  [Abridged] Over the past two decades observations and theoretical simulations
have established a global frame-work of galaxy formation and evolution in the
young Universe. Galaxies formed as baryonic gas cooled at the centres of
collapsing dark matter halos. Mergers of halos led to the build up of galaxy
mass. A major step forward in understanding these issues requires well resolved
physical information on individual galaxies at high redshift. Here we report
adaptive optics, spectroscopic observations of a representative luminous star
forming galaxy when the Universe was only twenty percent of its age. The
superior angular resolution of these data reveals the physical and dynamical
properties of a high redshift galaxy in unprecedented detail. A large and
massive rotating proto-disk is channelling gas towards a growing central
stellar bulge hosting an accreting massive black hole.


  We have observed 1999 RZ253 with the Hubble Space Telescope at seven separate
epochs and have fit an orbit to the observed relative positions of this binary.
Two orbital solutions have been identified that differ primarily in the
inclination of the orbit plane. The best fit corresponds to an orbital period,
P=46.263 +0.006/-0.074 days, semimajor axis a=4,660 +/-170 km and orbital
eccentricity e=0.460 +/-0.013 corresponding to a system mass m=3.7 +/-0.4
x10^18 kg. For a density of rho = 1000 kg m^-3 the albedo at 477 nm is p = 0.12
+/-0.01, significantly higher than has been commonly assumed for objects in the
Kuiper Belt. Multicolor, multiepoch photometry shows this pair to have colors
typical for the Kuiper belt with a spectral gradient of 0.35 per 100 nm in the
range between 475 and 775 nm. Photometric variations at the four epochs we
observed were as large as 12 +/-3% but the sampling is insufficient to confirm
the existence of a lightcurve.


  Transport of fluid through a pipe is essential for the operation of
macroscale machines and microfluidic devices. Conventional fluids only flow in
response to external pressure. We demonstrate that an active isotropic fluid,
comprised of microtubules and molecular motors, autonomously flows through
meter-long three-dimensional channels. We establish control over the magnitude,
velocity profile and direction of the self-organized flows, and correlate these
to the structure of the extensile microtubule bundles. The inherently
three-dimensional transition from bulk-turbulent to confined-coherent flows
occurs concomitantly with a transition in the bundle orientational order near
the surface, and is controlled by a scale-invariant criterion related to the
channel profile. The non-equilibrium transition of confined isotropic active
fluids can be used to engineer self-organized soft machines.


  Let $(R,\fm)$ be a local ring and let $C$ be a semidualizing $R$--module. In
this paper, we are concerned in $C$--injective and $G_{C}$--injective
dimensions of certain local cohomology modules of $R$. Firstly, the injective
dimension of $C$ and the above quantities of dimensions is compared. Then, as
an application of the above comparisons, a characterization of a dualizing
module of $R$ is given. Finally, it is shown that if $R$ is Cohen-Macaulay of
dimension $d$ such that $\H_{\fm}^{d}(C)$ is $C$--injective, then $R$ is
Gorernstein. This is an answer to the question which was recently presented.


  It is developed a Riemannian reformulation of classical statistical mechanics
for systems in thermodynamic equilibrium, which arises as a natural extension
of Ruppeiner geometry of thermodynamics. The present proposal leads to
interpret entropy $\mathcal{S}_{g}(I|\theta)$ and all its associated
thermo-statistical quantities as purely geometric notions derived from the
Riemannian structure on the manifold of macroscopic observables
$\mathcal{M}_{\theta}$ (existence of a distance
$ds^{2}=g_{ij}(I|\theta)dI^{i}dI^{j}$ between macroscopic configurations $I$
and $I+dI$). Moreover, the concept of statistical curvature scalar
$R(I|\theta)$ arises as an invariant measure to characterize the existence of
an \textit{irreducible statistical dependence} among the macroscopic
observables $I$ for a given value of control parameters $\theta$. This feature
evidences a certain analogy with Einstein General Relativity, where the
spacetime curvature $R(\mathbf{r},t)$ distinguishes the geometric nature of
gravitation and the reducible character inertial forces with an appropriate
selection of the reference frame.


  Applying large magnetic fields to gain access to the hyperfine Paschen-Back
regime can isolate three-level systems in a hot alkali metal vapors, thereby
simplifying usually complex atom-light interactions. We use this method to make
the first direct measurement of the $\vert\langle 5P\vert\vert er\vert\vert
5D\rangle\vert$ matrix element in $^{87}$Rb. An analytic model with only three
levels accurately models the experimental electromagnetically induced
transparency spectra and extracted Rabi frequencies are used to determine the
dipole matrix element. We measure $\vert\langle 5P_{3/2}\vert\vert er\vert\vert
5D_{5/2}\rangle\vert = (2.290\pm0.002_{\rm stat}\pm0.04_{\rm syst})~ea_{0}$
which is in excellent agreement with the theoretical calculations of Safronova,
Williams, and Clark [Phys. Rev. A \textbf{69}, 022509 (2004)].


  We apply density functional theory band structure calculations, the
coupled-cluster method, and exact diagonalization to investigate the
microscopic magnetic model of the spin-1/2 compound Cu2GeO4. The model is
quasi-two-dimensional, with uniform spin chains along one direction and
frustrated spin chains along the other direction. The coupling along the
uniform chains is antiferromagnetic, J 130 K. The couplings along the
frustrated chains are J1 -60 K and J2 80 K between nearest neighbors and
next-nearest neighbors, respectively. The ground state of the quantum model is
a spiral, with the reduced sublattice magnetization of 0.62 mu_B and the pitch
angle of 84 deg, both renormalized by quantum effects. The proposed spiral
ground state of Cu2GeO4 opens a way to magnetoelectric effects in this
compound.


  We propose a numerical technique to compute the equilibrium free energy of
glasses that cannot be prepared quasi-reversibly. For such systems, standard
techniques for estimating the free energy by extrapolation, cannot be used.
Instead, we use a procedure that samples the equilibrium partition function of
the basins of attraction of the different inherent structures (local potential
energy minima) of the system. If all relevant inherent structures could be
adequately sampled in the (supercooled) liquid phase, our approach would be
rigorous. In any finite simulation, we will miss the lower-energy inherent
structures that become dominant at very low temperatures. We find that our free
energy estimates for a Kob-Andersen glass are lower than those obtained by very
slow cooling, even at temperatures down to one third of the glass transition
temperature. The current approach could be applied to compute the chemical
potential of ultra-stable glassy materials, and should enable the estimation of
their solubility.


  Label inventories for fine-grained entity typing have grown in size and
complexity. Nonetheless, they exhibit a hierarchical structure. Hyperbolic
spaces offer a mathematically appealing approach for learning hierarchical
representations of symbolic data. However, it is not clear how to integrate
hyperbolic components into downstream tasks. This is the first work that
proposes a fully hyperbolic model for multi-class multi-label classification,
which performs all operations in hyperbolic space. We evaluate the proposed
model on two challenging datasets and compare to different baselines that
operate under Euclidean assumptions. Our hyperbolic model infers the latent
hierarchy from the class distribution, captures implicit hyponymic relations in
the inventory, and shows performance on par with state-of-the-art methods on
fine-grained classification with remarkable reduction of the parameter size. A
thorough analysis sheds light on the impact of each component in the final
prediction and showcases its ease of integration with Euclidean layers.


  Large language models (LLMs) have achieved remarkable progress in many
natural language processing tasks. However, our experiment reveals that, in
stance detection tasks, LLMs may generate biased stances due to
sentiment-stance spurious correlations and preference towards certain
individuals and topics, thus harming their performance. Therefore, in this
paper, we propose to Mitigate Biases of LLMs in stance detection with
Calibration (MB-Cal). To be specific, a novel calibration network is devised to
calibrate potential bias in the stance prediction of LLMs. Further, to address
the challenge of effectively learning bias representations and the difficulty
in the generalizability of debiasing, we construct counterfactual augmented
data. This approach enhances the calibration network, facilitating the
debiasing and out-of-domain generalization. Experimental results on in-target
and zero-shot stance detection tasks show that the proposed MB-Cal can
effectively mitigate biases of LLMs, achieving state-of-the-art results.


  Charged lepton flavour violation is reappraised in the context of
supersymmetric see-saw mechanism. It is pointed out that a non-trivial flavour
structure of right-handed neutrinos, whose effect has been thus far less
studied, can give rise to significant slepton flavour transitions. Under the
premise that the neutrino Yukawa couplings are of O(1), the right-handed
neutrino mixing contribution could form a basis of the $\mu \rightarrow e
\gamma$ amplitude which by itself might lead to an experimentally accessible
rate, given a typical low-energy sparticle spectrum. Emphasis is placed on the
crucial role of the recently measured lepton mixing angle $\theta_{13}$ as well
as the leptonic CP-violating phases.


  Coupling dark matter to light new particles is an attractive way to combine
thermal production with strong velocity-dependent self-interactions. Here we
point out that in such models the dark matter annihilation rate is generically
enhanced by the Sommerfeld effect, and we derive the resulting constraints from
the Cosmic Microwave Background and other indirect detection probes. For the
frequently studied case of s-wave annihilation these constraints exclude the
entire parameter space where the self-interactions are large enough to address
the small-scale problems of structure formation.


  A TeV-scale Higgs doublet with a small mixing to the standard model Higgs
doublet can have the sizable Yukawa couplings to several right-handed neutrinos
and the standard model lepton doublets. This provides a testable Dirac neutrino
mass generation. We further consider a seesaw mechanism involving a
$U(1)_{B-L}^{}$ gauge symmetry, which predicts the existence of two
right-handed neutrinos and a stable Dirac fermionic dark matter, to
simultaneously explain the small mixing between the two Higgs doublets and the
generation of the cosmic baryon asymmetry.


  A variational coarse-graining framework for heterogeneous media is developed
that allows for a seamless transition from the traditional static scenario to a
arbitrary loading conditions, including inertia effects and body forces. The
strategy is formulated in the spirit of computational homogenization methods
(FE$^2$) and is based on the discrete version of Hill's averaging results
recently derived by the authors. In particular, the traditional static
multiscale scheme is proved here to be equivalent to a direct homogenization of
the principle of minimum potential energy and to hold exactly under a finite
element discretization. This perspective provides a unifying variational
framework for the FE$^2$ method, in the static setting, with Dirichlet or
Neumann boundary conditions on the representative volume element; and it
directly manifests the approximate duality of the effective strain energy
density obtained with these two types of boundary conditions in the sense of
Legendre transformation. Its generalization to arbitrary loading conditions and
material constitutive relations is then immediate through the incremental
minimum formulation of the dynamic problem \`a la Radovitzky and Ortiz (1999),
which, in the discrete setting, is in full analogy to the static problem. These
theoretical developments are then translated into an efficient multiscale
FE$^2$ computational strategy for the homogenization of a microscopic explicit
dynamics scheme, with two noteworthy properties. Firstly, each time incremental
problem can be solved exactly with a single Newton-Raphson iteration with a
constant Hessian, regardless of the specific non-linearities or
history-dependence of the micro-constituents' behavior. Secondly, the scheme
concurrently solves for the microscopic and macroscopic degrees of freedom, in
contrast to standard approaches based on sequential or nested minimizations.


  We present Keck/NIRSPEC spectroscopic observations of three Lyman alpha
emitting galaxies (LAEs) at z ~ 2.3 discovered with the HETDEX pilot survey. We
detect Halpha, [OIII], and Hbeta emission from two galaxies at z = 2.29 and
2.49, designated HPS194 and HPS256, respectively, representing the first
detection of multiple rest-frame optical emission lines in galaxies at
high-redshift selected on the basis of their Lyman alpha emission. The
redshifts of the Lyman alpha emission from these galaxies are offset redward of
the systemic redshifts by Delta_v = 162 +/- 37 (photometric) +/- 42
(systematic) km/s for HPS194, and Delta_v = 36 +/- 35 +/- 18 km/s for HPS256.
An interpretation for HPS194 is that a large-scale outflow may be occurring in
its interstellar medium. The emission line ratios imply that neither LAE hosts
an active galactic nucleus. Using the upper limits on the [NII] emission we
place meaningful constraints on the gas-phase metallicities in these two LAEs
of Z < 0.17 and < 0.28 Zsol (1 sigma). Measuring the stellar masses of these
objects via spectral energy distribution (SED) fitting (~ 10^10 and 6 x 10^8
Msol, respectively), we study the nature of LAEs in a mass-metallicity plane.
At least one of these two LAEs appears to be more metal poor than
continuum-selected star-forming galaxies at the same redshift and stellar mass,
implying that objects exhibiting Lyman alpha emission may be systematically
less chemically enriched than the general galaxy population. We use the SEDs of
these two galaxies to show that neglecting the emission lines when fitting
stellar population models to the observed photometry can result in
overestimates of the population age by orders of magnitude, and the stellar
mass by a factor of ~ 2. This effect is particularly important at z > 7, where
similarly strong emission lines may masquerade in the photometry as a 4000 A
break (abridged).


  Energy management systems (EMS) are becoming increasingly important in order
to utilize the continuously growing curtailed renewable energy. Promising
energy storage systems (ESS), such as batteries and green hydrogen should be
employed to maximize the efficiency of energy stakeholders. However, optimal
decision-making, i.e., planning the leveraging between different strategies, is
confronted with the complexity and uncertainties of large-scale problems. Here,
we propose a sophisticated deep reinforcement learning (DRL) methodology with a
policy-based algorithm to realize the real-time optimal ESS planning under the
curtailed renewable energy uncertainty. A quantitative performance comparison
proved that the DRL agent outperforms the scenario-based stochastic
optimization (SO) algorithm, even with a wide action and observation space.
Owing to the uncertainty rejection capability of the DRL, we could confirm a
robust performance, under a large uncertainty of the curtailed renewable
energy, with a maximizing net profit and stable system. Action-mapping was
performed for visually assessing the action taken by the DRL agent according to
the state. The corresponding results confirmed that the DRL agent learns the
way like what a human expert would do, suggesting reliable application of the
proposed methodology.


  In this paper we obtain some essential generalizations of certain
Ramachandra's inequality, i. e. we obtain new lower estimates for the energies
of some complicated signals generated by the Riemann zeta-function on the
critical line.


  The anomalous properties of water in the supercooled state are numerous and
well-known. Particularly striking are the strong changes in dynamic properties
that appear to display divergences at temperatures close to -- but beyond --
the lowest temperatures attainable either experimentally or in computer
simulations. Recent work on slow or glassy dynamics in water suggests analogies
with simple liquids not previously appreciated, and at the same time highlights
some aspects that remain peculiar to water. A comparison of the behavior of
water with normal liquids, with respect to its dynamic, thermodynamic and
structural changes in the supercooled regime is made by analyzing, via computer
simulations, the properties of local potential energy minima sampled by water
in supercooled temperatures and pressures.


  The observation of significant dipolar effects in gases of ultra-cold polar
molecules typically demands a strong external electric field to polarize the
molecules. We show that even in the absence of a significant polarization,
dipolar effects may play a crucial role in the physics of polar molecules in
bilayers, provided that the molecules in each layer are initially prepared in a
different rotational state. Then, inter-layer dipolar interactions result in a
non-local swap of the rotational state between molecules in different layers,
even for weak applied electric fields. The inter-layer scattering due to the
dipole-dipole interaction leads to a non-trivial dependence of the swapping
rate on density, temperature, inter-layer spacing, and population imbalance.
For reactive molecules like KRb, chemical recombination immediately follows a
non-local swap and dominates the losses even for temperatures well above
quantum degeneracy, and could be hence observed under current experimental
conditions.


  The Standard Quantum Limit (SQL) for the measurement of a free mass position
is illustrated, along with two necessary conditions for breaching it. A
measurement scheme that overcomes the SQL is engineered. It can be achieved in
three-steps: i) a pre-squeezing stage; ii) a standard von Neumann measurement
with momentum-position object-probe interaction and iii) a feedback. Advantages
and limitations of this scheme are discussed. It is shown that all of the three
steps are needed in order to overcome the SQL. In particular, the von Neumann
interaction is crucial in getting the right state reduction, whereas other
experimentally achievable Hamiltonians, as, for example, the radiation-pressure
interaction, lead to state reductions that on the average cannot overcome the
SQL.


  Detecting and measuring a non-Gaussian signature of primordial origin in the
density field is a major science goal of next-generation galaxy surveys. The
signal will permit us to determine primordial physics processes and constrain
models of cosmic inflation. While traditional approaches utilise a limited set
of statistical summaries of the galaxy distribution to constrain primordial
non-Gaussianity, we present a field-level approach by Bayesian
forward-modelling the entire three-dimensional galaxy survey. Our method
naturally and fully self-consistently exploits the entirety of the large-scale
structure, e.g., higher-order statistics, peculiar velocity fields, and
scale-dependent galaxy bias, to extract information on the local
non-Gaussianity parameter, $\fnl$. We demonstrate the performance of our
approach through various tests with mock galaxy data emulating relevant
features of the \sdssiii{}-like survey, and additional tests with a
\textit{Stage IV} mock data set. These tests reveal that the method infers
unbiased values of $\fnl$ by accurately handling survey geometries, noise, and
unknown galaxy biases. We demonstrate that our method can achieve constraints
of $\sigma_{\fnl} \approx 8.78$ for \sdssiii{}-like data, an improvement of a
factor $\sim 2.5$ over currently published constraints. Tests with
next-generation mock data show that significant further improvements are
feasible with sufficiently high resolution. Furthermore, the results
demonstrate that our method can consistently marginalise all nuisance
parameters of the data model. The method further provides an inference of the
three-dimensional primordial density field, providing opportunities to explore
additional signatures of primordial physics.


  We describe the parameters of a low-frequency all-sky survey of compact radio
sources using Interplanetary Scintillation (IPS), undertaken with the Murchison
Widefield Array (MWA). While this survey gives important complementary
information to low-resolution survey such as the MWA GLEAM survey, providing
information on the subarsecond structure of every source, a survey of this kind
has not been attempted in the era of low-frequency imaging arrays such as the
MWA and LOFAR. Here we set out the capabilities of such a survey, describing
the limitations imposed by the heliocentric observing geometry and by the
instrument itself. We demonstrate the potential for IPS measurements at any
point on the celestial sphere and we show that at 160MHz, reasonable results
can be obtained within 30deg of the ecliptic (2{\pi} str: half the sky). We
also suggest some observational strategies and describe the first such survey,
the MWA Phase I IPS survey. Finally we analyse the potential of the
recently-upgraded MWA and discuss the potential of the SKA-low to use IPS to
probe sub-mJy flux density levels at sub-arcsecond angular resolution.


  We describe a general formalism based on the partial-wave decomposition to
compute the iterative $s$-channel discontinuity of four-point amplitudes at any
loop order. As an application, we focus on the low-energy expansions of type I
and II superstring amplitudes. Besides providing new results for their leading
and sub-leading logarithmic contributions beyond genus one, our approach
elucidates the general structure of non-analytic threshold terms. In the case
of open strings, the use of orthogonal colour projectors allows us to
efficiently compute all contributions from different worldsheet topologies at a
given loop order.


  Alice gives Bob an unknown localized physical state at some point P. At some
point Q in the causal future of P, Alice will ask Bob for the state back. Bob
knows this, but does not know at which point Q until the request is made. Bob
can satisfy Alice's summons, with arbitrarily short delay, for a quantum state
in Galilean space-time or a classical state in Minkowski space-time. However,
given an unknown quantum state in Minkowski space-time, he cannot generally
fulfil her summons. This {\it no-summoning theorem} is a fundamental feature
of, and intrinsic to, relativistic quantum theory. It follows from the
no-signalling principle and the no-cloning theorem, but not from either alone.


  We present our first efforts towards building a single multilingual automatic
speech recognition (ASR) system that can process code-switching (CS) speech in
five languages spoken within the same population. This contrasts with related
prior work which focuses on the recognition of CS speech in bilingual
scenarios. Recently, we have compiled a small five-language corpus of South
African soap opera speech which contains examples of CS between 5 languages
occurring in various contexts such as using English as the matrix language and
switching to other indigenous languages. The ASR system presented in this work
is trained on 4 corpora containing English-isiZulu, English-isiXhosa,
English-Setswana and English-Sesotho CS speech. The interpolation of multiple
language models trained on these language pairs enables the ASR system to
hypothesize mixed word sequences from these 5 languages. We evaluate various
state-of-the-art acoustic models trained on this 5-lingual training data and
report ASR accuracy and language recognition performance on the development and
test sets of the South African multilingual soap opera corpus.


  A bottom-up Einstein-Maxwell-Dilaton holographic model is used to compute,
for the first time, the behavior of several transport coefficients of the hot
and baryon-rich strongly coupled quark-gluon plasma at the critical point and
also across the first-order phase transition line in the phase diagram. The
observables under study are of the shear and bulk viscosities, baryon
diffusion, thermal conductivity, the jet quenching parameter $\hat{q}$, as well
as the heavy-quark drag force and Langevin diffusion coefficients. These
calculations provide a phenomenologically promising estimate for these
coefficients, given that our model quantitatively reproduces lattice QCD
thermodynamics results, both at zero and finite baryon density, besides
naturally incorporating the nearly-perfect fluidity of the quark-gluon plasma.
We find that the diffusion of baryon charge, and also the shear and bulk
viscosities, are suppressed with increasing baryon density, indicating that the
medium becomes even closer to perfect fluidity at large densities. On the other
hand, the jet quenching parameter and the heavy-quark momentum diffusion are
enhanced with increasing density. The observables display a discontinuity gap
when crossing the first-order phase transition line, while developing an
infinite slope at the critical point. The transition temperatures associated
with different transport coefficients differ in the crossover region but are
found to converge at the critical point.


  Quantization is studied from a viewpoint of field extension. If the dynamical
fields and their action have a periodicity, the space of wave functions should
be algebraically extended `a la Galois, so that it may be consistent with the
periodicity. This was pointed out by Y. Nambu three decades ago. Having chosen
quantum mechanics (one dimensional field theory), this paper shows that a
different Galois extension gives a different quantization scheme. A new scheme
of quantization appears when the invariance under Galois group is imposed as a
physical state condition. Then, the normalization condition appears as a sum
over the product of more than three wave functions, each of which is given for
a different root adjoined by the field extension.


  In quantum physics, all measured observables are subject to statistical
uncertainties, which arise from the quantum nature as well as the experimental
technique. We consider the statistical uncertainty of the so-called sampling
method, in which one estimates the expectation value of a given observable by
empirical means of suitable pattern functions. We show that if the observable
can be written as a function of a single directly measurable operator, the
variance of the estimate from the sampling method equals to the quantum
mechanical one. In this sense, we say that the estimate is on the quantum
mechanical level of uncertainty. In contrast, if the observable depends on
non-commuting operators, e.g. different quadratures, the quantum mechanical
level of uncertainty is not achieved. The impact of the results on quantum
tomography is discussed, and different approaches to quantum tomographic
measurements are compared. It is shown explicitly for the estimation of
quasiprobabilities of a quantum state, that balanced homodyne tomography does
not operate on the quantum mechanical level of uncertainty, while the
unbalanced homodyne detection does.


  Semantic Pattern Similarity is an interesting, though not often encountered
NLP task where two sentences are compared not by their specific meaning, but by
their more abstract semantic pattern (e.g., preposition or frame). We utilize
Siamese Networks to model this task, and show its usefulness in determining SQL
patterns for unseen questions in a database-backed question answering scenario.
Our approach achieves high accuracy and contains a built-in proxy for
confidence, which can be used to keep precision arbitrarily high.


  Microcompression testing is used to probe the uniaxial stress-strain response
of a nanocrystalline alloy, with an emphasis on exploring how grain size and
grain boundary relaxation state impact the complete flow curve and failure
behavior. The yield strength, strain hardening, strain-to-failure, and failure
mode of nanocrystalline Ni-W films with mean grain sizes of 5, 15, and 90 nm
are studied using taper-free micropillars that are large enough to avoid
extrinsic size effects. Strengthening is observed with grain refinement, but
catastrophic failure through strain localization is found as well. Shear
banding is found to cause failure, resembling the deformation of metallic
glasses. Finally, we study the influence of grain boundary state by employing
heat treatments that relax nonequilibrium boundary structure but leave grain
size unchanged. A pronounced strengthening effect and increased strain
localization is observed after relaxation in the finer grained samples.


  In an influential critique of empirical practice, Freedman (2008) showed that
the linear regression estimator was biased for the analysis of randomized
controlled trials under the randomization model. Under Freedman's assumptions,
we derive exact closed-form bias corrections for the linear regression
estimator with and without treatment-by-covariate interactions. We show that
the limiting distribution of the bias corrected estimator is identical to the
uncorrected estimator, implying that the asymptotic gains from adjustment can
be attained without introducing any risk of bias. Taken together with results
from Lin (2013), our results show that Freedman's theoretical arguments against
the use of regression adjustment can be completely resolved with minor
modifications to practice.


  Two classes of measure-valued valuations on convex functions related to
Monge-Amp\`ere operators are investigated and classified. It is shown that the
space of all valuations with values in the space of complex Radon measures on
$\mathbb{R}^n$ that are locally determined, continuous, dually epi-translation
invariant as well as translation equivariant, is finite dimensional. Integral
representations of these valuations and a description in terms of mixed
Monge-Amp\`ere operators are established, as well as a characterization of
$\mathrm{SO}(n)$-equivariant valuations in terms of Hessian measures.


  Let $E(0,1)$ be a symmetric space on $(0,1)$ and $C_F$ be a symmetric ideal
of compact operators on the Hilbert space $\ell_2$ associated with a symmetric
sequence space $F$. We give several criteria for $E(0,1)$ and $ F$ so that
$E(0,1)$ does not embed into the ideal $C_F$, extending the result for the case
when $E(0,1)=L_p(0,1)$ and $F=\ell_p $, $1\le p<\infty$, due to Arazy and
Lindenstrauss.


  We study the leptogenesis scenario in models with multi-Higgs doublets. It is
pointed out that the washing-out process through the effective dimension five
interactions, which has not been taken into account seriously in the
conventional scenario, can be effective, and the resultant baryon asymmetry can
be exponentially suppressed. This fact implies new possible scenario where the
observed baryon asymmetry is the remnant of the washed out lepton asymmetry
which was originally much larger than the one in the conventional scenario. Our
new scenario is applicable to some neutrino mass matrix models which predict
too large CP-violating parameter and makes them viable through the washing-out
process.


  We have made a detailed study of the signals produced at LEP-2 from charged
scalar bosons whose dominant decay channels are into four fermions. The event
rates as well as kinematics of the final states are discussed when such scalars
are either pair-produced or are generated through a tree-level interaction
involving a charged scalar, the W and the Z. The backgrounds in both cases are
discussed. We also suggest the possibility of reconstructing the mass of such a
scalar at LEP-2.


  This paper addresses the detection of a stochastic process in noise from
irregular samples. We consider two hypotheses. The \emph{noise only} hypothesis
amounts to model the observations as a sample of a i.i.d. Gaussian random
variables (noise only). The \emph{signal plus noise} hypothesis models the
observations as the samples of a continuous time stationary Gaussian process
(the signal) taken at known but random time-instants corrupted with an additive
noise. Two binary tests are considered, depending on which assumptions is
retained as the null hypothesis. Assuming that the signal is a linear
combination of the solution of a multidimensional stochastic differential
equation (SDE), it is shown that the minimum Type II error probability
decreases exponentially in the number of samples when the False Alarm
probability is fixed. This behavior is described by \emph{error exponents} that
are completely characterized. It turns out that they are related with the
asymptotic behavior of the Kalman Filter in random stationary environment,
which is studied in this paper. Finally, numerical illustrations of our claims
are provided in the context of sensor networks.


  Bound states, such as $qq$ and $q\bar{q}$, may exist in the Quark Gluon
Plasma. As the system is at high density, the volume of the bound states may
evoke a reduction to the phase space. We introduce an extended bag model to
investigate qualitatively the volume effect on the properties of the system. We
find a limit temperature where the bound states start to be completely melted.


  Axion-like particles (ALPs) are hypothetical entities often invoked to solve
various problems in particle physics to cosmology. They are one of the most
promising candidates to explain the elusive dark matter. A way to search for
ALPs is through their effects on photons. In the presence of external magnetic
fields, ALPs and photons can convert into one another, leading to measurable
signals. In this contribution we present results of Monte Carlo simulations of
ALP-photon interconversion in magnetised environments. We focus on high-energy
gamma rays with TeV energies travelling over cosmological distances. We include
a full treatment of the intergalactic electromagnetic cascades triggered by the
gamma rays. Finally, we discuss the impact of this improved treatment of the
propagation for current and future ALP searches.


  A three-dimensional light-like foliation of a spacetime geometry is one
particular way of studying its light cone structure and has important
applications in numerical relativity. In this paper, we execute such a
foliation for the Kerr-Newman-AdS black hole geometry and compare it with the
lightlike foliations of the Kerr-AdS and Kerr-Newman black holes. We derive the
equations that govern this slicing and study their properties. In particular,
we find that these null hypersurfaces develop caustics inside the inner horizon
of the Kerr-Newman-AdS black hole, in strong contrast to the Kerr-AdS case. We
then take the ultra-spinning limit of the Kerr-Newman-AdS spacetime, leading to
what is known as a Super-Entropic black hole, and show that the null
hypersurfaces develop caustics at a finite distance outside the event horizon
of this black hole. As an application, we construct Kruskal coordinates for
both the Kerr-Newman-AdS black hole and its ultra-spinning counterpart.


  We consider the corrector equation from the stochastic homogenization of
uniformly elliptic finite-difference equations with random, possibly
non-symmetric coefficients. Under the assumption that the coefficients are
stationary and ergodic in the quantitative form of a Logarithmic Sobolev
inequality (LSI), we obtain optimal bounds on the corrector and its gradient in
dimensions $d \geq 2$. Similar estimates have recently been obtained in the
special case of diagonal coefficients making extensive use of the maximum
principle and scalar techniques. Our new method only invokes arguments that are
also available for elliptic systems and does not use the maximum principle. In
particular, our proof relies on the LSI to quantify ergodicity and on
regularity estimates on the derivative of the discrete Green's function in
weighted spaces.


  In this paper we derive the Anti de Sitter counterpart of the super-Maxwell
algebra presented recently by Bonanos et.\ al. Then we gauge this algebra and
derive the corresponding supergravity theory, which turns out to be described
by the standard N=1 supergravity lagrangian, up to topological terms.


  We prove several sharp one-sided pinching estimates for immersed and embedded
hypersurfaces evolving by various fully nonlinear, one-homogeneous curvature
flows by the method of Stampacchia iteration. These include sharp estimates for
the largest principal curvature and the inscribed curvature ('cylindrical
estimates') for flows by concave speeds and a sharp estimate for the exscribed
curvature for flows by convex speeds. Making use of a recent idea of Huisken
and Sinestrari, we then obtain corresponding estimates for ancient solutions.
In particular, this leads to various characterisations of the shrinking sphere
amongst ancient solutions of these flows.


  We prove necessary and sufficient conditions on a family of (generalised)
gridding matrices to determine when the corresponding permutation classes are
partially well-ordered. One direction requires an application of Higman's
Theorem and relies on there being only finitely many simple permutations in the
only non-monotone cell of each component of the matrix. The other direction is
proved by a more general result that allows the construction of infinite
antichains in any grid class of a matrix whose graph has a component containing
two or more non-monotone-griddable cells. The construction uses a
generalisation of pin sequences to grid classes, together with a number of
symmetry operations on the rows and columns of a gridding.


  The strictly isospectral double Darboux method is applied to the quantum Taub
model in order to generate a one-parameter family of strictly isospectral
potentials for this case. The family we build is based on a scattering
Wheeler-DeWitt solution first discussed by Ryan and collaborators that we
slightly modified according to a suggestion due to Dunster. The strictly
isospectral Taub potentials possess different (attenuated) scattering states
with respect to the original Taub potential


  We show that the Quantum Isometry Group(as introduced in \cite{goswami}) of
the n tori is the classical isometry group. Moreover, using a result in
\cite{bhowmick goswami}, we conclude that the Quantum Isometry group of the
noncommutative n tori is a Rieffel deformation of the Quantum Isometry Group of
the commutative n tori.


  We prove that, if an isospectral torus contains a discrete Schr\"odinger
operator with nonconstant potential, the shift dynamics on that torus cannot be
minimal. Consequently, we specify a generic sense in which finite unions of
nondegenerate closed intervals having capacity one are not the spectrum of any
reflectionless discrete Schr\"odinger operator. We also show that the only
reflectionless discrete Schr\"odinger operators having zero, one, or two
spectral gaps are periodic.


  The MAGIC stereoscopic system collected 69 hours of Crab Nebula data between
October 2009 and April 2011. Analysis of this data sample using the latest
improvements in the MAGIC stereoscopic software provided an unprecedented
precision of spectral and night-by-night light curve determination at gamma
rays. We derived a differential spectrum with a single instrument from 50 GeV
up to almost 30 TeV with 5 bins per energy decade. At low energies, MAGIC
results, combined with Fermi-LAT data, show a flat and broad Inverse Compton
peak. The overall fit to the data between 1 GeV and 30 TeV is not well
described by a log-parabola function. We find that a modified log-parabola
function with an exponent of 2.5 instead of 2 provides a good description of
the data ($\chi^2=35/26$). Using systematic uncertainties of red the MAGIC and
Fermi-LAT measurements we determine the position of the Inverse Compton peak to
be at (53 $\pm$ 3stat + 31syst -13syst) GeV, which is the most precise
estimation up to date and is dominated by the systematic effects. There is no
hint of the integral flux variability on daily scales at energies above 300 GeV
when systematic uncertainties are included in the flux measurement. We consider
three state- of-the-art theoretical models to describe the overall spectral
energy distribution of the Crab Nebula. The constant B-field model cannot
satisfactorily reproduce the VHE spectral measurements presented in this work,
having particular difficulty reproducing the broadness of the observed IC peak.
Most probably this implies that the assumption of the homogeneity of the
magnetic field inside the nebula is incorrect. On the other hand, the
time-dependent 1D spectral model provides a good fit of the new VHE results
when considering a 80 {\mu}G magnetic field. However, it fails to match the
data when including the morphology of the nebula at lower wavelengths.


  We present a novel scheme of structured light laser with an astigmatic mode
converter (AMC) as intracavity element, first enabling the generation of
Hermite-Gaussian (HG) modes with fully controlled two-dimensional (2D) indices
(m,n) and vortex beams carrying orbital angular momentum (OAM) directly from
cavity. The 2D tunability was realized by controlling the off-axis
displacements of both pump and intracavity AMC. The output HGm,n beam could be
externally converted into OAM beam with 2D tunable radial and azimuthal indices
(p,l). With the certain parameter control, vortex beam carrying OAM also could
be directly generated from the cavity. Our setup provides a compact and concise
structured light source. It has great potential in extending various
applications of optical tweezers, communications, and nonlinearity.


  The paper intends to perform a relevant study on the closed-end fund puzzle
in the perspective of an emerging market. Quarterly data of 36 closed-end
mutual funds traded in Dhaka Stock Exchange are collected over the sample
period of 2016 to 2019. Dependent and independent variables are mapped down by
exploring previous researches. Weight of top 10 investments, fund size, fund
age, fund maturity, turnover and dividend yield are taken as explanatory
variable to analyze the impact on CEF discount. A fixed effects panel
regression is performed on the data set with few diagnostic tests to ensure the
reliability of the analysis conducted. The results show that, the variable fund
size and fund maturity have a significant positive and turnover has a
significant negative impact on CEF discount while the impact of weight of top
10 investments, dividend yield and fund age are found insignificant.


  A photometric study of the Near Contact Binary (NCB) system V609 Aql reveals
it to be the westernmost star of a close double, with brightness variations and
implied parameters more extreme than those derived in an earlier photographic
study, in which images of the variable and companion were almost certainly
blended. The system's brightness variations exhibit deep primary eclipses
($\delta$ V = 1.04) and secondary eclipses ($\delta$ V = 0.44) matched to a
model fit with a derived orbital inclination of i = 84.8$\pm$0.2 degrees and
estimated component spectral types of F8-F9 and K2-K3. The primary overfills
its Roche lobe in the optimum eclipse solution, inconsistent with the
definition of NCBs. Period changes in the system are studied from 23 published
times of light minimum and 21 newly-established values: 18 from examination of
archival Harvard plates, and 3 from ASAS data and new CCD observations. O-C
variations from 1891 to 2007 exhibit a long-term parabolic trend indicative of
a period decrease, dP/dt = -(7.75$\pm$1.39) x 10^-8 d/yr, corresponding to mass
transfer to the secondary of (6.5$pm$1.2) x 10^-8 SM/yr. Superposed variations
may indicate fluctuations in the mass flow. The system is estimated to be ~513
pc distant.


  Search for stable high-pressure compounds in the Ti--H system reveals the
existence of titanium hydrides with new stoichiometries, including
Ibam-Ti$_2$H$_5$, I4/m-Ti$_5$H$_{13}$, I$\bar{4}$-Ti$_5$H$_{14}$, Fddd-TiH$_4$,
Immm-Ti$_2$H$_{13}$, P$\bar{1}$-TiH$_{12}$, and C2/m-TiH$_{22}$. Our
calculations predict I4/mmm $\rightarrow$ R$\bar{3}$m and I4/mmm $\rightarrow$
Cmma transitions in TiH and TiH$_2$, respectively. Phonons and the
electron--phonon coupling of all searched titanium hydrides are analyzed at
high pressure. It is found that Immm-Ti$_2$H$_{13}$ rather than the highest
hydrogen content C2/m-TiH$_{22}$, exhibits the highest superconducting critical
temperature T$_{c}$. The estimated T$_{c}$ of Immm-Ti$_2$H$_{13}$ and
C2/m-TiH$_{22}$ are respectively 127.4--149.4 K ($\mu^{*}$=0.1-0.15) at 350 GPa
and 91.3--110.2 K at 250 GPa by numerically solving the Eliashberg equations.
One of the effects of pressure on T$_{c}$ can be attributed to the softening
and hardening of phonons with increasing pressure.


  A constantly growing amount of information is available through the web.
Unfortunately, extracting useful content from this massive amount of data still
remains an open issue. The lack of standard data models and structures forces
developers to create adhoc solutions from the scratch. The figure of the expert
is still needed in many situations where developers do not have the correct
background knowledge. This forces developers to spend time acquiring the needed
background from the expert. In other directions, there are promising solutions
employing machine learning techniques. However, increasing accuracy requires an
increase in system complexity that cannot be endured in many projects. In this
work, we approach the web knowledge extraction problem using an expertcentric
methodology. This methodology defines a set of configurable, extendible and
independent components that permit the reutilisation of large pieces of code
among projects. Our methodology differs from similar solutions in its
expert-driven design. This design, makes it possible for subject-matter expert
to drive the knowledge extraction for a given set of documents. Additionally,
we propose the utilization of machine assisted solutions that guide the expert
during this process. To demonstrate the capabilities of our methodology, we
present a real use case scenario in which public procurement data is extracted
from the web-based repositories of several public institutions across Europe.
We provide insightful details about the challenges we had to deal with in this
use case and additional discussions about how to apply our methodology.


  This paper presents a new approach to the analysis of mixed processes
\[X_t=B_t+G_t,\qquad t\in[0,T],\] where $B_t$ is a Brownian motion and $G_t$ is
an independent centered Gaussian process. We obtain a new canonical innovation
representation of $X$, using linear filtering theory. When the kernel
\[K(s,t)=\frac{\partial^2}{\partial s\,\partial t}\mathbb{E}G_tG_s,\qquad s\ne
t\] has a weak singularity on the diagonal, our results generalize the
classical innovation formulas beyond the square integrable setting. For kernels
with stronger singularity, our approach is applicable to processes with
additional "fractional" structure, including the mixed fractional Brownian
motion from mathematical finance. We show how previously-known measure
equivalence relations and semimartingale properties follow from our canonical
representation in a unified way, and complement them with new formulas for
Radon-Nikodym densities.


  Magnetic fields of planets, stars and galaxies are generated by
self-excitation in moving electrically conducting fluids. Once produced,
magnetic fields can play an active role in cosmic structure formation by
destabilizing rotational flows that would be otherwise hydrodynamically stable.
For a long time, both hydromagnetic dynamo action as well as magnetically
triggered flow instabilities had been the subject of purely theoretical
research. Meanwhile, however, the dynamo effect has been observed in
large-scale liquid sodium experiments in Riga, Karlsruhe and Cadarache. In this
paper, we summarize the results of liquid metal experiments devoted to the
dynamo effect and various magnetic instabilities such as the helical and the
azimuthal magnetorotational instability and the Tayler instability. We discuss
in detail our plans for a precession-driven dynamo experiment and a large-scale
Tayler-Couette experiment using liquid sodium, and on the prospects to observe
magnetically triggered instabilities of flows with positive shear.


  Unsupervised video object segmentation (UVOS) aims at detecting the primary
objects in a given video sequence without any human interposing. Most existing
methods rely on two-stream architectures that separately encode the appearance
and motion information before fusing them to identify the target and generate
object masks. However, this pipeline is computationally expensive and can lead
to suboptimal performance due to the difficulty of fusing the two modalities
properly. In this paper, we propose a novel UVOS model called SimulFlow that
simultaneously performs feature extraction and target identification, enabling
efficient and effective unsupervised video object segmentation. Concretely, we
design a novel SimulFlow Attention mechanism to bridege the image and motion by
utilizing the flexibility of attention operation, where coarse masks predicted
from fused feature at each stage are used to constrain the attention operation
within the mask area and exclude the impact of noise. Because of the
bidirectional information flow between visual and optical flow features in
SimulFlow Attention, no extra hand-designed fusing module is required and we
only adopt a light decoder to obtain the final prediction. We evaluate our
method on several benchmark datasets and achieve state-of-the-art results. Our
proposed approach not only outperforms existing methods but also addresses the
computational complexity and fusion difficulties caused by two-stream
architectures. Our models achieve 87.4% J & F on DAVIS-16 with the highest
speed (63.7 FPS on a 3090) and the lowest parameters (13.7 M). Our SimulFlow
also obtains competitive results on video salient object detection datasets.


  In this paper, we define k-equivalence, a relation on graphs that relies on
their associated cellular algebras. We show that a k-Boson quantum walk cannot
distinguish pairs of graphs that are k- equivalent. The existence of pairs of
k-equivalent graphs has been shown by Ponomarenko et al. [2, 6]. This gives a
negative answer to a question posed by Gamble et al. [7].


  We describe a $\frac{3}{2}$-approximation algorithm for the Matching
Augmentation Problem, which is a special case of the weighted 2-edge-connected
spanning subgraph problem. This improves upon the previous best ratio
$\frac{13}{8}$.


  We study the first Steklov-Dirichlet eigenvalue on eccentric spherical shells
in $\mathbb{R}^{n+2}$ with $n\geq 1$, imposing the Steklov condition on the
outer boundary sphere, denoted by $\Gamma_S$, and the Dirichlet condition on
the inner boundary sphere. The first eigenfunction admits a Fourier--Gegenbauer
series expansion via the bispherical coordinates, where the
Dirichlet-to-Neumann operator on $\Gamma_S$ can be recursively expressed in
terms of the expansion coefficients arXiv:2309.09587. In this paper, we develop
a finite section approach for the Dirichlet-to-Neumann operator to approximate
the first Steklov--Dirichlet eigenvalue on eccentric spherical shells. We prove
the exponential convergence of this approach by using the variational
characterization of the first eigenvalue. Furthermore, based on the convergence
result, we propose a numerical computation scheme as an extension of the
two-dimensional result in [Hong et al., Ann. Mat. Pura Appl., 2022] to general
dimensions. We provide numerical examples of the first Steklov-Dirichlet
eigenvalue on eccentric spherical shells with various geometric configurations.


  We show how for every integer n one can explicitly construct n distinct plane
quartics and one hyperelliptic curve over the complex numbers all of whose
Jacobians are isomorphic to one another as abelian varieties without
polarization. When we say that the curves can be constructed ``explicitly'', we
mean that the coefficients of the defining equations of the curves are simple
rational expressions in algebraic numbers in R whose minimal polynomials over Q
can be given exactly and whose decimal approximations can be given to as many
places as is necessary to distinguish them from their conjugates. We also prove
a simply-stated theorem that allows one to decide whether or not two plane
quartics over the complex numbers, each with a pair of commuting involutions,
are isomorphic to one another.


  Given the lidar measurements from an autonomous vehicle, we can project the
points and generate a sparse depth image. Depth completion aims at increasing
the resolution of such a depth image by infilling and interpolating the sparse
depth values. Like most existing approaches, we make use of camera images as
guidance in very sparse or occluded regions. In addition, we propose a temporal
algorithm that utilizes information from previous timesteps using recurrence.
In this work, we show how a state-of-the-art method PENet can be modified to
benefit from recurrency. Our algorithm achieves state-of-the-art results on the
KITTI depth completion dataset while adding only less than one percent of
additional overhead in terms of both neural network parameters and floating
point operations. The accuracy is especially improved for faraway objects and
regions containing a low amount of lidar depth samples. Even in regions without
any ground truth (like sky and rooftops) we observe large improvements which
are not captured by the existing evaluation metrics.


  Quadratic gravity is a UV completion of general relativity, which also solves
the hierarchy problem. The presence of 4 derivatives implies via the
Ostrogradsky theorem that the $classical$ Hamiltonian is unbounded from below.
Here we solve this issue by showing that the relevant solutions are not
unstable but metastable. When the energies are much below a threshold (that is
high enough to describe the whole cosmology) runaways are avoided. Remarkably,
the chaotic inflation theory of initial conditions ensures that such bound is
satisfied and we work out testable implications for the early universe. The
possible instability occurring when the bound is violated not only is
compatible with cosmology but would also explain why we live in a homogeneous
and isotropic universe.


  We give a one dimensional octonionic representation of the different Clifford
algebra Cliff(5,5)\sim Cliff(9,1), Cliff(6,6)\sim Cliff(10,2) and lastly
Cliff(7,6)\sim Cliff(10,3) which can be given by (8x8) real matrices taking
into account some suitable manipulation rules.


  Let $p\in(0,1]$, $q\in(0,\infty]$ and $A$ be a general expansive matrix on
$\mathbb{R}^n$. The authors introduce the anisotropic Hardy-Lorentz space
$H^{p,q}_A(\mathbb{R}^n)$ associated with $A$ via the non-tangential grand
maximal function and then establish its various real-variable characterizations
in terms of the atomic or the molecular decompositions, the radial or the
non-tangential maximal functions, or the finite atomic decompositions. All
these characterizations except the $\infty$-atomic characterization are new
even for the classical isotropic Hardy-Lorentz spaces on $\mathbb{R}^n$. As
applications, the authors first prove that $H^{p,q}_A(\mathbb{R}^n)$ is an
intermediate space between $H^{p_1,q_1}_A(\mathbb{R}^n)$ and
$H^{p_2,q_2}_A(\mathbb{R}^n)$ with $0<p_1<p<p_2<\infty$ and
$q_1,\,q,\,q_2\in(0,\infty]$, and also between $H^{p,q_1}_A(\mathbb{R}^n)$ and
$H^{p,q_2}_A(\mathbb{R}^n)$ with $p\in(0,\infty)$ and $0<q_1<q<q_2\leq\infty$
in the real method of interpolation. The authors then establish a criterion on
the boundedness of sublinear operators from $H^{p,q}_A(\mathbb{R}^n)$ into a
quasi-Banach space; moreover, the authors obtain the boundedness of
$\delta$-type Calder\'{o}n-Zygmund operators from $H^p_A(\mathbb{R}^n)$ to the
weak Lebesgue space $L^{p,\infty}(\mathbb{R}^n)$ (or
$H^{p,\infty}_A(\mathbb{R}^n)$) in the critical case, from
$H_A^{p,q}(\mathbb{R}^n)$ to $L^{p,q}(\mathbb{R}^n)$ (or
$H_A^{p,q}(\mathbb{R}^n)$) with $\delta\in(0,\frac{\ln\lambda_-}{\ln b}]$,
$p\in(\frac1{1+\delta},1]$ and $q\in(0,\infty]$, as well as the boundedness of
some Calder\'{o}n-Zygmund operators from $H_A^{p,q}(\mathbb{R}^n)$ to
$L^{p,\infty}(\mathbb{R}^n)$, where $b:=|\det A|$,
$\lambda_-:=\min\{|\lambda|:\ \lambda\in\sigma(A)\}$ and $\sigma(A)$ denotes
the set of all eigenvalues of $A$.


  Clustering is one of the most fundamental tasks in machine learning.
Recently, deep clustering has become a major trend in clustering techniques.
Representation learning often plays an important role in the effectiveness of
deep clustering, and thus can be a principal cause of performance degradation.
In this paper, we propose a clustering-friendly representation learning method
using instance discrimination and feature decorrelation. Our
deep-learning-based representation learning method is motivated by the
properties of classical spectral clustering. Instance discrimination learns
similarities among data and feature decorrelation removes redundant correlation
among features. We utilize an instance discrimination method in which learning
individual instance classes leads to learning similarity among instances.
Through detailed experiments and examination, we show that the approach can be
adapted to learning a latent space for clustering. We design novel
softmax-formulated decorrelation constraints for learning. In evaluations of
image clustering using CIFAR-10 and ImageNet-10, our method achieves accuracy
of 81.5% and 95.4%, respectively. We also show that the softmax-formulated
constraints are compatible with various neural networks.


  Final-state kinematic imbalances are measured in mesonless production of
$\nu_\mu + A \to \mu^- + p + X$ in the MINERvA tracker. Initial- and
final-state nuclear effects are probed using the direction of the $\mu^-$-p
transverse momentum imbalance and the initial-state momentum of the struck
neutron. Differential cross sections are compared to predictions based on
current approaches to medium modeling. These models under-predict the cross
section at intermediate intranuclear momentum transfers that generally exceed
the Fermi momenta. As neutrino interaction models need to correctly incorporate
the effect of the nucleus in order to predict neutrino energy resolution in
oscillation experiments, this result points to a region of phase space where
additional cross section strength is needed in current models, and demonstrates
a new technique that would be suitable for use in fine grained liquid argon
detectors where the effect of the nucleus may be even larger.


  In this paper, we introduce a time-continuous production model that enables
random machine failures, where the failure probability depends historically on
the production itself. This bidirectional relationship between historical
failure probabilities and production is mathematically modeled by the theory of
piecewise deterministic Markov processes (PDMPs). On this way, the system is
rewritten into a Markovian system such that classical results can be applied.
In addition, we present a suitable solution, taken from machine reliability
theory, to connect past production and the failure rate. Finally, we
investigate the behavior of the presented model numerically in examples by
considering sample means of relevant quantities and relative frequencies of
number of repairs.


  We present a theory for band-tuned metal-insulator transitions based on the
Kubo formalism. Such a transition exhibits scaling of the resistivity curves,
in the regime where $T\tau >1$ or $\mu \tau>1$, where $\tau$ is the scattering
time and $\mu$ the chemical potential. At the critical value of the chemical
potential, the resistivity diverges as a power law, $R_c \sim 1/T$.
Consequently, on the metallic side there is a regime with negative $dR/dT$,
which is often misinterpreted as insulating. We show that scaling and this
`fake insulator' regime is observed in a wide range of experimental systems. In
particular, we show that Mooij correlations in high-temperature metals with
negative $dR/dT$ can be quantitatively understood with our scaling theory in
the presence of $T$-linear scattering.


  In this research, we investigate the high-dimensional linear contextual
bandit problem where the number of features $p$ is greater than the budget $T$,
or it may even be infinite. Differing from the majority of previous works in
this field, we do not impose sparsity on the regression coefficients. Instead,
we rely on recent findings on overparameterized models, which enables us to
analyze the performance the minimum-norm interpolating estimator when data
distributions have small effective ranks. We propose an explore-then-commit
(EtC) algorithm to address this problem and examine its performance. Through
our analysis, we derive the optimal rate of the ETC algorithm in terms of $T$
and show that this rate can be achieved by balancing exploration and
exploitation. Moreover, we introduce an adaptive explore-then-commit (AEtC)
algorithm that adaptively finds the optimal balance. We assess the performance
of the proposed algorithms through a series of simulations.


  The development of efficient and effective evolutionary multi-objective
optimization (EMO) algorithms has been an active research topic in the
evolutionary computation community. Over the years, many EMO algorithms have
been proposed. The existing EMO algorithms are mainly developed based on the
final population framework. In the final population framework, the final
population of an EMO algorithm is presented to the decision maker. Thus, it is
required that the final population produced by an EMO algorithm is a good
solution set. Recently, the use of solution selection framework was suggested
for the design of EMO algorithms. This framework has an unbounded external
archive to store all the examined solutions. A pre-specified number of
solutions are selected from the archive as the final solutions presented to the
decision maker. When the solution selection framework is used, EMO algorithms
can be designed in a more flexible manner since the final population is not
necessarily to be a good solution set. In this paper, we examine the design of
MOEA/D under these two frameworks. We use an offline genetic algorithm-based
hyper-heuristic method to find the optimal configuration of MOEA/D in each
framework. The DTLZ and WFG test suites and their minus versions are used in
our experiments. The experimental results suggest the possibility that a more
flexible, robust and high-performance MOEA/D algorithm can be obtained when the
solution selection framework is used.


  We will summarize some aspects of the scenario of a strongly interacting
symmetry breaking sector via which the longitudinal vector boson scattering
becomes strong. We will examine the feasibility of observing such strong $W_L
W_L$ signal at the future hadronic supercolliders.


  Using the computational resources of an untrusted third party to crack a
password hash can pose a high number of privacy and security risks. The act of
revealing the hash digest could in itself negatively impact both the data
subject who created the password, and the data controller who stores the hash
digest. This paper solves this currently open problem by presenting a
Privacy-Preserving Password Cracking protocol (3PC), that prevents the third
party cracking server from learning any useful information about the hash
digest, or the recovered cleartext. This is achieved by a tailored anonymity
set of decoy hashes, based on the concept of predicate encryption, where we
extend the definition of a predicate function, to evaluate the output of a one
way hash function. The protocol allows the client to maintain plausible
deniability where the real choice of hash digest cannot be proved, even by the
client itself. The probabilistic information the server obtains during the
cracking process can be calculated and minimized to a desired level. While in
theory cracking a larger set of hashes would decrease computational speed, the
3PC protocol provides constant-time lookup on an arbitrary list size, bounded
by the input/output operation per second (IOPS) capabilities of the third party
server, thereby allowing the protocol to scale efficiently. We demonstrate
these claims both theoretically and in practice, with a real-life use case
implemented on an FPGA architecture.


  In this work we present a formal theoretical framework for assessing and
analyzing two classes of malevolent action towards generic Artificial
Intelligence (AI) systems. Our results apply to general multi-class classifiers
that map from an input space into a decision space, including artificial neural
networks used in deep learning applications. Two classes of attacks are
considered. The first class involves adversarial examples and concerns the
introduction of small perturbations of the input data that cause
misclassification. The second class, introduced here for the first time and
named stealth attacks, involves small perturbations to the AI system itself.
Here the perturbed system produces whatever output is desired by the attacker
on a specific small data set, perhaps even a single input, but performs as
normal on a validation set (which is unknown to the attacker). We show that in
both cases, i.e., in the case of an attack based on adversarial examples and in
the case of a stealth attack, the dimensionality of the AI's decision-making
space is a major contributor to the AI's susceptibility. For attacks based on
adversarial examples, a second crucial parameter is the absence of local
concentrations in the data probability distribution, a property known as
Smeared Absolute Continuity. According to our findings, robustness to
adversarial examples requires either (a) the data distributions in the AI's
feature space to have concentrated probability density functions or (b) the
dimensionality of the AI's decision variables to be sufficiently small. We also
show how to construct stealth attacks on high-dimensional AI systems that are
hard to spot unless the validation set is made exponentially large.


  Diagrammatic reasoning (DR) is pervasive in human problem solving as a
powerful adjunct to symbolic reasoning based on language-like representations.
The research reported in this paper is a contribution to building a general
purpose DR system as an extension to a SOAR-like problem solving architecture.
The work is in a framework in which DR is modeled as a process where subtasks
are solved, as appropriate, either by inference from symbolic representations
or by interaction with a diagram, i.e., perceiving specified information from a
diagram or modifying/creating objects in a diagram in specified ways according
to problem solving needs. The perceptions and actions in most DR systems built
so far are hand-coded for the specific application, even when the rest of the
system is built using the general architecture. The absence of a general
framework for executing perceptions/actions poses as a major hindrance to using
them opportunistically -- the essence of open-ended search in problem solving.
Our goal is to develop a framework for executing a wide variety of specified
perceptions and actions across tasks/domains without human intervention. We
observe that the domain/task-specific visual perceptions/actions can be
transformed into domain/task-independent spatial problems. We specify a spatial
problem as a quantified constraint satisfaction problem in the real domain
using an open-ended vocabulary of properties, relations and actions involving
three kinds of diagrammatic objects -- points, curves, regions. Solving a
spatial problem from this specification requires computing the equivalent
simplified quantifier-free expression, the complexity of which is inherently
doubly exponential. We represent objects as configuration of simple elements to
facilitate decomposition of complex problems into simpler and similar
subproblems. We show that, if the symbolic solution to a subproblem can be
expressed concisely, quantifiers can be eliminated from spatial problems in
low-order polynomial time using similar previously solved subproblems. This
requires determining the similarity of two problems, the existence of a mapping
between them computable in polynomial time, and designing a memory for storing
previously solved problems so as to facilitate search. The efficacy of the idea
is shown by time complexity analysis. We demonstrate the proposed approach by
executing perceptions and actions involved in DR tasks in two army
applications.


  The set of equivalence classes of cobounded actions of a group on different
hyperbolic metric spaces carries a natural partial order. The resulting poset
thus gives rise to a notion of the "best" hyperbolic action of a group as the
largest element of this poset, if such an element exists. We call such an
action a largest hyperbolic action. While hyperbolic groups admit largest
hyperbolic actions, we give evidence in this paper that this phenomenon is rare
for non-hyperbolic groups. In particular, we prove that many families of groups
of geometric origin do not have largest hyperbolic actions, including for
instance many 3-manifold groups and most mapping class groups. Our proofs use
the quasi-trees of metric spaces of Bestvina--Bromberg--Fujiwara, among other
tools. In addition, we give a complete characterization of the poset of
hyperbolic actions of Anosov mapping torus groups, and we show that mapping
class groups of closed surfaces of genus at least two have hyperbolic actions
which are comparable only to the trivial action.


  The paper considers the application of feedback control to orbital transfer
maneuvers subject to constraints on the spacecraft thrust and on avoiding the
collision with the primary body. Incremental reference governor (IRG)
strategies are developed to complement the nominal Lyapunov controller, derived
based on Gauss Variational Equations, and enforce the constraints. Simulation
results are reported that demonstrate the successful constrained orbital
transfer maneuvers with the proposed approach. A Lyapunov function based IRG
and a prediction-based IRG are compared. While both implementations
successfully enforce the constraints, a prediction-based IRG is shown to result
in faster maneuvers.


  We propose a new approach for universal lossless text compression, based on
grammar compression. In the literature, a target string $T$ has been compressed
as a context-free grammar $G$ in Chomsky normal form satisfying $L(G) = \{T\}$.
Such a grammar is often called a \emph{straight-line program} (SLP). In this
paper, we consider a probabilistic grammar $G$ that generates $T$, but not
necessarily as a unique element of $L(G)$. In order to recover the original
text $T$ unambiguously, we keep both the grammar $G$ and the derivation tree of
$T$ from the start symbol in $G$, in compressed form. We show some simple
evidence that our proposal is indeed more efficient than SLPs for certain
texts, both from theoretical and practical points of view.


  We present a simple proof that the competitive ratio of any randomized online
matching algorithm for the line is at least $\sqrt{\log_2(n\!+\!1)}/12$ for all
$n=2^i\!-\!1: i\in\mathbb{N}$.


  The simplest and often most effective way of parallelizing the training of
complex machine learning models is to execute several training instances on
multiple machines, possibly scanning the hyperparameter space to optimize the
underlying statistical model and the learning procedure. Often, such a meta
learning procedure is limited by the ability of accessing securely a common
database organizing the knowledge of the previous and ongoing trials.
Exploiting opportunistic GPUs provided in different environments represents a
further challenge when designing such optimization campaigns. In this
contribution we discuss how a set of RestAPIs can be used to access a dedicated
service based on INFN Cloud to monitor and possibly coordinate multiple
training instances, with gradient-less optimization techniques, via simple HTTP
requests. The service, named Hopaas (Hyperparameter OPtimization As A Service),
is made of web interface and sets of APIs implemented with a FastAPI back-end
running through Uvicorn and NGINX in a virtual instance of INFN Cloud. The
optimization algorithms are currently based on Bayesian techniques as provided
by Optuna. A Python front-end is also made available for quick prototyping. We
present applications to hyperparameter optimization campaigns performed
combining private, INFN Cloud and CINECA resources.


  To address the computational complexity associated with state-vector
simulation for quantum circuits, we propose a combination of advanced
techniques to accelerate circuit execution. Quantum gate matrix caching reduces
the overhead of repeated applications of the Kronecker product when applying a
gate matrix to the state vector by storing decomposed partial matrices for each
gate. Circuit splitting divides the circuit into sub-circuits with fewer gates
by constructing a dependency graph, enabling parallel or sequential execution
on disjoint subsets of the state vector. These techniques are implemented using
the PyTorch machine learning framework. We demonstrate the performance of our
approach by comparing it to other PyTorch-compatible quantum state-vector
simulators. Our implementation, named Qandle, is designed to seamlessly
integrate with existing machine learning workflows, providing a user-friendly
API and compatibility with the OpenQASM format. Qandle is an open-source
project hosted on GitHub https://github.com/gstenzel/qandle and PyPI
https://pypi.org/project/qandle/ .


  In this review, we discuss the impact of the development of lasers on
ultracold atoms and molecules and their applications. After a brief historical
review of laser cooling and Bose-Einstein condensation, we present important
applications of ultra cold atoms, including time and frequency metrology, atom
interferometry and inertial sensors, atom lasers, simulation of condensed
matter systems, production and study of strongly correlated systems, and
production of ultracold molecules.


  To reduce the resource demand of neural network (NN) inference systems, it
has been proposed to use approximate memory, in which the supply voltage and
the timing parameters are tuned trading accuracy with energy consumption and
performance. Tuning these parameters aggressively leads to bit errors, which
can be tolerated by NNs when bit flips are injected during training. However,
bit flip training, which is the state of the art for achieving bit error
tolerance, does not scale well; it leads to massive overheads and cannot be
applied for high bit error rates (BERs). Alternative methods to achieve bit
error tolerance in NNs are needed, but the underlying principles behind the bit
error tolerance of NNs have not been reported yet. With this lack of
understanding, further progress in the research on NN bit error tolerance will
be restrained.
  In this study, our objective is to investigate the internal changes in the
NNs that bit flip training causes, with a focus on binarized NNs (BNNs). To
this end, we quantify the properties of bit error tolerant BNNs with two
metrics. First, we propose a neuron-level bit error tolerance metric, which
calculates the margin between the pre-activation values and batch normalization
thresholds. Secondly, to capture the effects of bit error tolerance on the
interplay of neurons, we propose an inter-neuron bit error tolerance metric,
which measures the importance of each neuron and computes the variance over all
importance values. Our experimental results support that these two metrics are
strongly related to bit error tolerance.


  The dynamic scaling properties of the one dimensional Burgers equation are
expected to change with the inclusion of additional conserved degrees of
freedom. We study this by means of 1-D driven lattice gas models that conserve
both mass and momentum. The most elementary version of this is the
Arndt-Heinzel-Rittenberg (AHR) process, which is usually presented as a two
species diffusion process, with particles of opposite charge hopping in
opposite directions and with a variable passing probability.
  From the hydrodynamics perspective this can be viewed as two coupled Burgers
equations, with the number of positive and negative momentum quanta
individually conserved. We determine the dynamic scaling dimension of the AHR
process from the time evolution of the two-point correlation functions, and
find numerically that the dynamic critical exponent is consistent with simple
Kardar-Parisi-Zhang (KPZ) type scaling. We establish that this is the result of
perfect screening of fluctuations in the stationary state. The two-point
correlations decay exponentially in our simulations and in such a manner that
in terms of quasi-particles, fluctuations fully screen each other at coarse
grained length scales. We prove this screening rigorously using the analytic
matrix product structure of the stationary state. The proof suggests the
existence of a topological invariant. The process remains in the KPZ
universality class but only in the sense of a factorization, as $({KPZ})^2$.
The two Burgers equations decouple at large length scales due to the perfect
screening.


  We measured the activation energy gap of the N = 2 bubble states using
tilted-field technique in two GaAs/AlGaAs samples with Corbino geometry, and
observed monotonically decreasing energy gaps with the increasing total
magnetic field. The observed tilt angle dependence is discussed based on
magneto-orbital coupling and finite-size effect. We also found a bubble state
at an uncommon filling factor of 4.67, with an unanticipated energy gap of up
to 5.35 K and opposite in-plane field dependence.


  We estimate the frequency of singular matrices and of matrices of a given
rank whose entries are parametrised by arbitrary polynomials over the integers
and modulo a prime $p$. In particular, in the integer case, we improve a recent
bound of V. Blomer and J. Li (2022).


  In this study, we present the ro-vibrationally resolved gas-phase spectrum of
the diatomic molecule TiO around 1000\,cm$^{-1}$. Molecules were produced in a
laser ablation source by vaporizing a pure titanium sample in the atmosphere of
gaseous nitrous oxide. Adiabatically expanded gas, containing TiO, formed a
supersonic jet and was probed perpendicularly to its propagation by infrared
radiation from quantum cascade lasers. Fundamental bands of $^{46-50}$TiO and
vibrational hotbands of $^{48}$TiO are identified and analyzed. In a
mass-independent fitting procedure combining the new infrared data with pure
rotational and electronic transitions from the literature, a Dunham-like
parameterization is obtained. From the present data set, the multi-isotopic
analysis allows to determine the spin-rotation coupling constant $\gamma$ and
the Born-Oppenheimer correction coefficient $\Delta_{\rm U_{10}}^{\mathrm{Ti}}$
for the first time. The parameter set enables to calculate the Born-Oppenheimer
correction coefficients $\Delta_{\rm U_{02}}^{\mathrm{Ti}}$ and $\Delta_{\rm
U_{02}}^{\mathrm{O}}$. In addition, the vibrational transition moments for the
observed vibrational transitions are reported.


  We have investigated the accelerating behaviour of the universe in $f(Q,T)$
gravity in an isotropic and homogeneous space-time. We have initially derive
the dynamical parameters in the general form of $f(Q,T)=\alpha Q^{m}+\beta T$
[Xu et al., Eur. Phys. J. C, \textbf{79}, 708 (2019)] and then split it into
two cases (i) one with $m=1$ and the (ii) other with $\beta=0$. In the first
case, it reduces to the linear form of the functional $f(Q,T)$ and second case
leads to the higher power of the nonmetricity $Q$. In an assumed form of the
hyperbolic scale factor, the models are constructed and its evolutionary
behaviours are studied. The geometrical parameters as well the equation of
state parameter are obtained and found to be in the preferred range of the
cosmological observations. Marginal variation has been noticed in the behaviour
of $\omega$ and $\omega_{eff}$ at present time. The violation of strong energy
conditions in both the cases are shown. The dynamical system analysis for the
models has been performed.


  Machine learning is a modern approach to problem-solving and task automation.
In particular, machine learning is concerned with the development and
applications of algorithms that can recognize patterns in data and use them for
predictive modeling. Artificial neural networks are a particular class of
machine learning algorithms and models that evolved into what is now described
as deep learning. Given the computational advances made in the last decade,
deep learning can now be applied to massive data sets and in innumerable
contexts. Therefore, deep learning has become its own subfield of machine
learning. In the context of biological research, it has been increasingly used
to derive novel insights from high-dimensional biological data. To make the
biological applications of deep learning more accessible to scientists who have
some experience with machine learning, we solicited input from a community of
researchers with varied biological and deep learning interests. These
individuals collaboratively contributed to this manuscript's writing using the
GitHub version control platform and the Manubot manuscript generation toolset.
The goal was to articulate a practical, accessible, and concise set of
guidelines and suggestions to follow when using deep learning. In the course of
our discussions, several themes became clear: the importance of understanding
and applying machine learning fundamentals as a baseline for utilizing deep
learning, the necessity for extensive model comparisons with careful
evaluation, and the need for critical thought in interpreting results generated
by deep learning, among others.


  General purpose segmentation models are able to generate (semantic)
segmentation masks from a variety of prompts, including visual (points, boxed,
etc.) and textual (object names) ones. In particular, input images are
pre-processed by an image encoder to obtain embedding vectors which are later
used for mask predictions. Existing adversarial attacks target the end-to-end
tasks, i.e. aim at altering the segmentation mask predicted for a specific
image-prompt pair. However, this requires running an individual attack for each
new prompt for the same image. We propose instead to generate prompt-agnostic
adversarial attacks by maximizing the $\ell_2$-distance, in the latent space,
between the embedding of the original and perturbed images. Since the encoding
process only depends on the image, distorted image representations will cause
perturbations in the segmentation masks for a variety of prompts. We show that
even imperceptible $\ell_\infty$-bounded perturbations of radius
$\epsilon=1/255$ are often sufficient to drastically modify the masks predicted
with point, box and text prompts by recently proposed foundation models for
segmentation. Moreover, we explore the possibility of creating universal, i.e.
non image-specific, attacks which can be readily applied to any input without
further computational cost.


  We generalize both the notion of polynomial functions on Lie groups and the
notion of horizontally affine maps on Carnot groups. We fix a subset $S$ of the
algebra $\mathfrak g$ of left-invariant vector fields on a Lie group $\mathbb
G$ and we assume that $S$ Lie generates $\mathfrak g$. We say that a function
$f:\mathbb G\to \mathbb R$ (or more generally a distribution on $\mathbb G$) is
$S$-polynomial if for all $X\in S$ there exists $k\in \mathbb N$ such that the
iterated derivative $X^k f$ is zero in the sense of distributions.
  First, we show that all $S$-polynomial functions (as well as distributions)
are represented by analytic functions and, if the exponent $k$ in the previous
definition is independent on $X\in S$, they form a finite-dimensional vector
space.
  Second, if $\mathbb G$ is connected and nilpotent we show that $S$-polynomial
functions are polynomial functions in the sense of Leibman. The same result may
not be true for non-nilpotent groups.
  Finally, we show that in connected nilpotent Lie groups, being polynomial in
the sense of Leibman, being a polynomial in exponential chart, and the
vanishing of mixed derivatives of some fixed degree along directions of
$\mathfrak g$ are equivalent notions.


  This paper studies permutation tests for regression parameters in a time
series setting, where the time series is assumed stationary but may exhibit an
arbitrary (but weak) dependence structure. In such a setting, it is perhaps
surprising that permutation tests can offer any type of inference guarantees,
since permuting of covariates can destroy its relationship with the response.
Indeed, the fundamental assumption of exchangeability of errors required for
the finite-sample exactness of permutation tests, can easily fail. However, we
show that permutation tests may be constructed which are asymptotically valid
for a wide class of stationary processes, but remain exact when exchangeability
holds. We also consider the problem of testing for no monotone trend and we
construct asymptotically valid permutation tests in this setting as well.


  Significant progress has been made in designing magnetic fields that provide
excellent confinement of the guiding enter trajectories of alpha particles
using quasisymmetry (QS). Given the reduction in this transport channel, we
assess the impact of resonant Alfv\'{e}n eigenmodes (AEs) on the guiding center
motion. The AE amplitudes are chosen to be consistent with experimental
measurements and large-scale simulations. We evaluate the drift resonance
condition, phase-space island width, and island overlap criterion for
quasisymmetric configurations. Kinetic Poincar\'{e} plots elucidate features of
the transport, including stiff transport above a critical perturbation
amplitude. Our analysis highlights key departures from the AE-driven transport
in tokamaks, such as the avoidance of phase-space island overlap in
quasihelical configurations and the enhanced transport due to wide phase-space
islands in low magnetic shear configurations. In configurations that are closer
to QS, with QS deviations $\delta B/B_0 \lesssim 10^{-3}$, the transport is
primarily driven by the AE, while configurations that are further from QS,
$\delta B/B_0 \sim 10^{-2}$, experience significant transport due to the
QS-breaking fields in addition to the AE.


  The Cosmic Microwave Background (CMB) anisotropy power on the largest angular
scales observed both by WMAP and COBE DMR appears to be lower than the one
predicted by the standard model of cosmology with almost scale free primordial
perturbations arising from a period of inflation
\cite{cobe,Bennett:2003bz,Spergel,Peiris}. One can either interpret this as a
manifestation of cosmic variance or as a physical effect that requires an
explanation. We discuss various mechanisms that could be responsible for the
suppression of such low $\ell$ multipoles. Features in the late time evolution
of metric fluctuations may do this via the integral Sachs-Wolfe effect. Another
possibility is a suppression of power at large scales in the primordial
spectrum induced by a fast rolling stage in the evolution of the inflaton field
at the beginning of the last 65 e-folds of inflation. We illustrate this effect
in a simple model of inflation and fit the resulting CMB spectrum to the
observed temperature-temperature (TT) power spectrum. We find that the WMAP
observations suggest a cutoff at $k_c=4.9^{+1.3}_{-1.6}\times
10^{-4}$Mpc$^{-1}$ at 68% confidence, while only an upper limit of $k_c <
7.4\times 10^{-4}$Mpc$^{-1}$ at 95%. Thus, although it improves the fit of the
data, the presence of a cutoff in power spectrum is only required at a level
close to $2\sigma$. This is obtained with a prior which corresponds to equal
distribution wrt $k_c$. We discuss how other choices (such as an equal
distribution wrt $\ln k_c$ which is natural in the context of inflation) can
affect the statistical interpretation.


  Hawkes process (HP) is a point process with a conditionally dependent
intensity function. This paper defines the tempered fractional Hawkes process
(TFHP) by time-changing the HP with an inverse tempered stable subordinator. We
obtained results that generalize the fractional Hawkes process defined in
Hainaut (2020) to a tempered version which has \textit{semi-heavy tailed}
decay. We derive the mean, the variance, covariance and the governing
fractional difference-differential equations of the TFHP. Additionally, we
introduce the generalized fractional Hawkes process (GFHP) by time-changing the
HP with the inverse L\'evy subordinator. This definition encompasses all
potential (inverse L\'evy) time changes as specific instances. We also explore
the distributional characteristics and the governing difference-differential
equation of the one-dimensional distribution for the GFHP.


  It is an important task to reconstruct surfaces from 3D point clouds. Current
methods are able to reconstruct surfaces by learning Signed Distance Functions
(SDFs) from single point clouds without ground truth signed distances or point
normals. However, they require the point clouds to be dense, which dramatically
limits their performance in real applications. To resolve this issue, we
propose to reconstruct highly accurate surfaces from sparse point clouds with
an on-surface prior. We train a neural network to learn SDFs via projecting
queries onto the surface represented by the sparse point cloud. Our key idea is
to infer signed distances by pushing both the query projections to be on the
surface and the projection distance to be the minimum. To achieve this, we
train a neural network to capture the on-surface prior to determine whether a
point is on a sparse point cloud or not, and then leverage it as a
differentiable function to learn SDFs from unseen sparse point cloud. Our
method can learn SDFs from a single sparse point cloud without ground truth
signed distances or point normals. Our numerical evaluation under widely used
benchmarks demonstrates that our method achieves state-of-the-art
reconstruction accuracy, especially for sparse point clouds.


  The optimal power flow (OPF) problem seeks to control power generation/demand
to optimize certain objectives such as minimizing the generation cost or power
loss in the network. It is becoming increasingly important for distribution
networks, which are tree networks, due to the emergence of distributed
generation and controllable loads. In this paper, we study the OPF problem in
tree networks. The OPF problem is nonconvex. We prove that after a "small"
modification to the OPF problem, its global optimum can be recovered via a
second-order cone programming (SOCP) relaxation, under a "mild" condition that
can be checked apriori. Empirical studies justify that the modification to OPF
is "small" and that the "mild" condition holds for the IEEE 13-bus distribution
network and two real-world networks with high penetration of distributed
generation.


  Using Hirota's method, solitons are constructed for affine Toda field
theories based on the simply-laced affine algebras. By considering
automorphisms of the simply-laced Dynkin diagrams, solutions to the remaining
algebras, twisted as well as untwisted, are deduced.


  Out-of-distribution (OOD) detection aims at enhancing standard deep neural
networks to distinguish anomalous inputs from original training data. Previous
progress has introduced various approaches where the in-distribution training
data and even several OOD examples are prerequisites. However, due to privacy
and security, auxiliary data tends to be impractical in a real-world scenario.
In this paper, we propose a data-free method without training on natural data,
called Class-Conditional Impressions Reappearing (C2IR), which utilizes image
impressions from the fixed model to recover class-conditional feature
statistics. Based on that, we introduce Integral Probability Metrics to
estimate layer-wise class-conditional deviations and obtain layer weights by
Measuring Gradient-based Importance (MGI). The experiments verify the
effectiveness of our method and indicate that C2IR outperforms other post-hoc
methods and reaches comparable performance to the full access (ID and OOD)
detection method, especially in the far-OOD dataset (SVHN).


  Conceived to host 5 kg of xenon at a pressure of 15 bar in the fiducial
volume, the NEXT- White (NEW) apparatus is currently the largest high pressure
xenon gas TPC using electroluminescent amplification in the world. It is also a
1:2 scale model of the NEXT-100 detector scheduled to start searching for
$\beta\beta 0\nu$ decays in 136Xe in 2019. Both detectors measure the energy of
the event using a plane of photomultipliers located behind a transparent
cathode. They can also reconstruct the trajectories of charged tracks in the
dense gas of the TPC with the help of a plane of silicon photomultipliers
located behind the anode. A sophisticated gas system, common to both detectors,
allows the high gas purity needed to guarantee a long electron lifetime.
NEXT-White has been operating since October 2017 at the Canfranc Underground
Laboratory (LSC), in Spain. This paper describes the detector and associated
infrastructures.


  We study the usual stochastic order between probability measures on
preordered topological abelian groups, focusing on asymptotic and catalytic
versions of the order. In the asymptotic version, a measure $\mu$ dominates a
measure $\nu$ if the i.i.d.~random walk generated by $\mu$ first-order
dominates the one generated by $\nu$ at late times. In the catalytic version,
$\mu$ dominates $\nu$ if there is a third $\tau$ such that the convolution $\mu
\ast \tau$ first-order dominates $\nu \ast \tau$.
  Provided that the preorder on $G$ is induced by a suitably large positive
cone and that both measures are compactly supported Radon, our main result
gives a sufficient condition for asymptotic and catalytic dominance to hold in
terms of a family of inequalities closely related to the cumulant-generating
functions. While this sufficient condition requires these inequalities to be
strict, the non-strict versions of these inequalities are easily seen to be
necessary. In this sense, our result gives conditions that are necessary and
sufficient in generic cases. This result has been known for $G = \mathbb{R}$,
but is new already for $\mathbb{R}^n$ with $n > 1$. It is a direct application
of a recently proven theorem of real algebra, namely a
\emph{Vergleichsstellensatz} for preordered semirings.
  We finally use our result to derive a formula for the rate at which the
probabilities of a random walk decay \emph{relative} to those of another, now
for walks on a preordered topological vector space with compactly supported
Radon steps. Taking one of these walks to be deterministic reproduces a version
of Cram\'er's large deviation theorem for infinite dimensions.


  We present a rigorous theoretical description of excitonic dynamics in
molecular light-harvesting aggregates photoexcited by weak-intensity radiation
of arbitrary properties. While the interaction with light is included up to the
second order, the treatment of the excitation-environment coupling is exact and
results in an exact expression for the reduced excitonic density matrix that is
manifestly related to the spectroscopic picture of the photoexcitation process.
This expression takes fully into account the environmental reorganization
processes triggered by the two interactions with light. This is particularly
important for slow environments and/or strong excitation-environment coupling.
Within the exponential decomposition scheme, we demonstrate how our result can
be recast as the hierarchy of equations of motion (HEOM) that explicitly and
consistently includes the photoexcitation step. We analytically describe the
environmental reorganization dynamics triggered by a delta-like excitation of a
single chromophore, and demonstrate how our HEOM, in appropriate limits,
reduces to the Redfield equations comprising a pulsed photoexcitation and the
nonequilibrium Foerster theory. We also discuss the relation of our formalism
to the combined Born-Markov-HEOM approaches in the case of excitation by
thermal light.


  We present infinite sequences of excluded minors for both the class of
lifted-graphic matroids and the class of frame matroids.


  An intriguing silhouette of a small dust patch can be seen against the disk
of the S0 galaxy NGC 3269 in the Antlia cluster in optical images. The images
do not provide any clue as to whether the patch is a local Jupiter mass-scale
cloudlet or a large extragalactic dust complex. We aim to resolve the nature of
this object: is it a small Galactic cloudlet or an extragalactic dust complex?
ALMA and APEX spectroscopy and Gemini GMOS long-slit spectroscopy were used to
measure the velocity of the patch and the NGC 3269 disk radial velocity curve.
A weak 16$\pm$2.5 km/s wide $^{12}$CO (2-1) T$_{MB}$ 19$\pm$2.5 mK line in a
2.0" by 2.12" beam associated with the object was detected with ALMA. The
observed heliocentric velocity, V$_r$,hel = 3878$\pm$5.0km/s, immediately
establishes the extragalactic nature of the object. The patch velocity is
consistent with the velocity of the nucleus of NGC 3269, but not with the
radial velocity of the NGC 3269 disk of the galaxy at its position. The
$\sim$4" angular size of the patch corresponds to a linear size of $\sim$1 kpc
at the galaxy's Hubble distance of (d/50.7 Mpc)$^2$ Msun, while the attenuation
derived from the optical spectrum implies a dust mass of $\sim$2.6x10$^4$
(d/50.7 Mpc)$^2$ Msun. The derived attenuation ratio A'$_B$/(A'$_B$-A'$_R$) of
1.6$\pm$0.11 is substantially lower than the corresponding value for the mean
Milky Way extinction curve for point sources (2.3). We established the
extragalactic nature of the patch, but its origin remains elusive. One
possibility is that the dust patch is left over from the removal of
interstellar matter in NGC 3269 through the interaction with its neighbour, NGC
3268.