  In a ~ 20 ksec XMM-Newton observation the X-ray transient radio-quiet quasar
PG 0844+349 was found in a historically high state compared to previous X-ray
observations. The quasar showed a featureless spectrum with a strong soft
excess over the extrapolation of a hard power law. Comptonization models or a
broken power law with Gamma_{soft} ~ 2.75, Gamma_{hard} ~ 2.25 and a break
energy of E_{break} ~ 1.35 keV represent acceptable descriptions of the
spectral continuum. In the Comptonization models the temperature of the
Comptonizing gas is considerably lower than generally found in (broad line)
Seyfert galaxies whereas the optical depth is much higher. As a similar
behavior has been seen in NLSy1 galaxies, it might be an indicator of the
different physical conditions in these two classes of AGN. During the
XMM-Newton observation the flux of PG 0844+349 varied achromatically in a
smooth, nearly linear fashion, by ~ 25 % on time scales of a few thousand
seconds, which puts some constraints on current models of Comptonizing
accretion disk coronae.


  Very short computer programs, sometimes consisting of as few as three
arithmetic operations in an infinite loop, can generate data that sounds like
music when output as raw PCM audio. The space of such programs was recently
explored by dozens of individuals within various on-line communities. This
paper discusses the programs resulting from this exploratory work and
highlights some rather unusual methods they use for synthesizing sound and
generating musical structure.


  Using numerical hydrodynamics code, we perform various idealized galaxy
merger simulations to study the star formation (SF) of two merging disk
galaxies. Our simulations include gas accretion onto supermassive black holes
and active galactic nucleus (AGN) feedback. By comparing AGN simulations with
those without AGNs, we attempt to understand when the AGN feedback effect is
significant. With ~70 simulations, we investigated the SF with the AGN effect
in mergers with variety of mass ratios, inclinations, orbits, galaxy structures
and morphologies. Using these merger simulations with AGN feedback, we measure
merger-driven SF using the burst efficiency parameter introduced by Cox et al.
We confirm the previous studies that, in galaxy mergers, AGN suppresses SF more
efficiently than in isolated galaxies. However, we additionally find that the
effect of AGNs on SF is larger in major mergers than in minor mergers. In minor
merger simulations with different primary bulge-to-total ratios, the effect of
bulge fraction on the merger-driven SF decreases due to AGN feedback. We create
models of Sa, Sb and Sc type galaxies and compare their SF properties while
undergoing mergers. With the current AGN prescriptions, the difference in
merger-driven SF is not as pronounced as that in the recent observational study
of Kaviraj. We discuss the implications of this discrepancy.


  In this paper we prove that Ext-analogues of Gan--Gross--Prasad models vanish
for tempered representations, as conjectured by D. Prasad. In particular, this
implies a conjectural Euler--Poincar\'e characteristic formula for the
Gan--Gross--Prasad models.


  In this paper, we study the existence of positive solutions of a three-point
integral boundary value problem (BVP) for the following second-order
differential equation \begin{equation*} \begin{gathered} {u^{\prime \prime
}}(t)+\lambda a(t)f(u(t))=0,\ \ 0<t<1, \\ u^{\prime}(0)=0, \
u(1)={\alpha}\int_{0}^{\eta}u(s)ds, \end{gathered} \end{equation*} where
$\lambda>0$ is a parameter, $0<{\eta}<1$, $0<{\alpha}< \frac{1}{{\eta}}$. By
using the properties of the Green's function and Krasnoselskii's fixed point
theorem on cones, the eigenvalue intervals of the nonlinear boundary value
problem are considered, some sufficient conditions for the existence of at
least one positive solutions are established.


  We investigate the dual superconductivity mechanism in the exceptional group
$G_2$. This is a centerless group (no 't Hooft flux vortices are allowed) and
we check for the presence of a magnetic monopole condensate in the confined
phase by measuring on the lattice a disorder parameter related to the vacuum
expectation value of an operator carrying magnetic charge. The behaviour of the
disorder parameter is consistent with the dual superconductor picture. A first
step of an analysis on the thermodynamical properties of the theory is
conducted by mean of this operator.


  We completed a new survey for H I emission for a large, well-defined sample
of 154 nearby (z < 0.1) galaxies with type 1 AGNs. We make use of the extensive
database presented in a companion paper to perform a comprehensive appraisal of
the cold gas content in active galaxies and to seek new strategies to
investigate the global properties of the host galaxies and their relationship
to their central black holes (BHs). We show that the BH mass obeys a strong,
roughly linear relation with the host galaxy's dynamical mass. BH mass follows
a looser, though still highly significant, correlation with the maximum
rotation velocity of the galaxy, as expected from the known scaling between
rotation velocity and central velocity dispersion. Neither of these H I-based
correlations is as tight as the more familiar relations between BH mass and
bulge luminosity or velocity dispersion, but they offer the advantage of being
insensitive to the glare of the nucleus and therefore are promising new tools
for probing the host galaxies of both nearby and distant AGNs. We present
evidence for substantial ongoing BH growth in the most actively accreting AGNs.
In these nearby systems, BH growth appears to be delayed with respect to the
assembly of the host galaxy but otherwise has left no detectable perturbation
to its mass-to-light ratio or its global gas content. The host galaxies of type
1 AGNs, including those luminous enough to qualify as quasars, are generally
gas-rich systems, possessing a cold interstellar medium reservoir at least as
abundant as that in inactive galaxies of the same morphological type. This
calls into question current implementations of AGN feedback in models of galaxy
formation that predict strong cold gas depletion in unobscured AGNs. (Abridged)


  Embedded devices are omnipresent in modern networks including the ones
operating inside critical environments. However, due to their constrained
nature, novel mechanisms are required to provide external, and non-intrusive
anomaly detection. Among such approaches, one that has gained traction is based
on the analysis of the electromagnetic (EM) signals that get emanated during a
device's operation. However, one of the most neglected challenges of this
approach is the requirement for manually gathering and fingerprinting the
signals that correspond to each execution path of the software/firmware.
Indeed, even simple programs are comprised of hundreds if not thousands of
branches thus, making the fingerprinting stage an extremely time-consuming
process that involves the manual labor of a human specialist. To address this
issue, we propose a framework for generating synthetic EM signals directly from
the machine code. The synthetic signals can be used to train a Machine Learning
based (ML) system for anomaly detection. The main advantage of the proposed
approach is that it completely removes the need for an elaborate and
error-prone fingerprinting stage, thus, dramatically increasing the scalability
of the corresponding protection mechanisms. The experimental evaluations
indicate that our method provides high detection accuracy (above 90% AUC score)
when employed for the detection of injection attacks. Moreover, the proposed
methodology inflicts only a small penalty (-1.3%) in accuracy for the detection
of the injection of as little as four malicious instructions when compared to
the same methods if real signals were to be used.


  The evolution and distribution of species body sizes for terrestrial mammals
is well-explained by a macroevolutionary tradeoff between short-term selective
advantages and long-term extinction risks from increased species body size,
unfolding above the 2g minimum size induced by thermoregulation in air. Here,
we consider whether this same tradeoff, formalized as a constrained
convection-reaction-diffusion system, can also explain the sizes of fully
aquatic mammals, which have not previously been considered. By replacing the
terrestrial minimum with a pelagic one, at roughly 7000g, the terrestrial
mammal tradeoff model accurately predicts, with no tunable parameters, the
observed body masses of all extant cetacean species, including the 175,000,000g
Blue Whale. This strong agreement between theory and data suggests that a
universal macroevolutionary tradeoff governs body size evolution for all
mammals, regardless of their habitat. The dramatic sizes of cetaceans can thus
be attributed mainly to the increased convective heat loss is water, which
shifts the species size distribution upward and pushes its right tail into
ranges inaccessible to terrestrial mammals. Under this macroevolutionary
tradeoff, the largest expected species occurs where the rate at which
smaller-bodied species move up into large-bodied niches approximately equals
the rate at which extinction removes them.


  From the onset of high-speed optical communications, lithium niobite (LN) has
been the material of choice for electro-optic modulators owing to its large
electro-optic response, wide transparent window, excellent thermal stability
and long-term material reliability. Conventional LN electro-optic modulators
while continue to be the workhorse of the optoelectronic industry become
progressively too bulky, expensive and power hungry to fully serve the needs of
this industry rapidly progressing towards highly integrated, cost-effective and
energy efficient components and circuits. Recently developed monolithic LN
nanophotonic platform enables the realization of electro-optic modulators that
are significantly improved in terms of compactness, bandwidth and energy
efficiency, while still demanding relatively long, on the mm-scale, interaction
lengths. Here we successfully deal with this challenge and demonstrate
plasmonic electro-optic directional coupler switches consisting of two closely
spaced nm-thin gold nanostripes monolithically fabricated on LN substrates that
guide both coupled electromagnetic modes and electrical signals influencing
their coupling and thereby enabling ultra-compact switching and
modulatiofunctionalities. The extreme confinement of both slow-plasmon modes
and electrostatic fields created by two nanostripes along with their nearly
perfect spatial overlap allowed us to achieve a 90% modulation depth with
20-$\mu$m-long switches characterized by a electro-optic modulation efficiency
of 0.3 Vcm. Our monolithic LN plasmonic platform enables ultra-dense
integration of high-performance active photonic components, enabling a wide
range of cost-effective optical communication applications demanding
$\mu$m-scale footprints, ultrafast operation, robust design and high
environmental stability.


  The Picard-Lefschetz theory has been attracting much attention as a tool to
evaluate a multi-variable integral with a complex weight, which appears in
various important problems in theoretical physics. The idea is to deform the
integration contour based on Cauchy's theorem using the so-called gradient flow
equation. In this paper, we propose a fast Hybrid Monte Carlo algorithm for
evaluating the integral, where we "backpropagate" the force of the fictitious
Hamilton dynamics on the deformed contour to that on the original contour,
thereby reducing the required computational cost by a factor of the system
size. Our algorithm can be readily extended to the case in which one integrates
over the flow time in order to solve not only the sign problem but also the
ergodicity problem that occurs when there are more than one thimbles
contributing to the integral. This enables, in particular, efficient
identification of all the dominant saddle points and the associated thimbles.
We test our algorithm by calculating the real-time evolution of the wave
function using the path integral formalism.


  Quantization techniques have been applied in many challenging finance
applications, including pricing claims with path dependence and early exercise
features, stochastic optimal control, filtering problems and efficient
calibration of large derivative books. Recursive Marginal Quantization of the
Euler scheme has recently been proposed as an efficient numerical method for
evaluating functionals of solutions of stochastic differential equations. This
method involves recursively quantizing the conditional marginals of the
discrete-time Euler approximation of the underlying process. By generalizing
this approach, we show that it is possible to perform recursive marginal
quantization for two higher-order schemes: the Milstein scheme and a simplified
weak order 2.0 scheme. As part of this generalization a simple matrix
formulation is presented, allowing efficient implementation. We further extend
the applicability of recursive marginal quantization by showing how absorption
and reflection at the zero boundary may be incorporated, when this is
necessary. To illustrate the improved accuracy of the higher order schemes,
various computations are performed using geometric Brownian motion and its
generalization, the constant elasticity of variance model. For both processes,
we show numerical evidence of improved weak order convergence and we compare
the marginal distributions implied by the three schemes to the known analytical
distributions. By pricing European, Bermudan and Barrier options, further
evidence of improved accuracy of the higher order schemes is demonstrated.


  The inclusive production of $D_s$, $D^0$, and $J/\psi$ mesons is studied
using a 1.86 fb$^{-1}$ data sample collected on the $\Upsilon$(5S) resonance by
the Belle detector at the KEKB asymmetric energy $e^+ e^-$ collider. The number
of $b\bar{b}$ events in the $\Upsilon$(5S) data sample is determined. We
measure the branching fractions $B(\Upsilon({\rm 5S}) \to D_s X) / 2 = (23.6
\pm 1.2 \pm 3.6)%$, $B(\Upsilon({\rm 5S}) \to D^0 X) / 2 = (53.8 \pm 2.0 \pm
3.4)%$}, and $B(\Upsilon({\rm 5S}) \to J/\psi X) / 2 = (1.030 \pm 0.080 \pm
0.067)%$. From the $D_s$ and $D^0$ inclusive branching fractions the ratio $f_s
= (18.0 \pm 1.3 \pm 3.2)%$} of $B_s^{(*)} \bar{B}_s^{(*)}$ to the total
$b\bar{b}$ quark pair production at the $\Upsilon$(5S) energy is obtained in a
model-dependent way.


  The principle of multi-critical-points (PMCP) may be a convincing approach to
determine the emerging parameter values in different kinds of
beyond-standard-model (BSM) models. This could certainly be applied to solve
the problem of undetermined new parameters in the ALP-Higgs interaction models.
In this paper, we apply this principle to such model and investigate whether
there are suitable solutions. Then, using the 1-loop effective potential, we
study the phase transition property of this model under the PMCP requirement.
It is gratifying to find that under the requirement of PMCP, the phase
transition can be not only first-order, but also strong enough to serve as a
solution for electroweak baryongenesis (EWBG). Finally, we show the parameter
space of ALP and provide the parameter range that leads to the first-order
phase transition.


  We study the effects of electron-phonon interactions on the transport
properties of a molecular quantum dot coupled to two Luttinger-liquid leads. In
particular, we investigate the effects on the steady state current and DC noise
characteristics. We consider both equilibrated and unequilibrated on-dot
phonons. The density matrix formalism is applied in the high temperature
approximation and the resulting semi-classical rate equation is numerically
solved for various strengths of electron-electron interactions in the leads and
electron-phonon coupling. The current and the noise are in general smeared out
and suppressed due to intralead electron interaction. On the other hand, the
Fano factor, which measures the noise normalized by the current, is more
enhanced as the intralead interaction becomes stronger. As the electron-phonon
coupling becomes greater than order one, the Fano factor exhibits
super-Poissonian behaviour.


  We have investigated the gauge dependence of the vacuum expectation
value(VEV) both in the $R_{\xi }$ and the $\overline{R_{\xi }}$ gauge in the
$\overline{MS}$ scheme. We have found that, in case of the $R_{\xi }$ gauge,
the gauge dependence of the VEV should be modified due to the presence of the
parameter in the gauge function that should be identified as a VEV in the
broken symmetry phase. However the pole mass remains gauge independent.


  A main topic at this meeting is how galaxies are affected when they enter for
the first time the cluster environment from the outskirts. Most of the times we
are forced to infer the environmental effects indirectly, relying on systematic
variations of galaxy properties with environment, but there aren't many
examples of direct observations able to unveil ongoing transformations taking
place, and the corresponding mechanism producing it. We present a case in which
it is possible to identify the cluster environment, and in particular the
intracluster medium and the recent infall history of galaxies onto the cluster,
as the cause for a recent, abrupt change in the evolutionary history of
galaxies.


  The 4 Color Theorem (4CT) implies that every $n$-vertex planar graph has an
independent set of size at least $\frac{n}4$; this is best possible, as shown
by the disjoint union of many copies of $K_4$. In 1968, Erd\H{o}s asked whether
this bound on independence number could be proved more easily than the full
4CT. In 1976 Albertson showed (independently of the 4CT) that every $n$-vertex
planar graph has an independent set of size at least $\frac{2n}9$. Until now,
this remained the best bound independent of the 4CT. Our main result improves
this bound to $\frac{3n}{13}$.


  A prominent model for transportation networks is branched transport, which
seeks the optimal transportation scheme to move material from a given initial
to a final distribution. The cost of the scheme encodes a higher transport
efficiency the more mass is moved together, which automatically leads to
optimal transportation networks with a hierarchical branching structure. The
two major existing model formulations, either using mass fluxes (vector-valued
measures) or patterns (probabilities on the space of particle paths), are
rather different. Once their equivalence was established, the analysis of
optimal networks could rest on both.
  The transportation cost of classical branched transport is a fractional power
of the transported mass, and several model properties and proof techniques
build on its strict concavity. We generalize the model and its analysis to the
most general class of reasonable transportation costs, essentially increasing,
subadditive functions. This requires several modifications or new approaches.
In particular, for the equivalence between mass flux and pattern formulation it
turns out advantageous to resort to a description via 1-currents, an intuition
which already Xia exploited. In addition, some already existing arguments are
given a more concise and perhaps simpler form. The analysis includes the
well-posedness, a metrization and a length space property of the model cost,
the equivalence between the different model formulations, as well as a few
network properties.


  Both the semi-classical and quantum transport properties of F-doped SnO$_2$
thick films ($\sim$1\,$\mu$m) were investigated experimentally. It is found
that the resistivity caused by the thermal phonons obeys Bloch-Gr\"{u}neisen
law from $\sim$90 to 300\,K, while only the diffusive thermopower, which varies
linearly with temperature from 300 down to 10\,K, can be observed.The
phonon-drag thermopower is completely suppressed due to the long
electron-phonon relaxation time in the compound. These observations, together
with the temperature independent characteristic of carrier concentration,
indicate that the conduction electron in F-doped SnO$_2$ films behaves
essentially like a free electron. At low temperatures, the electron-electron
scattering dominates over the electron-phonon scattering and governs the
inelastic scattering process. The theoretical predicated scattering rates for
both large- and small-energy-transfer electron-electron scattering processes,
which are negligibly weak in three-dimensional disordered conventional
conductors, are quantitatively tested in this lower carrier concentration and
free-electron-like highly degenerate semiconductor.


  We use mono-crystalline gold platelets with ultra-smooth surfaces and
superior plasmonic properties to investigate the formation of interference
patterns caused by surface plasmon polaritons (SPPs) with scattering-type
scanning near-field microscopy (s-SNOM) at 521~nm and 633~nm. By applying a
Fourier analysis approach, we can identify and separate several signal channels
related to SPPs launched and scattered by the AFM tip and the edges of the
platelet. Especially at the excitation wavelength of 633~nm, we can isolate a
region in the center of the platelets where we find only contributions of SPPs
which are launched by the tip and reflected at the edges. These signatures are
used to determine the SPP wavelength of $\lambda_{SPP}=606$ nm in good
agreement with theoretical predictions. Furthermore, we were still able to
measure SPP signals after 20~$\upmu$m propagation, which demonstrates
impressively the superior plasmonic quality of these mono-crystalline gold
platelets.


  Collaborations such as Wikipedia are a key part of the value of the modern
Internet. At the same time there is concern that these collaborations are
threatened by high levels of member turnover. In this paper we borrow ideas
from topic analysis to editor activity on Wikipedia over time into a latent
space that offers an insight into the evolving patterns of editor behavior.
This latent space representation reveals a number of different categories of
editor (e.g. content experts, social networkers) and we show that it does
provide a signal that predicts an editor's departure from the community. We
also show that long term editors gradually diversify their participation by
shifting edit preference from one or two namespaces to multiple namespaces and
experience relatively soft evolution in their editor profiles, while short term
editors generally distribute their contribution randomly among the namespaces
and experience considerably fluctuated evolution in their editor profiles.


  Using an extended chromomagnetic model, we perform a systematic study of the
masses of the doubly heavy tetraquarks. We find that the ground states of the
doubly heavy tetraquarks are dominated by color-triplet
$\ket{(qq)^{\bar{3}_{c}}(\bar{Q}\bar{Q})^{3_{c}}}$ configuration, which is
opposite to that of the fully heavy tetraquarks. The combined results suggest
that the color-triplet configuration becomes more important when the mass
difference between the quarks and antiquarks increases. We find three stable
states which lie below the thresholds of two pseudoscalar mesons. They are the
$IJ^{P}=01^{+}$ $nn\bar{b}\bar{b}$ tetraquark, the $IJ^{P}=00^{+}$
$nn\bar{c}\bar{b}$ tetraquark and the $J^{P}=1^{+}$ $ns\bar{b}\bar{b}$
tetraquark.


  A feasibility study for an experimental analysis searching for
$t\bar{t}H(H\rightarrow b\bar{b})$ production at the LHC and its high
luminosity phase is presented in this note. Unlike search strategies currently
being used in experimental collaborations, the present analysis exploits jet
substructure techniques and focuses on the reconstruction of boosted Higgs
bosons, to obtain sensitivity to the signal in a simple cut-based analysis. The
$t\bar{t} +$ jets background may be constrained in the proposed analysis
through a control region with very small signal contamination. Using this
analysis strategy, the $t\bar{t}H(H\rightarrow b\bar{b})$ process could be
observed at the LHC, in the semi-leptonic channel alone, with a significance of
$5.41\pm 0.12$ for $\mathcal{L}=300\,\mbox{fb}^{-1}$. For the same integrated
luminosity, in the High Luminosity LHC scenario with an upgraded detector, a
significance of $6.13\pm 0.11$ may be obtained. The top Yukawa coupling could
be measured with a 35\% uncertainty using $\mathcal{L}=300\,\mbox{fb}^{-1}$ of
LHC data and of 17\% at the HL-LHC scenario with
$\mathcal{L}=3000\,\mbox{fb}^{-1}$. In the same luminosity scenarios, the
signal strength is equally expected to have a 18$\%$ and 5$\%$ uncertainty,
respectively. Finally, it was found that re-clustered jets may be used without
loss of efficiency.


  Assuming the source label space subsumes the target one, Partial Video Domain
Adaptation (PVDA) is a more general and practical scenario for cross-domain
video classification problems. The key challenge of PVDA is to mitigate the
negative transfer caused by the source-only outlier classes. To tackle this
challenge, a crucial step is to aggregate target predictions to assign class
weights by up-weighing target classes and down-weighing outlier classes.
However, the incorrect predictions of class weights can mislead the network and
lead to negative transfer. Previous works improve the class weight accuracy by
utilizing temporal features and attention mechanisms, but these methods may
fall short when trying to generate accurate class weight when domain shifts are
significant, as in most real-world scenarios. To deal with these challenges, we
propose the Multi-modality Cluster-calibrated partial Adversarial Network
(MCAN). MCAN enhances video feature extraction with multi-modal features from
multiple temporal scales to form more robust overall features. It utilizes a
novel class weight calibration method to alleviate the negative transfer caused
by incorrect class weights. The calibration method tries to identify and weigh
correct and incorrect predictions using distributional information implied by
unsupervised clustering. Extensive experiments are conducted on prevailing PVDA
benchmarks, and the proposed MCAN achieves significant improvements when
compared to state-of-the-art PVDA methods.


  We consider a 1D Klein-Fock-Gordon particle in a finite interval, or box. We
construct for the first time the most general set of pseudo self-adjoint
boundary conditions for the Hamiltonian operator that is present in the first
order in time 1D Klein-Fock-Gordon wave equation, or the 1D Feshbach-Villars
wave equation. We show that this set depends on four real parameters and can be
written in terms of the one-component wavefunction for the second order in time
1D Klein-Fock-Gordon wave equation and its spatial derivative, both evaluated
at the endpoints of the box. Certainly, we write the general set of pseudo
self-adjoint boundary conditions also in terms of the two-component
wavefunction for the 1D Feshbach-Villars wave equation and its spatial
derivative, evaluated at the ends of the box; however, the set actually depends
on these two column vectors each multiplied by the singular matrix that is
present in the kinetic energy term of the Hamiltonian. As a consequence, we
found that the two-component wavefunction for the 1D Feshbach-Villars equation
and its spatial derivative do not necessarily satisfy the same boundary
condition that these quantities satisfy when multiplied by the singular matrix.
In any case, given a particular boundary condition for the one-component
wavefunction of the standard 1D Klein-Fock-Gordon equation and using the pair
of relations that arise from the very definition of the two-component
wavefunction for the 1D Feshbach-Villars equation, the respective boundary
condition for the latter wavefunction and its derivative can be obtained. Our
results can be extended to the problem of a 1D Klein-Fock-Gordon particle
moving on a real line with a point interaction (or a hole) at one point.


  Multilingual speech recognition with supervised learning has achieved great
results as reflected in recent research. With the development of pretraining
methods on audio and text data, it is imperative to transfer the knowledge from
unsupervised multilingual models to facilitate recognition, especially in many
languages with limited data. Our work investigated the effectiveness of using
two pretrained models for two modalities: wav2vec 2.0 for audio and MBART50 for
text, together with the adaptive weight techniques to massively improve the
recognition quality on the public datasets containing CommonVoice and Europarl.
Overall, we noticed an 44% improvement over purely supervised learning, and
more importantly, each technique provides a different reinforcement in
different languages. We also explore other possibilities to potentially obtain
the best model by slightly adding either depth or relative attention to the
architecture.


  We give a new method for numerically solving Abel integral equations of first
kind. An estimation for the error is obtained. The method is based on
approximations of fractional integrals and Caputo derivatives. Using
trapezoidal rule and Computer Algebra System Maple, the exact and approximation
values of three Abel integral equations are found, illustrating the
effectiveness of the proposed approach.


  In this paper we propose a sequence of tests which gives a definitive test
for checking $2\times M$ separability. The test is definitive in the sense that
each test corresponds to checking membership in a cone, and that the closure of
the union of all these cones consists exactly of {\it all} $2 \times M$
separable states. Membership in each single cone may be checked via
semidefinite programming, and is thus a tractable problem. This sequential test
comes about by considering the dual problem, the characterization of all
positive maps acting ${\mathbb C}^{2 \times 2} \to {\mathbb C}^{M\times M}$.
The latter in turn is solved by characterizing all positive quadratic matrix
polynomials in a complex variable.


  A search is presented for exotic decays of a Higgs boson into undetectable
particles and one or two isolated photons in pp collisions at a center-of-mass
energy of 8 TeV. The data correspond to an integrated luminosity of up to 19.4
inverse femtobarns collected with the CMS detector at the LHC. Higgs bosons
produced in gluon-gluon fusion and in association with a Z boson are
investigated, using models in which the Higgs boson decays into a gravitino and
a neutralino or a pair of neutralinos, followed by the decay of the neutralino
to a gravitino and a photon. The selected events are consistent with the
background-only hypothesis, and limits are placed on the product of cross
sections and branching fractions. Assuming a standard model Higgs boson
production cross section, a 95% confidence level upper limit is set on the
branching fraction of a 125 GeV Higgs boson decaying into undetectable
particles and one or two isolated photons as a function of the neutralino mass.
For this class of models and neutralino masses from 1 to 120 GeV an upper limit
in the range of 7 to 13% is obtained. Further results are given as a function
of the neutralino lifetime, and also for a range of Higgs boson masses.


  Non-Gaussian component analysis (NGCA) is aimed at identifying a linear
subspace such that the projected data follows a non-Gaussian distribution. In
this paper, we propose a novel NGCA algorithm based on log-density gradient
estimation. Unlike existing methods, the proposed NGCA algorithm identifies the
linear subspace by using the eigenvalue decomposition without any iterative
procedures, and thus is computationally reasonable. Furthermore, through
theoretical analysis, we prove that the identified subspace converges to the
true subspace at the optimal parametric rate. Finally, the practical
performance of the proposed algorithm is demonstrated on both artificial and
benchmark datasets.


  The design of numerical tools to model the behavior of building materials is
a challenging task. The crucial point is to save computational cost and
maintain high accuracy of predictions. There are two main limitations on the
time scale choice, which put an obstacle to solve the above issues. First one
is the numerical restriction. A number of research is dedicated to overcome
this limitation and it is shown that it can be relaxed with innovative
numerical schemes. The second one is the physical restriction. It is imposed by
properties of a material, phenomena itself and corresponding boundary
conditions. This work is focused on the study of a methodology that enables to
overcome the physical restriction on the time grid. So-called Average Reduced
Model (ARM) is suggested. It is based on smoothing the time-dependent boundary
conditions. Besides, the approximate solution is decomposed into average and
fluctuating components. The primer is obtained by integrating the equations
over time, whereas the latter is an user-defined empirical model. The
methodology is investigated for both heat diffusion and coupled heat and mass
transfer. It is demonstrated that the signal core of the boundary conditions is
preserved and the physical restriction can be relaxed. The model proved to be
reliable, accurate and efficient also in comparison with the experimental data
of two years. The implementation of the scarce time-step of $1 \, \sf{h}$ is
justified.
  It is shown, that by maintaining the tolerable error it is possible to cut
computational effort up to almost four times in comparison with the complete
model with the same time grid.


  Rare-earth ions doped into desired locations of optical crystals might enable
a range of novel integrated photonic devices for quantum applications. With
this aim, we have investigated the production yield of cerium and praseodymium
by means of ion implantation. As a measure, the collected fluorescence
intensity from both, implanted samples and single centers was used. With a
tailored annealing procedure for cerium, a yield up to 53% was estimated.
Praseodymium yield amounts up to 91%.


  Recent experiments have shown remarkable dynamics of solid $^4$He melting and
growth, driven by the normal incidence of an acoustic pulse on the solid-liquid
interface. The theory of solid growth/melting, driven by the radiation pressure
of the acoustic pulse, accounts well for the temperature dependence of the
measured data. There is however an observed source of extra,
temperature-independent, melting. We here propose that this extra melting is
due to solid-liquid mixing (and consequent melting) at the interface, in a
process similar to the Richtmyer-Meshkov instability: Initial undulations of
the rough interface, grow when accelerated by the acoustic pressure
oscillations. This model predicts a temperature-independent extra melting and
its dependence on the acoustic power, which is in agreement with the measured
data.


  Testing remains the primary method to evaluate the accuracy of neural network
perception systems. Prior work on the formal verification of neural network
perception models has been limited to notions of local adversarial robustness
for classification with respect to individual image inputs. In this work, we
propose a notion of global correctness for neural network perception models
performing regression with respect to a generative neural network with a
semantically meaningful latent space. That is, against an infinite set of
images produced by a generative model over an interval of its latent space, we
employ neural network verification to prove that the model will always produce
estimates within some error bound of the ground truth. Where the perception
model fails, we obtain semantically meaningful counter-examples which carry
information on concrete states of the system of interest that can be used
programmatically without human inspection of corresponding generated images.
Our approach, Generate and Verify, provides a new technique to gather insight
into the failure cases of neural network perception systems and provide
meaningful guarantees of correct behavior in safety critical applications.


  Analog tunable memristors are widely utilized as artificial synapses in
various neural network applications. However, exploiting the dynamical aspects
of their conductance change to implement active neurons is still in its
infancy, awaiting the realization of efficient neural signal recognition
functionalities. Here we experimentally demonstrate an artificial neural
information processing unit that can detect a temporal pattern in a very noisy
environment, fire a single output spike upon successful detection and reset
itself in a fully unsupervised, autonomous manner. This circuit relies on the
dynamical operation of only two memristive blocks: a non-volatile Ta$_2$O$_5$
device and a volatile VO$_2$ unit. A fading functionality with exponentially
tunable memory time constant enables adaptive operation dynamics, which can be
tailored for the targeted temporal pattern recognition task. In the trained
circuit false input patterns only induce short-term variations. In contrast,
the desired signal activates long-term memory operation of the non-volatile
component, which triggers a firing output of the volatile block.


  We show that any group $G$ is contained in some sharply 2-transitive group
$\mathcal{G}$ without a non-trivial abelian normal subgroup. This answers a
long-standing open question. The involutions in the groups $\mathcal{G}$ that
we construct have no fixed points.


  Generalizing to novel classes unseen during training is a key challenge of
few-shot classification. Recent metric-based methods try to address this by
local representations. However, they are unable to take full advantage of them
due to (i) improper supervision for pretraining the feature extractor, and (ii)
lack of adaptability in the metric for handling various possible compositions
of local feature sets. In this work, we unleash the power of local
representations in improving novel-class generalization. For the feature
extractor, we design a novel pretraining paradigm that learns randomly cropped
patches by soft labels. It utilizes the class-level diversity of patches while
diminishing the impact of their semantic misalignments to hard labels. To align
network output with soft labels, we also propose a UniCon KL-Divergence that
emphasizes the equal contribution of each base class in describing "non-base"
patches. For the metric, we formulate measuring local feature sets as an
entropy-regularized optimal transport problem to introduce the ability to
handle sets consisting of homogeneous elements. Furthermore, we design a
Modulate Module to endow the metric with the necessary adaptability. Our method
achieves new state-of-the-art performance on three popular benchmarks.
Moreover, it exceeds state-of-the-art transductive and cross-modal methods in
the fine-grained scenario.


  We consider the polyhedral properties of two spanning tree problems with
additional constraints. In the first problem, it is required to find a tree
with a minimum sum of edge weights among all spanning trees with the number of
leaves less or equal a given value. In the second problem, an additional
constraint is the assumption that the degree of all vertices of the spanning
tree does not exceed a given value. The decision versions of both problems are
NP-complete.
  We consider the polytopes of these problems and their 1-skeletons. We prove
that in both cases it is a NP-complete problem to determine whether the
vertices of 1-skeleton are adjacent. Although it is possible to obtain a
superpolynomial lower bounds on the clique numbers of these graphs. These
values characterize the time complexity in a broad class of algorithms based on
linear comparisons. The results indicate a fundamental difference in
combinatorial and geometric properties between the considered problems and the
classical minimum spanning tree problem.


  The existence of cracks and other damages pose a significant threat to the
safe operation of transportation infrastructure. Traditional manual detection
and ultrasound equipment testing consume a lot of time and resources. With the
development of deep learning technology, many deep learning models have been
widely applied to practical visual segmentation tasks. The detection method
based on deep learning models has the advantages of high detection accuracy,
fast detection speed, and simple operation. However, deep learning-based crack
segmentation models are sensitive to background noise, have rough edges, and
lack robustness. Therefore, this paper proposes a crack segmentation model
based on the fusion of dual streams. The image is inputted simultaneously into
two designed processing streams to independently extract long-distance
dependence and local detail features. The adaptive prediction is achieved
through the dual-headed mechanism. Meanwhile, a novel interaction fusion
mechanism is proposed to guide the complementary of different feature layers to
achieve crack location and recognition in complex backgrounds. Finally, an edge
optimization method is proposed to improve the accuracy of segmentation.
Experiments show that the F1 value of segmentation results on the DeepCrack[1]
public dataset is 93.7% and the IOU value is 86.6%. The F1 value of
segmentation results on the CRACK500[2] dataset is 78.1%, and the IOU value is
66.0%.


  Many works related to Twitter aim at characterizing its users in some way:
role on the service (spammers, bots, organizations, etc.), nature of the user
(socio-professional category, age, etc.), topics of interest , and others.
However, for a given user classification problem, it is very difficult to
select a set of appropriate features, because the many features described in
the literature are very heterogeneous, with name overlaps and collisions, and
numerous very close variants. In this article, we review a wide range of such
features. In order to present a clear state-of-the-art description, we unify
their names, definitions and relationships, and we propose a new, neutral,
typology. We then illustrate the interest of our review by applying a selection
of these features to the offline influence detection problem. This task
consists in identifying users which are influential in real-life, based on
their Twitter account and related data. We show that most features deemed
efficient to predict online influence, such as the numbers of retweets and
followers, are not relevant to this problem. However, We propose several
content-based approaches to label Twitter users as Influencers or not. We also
rank them according to a predicted influence level. Our proposals are evaluated
over the CLEF RepLab 2014 dataset, and outmatch state-of-the-art methods.


  In this work, we discuss a new method for calculation of extremal
eigenvectors and eigenvalues in systems or regions of parameter space where
direct calculation is problematic. This technique relies on the analytic
continuation of the power series expansion for the eigenvector around a point
in the complex plane. We start this document by introducing the background
material relevant to understand the basics of quantum mechanics and quantum
field theories on the lattice, how we perform our numerical simulations, and
how this relates to the nuclear physics we probe. We then move to the
mathematical formalism of the eigenvector continuation, which is rooted in
analytic function theory and linear algebra. We then discuss how these
techniques are implemented numerically, with a discussion about the
computational costs. Finally, we discuss applications of this method to full,
quantum many-body systems. These include neutron matter, the Bose-Hubbard
model, the Lipkin model, and the Coulomb interaction in light nuclei with LO
chiral forces. These systems cover two categories of interest to the field:
systems with a substantial sign problem, or systems that exhibit quantum phase
transitions.


  Let A be an association scheme on q\geq 3 vertices. We show that the
Bose-Mesner algebra of the generalized Hamming scheme H(n,A), for n\geq 2, is
not the Nomura algebra of a type II matrix. This result gives examples of
formally self-dual Bose-Mesner algebras that are not the Nomura algebras of
type II matrices.


  Generative AI models continue to become more powerful. The launch of ChatGPT
in November 2022 has ushered in a new era of AI. ChatGPT and other similar
chatbots have a range of capabilities, from answering student homework
questions to creating music and art. There are already concerns that humans may
be replaced by chatbots for a variety of jobs. Because of the wide spectrum of
data chatbots are built on, we know that they will have human errors and human
biases built into them. These biases may cause significant harm and/or inequity
toward different subpopulations. To understand the strengths and weakness of
chatbot responses, we present a position paper that explores different use
cases of ChatGPT to determine the types of questions that are answered fairly
and the types that still need improvement. We find that ChatGPT is a fair
search engine for the tasks we tested; however, it has biases on both text
generation and code generation. We find that ChatGPT is very sensitive to
changes in the prompt, where small changes lead to different levels of
fairness. This suggests that we need to immediately implement "corrections" or
mitigation strategies in order to improve fairness of these systems. We suggest
different strategies to improve chatbots and also advocate for an impartial
review panel that has access to the model parameters to measure the levels of
different types of biases and then recommends safeguards that move toward
responses that are less discriminatory and more accurate.


  A new statistical interpretation of the nuclear collective states is
suggested and applied recently in rare earths and actinide nuclei by the
two-neutron transfer reactions in terms of the nearest neighbor-spacing
distributions (NNSDs). Experimental NNSDs were obtained by using the complete
and pure sequences of the collective states through an unfolding procedure. The
two-neutron transfer reactions allow to obtain such a sequence of the
collective states that meets the requirements for a statistical analysis. Their
theoretical analysis is based on the linear approximation of a repulsion level
density within the Wigner-Dyson theory. This approximation is successful to
evaluate separately the Wigner chaos and Poisson order contributions. We found
an intermediate behavior of NNSDs between the Wigner and Poisson limits. NNSDs
turn out to be shifted from a chaos to order with increasing the length of
spectra and the angular momentum of collective states. Perspectives for the
statistical analysis of the symmetry breaking of states with the fixed
projection of angular momenta $K$ are discussed.


  The question of association between outcome and feature is generally framed
in the context of a model on functional and distributional forms. Our
motivating application is that of identifying serum biomarkers of angiogenesis,
energy metabolism, apoptosis, and inflammation, predictive of recurrence after
lung resection in node-negative non-small cell lung cancer patients with tumor
stage T2a or less. We propose an omnibus approach for testing association that
is free of assumptions on functional forms and distributions and can be used as
a black box method. This proposed maximal permutation test is based on the idea
of thresholding, is readily implementable and is computationally efficient. We
illustrate that the proposed omnibus tests maintain their levels and have
strong power as black box tests for detecting linear, nonlinear and
quantile-based associations, even with outlier-prone and heavy-tailed error
distributions and under nonparametric setting. We additionally illustrate the
use of this approach in model-free feature screening and further examine the
level and power of these tests for binary outcome. We compare the performance
of the proposed omnibus tests with comparator methods in our motivating
application to identify preoperative serum biomarkers associated with non-small
cell lung cancer recurrence in early stage patients.


  In this work, we performed a detailed study of the fractional order phase
transition (FPT) for several AdS black hole prototypes [1]. Our objective is to
see whether the FPT 4/3 order at critical points reported in [2] is universal.
Our analysis shows two remarkable features: Firstly, the FPT is located at 4/3
order only when the black hole possesses a spherical symmetry. Secondly, this
fractional order is not universal and can be altered by the geometric symmetry.


  The evolution of a determining form for the 2D Navier-Stokes equations (NSE),
which is an ODE on a space of trajectories is completely described. It is
proved that at every stage of its evolution, the solution is a convex
combination of the initial trajectory and the fixed steady state, with a
dynamical convexity parameter $\theta$, which will be called the characteristic
determining parameter. That is, we show a remarkable separation of variables
formula for the solution of the determining form. Moreover, for a given initial
trajectory, the dynamics of the infinite-dimensional determining form are
equivalent to those of the characteristic determining parameter $\theta$ which
is governed by a one-dimensional ODE. %for the parameter specifying the
position on the line segment. This one-dimensional ODE is used to show that if
the solution to the determining form converges to the fixed state it does so no
faster than $\mathcal{O}(\tau^{-1/2})$, otherwise it converges to a projection
of some other trajectory in the global attractor of the NSE, but no faster than
$\mathcal{O}(\tau^{-1})$, as $\tau \to \infty$, where $\tau$ is the
evolutionary variable in determining form. The one-dimensional ODE also
exploited in computations which suggest that the one-sided convergence rate
estimates are in fact achieved. The ODE is then modified to accelerate the
convergence to an exponential rate. Remarkably, it is shown that the zeros of
the scalar function that governs the dynamics of $\theta$, which are called
characteristic determining values, identify in a unique fashion the
trajectories in the global attractor of the 2D NSE. Furthermore, the
one-dimensional characteristic determining form enables us to find
unanticipated geometric features of the global attractor, a subject of future
research.


  We construct $n$-node graphs on which any $O(n)$-size spanner has additive
error at least $+\Omega(n^{3/17})$, improving on the previous best lower bound
of $\Omega(n^{1/7})$ [Bodwin-Hoppenworth FOCS '22]. Our construction completes
the first two steps of a particular three-step research program, introduced in
prior work and overviewed here, aimed at producing tight bounds for the problem
by aligning aspects of the upper and lower bound constructions. More
specifically, we develop techniques that enable the use of inner graphs in the
lower bound framework whose technical properties are provably tight with the
corresponding assumptions made in the upper bounds. As an additional
application of our techniques, we improve the corresponding lower bound for
$O(n)$-size additive emulators to $+\Omega(n^{1/14})$.


  We introduce GEDI, a Bayesian framework that combines existing
self-supervised learning objectives with likelihood-based generative models.
This framework leverages the benefits of both GEnerative and DIscriminative
approaches, resulting in improved symbolic representations over standalone
solutions. Additionally, GEDI can be easily integrated and trained jointly with
existing neuro-symbolic frameworks without the need for additional supervision
or costly pre-training steps. We demonstrate through experiments on real-world
data, including SVHN, CIFAR10, and CIFAR100, that GEDI outperforms existing
self-supervised learning strategies in terms of clustering performance by a
significant margin. The symbolic component further allows it to leverage
knowledge in the form of logical constraints to improve performance in the
small data regime.


  We show how to solve computationally a combinatorial problem about the
possible number of roots orthogonal to a vector of given length in $E_8$. We
show that the moduli space of K3 surfaces with polarisation of degree 2d is
also of general type for d=52. This case was omitted from the earlier work of
Gritsenko, Hulek and the second author. We also apply this method to some
related problems. In Appendix A, V. Gritsenko shows how to arrive at the case
d=52 and some others directly.


  We study stochastic team (known also as decentralized stochastic control or
identical interest stochastic dynamic game) problems with large or countably
infinite number of decision makers, and characterize existence and structural
properties for (globally) optimal policies. We consider both static and dynamic
non-convex team problems where the cost function and dynamics satisfy an
exchangeability condition. To arrive at existence and structural results on
optimal policies, we first introduce a topology on control policies, which
involves various relaxations given the decentralized information structure.
This is then utilized to arrive at a de Finetti type representation theorem for
exchangeable policies. This leads to a representation theorem for policies
which admit an infinite exchangeability condition. For a general setup of
stochastic team problems with $N$ decision makers, under exchangeability of
observations of decision makers and the cost function, we show that without
loss of global optimality, the search for optimal policies can be restricted to
those that are $N$-exchangeable. Then, by extending $N$-exchangeable policies
to infinitely-exchangeable ones, establishing a convergence argument for the
induced costs, and using the presented de Finetti type theorem, we establish
the existence of an optimal decentralized policy for static and dynamic teams
with countably infinite number of decision makers, which turns out to be
symmetric (i.e., identical) and randomized. In particular, unlike prior work,
convexity of the cost in policies is not assumed. Finally, we show near
optimality of symmetric independently randomized policies for finite
$N$-decision maker team problems and thus establish approximation results for
$N$-decision maker weakly coupled stochastic teams.


  We investigate the role of projection heads, also known as projectors, within
the encoder-projector framework (e.g., SimCLR) used in contrastive learning. We
aim to demystify the observed phenomenon where representations learned before
projectors outperform those learned after -- measured using the downstream
linear classification accuracy, even when the projectors themselves are linear.
  In this paper, we make two significant contributions towards this aim.
Firstly, through empirical and theoretical analysis, we identify two crucial
effects -- expansion and shrinkage -- induced by the contrastive loss on the
projectors. In essence, contrastive loss either expands or shrinks the signal
direction in the representations learned by an encoder, depending on factors
such as the augmentation strength, the temperature used in contrastive loss,
etc. Secondly, drawing inspiration from the expansion and shrinkage phenomenon,
we propose a family of linear transformations to accurately model the
projector's behavior. This enables us to precisely characterize the downstream
linear classification accuracy in the high-dimensional asymptotic limit. Our
findings reveal that linear projectors operating in the shrinkage (or
expansion) regime hinder (or improve) the downstream classification accuracy.
This provides the first theoretical explanation as to why (linear) projectors
impact the downstream performance of learned representations. Our theoretical
findings are further corroborated by extensive experiments on both synthetic
data and real image data.


  We present INTEGRAL and XMM-Newton observations of the prompt gamma-ray
emission and the X-ray afterglow of GRB030227, the first GRB for which the
quick localization obtained with the INTEGRAL Burst Alert System (IBAS) has led
to the discovery of X-ray and optical afterglows. GRB030227 had a duration of
about 20 s and a peak flux of 1.1 photons cm^-2 s^-1 in the 20-200 keV energy
range. The time averaged spectrum can be fit by a single power law with photon
index about 2 and we find some evidence for a hard to soft spectral evolution.
The X-ray afterglow has been detected starting only 8 hours after the prompt
emission, with a 0.2-10 keV flux decreasing as t^-1 from 1.3x10e-12 to 5x10e-13
erg cm^-2 s^-1. The afterglow spectrum is well described by a power law with
photon index 1.94+/-0.05 modified by a redshifted neutral absorber with column
density of several 10e22 cm^-2. A possible emission line at 1.67 keV could be
due to Fe for a redshift z=3, consistent with the value inferred from the
absorption.


  Synthetic data can be used in various applications, such as correcting bias
datasets or replacing scarce original data for simulation purposes. Generative
Adversarial Networks (GANs) are considered state-of-the-art for developing
generative models. However, these deep learning models are data-driven, and it
is, thus, difficult to control the generation process. It can, therefore, lead
to the following issues: lack of representativity in the generated data, the
introduction of bias, and the possibility of overfitting the sample's noise.
This article presents the Directed Acyclic Tabular GAN (DATGAN) to address
these limitations by integrating expert knowledge in deep learning models for
synthetic tabular data generation. This approach allows the interactions
between variables to be specified explicitly using a Directed Acyclic Graph
(DAG). The DAG is then converted to a network of modified Long Short-Term
Memory (LSTM) cells to accept multiple inputs. Multiple DATGAN versions are
systematically tested on multiple assessment metrics. We show that the best
versions of the DATGAN outperform state-of-the-art generative models on
multiple case studies. Finally, we show how the DAG can create hypothetical
synthetic datasets.


  Advances in Visually Rich Document Understanding (VrDU) have enabled
information extraction and question answering over documents with complex
layouts. Two tropes of architectures have emerged -- transformer-based models
inspired by LLMs, and Graph Neural Networks. In this paper, we introduce
DocGraphLM, a novel framework that combines pre-trained language models with
graph semantics. To achieve this, we propose 1) a joint encoder architecture to
represent documents, and 2) a novel link prediction approach to reconstruct
document graphs. DocGraphLM predicts both directions and distances between
nodes using a convergent joint loss function that prioritizes neighborhood
restoration and downweighs distant node detection. Our experiments on three
SotA datasets show consistent improvement on IE and QA tasks with the adoption
of graph features. Moreover, we report that adopting the graph features
accelerates convergence in the learning process during training, despite being
solely constructed through link prediction.


  We present a brief numerical study of the Darwin-Fowler method applied to the
analysis of the energy partition of essembles of bosons and fermions. We
analyze the assertion of the existence of a "strong maximum" made in the
original paper of Darwin and Fowler and other studies and show that although
the presumed saddle point along the real axis of the grand canonical parameters
may exist it cannot, in general, be characterized as "strong", in the sense of
having much larger magnitude than the other points along the path of
integration. We show that in some cases the saddle point is not even present
and the various approximations of the method can be interpreted as a tricky
reformulation of usual thermodynamic relations. The close connection of the
method with the formalism of the Laplace transform may produce wrong results if
the internal energy of the components of the ensemble is not large enough.
Therefore, although useful in many applications the Darwin-Fowler method may
not be suitable, in general, for a detailed microscopic analysis of the nuclear
structure in connection with the Shell Model approach, as it is usually done in
studies of the pre-equilibrium stage of nuclear reactions.


  This paper presents a web-based tool that facilitates the production of
tailored summaries for online sharing on social media. Through an interactive
user interface, it supports a ``one-click'' video summarization process. Based
on the integrated AI models for video summarization and aspect ratio
transformation, it facilitates the generation of multiple summaries of a
full-length video according to the needs of target platforms with regard to the
video's length and aspect ratio.


  We present a method to perform novel view and time synthesis of dynamic
scenes, requiring only a monocular video with known camera poses as input. To
do this, we introduce Neural Scene Flow Fields, a new representation that
models the dynamic scene as a time-variant continuous function of appearance,
geometry, and 3D scene motion. Our representation is optimized through a neural
network to fit the observed input views. We show that our representation can be
used for complex dynamic scenes, including thin structures, view-dependent
effects, and natural degrees of motion. We conduct a number of experiments that
demonstrate our approach significantly outperforms recent monocular view
synthesis methods, and show qualitative results of space-time view synthesis on
a variety of real-world videos.


  We consider multi-source free domain adaptation, the problem of adapting
multiple existing models to a new domain without accessing the source data.
Among existing approaches, methods based on model ensemble are effective in
both the source and target domains, but incur significantly increased
computational costs. Towards this dilemma, in this work, we propose a novel
framework called SepRep-Net, which tackles multi-source free domain adaptation
via model Separation and Reparameterization.Concretely, SepRep-Net reassembled
multiple existing models to a unified network, while maintaining separate
pathways (Separation). During training, separate pathways are optimized in
parallel with the information exchange regularly performed via an additional
feature merging unit. With our specific design, these pathways can be further
reparameterized into a single one to facilitate inference (Reparameterization).
SepRep-Net is characterized by 1) effectiveness: competitive performance on the
target domain, 2) efficiency: low computational costs, and 3) generalizability:
maintaining more source knowledge than existing solutions. As a general
approach, SepRep-Net can be seamlessly plugged into various methods. Extensive
experiments validate the performance of SepRep-Net on mainstream benchmarks.


  It is known that exactly self-dual gauge-field configurations with
topological charge |Q|=1 cannot exist on the untwisted continuum 4-torus. We
explore the manifestation of this remarkable fact on the lattice 4-torus for
SU(3) using advanced techniques for controlling lattice discretization errors,
extending earlier work of De Forcrand et. al. for SU(2). We identify three
distinct signals for the instability of |Q|=1 configurations, and show that
these manifest themselves early in the cooling process, long before the
would-be instanton has shrunk to a size comparable to the lattice
discretization threshold. These signals do not appear for our |Q|=2
configurations. This indicates that these signals reflect the truly global
nature of the instability, rather than local discretization effects.
Monte-Carlo generated SU(3) gauge field configurations are cooled to the
self-dual limit using an O(a^4)-improved gauge action chosen to have small but
positive O(a^6) errors. This choice prevents lattice discretization errors from
destroying instantons provided their size exceeds the dislocation threshold of
the cooling algorithm. Lattice discretization errors are evaluated by comparing
the O(a^4)-improved gauge-field action with an O(a^4)-improved action
constructed from the square of an O(a^4)-improved lattice field-strength
tensor, thus having different O(a^6) discretization errors. The number of
action-density peaks, the instanton size and the topological charge of
configurations is monitored. We observe a fluctuation in the total topological
charge of |Q|=1 configurations, and demonstrate that the onset of this unusual
behavior corresponds with the disappearance of multiple-peaks in the action
density. At the same time discretization errors are minimal.


  We investigate the electron-positron pair cascade taking place in the
magnetosphere of a rapidly rotating black hole. Because of the spacetime frame
dragging, the Goldreich-Julian charge density changes sign in the vicinity of
the event horizon, which leads to an occurrence of a magnetic-field aligned
electric field, in the same way as the pulsar outer-magnetospheric accelerator.
In this lepton accelerator, electrons and positrons are accelerated in the
opposite directions, to emit copious gamma-rays via the curvature and
inverse-Compton processes. We examine a stationary pair cascade, and show that
a stellar-mass black hole moving in a gaseous cloud can emit a detectable
very-high-energy flux, provided that the black hole is extremely rotating and
that the distance is less than about 1 kpc. We argue that the gamma-ray image
will have a point-like morphology, and demonstrate that their gamma-ray spectra
have a broad peak around 0.01-1 GeV and a sharp peak around 0.1 TeV, that the
accelerators become most luminous when the mass accretion rate becomes about
0.01% of the Eddington rate, and that the predicted gamma-ray flux little
changes in a wide range of magnetospheric currents. An implication of the
stability of such a stationary gap is discussed.


  We show the localization transition and its effect on two dynamical processes
for an extended Aubry-Andr\'e-Harper model with incommensurate on-site and
hopping potentials. After specifying an extended Aubry-Andr\'e-Harper model, we
check the localization transition for all the eigenstates and eigenenergy band
splitting behavior versus a system parameter. To examine the effect of
localization transition on dynamical processes, firstly, the slowly pumping of
the edge states are examined. In the dynamical processes, the system acts as
conductor for the excitation in the nonlocal region and insulator in the
localized region. Then by quantum Lyapunov control method with different
control Hamiltonians, we prepare an edge localized state which exists in the
nonlocal region. Compared to that in the nonlocal region, the control effect is
suppressed in the localized region. Then we employ the entropy and occupation
imbalance between even and odd sites to indicate the localization transition
further. Finally, the experimental schemes based on cold atoms trapped
quasiperiodic optical lattice and coupled optical waveguide arrays are
suggested.


  How superconductivity interacts with charge or nematic order is one of the
great unresolved issues at the center of research in quantum materials.
Ba$_{1-x}$Sr$_{x}$Ni$_{2}$As$_{2}$ (BSNA) is a charge ordered pnictide
superconductor recently shown to exhibit a six-fold enhancement of
superconductivity due to nematic fluctuations near a quantum phase transition
(at $x_c=0.7$). The superconductivity is, however, anomalous, with the
resistive transition for $0.4 < x< x_c$ occurring at a higher temperature than
the specific heat anomaly. Using x-ray scattering, we discovered a new charge
density wave (CDW) in BSNA in this composition range. The CDW is commensurate
with a period of two lattice parameters, and is distinct from the two CDWs
previously reported in this material. We argue that the anomalous transport
behavior arises from heterogeneous superconductivity nucleating at antiphase
domain walls in this CDW. We also present new data on the incommensurate CDW,
previously identified as being unidirectional, showing that is a rotationally
symmetric, "4$Q$" state with $C_4$ symmetry. Our study establishes BSNA as a
rare material containing three distinct CDWs, and an exciting testbed for
studying coupling between CDW, nematic, and SC orders.


  Many recent works on understanding deep learning try to quantify how much
individual data instances influence the optimization and generalization of a
model. Such attempts reveal characteristics and importance of individual
instances, which may provide useful information in diagnosing and improving
deep learning. However, most of the existing works on data valuation require
actual training of a model, which often demands high-computational cost. In
this paper, we provide a training-free data valuation score, called
complexity-gap score, which is a data-centric score to quantify the influence
of individual instances in generalization of two-layer overparameterized neural
networks. The proposed score can quantify irregularity of the instances and
measure how much each data instance contributes in the total movement of the
network parameters during training. We theoretically analyze and empirically
demonstrate the effectiveness of the complexity-gap score in finding `irregular
or mislabeled' data instances, and also provide applications of the score in
analyzing datasets and diagnosing training dynamics. Our code is publicly
available at https://github.com/JJchy/CG_score


  Time series has been left behind in the era of pre-training and transfer
learning. While research in the fields of natural language processing and
computer vision are enjoying progressively larger datasets to train massive
models, the most popular time series datasets consist of only tens of thousands
of time steps, limiting our ability to study the effectiveness of pre-training
and scaling. Recent studies have also cast doubt on the need for expressive
models and scale. To alleviate these issues, we introduce three large-scale
time series forecasting datasets from the cloud operations (CloudOps) domain,
the largest having billions of observations, enabling further study into
pre-training and scaling of time series models. We build the empirical
groundwork for studying pre-training and scaling of time series models and pave
the way for future research by identifying a promising candidate architecture.
We show that it is a strong zero-shot baseline and benefits from further
scaling, both in model and dataset size. Accompanying these datasets and
results is a suite of comprehensive benchmark results comparing classical and
deep learning baselines to our pre-trained method - achieving a 27% reduction
in error on the largest dataset. Code and datasets can be found
https://github.com/SalesforceAIResearch/pretrain-time-series-cloudops.


  We present a large forward-modeling analysis for 55 late-T (T7-T9) dwarfs,
using low-resolution ($R\approx150$) near-infrared spectra and cloudless
Sonora-Bobcat model atmospheres. We derive the objects' effective temperatures,
surface gravities, metallicities, radii, masses, and luminosities using our
newly developed Bayesian framework, and use the resulting population properties
to test the model atmospheres. We find (1) our objects' fitted metallicities
are 0.3-0.4 dex lower than those of nearby stars; (2) their ages derived from
spectroscopic parameters are implausibly young; (3) their fitted temperatures
show a similar spread as empirical temperature scales at a given spectral type
but are $\sim100$ K hotter for $\geqslant$T8 dwarfs; and (4) their
spectroscopically inferred masses are unphysically small. These results suggest
the Sonora-Bobcat assumptions of cloudless and chemical-equilibrium atmospheres
do not adequately reproduce late-T dwarf spectra. We also find a gravity- and a
metallicity-dependence of temperatures. Combining the resulting parameter
posteriors of our sample, we quantify the degeneracy between surface gravity
and metallicity such that an increase in $Z$ combined with a $3.4\times$
increase in $\log{g}$ results in a spectrum that has similar fitted parameters.
We note the systematic difference between our 1.0-2.5 $\mu$m spectra and the
Sonora-Bobcat models is $\approx$2-4% of the objects' peak $J$-band fluxes,
implying modeling systematics will exceed measurement uncertainties when
analyzing data with $J$-band S/N $\gtrsim50$. Using our large sample, we
examine the fitting residuals as a function of wavelength and atmospheric
properties to discern how to improve the models. Our work constitutes the
largest analysis of brown dwarf spectra using multi-metallicity models and the
most systematic examination of ultracool model atmospheres to date.


  This paper presents an end-to-end approach for tracking static and dynamic
objects for an autonomous vehicle driving through crowded urban environments.
Unlike traditional approaches to tracking, this method is learned end-to-end,
and is able to directly predict a full unoccluded occupancy grid map from raw
laser input data. Inspired by the recently presented DeepTracking approach
[Ondruska, 2016], we employ a recurrent neural network (RNN) to capture the
temporal evolution of the state of the environment, and propose to use Spatial
Transformer modules to exploit estimates of the egomotion of the vehicle. Our
results demonstrate the ability to track a range of objects, including cars,
buses, pedestrians, and cyclists through occlusion, from both moving and
stationary platforms, using a single learned model. Experimental results
demonstrate that the model can also predict the future states of objects from
current inputs, with greater accuracy than previous work.


  We investigate the collective behavior of sterically interacting
self-propelled particles confined in a harmonic potential. Our theoretical and
numerical study unveils the emergence of distinctive collective polar
organizations, revealing how different levels of interparticle torques and
noise influence the system. The observed phases include the shear-banded
vortex, where the system self organizes in two concentric bands rotating in
opposite directions around the potential center; the uniform vortex, where the
two bands merge into a close packed configurations rotating uniformly as a
quasi-rigid body; and the orbiting polar state, characterized by parallel
orientation vectors and the cluster revolving around the potential center,
without rotation, as a rigid body. Intriguingly, at lower filling fractions,
the vortex and polar phases merge into a single phase where the trapped cluster
breaks into smaller polarized clusters, each one orbiting the potential center
as a rigid body.


  We show that local structure approximation of sufficiently high order can
predict the existence of second order phase transitions belonging to the
directed percolation university class in $\alpha$-asynchronous cellular
automata.


  We investigate the exact behavior of the energy density of a real massless
scalar field inside a cavity with a single moving mirror executing a resonant
oscillatory law of motion, satisfying Dirichlet boundary conditions at finite
temperature. Our results are compared with those found in literature through
analytical approximative methods.


  We study the probability distribution of the ratio between the second
smallest and smallest eigenvalue in the $n\times n$ Laguerre Unitary Ensemble.
The probability that this ratio is greater than $r>1$ is expressed in terms of
an $n \times n$ Hankel determinant with a perturbed Laguerre weight. The
limiting probability distribution for the ratio as $n\to\infty$ is found as an
integral over $(0,\infty)$ containing two functions $q_{1}(x)$ and $q_{2}(x)$.
These functions satisfy a system of two coupled Painlev\'e V equations, which
are derived from a Lax pair of a Riemann-Hilbert problem. We compute asymptotic
behaviours of these functions as $rx \to 0_{+}$ and $(r-1)x \to \infty$, as
well large $n$ asymptotics for the associated Hankel determinants in several
regimes of $r$ and $x$.


  Sparse classifiers such as the support vector machines (SVM) are efficient in
test-phases because the classifier is characterized only by a subset of the
samples called support vectors (SVs), and the rest of the samples (non SVs)
have no influence on the classification result. However, the advantage of the
sparsity has not been fully exploited in training phases because it is
generally difficult to know which sample turns out to be SV beforehand. In this
paper, we introduce a new approach called safe sample screening that enables us
to identify a subset of the non-SVs and screen them out prior to the training
phase. Our approach is different from existing heuristic approaches in the
sense that the screened samples are guaranteed to be non-SVs at the optimal
solution. We investigate the advantage of the safe sample screening approach
through intensive numerical experiments, and demonstrate that it can
substantially decrease the computational cost of the state-of-the-art SVM
solvers such as LIBSVM. In the current big data era, we believe that safe
sample screening would be of great practical importance since the data size can
be reduced without sacrificing the optimality of the final solution.


  We can associate $p$ -adic admissible unitary representation of $\GL_2(\Q_p)$
to every local Galois representation. We prove if local Galois representations
is ordinary then there exists a sub representation of this representation of
$\GL_2(\Q_p)$ that appears in ordinary parts of the cohomology.
  We give a positive answer to a question raised by Chojecki \cite{Chojecki18}.


  We investigate the magnetization reversal of single-domain magnetic
nanoparticle driven by linear down-chirp microwave magnetic field pulse.
Numerical simulations based on the Landau-Lifshitz-Gilbert equation reveal that
solely down-chirp pulse is capable of inducing subnanosecond magnetization
reversal. With a certain range of initial frequency and chirp rate, the
required field amplitude is much smaller than that of constant-frequency
microwave field. The fast reversal is because the down-chirp microwave field
acts as an energy source and sink for the magnetic particle before and after
crossing over the energy barrier, respectively. Applying a spin-polarized
current additively to the system further reduces the microwave field amplitude.
Our findings provide a new way to realize low-cost and fast magnetization
reversal.


  We present a simple framework for specifying and proving facts about the
input/output behavior of ELF binary files on the x86-64 architecture. A strong
emphasis has been placed on simplicity at all levels: the specification says
only what it needs to about the target executable, the specification is
performed inside a simple logic (equivalent to first-order Peano Arithmetic),
and the verification language and proof checker are custom-designed to have
only what is necessary to perform efficient general purpose verification. This
forms a part of the Metamath Zero project, to build a minimal verifier that is
capable of verifying its own binary. In this paper, we will present the
specification of the dynamic semantics of x86 machine code, together with
enough information about Linux system calls to perform simple IO.


  We study the homogeneous Boltzmann equation with the fractional Laplacian
term. Working on the Fourier side we solve the resulting integral equation, and
improve a previous result by Y.-K. Cho. We replace the initial data space with
a certain space $\mathcal{M}^\alpha$ introduced by Morimoto, Wang, and Yang.
This space precisely captures the Fourier image of probability measures with
bounded fractional moments, providing a more natural initial condition. We show
existence of a unique global solution, in addition to the expected maximal
growth estimates and stability estimates. As a consequence we obtain a
continuous density solution of the original equation.


  Artificial neural networks and machine learning have now reached a new era
after several decades of improvement where applications are to explode in many
fields of science, industry, and technology. Here, we use artificial neural
networks to study an intriguing phenomenon in quantum physics--- the
topological phases of matter. We find that certain topological states, either
symmetry-protected or with intrinsic topological order, can be represented with
classical artificial neural networks. This is demonstrated by using three
concrete spin systems, the one-dimensional (1D) symmetry-protected topological
cluster state and the 2D and 3D toric code states with intrinsic topological
orders. For all three cases we show rigorously that the topological ground
states can be represented by short-range neural networks in an \textit{exact}
and \textit{efficient} fashion---the required number of hidden neurons is as
small as the number of physical spins and the number of parameters scales only
\textit{linearly} with the system size. For the 2D toric-code model, we find
that the proposed short-range neural networks can describe the excited states
with abelain anyons and their nontrivial mutual statistics as well. In
addition, by using reinforcement learning we show that neural networks are
capable of finding the topological ground states of non-integrable Hamiltonians
with strong interactions and studying their topological phase transitions. Our
results demonstrate explicitly the exceptional power of neural networks in
describing topological quantum states, and at the same time provide valuable
guidance to machine learning of topological phases in generic lattice models.


  We present new results on the chemical composition of the Galactic ring
nebula NGC6888 surrounding the WN6(h) star WR136. The data are based on deep
spectroscopical observations taken with the High Dispersion Spectrograph at the
8.2m Subaru Telescope. The spectra cover the optical range from 3700 to 7400 A.
The effect of the CNO cycle is well identified in the abundances of He, N, and
O, while elements not involved in the synthesis such as Ar, S, and Fe present
values consistent with the solar vicinity and the ambient gas. The major
achievement of this work is the first detection of the faint CII 4267
recombination line in a Wolf-Rayet nebula. This allows to estimate the C
abundance in NGC6888 and therefore investigate for the first time the trace of
the CNO cycle in a ring nebula around a Wolf-Rayet star. Although the detection
of the CII line has a low signal-to-noise ratio, the C abundance seems to be
higher than the predictions of recent stellar evolution models of massive
stars. The Ne abundance also show a puzzling pattern with an abundance of about
0.5 dex lower than the solar vicinity, which may be related to the action of
the NeNa cycle. Attending to the constraints imposed by the dynamical timescale
and the He/H and N/O ratios of the nebula, the comparison with stellar
evolution models indicates that the initial mass of the stellar progenitor of
NGC6888 is between 25 Msun and 40 Msun.


  Phase synchronization in unidirectionally coupled Ikeda time-delay systems
exhibiting non-phase-coherent hyperchaotic attractors of complex topology with
highly interwoven trajectories is studied. It is shown that in this set of
coupled systems phase synchronization (PS) does exist in a range of the
coupling strength which is preceded by a transition regime (approximate PS) and
a nonsynchronous regime. However, exact generalized synchronization does not
seem to occur in the coupled Ikeda systems (for the range of parameters we have
studied) even for large coupling strength, in contrast to our earlier studies
in coupled piecewise-linear and Mackey-Glass systems
\cite{dvskml2006,dvskml2008}. The above transitions are characterized in terms
of recurrence based indices, namely generalized autocorrelation function
$P(t)$, correlation of probability of recurrence (CPR), joint probability of
recurrence (JPR) and similarity of probability of recurrence (SPR). The
existence of phase synchronization is also further confirmed by typical
transitions in the Lyapunov exponents of the coupled Ikeda time-delay systems
and also using the concept of localized sets.


  The hidden Markov model (HMM) is a generative model that treats sequential
data under the assumption that each observation is conditioned on the state of
a discrete hidden variable that evolves in time as a Markov chain. In this
paper, we derive a novel algorithm to cluster HMMs through their probability
distributions. We propose a hierarchical EM algorithm that i) clusters a given
collection of HMMs into groups of HMMs that are similar, in terms of the
distributions they represent, and ii) characterizes each group by a "cluster
center", i.e., a novel HMM that is representative for the group. We present
several empirical studies that illustrate the benefits of the proposed
algorithm.


  The similarity between Finsler and Riemann geometry is an intriguing starting
point to extend general relativity. The lack of quadratic restriction over the
line element (color) naturally generalize the Riemannian case and breaks the
local symmetries of general relativity. In addition, the Finsler manifold is
enriched with new geometric entities and all the classical identities are
suitably extended. We investigate the covariant kinematics of a medium formed
by a time-like congruence. After a brief view in the general case we impose
particular geometric restrictions to get some analytic insight. Central role to
our analysis plays the Lie derivative where even in case of irrotational
Killing vectors the bundle still deforms. We demonstrate an example of an
isotropic and exponentially expanding cross-section that finally deflates or
forms a caustic. Furthermore, using the 1+3 covariant formalism we investigate
the expansion dynamics of the congruence. For certain geometric restrictions we
retrieve the Raychaudhuri equation where a color-curvature coupling is
revealed. The condition to prevent the focusing of neighboring particles is
given and is more likely to fulfilled in highly curved regions. Then, we
introduce the Levi-Civita connection for the osculating Riemannian metric and
develop a (spatially) isotropic and homogeneous dust-like model with a
non-singular bounce.


  We present imaging and spectroscopy of a hydrogen-poor superluminous
supernova (SLSN) discovered by the intermediate Palomar Transient Factory:
iPTF13ajg. At a redshift of z=0.7403, derived from narrow absorption lines,
iPTF13ajg peaked at an absolute magnitude M(u,AB)=-22.5, one of the most
luminous supernovae to date. The uBgRiz light curves, obtained with the P48,
P60, NOT, DCT, and Keck telescopes, and the nine-epoch spectral sequence
secured with the Keck and the VLT (covering 3 rest-frame months), are tied
together photometrically to provide an estimate of the flux evolution as a
function of time and wavelength. The observed bolometric peak luminosity of
iPTF13ajg is 3.2x10^44 erg/s, while the estimated total radiated energy is
1.3x10^51 erg. We detect narrow absorption lines of Mg I, Mg II, and Fe II,
associated with the cold interstellar medium in the host galaxy, at two
different epochs with X-shooter at the VLT. From Voigt-profile fitting, we
derive the column densities log N(Mg I)=11.94+-0.06, log N(Mg II)=14.7+-0.3,
and log N(Fe II)=14.25+-0.10. These column densities, as well as the Mg I and
Mg II equivalent widths of a sample of hydrogen-poor SLSNe taken from the
literature, are at the low end of those derived for gamma-ray bursts (GRBs),
whose progenitors are also thought to be massive stars. This suggests that the
environments of SLSNe and GRBs are different. From the nondetection of Fe II
fine-structure absorption lines, we derive a strict lower limit on the distance
between the supernova and the narrow-line absorbing gas of 50 pc. No
host-galaxy emission lines are detected, leading to an upper limit on the
unobscured star-formation rate of SFR([OII])<0.07 Msun/yr. Late-time imaging
shows the host galaxy of iPTF13ajg to be faint, with g(AB)~27.0 and R(AB)>=26.0
mag, which roughly corresponds to M(B,Vega) >~ -17.7 mag. [abridged]


  Motivated by recent experiments on TmB4 (arXiv:0712.1537), we examine the
phase diagram of the Shastry-Sutherland antiferromagnet in an applied magnetic
field in the limit of strong Ising anisotropy. In classical Ising limit, we
demonstrate that the only fractional magnetization plateau is at 1/3 of the
saturated magnetization. We study the perturbative influence of transverse
quantum spin fluctuations, and present evidence that they can stabilize a
narrow 1/2 magnetization plateau.


  An ongoing challenge for learning algorithms formulated in the Minimally
Adequate Teacher framework is to efficiently obtain counterexamples. In this
paper we compare and combine conformance testing and mutation-based fuzzing
methods for obtaining counterexamples when learning finite state machine models
for the reactive software systems of the Rigorous Exampination of Reactive
Systems (RERS) challenge. We have found that for the LTL problems of the
challenge the fuzzer provided an independent confirmation that the learning
process had been successful, since no additional counterexamples were found.
For the reachability problems of the challenge, however, the fuzzer discovered
more reachable error states than the learner and tester, albeit in some cases
the learner and tester found some that were not discovered by the fuzzer. This
leads us to believe that these orthogonal approaches are complementary in the
context of model learning.


  Self induced neutrino flavor conversions in the dense regions of stellar core
collapse are almost exclusively studied in the standard two flavor scenario.
Linear stability analysis has been successfully used to understand these flavor
conversions. This is the first linearized study of $\textit{three flavor}$ fast
instabilities. The `fast' conversions are fascinating distinctions of the dense
neutrino systems. In the fast modes the collective oscillation dynamics are
independent of the neutrino mass, growing at the scale of the large
neutrino-neutrino interaction strength ($10^5$ km$^{-1}$) of the dense core.
This is extremely fast, in comparison to the usual `slow' collective modes
driven by much smaller vacuum oscillation frequencies ($10^0$ km$^{-1}$). The
three flavor analysis shows distinctive characteristics for both the slow and
the fast conversions. The slow oscillation results are in qualitative agreement
with the existing nonlinear three flavor studies. For the fast modes, addition
of the third flavor opens up possibilities of influencing the growth rates of
flavor instabilities when compared to a two flavor scenario.


  Providing seamless connection to a large number of devices is one of the
biggest challenges for the Internet of Things (IoT) networks. Using a drone as
an aerial base station (ABS) to provide coverage to devices or users on ground
is envisaged as a promising solution for IoT networks. In this paper, we
consider a communication network with an underlay ABS to provide coverage for a
temporary event, such as a sporting event or a concert in a stadium. Using
stochastic geometry, we propose a general analytical framework to compute the
uplink and downlink coverage probabilities for both the aerial and the
terrestrial cellular system. Our framework is valid for any aerial channel
model for which the probabilistic functions of line-of-sight (LOS) and
non-line-of-sight (NLOS) links are specified. The accuracy of the analytical
results is verified by Monte Carlo simulations considering two commonly adopted
aerial channel models. Our results show the non-trivial impact of the different
aerial channel environments (i.e., suburban, urban, dense urban and high-rise
urban) on the uplink and downlink coverage probabilities and provide design
guidelines for best ABS deployment height.


  Diabetes Mellitus (DM) is a chronic disease characterized by an increase in
blood glucose (sugar) above normal levels and it appears when human body is not
able to produce enough insulin to cover the peripheral tissue demand. Nowadays,
DM affects the 8.5% of the world's population and, even though no cure for it
has been found, an adequate monitoring and treatment allow patients to have an
almost normal life. This paper introduces Diabetes Link, a comprehensive
platform for control and monitoring people with DM. Diabetes Link allows
recording various parameters relevant for the treatment and calculating
different statistical charts using them. In addition, it allows connecting with
other users (supervisors) so they can monitor the controls. Even more, the
extensive comparative study carried out reflects that Diabetes Link presents
distinctive and superior features against other proposals. We conclude that
Diabetes Link represents a broad and accessible tool that can help make
day-to-day control easier and optimize the efficacy in DM control and
treatment.


  Theoretical and experimental results for in-plane vibrations of a uniform
rectangular plate with free boundary conditions are obtained. The experimental
setup uses electromagnetic-acoustic transducers and a vector network analyzer.
The theoretical calculations were obtained using the plane wave expansion
method applied to the in-plane thin plate vibration theory. The agreement
between theory and experiment is excellent for the lower 95 modes covering a
very wide frequency range from DC to 20 kHz. Some measured normal-mode wave
amplitudes were compared with the theoretical predictions; very good agreement
was observed. The excellent agreement of the classical theory of in-plane
vibrations confirms its reliability up to very high frequencies.


  The quantum walk is a counterpart of the random walk. The 2-state quantum
walk in one dimension can be determined by a measure on the unit circle in the
complex plane. As for the singular continuous measure, results on the
corresponding quantum walk are limited. In this situation, we focus on a
quantum walk, called the Riesz walk, given by the Riesz measure which is one of
the famous singular continuous measures. The present paper is devoted to the
return probability of the Riesz walk. Furthermore, we present some conjectures
on the self-similarity of the walk.


  New observable integer-valued numbers of the topological origin were revealed
by the present authors studying the conductivity theory of single crystal 3D
normal metals in the reasonably strong magnetic field ($B \leq 10^{3} Tl$). Our
investigation is based on the study of dynamical systems on Fermi surfaces for
the motion of semi-classical electron in magnetic field. All possible
asymptotic regimes are also found for $B \to \infty$ based on the topological
classification of trajectories.


  Distributed quantum systems and especially the Quantum Internet have the
ever-increasing potential to fully demonstrate the power of quantum
computation. This is particularly true given that developing a general-purpose
quantum computer is much more difficult than connecting many small quantum
devices. One major challenge of implementing distributed quantum systems is
programming them and verifying their correctness. In this paper, we propose a
CSP-like distributed programming language to facilitate the specification and
verification of such systems. After presenting its operational and denotational
semantics, we develop a Hoare-style logic for distributed quantum programs and
establish its soundness and (relative) completeness with respect to both
partial and total correctness. The effectiveness of the logic is demonstrated
by its applications in the verification of quantum teleportation and local
implementation of non-local CNOT gates, two important algorithms widely used in
distributed quantum systems.


  We identify a new dynamic agency problem: that of incentivising the prompt
disclosure of productive information. To study it, we introduce a general model
in which a technological breakthrough occurs at an uncertain time and is
privately observed by an agent, and a principal must incentivise disclosure via
her control of a payoff-relevant physical allocation. We uncover a deadline
structure of optimal mechanisms: they have a simple deadline form in an
important special case, and a graduated deadline structure in general. We apply
our results to the design of unemployment insurance schemes.


  Intelligent transportation systems play a crucial role in modern traffic
management and optimization, greatly improving traffic efficiency and safety.
With the rapid development of generative artificial intelligence (Generative
AI) technologies in the fields of image generation and natural language
processing, generative AI has also played a crucial role in addressing key
issues in intelligent transportation systems, such as data sparsity, difficulty
in observing abnormal scenarios, and in modeling data uncertainty. In this
review, we systematically investigate the relevant literature on generative AI
techniques in addressing key issues in different types of tasks in intelligent
transportation systems. First, we introduce the principles of different
generative AI techniques, and their potential applications. Then, we classify
tasks in intelligent transportation systems into four types: traffic
perception, traffic prediction, traffic simulation, and traffic
decision-making. We systematically illustrate how generative AI techniques
addresses key issues in these four different types of tasks. Finally, we
summarize the challenges faced in applying generative AI to intelligent
transportation systems, and discuss future research directions based on
different application scenarios.


  Spin foam models are an approach to quantum gravity based on the concept of
sum over states, which aims to describe quantum spacetime dynamics in a way
that its parent framework, loop quantum gravity, has not as of yet succeeded.
Since these models' relation to classical Einstein gravity is not explicit, an
important test of their viabilitiy is the study of asymptotics - the classical
theory should be obtained in a limit where quantum effects are negligible,
taken to be the limit of large triangle areas in a triangulated manifold with
boundary. In this paper we will briefly introduce the EPRL/FK spin foam model
and known results about its asymptotics, proceeding then to describe a
practical computation of spin foam and semiclassical geometric data for a
simple triangulation with only one interior triangle. The results are used to
comment on the "flatness problem" - a hypothesis raised by Bonzom (2009)
suggesting that EPRL/FK's classical limit only describes flat geometries in
vacuum.