  As part of the recent research effort on quantum natural language processing
(QNLP), variational quantum sentence classifiers (VQSCs) have been implemented
and supported in lambeq / DisCoPy, based on the DisCoCat model of sentence
meaning. We discuss in some detail VQSCs, including category theory, DisCoCat
for modeling sentence as string diagram, and DisCoPy for encoding string
diagram as parameterized quantum circuit. Many NLP tasks, however, require the
handling of text consisting of multiple sentences, which is not supported in
lambeq / DisCoPy. A good example is sentiment classification of customer
feedback or product review. We discuss three potential approaches to
variational quantum text classifiers (VQTCs), in line with VQSCs. The first is
a weighted bag-of-sentences approach which treats text as a group of
independent sentences with task-specific sentence weighting. The second is a
coreference resolution approach which treats text as a consolidation of its
member sentences with coreferences among them resolved. Both approaches are
based on the DisCoCat model and should be implementable in lambeq / DisCoCat.
The third approach, on the other hand, is based on the DisCoCirc model which
considers both ordering of sentences and interaction of words in composing text
meaning from word and sentence meanings. DisCoCirc makes fundamental
modification of DisCoCat since a sentence in DisCoCirc updates meanings of
words, whereas all meanings are static in DisCoCat. It is not clear if
DisCoCirc can be implemented in lambeq / DisCoCat without breaking DisCoCat.


  We report measurement of a spin pumping effect due to fluctuations of the
magnetic order of IrMn thin films. A precessing NiFe ferromagnet injected spins
into IrMn spin sinks, and enhanced damping was observed around the IrMn
magnetic phase transition. Our data was compared to a recently developed theory
and converted into interfacial spin mixing conductance enhancements. By
spotting the spin pumping peak, the thickness dependence of the IrMn critical
temperature could be determined and the characteristic length for the spin-spin
interactions was deduced.


  We propose a Machine Learning (ML) non-Markovian closure modeling framework
for accurate predictions of statistical responses of turbulent dynamical
systems subjected to external forcings. One of the difficulties in this
statistical closure problem is the lack of training data, which is a
configuration that is not desirable in supervised learning with neural network
models. In this study with the 40-dimensional Lorenz-96 model, the shortage of
data (in temporal) is due to the stationarity of the statistics beyond the
decorrelation time, thus, the only informative content in the training data is
on short-time transient statistics. We adopted a unified closure framework on
various truncation regimes, including and excluding the detailed dynamical
equations for the variances. The closure frameworks employ a
Long-Short-Term-Memory architecture to represent the higher-order unresolved
statistical feedbacks with careful consideration to account for the intrinsic
instability yet producing stable long-time predictions. We found that this
unified agnostic ML approach performs well under various truncation scenarios.
Numerically, the ML closure model can accurately predict the long-time
statistical responses subjected to various time-dependent external forces that
are not (and maximum forcing amplitudes that are relatively larger than those)
in the training dataset.


  Target ionization processes of alkali atoms by Positronium impact are
investigated. Calculations are performed in the frame work of model potential
formalism using the Coulomb distorted eikonal approximation. Interesting
qualitative features are noted both in the scattered Ps and the ejected
electron distributions in differential as well as double differential levels of
the collision cross sections.


  Coherence distillation is one of the central problems in the resource theory
of coherence. In this Letter, we complete the deterministic distillation of
quantum coherence for a finite number of coherent states under strictly
incoherent operations. Specifically, we find the necessary and sufficient
condition for the transformation from a mixed coherent state into a pure state
via strictly incoherent operations, which recovers a connection between the
resource theory of coherence and the algebraic theory of majorization lattice.
With the help of this condition, we present the deterministic coherence
distillation scheme and derive the maximum number of maximally coherent states
obtained via this scheme.


  We show that the 4-dimensional N=1/2 supersymmetry algebra admits central
extension. The central charges are supported by domain wall and the central
charges are computed. We also determine the Konishi anomaly for N=1/2
supersymmetric gauge theory. Due to the new couplings in the Lagrangian, many
terms appears. We show that these terms sum up to give the expected form for
the holomorphic part of the Konishi anomaly. For the anti-holomorphic part, we
give a simple argument that the naive generalization has to be modified. We
suggest that the anti-holomorphic Konishi anomaly is given by a gauge invariant
completion using open Wilson line.


  Collecting and training over sensitive personal data raise severe privacy
concerns in personalized recommendation systems, and federated learning can
potentially alleviate the problem by training models over decentralized user
data.However, a theoretically private solution in both the training and serving
stages of federated recommendation is essential but still lacking.Furthermore,
naively applying differential privacy (DP) to the two stages in federated
recommendation would fail to achieve a satisfactory trade-off between privacy
and utility due to the high-dimensional characteristics of model gradients and
hidden representations.In this work, we propose a federated news recommendation
method for achieving a better utility in model training and online serving
under a DP guarantee.We first clarify the DP definition over behavior data for
each round in the life-circle of federated recommendation systems.Next, we
propose a privacy-preserving online serving mechanism under this definition
based on the idea of decomposing user embeddings with public basic vectors and
perturbing the lower-dimensional combination coefficients. We apply a random
behavior padding mechanism to reduce the required noise intensity for better
utility. Besides, we design a federated recommendation model training method,
which can generate effective and public basic vectors for serving while
providing DP for training participants. We avoid the dimension-dependent noise
for large models via label permutation and differentially private attention
modules. Experiments on real-world news recommendation datasets validate that
our method achieves superior utility under a DP guarantee in both training and
serving of federated news recommendations.


  We study the fragmentation of a jet propagating in a dense quark-gluon
plasma. Using a leading, double-logarithmic approximation in perturbative QCD,
we compute for the first time the effects of the medium on the vacuum-like
emissions. We show that, due to the scatterings off the plasma, the in-medium
parton showers differ from the vacuum ones in two crucial aspects: their
phase-space is reduced and the first emission outside the medium can violate
angular ordering. We compute the jet fragmentation function and find results in
qualitative agreement with measurements at the LHC.


  Hyperbolic random graphs (HRG) and geometric inhomogeneous random graphs
(GIRG) are two similar generative network models that were designed to resemble
complex real world networks. In particular, they have a power-law degree
distribution with controllable exponent $\beta$, and high clustering that can
be controlled via the temperature $T$.
  We present the first implementation of an efficient GIRG generator running in
expected linear time. Besides varying temperatures, it also supports underlying
geometries of higher dimensions. It is capable of generating graphs with ten
million edges in under a second on commodity hardware. The algorithm can be
adapted to HRGs. Our resulting implementation is the fastest sequential HRG
generator, despite the fact that we support non-zero temperatures. Though
non-zero temperatures are crucial for many applications, most existing
generators are restricted to $T = 0$. Our generators support parallelization,
although this is not the focus of this paper. We note that our generators draw
from the correct probability distribution, i.e., they involve no approximation.
  Besides the generators themselves, we also provide an efficient algorithm to
determine the non-trivial dependency between the average degree of the
resulting graph and the input parameters of the GIRG model. This makes it
possible to specify the expected average degree as input.
  Moreover, we investigate the differences between HRGs and GIRGs, shedding new
light on the nature of the relation between the two models. Although HRGs
represent, in a certain sense, a special case of the GIRG model, we find that a
straight-forward inclusion does not hold in practice. However, the difference
is negligible for most use cases.


  We study the problem of scheduling periodic real-time tasks so as to meet
their individual minimum reward requirements. A task generates jobs that can be
given arbitrary service times before their deadlines. A task then obtains
rewards based on the service times received by its jobs. We show that this
model is compatible to the imprecise computation models and the increasing
reward with increasing service models. In contrast to previous work on these
models, which mainly focus on maximize the total reward in the system, we aim
to fulfill different reward requirements by different tasks, which offers
better fairness and allows fine-grained tradeoff between tasks. We first derive
a necessary and sufficient condition for a system, along with reward
requirements of tasks, to be feasible. We also obtain an off-line feasibility
optimal scheduling policy. We then studies a sufficient condition for a policy
to be feasibility optimal or achieves some approximation bound. This condition
can serve as a guideline for designing on-line scheduling policy and we obtains
a greedy policy based on it. We prove that the on-line policy is feasibility
optimal when all tasks have the same periods and also obtain an approximation
bound for the policy under general cases.


  Both ecological systems and convective fluid systems are examples of open
systems which operate far-from-equilibrium. This article demonstrates that
there is a correspondence between a resource-consumer chemostat ecosystem and
the Rayleigh-Benard (RB) convective fluid system. The Lorenz dynamics of the RB
system can be translated into an ecosystem dynamics. Not only is there a
correspondence between the dynamical equations, also the physical
interpretations show interesting analogies. By using this fluid-ecosystem
analogy, we are able to derive the correct value of the size of convection
rolls by competitive fitness arguments borrowed from ecology. We finally
conjecture that the Lorenz dynamics can be extended to describe more complex
convection patterns that resemble ecological predation.


  It has been shown that the primary, old-fashioned idea of Sakharov's induced
gravity and gauge interactions, in the "one-loop dominance" version, works
astonishingly well yielding phenomenologically reasonable results. As a
byproduct, the issue of the role of the UV cutoff in the context of the induced
gravity has been reexamined (an idea of self-cutoff induced gravity). As an
additional check, the black hole entropy has been used in the place of the
action. Finally, it has been explicitly shown that the induced coupling
constants of gauge interactions of the standard model assume qualitatively
realistic values.


  The affine current algebra for Lie superalgebras is examined. The bilinear
invariant forms of the Lie superalgebra can be either degenerate or
non-degenerate. We give the conditions for a Virasoro construction, in which
the currents are primary fields of weight one, to exist. In certain cases, the
Virasoro central charge is an integer equal to the super dimension of the group
supermanifold. A Wess-Zumino-Novikov-Witten action based on these Lie
superalgebras is also found.


  Two groups recently analyzed the long-term orbital evolution of HM Cancri,
which is one of the most important verification binaries for the space
gravitational wave detector LISA. By using the reported first and second
derivatives of its orbital frequency $f$, we discuss potential tertiary effects
on this binary. We found that, in contrast to the first derivative $\dot f$,
the second derivative $\ddot f$ might be strongly affected by a dark tertiary
component such as an old white dwarf with an outer orbital period of $\sim$250
years.


  Based on the recently published generalized Miller formula, we derive the
spectral dependence of the contribution of arbitrary-order non-linear indices
to the group-velocity index. We show that in the context of laser filamentation
in gases all experimentally-accessible orders (up to the $9^{th}$-order
non-linear susceptibility $\chi^{(9)}$ in air and $\chi^{(11)}$ in argon) have
contributions of alternative signs and similar magnitudes. Moreover, we show
both analytically and numerically that the dispersion term of the non-linear
indices must be considered when computing the intensity-dependent group
velocity.


  We explore the potential for detecting rotational instabilities in the
post-merger phase of binary neutron star mergers using different network
configurations of upgraded and next-generation gravitational wave detectors.
Our study employs numerically generated post-merger waveforms, which reveal the
re-excitation of the $l=m=2$ $f$-mode at a time of $O(10{\rm})$ms after merger.
We evaluate the detectability of these signals by injecting them into colored
Gaussian noise and performing a reconstruction as a sum of wavelets using
Bayesian inference. Computing the overlap between the reconstructed and
injected signal, restricted to the instability part of the post-merger phase,
we find that one could infer the presence of rotational instabilities with a
network of planned 3rd-generation detectors, depending on the total mass and
distance to the source. For a recently suggested high-frequency detector
design, we find that the instability part would be detectable even at 200 Mpc,
significantly increasing the anticipated detection rate. For a network
consisting of the existing HLV detectors, but upgraded to twice the A+
sensitivity, we confirm that the peak frequency of the whole post-merger
gravitational-wave emission could be detectable with a network signal-to-noise
ratio of 8 at a distance of 40Mpc.


  Frequency-resolved optical gating (FROG) is probably the most popular
technique for complete characterization of ultrashort laser pulses. In FROG, a
reconstruction algorithm retrieves the pulse from a measured spectrogram, yet
current FROG reconstruction algorithms require and exhibit several restricting
features that weaken FROG performances. For example, the delay step must
correspond to the spectral bandwidth measured with large enough SNR a condition
that limits the temporal resolution of the reconstructed pulse, obscures
measurements of weak broadband pulses, and makes measurement of broadband
mid-IR pulses hard and slow because the spectrograms become huge. We develop a
new approach for FROG reconstruction, based on ptychography (a scanning
coherent diffraction imaging technique), that removes many of the algorithmic
restrictions. The ptychographic reconstruction algorithm is significantly
faster and more robust to noise than current FROG algorithms, which are based
on generalized projections (GP). We demonstrate, numerically and
experimentally, that ptychographic reconstruction works well with very partial
spectrograms, e. g. spectrograms with reduced number of measured delays and
spectrograms that have been substantially spectrally filtered. In addition, we
implement the ptychogrpahic approach to blind second harmonic generation (SHG)
FROG and demonstrate robust and complete characterization of two unknown pulses
from a single measured spectrogram and power spectrum of only one of the
pulses. We believe that the ptychograpy-based approach will become the standard
reconstruction procedure in FROG and related diagnostics methods, allowing
successful reconstructions from so far unreconstructable spectrograms.


  We present results from a search for gravitational microlensing of stars in
the Large Magellanic Cloud by low mass objects in the Galactic Halo. The search
uses the CCD light curves of about 82,000 stars with up to 46 measurements per
night over a period of 10 months. No light curve exhibits a form that is
consistent with a microlensing event of maximum amplification greater than 1.2.
This null result makes it unlikely that the Halo is dominated by objects in the
mass range $5 10^{-8}M_{\odot} < M <5 10^{-4}M_{\odot} $. keywords{Galaxy :
Halo, kinematics and dynamics, stellar content -- Cosmology : dark matter,
gravitational lensing}


  We study the scattering of light (null geodesics) by two fixed extreme
Reissner-Nordstr\"om black holes, in which the gravitational attraction of
their masses is exactly balanced with the electrostatic repulsion of their
charges, allowing a static spacetime. We identify the set of unstable periodic
orbits that constitute the fractal repeller that completely describes the
chaotic escape dynamics of photons. In the framework of periodic orbit theory,
the analysis of the linear stability of the unstable periodic orbits is used to
obtain the main quantities of chaos that characterize the escape dynamics of
the photons scattered by the black holes. In particular, the escape rate which
is compared with the result obtained from numerical simulations that consider
statistical ensembles of photons. We also analyze the dynamics of photons in
the proximity of a perturbed black hole and give an analytical estimation for
the escape rate in this system.


  The LIGO Scientific Collaboration (LSC) is developing and running analysis
pipelines to search for gravitational-wave transients emitted by astrophysical
events such as compact binary mergers or core-collapse supernovae. However,
because of the non-Gaussian, non-stationary nature of the noise exhibited by
the LIGO detectors, residual false alarms might be found at the end of the
pipelines. A critical aspect of the search is then to assess our confidence for
gravitational waves and to distinguish them from those false alarms. Both the
'Compact Binary Coalescence' and the 'Burst' working groups have been
developing a detection checklist for the validation of candidate-events,
consisting of a series of tests which aim to corroborate a detection or to
eliminate a false alarm. These tests include for example data quality checks,
analysis of the candidate appearance, parameter consistency studies and
coherent analysis. In this paper, the general methodology used for candidate
validation is presented. The method is illustrated with an example of simulated
gravitational-wave signal and a false alarm.


  The recent detection of the gravitational wave event GW170817, produced by
the coalescence of two neutron stars, and of its optical-infrared counterpart,
powered by the radioactive decay of r-process elements, has opened a new window
to gamma-ray astronomy: the direct detection of photons coming from such
decays. Here we calculate the contribution of kilonovae to the diffuse
gamma-ray background in the MeV range, using recent results on the spectra of
the gamma-rays emitted in individual events, and we compare it with that from
other sources. We find that the contribution from kilonovae is not dominant in
such energy range, but within current uncertainties, its addition to other
sources might help to fit the observational data.


  The systematic approach to solving the recurrence relations for multi-loop
integrals is described. In particular, the criteria of their reducibility is
suggested.


  We present the possibility that Dirac and Majorana neutrino wave packets can
be distinguished when subject to spin-gravity interaction while propagating
through vacuum described by the Lense-Thirring metric. By adopting the
techniques of gravitational phase and time-independent perturbation theory
following the Brillouin-Wigner method, we generate spin-gravity matrix elements
from a perturbation Hamiltonian and show that this distinction is easily
reflected in well-defined gravitational corrections to the neutrino oscillation
length for a two-flavour system. Explicit examples are presented using the Sun
and SN1987A as the gravitational sources for the Lense-Thirring metric. This
approach offers the possibility to determine the absolute neutrino masses by
this method and identify a theoretical upper bound for the absolute neutrino
mass difference, where the distinctions between the Dirac and Majorana cases
are evident. We discuss the relevance of this analysis to the upcoming attempts
to measure the properties of low-energy neutrinos by SNO and other solar
neutrino observatories.


  This work discusses the possibility of inducing the emission of gamma
radiation from nuclear isomers by two-photon transitions, in the more general
context of the problem of the amplification of gamma radiation.


  We prove that for any integer $k\geq 2$ and $\varepsilon>0$, there is an
integer $\ell_0\geq 1$ such that any $k$-uniform hypergraph on $n$ vertices
with minimum codegree at least $(1/2+\varepsilon)n$ has a fractional
decomposition into tight cycles of length $\ell$ ($\ell$-cycles for short)
whenever $\ell\geq \ell_0$ and $n$ is large in terms of $\ell$. This is
essentially tight.
  This immediately yields also approximate integral decompositions for these
hypergraphs into $\ell$-cycles. Moreover, for graphs this even guarantees
integral decompositions into $\ell$-cycles and solves a problem posed by Glock,
K\"uhn and Osthus. For our proof, we introduce a new method for finding a set
of $\ell$-cycles such that every edge is contained in roughly the same number
of $\ell$-cycles from this set by exploiting that certain Markov chains are
rapidly mixing.


  Many new $\gamma$-rays have been observed, following muon capture on
$^{14}$N. One had been reported before, and the low yield is confirmed,
indicating that the nuclear structure of $^{14}$N is still not understood.
Gamma-rays from $^{13}$C resulting from the reaction
$^{14}$N($\mu^{-}$,$\nu$n)$^{13}$C compare favourably with states observed in
the reaction $^{14}$N($\gamma$,p)$^{13}$C. More precise energies are also given
for the 7017 and 6730 keV $\gamma$-rays in $^{14}$C.


  Development of dry, ultra-conformable and unperceivable temporary tattoo
electrodes (TTEs), based on the ink-jet printing of PEDOT:PSS on top of
commercially available temporary tattoo paper, has gained increasing attention
as a new and promising technology for electrophysiological recordings on skin.
In this work we present a TTEs epidermal sensor for real time monitoring of
respiration through transthoracic impedance measurements, exploiting a new
design based on the application of soft screen printed Ag ink and magnetic
interlink, that guarantees a repositionable, long term stable and robust
interconnection of TTEs with external docking devices. The efficiency of the
TTE and the proposed interconnection strategy under stretching (up to 10%) and
over time (up to 96 hours) has been verified on a dedicated experimental setup
and on humans, fulfilling the proposed specific application of transthoracic
impedance measurements. The proposed approach makes this technology suitable
for large-scale production and suitable not only for the specific use case
presented, but also for real time monitoring of different bio-electric signals,
as demonstrated through specific proof of concept demonstrators.


  We present development of the collimated bipolar jets from the symbiotic
prototype Z And that appeared and disappeared during its 2006 outburst. In 2006
July Z And reached its historical maximum at U ~ 8.0. During this period, rapid
photometric variations with Dm ~ 0.06 mag on the timescale of hours developed.
Simultaneously, high-velocity satellite components appeared on both sides of
the H-alpha and H-beta emission line profiles. They were launched
asymmetrically with the red/blue velocity ratio of 1.2 - 1.3. From about
mid-August they became symmetric. Their spectral properties indicated ejection
of bipolar jets collimated within an average opening angle of 6.1 degrees. We
estimated average outflow rate via jets to dM(jet)/dt ~
2xE10-6(R(jet)/1AU)**(1/2) M(Sun)/year, during their August - September
maximum, which corresponds to the emitting mass in jets, M(jet, emitting) ~
6xE-10(Rjet)/1AU)^{3/2} M(Sun). During their lifetime, the jets released the
total mass of M(jet, total) approx 7.4x1E-7 M(Sun). Evolution in the rapid
photometric variability and asymmetric ejection of jets around the optical
maximum can be explained by a disruption of the inner parts of the disk caused
by radiation-induced warping of the disk.


  Magnetization measurements were carried out in the in field-cooled (FC) and
in zero-field-cooled (ZFC) conditions versus temperature, T, and external
magnetic field, H, on a sigma-phase Fe47Mo53 compound. Analysis of the measured
M_FC and M_ZFC curves yielded values of characteristic temperatures: magnetic
ordering (Curie) temperature, T_C, irreversibility temperature, T_ir,
temperature of the maximum in M_ZFC, T_m, identified as the N\.eel (T_N)
temperature, and cross-over temperature, T_co. Based on these temperatures a
magnetic phase diagram in the H-T plane was outlined. The field dependences of
the characteristic temperatures viz. of the irreversibility and of the
cross-over temperatures were described in terms of a power law with the
exponent 0.5(1). In the whole range of H i.e. up to 800 Oe, except the one H>50
Oe, a rare double re-entrant transition viz. PM-FM-AF-SG takes place. For small
fields i.e. H<50 Oe a single re-entrant transition viz. PM-FM-SG is revealed.


  Though deep neural networks (DNNs) have shown superiority over other
techniques in major fields like computer vision, natural language processing,
robotics, recently, it has been proven that they are vulnerable to adversarial
attacks. The addition of a simple, small and almost invisible perturbation to
the original input image can be used to fool DNNs into making wrong decisions.
With more attack algorithms being designed, a need for defending the neural
networks from such attacks arises. Retraining the network with adversarial
images is one of the simplest techniques. In this paper, we evaluate the
effectiveness of such a retraining strategy in defending against adversarial
attacks. We also show how simple algorithms like KNN can be used to determine
the labels of the adversarial images needed for retraining. We present the
results on two standard datasets namely, CIFAR-10 and TinyImageNet.


  In this paper, we propose POTATOES (Partitioning OverfiTting AuTOencoder
EnSemble), a new method for unsupervised outlier detection (UOD). More
precisely, given any autoencoder for UOD, this technique can be used to improve
its accuracy while at the same time removing the burden of tuning its
regularization. The idea is to not regularize at all, but to rather randomly
partition the data into sufficiently many equally sized parts, overfit each
part with its own autoencoder, and to use the maximum over all autoencoder
reconstruction errors as the anomaly score. We apply our model to various
realistic datasets and show that if the set of inliers is dense enough, our
method indeed improves the UOD performance of a given autoencoder
significantly. For reproducibility, the code is made available on github so the
reader can recreate the results in this paper as well as apply the method to
other autoencoders and datasets.


  Let X be a Banach space. Given a subset A of the dual space X* denote by
$A_{(1)}$ the weak* sequential closure of A, i.e., the set of all limits of
weak*-convergent sequences in A.
  The study of weak* sequential closures of linear subspaces of the duals of
separable Banach spaces was initiated by S.Banach. The first results of this
study were presented in the appendix to his book "Theorie des operations
lineaires" (1932).
  It is natural to suppose that the reason for studying weak* sequential
closures by S. Banach and S. Mazurkiewicz was the lack of acquaintance of S.
Banach and his school with concepts of general topology. Although the name
"General topology" was introduced later, the subject did already existed. F.
Hausdorff introduced topological spaces in his book published in 1914,
Alexandroff-Urysohn (1924) studied compactness, and A.Tychonoff published his
theorem on compactness of products in 1929. Also J.von Neumann introduced the
notion of a weak topology in his paper published in 1929.
  Using the notions of a topological space and the Tychonoff theorem, more
elegant treatment of weak and weak* topologies, and the duality of Banach
spaces was developed by L.Alaoglu, N.Bourbaki and S.Kakutani (1938-1940).
  Nevertheless, an "old-fashioned" treatment of S.Banach still attracts
attention. It happens because the "sequential" approach is very useful in
several contexts.
  The main purpose of the paper is to describe the history of this direction of
research and to give an up-to-date (1999) survey of the results on weak*
sequential closures and their applications.


  A calculation of the fluxes of primary particles arriving to the Earth's
vicinity as well as those produced in the interactions of the primaries with
the atmosphere is presented. The result of calculations is compared with the
experimental data obtained with the Alpha Magnetic Spectrometer (AMS). A good
agreement of calculated and measured fluxes of charged particles supports the
viability of the atmospheric neutrino flux calculation.


  Crowdsourced smartphone-based earthquake early warning systems recently
emerged as reliable alternatives to the more expensive solutions based on
scientific-grade instruments. For instance, during the 2023 Turkish-Syrian
deadly event, the system implemented by the Earthquake Network citizen science
initiative provided a forewarning up to 25 seconds. We develop a statistical
methodology based on a survival mixture cure model which provides full Bayesian
inference on epicentre, depth and origin time, and we design an efficient
tempering MCMC algorithm to address multi-modality of the posterior
distribution. The methodology is applied to data collected by the Earthquake
Network, including the 2023 Turkish-Syrian and 2019 Ridgecrest events.


  We processed the data about radial velocities and HI linewidths for 1678 flat
edge-on spirals from the Revised Flat Galaxy Catalogue. We obtained the
parameters of the multipole components of large-scale velocity field of
collective non-Hubble galaxy motion as well as the parameters of the
generalized Tully-Fisher relationship in the "HI line width - linear diameter"
version. All the calculations were performed independently in the framework of
three models, where the multipole decomposition of the galaxy velocity field
was limited to a dipole, quadrupole and octopole terms respectively. We showed
that both the quadrupole and the octopole components are statistically
significant.
  On the basis of the compiled list of peculiar velocities of 1623 galaxies we
obtained the estimations of cosmological parameters Omega_m and sigma_8. This
estimation is obtained in both graphical form and as a constraint of the value
S_8=sigma_8(Omega_m/0.3)^0.35 = 0.91 +/- 0.05.


  This Case Study will be used in order to investigate and establish the
importance of role of Management Information System in time saving during the
payment of automobile tax in Sindh through e-filling methods. Moreover it will
also highlight the important factors which are involved as barriers and limit
the role of MIS in time saving techniques. The approach which is used in this
case study is descriptive research type along with the survey. The data used
was collected from the specimen of common people working in different
environments along with the officers working at Civic Centre (Automobile Tax
Collection Branch excise department). The audience included were all well
informed by the process and were eligible to give their opinions on the
following research. A system design is also proposed along with an Erd which
can be useful in the coming future. This research could likewise be expanded to
include different of respondents, for example, paid taxpayers and different
types of taxpayers. Paid tax payers are given the rights by their clients to
prepare their assessment matters. They use the e-filing system for different
types of clients and are more frequent users of the e-filing system than
taxpayers who file for themselves. It would be interesting to understand which
facets of hazard are larger to them. Different types of taxpayers, for example,
company authorized cars may deal with more complex exchanges than single car
taxpayers, consequently, they may emphasize different hazard facets when filing
in the government form frame electronically


  We address an issue what kind of tuning realizes a flat potential for
inflaton in supergravity. We restrict ourselves to the case that inflationary
dynamics is not affected by separate supersymmetry-breaking effects, and
consider examples of a single chiral superfield as an inflaton with its vacuum
of the vanishing cosmological constant. The examples potentially include the
cases of Polonyi field or right-handed sneutrino as an inflaton.


  We study pseudo-Goldstone boson corrections to top quark pair production
rates in technicolor models with and without topcolor at the E=0.5 teV and 1.5
TeV photon colliders. We find that, for reasonable ranges of the parameters,
the corrections are large enough to be observable, and the corrections in
models with topcolor are considerably larger than those in models without
topcolor, and they are all significantly larger than the corresponding
corrections in the minimal supersymmetric standard model (MSSM) with tan(beta)
of the order of 1. So that the two kinds of technicolor models and the MSSM
with tan(beta) of the order of 1 can be experimentally distinguished.


  The goal of this paper is twofold. In the first part we discuss a general
approach to determine Lyapunov exponents from ensemble- rather than
time-averages. The approach passes through the identification of locally stable
and unstable manifolds (the Lyapunov vectors), thereby revealing an analogy
with generalized synchronization. The method is then applied to a periodically
forced chaotic oscillator to show that the modulus of the Lyapunov exponent
associated to the phase dynamics increases quadratically with the coupling
strength and it is therefore different from zero already below the onset of
phase-synchronization. The analytical calculations are carried out for a model,
the generalized special flow, that we construct as a simplified version of the
periodically forced Rossler oscillator.


  This paper is devoted to the study of isometrically homogeneous spaces from
the view point of metric geometry. Mainly we focus on those spaces that are
homeomorphic to lines. One can reduce the study to those distances on $\R$ that
are translation invariant. We study possible values of various metric
dimensions of such spaces. One of the main results is the equivalence of two
properties: the first one is linear connectedness and the second one is
1-dimensionality, with respect to Nagata dimension. Several concrete
pathological examples are provided.


  Consider an $n \times p$ data matrix $X$ whose rows are independently sampled
from a population with covariance $\Sigma$. When $n,p$ are both large, the
eigenvalues of the sample covariance matrix are substantially different from
those of the true covariance. Asymptotically, as $n,p \to \infty$ with $p/n \to
\gamma$, there is a deterministic mapping from the population spectral
distribution (PSD) to the empirical spectral distribution (ESD) of the
eigenvalues. The mapping is characterized by a fixed-point equation for the
Stieltjes transform.
  We propose a new method to compute numerically the output ESD from an
arbitrary input PSD. Our method, called Spectrode, finds the support and the
density of the ESD to high precision; we prove this for finite discrete
distributions. In computational experiments it outperforms existing methods by
several orders of magnitude in speed and accuracy. We apply Spectrode to
compute expectations and contour integrals of the ESD. These quantities are
often central in applications of random matrix theory (RMT).
  We illustrate that Spectrode is directly useful in statistical problems, such
as estimation and hypothesis testing for covariance matrices. Our proposal may
make it more convenient to use asymptotic RMT in aspects of high-dimensional
data analysis.


  We investigated the numerical and physical reasons leading to a flat
distribution of low gas entropy in the core region of galaxy clusters, as
commonly found in grid cosmological simulations. To this end, we run a set of
30 high resolution re-simulations of a 3 x 10^14 M_sol/h cluster of galaxies
with the AMR code ENZO, exploring and investigating the details involved in the
production of entropy in simulated galaxy clusters. The occurrence of the flat
entropy core is found to be mainly due to hydro-dynamical processes resolved in
the code and that additional spurious effects of numerical origin (e.g.
artificial heating due to softening effects or N-body noise) can affect the
size and level of the entropy core only in a minor way. We show that the
entropy profile of non-radiative simulations is produced by a mechanism of
"sorting in entropy" which takes place with regularity during the cluster
evolution. Using gas tracers we prove that the flat entropy core is caused by
physical mixing of gravity-driven subsonic motions within the shallow inner
cluster potential. Re-simulations were also produced for the same cluster
object with the addition of radiative cooling, uniform pre-heating at high
redshift (z=10) and late (z<1) thermal energy feedback from AGN activity in the
cluster, in order to assess the effects of such mechanisms on the final entropy
profile of the cluster. We report on the infeasibility of balancing the
catastrophic cooling and recovering a flat entropy profile with the
investigated trials for AGN activity alone, while for a sub-set of pre-heating
models, or AGN feedback plus pre-heating models, a flat entropy distribution
similar to non-radiative runs can be obtained with a viable energy requirement,
and in good consistency with X-ray observations.


  Sharp $L^p$ extensions of Pitt's inequality expressed as a weighted Sobolev
inequality are obtained using convolution estimates and Stein-Weiss potentials.
More generally, optimal constants are obtained for the full Stein-Weiss
potential as a map from $L^p$ to itself, and new proofs are given for the
classical Pitt and Stein-Weiss estimates.


  We study a classical $\Lambda$-type three-level system based on three
high-$Q$ micromechanical beam resonators embedded in a gradient electric field.
By modulating the strength of the field at the difference frequency between
adjacent beam modes, we realize strong dynamic two-mode coupling, via the
dielectric force. Driving adjacent pairs simultaneously, we observe the
formation of a purely mechanical 'dark' state and an all-phononic analog of
coherent population trapping --- signatures of strong three-mode coupling. The
$\Lambda$-type micromechanical system is a natural extention of previously
demonstrated 'two-level' micromechanical systems and offers new perspectives on
the architecture of all-phononic micromechanical circuits and arrays.


  We discuss the prospects of high precision pointing of our transmitter to
habitable planets around Galactic main sequence stars. For an efficient signal
delivery, the future sky positions of the host stars should be appropriately
extrapolated with accuracy better than the beam opening angle $\Theta$ of the
transmitter. Using the latest data release (DR2) of Gaia, we estimate the
accuracy of the extrapolations individually for $4.7\times 10^7$ FGK stars, and
find that the total number of targets could be $\sim 10^7$ for the accuracy
goal better than 1". Considering the pairwise nature of communication, our
study would be instructive also for SETI (Search for Extraterrestrial
Intelligence), not only for sending signals outward.


  The quantum resonances occurring with delta-kicked particles are studied with
the help of a fictitious classical limit, establishing a direct correspondence
between the nearly resonant quantum motion and the classical resonances of a
related system. A scaling law which characterizes the structure of the resonant
peaks is derived and numerically demonstrated.


  For a precise determination of the m_u and m_d quark masses, it is useful to
combine isospin symmetric results of lattice or sum rules QCD techniques with
some isospin breaking study performed in chiral perturbation theory (ChPT). The
most promising process for the later is $\eta \to 3\pi$ decay. However, this
process is affected by large chiral corrections and there are observed
discrepancies between the values of Dalitz plot parameters stemming from the
standard ChPT computation of its amplitude and those experimentally measured.
We describe here the method based on analytic dispersive representation which
attempts to obtain the information on the masses by taking this discrepancy
into account independently of its exact origin. Together with the results of
this analysis we present a review of some other constraints on the light quark
masses and conclude with values of the masses compatible with them.


  In this paper we will show how the boundedness condition for the weighted
composition operators on a class of spaces of analytic functions on the open
right complex half-plane called Zen spaces (which include the Hardy spaces and
weighted Bergman spaces) can be stated in terms of Carleson measures and
Bergman kernels. In Hilbertian setting we will also show how the norms of
\emph{causal} weighted composition operators on these spaces are related to
each other and use it to show that an \emph{(unweighted) composition operator}
$C_\varphi$ is bounded on a Zen space if and only if $\varphi$ has a finite
angular derivative at infinity. Finally, we will show that there is no compact
composition operator on Zen spaces.


  We theoretically show that in the presence of conventional or higher order
van Hove singularities(VHS), the bare finite momentum pairing, also known as
the pair density wave (PDW), susceptibility can be promoted to the same order
of the most divergent bare BCS susceptibility through a valley-contrasting flux
3$\phi$ in each triangular plaquette at $\phi=\frac{\pi}{3}$ and
$\phi=\frac{\pi}{6}$ in moir\'e systems. This makes the PDW order a possible
leading instability for an electronic system with repulsive interactions. We
confirm that it indeed wins over all other instabilities and becomes the ground
state under certain conditions through the renormalization group calculation
and a flux insertion argument. Moreover, we also find that a topological
nontrivial loop current order becomes the leading instability if the Fermi
surface with conventional VHS is perfectly nested at $\phi=\frac{\pi}{3}$.
Similar to the Haldane model, this loop current state has the quantum anomalous
Hall effect. If we dope this loop current state or introduce a finite
next-nearest neighbour hopping $t^{\prime}$, the chiral $d$-wave PDW becomes
the dominant instability. Experimentally, the flux can be effectively tuned by
an out-of-plane electric field in moir\'e systems based on graphene and
transition metal dichalcogenides.


  Semi-supervised learning (SSL) is a common approach to learning predictive
models using not only labeled examples, but also unlabeled examples. While SSL
for the simple tasks of classification and regression has received a lot of
attention from the research community, this is not properly investigated for
complex prediction tasks with structurally dependent variables. This is the
case of multi-label classification and hierarchical multi-label classification
tasks, which may require additional information, possibly coming from the
underlying distribution in the descriptive space provided by unlabeled
examples, to better face the challenging task of predicting simultaneously
multiple class labels.
  In this paper, we investigate this aspect and propose a (hierarchical)
multi-label classification method based on semi-supervised learning of
predictive clustering trees. We also extend the method towards ensemble
learning and propose a method based on the random forest approach. Extensive
experimental evaluation conducted on 23 datasets shows significant advantages
of the proposed method and its extension with respect to their supervised
counterparts. Moreover, the method preserves interpretability and reduces the
time complexity of classical tree-based models.


  In this paper, we focus on intelligent reflecting surface (IRS) assisted
multi-antenna communications with transceiver hardware impairments encountered
in practice. In particular, we aim to maximize the received signal-to-noise
ratio (SNR) taking into account the impact of hardware impairments, where the
source transmit beamforming and the IRS reflect beamforming are jointly
designed under the proposed optimization framework. To circumvent the
non-convexity of the formulated design problem, we first derive a closed-form
optimal solution to the source transmit beamforming. Then, for the optimization
of IRS reflect beamforming, we obtain an upper bound to the optimal objective
value via solving a single convex problem. A low-complexity
minorization-maximization (MM) algorithm was developed to approach the upper
bound. Simulation results demonstrate that the proposed beamforming design is
more robust to the hardware impairments than that of the conventional SNR
maximized scheme. Moreover, compared to the scenario without deploying an IRS,
the performance gain brought by incorporating the hardware impairments is more
evident for the IRS-aided communications.


  To deal with complicated constraints via locally light computations in
distributed online learning, a recent study has presented a projection-free
algorithm called distributed online conditional gradient (D-OCG), and achieved
an $O(T^{3/4})$ regret bound for convex losses, where $T$ is the number of
total rounds. However, it requires $T$ communication rounds, and cannot utilize
the strong convexity of losses. In this paper, we propose an improved variant
of D-OCG, namely D-BOCG, which can attain the same $O(T^{3/4})$ regret bound
with only $O(\sqrt{T})$ communication rounds for convex losses, and a better
regret bound of $O(T^{2/3}(\log T)^{1/3})$ with fewer $O(T^{1/3}(\log
T)^{2/3})$ communication rounds for strongly convex losses. The key idea is to
adopt a delayed update mechanism that reduces the communication complexity, and
redefine the surrogate loss function in D-OCG for exploiting the strong
convexity. Furthermore, we provide lower bounds to demonstrate that the
$O(\sqrt{T})$ communication rounds required by D-BOCG are optimal (in terms of
$T$) for achieving the $O(T^{3/4})$ regret with convex losses, and the
$O(T^{1/3}(\log T)^{2/3})$ communication rounds required by D-BOCG are
near-optimal (in terms of $T$) for achieving the $O(T^{2/3}(\log T)^{1/3})$
regret with strongly convex losses up to polylogarithmic factors. Finally, to
handle the more challenging bandit setting, in which only the loss value is
available, we incorporate the classical one-point gradient estimator into
D-BOCG, and obtain similar theoretical guarantees.


  The intrinsic fluctuations of the underlying, immutable quantum fields that
fill all space and time can support the element of reality of a wave function
in quantum mechanics. The mysterious non-locality of quantum entanglement may
also be understood in terms of these inherent quantum fluctuations,
ever-present at the most fundamental level of the universe.


  The gravitational form factors which are obtained from the matrix elements of
the energy-momentum tensor provide us information about internal distributions
of mass, energy, pressure and shear. The Druck term is the least understood
among all the gravitational form factors. In a light front quark-diquark model
of proton, we investigate the Druck form factor. Using Abel transformation, we
evaluate the 3D distribution in the Breit frame from the 2D light front
distributions. The results are compared with other models and lattice
predictions.


  In investigation of boundary-value problems for certain partial differential
equations arising in applied mathematics, we often need to study the solution
of system of partial differential equations satisfied by hypergeometric
functions and find explicit linearly independent solutions for the system. In
this present investigation, we give the solutions of systems of partial
differential equations for two Kamp\'e de F\'eriet type functions of third and
fourth orders and of two variables.


  Transient X-ray emission, with an approximate t^{-0.7} decay, was observed
from SGR 1900+14 over 40 days following the the giant flare of 27 Aug 1998. We
calculate in detail the diffusion of heat to the surface of a neutron star
through an intense 10^{14}-10^{15} G magnetic field, following the release of
magnetic energy in its outer layers. We show that the power law index, the
fraction of burst energy in the afterglow, and the return to persistent
emission can all be understood if the star is composed of normal baryonic
material.


  State-of-the-art classifiers have been shown to be largely vulnerable to
adversarial perturbations. One of the most effective strategies to improve
robustness is adversarial training. In this paper, we investigate the effect of
adversarial training on the geometry of the classification landscape and
decision boundaries. We show in particular that adversarial training leads to a
significant decrease in the curvature of the loss surface with respect to
inputs, leading to a drastically more "linear" behaviour of the network. Using
a locally quadratic approximation, we provide theoretical evidence on the
existence of a strong relation between large robustness and small curvature. To
further show the importance of reduced curvature for improving the robustness,
we propose a new regularizer that directly minimizes curvature of the loss
surface, and leads to adversarial robustness that is on par with adversarial
training. Besides being a more efficient and principled alternative to
adversarial training, the proposed regularizer confirms our claims on the
importance of exhibiting quasi-linear behavior in the vicinity of data points
in order to achieve robustness.


  We revisit our previous work [Phys. Rev. D 95, 096014 (2017)] where neutrino
oscillation and nonoscillation data were analyzed in the standard framework
with three neutrino families, in order to constrain their absolute masses and
to probe their ordering (either normal, NO, or inverted, IO). We include
updated oscillation results to discuss best fits and allowed ranges for the two
squared mass differences $\delta m^2$ and $\Delta m^2$, the three mixing angles
$\theta_{12}$, $\theta_{23}$ and $\theta_{13}$, as well as constraints on the
CP-violating phase $\delta$, plus significant indications in favor of NO vs IO
at the level of $\Delta\chi^2=10.0$. We then consider nonoscillation data from
beta decay, from neutrinoless double beta decay (if neutrinos are Majorana),
and from various cosmological input variants (in the data or the model) leading
to results dubbed as default, aggressive, and conservative. In the default
option, we obtain from nonoscillation data an extra contribution $\Delta\chi^2
= 2.2$ in favor of NO, and an upper bound on the sum of neutrino masses $\Sigma
< 0.15$ eV at $2\sigma$; both results - dominated by cosmology - can be
strengthened or weakened by using more aggressive or conservative options,
respectively. Taking into account such variations, we find that the combination
of all (oscillation and nonoscillation) neutrino data favors NO at the level of
$3.2-3.7\sigma$, and that $\Sigma$ is constrained at the $2\sigma$ level within
$\Sigma < 0.12-0.69$ eV. The upper edge of this allowed range corresponds to an
effective $\beta$-decay neutrino mass $m_\beta = \Sigma/3 = 0.23$ eV, at the
sensitivity frontier of the KATRIN experiment.


  We examine the stability of l=1 and l=2 g modes in a pair of nitrogen-rich
Wolf-Rayet stellar models characterized by differing hydrogen abundances. We
find that modes with intermediate radial orders are destabilized by a kappa
mechanism operating on an opacity bump at an envelope temperature log T ~ 6.25.
This `deep opacity bump' is due primarily to L-shell bound-free transitions of
iron. Periods of the unstable modes span ~ 11-21 hr in the model containing
some hydrogen, and ~ 3-12 hr in the hydrogen-depleted model. Based on the
latter finding, we suggest that self-excited g modes may be the source of the
9.8 hr-periodic variation of WR 123 recently reported by Lefevre et al. (2005).


  Determining the Shannon capacity of graphs is a long-standing open problem in
information theory, graph theory and combinatorial optimization. Over decades,
a wide range of upper and lower bound methods have been developed to analyze
this problem. However, despite tremendous effort, even small instances of the
problem have remained open.
  In recent years, a new dual characterization of the Shannon capacity of
graphs, asymptotic spectrum duality, has unified and extended known upper bound
methods and structural theorems. In this paper, building on asymptotic spectrum
duality, we develop a new theory of graph distance, that we call asymptotic
spectrum distance, and corresponding limits (reminiscent of, but different
from, the celebrated theory of cut-norm, graphons and flag algebras). We
propose a graph limit approach to the Shannon capacity problem: to determine
the Shannon capacity of a graph, construct a sequence of easier to analyse
graphs converging to it.
  (1) We give a very general construction of non-trivial converging sequences
of graphs (in a family of circulant graphs).
  (2) We construct Cauchy sequences of finite graphs that do not converge to
any finite graph, but do converge to an infinite graph. We establish strong
connections between convergence questions of finite graphs and the asymptotic
properties of Borsuk-like infinite graphs on the circle.
  (3) We observe that all best-known lower bound constructions for Shannon
capacity of small odd cycles can be obtained from a "finite" version of the
graph limit approach. We develop computational and theoretical aspects of this
approach and use these to obtain a new Shannon capacity lower bound for the
fifteen-cycle.
  The theory of asymptotic spectrum distance applies not only to Shannon
capacity of graphs; indeed, we will develop it for a general class of
mathematical objects and their asymptotic properties.


  Collective behavior widely exists in nature, ranging from the macroscopic
cloud of swallows to the microscopic cloud of colloidal particles. The behavior
of an individual inside the collective is distinctive from its behavior alone,
as it follows its neighbors. The introduction of such collective behavior in
two-dimensional (2D) materials may offer new possibilities to achieve desired
but unattained properties. Here, we report a highly sensitive magneto-optic
effect and transmissive magneto-coloration via introducing collective behavior
into magnetic 2D material dispersions. The increase of ionic strength in the
dispersion enhances the collective behavior of colloidal particles, giving rise
to a magneto-optic Cotton-Mouton coefficient up to 2700 T-2m-1 which is the
highest value obtained so far, being three orders of magnitude larger than
other known transparent media. We also reveal linearly dependence of
magneto-coloration on the concentration and hydration radius of ions. Such
linear dependence and the extremely large Cotton-Mouton coefficient
cooperatively allow fabrication of giant magneto-birefringent devices for
color-centered visual sensing.


  While existing machine learning models have achieved great success for
sentiment classification, they typically do not explicitly capture
sentiment-oriented word interaction, which can lead to poor results for
fine-grained analysis at the snippet level (a phrase or sentence).
Factorization Machine provides a possible approach to learning element-wise
interaction for recommender systems, but they are not directly applicable to
our task due to the inability to model contexts and word sequences. In this
work, we develop two Position-aware Factorization Machines which consider word
interaction, context and position information. Such information is jointly
encoded in a set of sentiment-oriented word interaction vectors. Compared to
traditional word embeddings, SWI vectors explicitly capture sentiment-oriented
word interaction and simplify the parameter learning. Experimental results show
that while they have comparable performance with state-of-the-art methods for
document-level classification, they benefit the snippet/sentence-level
sentiment analysis.


  We study the formation and evolution of small groups of galaxies using new
Monte Carlo simulations. These are directly based on the random walk approach
to the statistics of condensations collapsing by gravitational instability, and
the results are consistent with the Press and Schechter formula. We stress how
observational features of groups, such as galaxy membership, depend on the
global dynamics of the Universe. This is because the amount of aggregations
between infalling galaxies is governed by the ratio of the group age to the
time scale for dynamical friction. This ratio strongly depends on cosmology.
Thus in low-density models, even if flat, collapse of group sized perturbations
followed by fast mergers, would often originate a single massive remnant. This
is contrast to observed associations as compact groups of galaxies.


  We estimate convergence rates for curves generated by Loewner's differential
equation under the basic assumption that a convergence rate for the driving
terms is known. An important tool is what we call the tip structure modulus, a
geometric measure of regularity for Loewner curves parameterized by capacity.
It is analogous to Warschawski's boundary structure modulus and closely related
to annuli crossings. The main application we have in mind is that of a random
discrete-model curve approaching a Schramm-Loewner evolution (SLE) curve in the
lattice size scaling limit. We carry out the approach in the case of
loop-erased random walk (LERW) in a simply connected domain. Under mild
assumptions of boundary regularity, we obtain an explicit power-law rate for
the convergence of the LERW path toward the radial SLE$_2$ path in the supremum
norm, the curves being parameterized by capacity. On the deterministic side, we
show that the tip structure modulus gives a sufficient geometric condition for
a Loewner curve to be H\"{o}lder continuous in the capacity parameterization,
assuming its driving term is H\"{o}lder continuous. We also briefly discuss the
case when the curves are a priori known to be H\"{o}lder continuous in the
capacity parameterization and we obtain a power-law convergence rate depending
only on the regularity of the curves.


  Strong Beltrami fields have long played a key role in fluid mechanics and
magnetohydrodynamics. In particular, they are the kind of stationary solutions
of the Euler equations where one has been able to show the existence of vortex
structures (vortex tubes and vortex lines) of arbitrarily complicated topology.
On the contrary, there are very few results about the existence of generalized
Beltrami fields, that is, divergence-free fields whose curl is the field itself
times a non-constant function. In fact, generalized Beltrami fields (which are
also stationary solutions to the Euler equations) have been recently shown to
be rare, in the sense that for "most" proportionality factors there are no
nontrivial Beltrami fields of high enough regularity (e.g., of class
$C^{6,\alpha}$), not even locally.
  We show that, nevertheless, there are "many" Beltrami fields with
non-constant factor, even realizing arbitrarily complicated vortex structures.
The core results are an "almost global" stability theorem for strong Beltrami
fields, which ensures that a global strong Beltrami field with suitable decay
at infinity can be perturbed to get "many" Beltrami fields with non-constant
factor of arbitrarily high regularity and defined in the exterior of an
arbitrarily small ball, and a "local" stability theorem for generalized
Beltrami fields, which is an analogous perturbative result that is valid for
any kind of Beltrami field (not just with a constant factor) but only applies
to small enough domains.
  The proof relies on an iterative scheme of Grad-Rubin type. For this purpose,
we study the Neumann problem for the inhomogeneous Beltrami equation in
exterior domains via a boundary integral equation method and we obtain H\"older
estimates, a sharp decay at infinity and some compactness properties for these
sequences of approximate solutions.


  We report long-range ordered antiferromagnetism concomitant with local iron
displacements in the spin-ladder compound BaFe$_2$Se$_3$. Short-range magnetic
correlations, present at room temperature, develop into long-range
antiferromagnetic order below T$_N$ = 256 K, with no superconductivity down to
1.8 K. Built of ferromagnetic Fe$_4$ plaquettes, the magnetic ground state
correlates with local displacements of the Fe atoms. These iron displacements
imply significant magnetoelastic coupling in FeX$_4$-based materials, an
ingredient hypothesized to be important in the emergence of superconductivity.
This result also suggests that knowledge of these local displacements is
essential for properly understanding the electronic structure of these systems.
As with the copper oxide superconductors two decades ago, our results highlight
the importance of reduced dimensionality spin ladder compounds in the study of
the coupling of spin, charge, and atom positions in superconducting materials.


  Recent studies on AI security have highlighted the vulnerability of
Vision-Language Pre-training (VLP) models to subtle yet intentionally designed
perturbations in images and texts. Investigating multimodal systems' robustness
via adversarial attacks is crucial in this field. Most multimodal attacks are
sample-specific, generating a unique perturbation for each sample to construct
adversarial samples. To the best of our knowledge, it is the first work through
multimodal decision boundaries to explore the creation of a universal,
sample-agnostic perturbation that applies to any image. Initially, we explore
strategies to move sample points beyond the decision boundaries of linear
classifiers, refining the algorithm to ensure successful attacks under the top
$k$ accuracy metric. Based on this foundation, in visual-language tasks, we
treat visual and textual modalities as reciprocal sample points and decision
hyperplanes, guiding image embeddings to traverse text-constructed decision
boundaries, and vice versa. This iterative process consistently refines a
universal perturbation, ultimately identifying a singular direction within the
input space which is exploitable to impair the retrieval performance of VLP
models. The proposed algorithms support the creation of global perturbations or
adversarial patches. Comprehensive experiments validate the effectiveness of
our method, showcasing its data, task, and model transferability across various
VLP models and datasets. Code: https://github.com/LibertazZ/MUAP


  A short proof is given of a necessary and sufficient condition for the
normalized occupation measure of a L\'evy process in a metrizable compact group
to be asymptotically uniform with probability one.


  Cosmological evolution of axionic string network is analyzed in terms of
field-theoretic simulations in a box of 512^3 grids, which are the largest
ever, using a new and more efficient identification scheme of global strings.
The scaling parameter is found to be \xi=0.87 +- 0.14 in agreement with
previous results. The energy spectrum is calculated precisely using a pseudo
power spectrum estimator which significantly reduces the error in the mean
reciprocal comoving momentum. The resultant constraint on the axion decay
constant leads to f_a <= 3*10^11 GeV. We also discuss implications for the
early Universe.


  Breathing is an essential part of human survival, which carries information
about a person's physiological and psychological state. Generally, breath
boundaries are marked by experts before using for any task. An unsupervised
algorithm for breath boundary detection has been proposed for breath sounds
recorded at the mouth also referred as vocal breath sounds (VBS) in this work.
Breath sounds recorded at the mouth are used in this work because they are easy
and contactless to record than tracheal breath sounds and lung breath sounds.
The periodic nature of breath signal energy is used to segment the breath
boundaries. Dynamic programming with the prior information of the number of
breath phases($P$) and breath phase duration($d$) is used to find the
boundaries. In this work, 367 breath boundaries from 60 subjects (31 healthy,
29 patients) having 307 breaths are predicted. With the proposed method, M
($89\%$), I ($13\%$), D ($11\%$) and S ($79\%$) is found. The proposed method
shows better performance than the baselines used in this work. Even the
classification performance between asthmatic and healthy subjects using
estimated boundaries by the proposed method is comparable with the ground truth
boundaries.


  Social media enables the rapid spread of many kinds of information, from
memes to social movements. However, little is known about how information
crosses linguistic boundaries. We apply causal inference techniques on the
European Twitter network to quantify multilingual users' structural role and
communication influence in cross-lingual information exchange. Overall,
multilinguals play an essential role; posting in multiple languages increases
betweenness centrality by 13%, and having a multilingual network neighbor
increases monolinguals' odds of sharing domains and hashtags from another
language 16-fold and 4-fold, respectively. We further show that multilinguals
have a greater impact on diffusing information less accessible to their
monolingual compatriots, such as information from far-away countries and
content about regional politics, nascent social movements, and job
opportunities. By highlighting information exchange across borders, this work
sheds light on a crucial component of how information and ideas spread around
the world.


  The Hearthstone AI framework and competition motivates the development of
artificial intelligence agents that can play collectible card games. A special
feature of those games is the high variety of cards, which can be chosen by the
players to create their own decks. In contrast to simpler card games, the value
of many cards is determined by their possible synergies. The vast amount of
possible decks, the randomness of the game, as well as the restricted
information during the player's turn offer quite a hard challenge for the
development of game-playing agents. This short paper introduces the competition
framework and goes into more detail on the problems and challenges that need to
be faced during the development process.


  We study the transmission probability of Dirac fermions in graphene scattered
by a triangular double barrier potential in the presence of an external
magnetic field. Our system made of two triangular potential barrier regions
separated by a well region characterized by an energy gap. Solving our
Dirac-like equation and matching the solutions at the boundaries allowed us to
express our transmission and reflection coefficients in terms of transfer
matrix. We show in particular that the transmission exhibits oscillation
resonances that are manifestations of the Klein tunneling effect. The
triangular barrier electrostatic field was found to play a key role in
controlling the peak of tunneling resistance. However, it only slightly
modifies the resonances at oblique incidence and leaves Klein paradox
unaffected at normal incidence.


  We propose a stereo vision-based approach for tracking the camera ego-motion
and 3D semantic objects in dynamic autonomous driving scenarios. Instead of
directly regressing the 3D bounding box using end-to-end approaches, we propose
to use the easy-to-labeled 2D detection and discrete viewpoint classification
together with a light-weight semantic inference method to obtain rough 3D
object measurements. Based on the object-aware-aided camera pose tracking which
is robust in dynamic environments, in combination with our novel dynamic object
bundle adjustment (BA) approach to fuse temporal sparse feature correspondences
and the semantic 3D measurement model, we obtain 3D object pose, velocity and
anchored dynamic point cloud estimation with instance accuracy and temporal
consistency. The performance of our proposed method is demonstrated in diverse
scenarios. Both the ego-motion estimation and object localization are compared
with the state-of-of-the-art solutions.


  We measure the size-luminosity relation of photometrically-selected galaxies
within the redshift range $z\sim6-9$, using galaxies lensed by six foreground
Hubble Frontier Fields (HFF) clusters. The power afforded by strong
gravitational lensing allows us to observe fainter and smaller galaxies than in
blank fields. We select our sample of galaxies and obtain their properties,
e.g., redshift, magnitude, from the photometrically-derived ASTRODEEP
catalogues. The intrinsic size is measured with the Lenstruction software, and
completeness maps are created as a function of size and luminosity via the
GLACiAR2 software. We perform a Bayesian analysis to estimate the intrinsic and
incompleteness-corrected size-luminosity distribution, with parameterization
$r_e \propto L^\beta$. We find slopes of $\beta\sim0.48\pm0.08$ at $z\sim6-7$
and $\beta\sim0.68\pm0.14$ at $z\sim8.5$, adopting the Bradac lens model. The
slope derived by lensed galaxies is steeper than that obtained in blank fields
and is consistent with other independent determinations of the size-luminosity
relation from the HFF dataset. We also investigate the systematic uncertainties
correlated with the choice of lens models, finding that the slopes of
size-luminosity relations derived from different models are consistent with
each other, i.e. the modeling errors are not a significant source of
uncertainty in the size-luminosity relation.


  Recently a short scale modified black hole metric, known as holographic
metric, has been proposed in order to capture the self-complete character of
gravity. In this paper we show that such a metric can reproduce some geometric
features expected from the quantum $N$-portrait beyond the semi-classical
limit. We show that for a generic $N$ this corresponds to having an effective
energy momentum tensor in Einstein equations or, equivalently, non-local terms
in the gravity action. We also consider the higher dimensional extension of the
metric and the case of an AdS cosmological term. We provide a detailed
thermodynamic analysis of both cases, with particular reference to the
repercussions on the Hawking-Page phase transition.


  In this paper, we are interested in the following singular stochastic
differential equation (SDE) $${\rm d} X_t = b(t,X_t) {\rm d} t + {\rm d}
B_{t},\ 0\leq t\leq T,\ X_0 = x \in \mathbb{R}^d,$$ where the drift coefficient
$b:[0,T]\times \mathbb{R}^{d}\longrightarrow \mathbb{R}^{d}$ is Borel
measurable, possibly unbounded and has spatial linear growth. The driving noise
$B_{t}$ is a $d-$ dimensional Brownian motion. The main objective of the paper
is to establish the existence and uniqueness of a strong solution and a Sobolev
differentiable stochastic flow for the above SDE. Malliavin differentiability
of the solution is also obtained (cf.\cite{MMNPZ13, MNP2015}). Our results
constitute significant extensions to those in \cite{Zvon74, Ver79, KR05,
MMNPZ13, MNP2015} by allowing the drift $b$ to be unbounded. We employ methods
from white-noise analysis and the Malliavin calculus. As application, we prove
existence of a unique strong Malliavin differentiable solution to the following
stochastic delay differential equation $${\rm d} X (t) = b (X(t-r),
X(t,0,(v,\eta)) {\rm d} t + {\rm d} B(t), \,t \geq 0 ,\textbf{ } (X(0), X_0)=
(v, \eta) \in \mathbb{R}^d \times L^2 ([-r,0], \mathbb{R}^d),$$ with the drift
coefficient $b: \mathbb{R}^d \times \mathbb{R}^d \rightarrow \mathbb{R}^d$ is a
Borel-measurable function bounded in the first argument and has linear growth
in the second argument.


  We use local stratified shearing-box simulations to elucidate the impact of
two-temperature thermodynamics on the thermal structure of coronae in
radiatively efficient accretion flows. Rather than treating the coronal plasma
as an isothermal fluid, we use a simple, parameterized cooling function that
models the collisional transfer of energy from the ions to the rapidly cooling
leptons. Two-temperature models naturally form temperature inversions, with a
hot, magnetically dominated corona surrounding a cold disc. Simulations with
net vertical flux (NF) magnetic fields launch powerful magnetocentrifugal winds
that would enhance accretion in a global system. The outflow rates are much
better converged with increasing box height than analogous isothermal
simulations, suggesting that the winds into two-temperature coronae may be
sufficiently strong to evaporate a thin disc and form a radiatively inefficient
accretion flow under some conditions. We find evidence for multiphase structure
in the corona, with broad density and temperature distributions, and we propose
criteria for the formation of a multiphase corona. The fraction of cooling in
the surface layers of the disc is substantially larger for NF fields compared
to zero net-flux configurations, with moderate NF simulations radiating
${\gtrsim}30$ per cent of the flow's total luminosity above two midplane
scale-heights. Our work shows that NF fields may efficiently power the coronae
of luminous Seyfert galaxies and quasars, providing compelling motivation for
future studies of the heating mechanisms available to NF fields and the
interplay of radiation with two-temperature thermodynamics.


  Let $\pi\colon P\to M$ be a principal bundle and $p$ an invariant polynomial
of degree r on the Lie algebra of the structure group. The theory of
Chern-Simons differential characters is exploited to define an homology map
$\chi^{k} : H_{2r-k-1}(M)\times H_{k}(\mathcal{F}/\mathcal{G})\to
\mathbb{R}/\mathbb{Z}$, for $k<r-1$, where $\mathcal{F} /\mathcal{G}$ is the
moduli space of flat connections of $\pi$ under the action of a subgroup
$\mathcal{G}$ of the gauge group. The differential characters of first order
are related to the Dijkgraaf-Witten action for Chern-Simons Theory. The second
order characters are interpreted geometrically as the holonomy of a connection
in a line bundle over $\mathcal{F}/\mathcal{G})$. The relationship with other
constructions in the literature is also analyzed.


  Organic molecules store the energy of absorbed light in the form of
charge-neutral molecular excitations -- Frenkel excitons. Usually, in amorphous
organic materials, excitons are viewed as quasiparticles, localized on single
molecules, which diffuse randomly through the structure. However, the picture
of incoherent hopping is not applicable to some classes of molecular aggregates
-- assemblies of molecules that have strong near field interaction between
electronic excitations in the individual subunits. Molecular aggregates can be
found in nature, in photosynthetic complexes of plants and bacteria, and they
can also be produced artificially in various forms including quasi-one
dimensional chains, two-dimensional films, tubes, etc. In these structures
light is absorbed collectively by many molecules and the following dynamics of
molecular excitation possesses coherent properties. This energy transfer
mechanism, mediated by the coherent exciton dynamics, resembles the propagation
of electromagnetic waves through a structured medium on the nanometer scale.
The absorbed energy can be transferred resonantly over distances of hundreds of
nanometers before exciton relaxation occurs. Furthermore, the spatial and
energetic landscape of molecular aggregates can enable the funneling of the
exciton energy to a small number of molecules either within or outside the
aggregate. In this review we establish a bridge between the fields of photonics
and excitonics by describing the present understanding of exciton dynamics in
molecular aggregates.


  Recently, transformer networks have outperformed traditional deep neural
networks in natural language processing and show a large potential in many
computer vision tasks compared to convolutional backbones. In the original
transformer, readout tokens are used as designated vectors for aggregating
information from other tokens. However, the performance of using readout tokens
in a vision transformer is limited. Therefore, we propose a novel fusion
strategy to integrate radar data into a dense prediction transformer network by
reassembling camera representations with radar representations. Instead of
using readout tokens, radar representations contribute additional depth
information to a monocular depth estimation model and improve performance. We
further investigate different fusion approaches that are commonly used for
integrating additional modality in a dense prediction transformer network. The
experiments are conducted on the nuScenes dataset, which includes camera
images, lidar, and radar data. The results show that our proposed method yields
better performance than the commonly used fusion strategies and outperforms
existing convolutional depth estimation models that fuse camera images and
radar.


  Aims: In this paper, we perform detailed modelling of the Spitzer and
Herschel observations of the LMC, in order to: (i) systematically study the
uncertainties and biases affecting dust mass estimates; and to (ii) explore the
peculiar ISM properties of the LMC.
  Methods: To achieve these goals, we have modelled the spatially resolved SEDs
with two alternate grain compositions, to study the impact of different
submillimetre opacities on the dust mass. We have rigorously propagated the
observational errors (noise and calibration) through the entire fitting
process, in order to derive consistent parameter uncertainties.
  Results: First, we show that using the integrated SED leads to
underestimating the dust mass by ~50 % compared to the value obtained with
sufficient spatial resolution, for the region we studied. This might be the
case, in general, for unresolved galaxies. Second, we show that Milky Way type
grains produce higher gas-to-dust mass ratios than what seems possible
according to the element abundances in the LMC. A spatial analysis shows that
this dilemma is the result of an exceptional property: the grains of the LMC
have on average a larger intrinsic submm opacity (emissivity index beta~1.7 and
opacity kappa_abs(160 microns)=1.6 m2/kg) than those of the Galaxy. By studying
the spatial distribution of the gas-to-dust mass ratio, we are able to
constrain the fraction of unseen gas mass between ~10, and ~100 % and show that
it is not sufficient to explain the gas-to-dust mass ratio obtained with Milky
Way type grains. Finally, we confirm the detection of a 500 microns extended
emission excess with an average relative amplitude of ~15 %, varying up to 40
%. This excess anticorrelates well with the dust mass surface density. Although
we do not know the origin of this excess, we show that it is unlikely the
result of very cold dust, or CMB fluctuations.


  We declare that we live in the quantum 4-dimensional Minkowski space-time
with, via the gauge principle, the force-fields gauge-group structure, $SU_c(3)
\times SU_L(2) \times U(1)$ applied to the quark world or $SU_L(2) \times U(1)
\times SU_f(3)$ applied to the lepton world, built-in from the very beginning.
The quark world with the triplets from the "quark" group $SU_Q(3)$ (used to be
called "flavor $SU(3)$ symmetry"), which is of nuclear sizes and is protected
by $SU_c(3) \times SU_L(2) \times U(1)$ (i.e. the (123) symmetry), can be seen
by our world, while the lepton world, as of atomic sizes and protected by
$SU_L(2) \times U(1) \times SU_f(3)$ or another (123) symmetry, can also be
seen, as singlets of the quark group $SU_Q(3)$, by our world. Apart from the
"ignition" term, the entire Standard Model is dimensionless and massless in the
quantum 4-dimensional Minkowski space-time; that is, all couplings are
dimensionless and there are no mass terms in the quantum 4-dimensional
Minkowski space-time.
  Therefore, there exist the well-studied $3^\circ \,K$ cosmic microwave
background (CMB) and the now clustered neutrino halos formed from cosmic
background (CB) neutrinos (of three flavors, and antineutrinos), both present
in the overall background of our Universe. In theory, it yields neutrino
oscillations, as some lepton-flavor-violating processes, in a natural manner.


  Ly$\alpha$ emission is an exceptionally informative tracer of the life cycle
of evolving galaxies and the escape of ionising photons. However, theoretical
studies of Ly$\alpha$ emission are often limited by insufficient numerical
resolution, incomplete sets of physical models, and poor line-of-sight (LOS)
statistics. To overcome such limitations, we utilize here the novel PANDORA
suite of high-resolution dwarf galaxy simulations that include a comprehensive
set of state-of-the-art physical models for ionizing radiation, magnetic
fields, supernova feedback and cosmic rays. We post-process the simulations
with the radiative transfer code \textsc{RASCAS} to generate synthetic
observations and compare to observed properties of Ly$\alpha$ emitters. Our
simulated Ly$\alpha$ haloes are more extended than the spatial region from
which the intrinsic emission emanates and our spatially resolved maps of
spectral parameters of the Ly$\alpha$ emission are very sensitive to the
underlying spatial distribution and kinematics of neutral hydrogen. Ly$\alpha$
and LyC emission display strongly varying signatures along different LOS
depending on how each LOS intersects low-density channels generated by stellar
feedback. Comparing galaxies simulated with different physics, we find the
Ly$\alpha$ signatures to exhibit systematic offsets determined by the different
levels of feedback strength and the clumpiness of the neutral gas. Despite this
variance, and regardless of the different physics included in each model, we
find universal correlations between Ly$\alpha$ observables and LyC escape
fraction, demonstrating a robust connection between Ly$\alpha$ and LyC
emission. Ly$\alpha$ observations from a large sample of dwarf galaxies should
thus give strong constraints on their stellar feedback-regulated LyC escape and
confirm their important role for the reionization of the Universe.


  We introduce a convergent iterative algorithm for finding the optimal coding
and decoding operations for an arbitrary noisy quantum channel. This algorithm
does not require any error syndrome to be corrected completely, and hence also
finds codes outside the usual Knill-Laflamme definition of error correcting
codes. The iteration is shown to improve the figure of merit "channel fidelity"
in every step.


  Polyhedra form an established abstract domain for inferring runtime
properties of programs using abstract interpretation. Computations on them need
to be certified for the whole static analysis results to be trusted. In this
work, we look at how far we can get down the road of a posteriori verification
to lower the overhead of certification of the abstract domain of polyhedra. We
demonstrate methods for making the cost of inclusion certificate generation
negligible. From a performance point of view, our single-representation,
constraints-based implementation compares with state-of-the-art
implementations.


  This thesis investigates optimal trajectory tracking of nonlinear dynamical
systems with affine controls. The control task is to enforce the system state
to follow a prescribed desired trajectory as closely as possible. The concept
of so-called exactly realizable trajectories is proposed. For exactly
realizable desired trajectories exists a control signal which enforces the
state to exactly follow the desired trajectory. For a given affine control
system, these trajectories are characterized by the so-called constraint
equation. This approach does not only yield an explicit expression for the
control signal in terms of the desired trajectory, but also identifies a
particularly simple class of nonlinear control systems. Based on that insight,
the regularization parameter is used as the small parameter for a perturbation
expansion. This results in a reinterpretation of affine optimal control
problems with small regularization term as singularly perturbed differential
equations. The small parameter originates from the formulation of the control
problem and does not involve simplifying assumptions about the system dynamics.
Combining this approach with the linearizing assumption, approximate and partly
linear equations for the optimal trajectory tracking of arbitrary desired
trajectories are derived. For vanishing regularization parameter, the state
trajectory becomes discontinuous and the control signal diverges. On the other
hand, the analytical treatment becomes exact and the solutions are exclusively
governed by linear differential equations. Thus, the possibility of linear
structures underlying nonlinear optimal control is revealed. This fact enables
the derivation of exact analytical solutions to an entire class of nonlinear
trajectory tracking problems with affine controls. This class comprises
mechanical control systems in one spatial dimension and the FitzHugh-Nagumo
model.


  The uncertainty of multidimensional shift spaces draws attracted attention of
researchers. For example, the emptiness problem is undecidable; there exist
aperiodic shifts of finite type; there is a nonempty shift of finite type
exhibiting nonextensible local patterns. This paper investigates symbolic
dynamics on Cayley trees and gives affirmative answers to the above questions
in tree-shifts. Beyond that, with introducing block gluing tree-shift, a
sufficient condition of exhibiting positive topological entropy is revealed.


  The determination of inter- and intra-filament transport characteristics in
superconducting composites such as BSCCO-Ag tapes is of great importance for
material evaluation towards applications. Most attempts to separate the two
contributions have relied on indirect methods based on magnetic measurements
such as SQUID or magneto-optic imaging techniques. Here we show that laser
patterning of superconducting BSCCO-Ag tapes constitutes a simple approach to
measure local transport properties in a direct way, even able to separate
inter- and intra-filament contributions to the overall transport behavior of
the sample. Our technique is potentially useful for sub-millimeter devices
based on superconducting tapes.


  In this survey, we report on the state of the art of some of the fundamental
problems in the Lie theory of Lie groups modeled on locally convex spaces, such
as integrability of Lie algebras, integrability of Lie subalgebras to Lie
subgroups, and integrability of Lie algebra extensions to Lie group extensions.
We further describe how regularity or local exponentiality of a Lie group can
be used to obtain quite satisfying answers to some of the fundamental problems.
These results are illustrated by specialization to some specific classes of Lie
groups, such as direct limit groups, linear Lie groups, groups of smooth maps
and groups of diffeomorphisms.


  A prominent threat to causal inference about peer effects over social
networks is the presence of homophily bias, that is, social influence between
friends and families is entangled with common characteristics or underlying
similarities that form close connections. Analysis of social network data has
suggested that certain health conditions such as obesity and psychological
states including happiness and loneliness can spread over a network. However,
such analyses of peer effects or contagion effects have come under criticism
because homophily bias may compromise the causal statement. We develop a
regression-based approach which leverages a negative control exposure for
identification and estimation of contagion effects on additive or
multiplicative scales, in the presence of homophily bias. We apply our methods
to evaluate the peer effect of obesity in Framingham Offspring Study.


  We consider the class of jet shapes known as angularities in dijet production
at hadron colliders. These angularities are modified from the original
definitions in e+e- collisions to be boost invariant along the beam axis. These
shapes apply to the constituents of jets defined with respect to either
k_T-type (anti-k_T, C/A, and k_T) algorithms and cone-type algorithms. We
present an SCET factorization formula and calculate the ingredients needed to
achieve next-to-leading-log (NLL) accuracy in kinematic regions where
non-global logarithms are not large. The factorization formula involves
previously unstudied "unmeasured beam functions," which are present for finite
rapidity cuts around the beams. We derive relations between the jet functions
and the shape-dependent part of the soft function that appear in the factorized
cross section and those previously calculated for e+e- collisions, and present
the calculation of the non-trivial, color-connected part of the soft-function
to O(\alpha_s). This latter part of the soft function is universal in the sense
that it applies to any experimental setup with an out-of-jet p_T veto and
rapidity cuts together with two tagged jets and it is independent of the choice
of jet (sub-)structure measurement. In addition, we implement the recently
introduced soft-collinear refactorization to resum logarithms of the jet size,
valid in the region of non-enhanced non-global logarithm effects. While our
results are valid for all 2 \to 2 channels, we compute explicitly for the qq'
\to qq' channel the color-flow matrices and plot the NLL resummed differential
dijet cross section as an explicit example, which shows that the normalization
and scale uncertainty is reduced when the soft function is refactorized. For
this channel, we also plot the jet size R dependence, the p_T^{\rm cut}
dependence, and the dependence on the angularity parameter a.


  We study the clustering properties of dark matter halos in real- and
redshift-space in cosmologies with massless and massive neutrinos through a
large set of state-of-the-art N-body simulations. We provide quick and
easy-to-use prescriptions for the halo bias on linear and mildly non-linear
scales, both in real and redshift space, which are valid also for massive
neutrinos cosmologies. Finally we present a halo bias
emulator,$\textbf{BE-HaPPY}$, calibrated on the N-body simulations, which is
fast enough to be used in the standard Markov Chain Monte Carlo approach to
cosmological inference. For a fiducial standard $\Lambda$CDM cosmology
$\textbf{BE-HaPPY}$ provides percent or sub-percent accuracy on the scales of
interest (linear and well into the mildly non-linear regime), meeting therefore
for the halo-bias the accuracy requirements for the analysis of next-generation
large--scale structure surveys.


  Despite the significant progress over the last 50 years in simulating flow
problems using numerical discretization of the Navier-Stokes equations (NSE),
we still cannot incorporate seamlessly noisy data into existing algorithms,
mesh-generation is complex, and we cannot tackle high-dimensional problems
governed by parametrized NSE. Moreover, solving inverse flow problems is often
prohibitively expensive and requires complex and expensive formulations and new
computer codes. Here, we review flow physics-informed learning, integrating
seamlessly data and mathematical models, and implementing them using
physics-informed neural networks (PINNs). We demonstrate the effectiveness of
PINNs for inverse problems related to three-dimensional wake flows, supersonic
flows, and biomedical flows.


  We analyse archival data from the Parkes radio telescope which was observing
a location 35.6$^\circ$ away from SGR 1806-20 during its giant $\gamma$-ray
flare of 2004 December 27. We show that no FRB-like burst counterpart was
detected, and set a radio limit of 110\,MJy at 1.4 GHz, including the estimated
70\,dB suppression of the signal due to its location in the far side lobe of
Parkes and the predicted scattering from the interstellar medium. The upper
limit for the magnetar giant flare radio to $\gamma$-ray fluence ratio is
$\eta_\mathrm{SGR} \lesssim 10^{7}\,\mathrm{Jy\,ms\,erg^{-1}\,cm^{2}}$. Based
on the non-detection of a short and prompt $\gamma$-ray counterpart of fifteen
FRBs in $\gamma$-ray transient monitors, we set a lower limit on the fluence
ratios of FRBs to be $\eta_\mathrm{FRB} \gtrsim
10^{7-9}\,\mathrm{Jy\,ms\,erg^{-1}\,cm^{2}}$. The fluence ratio limit for \sgr\
is inconsistent with all but one of the fifteen FRBs. We discuss possible
variations in the magnetar-FRB emission mechanism and observational caveats
that may reconcile the theory with observations.


  For a dualizing module $D$ over a commutative Noetherian ring $R$ with
identity, it is known that its Auslander class $\mathscr{A}_D\left(R\right)$
(respectively, Bass class $\mathscr{B}_D\left(R\right)$) is characterized as
those $R$-modules with finite Gorenstein flat dimension (respectively, finite
Gorenstein injective dimension). We establish an analogue of this result in the
context of cotilting modules over general Noetherain rings.


  Unsupervised domain adaptation is one of the challenging problems in computer
vision. This paper presents a novel approach to unsupervised domain adaptations
based on the optimal transport-based distance. Our approach allows aligning
target and source domains without the requirement of meaningful metrics across
domains. In addition, the proposal can associate the correct mapping between
source and target domains and guarantee a constraint of topology between source
and target domains. The proposed method is evaluated on different datasets in
various problems, i.e. (i) digit recognition on MNIST, MNIST-M, USPS datasets,
(ii) Object recognition on Amazon, Webcam, DSLR, and VisDA datasets, (iii)
Insect Recognition on the IP102 dataset. The experimental results show that our
proposed method consistently improves performance accuracy. Also, our framework
could be incorporated with any other CNN frameworks within an end-to-end deep
network design for recognition problems to improve their performance.


  In a previous work, we used a polarization condition to show that there is a
family of U(1) symmetric solutions of the vacuum Einstein equations such that
each exhibits AVTD (Asymptotic Velocity Term Dominated) behavior in the
neighborhood of its singularity. Here we consider the general case of U(1)
bundles and determine a condition, called the half polarization condition,
necessary and sufficient in our context, for AVTD behavior near the
singularity.


  Precision tests of decay law of radioactive nuclei have not so far found any
deviation from the exponential decay law at early time, as predicted by quantum
mechanics. In this paper, we show that the quantum decoherence time (i.e. the
timescale of nonexponential decay) of the quasifission or fission process
should be of the order of attosecond considering the atom of the fissioning
nucleus as a quantum detector. Hence, the observed decay timescale of the
quasifission or fission process of even highly excited (EX greater than 50 MeV)
transuranium and uraniumlike complexes should be rather long (of the order of
attosecond) in spite of their very fast exponential decay timescale (of the
order of zeptosecond) as measured by the nuclear techniques. Recent controversy
regarding the observation of very long (of the order of attosecond ) and very
short (of the order of zeptosecond ) quasifission or fission timescales for
similar systems at similar excitation energies as obtained by direct techniques
(crystal blocking, X ray fission fragment) and nuclear techniques could be
interpreted as evidence for nonexponential decays in nuclear systems


  Multiple defendants in a criminal fact description generally exhibit complex
interactions, and cannot be well handled by existing Legal Judgment Prediction
(LJP) methods which focus on predicting judgment results (e.g., law articles,
charges, and terms of penalty) for single-defendant cases. To address this
problem, we propose the task of multi-defendant LJP, which aims to
automatically predict the judgment results for each defendant of
multi-defendant cases. Two challenges arise with the task of multi-defendant
LJP: (1) indistinguishable judgment results among various defendants; and (2)
the lack of a real-world dataset for training and evaluation. To tackle the
first challenge, we formalize the multi-defendant judgment process as
hierarchical reasoning chains and introduce a multi-defendant LJP method, named
Hierarchical Reasoning Network (HRN), which follows the hierarchical reasoning
chains to determine criminal relationships, sentencing circumstances, law
articles, charges, and terms of penalty for each defendant. To tackle the
second challenge, we collect a real-world multi-defendant LJP dataset, namely
MultiLJP, to accelerate the relevant research in the future. Extensive
experiments on MultiLJP verify the effectiveness of our proposed HRN.


  The goal of our work is to investigate the oscillation and asymptotic
properties of a class of difference equations with a condition. In contrast to
most previous studies, the oscillation of the investigated equation is obtained
with only one condition.