  We present a systematic numerical method to compute the elementary braiding
operations for topological quantum computation (TQC). Braiding non-Abelian
anyons is a crucial technique in TQC, offering a topologically protected
implementation of quantum gates. However, obtaining matrix representations for
braid generators can be challenging, especially for systems with numerous
anyons or complex fusion patterns. Our proposed method addresses this
challenge, allowing for the inclusion of an arbitrary number of anyons per
qubit or qudit. This approach serves as a fundamental component in a general
topological quantum circuit simulator, facilitating the exploration and
analysis of intricate quantum circuits within the TQC framework. We have
implemented and tested the method using algebraic conditions. Furthermore, we
provide a proof of concept by successfully reproducing the CNOT gate.


  Recently, the field of few-shot detection within remote sensing imagery has
witnessed significant advancements. Despite these progresses, the capacity for
continuous conceptual learning still poses a significant challenge to existing
methodologies. In this paper, we explore the intricate task of incremental
few-shot object detection in remote sensing images. We introduce a pioneering
fine-tuningbased technique, termed InfRS, designed to facilitate the
incremental learning of novel classes using a restricted set of examples, while
concurrently preserving the performance on established base classes without the
need to revisit previous datasets. Specifically, we pretrain the model using
abundant data from base classes and then generate a set of class-wise
prototypes that represent the intrinsic characteristics of the data. In the
incremental learning stage, we introduce a Hybrid Prototypical Contrastive
(HPC) encoding module for learning discriminative representations. Furthermore,
we develop a prototypical calibration strategy based on the Wasserstein
distance to mitigate the catastrophic forgetting problem. Comprehensive
evaluations on the NWPU VHR-10 and DIOR datasets demonstrate that our model can
effectively solve the iFSOD problem in remote sensing images. Code will be
released.


  We present in this paper the Banach space representation for the set of
random finite-dimensional vectors with exponential decreasing tails of
distributions.
  We show that there are at last three types of these multidimensional Banach
spaces, i.e. which can completely describe the random vectors with exponential
decreasing tails of distributions: exponential Orlicz spaces, Young spaces and
Grand Lebesgue spaces.
  We discuss in the last section the possible applications of obtained results.


  We prove a Prodi-Serrin-type global regularity condition for the
three-dimensional Magnetohydrodynamic-Boussinesq system (3D MHD-Boussinesq)
without thermal diffusion, in terms of only two velocity and two magnetic
components. This is the first Prodi-Serrin-type criterion for a hydrodynamic
system which is not fully dissipative, and indicates that such an approach may
be successful on other systems. In addition, we provide a constructive proof of
the local well-posedness of solutions to the fully dissipative 3D
MHD-Boussinesq system, and also the fully inviscid, irresistive, non-diffusive
MHD-Boussinesq equations. We note that, as a special case, these results
include the 3D non-diffusive Boussinesq system and the 3D MHD equations.
Moreover, they can be extended without difficulty to include the case of a
Coriolis rotational term.


  In this paper we use theory of embedded graphs on oriented and compact
$PL$-surfaces to construct minimal realizations of signed Gauss paragraphs. We
prove that the genus of the ambient surface of these minimal realizations can
be seen as a function of the maximum number of Carter's circles. For the case
of signed Gauss words, we use a generating set of $H_1(S_w,\mathbb{Z})$, given
in \cite{CaEl}, and the intersection pairing of immersed $PL$-normal curves to
present a short solution of the signed Gauss word problem. Moreover, we define
the join operation on signed Gauss paragraphs to produce signed Gauss words
such that both can be realized on the same minimal genus $PL$-surface.


  The Coulomb gauge in QCD is the only explicitly unitary gauge. But it suffers
from energy-divergences which means that it is not rigorously well-defined. One
way to define it unambiguously is as the limit of a gauge interpolating between
the Feynman gauge and the Coulomb gauge. This interpolating gauge is
characterized by a parameter theta and the Coulomb gauge is obtained in the
limit theta tends to 0. We study the renormalization of this theta-gauge for
all values of theta. Novel features include field mixing as well as scaling,
the renormalization of the theta parameter itself, and the appearance of new
counter-term structures at two-loop order.


  The IceCube Neutrino Observatory has observed highly energetic neutrinos in
excess of the expected atmospheric neutrino background. It is intriguing to
consider the possibility that such events are probing physics beyond the
standard model. In this context, $\mathcal{O}$(PeV) dark matter particles
decaying to neutrinos have been considered while dark matter annihilation has
been dismissed invoking the unitarity bound as a limiting factor. However, the
latter claim was done ignoring the contribution from dark matter substructure,
which for PeV Cold Dark Matter would extend down to a free streaming mass of
$\mathcal{O}$($10^{-18}$M$_\odot$). Since the unitarity bound is less stringent
at low velocities, ($\sigma_{\rm ann}$v)$\leq4\pi/m_\chi^2v$, then, it is
possible that these cold and dense subhalos would contribute dominantly to a
dark-matter-induced neutrino flux and easily account for the events observed by
IceCube. A Sommerfeld-enhanced dark matter model can naturally support such
scenario. Interestingly, the spatial distribution of the events shows features
that would be expected in a dark matter interpretation. Although not
conclusive, 9 of the 37 events appear to be clustered around a region near the
Galactic Center while 6 others spatially coincide, within the reported angular
errors, with 5 of 26 Milky Way satellites. However, a simple estimate of the
probability of the latter occurring by chance is $\sim35\%$. More events are
needed to statistically test this hypothesis. PeV dark matter particles are
massive enough that their abundance as standard thermal relics would overclose
the Universe. This issue can be solved in alternative scenarios, for instance
if the decay of new massive unstable particles generates significant entropy
reheating the Universe to a slightly lower temperature than the freeze-out
temperature, $T_{\rm RH} \lesssim T_{\rm f}\sim4\times10^4$~GeV.


  This paper addresses two problems lying at the intersection of geometric
analysis and theoretical computer science: The non-linear isomorphic Dvoretzky
theorem and the design of good approximate distance oracles for large
distortion. We introduce the notion of Ramsey partitions of a finite metric
space, and show that the existence of good Ramsey partitions implies a solution
to the metric Ramsey problem for large distortion (a.k.a. the non-linear
version of the isomorphic Dvoretzky theorem, as introduced by Bourgain, Figiel,
and Milman). We then proceed to construct optimal Ramsey partitions, and use
them to show that for every e\in (0,1), any n-point metric space has a subset
of size n^{1-e} which embeds into Hilbert space with distortion O(1/e). This
result is best possible and improves part of the metric Ramsey theorem of
Bartal, Linial, Mendel and Naor, in addition to considerably simplifying its
proof. We use our new Ramsey partitions to design the best known approximate
distance oracles when the distortion is large, closing a gap left open by
Thorup and Zwick. Namely, we show that for any $n$ point metric space X, and
k>1, there exists an O(k)-approximate distance oracle whose storage requirement
is O(n^{1+1/k}), and whose query time is a universal constant. We also discuss
applications of Ramsey partitions to various other geometric data structure
problems, such as the design of efficient data structures for approximate
ranking.


  In this paper we give a detailed analysis of deterministic and randomized
algorithms that enumerate any number of irreducible polynomials of degree $n$
over a finite field and their roots in the extension field in quasilinear where
$N=n^2$ is the size of the output.} time cost per element.
  Our algorithm is based on an improved algorithm for enumerating all the
Lyndon words of length $n$ in linear delay time and the known reduction of
Lyndon words to irreducible polynomials.


  Given a set $X$ and a family $G$ of self-maps of $X$, we study the problem of
the existence of a non-discrete Hausdorff topology on $X$ with respect to which
all functions $f\in G$ are continuous. A topology on $X$ with this property is
called a $G$-topology. The answer is given in terms of the Zariski $G$-topology
$\zeta_G$ on $X$, that is, the topology generated by the subbase consisting of
the sets $\{x\in X:f(x)\ne g(x)\}$ and $\{x\in X:f(x)\ne c\}$, where $f,g\in G$
and $c\in X$. We prove that, for a countable monoid $G\subset X^X$, $X$ admits
a non-discrete Hausdorff $G$-topology if and only if the Zariski $G$-topology
$\zeta_G$ is non-discrete; moreover, in this case, $X$ admits $2^{\mathfrak c}$
hereditarily normal $G$-topologies.


  A facial morph is a synthetically created image of a face that looks similar
to two different individuals and can even trick biometric facial recognition
systems into recognizing both individuals. This attack is known as face
morphing attack. The process of creating such a facial morph is well documented
and a lot of tutorials and software to create them are freely available.
Therefore, it is mandatory to be able to detect this kind of fraud to ensure
the integrity of the face as reliable biometric feature. In this work, we study
the effects of face morphing on the physically correctness of the illumination.
We estimate the direction to the light sources based on specular highlights in
the eyes and use them to generate a synthetic map for highlights on the skin.
This map is compared with the highlights in the image that is suspected to be a
fraud. Morphing faces with different geometries, a bad alignment of the source
images or using images with different illuminations, can lead to
inconsistencies in reflections that indicate the existence of a morphing
attack.


  The Dong Lemma in the theory of vertex algebras states that the locality
property of formal distributions over a Lie algebra is preserved under the
action of a vertex operator. A~similar statement is known for associative
algebras. We study local formal distributions over pre-Lie (right-symmetric),
pre-associative (dendriform), and Novikov algebras to show that the analogue of
the Dong Lemma holds for Novikov algebras but does not hold for pre-Lie and
pre-associative ones.


  We present total intensity and linear polarization images of OJ287 at
1.68GHz, obtained through space-based VLBI observations with RadioAstron on
April 16, 2016. The observations were conducted using a ground array consisting
of the VLBA and the EVN. Ground-space fringes were detected with a maximum
projected baseline length of 5.6 Earth's diameter, resulting in an angular
resolution of 530 uas. With this unprecedented resolution at such a low
frequency, the progressively bending jet structure of OJ287 has been resolved
up to 10 pc of the projected distance from the radio core. In comparison with
close-in-time VLBI observations at 15, 43, 86 GHz from MOJAVE and
VLBA-BU-BLAZAR monitoring projects, we obtain the spectral index map showing
the opaque core and optically thin jet components. The optically thick core has
a brightness temperature of 10$^{13}$ K, and is further resolved into two
sub-components at higher frequencies labeled C1 and C2. These sub-components
exhibit a transition from optically thick to thin, with a SSA turnover
frequency estimated to be 33 and 11.5 GHz, and a turnover flux density 4 and
0.7 Jy, respectively. Assuming a Doppler boosting factor of 10, the SSA values
provide the estimate of the magnetic field strengths from SSA of 3.4 G for C1
and 1.0 G for C2. The magnetic field strengths assuming equipartition arguments
are also estimated as 2.6 G and 1.6 G, respectively. The integrated degree of
linear polarization is found to be approximately 2.5 %, with the electric
vector position angle being well aligned with the local jet direction at the
core region. This alignment suggests a predominant toroidal magnetic field,
which is in agreement with the jet formation model that requires a helical
magnetic field anchored to either the black hole ergosphere or the accretion
disk. Further downstream, the jet seems to be predominantly threaded by a
poloidal magnetic field.


  Human gesture recognition has assumed a capital role in industrial
applications, such as Human-Machine Interaction. We propose an approach for
segmentation and classification of dynamic gestures based on a set of
handcrafted features, which are drawn from the skeleton data provided by the
Kinect sensor. The module for gesture detection relies on a feedforward neural
network which performs framewise binary classification. The method for gesture
recognition applies a sliding window, which extracts information from both the
spatial and temporal dimensions. Then we combine windows of varying durations
to get a multi-temporal scale approach and an additional gain in performance.
Encouraged by the recent success of Recurrent Neural Networks for time series
domains, we also propose a method for simultaneous gesture segmentation and
classification based on the bidirectional Long Short-Term Memory cells, which
have shown ability for learning the temporal relationships on long temporal
scales. We evaluate all the different approaches on the dataset published for
the ChaLearn Looking at People Challenge 2014. The most effective method
achieves a Jaccard index of 0.75, which suggests a performance almost on pair
with that presented by the state-of-the-art techniques. At the end, the
recognized gestures are used to interact with a collaborative robot.


  For a graph $G$, we call an edge coloring of $G$ an \textit{improper}
\textit{interval edge coloring} if for every $v\in V(G)$ the colors, which are
integers, of the edges incident with $v$ form an integral interval. The
\textit{interval coloring impropriety} of $G$, denoted by $\mu_{int}(G)$, is
the smallest value $k$ such that $G$ has an improper interval edge coloring
where at most $k$ edges of $G$ with a common endpoint have the same color.
  The purpose of this note is to communicate solutions to two previous
questions on interval coloring impropriety, mainly regarding planar graphs.
First, we prove $\mu_{int}(G) \leq 2$ for every outerplanar graph $G$. This
confirms the conjecture by Casselgren and Petrosyan in the affirmative.
Secondly, we prove that for each $k\geq 2$, the interval coloring impropriety
of $k$-trees is unbounded. This refutes the conjecture by Carr, Cho, Crawford,
Ir\v{s}i\v{c}, Pai and Robinson.


  In many natural and engineered systems, unknown quantum channels act on a
subsystem that cannot be directly controlled and measured, but is instead
learned through a controllable subsystem that weakly interacts with it. We
study quantum channel discrimination (QCD) under these restrictions, which we
call hidden system QCD (HQCD). We find that sequential protocols achieve
perfect discrimination and saturate the Heisenberg limit. In contrast, depth-1
parallel and multi-shot protocols cannot solve HQCD. This suggests that
sequential protocols are superior in experimentally realistic situations.


  In this work we study the semileptonic decays of $B_c$ meson. We evaluated
$B_{c}\rightarrow D(D^{\ast})$, $B_{c}\rightarrow D_s(D_s^{\ast})$ and
$B_{c}\rightarrow \eta_{c}(J/\psi)$ transitions form factors in the full
kinematical region within the covariant quark model. The calculated form
factors are used to evaluate the semileptonic decays of $B_c$ meson and it was
defined ratios ($R_{\eta_{c}}$, $R_{J/\psi}$, $R_{ D}$ ,$R_{ D^{\ast}}$) of the
branching ratios, which will be hopefully tested on LHC experiments.We compare
the obtained results with the results from other theoretical approaches.


  We address a physics-informed neural network based on the concept of random
projections for the numerical solution of IVPs of nonlinear ODEs in
linear-implicit form and index-1 DAEs, which may also arise from the spatial
discretization of PDEs. The scheme has a single hidden layer with appropriately
randomly parametrized Gaussian kernels and a linear output layer, while the
internal weights are fixed to ones. The unknown weights between the hidden and
output layer are computed by Newton's iterations, using the Moore-Penrose
pseudoinverse for low to medium, and sparse QR decomposition with
regularization for medium to large scale systems. To deal with stiffness and
sharp gradients, we propose a variable step size scheme for adjusting the
interval of integration and address a continuation method for providing good
initial guesses for the Newton iterations. Based on previous works on random
projections, we prove the approximation capability of the scheme for ODEs in
the canonical form and index-1 DAEs in the semiexplicit form. The optimal
bounds of the uniform distribution are parsimoniously chosen based on the
bias-variance trade-off. The performance of the scheme is assessed through
seven benchmark problems: four index-1 DAEs, the Robertson model, a model of
five DAEs describing the motion of a bead, a model of six DAEs describing a
power discharge control problem, the chemical Akzo Nobel problem and three
stiff problems, the Belousov-Zhabotinsky, the Allen-Cahn PDE and the
Kuramoto-Sivashinsky PDE. The efficiency of the scheme is compared with three
solvers ode23t, ode23s, ode15s of the MATLAB ODE suite. Our results show that
the proposed scheme outperforms the stiff solvers in several cases, especially
in regimes where high stiffness or sharp gradients arise in terms of numerical
accuracy, while the computational costs are for any practical purposes
comparable.


  We examine a degenerate, dispersive, nonlinear wave equation related to the
evolution of partially molten rock in dimensions two and higher. This
simplified model, for a scalar field capturing the melt fraction by volume, has
been studied by direct numerical simulation where it has been observed to
develop stable solitary waves. In this work, we prove local in time
well-posedness results for the time dependent equation, on both the whole space
and the torus, for dimensions two and higher. We also prove the existence of
the solitary wave solutions in dimensions two and higher.


  The resonator-induced phase gate is a two-qubit operation in which driving a
bus resonator induces a state-dependent phase shift on the qubits equivalent to
an effective $ZZ$ interaction. In principle, the dispersive nature of the gate
offers flexibility for qubit parameters. However, the drive can cause resonator
and qubit leakage, the physics of which cannot be fully captured using either
the existing Jaynes-Cummings or Kerr models. In this paper, we adopt an
ab-initio model based on Josephson nonlinearity for transmon qubits. The
ab-initio analysis agrees well with the Kerr model in terms of capturing the
effective $ZZ$ interaction in the weak-drive dispersive regime. In addition,
however, it reveals numerous leakage transitions involving high-excitation
qubit states. We analyze the physics behind such novel leakage channels,
demonstrate the connection with specific qubits-resonator frequency collisions,
and lay out a plan towards device parameter optimization. We show this type of
leakage can be substantially suppressed using very weakly anharmonic transmons.
In particular, weaker qubit anharmonicity mitigates both collision density and
leakage amplitude, while larger qubit frequency moves the collisions to occur
only at large anharmonicity not relevant to experiment. Our work is broadly
applicable to the physics of weakly anharmonic transmon qubits coupled to
linear resonators. In particular, our analysis confirms and generalizes the
measurement-induced state transitions noted in Sank et al. (Phys. Rev. Lett.
117, 190503) and lays the groundwork for both strong-drive resonator-induced
phase gate implementation and strong-drive dispersive qubit measurement.


  Event collections are frequently built by crawling the live web on the basis
of seed URIs nominated by human experts. Focused web crawling is a technique
where the crawler is guided by reference content pertaining to the event. Given
the dynamic nature of the web and the pace with which topics evolve, the timing
of the crawl is a concern for both approaches. We investigate the feasibility
of performing focused crawls on the archived web. By utilizing the Memento
infrastructure, we obtain resources from 22 web archives that contribute to
building event collections. We create collections on four events and compare
the relevance of their resources to collections built from crawling the live
web as well as from a manually curated collection. Our results show that
focused crawling on the archived web can be done and indeed results in highly
relevant collections, especially for events that happened further in the past.


  A general technique of exact calculation of any correlation functions for the
special class of one-dimensional spin models containing small clusters of
quantum spins assembled to a chain by alternating with the single Ising spins
is proposed. The technique is a natural generalization of that in the models
solved by a classical transfer matrix. The general expressions for
corresponding matrix operators which are the key components of the technique
are obtained. As it is clear from the general principles, the decay of the
correlation functions of various types is explicitly shown to be governed by a
single correlation length. The technique is illustrated by two examples:
symmetric diamond chain and asymmetric sawtooth chain.


  We study the normalized eigenvalue counting measure d\sigma of matrices of
long-range percolation model. These are (2n+1)\times (2n+1) random real
symmetric matrices H=\{H(i,j)\}_{i,j} whose elements are independent random
variables taking zero value with probability 1-\psi [(i-j)/b], b\in
\mathbb{R}^{+}, where \psi is an even positive function \psi(t)\le{1} vanishing
at infinity. It is shown that if the third moment of \sqrt{b}H(i,j), i\leq{j}
is uniformly bounded then the measure d\sigma:=d\sigma_{n,b} weakly converges
in probability in the limit n,b\to\infty, b=o(n) to the semicircle (or Wigner)
distribution. The proof uses the resolvent technique combined with the cumulant
expansions method. We show that the normalized trace of resolvent g_{n,b}(z)
converges in average and that the variance of g_{n,b}(z) vanishes. In the
second part of the paper, we estimate the rate of decreasing of the variance of
g_{n,b}(z), under further conditions on the moments of \sqrt{b}H(i,j), \
i\le{j}.


  By carrying out density functional theory (DFT) analysis within the Keldysh
non-equilibrium Green's function (NEGF) framework, we investigate the quantum
transport properties of Au-C60-Au molecular junctions from the first
principles. We briefly review the NEGF-DFT formalism and present some of our
data. We found that at small electrode separations (9-13 Angstrom), the
Au-C60-Au junctions show metallic behaviour with conductance G ~ 2e^{2}/h and
current I ~ 1-3 microAmpere in the linear regime (bias voltage V < 0.3 Volt).
The physical mechanism is the resonance tunnelling through partially occupied
states originated from the lowest unoccupied molecular orbital (LUMO) of the
free C60. We also found that the charge transfer from the Au electrodes to the
C60 molecule can be controlled by gate potential.


  The various decay mechanisms of excitons in cuprous oxide (Cu2O) are highly
sensitive to defects which can relax selection rules. Here we report cryogenic
hyperspectral imaging of exciton luminescence from cuprous oxide crystals grown
via the floating zone method showing the samples have few defects. Some
locations, however, show strain splitting of the 1s orthoexciton triplet
polariton luminescence. Strain is reduced by annealing. In addition, annealing
causes annihilation of oxygen and copper vacancies, which leads to a negative
correlation between luminescence of unlike vacancies.


  Using the idea of the quantum inverse scattering method, we introduce the
operators $\mathbf{B}(x), \mathbf{C}(x)$ and $\mathbf{\tilde{B}}(x),
\mathbf{\tilde{C}}(x)$ corresponding to the off-diagonal entries of the
monodromy matrix $T$ for the phase model and $i$-boson model in terms of bc
fermions and neutral fermions respectively, thus giving alternative treatment
of the KP and BKP hierarchies.
  We also introduce analogous operators $\mathbf{B}^{*}(x)$ and
$\mathbf{C}^{*}(x)$ for the charged free boson system and show that they are in
complete analogy to those of $bc$ fermionic fields. It is proved that the
correlation function $\langle
0|\mathbf{C}(x_N)\cdots\mathbf{C}(x_1)\mathbf{B}(y_1)\cdots $
$\mathbf{B}(y_N)|0\rangle$ in the $bc$ fermionic fields is the inverse of the
correlation function $\langle
0|\mathbf{C}^{*}(x_N)\cdots\mathbf{C}^{*}(x_1)\mathbf{B}^{*}(y_1)\cdots
\mathbf{B}^{*}(y_N)|0\rangle$ in the charged free bosons.


  A 1 $\mu$m long, field emitting, (5,5) single-walled, carbon nanotube (SWCNT)
closed with a fullerene cap, and a similar open nanotube with hydrogen-atom
termination, have been simulated using the MNDO (Modified Neglect of Diatomic
Overlap) quantum-mechanical method. Both contain about 80 000 atoms. It is
found that field penetration and band-bending, and various forms of chemically
and electrically induced apex dipole, play roles. Field penetration may help to
explain electroluminescence associated with field emitting carbon nanotubes.
Charge-density oscillations, induced by the hydrogen adsorption, are also
found. Many of the effects can be related to known effects that occur with
metallic or semiconductor field emitters; this helps both to explain the
effects and to unify our knowledge about field electron emitters. However, it
is currently unclear how best to treat correlation-and-exchange effects when
defining the CNT emission barrier. A new form of definition for the field
enhancement factor (FEF) is used. Predicted FEF values for these SWCNTs are
significantly less than values predicted by simple classical formulae. The FEF
for the closed SWCNT decreases with applied field; the FEF for the H-terminated
open SWCNT is less than the FEF for the closed SWCNT, but increases with
applied field. Physical explanations for this behavior are proposed. Curved
Fowler-Nordheim plots are predicted. Overall, the predicted field emission
performance of the H-terminated open SWCNT is slightly better than that of the
closed SWCNT, essentially because a C-H dipole is formed that reduces the
height of the tunnelling barrier. In general, the physics of a charged SWCNT
seems more complex than hitherto realised.


  We study a family of Lie algebras {hO} which are defined for cyclic operads
O. Using his graph homology theory, Kontsevich identified the homology of two
of these Lie algebras (corresponding to the Lie and associative operads) with
the cohomology of outer automorphism groups of free groups and mapping class
groups of punctured surfaces, respectively. In this paper we introduce a hairy
graph homology theory for O. We show that the homology of hO embeds in hairy
graph homology via a trace map which generalizes the trace map defined by S.
Morita. For the Lie operad we use the trace map to find large new summands of
the abelianization of hO which are related to classical modular forms for
SL(2,Z). Using cusp forms we construct new cycles for the unstable homology of
Out(F_n), and using Eisenstein series we find new cycles for Aut(F_n). For the
associative operad we compute the first homology of the hairy graph complex by
adapting an argument of Morita, Sakasai and Suzuki, who determined the complete
abelianization of hO in the associative case.


  We show that volume-preserving diffomorphisms and the chemical shift symmetry
defining relativistic lagrangian ideal fluid dynamics can be derived as an
emerging symmetry when ergodicity is assumed to apply locally in a way that is
invariant under smooth spacetime foliations. This can be used as a way to
derive the ideal hydrodynamic limit in a strongly coupled but strongly
fluctuating medium.
  We comment on the connection with thermalization in small systems, the
Eigenstate thermalization hypothesis and deviations from the ideal limit.


  In this paper we address the abnormality detection problem in crowded scenes.
We propose to use Generative Adversarial Nets (GANs), which are trained using
normal frames and corresponding optical-flow images in order to learn an
internal representation of the scene normality. Since our GANs are trained with
only normal data, they are not able to generate abnormal events. At testing
time the real data are compared with both the appearance and the motion
representations reconstructed by our GANs and abnormal areas are detected by
computing local differences. Experimental results on challenging abnormality
detection datasets show the superiority of the proposed method compared to the
state of the art in both frame-level and pixel-level abnormality detection
tasks.


  This research in progress paper describes the role of cyber security measures
undertaken in an ICT system for integrating electric storage technologies into
the grid. To do so, it defines security requirements for a communications
gateway and gives detailed information and hands-on configuration advice on
node and communication line security, data storage, coping with backend M2M
communications protocols and examines privacy issues. The presented research
paves the road for developing secure smart energy communications devices that
allow enhancing energy efficiency. The described measures are implemented in an
actual gateway device within the HORIZON 2020 project STORY, which aims at
developing new ways to use storage and demonstrating these on six different
demonstration sites.


  An \emph{asteroidal triple} is a set of three independent vertices in a graph
such that any two vertices in the set are connected by a path which avoids the
neighbourhood of the third.
  A classical result by Lekkerkerker and Boland \cite{6} showed that interval
graphs are precisely the chordal graphs that do not have asteroidal triples.
  Interval graphs are chordal, as are the \emph{directed path graphs} and the
\emph{path graphs}.
  Similar to Lekkerkerker and Boland, Cameron, Ho\'{a}ng, and L\'{e}v\^{e}que
\cite{4} gave a characterization of directed path graphs by a "special type" of
asteroidal triple, and asked whether or not there was such a characterization
for path graphs.
  We give strong evidence that asteroidal triples alone are insufficient to
characterize the family of path graphs, and give a new characterization of path
graphs via a forbidden induced subgraph family that we call \emph{sun systems}.
  Key to our new characterization is the study of \emph{asteroidal sets} in sun
systems, which are a natural generalization of asteroidal triples.
  Our characterization of path graphs by forbidding sun systems also
generalizes a characterization of directed path graphs by forbidding odd suns
that was given by Chaplick et al.~\cite{9}.


  We use multi-band optical and near-infrared photometric observations of
galaxies in the Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey
(CANDELS) to predict photometric redshifts using artificial neural networks.
The multi-band observations span over 0.39 microns to 8.0 microns for a sample
of about 1000 galaxies in the GOODS-S field for which robust size measurements
are available from Hubble Space Telescope Wide Field Camera 3 observations. We
use Self Organizing Maps (SOMs) to map the multi dimensional photometric and
galaxy size observations while taking advantage of existing spectroscopic
redshifts at 0 < z < 2 for independent training and testing sets. We show that
use of photometric and morphological data led to redshift estimates comparable
to redshift measurements from SED modeling and from self-organizing maps
without morphological measurements.


  Ageing populations in developing countries have spurred the introduction of
public pension programs to preserve the standard of living for the elderly. The
often-overlooked mechanism of intergenerational transfers, however, can dampen
these intended policy effects as adult children who make income contributions
to their parents could adjust their behavior to changes in their parents'
income. Exploiting a unique policy intervention in China, we examine using a
difference-in-difference-in-differences (DDD) approach how a new pension
program impacts inter vivos transfers. We show that pension benefits lower the
propensity of receiving transfers from adult children in the context of a large
middle-income country and we also estimate a small crowd-out effect. Taken
together, these estimates fit the pattern of previous research in high-income
countries, although our estimates of the crowd-out effect are significantly
smaller than previous studies in both high-income and middle-income countries.


  We present Period-Luminosity and Period-Luminosity-Color relations at
maximum-light for Mira variables in the Magellanic Clouds using time-series
data from the Optical Gravitational Lensing Experiment (OGLE-III) and {\it
Gaia} data release 2. The maximum-light relations exhibit a scatter typically
up to $\sim 30\%$ smaller than their mean-light counterparts. The apparent
magnitudes of Oxygen-rich Miras at maximum-light display significantly smaller
cycle-to-cycle variations than at minimum-light. High-precision photometric
data for Kepler Mira candidates also exhibit stable magnitude variations at the
brightest epochs while their multi-epoch spectra display strong Balmer emission
lines and weak molecular absorption at maximum-light. The stability of
maximum-light magnitudes for Miras possibly occurs due to the decrease in the
sensitivity to molecular bands at their warmest phase. At near-infrared
wavelengths, the Period-Luminosity relations of Miras display similar
dispersion at mean and maximum-light with limited time-series data in the
Magellanic Clouds. A kink in the Oxygen-rich Mira Period-Luminosity relations
is found at 300 days in the $VI$-bands which shifts to longer-periods ($\sim
350$~days) at near-infrared wavelengths. Oxygen-rich Mira Period-Luminosity
relations at maximum-light provide a relative distance modulus, $\Delta \mu =
0.48\pm0.08$~mag, between the Magellanic Clouds with a smaller statistical
uncertainty than the mean-light relations. The maximum-light properties of
Miras can be very useful for stellar atmosphere modeling and distance scale
studies provided their stability and the universality can be established in
other stellar environments in the era of extremely large telescopes.


  We perform extensive Monte Carlo simulations to systematically compare the
frequentist and Bayesian treatments of the Lomb--Scargle periodogram. The goal
is to investigate whether the Bayesian period search is advantageous over the
frequentist one in terms of the detection efficiency, how much if yes, and how
sensitive it is regarding the choice of the priors, in particular in case of a
misspecified prior (whenever the adopted prior does not match the actual
distribution of physical objects). We find that the Bayesian and frequentist
analyses always offer nearly identical detection efficiency in terms of their
tradeoff between type-I and type-II mistakes. Bayesian detection may reveal a
formal advantage if the frequency prior is nonuniform, but this results in only
$\sim 1$ per cent extra detected signals. In case if the prior was misspecified
(adopting nonuniform one over the actual uniform) this may turn into an
opposite advantage of the frequentist analysis. Finally, we revealed that Bayes
factor of this task appears rather overconservative if used without a
calibration against type-I mistakes (false positives), thereby necessitating
such a calibration in practice.


  We report the discovery of two new large HI shells in the direction of the
Coalsack nebula. Both shells were observed with the Parkes Radio Telescope as
part of the Southern Galactic Plane Survey. The largest shell, GSH 304-00-12,
is at a distance of ~1.2 kpc and has derived physical dimensions of 280 x 200
pc. The second shell, GSH 305+01-24, is at a distance of ~2.2 kpc and has
derived dimensions of 280 x 440 pc. We present a simple numerical model to show
that GSH 305+01-24 most likely formed from stellar winds in the Centaurus OB1
stellar association. There is associated radio, infrared and H-alpha continuum
emission. Both shells are situated in the Sagittarius-Carina arm, with GSH
305+01-24 more distant. The far edge of GSH 304-00-12 is at the near side of
the arm and opens into the interarm region. We find no evidence for closure at
the near side of the shell and therefore describe the geometry as conical.
Emission from the near side of the shell may be lost in absorption by the
Coalsack Nebula.


  We investigate the behavior of a circuit QED device when the resonator is
initially populated with a mesoscopic coherent field. The strong coupling
between the cavity and the qubit produces an entangled state involving
mesoscopic quasi-pointer states with respect to cavity dissipation. The overlap
of the associated field components results in collapse and revivals for the
Rabi oscillation. Although qubit relaxation and dephasing do not preserve these
states, a simple analytical description of the dissipative dynamics of the
circuit QED device including cavity relaxation as well as qubit dissipation is
obtained from the Monte-Carlo approach. Explicit predictions for the
spontaneous and induced Rabi oscillation signals are derived and sucessfully
compared with exact calculations. We show that these interesting effects could
be observed with a 10 photon field in forthcoming circuit QED experiments.


  We apply the holographic principle in the cosmological context through the
nonadditive Tsallis entropy, used to describe the thermodynamic properties of
nonstandard statistical systems such as the gravitational ones. Assuming the
future event horizon as the infrared cutoff, we build a dark energy model free
from cosmological inconsistencies, which includes standard thermodynamics and
standard holographic dark energy as a limiting case. We thus describe the
dynamics of Tsallis holographic dark energy in a flat FLRW background. Hence,
we investigate cosmological perturbations in the linear regime on sub-horizon
scales. We study the growth of matter fluctuations in the case of clustering
dark matter and a homogeneous dark energy component. Furthermore, we employ the
most recent late-time cosmic data to test the observational viability of our
theoretical scenario. We thus obtain constraints on the free parameters of the
model by means of Monte Carlo numerical method. We also used Bayesian selection
criteria to estimate the statistical preference for Tsallis holographic dark
energy compared to the concordance $\Lambda$CDM paradigm. Our results show
deviations from standard holographic dark energy within the $2\sigma$
confidence level. Finally, the analysis of the dark energy equation of state
indicates a quintessence-like behaviour with no evidence for phantom-divide
crossing at the $1\sigma$ level.


  Scientific facts are often spun in the popular press with the intent to
influence public opinion and action, as was evidenced during the COVID-19
pandemic. Automatic detection of misinformation in the scientific domain is
challenging because of the distinct styles of writing in these two media types
and is still in its nascence. Most research on the validity of scientific
reporting treats this problem as a claim verification challenge. In doing so,
significant expert human effort is required to generate appropriate claims. Our
solution bypasses this step and addresses a more real-world scenario where such
explicit, labeled claims may not be available. The central research question of
this paper is whether it is possible to use large language models (LLMs) to
detect misinformation in scientific reporting. To this end, we first present a
new labeled dataset SciNews, containing 2.4k scientific news stories drawn from
trusted and untrustworthy sources, paired with related abstracts from the
CORD-19 database. Our dataset includes both human-written and LLM-generated
news articles, making it more comprehensive in terms of capturing the growing
trend of using LLMs to generate popular press articles. Then, we identify
dimensions of scientific validity in science news articles and explore how this
can be integrated into the automated detection of scientific misinformation. We
propose several baseline architectures using LLMs to automatically detect false
representations of scientific findings in the popular press. For each of these
architectures, we use several prompt engineering strategies including
zero-shot, few-shot, and chain-of-thought prompting. We also test these
architectures and prompting strategies on GPT-3.5, GPT-4, and Llama2-7B,
Llama2-13B.


  We construct the mean thermal Sunyaev-Zel'dovich (tSZ) Comptonization y
profile around Luminous Red Galaxies (LRGs) in the redshift range 0.16 < z <
0.47 from the Sloan Digital Sky Survey (SDSS) Data Release 7 (DR7) using the
Planck y map. The mean central tSZ signal for the full sample is y ~ 1.8 *
10^(-7) and we detect tSZ emission out to ~30 arcmin, which is well beyond the
10 arcmin angular resolution of the y map and well beyond the virial radii of
the LRGs. We compare the measured profile with predictions from the cosmo-OWLS
suite of cosmological hydrodynamical simulations. This comparison agrees well
for models that include feedback from active galactic nuclei (AGN), but not
with hydrodynamic models without this energetic feedback mechanism. This
suggests that an additional heating mechanism is required over SNe feedback and
star formation to explain the y data profile. We also compare our results with
predictions based on the halo model with a universal pressure profile (UPP)
giving the y signal. The predicted profile is consistent with the data, but
only if we account for the clustering of haloes via a two-halo term and if halo
masses are estimated using the mean stellar-to-halo mass (SHM) relation of
Coupon et al. (2015) or Wang et al.(2016) estimated from gravitational lensing
measurements. We also discuss the importance of scatter in the SHM relation on
the model predictions.


  In this position paper, we reflect on fictional stories dealing with the
infinite and how they connect with the current, fast-evolving field of image
generation models. We draw attention to how some of these literary constructs
can serve as powerful metaphors for guiding human-centered design and technical
thinking in the space of these emerging technologies and the experiences we
build around them. We hope our provocations seed conversations about current
and yet-to-be developed interactions with these emerging models in ways that
may amplify human agency.


  It was recently shown that the use of feedback control can improve the
performance of a flashing ratchet. We investigate the effect of a time delay in
the implementation of feedback control in a closed-loop collective flashing
ratchet, using Langevin dynamics simulations. Surprisingly, for a large
ensemble, a well-chosen delay time improves the ratchet performance by allowing
the system to synchronize into a quasi-periodic stable mode of oscillation that
reproduces the optimal average velocity for a periodically flashing ratchet.
For a small ensemble, on the other hand, finite delay times significantly
reduce the benefit of feedback control for the time-averaged velocity, because
the relevance of information decays on a time scale set by the diffusion time
of the particles. Based on these results, we establish that experimental use of
feedback control is realistic.


  The topologically nontrivial edge states induce PT transition in
Su-Schrieffer-Heeger (SSH) chain with one pair of gain and loss at boundaries.
In this study, we investigated a pair of PT-symmetric defects located inside
the SSH chain, in particular, the defects locations are at the chain centre.
The PT symmetry breaking of the bound states leads to the PT transition, the
PT-symmetric phases and the localized states were studied. In the broken
PT-symmetric phase, all energy levels break simultaneously in topologically
trivial phase; however, two edge states in topologically nontrivial phase are
free from the influence of the PT-symmetric defects. We discovered PT-symmetric
bound states induced by the PT-symmetric local defects at the SSH chain centre.
The PT-symmetric bound states significantly increase the PT transition
threshold and coalesce to the topologically protected zero mode with vanishing
probabilities on every other site of the left-half chain and the right-half
chain, respectively.


  We derive a general theorem relating the energy, momentum and velocity of any
solitary wave solution of the generalized KdV equation which enables us to
relate the amplitude, width, and momentum to the velocity of these solutions.
We obtain the general condition for linear and Lyapunov stability. We then
obtain a two parameter family of exact solutions to these equations which
include elliptic and hyper-elliptic compacton solutions. For this general
family we explicitly verify both the theorem and the stability criteria.


  GAN inversion has been exploited in many face manipulation tasks, but 2D GANs
often fail to generate multi-view 3D consistent images. The encoders designed
for 2D GANs are not able to provide sufficient 3D information for the inversion
and editing. Therefore, 3D-aware GAN inversion is proposed to increase the 3D
editing capability of GANs. However, the 3D-aware GAN inversion remains
under-explored. To tackle this problem, we propose a 3D-aware (3Da) encoder for
GAN inversion and face editing based on the powerful StyleNeRF model. Our
proposed 3Da encoder combines a parametric 3D face model with a learnable
detail representation model to generate geometry, texture and view direction
codes. For more flexible face manipulation, we then design a dual-branch
StyleFlow module to transfer the StyleNeRF codes with disentangled geometry and
texture flows. Extensive experiments demonstrate that we realize 3D consistent
face manipulation in both facial attribute editing and texture transfer.
Furthermore, for video editing, we make the sequence of frame codes share a
common canonical manifold, which improves the temporal consistency of the
edited attributes.


  We study possible spatial domains containing expanding extra dimensions. We
show that they are predicted in the framework of $f(R)$ gravity and could
appear due to quantum fluctuations during inflation. Their interior is
characterized by the multidimensional curvature ultimately tending to zero and
a slowly growing size of the extra dimensions.


  An automatic word classification system has been designed which processes
word unigram and bigram frequency statistics extracted from a corpus of natural
language utterances. The system implements a binary top-down form of word
clustering which employs an average class mutual information metric. Resulting
classifications are hierarchical, allowing variable class granularity. Words
are represented as structural tags --- unique $n$-bit numbers the most
significant bit-patterns of which incorporate class information. Access to a
structural tag immediately provides access to all classification levels for the
corresponding word. The classification system has successfully revealed some of
the structure of English, from the phonemic to the semantic level. The system
has been compared --- directly and indirectly --- with other recent word
classification systems. Class based interpolated language models have been
constructed to exploit the extra information supplied by the classifications
and some experiments have shown that the new models improve model performance.


  In this review we identify a new category of structural optimization methods
that has emerged over the last 20 years, which we propose to call
feature-mapping methods. The two defining aspects of these methods are that the
design is parameterized by a high-level geometric description and that features
are mapped onto a fixed grid for analysis. The main motivation for using these
methods is to gain better control over the geometry to, for example, facilitate
imposing direct constraints on geometric features, whilst avoiding issues with
re-meshing. The review starts by providing some key definitions and then
examines the ingredients that these methods use to map geometric features onto
a fixed-grid. One of these ingredients corresponds to the mechanism for mapping
the geometry of a single feature onto a fixed analysis grid, from which an
ersatz material or an immersed boundary approach is used for the analysis. For
the former case, which we refer to as the pseudo-density approach, a test
problem is formulated to investigate aspects of the material interpolation,
boundary smoothing and numerical integration. We also review other ingredients
of feature-mapping techniques, including approaches for combining features
(which are required to perform topology optimization) and methods for imposing
a minimum separation distance among features. A literature review of
feature-mapping methods is provided for shape optimization, combined
feature/free-form optimization, and topology optimization. Finally, we discuss
potential future research directions for feature-mapping methods.


  The calculations of the elastic scattering properties of ultracold-francium
isotopes are reported in the detail. A parametric model potential for the
triplet molecular states of the Fr$_{2}$ is represented, and the scattering
lengths at and the effective range $r_{e}$ are calculated using WKB and Numerov
methods for Fr-Fr in the triplet state. The convergence of these scattering
properties as the depending on a $K_{0}$ parameter and core radius is also
investigated using two method as Quantum Defect Theory and analytical formula
of symskovisky.


  The paper analyzes the spreading of population in Indonesia. The spreading of
population in Indonesia is clustered in two regional terms, i.e.: kabupaten and
kotamadya. It is interestingly found that the rank in all kabupaten respect to
the population does not have fat tail properties, while in the other hand;
there exists power-law signature in kotamadya. We analyzed that this fact could
be caused by the equal or similar infrastructural development in all regions;
nevertheless, we also note that the first 20 kabupatens are dominated in Java
and Sumatera. Furthermore, the fat tail character in the rank of kotamadya
could be caused by the big gap between big cities one another, e.g.: Jakarta,
Surabaya, and others. The paper ends with some suggestions of more attention to
infrastructural development in eastern regional cities.


  In the course of a comprehensive mm/submm survey of massive star-forming
regions, a particularly interesting object has been found in the surroundings
of the bright FIR source IRAS 07029-1215, in a distance of 1 kpc. The object --
named UYSO 1 (Unidentified Young Stellar Object 1) -- is cold (T=40 K), it has
a massive envelope, and it is associated with an energetic molecular outflow.
No infrared point source has been detected at its position for wavelengths
below 20 micron. Therefore, it is a very good candidate for a member of the
long searched for group of massive protostars.


  The capabilities of natural neural systems have inspired new generations of
machine learning algorithms as well as neuromorphic very large-scale integrated
(VLSI) circuits capable of fast, low-power information processing. However, it
has been argued that most modern machine learning algorithms are not
neurophysiologically plausible. In particular, the workhorse of modern deep
learning, the backpropagation algorithm, has proven difficult to translate to
neuromorphic hardware. In this study, we present a neuromorphic, spiking
backpropagation algorithm based on synfire-gated dynamical information
coordination and processing, implemented on Intel's Loihi neuromorphic research
processor. We demonstrate a proof-of-principle three-layer circuit that learns
to classify digits from the MNIST dataset. To our knowledge, this is the first
work to show a Spiking Neural Network (SNN) implementation of the
backpropagation algorithm that is fully on-chip, without a computer in the
loop. It is competitive in accuracy with off-chip trained SNNs and achieves an
energy-delay product suitable for edge computing. This implementation shows a
path for using in-memory, massively parallel neuromorphic processors for
low-power, low-latency implementation of modern deep learning applications.


  Non-orthogonal Multiple Access (NOMA) has become a salient technology for
improving the spectral efficiency of the next generation 5G wireless
communication networks. In this paper, the achievable average rate of an
Opportunistic Non-Orthogonal Multiple Access (O-NOMA) based Cooperative
Relaying System (CRS) is studied under Rician fading channels with Channel
State Information (CSI) available at the source terminal. Based on CSI, for
opportunistic transmission, the source immediately chooses either the direct
transmission or the cooperative NOMA transmission using the relay, which can
provide better achievable average rate performance than the existing
Conventional-NOMA (C-NOMA) based CRS with no CSI at the source node.
Furthermore, a mathematical expression is also derived for the achievable
average rate and the results are compared with C-NOMA based CRS with no CSI at
the transmitter end, over a range of increasing power allocation coefficients,
transmit Signal-to-Noise Ratios (SNRs) and average channel powers. Numerical
results show that the CRS using O-NOMA with CSI achieves better spectral
efficiency in terms of the achievable average rate than the Conventional-NOMA
based CRS without CSI. To check the consistency of the derived analytical
results, Monte Carlo simulations are performed which verify that the results
are consistent and matched well with the simulation results.


  Boundary value problems involving elliptic PDEs such as the Laplace and the
Helmholtz equations are ubiquitous in mathematical physics and engineering.
Many such problems can be alternatively formulated as integral equations that
are mathematically more tractable. However, an integral-equation formulation
poses a significant computational challenge: solving large dense linear systems
that arise upon discretization. In cases where iterative methods converge
rapidly, existing methods that draw on fast summation schemes such as the Fast
Multipole Method are highly efficient and well-established. More recently,
linear complexity direct solvers that sidestep convergence issues by directly
computing an invertible factorization have been developed. However, storage and
computation costs are high, which limits their ability to solve large-scale
problems in practice. In this work, we introduce a distributed-memory parallel
algorithm based on an existing direct solver named ``strong recursive
skeletonization factorization.'' Specifically, we apply low-rank compression to
certain off-diagonal matrix blocks in a way that minimizes computation and data
movement. Compared to iterative algorithms, our method is particularly suitable
for problems involving ill-conditioned matrices or multiple right-hand sides.
Large-scale numerical experiments are presented to show the performance of our
Julia implementation.


  RSA cryptography is still widely used. Some of its applications (e.g.,
distributed signature schemes, cryptosystems) do not allow the RSA modulus to
be generated by a centralized trusted entity. Instead, the factorization must
remain unknown to all the network participants. To this date, the existing
algorithms are either computationally expensive, or limited to two-party
settings. In this work, we design a decentralized multi-party computation
algorithm able to generate efficiently the RSA modulus.


  In text generation evaluation, many practical issues, such as inconsistent
experimental settings and metric implementations, are often ignored but lead to
unfair evaluation and untenable conclusions. We present CoTK, an open-source
toolkit aiming to support fast development and fair evaluation of text
generation. In model development, CoTK helps handle the cumbersome issues, such
as data processing, metric implementation, and reproduction. It standardizes
the development steps and reduces human errors which may lead to inconsistent
experimental settings. In model evaluation, CoTK provides implementation for
many commonly used metrics and benchmark models across different experimental
settings. As a unique feature, CoTK can signify when and which metric cannot be
fairly compared. We demonstrate that it is convenient to use CoTK for model
development and evaluation, particularly across different experimental
settings.


  Conducting efficient performance estimations of neural architectures is a
major challenge in neural architecture search (NAS). To reduce the architecture
training costs in NAS, one-shot estimators (OSEs) amortize the architecture
training costs by sharing the parameters of one "supernet" between all
architectures. Recently, zero-shot estimators (ZSEs) that involve no training
are proposed to further reduce the architecture evaluation cost. Despite the
high efficiency of these estimators, the quality of such estimations has not
been thoroughly studied. In this paper, we conduct an extensive and organized
assessment of OSEs and ZSEs on five NAS benchmarks: NAS-Bench-101/201/301, and
NDS ResNet/ResNeXt-A. Specifically, we employ a set of NAS-oriented criteria to
study the behavior of OSEs and ZSEs and reveal that they have certain biases
and variances. After analyzing how and why the OSE estimations are
unsatisfying, we explore how to mitigate the correlation gap of OSEs from
several perspectives. Through our analysis, we give out suggestions for future
application and development of efficient architecture performance estimators.
Furthermore, the analysis framework proposed in our work could be utilized in
future research to give a more comprehensive understanding of newly designed
architecture performance estimators. All codes are available at
https://github.com/walkerning/aw_nas.


  Resonant rectifier topologies would be a promising candidate for achieving
simple, compact, and reliable single-stage wireless power transfer (WPT)
receiver if not for the lack of good DC regulation capability. This paper
investigates the problems that prevent the feasibility of single-stage DC
regulation in resonant rectifier topologies. A possible solution is the
proposed differential resonant rectifier topology, of which the rectifier is
designed to have a relatively constant AC voltage, and that phase shift control
is used to achieve relatively good output regulation. Design considerations on
the reactive component sizing, magnetic component design, frequency and phase
synchronization, small signal modelling, and closed-loop feedback control
design, are discussed. Experimental results verified that the proposed WPT
receiver system can achieve single-stage AC rectification and DC regulation
while attaining the key features of low harmonic distortion in its AC output
voltage, continuous DC current, and zero-voltage-switching (ZVS) operation over
a wide operating range.


  The ability of healthcare workers to learn proper hand hygiene has been an
understudied area of research. Generally, hand hygiene skills are regarded as a
key contributor to reduce critical infections and healthcare-associated
infections. In a clinical setup, at a Neonatal Intensive Care Unit (NICU), the
outcome of a multi-modal training initiative was recorded, where objective
feedback was provided to the staff. It was hypothesized that staff at the NICU
are more sensitive towards applying increased patient safety measures. Outcomes
were recorded as the ability to cover all hand surfaces with Alcohol-Based
Handrub (ABHR), modelled as a time-series of measurements. The learning ability
to rub in with 1.5 mL and with 3 mL was also assessed. As a secondary outcome,
handrub consumption and infection numbers were recorded. It has been observed
that some staff members were able to quickly learn the proper hand hygiene,
even with the limited 1.5 mL, while others were not capable of acquiring the
technique even with 3 mL. When analyzing the 1.5 mL group, it was deemed an
insufficient ABHR amount, while with 3 mL, the critical necessity of skill
training to achieve complete coverage was documented. Identifying these
individuals helps the infection control staff to better focus their training
efforts. The training led to a 157% increase in handrub consumption. The
setting of the study did not allow to show a measurable reduction in the number
of hospital infections. It has been concluded that the training method chosen
by the staff greatly affects the quality of the outcomes.


  How to model and encode the semantics of human-written text and select the
type of neural network to process it are not settled issues in sentiment
analysis. Accuracy and transferability are critical issues in machine learning
in general. These properties are closely related to the loss estimates for the
trained model. I present a computationally-efficient and accurate feedforward
neural network for sentiment prediction capable of maintaining low losses. When
coupled with an effective semantics model of the text, it provides highly
accurate models with low losses. Experimental results on representative
benchmark datasets and comparisons to other methods show the advantages of the
new approach.


  The GW approximation for the electronic self-energy is an important tool for
the quantitative prediction of excited states in solids, but its mathematical
exploration is hampered by the fact that it must, in general, be evaluated
numerically even for very simple systems. In this paper I describe a nontrivial
model consisting of two electrons on the surface of a sphere, interacting with
the normal long-range Coulomb potential, and show that the GW self-energy, in
the absence of self-consistency, can in fact be derived completely analytically
in this case. The resulting expression is subsequently used to analyze the
convergence of the energy gap between the highest occupied and the lowest
unoccupied quasiparticle orbital with respect to the total number of states
included in the spectral summations. The asymptotic formula for the truncation
error obtained in this way, whose dominant contribution is proportional to the
cutoff energy to the power -3/2, may be adapted to extrapolate energy gaps in
other systems.


  There exists a large literature of construction of convolutional codes with
maximal or near maximal free distance. Much less is known about constructions
of convolutional codes having optimal or near optimal column distances. In this
paper, a new construction of convolutional codes over the binary field with
optimal column distances is presented.


  In this paper, we study two variations of the time discrete Taylor schemes
for rough differential equations and for stochastic differential equations
driven by fractional Brownian motions. One is the incomplete Taylor scheme
which excludes some terms of an Taylor scheme in its recursive computation so
as to reduce the computation time. The other one is to add some deterministic
terms to an incomplete Taylor scheme to improve the mean rate of convergence.
Almost sure rate of convergence and $L_p$-rate of convergence are obtained for
the incomplete Taylor schemes. Almost sure rate is expressed in terms of the
H\"older exponents of the driving signals and the $L_p$-rate is expressed by
the Hurst parameters. Both rates involves with the incomplete Taylor scheme in
a very explicit way and then provide us with the best incomplete schemes,
depending on that one needs the almost sure convergence or one needs
$L_p$-convergence. As in the smooth case, general Taylor schemes are always
complicated to deal with. The incomplete Taylor scheme is even more
sophisticated to analyze. A new feature of our approach is the explicit
expression of the error functions which will be easier to study. Estimates for
multiple integrals and formulas for the iterated vector fields are obtained to
analyze the error functions and then to obtain the rates of convergence.


  This paper concerns the construction and analysis of a new family of exact
general relativistic shock waves. The construction resolves the open problem of
determining the expanding waves created behind a shock-wave explosion into a
static isothermal sphere with an inverse square density and pressure profile.
The construction involves matching two self-similar families of solutions to
the perfect fluid Einstein field equations across a spherical shock surface.
The matching is accomplished in Schwarzschild coordinates where the shock waves
appear one derivative less regular than they actually are. Separately, both
families contain singularities, but as matched shock-wave solutions, they are
singularity free. There was no guarantee ahead of time that the matching of the
two families could be achieved within the regions where both families are
nonsingular. Indeed, for pure radiation equations of state, the matching occurs
near the sonic point of the interior expanding wave and this makes the analysis
quite delicate, both numerically and formally. It is for this reason the
construction is accompanied by a rigorous existence proof in the pure radiation
case. The analysis is extended to demonstrate Lax stability in the pure
radiation case and provide a criterion for Lax stability in all other cases.
These shock-wave solutions represent an intriguing new mechanism in General
Relativity for exhibiting accelerations in asymptotically Friedmann spacetimes,
analogous to the accelerations modelled by the cosmological constant in the
Standard Model of Cosmology. However, unlike in the Standard Model of
Cosmology, these shock-wave solutions solve the Einstein field equations in the
absence of a cosmological constant, opening up the question of whether a purely
mathematical mechanism could account for the cosmic acceleration observed
today, rather than dark energy.


  We document a well-developed log-periodic power-law antibubble in China's
stock market, which started in August 2001. We argue that the current stock
market antibubble is sustained by a contemporary active unsustainable
real-estate bubble in China. The characteristic parameters of the antibubble
have exhibited remarkable stability over one year (Oct. 2002-Oct. 2003). Many
tests, including predictability over different horizons and time periods,
confirm the high significance of the antibubble detection. We predict that the
Chinese stock market will stop its negative trend around the end of 2003 and
start going up, appreciating by at least 25% in the following 6 months.
Notwithstanding the immature nature of the Chinese equity market and the strong
influence of government policy, we have found maybe even stronger imprints of
herding than in other mature markets. This is maybe due indeed to the
immaturity of the Chinese market which seems to attract short-term investors
more interested in fast gains than in long-term investments, thus promoting
speculative herding.


  This paper presents novel systems and methodologies for the development of
efficient large language models (LLMs). It explores the trade-offs between
model size, performance, and computational resources, with the aim of
maximizing the efficiency of these AI systems. The research explores novel
methods that allow different parts of the model to share parameters, reducing
the total number of unique parameters required. This approach ensures that the
model remains compact without sacrificing its ability to learn and represent
complex language structures. This study provides valuable insights and tools
for creating more efficient and effective LLMs, contributing to a more
sustainable and accessible future for AI language modeling.


  In the special case of a spherically symmetric solution of Einstein equations
coupled to a scalar massless field, we examine the consequences on the exact
solution imposed by a semiclassical treatment of gravitational interaction when
the scalar field is quantized. In agreement with the work of Doplicher,
Fredenhagen and Roberts (DFR), imposing the principle of gravitational
stability against localization of events, we find that the region where an
event is localized, or where initial conditions can be assigned, has a minimal
extension, of the order of the Planck length. This conclusion, though limited
to the case of spherical symmetry, is more general than that of DFR, since it
does not require the use of the notion of energy through the Heisenberg
Principle, nor of any approximation as the linearized Einstein equations.
  We shall then describe the influence of this minimal length scale in a
cosmological model, namely a simple universe filled with radiation, which is
effectively described by a conformally coupled scalar field in a conformal KMS
state. Solving the backreaction, a power law inflation scenario appears close
to the initial singularity. Furthermore, the initial singularity becomes light
like and thus the standard horizon problem is avoided in this simple model.
This indication goes in the same direction as those drawn at a heuristic level
from a full use of the principle of gravitational stability against
localization of events, which point to a background dependence of the effective
Planck length, through which a-causal effects may be transmitted.


  Results on the production of $\pi^{\pm}$, $\textrm{K}^{\pm}$,
$\textrm{p}(\bar{\textrm{p}})$, $\Lambda(\bar{\Lambda})$, $\Xi^{-}
\left(\bar{\Xi}^{+}\right)$ and $\Omega^{-} \left(\bar{\Omega}^{+}\right)$ at
midrapidity (${|y|<0.5}$) as a function of multiplicity in
$\sqrt{s}~=~7~\textrm{TeV}$ pp collisions are reported. Transverse momentum
distributions and integrated yields are compared to expectations from
statistical hadronization models along with results from different colliding
systems and center-of-mass energies. The evolution of spectral shapes with
multiplicity show similar patterns to those seen in p-Pb and Pb-Pb collisions.
The $p_{\textrm{T}}$-integrated baryon yields relative to pions exhibit a
significant strangeness-related enhancement in both pp and p-Pb collisions.


  In a joint work with Palmer we have formulated sufficient conditions under
which there exist continuous and invertible transformations of the form
$H_n(x,y)$ taking solutions of a coupled system \begin{equation*} x_{n+1}
=A_nx_n+f_n(x_n, y_n), \quad y_{n+1}=g_n( y_n), \end{equation*} onto the
solutions of the associated partially linearized uncoupled system
\begin{equation*} x_{n+1} =A_nx_n, \quad y_{n+1}=g_n( y_n). \end{equation*} In
the present work we go one step further and provide conditions under which
$H_n$ and $H_n^{-1}$ are smooth in one of the variables $x$ and $y$.
  We emphasise that our conditions are of a general form and do not involve any
kind of dichotomy, nonresonance or spectral gap assumptions for the linear part
which are present on most of the related works.


  As Large Language Models (LLMs) have become integral to both research and
daily operations, rigorous evaluation is crucial. This assessment is important
not only for individual tasks but also for understanding their societal impact
and potential risks. Despite extensive efforts to examine LLMs from various
perspectives, there is a noticeable lack of multi-agent AI models specifically
designed to evaluate the performance of different LLMs. To address this gap, we
introduce a novel multi-agent AI model that aims to assess and compare the
performance of various LLMs. Our model consists of eight distinct AI agents,
each responsible for retrieving code based on a common description from
different advanced language models, including GPT-3.5, GPT-3.5 Turbo, GPT-4,
GPT-4 Turbo, Google Bard, LLAMA, and Hugging Face. Our developed model utilizes
the API of each language model to retrieve code for a given high-level
description. Additionally, we developed a verification agent, tasked with the
critical role of evaluating the code generated by its counterparts. We
integrate the HumanEval benchmark into our verification agent to assess the
generated code's performance, providing insights into their respective
capabilities and efficiencies. Our initial results indicate that the GPT-3.5
Turbo model's performance is comparatively better than the other models. This
preliminary analysis serves as a benchmark, comparing their performances side
by side. Our future goal is to enhance the evaluation process by incorporating
the Massively Multitask Benchmark for Python (MBPP) benchmark, which is
expected to further refine our assessment. Additionally, we plan to share our
developed model with twenty practitioners from various backgrounds to test our
model and collect their feedback for further improvement.


  A flat mounting unit with electronically variable thermal resistance [1] has
been presented in the last year [2]. The design was based on a Peltier cell and
the appropriate control electronics and software. The device is devoted
especially to the thermal characterization of packages, e.g. in dual cold plate
arrangements. Although this design meets the requirements of the static
measurement we are intended to improve its parameters as the settling time and
dynamic thermal impedance and the range of realized thermal resistance. The new
design applies the heat flux sensor developed by our team as well [3], making
easier the control of the device. This development allows even the realization
of negative thermal resistances.


  Bacterial vaginosis (BV) increases transmission of HIV, enhances the risk of
preterm labour, and its associated malodour impacts the quality of life for
many women. Clinical diagnosis primarily relies on microscopy to presumptively
detect a loss of lactobacilli and acquisition of anaerobes. This diagnostic
does not reflect the microbiota composition accurately as lactobacilli can
assume different morphotypes, and assigning BV associated morphotypes to
specific organisms is challenging. Using an untargeted metabolomics approach we
identify novel biomarkers for BV in a cohort of 131 Rwandan women, and
demonstrate that metabolic products in the vagina are strongly associated with
bacterial diversity. Metabolites associated with high diversity and clinical BV
include 2-hydroxyisovalerate and gamma-hydroxybutyrate (GHB), but not the
anaerobic end-product succinate. Low diversity, and high relative abundance of
lactobacilli, is characterized by lactate and amino acids. Biomarkers
associated with diversity and BV are independent of pregnancy status, and were
validated in a blinded replication cohort from Tanzania (n=45), in which we
predicted clinical BV with 91% accuracy. Correlations between the metabolome
and microbiota identified Gardnerella vaginalis as a putative producer of GHB,
and we demonstrate production by this species in vitro. This work provides a
deeper understanding of the relationship between the vaginal microbiota and
biomarkers of vaginal health and dysbiosis.


  This paper addresses the Capacitated Arc Routing Problem (CARP) using an Ant
Colony Optimization scheme. Ant Colony schemes can compute solutions for medium
scale instances of VRP. The proposed Ant Colony is dedicated to large-scale
instances of CARP with more than 140 nodes and 190 arcs to service. The Ant
Colony scheme is coupled with a local search procedure and provides high
quality solutions. The benchmarks we carried out prove possible to obtain
solutions as profitable as CARPET ones can be obtained using such scheme when a
sufficient number of iterations is devoted to the ants. It competes with the
Genetic Algorithm of Lacomme et al. regarding solution quality but it is more
time consuming on large scale instances. The method has been intensively
benchmarked on the well-known instances of Eglese, DeArmon and the last ones of
Belenguer and Benavent. This research report is a step forward CARP resolution
by Ant Colony proving ant schemes can compete with Taboo search methods and
Genetic Algorithms


  In the last few years a number of works reported the appearance of thick
branes with internal structure, induced by the parameter which controls the
interaction between two scalar fields coupled to gravity in (4,1) dimensions in
warped space-time with one extra dimension. Here we show that one can implement
the control over the brane thickness without needing to change the potential
parameter. On the contrary, this is going to be done by means of the variation
of a parameter associated to the domain wall degeneracy. We also report the
existence of novel and qualitatively different solutions for a critical value
of the degeneracy parameter, which could be called critical Bloch branes.


  Suppose X is a multivariate diffusion process that is observed discretely in
time. At each observation time, a transformation of the state of the process is
observed with noise. The smoothing problem consists of recovering the path of
the process, consistent with the observations. We derive a novel Markov Chain
Monte Carlo algorithm to sample from the exact smoothing distribution. The
resulting algorithm is called the Backward Filtering Forward Guiding (BFFG)
algorithm. We extend the algorithm to include parameter estimation. The
proposed method relies on guided proposals introduced in Schauer et al. (2017).
We illustrate its efficiency in a number of challenging problems.


  We present SmartChoices, an approach to making machine learning (ML) a first
class citizen in programming languages which we see as one way to lower the
entrance cost to applying ML to problems in new domains. There is a growing
divide in approaches to building systems: on the one hand, programming
leverages human experts to define a system while on the other hand behavior is
learned from data in machine learning. We propose to hybridize these two by
providing a 3-call API which we expose through an object called SmartChoice. We
describe the SmartChoices-interface, how it can be used in programming with
minimal code changes, and demonstrate that it is an easy to use but still
powerful tool by demonstrating improvements over not using ML at all on three
algorithmic problems: binary search, QuickSort, and caches. In these three
examples, we replace the commonly used heuristics with an ML model entirely
encapsulated within a SmartChoice and thus requiring minimal code changes. As
opposed to previous work applying ML to algorithmic problems, our proposed
approach does not require to drop existing implementations but seamlessly
integrates into the standard software development workflow and gives full
control to the software developer over how ML methods are applied. Our
implementation relies on standard Reinforcement Learning (RL) methods. To learn
faster, we use the heuristic function, which they are replacing, as an initial
function. We show how this initial function can be used to speed up and
stabilize learning while providing a safety net that prevents performance to
become substantially worse -- allowing for a safe deployment in critical
applications in real life.


  The Crank-Nicolson (CN) method is a well-known time integrator for
evolutionary partial differential equations (PDEs) arising in many real-world
applications. Since the solution at any time depends on the solution at
previous time steps, the CN method is inherently difficult to parallelize. In
this paper, we consider a parallel method for the solution of evolutionary PDEs
with the CN scheme. Using an all-at-once approach, we can solve for all time
steps simultaneously using a parallelizable over time preconditioner within a
standard iterative method. Due to the diagonalization of the proposed
preconditioner, we can prove that most eigenvalues of preconditioned matrices
are equal to 1 and the others lie in the set: $\left\{z\in\mathbb{C}: 1/(1 +
\alpha) < |z| < 1/(1 - \alpha)~{\rm and}~\Re{\rm e}(z) > 0\right\}$, where $0 <
\alpha < 1$ is a free parameter. Besides, the efficient implementation of the
proposed preconditioner is described. Given certain conditions, we prove that
the preconditioned GMRES method exhibits a mesh-independent convergence rate.
Finally, we will verify both theoretical findings and the efficacy of the
proposed preconditioner via numerical experiments on financial option pricing
PDEs.


  Several applications have been implemented with access via the Clarens web
service infrastructure, including virtual organization management, JetMET
physics data analysis using relational databases, and Storage Resource Broker
(SRB) access. This functionality is accessible transparently from Python
scripts, the Root analysis framework and from Java applications and browser
applets.


  This work aims to study different probes of Type Ia supernova progenitors
that have been suggested to be linked to the presence of circumstellar material
(CSM). In particular, we have investigated, for the first time, the link
between narrow blueshifted NaID absorption profiles and the presence and
strength of the broad high-velocity CaII near infrared triplet absorption
features seen in Type Ia supernovae around maximum light. With the probes
exploring different distances from the supernova; NaID > 10$^{17}$cm,
high-velocity CaII features < 10$^{15}$cm. For this, we have used a new
intermediate-resolution X-shooter spectral sample of 15 Type Ia supernovae. We
do not identify a link between these two probes, implying either that, one (or
both) is not physically related to the presence of CSM or that the occurrence
of CSM at the distance explored by one probe is not linked to its presence at
the distance probed by the other. However, the previously identified
statistical excess in the presence of blueshifted (over redshifted) NaID
absorption is confirmed in this sample at high significance and is found to be
stronger in Type Ia supernovae hosted by late-type galaxies. This excess is
difficult to explain as being from an interstellar-medium origin as has been
suggested by some recent modelling, as such an origin is not expected to show a
bias for blueshifted absorption. However, a circumstellar origin for these
features also appears unsatisfactory based on our new results given the lack of
link between the two probes of CSM investigated.


  Information engines model ``Maxwell's demon" mechanistically. However, the
demon's strategy is pre-described by an external experimenter, and information
engines are conveniently designed such that observables contain complete
information about variables pertinent to work extraction. In real world
scenarios, it is more realistic to encounter partial observability, which
forces the physical observer, an integral part of the information engine, to
make inferences from incomplete knowledge. Here, we use the fact that an
algorithm for computing optimal strategies can be directly derived from
maximizing overall engine work output. For a simple binary decision problem, we
discover interesting optimal strategies that differ notably from naive coarse
graining. They inspire a model class of simple, yet compelling, parameterized
soft partitionings of the observable.


  We study the magnetization of a Heisenberg spin ladder using exact
diagonalization techniques, finding three distinct magnetic phases. We consider
the results in relation to the experimental behaviour of the new copper
compound Cu_2(C_5H_{12}N_2)_2Cl_4 and deduce that the compound is well
described by such a model with a ratio of `chain' to `rung' bond strengths
(J/J^\prime) of the order of 0.2, consistent with results from the magnetic
susceptibility. The effects of temperature, spin impurities and additional
diagonal bonds are presented and we give evidence that these diagonal bonds are
indeed of a ferromagnetic nature.


  Using metal-clad (or plasmonic) waveguide structures in semiconductor lasers
carries a promise of reduced size, threshold, and power consumption. This
promise is put to a rigorous theoretical test, that takes into account
increased waveguide loss, Auger recombination, and Purcell enhancement of
spontaneous recombination. The conclusion is that purported benefits of metal
waveguides are small to nonexistent for all the band-to-band and intersubband
lasers operating from UV to Mid-IR range, with a prominent exception of far-IR
and THz quantum cascade lasers. For these devices, however, metal waveguides
already represent the state of the art, and the guiding mechanism in them has
far more in common with a ubiquitous transmission line than with plasmonics.


  Adoption of machine learning models in healthcare requires end users' trust
in the system. Models that provide additional supportive evidence for their
predictions promise to facilitate adoption. We define consistent evidence to be
both compatible and sufficient with respect to model predictions. We propose
measures of model inconsistency and regularizers that promote more consistent
evidence. We demonstrate our ideas in the context of edema severity grading
from chest radiographs. We demonstrate empirically that consistent models
provide competitive performance while supporting interpretation.


  We discuss the geometry of the Ramond-Ramond fields of low degree via the
language of abelian p-gerbes, working in type II superstring theory with
vanishing H-flux. A Dp-brane is a source for the Ramond-Ramond field strength
G_(p+2), which can be thought of as the curvature of a connection on a p-gerbe
with U(1)-band. The highest degree forms of the connection are the local
Ramond-Ramond potentials C_(p+1). This picture is clear for p >= 0, a 0-gerbe
being a line bundle. Actually, one can encounter also lower degree
Ramond-Ramond field strengths: G_1 in IIB superstring theory, and G_0 in IIA
superstring theory. Although the geometrical nature of these objects is simple
in itself, it is interesting to explicitly clarify the analogy with the
geometry of the higher degree fields and potentials, introducing the notions of
(-1)-gerbes with connection and (-2)-gerbes. We define these gerbes and we show
that they are better described by a variant of ordinary Cech cohomology.


  Managing the semantic quality of the categorization in large textual
datasets, such as Wikipedia, presents significant challenges in terms of
complexity and cost. In this paper, we propose leveraging transformer models to
distill semantic information from texts in the Wikipedia dataset and its
associated categories into a latent space. We then explore different approaches
based on these encodings to assess and enhance the semantic identity of the
categories. Our graphical approach is powered by Convex Hull, while we utilize
Hierarchical Navigable Small Worlds (HNSWs) for the hierarchical approach. As a
solution to the information loss caused by the dimensionality reduction, we
modulate the following mathematical solution: an exponential decay function
driven by the Euclidean distances between the high-dimensional encodings of the
textual categories. This function represents a filter built around a contextual
category and retrieves items with a certain Reconsideration Probability (RP).
Retrieving high-RP items serves as a tool for database administrators to
improve data groupings by providing recommendations and identifying outliers
within a contextual framework.


  A comprehensive phase diagram of lowest-energy structures and compositions of
the rutile TiO_2(110) surface in equilibrium with a surrounding gas phase at
finite temperatures and pressures has been determined using density functional
theory in combination with a thermodynamic formalism. The exchange of oxygen,
hydrogen, and water molecules with the gas phase is considered. Particular
attention is given to the convergence of all calculations with respect to
lateral system size and slab thickness. In addition, the reliability of
semilocal density functionals to describing the energetics of the reduced
surfaces is critically evaluated. For ambient conditions the surface is found
to be fully covered by molecularly adsorbed water. At low coverages, in the
limit of single, isolated water molecules, molecular and dissociative
adsorption become energetically degenerate. Oxygen vacancies form in strongly
reducing, oxygen-poor environments. However, already at slightly more moderate
conditions it is shown that removing full TiO_2 units from the surface is
thermodynamically preferred. In agreement with recent experimental observations
it is furthermore confirmed that even under extremely hydrogen-rich
environments the surface cannot be fully hydroxylated, but only a maximum
coverage with hydrogen of about 0.6-0.7 monolayer can be reached. Finally,
calculations of migration paths strongly suggest that hydrogen prefers to
diffuse into the bulk over desorbing from the surface into the gas phase.


  We consider spatially coupled code ensembles. A particular instance are
convolutional LDPC ensembles. It was recently shown that, for transmission over
the binary erasure channel, this coupling increases the belief propagation
threshold of the ensemble to the maximum a-priori threshold of the underlying
component ensemble. We report on empirical evidence which suggest that the same
phenomenon also occurs when transmission takes place over a general binary
memoryless symmetric channel. This is confirmed both by simulations as well as
by computing EBP GEXIT curves and by comparing the empirical BP thresholds of
coupled ensembles to the empirically determined MAP thresholds of the
underlying regular ensembles. We further consider ways of reducing the
rate-loss incurred by such constructions.


  We consider possible non-signaling composites of probabilistic models based
on euclidean Jordan algebras (EJAs), satisfying some reasonable additional
constraints motivated by the desire to construct dagger-compact categories of
such models. We show that no such composite has the exceptional Jordan algebra
as a direct summand, nor does any such composite exist if one factor has an
exceptional summand, unless the other factor is a direct sum of one-dimensional
Jordan algebras (representing essentially a classical system). Moreover, we
show that any composite of simple, non-exceptional EJAs is a direct summand of
their universal tensor product, sharply limiting the possibilities.
  These results warrant our focussing on concrete Jordan algebras of hermitian
matrices, i.e., euclidean Jordan algebras with a preferred embedding in a
complex matrix algebra}. We show that these can be organized in a natural way
as a symmetric monoidal category, albeit one that is not compact closed. We
then construct a related category InvQM of embedded euclidean Jordan algebras,
having fewer objects but more morphisms, that is not only compact closed but
dagger-compact. This category unifies finite-dimensional real, complex and
quaternionic mixed-state quantum mechanics, except that the composite of two
complex quantum systems comes with an extra classical bit.
  Our notion of composite requires neither tomographic locality, nor
preservation of purity under tensor product. The categories we construct
include examples in which both of these conditions fail. In such cases, the
information capacity (the maximum number of mutually distinguishable states) of
a composite is greater than the product of the capacities of its constituents.


  Representation learning on temporal graphs has drawn considerable research
attention owing to its fundamental importance in a wide spectrum of real-world
applications. Though a number of studies succeed in obtaining time-dependent
representations, it still faces significant challenges. On the one hand, most
of the existing methods restrict the embedding space with a certain curvature.
However, the underlying geometry in fact shifts among the positive curvature
hyperspherical, zero curvature Euclidean and negative curvature hyperbolic
spaces in the evolvement over time. On the other hand, these methods usually
require abundant labels to learn temporal representations, and thereby notably
limit their wide use in the unlabeled graphs of the real applications. To
bridge this gap, we make the first attempt to study the problem of
self-supervised temporal graph representation learning in the general
Riemannian space, supporting the time-varying curvature to shift among
hyperspherical, Euclidean and hyperbolic spaces. In this paper, we present a
novel self-supervised Riemannian graph neural network (SelfRGNN). Specifically,
we design a curvature-varying Riemannian GNN with a theoretically grounded time
encoding, and formulate a functional curvature over time to model the
evolvement shifting among the positive, zero and negative curvature spaces. To
enable the self-supervised learning, we propose a novel reweighting
self-contrastive approach, exploring the Riemannian space itself without
augmentation, and propose an edge-based self-supervised curvature learning with
the Ricci curvature. Extensive experiments show the superiority of SelfRGNN,
and moreover, the case study shows the time-varying curvature of temporal graph
in reality.


  Within General Relativity, the unique stationary solution of an isolated
black hole is the Kerr spacetime, which has a peculiar multipolar structure
depending only on its mass and spin. We develop a general method to extract the
multipole moments of arbitrary stationary spacetimes and apply it to a large
family of horizonless microstate geometries. The latter can break the axial and
equatorial symmetry of the Kerr metric and have a much richer multipolar
structure, which provides a portal to constrain fuzzball models
phenomenologically. We find numerical evidence that all multipole moments are
typically larger (in absolute value) than those of a Kerr black hole with the
same mass and spin. Current measurements of the quadrupole moment of black-hole
candidates could place only mild constraints on fuzzballs, while future
gravitational-wave detections of extreme mass-ratio inspirals with the space
mission LISA will improve these bounds by orders of magnitude.


  Using the non-equilibrium quantum field theory, photon production from the
coherently oscillating axion field in a flat Robertson-Walker cosmology is
re-examined. First neglecting the Debye screening of the baryon plasma to
photons, we find that the axions will dissipate into photons via spinodal
instability in addition to parametric resonance. As a result of the
pseudo-scalar nature of the axion-photon coupling, we observe a circular
polarization asymmetry in the produced photons. However, these effects are
suppressed to an insignificant level in the expanding universe. We then briefly
discuss a systematic way of including the plasma effect which can further
suppress the photon production. We note that the formalism of the problem can
be applied to any pseudo-scalar field coupled to photon in a thermal background
in a general curved spacetime.


  We study mechanism of magnetic energy transport, motivated by recent
measurements of the thermal conductivity in low dimensional quantum magnets. We
point out a possible mechanism of enhancement of the thermal conductivity in
gapped magnetic system, where the magnetic energy transport plays a crucial
role. This mechanism gives an interpretation for the recent experiment of
CuGeO_3, where the thermal conductivity depends on the crystal direction.


  Throughout this paper we investigate the complex structure of the conifold
$C(T^{1,1})$ basically making use of the interplay between symplectic and
complex approaches of the K\"{a}hler toric manifolds. The description of the
Calabi-Yau manifold $C(T^{1,1})$ using toric data allows us to write explicitly
the complex coordinates and apply standard methods for extracting special
Killing forms on the base manifold. As an outcome, we obtain the complete set
of special Killing forms on the five-dimensional Sasaki-Einstein space
$T^{1,1}$.


  The relativistic fluid is a highly successful model used to describe the
dynamics of many-particle systems moving at high velocities and/or in strong
gravity. It takes as input physics from microscopic scales and yields as output
predictions of bulk, macroscopic motion. By inverting the process-e.g., drawing
on astrophysical observations-an understanding of relativistic features can
lead to insight into physics on the microscopic scale. Relativistic fluids have
been used to model systems as "small" as colliding heavy ions in laboratory
experiments, and as large as the Universe itself, with "intermediate" sized
objects like neutron stars being considered along the way. The purpose of this
review is to discuss the mathematical and theoretical physics underpinnings of
the relativistic (multi-) fluid model. We focus on the variational principle
approach championed by Brandon Carter and collaborators, in which a crucial
element is to distinguish the momenta that are conjugate to the particle number
density currents. This approach differs from the "standard" text-book
derivation of the equations of motion from the divergence of the stress-energy
tensor in that one explicitly obtains the relativistic Euler equation as an
"integrability" condition on the relativistic vorticity. We discuss the
conservation laws and the equations of motion in detail, and provide a number
of (in our opinion) interesting and relevant applications of the general
theory. The formalism provides a foundation for complex models, e.g., including
electromagnetism, superfluidity and elasticity-all of which are relevant for
state of the art neutron-star modelling.