Recognition dataset, which is an open
dataset comprising acceleration signals of human activities. In addition, we
checked the average 6.55 signals from a total of 15 acceleration signals (five
triaxial sensors) that were removed by FG-SSA while maintaining high
generalization scores of classification. Therefore, the proposed algorithm
FG-SSA has an effect on finding and removing signals that are not important for
CNN-based classification.


  The design or simulation analysis of special equipment products must follow
the national standards, and hence it may be necessary to repeatedly consult the
contents of the standards in the design process. However, it is difficult for
the traditional question answering system based on keyword retrieval to give
accurate answers to technical questions. Therefore, we use natural language
processing techniques to design a question answering system for the
decision-making process in pressure vessel design. To solve the problem of
insufficient training data for the technology question answering system, we
propose a method to generate questions according to a declarative sentence from
several different dimensions so that multiple question-answer pairs can be
obtained from a declarative sentence. In addition, we designed an interactive
attention model based on a bidirectional long short-term memory (BiLSTM)
network to improve the performance of the similarity comparison of two question
sentences. Finally, the performance of the question answering system was tested
on public and technical domain datasets.


  We incorporate discrete and continuous time Markov processes as building
blocks into probabilistic graphical models with latent and observed variables.
We introduce the automatic Backward Filtering Forward Guiding (BFFG) paradigm
(Mider et al., 2021) for programmable inference on latent states and model
parameters. Our starting point is a generative model, a forward description of
the probabilistic process dynamics. We backpropagate the information provided
by observations through the model to transform the generative (forward) model
into a pre-conditional model guided by the data. It approximates the actual
conditional model with known likelihood-ratio between the two. The backward
filter and the forward change of measure are suitable to be incorporated into a
probabilistic programming context because they can be formulated as a set of
transformation rules.
  The guided generative model can be incorporated in different approaches to
efficiently sample latent states and parameters conditional on observations. We
show applicability in a variety of settings, including Markov chains with
discrete state space, interacting particle systems, state space models,
branching diffusions and Gamma processes.


  Inspired by recent developments in learning smoothed densities with empirical
Bayes, we study variational autoencoders with a decoder that is tailored for
the random variable $Y=X+N(0,\sigma^2 I_d)$. A notion of smoothed variational
inference emerges where the smoothing is implicitly enforced by the noise model
of the decoder; "implicit", since during training the encoder only sees clean
samples. This is the concept of imaginary noise model, where the noise model
dictates the functional form of the variational lower bound
$\mathcal{L}(\sigma)$, but the noisy data are never seen during learning. The
model is named $\sigma$-VAE. We prove that all $\sigma$-VAEs are equivalent to
each other via a simple $\beta$-VAE expansion: $\mathcal{L}(\sigma_2) \equiv
\mathcal{L}(\sigma_1,\beta)$, where $\beta=\sigma_2^2/\sigma_1^2$. We prove a
similar result for the Laplace distribution in exponential families.
Empirically, we report an intriguing power law $\mathcal{D}_{\rm KL} \sim
\sigma^{-\nu}$ for the learned models and we study the inference in the
$\sigma$-VAE for unseen noisy data. The experiments were performed on MNIST,
where we show that quite remarkably the model can make reasonable inferences on
extremely noisy samples even though it has not seen any during training. The
vanilla VAE completely breaks down in this regime. We finish with a hypothesis
(the XYZ hypothesis) on the findings here.


  Ternary tetradymites Bi2Te2S, Bi2Te2Se and Bi2Se2Te are found to be stable,
bulk topological insulators via theory, showing band inversion between group V
and VI pz orbitals. We identify Bi2Se2Te as a good candidate to study massive
Dirac Fermions, with a (111) cleavage-surface-derived Dirac point (DP) isolated
in the bulk band gap at the Fermi energy Ef like Bi2Se3 but with a spin texture
alterable by layer chemistry. In contrast, Bi2Te2S and Bi2Te2Se (111) behave
like Bi2Te3, with a DP below Ef buried in bulk bands. Bi2Te2S offers large bulk
resistivity needed for devices.


  We study the time evolution of the antiferromagnetic order parameter after
interaction quenches in the Hubbard model. Using the nonequilibrium dynamical
mean field formalism, we show that the system, after a quench from intermediate
to strong interaction, is trapped in a nonthermal state which is reminiscent of
a photo-doped state and protected by the slow decay of doublons. If the
effective doping of this state is low enough, it exhibits robust
antiferromagnetic order, even if the system is highly excited and the thermal
state thus expected to be paramagnetic. We comment on the implication of our
findings for the stability of nonthermal superconducting states.


  We explore the influence of the Milky Way galaxy's chemical evolution on the
formation, structure, and habitability of the Solar system. Using a multi-zone
Galactic Chemical Evolution (GCE) model, we successfully reproduce key
observational constraints, including the age-metallicity ([Fe/H]) relation,
metallicity distribution functions, abundance gradients, and [X/Fe] ratio
trends for critical elements involved in planetary mineralogy, including C, O,
Mg, and Si. Our GCE model suggests that the Sun formed in the inner Galactic
disc, $R_{\rm birth,\odot}\approx 5$ kpc. We also combined a stoichiometric
model with the GCE model to examine the temporal evolution and spatial
distribution of planet building blocks (PBBs) within the Milky Way galaxy,
revealing trends in the condensed mass fraction ($f_{\rm cond}$),
iron-to-silicon mass fraction ($f_{\rm iron}$), and water mass fraction
($f_{\rm water}$) over time and towards the inner Galactic disc regions.
Specifically, our model predicts a higher $f_{\rm cond}$ in the protoplanetary
disc within the inner regions of the Milky Way galaxy, as well as an increased
$f_{\rm iron}$ and a decreased $f_{\rm water}$ in the inner regions. Based on
these findings, we discuss the potential impact of the Sun's birth location on
the overall structure and habitability of the Solar System.


  Unlike many urban localization methods that return point-valued estimates, a
set-valued representation enables robustness by ensuring that a continuum of
possible positions obeys safety constraints. One strategy with the potential
for set-valued estimation is GNSS-based shadow matching~(SM), where one uses a
three-dimensional (3-D) map to compute GNSS shadows (where line-of-sight is
blocked). However, SM requires a point-valued grid for computational
tractability, with accuracy limited by grid resolution. We propose zonotope
shadow matching (ZSM) for set-valued 3-D map-aided GNSS localization. ZSM
represents buildings and GNSS shadows using constrained zonotopes, a convex
polytope representation that enables propagating set-valued estimates using
fast vector concatenation operations. Starting from a coarse set-valued
position, ZSM refines the estimate depending on the receiver being inside or
outside each shadow as judged by received carrier-to-noise density. We
demonstrated our algorithm's performance using simulated experiments on a
simple 3-D example map and on a dense 3-D map of San Francisco.


  The theory of internal waves between two layers of immiscible fluids is
important both for its applications in oceanography and engineering, and as a
source of interesting mathematical model equations that exhibit nonlinearity
and dispersion. A Boussinesq system for two-way propagation of interfacial
waves in a rigid lid configuration is derived. In most cases, the nonlinearity
is quadratic. However, when the square of the depth ratio is close to the
density ratio, the coefficients of the quadratic nonlinearities become small
and cubic nonlinearities must be considered. The propagation as well as the
collision of solitary waves and/or fronts is studied numerically.


  A brief discussion of the DVCS program at the Jefferson Lab 12 GeV energy
upgrade is given. Emphasis is on what can be learned from using both polarized
electron and polarized positron beams in conjunction with polarized nucleon
targets.


  We examine the projective dimensions of Mackey functors and cohomological
Mackey functors. We show over a field of characteristic $p$ that cohomological
Mackey functors are Gorenstein if and only if Sylow $p$-subgroups are cyclic or
dihedral, and they have finite global dimension if and only if the group order
is invertible or Sylow subgroups are cyclic of order 2. By contrast, we show
that the only Mackey functors of finite projective dimension over a field are
projective. This allows us to give a new proof of a theorem of Greenlees on the
projective dimension of Mackey functors over a Dedekind domain. We conclude by
completing work of Arnold on the global dimension of cohomological Mackey
functors over $\ZZ$.


  Graphene is thought to be a promising materials for many applications.
However, pristine graphene is not suitable for most electrochemical devices,
where defect engineering is crucial for its performance. We demonstrate how
boron doping of graphene can alter its reactivity, electrical conductivity and
potential application for sodium and aluminium storage, with the emphasis on
novel metal-ion batteries. Using DFT calculations, we investigate both the
influence of boron concentration and the oxidation of the material, on the
mentioned properties. It is demonstrated that the presence of boron in graphene
increases its reactivity towards atomic hydrogen and oxygen-containing species,
in other words, it makes B-doped graphene more prone to oxidation.
Additionally, the presence of these surface functional groups significantly
alters the type and strength of the interaction of Na and Al with the given
materials. Boron-doping and oxidation of graphene is found to increase Na
storage capacity of graphene by the factor of up to 4.


  While graph convolutional networks show great practical promises, the
theoretical understanding of their generalization properties as a function of
the number of samples is still in its infancy compared to the more broadly
studied case of supervised fully connected neural networks. In this article, we
predict the performances of a single-layer graph convolutional network (GCN)
trained on data produced by attributed stochastic block models (SBMs) in the
high-dimensional limit. Previously, only ridge regression on contextual-SBM
(CSBM) has been considered in Shi et al. 2022; we generalize the analysis to
arbitrary convex loss and regularization for the CSBM and add the analysis for
another data model, the neural-prior SBM. We also study the high
signal-to-noise ratio limit, detail the convergence rates of the GCN and show
that, while consistent, it does not reach the Bayes-optimal rate for any of the
considered cases.


  Generics (unquantified generalizations) are thought to be pervasive in
communication and when they are about social groups, this may offend and
polarize people because generics gloss over variations between individuals.
Generics about social groups might be particularly common on Twitter (X). This
remains unexplored, however. Using machine learning (ML) techniques, we
therefore developed an automatic classifier for social generics, applied it to
more than a million tweets about people, and analyzed the tweets. We found that
most tweets (78%) about people contained no generics. However, tweets with
social generics received more 'likes' and retweets. Furthermore, while recent
psychological research may lead to the prediction that tweets with generics
about political groups are more common than tweets with generics about ethnic
groups, we found the opposite. However, consistent with recent claims that
political animosity is less constrained by social norms than animosity against
gender and ethnic groups, negative tweets with generics about political groups
were significantly more prevalent and retweeted than negative tweets about
ethnic groups. Our study provides the first ML-based insights into the use and
impact of social generics on Twitter.


  We use a Nambu-Jona Lasinio type model to investigate the phase diagram of
dense quark matter under neutron star conditions in mean field approximation.
The model contains selfconsistently determined quark masses and allows for
diquark condensation in the scalar as well as in the pseudoscalar channel. The
latter gives rise to the possibility of K^0 condensation in the CFL phase. In
agreement with earlier studies we find that this CFLK^0 phase covers large
regions of the phase diagram and that the predominant part of this phase is
fully gapped. We show, however, that there exists a region at very low
temperatures where the CFLK^0 solutions become gapless, possibly indicating an
instability towards anisotropic or inhomogeneous phases. The physical
significance of solutions with pseudoscalar diquark condensates in the 2SC
phase is discussed as well.


  Few-shot algorithms aim at learning new tasks provided only a handful of
training examples. In this work we investigate few-shot learning in the setting
where the data points are sequences of tokens and propose an efficient learning
algorithm based on Transformers. In the simplest setting, we append a token to
an input sequence which represents the particular task to be undertaken, and
show that the embedding of this token can be optimized on the fly given few
labeled examples. Our approach does not require complicated changes to the
model architecture such as adapter layers nor computing second order
derivatives as is currently popular in the meta-learning and few-shot learning
literature. We demonstrate our approach on a variety of tasks, and analyze the
generalization properties of several model variants and baseline approaches. In
particular, we show that compositional task descriptors can improve
performance. Experiments show that our approach works at least as well as other
methods, while being more computationally efficient.


  Determining the state of a mobile robot is an essential building block of
robot navigation systems. In this paper, we address the problem of estimating
the robots pose in an indoor environment using 2D LiDAR data and investigate
how modern environment models can improve gold standard Monte-Carlo
localization (MCL) systems. We propose a neural occupancy field to implicitly
represent the scene using a neural network. With the pretrained network, we can
synthesize 2D LiDAR scans for an arbitrary robot pose through volume rendering.
Based on the implicit representation, we can obtain the similarity between a
synthesized and actual scan as an observation model and integrate it into an
MCL system to perform accurate localization. We evaluate our approach on
self-recorded datasets and three publicly available ones. We show that we can
accurately and efficiently localize a robot using our approach surpassing the
localization performance of state-of-the-art methods. The experiments suggest
that the presented implicit representation is able to predict more accurate 2D
LiDAR scans leading to an improved observation model for our particle
filter-based localization. The code of our approach will be available at:
https://github.com/PRBonn/ir-mcl.


  Partons are defined as the quanta in a Fock space description of a hadron.
Gluon saturation is described in the Weizs\"{a}cker-Williams approximation for
a large nucleus. The elements of DGLAP and BFKL evolution are given with the
BFKL equation derived in a large-$N_c$ dipole formalism. A more general
discussion of saturation is given in terms of a dipole scattering on a nucleon
or nucleus. Possible evidence for saturation at HERA is discussed.


  While the existence of a chiral spin liquid (CSL) on a class of spin-1/2
kagome antiferromagnets is by now well-established numerically, a controlled
theoretical path from the lattice model leading to a low energy topological
field theory is still lacking. This we provide via an explicit construction,
starting from reformulating a microscopic model for a CSL as a lattice gauge
theory, and deriving the low-energy form of its continuum limit. A crucial
ingredient is the realisation that the bosonic spinons of the gauge theory
exhibit a $U(1)$ symmetry-protected topological (SPT) phase, which upon
promoting its $U(1)$ global symmetry to a local gauge structure ("gauging")
yields the CSL. We suggest that such an explicit lattice-based construction
involving gauging of an SPT phase can be applied more generally to understand
topological spin liquids.


  The COHERENT experiment is well poised to test sub-GeV dark matter models
using low-energy recoil detectors sensitive to coherent elastic
neutrino-nucleus scattering (CEvNS) in the $\pi$-DAR neutrino beam produced by
the Spallation Neutron Source. We show how a planned 750-kg liquid argon
scintillation detector would place leading limits on scalar light dark matter
models, over two orders of magnitude of dark matter mass, for dark matter
particles produced through vector and leptophobic portals in the absence of
other effects beyond the standard model. The characteristic timing structure of
a $\pi$-DAR beam allows a unique opportunity for constraining systematic
uncertainties on the standard model background in a time window where signal is
not expected, enhancing expected sensitivity. Additionally, we discuss future
prospects, further increasing the discovery potential of CEvNS detectors. Such
methods would test the calculated thermal dark matter abundance for all
couplings $\alpha'\leq1$ within the vector portal model over an order of
magnitude of dark matter masses.


  We present the first measurement of decay amplitudes in B -> phi K^* and
measurements of branching fractions in B -> phi K^{(*)} decays based on 78.1
fb^{-1} of data recorded at the Upsilon(4S) resonance with the Belle detector
at the KEKB e^+ e^- storage ring. The decay amplitudes for the different phi
K^{*0} helicity states are measured from the angular distributions of final
state particles in the transversity basis. The longitudinal and transverse
complex amplitudes are |A_0|^2 = 0.43 +- 0.09 +- 0.04, |A_perp|^2 = 0.41 +-
0.10 +- 0.04, arg(A_parallel) = -2.57 +- 0.39 +- 0.09, and arg(A_perp) = 0.48
+- 0.32 +- 0.06. The direct CP-violating asymmetries are found to be consistent
with zero.


  The experiment NA62 at CERN collected a large sample of K+- leptonic decays
in order to perform precise test of lepton universality, by measuring the
helicity suppressed ratio R_K=Gamma(K+- --> e+- nu)/Gamma(K+- --> mu+- nu). The
preliminary result of the analysis based on 51089 K+ --> e+nu candidates is
R_K=(2.500+-0.016)x10^{-5}, consistent with the predictions of the Standard
Model.


  Considering that prior research has found older users undergo a different
privacy decision-making process compared to younger adults, more research is
needed to inform the behavioral privacy disclosure effects of these strategies
for different age groups. To address this gap, we used an existing dataset of
an experiment with a photo-tagging Facebook application. This experiment had a
2x2x5 between-subjects design where the manipulations were common dark pattern
design strategies: framing (positive vs. negative), privacy defaults (opt-in
vs. opt-out), and justification messages (positive normative, negative
normative, positive rationale, negative rationale, none). We compared older
(above 65 years old, N=44) and young adults (18 to 25 years old, N=162) privacy
concerns and disclosure behaviors (i.e., accepting or refusing automated photo
tagging) in the scope of dark pattern design. Overall, we find support for the
effectiveness of dark pattern designs in the sense that positive framing and
opt-out privacy defaults significantly increased disclosure behavior, while
negative justification messages significantly decreased privacy concerns.
Regarding older adults, our results show that certain dark patterns do lead to
more disclosure than for younger adults, but also to increased privacy concerns
for older adults than for younger.


  The direct $\gamma$-decays of the giant dipole resonance (GDR) and the giant
quadrupole resonance (GQR) of $^{208}$Pb to low-lying states are investigated
by means of a microscopic self-consistent model. The model considers effects
beyond the linear response approximation. The strong sensitivity of
$\gamma$-decay to the isospin of the involved states is proven. By comparing
their decay widths, a much larger weight of the $3_{1}^{-}$ component in the
GQR wave function of $^{208}$Pb is deduced, with respect to the weight of the
$2_{1}^{+}$ component in the GDR wave function. Thus, we have shown that
$\gamma$-decay is a unique probe of the resonance wave functions, and a
testground for nuclear structure models.


  Graphene is a promising material for applications as a channel in graphene
field-effect transistors (GFETs) which may be used as a building block for
optoelectronics, high-frequency devices and sensors. However, these devices
require gate insulators which ideally should form atomically flat interfaces
with graphene and at the same time contain small densities of traps to maintain
high device stability. Previously used amorphous oxides, such as SiO2 and
Al2O3, however, typically suffer from oxide dangling bonds at the interface,
high surface roughness and numerous border oxide traps. In order to address
these challenges, here we use for the first time 2nm thick epitaxial CaF2 as a
gate insulator in GFETs. By analyzing device-to-device variability for over 200
devices fabricated in two batches, we find that tens of them show similar gate
transfer characteristics. Our statistical analysis of the hysteresis up to 175C
has revealed that while an ambient-sensitive counterclockwise hysteresis can be
present in some devices, the dominant mechanism is thermally activated charge
trapping by border defects in CaF2 which results in the conventional clockwise
hysteresis. We demonstrate that both the hysteresis and bias-temperature
instabilities in our GFETs with CaF2 are comparable to similar devices with
SiO2 and Al2O3. In particular, we achieve a small hysteresis below 0.01 V for
equivalent oxide thickness (EOT) of about 1 nm at the electric fields up to 15
MV/cm and sweep times in the kilosecond range. Thus, our results demonstrate
that crystalline CaF2 is a promising insulator for highly-stable GFETs.


  Temporal graph signals are multivariate time series with individual
components associated with nodes of a fixed graph structure. Data of this kind
arises in many domains including activity of social network users, sensor
network readings over time, and time course gene expression within the
interaction network of a model organism. Traditional matrix decomposition
methods applied to such data fall short of exploiting structural regularities
encoded in the underlying graph and also in the temporal patterns of the
signal. How can we take into account such structure to obtain a succinct and
interpretable representation of temporal graph signals?
  We propose a general, dictionary-based framework for temporal graph signal
decomposition (TGSD). The key idea is to learn a low-rank, joint encoding of
the data via a combination of graph and time dictionaries. We propose a highly
scalable decomposition algorithm for both complete and incomplete data, and
demonstrate its advantage for matrix decomposition, imputation of missing
values, temporal interpolation, clustering, period estimation, and rank
estimation in synthetic and real-world data ranging from traffic patterns to
social media activity. Our framework achieves 28% reduction in RMSE compared to
baselines for temporal interpolation when as many as 75% of the observations
are missing. It scales best among baselines taking under 20 seconds on 3.5
million data points and produces the most parsimonious models. To the best of
our knowledge, TGSD is the first framework to jointly model graph signals by
temporal and graph dictionaries.


  Magnetic fields of debated origin appear to permeate the Universe on all
large scales. There is mounting evidence that supernovae produce not only
roughly spherical ejecta and winds, but also highly relativistic jets of
ordinary matter. These jets, which travel long distances, slow down by
accelerating the matter encountered on their path to cosmic-ray energies. We
show that, if the turbulent motions induced by the winds and the cosmic rays
generate magnetic fields in rough energy equipartition, the predicted
magnetic-field strengths coincide with the ones observed not only in galaxies
(5 $\mu$G in the Milky Way) but also in galaxy clusters (6 $\mu$G in Coma). The
prediction for the intergalactic (or inter-cluster) field is 50 nG.


  In the long-wave regime, nonlinear waves may undergo a phase transition from
a smooth to a fast oscillatory behaviour. We study this phenomenon, commonly
known as dispersive shock, in the light of Dubrovin's universality conjecture ,
and we argue that the transition can be described by a special solution of a
model universal partial differential equation. This universal solution is
constructed by means of a string equation. We provide a classification of
universality classes and the explicit description of the transition by means of
special functions, extending Dubrovin's universality conjecture to a wider
class of equations. In particular, we show that Benjamin-Ono equation belongs
to a novel universality class with respect to the ones known in the literature,
and we compute its string equation exactly. We describe our results using the
language of statistical mechanics, showing that dispersive shocks share many
features of the tri-critical point in statistical systems, and building a
dictionary between nonlinear waves and statistical mechanics.


  Text-guided diffusion models catalyze a paradigm shift in audio generation,
facilitating the adaptability of source audio to conform to specific textual
prompts. Recent advancements introduce inversion techniques, like DDIM
inversion, to zero-shot editing, exploiting pre-trained diffusion models for
audio modification. Nonetheless, our investigation exposes that DDIM inversion
suffers from an accumulation of errors across each diffusion step, undermining
its efficacy. And the lack of attention control hinders the fine-grained
manipulations of music. To counteract these limitations, we introduce the
\textit{Disentangled Inversion} technique, which is designed to disentangle the
diffusion process into triple branches, thereby magnifying their individual
capabilities for both precise editing and preservation. Furthermore, we propose
the \textit{Harmonized Attention Control} framework, which unifies the mutual
self-attention and cross-attention with an additional Harmonic Branch to
achieve the desired composition and structural information in the target music.
Collectively, these innovations comprise the \textit{Disentangled Inversion
Control (DIC)} framework, enabling accurate music editing whilst safeguarding
structural integrity. To benchmark audio editing efficacy, we introduce
\textit{ZoME-Bench}, a comprehensive music editing benchmark hosting 1,100
samples spread across 10 distinct editing categories, which facilitates both
zero-shot and instruction-based music editing tasks. Our method demonstrates
unparalleled performance in edit fidelity and essential content preservation,
outperforming contemporary state-of-the-art inversion techniques.


  We study the capabilities of future electron-positron Linear Colliders, with
centre-of-mass energy at the TeV scale, in accessing the parameter space of a
$Z'$ boson within the minimal $B-L$ model. In such a model, wherein the
Standard Model gauge group is augmented by a broken $U(1)_{B-L}$ symmetry --
with $B(L)$ being the baryon(lepton) number -- the emerging $Z'$ mass is
expected to be in the above energy range. We carry out a detailed comparison
between the discovery regions mapped over a two-dimensional configuration space
($Z'$ mass and coupling) at the Large Hadron Collider and possible future
Linear Colliders for the case of di-muon production. As known in the literature
for other $Z'$ models, we confirm that leptonic machines, as compared to the
CERN hadronic accelerator, display an additional potential in discovering a
$B-L$ $Z'$ boson as well as in allowing one to study its properties at a level
of precision well beyond that of any of the existing colliders.


  An automorphism $\alpha$ of a Cayley graph $Cay(G,S)$ of a group $G$ with
connection set $S$ is color-preserving if $\alpha(g,gs) = (h,hs)$ or
$(h,hs^{-1})$ for every edge $(g,gs)\in E(Cay(G,S))$. If every color-preserving
automorphism of $Cay(G,S)$ is also affine, then $Cay(G,S)$ is a CCA (Cayley
color automorphism) graph. If every Cayley graph $Cay(G,S)$ is a CCA graph,
then $G$ is a CCA group. Hujdurovi\'c, Kutnar, D.W. Morris, and J. Morris have
shown that every non-CCA group $G$ contains a section isomorphic to the
nonabelian group $F_{21}$ of order $21$. We first show that there is a unique
non-CCA Cayley graph $\Gamma$ of $F_{21}$. We then show that if $Cay(G,S)$ is a
non-CCA graph of a group $G$ of odd square-free order, then $G = H\times
F_{21}$ for some CCA group $H$, and $Cay(G,S) = Cay(G,T)\Box\Gamma$.


  The following system of equations {x_1 \cdot x_1=x_2, x_2 \cdot x_2=x_3,
2^{2^{x_1}}=x_3, x_4 \cdot x_5=x_2, x_6 \cdot x_7=x_2} has exactly one solution
in ({\mathbb N}\{0,1})^7, namely (2,4,16,2,2,2,2). Hypothesis 1 states that if
a system of equations S \subseteq {x_i \cdot x_j=x_k: i,j,k \in {1,...,7}} \cup
{2^{2^{x_j}}=x_k: j,k \in {1,...,7}} has at most five equations and at most
finitely many solutions in ({\mathbb N}\{0,1})^7, then each such solution
(x_1,...,x_7) satisfies x_1,...,x_7 \leq 16. Hypothesis 1 implies that there
are infinitely many composite numbers of the form 2^{2^{n}}+1. Hypotheses 2 and
3 are of similar kind. Hypothesis 2 implies that if the equation x!+1=y^2 has
at most finitely many solutions in positive integers x and y, then each such
solution (x,y) belongs to the set {(4,5),(5,11),(7,71)}. Hypothesis 3 implies
that if the equation x(x+1)=y! has at most finitely many solutions in positive
integers x and y, then each such solution (x,y) belongs to the set
{(1,2),(2,3)}. We describe semi-algorithms sem_j (j=1,2,3) that never
terminate. For every j \in {1,2,3}, if Hypothesis j is true, then sem_j
endlessly prints consecutive positive integers starting from 1. For every j \in
{1,2,3}, if Hypothesis j is false, then sem_j prints a finite number (including
zero) of consecutive positive integers starting from 1.


  Building on earlier work, we discuss a general framework for exploring the
cosmological dynamics of Higher Order Theories of Gravity. We show that once
the theory of gravity has been specified, the cosmological equations can be
written as a first-order autonomous system and we give several examples which
illustrate the utility of our method. We also discuss a number of results which
have appeared recently in the literature.


  We present two rigorous results on the Sherrington-Kirkpatrick mean field
model for spin glasses, proven by elementary methods, based on properties of
fluctuations, with respect to the external quenched noise, of the thermodynamic
variables and order parameters. The first result gives the uniform convergence
of the quenched average of the free energy in the thermodynamic limit to its
annealed approximation, in the high temperature regime, including the assumed
critical point beta=1 in our notations. The second result shows that the free
energy can be expressed through a functional order parameter, of the type
introduced by Parisi in the frame of the replica symmetry breaking method. The
functional order parameter is implicitely given in terms of fluctuations of
thermodynamic variables.


  Cerium 4f electronic spin dynamics in single crystals of the heavy-fermion
system CeFePO is studied by means of ac-susceptibility, specific heat and
muon-spin relaxation ($\mu$SR). Short-range static magnetism occurs below the
freezing temperature Tg ~ 0.7 K, which prevents the system from accessing the
putative ferromagnetic quantum critical point. In the $\mu$SR, the
sample-averaged muon asymmetry function is dominated by strongly inhomogeneous
spin fluctuations below 10 K and exhibits a characteristic time-field scaling
relation expected from glassy spin dynamics, strongly evidencing cooperative
and critical spin fluctuations. The overall behavior can be ascribed neither to
canonical spin glasses nor other disorder-driven mechanisms.


  In this paper, we present a systematic way of deriving (1) languages of
(generalised) regular expressions, and (2) sound and complete axiomatizations
thereof, for a wide variety of systems. This generalizes both the results of
Kleene (on regular languages and deterministic finite automata) and Milner (on
regular behaviours and finite labelled transition systems), and includes many
other systems such as Mealy and Moore machines.


  A degree of a module $M$ is a numerical measure of information carried by
$M$. We highlight some of Vasconcelos' outstanding contributions to the theory
of degrees, bridging commutative algebra and computational algebra. We present
several degrees he introduced and developed, including arithmetic degree, jdeg,
homological degree, cohomological degrees, canonical degree and bi-canonical
degree. For the canonical and bi-canonical degrees we discuss recent
developments motivated by our joint works.


  The existence of supermassive black holes (SMBHs) with masses greater than
$\sim 10^{9}M_{\odot}$ at high redshift ($z\gtrsim 7$) is difficult to
accommodate in standard astrophysical scenarios. We study the possibility that
(nearly) totally dissipative self-interacting dark matter (tdSIDM)--in rare,
high density dark matter fluctuations in the early Universe--produces SMBH
seeds through catastrophic collapse. We use a semi-analytic model, tested and
calibrated by a series of N-body simulations of isolated dark matter halos, to
compute the collapse criteria and timescale of tdSIDM halos, where dark matter
loses nearly all of its kinetic energy in a single collision in the
center-of-momentum frame. Applying this model to halo merger trees, we
empirically assign SMBH seeds to halos and trace the formation and evolution
history of SMBHs. We make predictions for the quasar luminosity function, the
$M_{\rm BH}$-$\sigma_{\rm v}^{\ast}$ relation, and cosmic SMBH mass density at
high redshift and compare them to observations. We find that a dissipative dark
matter interaction cross-section of $\sigma/m \sim 0.05~\rm cm^2/g$ is
sufficient to produce the SMBHs observed in the early Universe while remaining
consistent with ordinary SMBHs in the late Universe.


  Recent work has aimed to capture nuances of human behavior by using LLMs to
simulate responses from particular demographics in settings like social science
experiments and public opinion surveys. However, there are currently no
established ways to discuss or evaluate the quality of such LLM simulations.
Moreover, there is growing concern that these LLM simulations are flattened
caricatures of the personas that they aim to simulate, failing to capture the
multidimensionality of people and perpetuating stereotypes. To bridge these
gaps, we present CoMPosT, a framework to characterize LLM simulations using
four dimensions: Context, Model, Persona, and Topic. We use this framework to
measure open-ended LLM simulations' susceptibility to caricature, defined via
two criteria: individuation and exaggeration. We evaluate the level of
caricature in scenarios from existing work on LLM simulations. We find that for
GPT-4, simulations of certain demographics (political and marginalized groups)
and topics (general, uncontroversial) are highly susceptible to caricature.


  We study when the Thurston norm is detected by twisted Alexander polynomials
associated to representations of the 3-manifold group to SL(2, C).
Specifically, we show that the hyperbolic torsion polynomial determines the
genus for a large class of hyperbolic knots in the 3-sphere which includes all
special arborescent knots and many knots whose ordinary Alexander polynomial is
trivial. This theorem follows from results showing that the tautness of certain
sutured manifolds can be certified by checking that they are a product from the
point of view of homology with coefficients twisted by an SL(2,
C)-representation.


  We study $D$-dimensional elastic manifolds driven by ac-forces in a
disordered environment using a perturbation expansion in the disorder strength
and the mean-field approximation. We find, that for $D\le 4$ perturbation
theory produces non-regular terms that grow unboundedly in time. The origin of
these non-regular terms is explained. By using a graphical representation we
argue that the perturbation expansion is regular to all orders for $D>4$.
Moreover, for the corresponding mean-field problem we prove that ill-behaved
diagrams can be resummed in a way, that their unbounded parts mutually cancel.
Our analytical results are supported by numerical investigations. Furthermore,
we conjecture the scaling of the Fourier coefficients of the mean velocity with
the amplitude of the driving force $h$.


  Hereditarily finite sets (sets which are finite and have only hereditarily
finite sets as members) are basic mathematical and computational objects, and
also stand at the basis of some programming languages. This raises the need for
efficient representation of such sets, for example by numbers. In 2008, Kirby
proposed an adjunctive hierarchy of hereditarily finite sets, based on the fact
that they can also be seen as built up from the empty set by repeated
adjunction, that is, by the addition of a new single element drawn from the
already existing sets to an already existing set. Determining the cardinality
$a_n$ of each level of this hierarchy, problem crucial in establishing whether
the natural adjunctive hierarchy leads to an efficient encoding by numbers, was
left open.
  In this paper we solve this problem. Our results can be generalized to
hereditarily finite sets with atoms, or can be further refined by imposing
restrictions on rank, on cardinality, or on the maximum level from where the
new adjoined element can be drawn. We also show that $a_n$ satisfies the
asymptotic formula $a_n = C^{2^n} + O(C^{2^{n-1}})$, for a constant $C \approx
1.3399$, which is a too fast asymptotic growth for practical purposes. We thus
propose a very natural variant of the adjunctive hierarchy, whose asymptotic
behavior we prove to be $\Theta(2^n)$. To our knowledge, this is the first
result of this kind.


  Maps that are not completely positive (CP) are often useful to describe the
dynamics of open systems. An apparent violation of complete positivity can
occur because there are prior correlations of the principal system with the
environment, or if the applied transformation is correlated with the state of
the system. We provide a physically motivated definition of accessible non-CP
maps and derive two necessary conditions for a map to be accessible. We also
show that entanglement between the system and the environment is not necessary
to generate a non-CP dynamics. We describe two simple approximations that may
be sufficient for some problems in process tomography, and then outline what
these methods may be able to tell us in other situations where non-CP dynamics
naturally arise.


  In contrast to the classical twistor spaces whose fibres are 2-spheres, we
introduce twistor spaces over manifolds with almost quaternionic structures of
the second kind in the sense of P. Libermann whose fibres are hyperbolic
planes. We discuss two natural almost complex structures on such a twistor
space and their holomorphic functions.


  Electrochemically switchable selective membranes play an important role in
selective filtration processes such as water desalination, industrial waste
treatment and hemodialysis. Currently, membranes for these purposes need to be
optimized in terms of electrical conductivity and stability against fouling and
corrosion. In this paper, we report the fabrication of boron-doped diamond
membrane by template diamond growth on quartz fiber filters. The morphology and
quality of the diamond coating are characterized via SEM and Raman
spectroscopy. The membrane is heavily boron doped (> 1021 cm-3) with > 3 V
potential window in aqueous electrolyte. By applying a membrane potential
against the electrolyte, redox active species can be removed via flow-through
electrolysis. Compared to planar diamond electrodes, the ~250 times surface
enlargement provided by such a membrane ensures an effective removal of target
chemicals from the input electrolyte. The high stability of diamond enables the
membrane to not only work at high membrane bias but also to be self-cleaning
via in situ electrochemical oxidation. Therefore, we believe that the diamond
membrane presented in this paper will provide a solution to future selective
filtration applications especially in extreme conditions.


  We offer an alternative proof, using the Stein-Chen method, of Bollob\'{a}s'
theorem concerning the distribution of the extreme degrees of a random graph.
Our proof also provides a rate of convergence of the extreme degree to its
asymptotic distribution. The same method also applies in a more general setting
where the probability of every pair of vertices being connected by edges
depends on the number of vertices.


  When considering a model architecture, there are several ways to reduce its
memory footprint. Historically, popular approaches included selecting smaller
architectures and creating sparse networks through pruning. More recently,
randomized parameter-sharing (RPS) methods have gained traction for model
compression at start of training. In this paper, we comprehensively assess the
trade-off between memory and accuracy across RPS, pruning techniques, and
building smaller models. Our findings demonstrate that RPS, which is both data
and model-agnostic, consistently outperforms/matches smaller models and all
moderately informed pruning strategies, such as MAG, SNIP, SYNFLOW, and GRASP,
across the entire compression range. This advantage becomes particularly
pronounced in higher compression scenarios. Notably, even when compared to
highly informed pruning techniques like Lottery Ticket Rewinding (LTR), RPS
exhibits superior performance in high compression settings. This points out
inherent capacity advantage that RPS enjoys over sparse models. Theoretically,
we establish RPS as a superior technique in terms of memory-efficient
representation when compared to pruning for linear models. This paper argues in
favor of paradigm shift towards RPS based models. During our rigorous
evaluation of RPS, we identified issues in the state-of-the-art RPS technique
ROAST, specifically regarding stability (ROAST's sensitivity to initialization
hyperparameters, often leading to divergence) and Pareto-continuity (ROAST's
inability to recover the accuracy of the original model at zero compression).
We provably address both of these issues. We refer to the modified RPS, which
incorporates our improvements, as STABLE-RPS.


  We propose an exactly solvable simplified statistical mechanical model for
the thermodynamics of beta-amyloid aggregation, generalizing a well-studied
model for protein folding. The monomer concentration is explicitly taken into
account as well as a non trivial dependence on the microscopic degrees of
freedom of the single peptide chain, both in the alpha-helix folded isolated
state and in the fibrillar one. The phase diagram of the model is studied and
compared to the outcome of fibril formation experiments which is qualitatively
reproduced.


  Tumor heterogeneity is a complex and widely recognized trait that poses
significant challenges in developing effective cancer therapies. In particular,
many tumors harbor a variety of subpopulations with distinct therapeutic
response characteristics. Characterizing this heterogeneity by determining the
subpopulation structure within a tumor enables more precise and successful
treatment strategies. In our prior work, we developed PhenoPop, a computational
framework for unravelling the drug-response subpopulation structure within a
tumor from bulk high-throughput drug screening data. However, the deterministic
nature of the underlying models driving PhenoPop restricts the model fit and
the information it can extract from the data. As an advancement, we propose a
stochastic model based on the linear birth-death process to address this
limitation. Our model can formulate a dynamic variance along the horizon of the
experiment so that the model uses more information from the data to provide a
more robust estimation. In addition, the newly proposed model can be readily
adapted to situations where the experimental data exhibits a positive time
correlation. We test our model on simulated data (in silico) and experimental
data (in vitro), which supports our argument about its advantages.


  We present a set of cosmological simulations with radiative transfer in order
to model the reionization history of the Universe. Galaxy formation and the
associated star formation are followed self-consistently with gas and dark
matter dynamics using the RAMSES code, while radiative transfer is performed as
a post-processing step using a moment-based method with M1 closure relation in
the ATON code. The latter has been ported to a multiple Graphics Processing
Units (GPU) architecture using CUDA + MPI, resulting in an overall acceleration
(x80) that allows us to tackle radiative transfer problems at resolution of
1024^3 + 2 levels of refinement for the hydro adaptive grid and 1024^3 for the
RT cartesian grid. We observe a good convergence between our different
resolution runs as long as the effects of finite resolution on the star
formation history are properly taken into account. We also show that the
neutral fraction depends on the total mass density, in a way close to the
predictions of photoionization equilibrium, as long as the effect of
self-shielding is included in the background radiation model. However we still
fail at reproducing the z=6 constraints on the H neutral fraction and the
intensity of the UV background. In order to account for unresolved density
fluctuations, we added a simple clumping factor model. Using our most spatially
resolved simulation (12.5 Mpc/h-1024^3) to calibrate our subgrid model, we have
resimulated our largest box (100 Mpc/h 1024^3), successfully reproducing the
observed level of H neutral fraction at z=6. We don't reproduce the
photoionization rate inferred from the same observations. We argue that this
discrepancy could be explained by the fact that the average radiation intensity
and the average neutral fraction depends on different regions of the gas
density distribution, so that one quantity cannot be simply deduced from the
other.


  We are going to prove that the Monopole and the Coulomb fields are duals
within the unifying structure provided by the Reissner-Nordstrom spacetime.
This is accomplished when noticing that in order to produce the tetrad that
locally and covariantly diagonalizes the stress-energy tensor, both the
Monopole and the Coulomb fields are necessary in the construction. Without any
of them it would be impossible to express the tetrad vectors that locally and
covariantly diagonalize the stress-energy tensor. Then, both electromagnetic
fields are an integral part of the same structure, the Reissner-Nordstrom
geometry.


  The advent of gravitational wave astronomy has rekindled interest in
primordial black holes (PBH) as a dark matter candidate. As there are many
different observational probes of the PBH density across different masses,
constraints on PBH models are dependent on the functional form of the PBH mass
function. This complicates general statements about the mass functions allowed
by current data, and, in particular, about the maximum total density of PBH.
Numerical studies suggest that some forms of extended mass functions face
tighter constraints than monochromatic mass functions, but they do not preclude
the existence of a functional form for which constraints are relaxed. We use
analytical arguments to show that the mass function which maximizes the
fraction of the matter density in PBH subject to all constraints is a finite
linear combination of monochromatic mass functions. We explicitly compute the
maximum fraction of dark matter in PBH for different combinations of current
constraints, allowing for total freedom of the mass function. Our framework
elucidates the dependence of the maximum PBH density on the form of
observational constraints, and we discuss the implications of current and
future constraints for the viability of the PBH dark matter paradigm.


  In this work we study phonon-phonon coupling in bismuth vanadate (BiVO4),
known for its second-order transition involving a variety of coupling
mechanisms. Using Raman spectroscopy as a probe, we identify two optical
coupled phonon modes of the VO4 tetrahedron and study them by varying light
polarization and temperature. The coupling manifests in non-Lorentzian
line-shapes of Raman peaks and frequency shifts. We use theoretical framework
of coupled damped harmonic oscillators to model the coupling and capture the
phenomena in the temperature evolution of the coupling parameters. The coupling
is negligible at temperatures below 100 K and later increases in magnitude with
temperature until 400 K. The sign of the coupling parameter depends on the
light polarization direction, causing either phonon attraction or repulsion.
After 400 K the phonon-phonon coupling diminishes when approaching phase
transition at which the phonon modes change their symmetry and the coupling is
no longer allowed.


  Fiducial Inference, introduced by Fisher in the 1930s, has a long history,
which at times aroused passionate disagreements. However, its application has
been largely confined to relatively simple parametric problems. In this paper,
we present what might be the first time fiducial inference, as generalized by
Hannig et al. (2016), is systematically applied to estimation of a
nonparametric survival function under right censoring. We find that the
resulting fiducial distribution gives rise to surprisingly good statistical
procedures applicable to both one sample and two sample problems. In
particular, we use the fiducial distribution of a survival function to
construct pointwise and curvewise confidence intervals for the survival
function, and propose tests based on the curvewise confidence interval. We
establish a functional Bernstein-von Mises theorem, and perform thorough
simulation studies in scenarios with different levels of censoring. The
proposed fiducial based confidence intervals maintain coverage in situations
where asymptotic methods often have substantial coverage problems. Furthermore,
the average length of the proposed confidence intervals is often shorter than
the length of competing methods that maintain coverage. Finally, the proposed
fiducial test is more powerful than various types of log-rank tests and sup
log-rank tests in some scenarios. We illustrate the proposed fiducial test
comparing chemotherapy against chemotherapy combined with radiotherapy using
data from the treatment of locally unresectable gastric cancer.


  In recent years several in-depth investigations of the three Galactic Of?p
stars were undertaken. These multiwavelength studies revealed the peculiar
properties of these objects (in the X-rays as well as in the optical): magnetic
fields, periodic line profile variations, recurrent photometric changes.
However, many questions remain unsolved. To clarify some of the properties of
the Of?p stars, we have continued their monitoring. A new XMM observation and
two new optical datasets were obtained. Additional information for the
prototypical Of?p trio has been found. HD108 has now reached its quiescent,
minimum-emission state, for the first time in 50--60yrs. The echelle spectra of
HD148937 confirm the presence of the 7d variations in the Balmer lines and
reveal similar periodic variations (though of lower amplitudes) in the HeI5876
and HeII4686 lines, underlining its similarities with the other two
prototypical Of?p stars. The new XMM observation of HD191612 was taken at the
same phase in the line modulation cycle but at a different orbital phase as
previous data. It clearly shows that the X-ray emission of HD191612 is
modulated by the 538d period and not the orbital period of 1542d - it is thus
not of colliding-wind origin and the phenomenon responsible for the optical
changes appears also at work in the high-energy domain. There are however
problems: our MHD simulations of the wind magnetic confinement predict both a
harder X-ray flux of a much larger strength than what is observed (the modeled
DEM peaks at 30-40MK, whereas the observed one peaks at 2MK) and narrow lines
(hot gas moving with velocities of 100--200km/s, whereras the observed FWHM is
~2000km/s).


  For the study of cosmological backreacktion an avaragng procedure is
required. In this work a covariant and gauge invariant averaging formalism for
finite volumes will be developed. This averaging will be applied to the scalar
parts of Einstein's equations. For this purpose dust as a physical laboratory
will be coupled to the gravitating system. The goal is to study the deviation
from the homogeneous universe and the impact of this deviation on the dynamics
of our universe. Fields of physical observers are included in the studied
system and used to construct a reference frame to perform the averaging without
a formal gauge fixing. The derived equations resolve the question whether
backreaction is gauge dependent.


  The Dresselhaus and Rashba effects are well-known phenomena in solid-state
physics, in which spin-orbit coupling (SOC) splits spin-up and spin-down energy
bands of nonmagnetic non-centrosymmetric crystals. Here, we discover a new
phenomenon, dubbed as band splitting with vanishing spin polarizations (BSVSP),
in which, as usual, SOC splits the energy bands in nonmagnetic
non-centrosymmetric systems; surprisingly, however, both split bands show no
net spin polarization along certain high-symmetry lines in the Brillouin zone.
In order to rationalize this phenomenon, we propose a new classification of
point groups into pseudo-polar and non-pseudo-polar groups. By means of
first-principles simulations, we demonstrate that BSVSP can take place in both
symmorphic (e.g., bulk GaAs) and non-symmorphic systems (e.g., two dimensional
ferroelectric SnTe). Furthermore, we propose a novel linear magnetoelectric
coupling in reciprocal space, which could be employed to tune the spin
polarization with an external electric field. The BSVSP effect and its
manipulation could therefore pave a new way to novel spintronic devices.


  Existing person re-identification (re-id) methods assume the provision of
accurately cropped person bounding boxes with minimum background noise, mostly
by manually cropping. This is significantly breached in practice when person
bounding boxes must be detected automatically given a very large number of
images and/or videos processed. Compared to carefully cropped manually,
auto-detected bounding boxes are far less accurate with random amount of
background clutter which can degrade notably person re-id matching accuracy. In
this work, we develop a joint learning deep model that optimises person re-id
attention selection within any auto-detected person bounding boxes by
reinforcement learning of background clutter minimisation subject to re-id
label pairwise constraints. Specifically, we formulate a novel unified re-id
architecture called Identity DiscriminativE Attention reinforcement Learning
(IDEAL) to accurately select re-id attention in auto-detected bounding boxes
for optimising re-id performance. Our model can improve re-id accuracy
comparable to that from exhaustive human manual cropping of bounding boxes with
additional advantages from identity discriminative attention selection that
specially benefits re-id tasks beyond human knowledge. Extensive comparative
evaluations demonstrate the re-id advantages of the proposed IDEAL model over a
wide range of state-of-the-art re-id methods on two auto-detected re-id
benchmarks CUHK03 and Market-1501.


  The task of classifying videos of natural dynamic scenes into appropriate
classes has gained lot of attention in recent years. The problem especially
becomes challenging when the camera used to capture the video is dynamic. In
this paper, we analyse the performance of statistical aggregation (SA)
techniques on various pre-trained convolutional neural network(CNN) models to
address this problem. The proposed approach works by extracting CNN activation
features for a number of frames in a video and then uses an aggregation scheme
in order to obtain a robust feature descriptor for the video. We show through
results that the proposed approach performs better than the-state-of-the arts
for the Maryland and YUPenn dataset. The final descriptor obtained is powerful
enough to distinguish among dynamic scenes and is even capable of addressing
the scenario where the camera motion is dominant and the scene dynamics are
complex. Further, this paper shows an extensive study on the performance of
various aggregation methods and their combinations. We compare the proposed
approach with other dynamic scene classification algorithms on two publicly
available datasets - Maryland and YUPenn to demonstrate the superior
performance of the proposed approach.


  Research on agent communication languages has typically taken the speech acts
paradigm as its starting point. Despite their manifest attractions, speech-act
models of communication have several serious disadvantages as a foundation for
communication in artificial agent systems. In particular, it has proved to be
extremely difficult to give a satisfactory semantics to speech-act based agent
communication languages. In part, the problem is that speech-act semantics
typically make reference to the "mental states" of agents (their beliefs,
desires, and intentions), and there is in general no way to attribute such
attitudes to arbitrary computational agents. In addition, agent programming
languages have only had their semantics formalised for abstract, stand-alone
versions, neglecting aspects such as communication primitives. With respect to
communication, implemented agent programming languages have tended to be rather
ad hoc. This paper addresses both of these problems, by giving semantics to
speech-act based messages received by an AgentSpeak agent. AgentSpeak is a
logic-based agent programming language which incorporates the main features of
the PRS model of reactive planning systems. The paper builds upon a structural
operational semantics to AgentSpeak that we developed in previous work. The
main contributions of this paper are as follows: an extension of our earlier
work on the theoretical foundations of AgentSpeak interpreters; a
computationally grounded semantics for (the core) performatives used in
speech-act based agent communication languages; and a well-defined extension of
AgentSpeak that supports agent communication.