  We compute all two-loop master integrals which are required for the
evaluation of next-to-leading order QCD corrections in Higgs boson production
via gluon fusion. Many two-loop amplitudes for 2 -> 1 processes in the Standard
Model and beyond can be expressed in terms of these integrals using automated
reduction techniques. These integrals also form a subset of the master
integrals for more complicated 2 -> 2 amplitudes with massive propagators in
the loops. As a first application, we evaluate the two-loop amplitude for Higgs
boson production in gluon fusion via a massive quark. Our result is the first
independent check of the calculation of Spira, Djouadi, Graudenz and Zerwas. We
also present for the first time the two-loop amplitude for gg -> h via a
massive squark.


  Statistical depth functions are a standard tool in nonparametric statistics
to extend order-based univariate methods to the multivariate setting. Since
there is no universally accepted total order for fuzzy data (even in the
univariate case) and there is a lack of parametric models, a fuzzy extension of
depth-based methods is very interesting. In this paper, we adapt projection
depth and $L^{r}$-type depth to the fuzzy setting, studying their properties
and illustrating their behaviour with a real data example.


  We propose an effective approach for generating significant amount of
entanglement and asymmetric steering between photon and phonon in a cavity
magnomechanical system which consists of a microwave cavity and a yttrium iron
garnet sphere. By driving the magnon mode of the yttrium iron garnet sphere
with blue-detuned microwave field, the magnon mode can be acted as an
engineered resevoir cools the Bogoliubov modes of microwave cavity mode and
mechanical mode via beam-splitter-like interaction. In this way, the microwave
cavity mode and mechanical mode are driven to two-mode squeezed states in the
stationary limit. In particular, strong two-way and one-way asymmetric quantum
steering between the photon and phonon modes can be obtained with even equal
dissipation. It is very different from the conventional proposal of asymmetric
quantum steering, where additional unbalanced losses or noises on the two
subsystems has been imposed. Our finding may be significant to expand our
understanding of the essential physics of asymmetric steering and extend the
potential application of the cavity spintronics to device-independent quantum
key distribution.


  Resummation, ie. reorganization of perturbative series, can result in an
inconsistent perturbation theory, unless the counterterms are reorganized in an
appropriate way. In this paper two methods are presented for resummation of
counterterms: one is a direct method where the necessary counterterms are
constructed order by order; the other is a general one, based on
renormalization group arguments. We demonstrate at one hand that, in mass
independent schemes, mass resummation can be performed by gap equations
renormalized prior to the substitution of the resummed mass for its argument.
On the other hand it is shown that any (momentum-independent) form of mass and
coupling constant resummation is compatible with renormalization, and one can
explicitly construct the corresponding counterterms.


  In this paper we present a new dataset and user simulator e-QRAQ (explainable
Query, Reason, and Answer Question) which tests an Agent's ability to read an
ambiguous text; ask questions until it can answer a challenge question; and
explain the reasoning behind its questions and answer. The User simulator
provides the Agent with a short, ambiguous story and a challenge question about
the story. The story is ambiguous because some of the entities have been
replaced by variables. At each turn the Agent may ask for the value of a
variable or try to answer the challenge question. In response the User
simulator provides a natural language explanation of why the Agent's query or
answer was useful in narrowing down the set of possible answers, or not. To
demonstrate one potential application of the e-QRAQ dataset, we train a new
neural architecture based on End-to-End Memory Networks to successfully
generate both predictions and partial explanations of its current understanding
of the problem. We observe a strong correlation between the quality of the
prediction and explanation.


  A recent attempt to make sense of scalars in AdS with "Neumann boundary
conditions" outside of the usual BF-window $-(d/2)^2 < m^2 l^2 < -(d/2)^2 + 1$
led to pathologies including (depending on the precise context) either IR
divergences or the appearance of ghosts. Here we argue that such ghosts may be
banished by imposing a UV cutoff. It is also possible to achieve this goal in
certain UV completions. An example is the above AdS theory with a radial cutoff
supplemented by particular boundary conditions on the cutoff surface. In this
case we explicitly identify a region of parameter space for which the theory is
ghost free. At low energies, this theory may be interpreted as the standard
dual CFT (defined with "Dirichlet" boundary conditions) interacting with an
extra scalar via an irrelevant interaction. We also discuss the relationship to
recent works on holographic fermi surfaces and quantum criticality.


  From optical spectropolarimetry of Cygnus A we conclude that the scattering
medium in the ionization cones in Cygnus A is moving outward at a speed of
170+-34 km/s, and that the required momentum can be supplied by the radiation
pressure of an average quasar. Such a process could produce a structure
resembling the observed ionization cones, which are thought to result from
shadowing by a circumnuclear dust torus. We detect a polarized red wing in the
[O III] emission lines arising from the central kiloparsec of Cygnus A. This
wing is consistent with line emission created close to the boundary of the
broad-line region.


  In order to interpret H2 (molecular hydrogen) quasar absorption line
observations of damped Ly-alpha systems (DLAs) and sub-DLAs, we model their H2
abundance as a function of dust-to-gas ratio, including H2 self-shielding and
dust extinction against dissociating photons. Then, we constrain the physical
state of gas by using H2 data. Using H2 excitation data for DLA with H2
detections, we derive a gas density 1.5 < log n [cm^-3] < 2.5, temperature 1.5
< log T [K] < 3, and internal UV radiation field (in units of the Galactic
value) 0.5 < log \chi < 1.5. We then find that the observed relation between
molecular fraction and dust-to-gas ratio of the sample is naturally explained
by the above conditions. However, it is still possible that H2 deficient DLAs
and sub-DLAs with H2 fractions less than ~ 10^-6 are in a more diffuse and
warmer state. The efficient photodissociation by the internal UV radiation
field explains the extremely small H2 fraction (< 10^-6) observed for \kappa <
1/30 (\kappa is the dust-to-gas ratio in units of the Galactic value); H2
self-shielding causes a rapid increase and the large variations of H2 abundance
for \kappa > 1/30. We finally propose an independent method to estimate the
star formation rates of DLAs from H2 abundances; such rates are then critically
compared with those derived from other proposed methods. The implications for
the contribution of DLAs to the cosmic star formation history are briefly
discussed.


  In the partonic (or light-front) description of relativistic systems the
electromagnetic form factors are expressed in terms of frame-independent charge
and magnetization densities in transverse space. This formulation allows one to
identify the chiral components of nucleon structure as the peripheral densities
at transverse distances b = O(M_pi^{-1}) and compute them in a parametrically
controlled manner. A dispersion relation connects the large-distance behavior
of the transverse charge and magnetization densities to the spectral functions
of the Dirac and Pauli form factors near the two-pion threshold at timelike t =
4 M_pi^2. Using relativistic chiral effective field theory in the leading-order
approximation, we (a) derive the asymptotic behavior (Yukawa tail) of the
isovector transverse densities in the "chiral" region b = O(M_pi^{-1}) and the
"molecular" region b = O(M_N^2/M_pi^3); (b) perform the heavy-baryon expansion;
(c) explain the relative magnitude of the peripheral charge and magnetization
densities in a simple mechanical picture; (d) include Delta intermediate states
and study the densities in the large-N_c limit of QCD; (e) quantify the spatial
region where the chiral components are numerically dominant; (f) calculate the
chiral divergences of the b^2-weighted moments of the transverse densities
(charge and magnetic radii) and determine their spatial support. Our approach
provides a concise formulation of the spatial structure of the nucleon's chiral
component and offers new insights into basic properties of the chiral
expansion. It relates the information extracted from low-t elastic form factors
to the generalized parton distributions probed in peripheral high-energy
scattering processes.


  We consider a scenario with axions/axion-like particles Chern-Simons gravity
coupling, such that gravitational waves can be produced directly from axion
wave parametric resonance in the early universe after inflation. This axion
gravity term is less constrained compared to the well-searched axion photon
coupling and can provide a direct and efficient production channel for
gravitational waves. Such stochastic gravitational waves can be detected by
either space/ground-based gravitational wave detectors or pulsar timing arrays
for a broad range of axion masses and decay constants.


  In order to interpret and predict the behavior and properties of fusion
plasma, accurate cross sections for electronic processes in collisions between
plasma impurities and atomic hydrogen are required. In this work, we
investigate the electron capture, target excitation, and ionization processes
occurring in collision of ${\rm N}^{4+}$ with atomic hydrogen in a broad energy
domain ranging from 0.06 to 225 keV/u. We consider ${\rm N}^{4+}$ ground state
${\rm N}^{4+} (2s)$ and also ${\rm N}^{4+} (2p)$ since the impurities in the
edge plasma environment may be excited due to collisions with electrons and
ions/atoms. Total and partial cross sections in both spin-averaged and
spin-resolved cases are calculated using a two-active-electron semiclassical
asymptotic-state close-coupling approach. For electron capture cross sections
the present results show the best overall agreement with available experimental
data for both total and partial cross sections, and the origins of observed
discrepancies are discussed. Furthermore, we provide new data for target
excitation and ionization processes, which are essential to improve our
understanding of this relevant collision system. The International Atomic
Energy Agency (IAEA) has recently published a report highlighting the
importance and the scarcity of such data. Our work therefore will allow a
better modeling and thus understanding of magnetically confined fusion plasma.


  This paper constructs two conforming finite element grad grad and elasticity
complexes on the cuboid meshes. For the finite element grad grad complex, an
$H^2$ conforming finite element space, an $\boldsymbol{H}(\operatorname{curl};
\mathbb{S})$ conforming finite element space, an
$\boldsymbol{H}(\operatorname{div}; \mathbb{T})$ conforming finite element
space and an $\boldsymbol{L}^2$ finite element space are constructed. Further,
a finite element complex with reduced regularity is also constructed, whose
degrees of freedom for the three diagonal components are coupled. For the
finite element elasticity complex, a vector $\boldsymbol{H}^1$ conforming space
and an $\boldsymbol{H}(\operatorname{curl}\operatorname{curl}^{T}; \mathbb{S})$
conforming space are constructed. Combining with an existing
$\boldsymbol{H}(\operatorname{div}; \mathbb{S}) \cap
\boldsymbol{H}(\operatorname{div}\operatorname{div}; \mathbb{S})$ element and
$\boldsymbol{H}(\operatorname{div}; \mathbb{S})$ element, respectively, these
finite element spaces form two different finite element elasticity complexes.
The exactness of all the finite element complexes is proved.


  We consider a simple experimental setup, based on a harmonic confinement,
where a Bose-Einstein condensate and a thermal cloud of weakly interacting
alkali atoms are trapped in two different vessels connected by a narrow
channel. Using the classical field approximation, as described in J. Phys. B
40, R1 (2007) and optimized in Phys. Rev. A 81, 013629 (2010) for an arbitrary
trapping potential, we theoretically investigate the analog of the celebrated
superfluid helium fountain effect. We show that this thermo-mechanical effect
might indeed be observed in this system. By analyzing the dynamics of the
system, we are able to identify the superfluid and normal components of the
flow as well as to distinguish the condensate fraction from the superfluid
component. We show that the superfluid component can easily flow from the
colder vessel to the hotter one while the normal component is practically
blocked in the latter.


  In applied mechanics Reye's law (1860) establishes, via energy arguments,
that the mass of the debris produced by dry friction in the contact of rigid
bodies is proportional to the work done by friction forces. This result has
long been used for the determination of the distribution of pressure in the
contact of rigid bodies, and hence for the design of brakes. In this work I
show that, when bodies losing mass due to friction are treated, as they should,
as variable mass systems, a relationship analogous to the relativistic mass
formula is recovered. This result suggests that mathematical structures typical
of relativistic physics could have been discovered prior to 1905, without
making any reference to electromagnetism, group theory or the speed of light.
Also this result could point to the existence of a physical theory depending on
two constants, the speed of light (Reye's constant) and a universal frictional
deceleration with respect to an absolute frame. The limit of the theory for
vanishing friction would give Special Relativity, as the absolute frame would
become unobservable, while the limit for the speed of light going to infinity
would lead to Aristotelian mechanics, i.e. a classical mechanics type theory
presenting universal friction. Finally, I present a reference frame
transformation that displays these features and I apply the theory to some open
cosmological problems.


  The world is changing fast, and so is the space sector. Planning for large
scientific experiments two decades ahead may no longer be the most sensible
approach. I develop the argument that large science experiments are becoming
comparable to terrestrial civil infrastructures in terms of cost. As a result,
these should incorporate plans for a return on investment (or impact, not
necessarily economic), require a different approach for inter-division
coordination within the European Space Agency(ESA), and a broader participation
of all society stakeholders (civil society representatives, and the broader
public). Defining which experiments will be relevant two decades ahead adds
rigidity and quenches creativity to the development of cutting edge science and
technology. This is likely to discourage both senior and earlier career
professionals into supporting such long-term (and often precarious) plans. A
more sensible strategy would be increasing the rate of smaller well understood
experiments, engage more society sectors in the development of a truly
space-bound infrastructure, and formulate a strategy more in tune with the
challenges faced by our society and planet. We argue that such strategy would
lead to equally large -- even larger -- scale experiments in the same
time-scale, while providing economic returns and a common sense of purpose. A
basic but aggressive road map is outlined.


  In this paper we preform a systematic study of the tau flavored dark matter
model by introducing two kinds of mediators (a scalar doublet and a charged
scalar singlet). The electromagnetic properties of the dark matter, as well as
their implications in dark matter direct detections, are analyzed in detail.
The model turns out contributing a significant radiative correction to the tau
lepton mass, in addition to loosing the tension between the measured dark
matter relic density and constraints of dark matter direct detections. The loop
corrections can be ${\cal O}(10\%)$ of the total tau mass. Signal rates of the
Higgs measurements from the LHC in the $h\to\tau \tau$ and $h\to \gamma \gamma$
channels, relative to the Standard Model expectations, can be explained in this
model.


  We review the current status of neutrino cosmology, focusing mainly on the
question of the absolute values of neutrino masses and the possibility of a
cosmological neutrino lepton asymmetry.


  A new Doppler radar initial orbit determination algorithm with embedded
uncertainty quantification capabilities is presented. The method is based on a
combination of Gauss' and Lambert's solvers. The whole process is carried out
in the Differential Algebra framework, which provides the Taylor expansion of
the state estimate with respect to the measurements' uncertainties. This
feature makes the approach particularly suited for handling data association
problems. A comparison with the Doppler integration method is performed using
both simulated and real data. The proposed approach is shown to be more
accurate and robust, and particularly suited for short-arc observations.


  Triboelectric nanogenerator (TENG), a class of mechanical to electrical
energy transducers, has emerged as a promising solution to self-power Internet
of Things (IoT) sensors, wearable electronics, etc. The use of synchronous
switched energy extraction circuits (EECs) as an interface between TENG and
battery load can deliver multi-fold energy gain over simple minded Full Wave
Rectification (FWR). This paper presents a detailed analysis of Parallel and
Series Synchronous Switched Harvesting on Inductor (P-SSHI and S-SSHI) EECs to
derive the energy delivered to the battery load and compare it with the
standard FWR (a 3rd circuit) in a common analytical framework, under both
realistic conditions, and also ideal conditions. Further, the optimal value of
battery load to maximize output and upper bound beyond which charging is not
feasible are derived for all the three considered circuits. These closed-form
results derived with general TENG electrical parameters and first-order circuit
non-idealities shed light on the physics of the modeling and guide the choice
and design of EECs for any given TENG. The derived analytical results are
verified against PSpice based simulation results as well as the experimentally
measured values.


  Since its start in 2014, the lightweight open source Python library Gammapy
has come a long way to become a popular data analysis package for high-energy
astrophysics. Selected as the official CTAO Science Analysis tool, it is also
an approved analysis software within the H.E.S.S. and MAGIC collaborations. The
first long-term version, Gammapy v1.0 was released on late 2022. It is
compliant with several well-established data conventions in high-energy
astrophysics, and provides serialised data products that are interoperable with
other software. Event lists and instrument response functions curated within
the same format from various instruments can be reduced to data binned in
energy, time or spatial coordinates. Thereafter, the flux and morphology of one
or more gamma-ray sources can be estimated using Poisson maximum likelihood
fitting and assuming a variety of spectral, temporal and spatial models. Flux
points, likelihood profiles and light curves extractions are supported. Complex
user defined likelihoods and models can also be implemented. In this
contribution, we will highlight the main features of Gammapy v1.0, including
data reduction and analysis examples from different space and ground-based
instruments, applications of various background rejection techniques, and a
simultaneous fitting across multiple instruments with astrophysical models. We
will also present our plans for the future, showcasing new features such as the
support of different event types, unbinned likelihood analysis, spectral
unfolding and transient source detections. In addition to an improved API with
distributed computing for scalable analysis, enhanced support for all-sky
instruments like Fermi-LAT and HAWC is foreseen.


  We analyze and study the effects of locality on the fault-tolerance threshold
for quantum computation. We analytically estimate how the threshold will depend
on a scale parameter r which estimates the scale-up in the size of the circuit
due to encoding. We carry out a detailed semi-numerical threshold analysis for
concatenated coding using the 7-qubit CSS code in the local and `nonlocal'
setting. First, we find that the threshold in the local model for the [[7,1,3]]
code has a 1/r dependence, which is in correspondence with our analytical
estimate. Second, the threshold, beyond the 1/r dependence, does not depend too
strongly on the noise levels for transporting qubits. Beyond these results, we
find that it is important to look at more than one level of concatenation in
order to estimate the threshold and that it may be beneficial in certain
places, like in the transportation of qubits, to do error correction only
infrequently.


  This manuscript portrays optimization as a process. In many practical
applications the environment is so complex that it is infeasible to lay out a
comprehensive theoretical model and use classical algorithmic theory and
mathematical optimization. It is necessary as well as beneficial to take a
robust approach, by applying an optimization method that learns as one goes
along, learning from experience as more aspects of the problem are observed.
This view of optimization as a process has become prominent in varied fields
and has led to some spectacular success in modeling and systems that are now
part of our daily lives.


  We study the partition function of three-dimensional ${\mathcal N}=4$
superconformal Chern-Simons theories of the circular quiver type, which are
natural generalizations of the ABJM theory, the worldvolume theory of
M2-branes. In the ABJM case, it was known that the perturbative part of the
partition function sums up to the Airy function as
$Z(N)=e^{A}C^{-1/3}\mathrm{Ai}[C^{-1/3}(N-B)]$ with coefficients $C$, $B$ and
$A$ and that for the non-perturbative part the divergences coming from the
coefficients of worldsheet instantons and membrane instantons cancel among
themselves. We find that many of the interesting properties in the ABJM theory
are extended to the general superconformal Chern-Simons theories. Especially,
we find an explicit expression of $B$ for general ${\mathcal N}=4$ theories, a
conjectural form of $A$ for a special class of theories, and cancellation in
the non-perturbative coefficients for the simplest theory next to the ABJM
theory.


  This project aimed to design, simulate, and implement a two-axis inertially
stabilised platform (ISP) for use in astronomical applications. It aimed to
approximate the stabilisation of a Meade ETX-90 3.5" compound telescope at
low-cost using a mechanical assembly designed to geometrically and inertially
model the telescope. A set of system specifications was developed to guide
design decisions and to provide an analysis framework against which the
performance of the implemented system was compared. The electro-mechanical
structure of the ISP was designed and manufactured, the associated electrical
systems were specified and configured, an image processing script capable of
detecting and locating the centre of the Moon in a camera field-of-view was
written, a complete simulation model for the system was developed and used to
design various classical controllers for the ISP control system. These
controllers were implemented on a STM32F051 microcontroller and a user
interface was written in LabVIEW to facilitate intuitive user control of the
system and perform datalogging of the system runtime data.


  We use the DBI action from string theory and holography to study the
magneto-resistance at quantum criticality with hyperscaling violation. We find
and analyze a rich class of scaling behaviors for the magneto-resistance. A
special case describes the scaling results found in pnictides by Hayers et al.
in~\cite{analytis}.


  If one tries to view de Sitter as a true (as opposed to a meta-stable)
vacuum, there is a tension between the finiteness of its entropy and the
infinite-dimensionality of its Hilbert space. We invetsigate the viability of
one proposal to reconcile this tension using $q$-deformation. After defining a
differential geometry on the quantum de Sitter space, we try to constrain the
value of the deformation parameter by imposing the condition that in the
undeformed limit, we want the real form of the (inherently complex) quantum
group to reduce to the usual SO(4,1) of de Sitter. We find that this forces $q$
to be a real number. Since it is known that quantum groups have
finite-dimensional representations only for $q=$ root of unity, this suggests
that standard $q$-deformations cannot give rise to finite dimensional Hilbert
spaces, ruling out finite entropy for q-deformed de Sitter.


  Comparison of their chemical compositions shows, to first order, a good
agreement between the cometary and interstellar abundances. However, a complex
O-bearing organic molecule, ethylene glycol (CH$_{2}$OH)$_{2}$, seems to depart
from this correlation because it was not easily detected in the interstellar
medium although it proved to be rather abundant with respect to other O-bearing
species in comet Hale-Bopp. Ethylene glycol thus appears, together with the
related molecules glycolaldehyde CH$_{2}$OHCHO and ethanol CH$_{3}$CH$_{2}$OH,
as a key species in the comparison of interstellar and cometary ices as well as
in any discussion on the formation of cometary matter. We focus here on the
analysis of ethylene glycol in the nearest and best studied hot core-like
region, Orion-KL. We use ALMA interferometric data because high spatial
resolution observations allow us to reduce the line confusion problem with
respect to single-dish observations since different molecules are expected to
exhibit different spatial distributions. Furthermore, a large spectral
bandwidth is needed because many individual transitions are required to
securely detect large organic molecules. Confusion and continuum subtraction
are major issues and have been handled with care. We have detected the aGg'
conformer of ethylene glycol in Orion-KL. The emission is compact and peaks
towards the Hot Core close to the main continuum peak, about 2" to the
south-west; this distribution is notably different from other O-bearing
species. Assuming optically thin lines and local thermodynamic equilibrium, we
derive a rotational temperature of 145 K and a column density of 4.6 10$^{15}$
cm$^{-2}$. The limit on the column density of the gGg' conformer is five times
lower.


  [Abridged] We compute the virial mass of the central black hole (M) and the
luminosity-to-mass (L/M) ratio of ~ 300 low-z quasars and luminous Seyfert 1
nuclei. We analyze: (1) whether radio-quiet and radio-loud objects show
systematic differences in terms of M and L/M; (2) the influence of M and L/M on
the shape of the H-beta broad component line profile; (3) the significance of
the so-called "blue outliers" i.e., sources showing a significant blueshift of
the [OIII] 4959, 5007 lines with respect to the narrow component of H-beta
which is used as an estimator of the quasar reference frame. We show that M and
L/M distributions for RQ and RL sources are likely different for samples
matched in luminosity and redshift. Line profile comparisons for median spectra
computed over narrow ranges of M and L/M indicate that a Lorentz function
provides a better fit for higher L/M sources and a double Gaussian for lower
L/M values. A second (redshifted) Gaussian component at low L/M appears as a
red asymmetry frequently observed in radio-loud and radio-quiet sources with
broader (FWHM > 4000 km/s) H-beta broad component profiles. This component
becomes stronger in larger mass and lower L/M sources. No specific influence of
radio loudness on the H-beta broad component profile is detected, although
equivalent widths of H-beta broad component and especially of [OIII] 4959,5007
are larger for radio-loud sources. We identify five more "blue outlier"
sources. Since these sources are, on average, one magnitude brighter than other
AGNs with similar mass, they are accreting at an Eddington ratio that is 2-3
times higher. We hint at evolutionary effects that explain some of these
results and reinforce the "Eigenvector 1" correlations.


  We discuss the structure of infrared singularities in on-shell QCD amplitudes
with massive partons and present a general factorization formula in the limit
of small parton masses. The factorization formula gives rise to an all-order
exponentiation of both, the soft poles in dimensional regularization and the
large collinear logarithms of the parton masses. Moreover, it provides a
universal relation between any on-shell amplitude with massive external partons
and its corresponding massless amplitude. For the form factor of a heavy quark
we present explicit results including the fixed-order expansion up to three
loops in the small mass limit. For general scattering processes we show how our
constructive method applies to the computation of all singularities as well as
the constant (mass-independent) terms of a generic massive n-parton QCD
amplitude up to the next-to-next-to-leading order corrections.


  A neutron star in an inspiraling binary system is tidally deformed by its
companion, and the effect leaves a measurable imprint on the emitted
gravitational waves. While the tidal interaction falls within the regime of
static tides during the early stages of inspiral, a regime of dynamical tides
takes over in the later stages. The description of dynamical tides found in the
literature makes integral use of a spectral representation of the tidal
deformation, in which it is expressed as a sum over the star's normal modes of
vibration. This description is deeply rooted in Newtonian fluid mechanics and
gravitation, and we point out that considerable obstacles manifest themselves
in an extension to general relativity. To remedy this we propose an
alternative, mode-less description of dynamical tides that can be formulated in
both Newtonian and relativistic mechanics. Our description is based on a
time-derivative expansion of the tidal dynamics. The tidal deformation is
characterized by two sets of Love numbers: the static Love numbers $k_\ell$ and
the dynamic Love numbers $\ddot{k}_\ell$. These are computed here for
polytropic stellar models in both Newtonian gravity and general relativity. The
time-derivative expansion of the tidal dynamics seems to preclude any attempt
to capture an approach to resonance, which occurs when the frequency of the
tidal field becomes equal to a normal-mode frequency. To overcome this
limitation we propose a pragmatic extension of the time-derivative expansion
which does capture an approach to resonance. We demonstrate that with this
extension, our formulation of dynamical tides should be just as accurate as the
$f$-mode truncation of the mode representation, in which the sum over modes is
truncated to a single term involving the star's fundamental mode of vibration.


  V1298 Tau hosts at least four planets. Since its discovery, this system has
been a target of intensive photometric and spectroscopic monitoring. The
characterisation of its architecture and planets' fundamental properties turned
out to be very challenging so far. The determination of the orbital ephemeris
of the outermost planet V1298 Tau $e$ remains an open question. Only two
transits have been detected so far by $Kepler/K2$ and TESS, allowing for a grid
of reference periods to be tested with new observations, without excluding the
possibility of transit timing variations. Observing a third transit would allow
to better constrain the orbital period, and would also help determining an
accurate radius of V1298 Tau $e$ because the former transits showed different
depths. We observed V1298 Tau with the CHEOPS space telescope to search for a
third transit of planet $e$ within observing windows that have been selected in
order to test three of the shortest predicted orbital periods. We also
collected ground-based observations to verify the result found with CHEOPS. We
reanalysed $Kepler/K2$ and TESS light curves to test how the results derived
from these data are affected by alternative photometric extraction and
detrending methods. We report the detection with CHEOPS of a transit that could
be attributed to V1298 Tau $e$. If so, that result implies that the orbital
period calculated from fitting a linear ephemeris to the three available
transits is close to $\sim45$ days. Results from the ground-based follow-up
marginally support this possibility. We found that $\textit{i}$) the transit
observed by CHEOPS has a longer duration compared to that of the transits
observed by $Kepler/K2$ and TESS; $\textit{ii}$) the transit observed by TESS
is $>30\%$ deeper than that of $Kepler/K2$ and CHEOPS, and deeper than the
measurement previously reported in the literature, according to our reanalysis.


  We study the effect of a power law drift on Brownian motion in the positive
half-line, where the order of the drift at 0 and infinity is different.


  On June 20, 2020, the LIGO-Virgo collaboration announced the discovery of
GW190814, a gravitational wave event originating from a binary system merger
between a black hole of mass $M_1 = 23.2^{+1.1} _ {-1.0}M_\odot$ and an
unidentified object with a mass of $M_2 = 2.59^{+0.08} _ {-0.09}M_\odot$. This
second object would be either the heaviest neutron star or lightest black hole
observed to date. Here we investigate the possibility of the $\sim 2.6M_\odot$
object being a primordial black hole (PBH). We find that a primordial black
hole explanation to GW190814 is unlikely as it is limited by the formation rate
of the primary stellar progenitor and the observed merger rates of
$\mathcal{O}(20)M_\odot$ massive black hole pairs.


  Reflection gratings enable light coupling to optical cavities without
transmission through substrates. Gratings that have three ports and are mounted
in second-order Littrow configuration even allow the coupling to high-finesse
cavities using low diffraction efficiencies. In contrast to conventional
transmissive cavity couplers, however, the phase of the diffracted light
depends on the lateral position of the grating, which introduces an additional
noise coupling. Here we experimentally demonstrate that this kind of noise
cancels out once both diffracted output ports of the grating are combined. We
achieve the same signal-to-shot-noise ratio as for a conventional coupler. From
this perspective, 3-port grating couplers in second-order Littrow configuration
remain a valuable approach to reducing optical absorption of cavity coupler
substrates in future gravitational wave detectors.


  Non-autoregressive automatic speech recognition (ASR) has become a mainstream
of ASR modeling because of its fast decoding speed and satisfactory result. To
further boost the performance, relaxing the conditional independence assumption
and cascading large-scaled pre-trained models are two active research
directions. In addition to these strategies, we propose a lexical-aware
non-autoregressive Transformer-based (LA-NAT) ASR framework, which consists of
an acoustic encoder, a speech-text shared encoder, and a speech-text shared
decoder. The acoustic encoder is used to process the input speech features as
usual, and the speech-text shared encoder and decoder are designed to train
speech and text data simultaneously. By doing so, LA-NAT aims to make the ASR
model aware of lexical information, so the resulting model is expected to
achieve better results by leveraging the learned linguistic knowledge. A series
of experiments are conducted on the AISHELL-1, CSJ, and TEDLIUM 2 datasets.
According to the experiments, the proposed LA-NAT can provide superior results
than other recently proposed non-autoregressive ASR models. In addition, LA-NAT
is a relatively compact model than most non-autoregressive ASR models, and it
is about 58 times faster than the classic autoregressive model.


  We have obtained near-infrared images and mid-infrared spectra of the
starburst core of the dwarf Wolf-Rayet galaxy He 2-10. We find that the
infrared continuum and emission lines are concentrated in a flattened ellipse
3-4'' or 150 pc across which may show where a recent accretion event has
triggered intense star formation. The ionizing radiation from this cluster has
an effective temperature of 40,000 K, corresponding to $30M_\odot$ stars, and
the starburst is $0.5-1.5 \times 10^7$ years old.


  We present both photometry and spectra of the individual images of the
quadruple gravitational lens system Q2237+0305. Comparison of spectra obtained
at two epochs, separated by $\sim~3\,$years, shows evidence for significant
changes in the emission line to continuum ratio of the strong ultraviolet
CIV~$\lambda$1549, CIII]~$\lambda$1909 and MgII~$\lambda$2798 lines. The short,
$\sim~1\,$day, light--travel time differences between the sight lines to the
four individual quasar images rule out any explanation based on intrinsic
variability of the source. The spectroscopic differences thus represent direct
detection of microlensing--induced spectroscopic differences in a quasar. The
observations allow constraints to be placed on the relative spatial scales in
the nucleus of the quasar, with the ultra--violet continuum arising in a region
of $\la~0.05~{\rm pc}$ in extent, while the broad emission line material is
distributed on scales much greater than this.


  The amount of sensitive information that service providers handle about their
users has become a concerning fact in many use cases, where users have no other
option but to trust that those companies will not misuse their personal
information. To solve that, Self-Sovereign Identity (SSI) systems have become a
hot topic of research in recent years: SSI systems allow users to manage their
identities transparently. Recent solutions represent the rights of users to use
services as Non-Fungible Tokens (NFTs) stored on Blockchains, and users prove
possession of these rights using Zero-Knowledge Proofs (ZKPs). However, even
when ZKPs do not leak any information about the rights, the NFTs are stored as
public values linked to known accounts, and thus, they can be traced. In this
paper, we design a native privacy-preserving NFT model for the Dusk Network
Blockchain, and on top of it, we deploy Citadel: our novel
full-privacy-preserving SSI system, where the rights of the users are privately
stored on the Dusk Network Blockchain, and users can prove their ownership in a
fully private manner.


  Estimating heritability remains a significant challenge in statistical
genetics. Diverse approaches have emerged over the years that are broadly
categorized as either random effects or fixed effects heritability methods. In
this work, we focus on the latter. We propose HEDE, an ensemble approach to
estimate heritability or the signal-to-noise ratio in high-dimensional linear
models where the sample size and the dimension grow proportionally. Our method
ensembles post-processed versions of the debiased lasso and debiased ridge
estimators, and incorporates a data-driven strategy for hyperparameter
selection that significantly boosts estimation performance. We establish
rigorous consistency guarantees that hold despite adaptive tuning. Extensive
simulations demonstrate our method's superiority over existing state-of-the-art
methods across various signal structures and genetic architectures, ranging
from sparse to relatively dense and from evenly to unevenly distributed
signals. Furthermore, we discuss the advantages of fixed effects heritability
estimation compared to random effects estimation. Our theoretical guarantees
hold for realistic genotype distributions observed in genetic studies, where
genotypes typically take on discrete values and are often well-modeled by
sub-Gaussian distributed random variables. We establish our theoretical results
by deriving uniform bounds, built upon the convex Gaussian min-max theorem, and
leveraging universality results. Finally, we showcase the efficacy of our
approach in estimating height and BMI heritability using the UK Biobank.


  The study by De Montjoye et al. ("Science", 30 January 2015, p. 536) claimed
that most individuals can be reidentified from a deidentified credit card
transaction database and that anonymization mechanisms are not effective
against reidentification. Such claims deserve detailed quantitative scrutiny,
as they might seriously undermine the willingness of data owners and subjects
to share data for research. In a recent Technical Comment published in
"Science" (18 March 2016, p. 1274), we demonstrate that the reidentification
risk reported by De Montjoye et al. was significantly overestimated (due to a
misunderstanding of the reidentification attack) and that the alleged
ineffectiveness of anonymization is due to the choice of poor and undocumented
methods and to a general disregard of 40 years of anonymization literature. The
technical comment also shows how to properly anonymize data, in order to reduce
unequivocal reidentifications to zero while retaining even more analytical
utility than with the poor anonymization mechanisms employed by De Montjoye et
al. In conclusion, data owners, subjects and users can be reassured that sound
privacy models and anonymization methods exist to produce safe and useful
anonymized data.
  Supplementary materials detailing the data sets, algorithms and extended
results of our study are available here. Moreover, unlike the De Montjoye et
al.'s data set, which was never made available, our data, anonymized results,
and anonymization algorithms can be freely downloaded from
http://crises-deim.urv.cat/opendata/SPD_Science.zip


  We give an assessment of the three approaches, undertaken by the D$\emptyset$
experiment, to probe CP violation in $B_s$ mixing without measurement of
$B_s$-$\bar B_s$ oscillations or tagging: dimuon charge asymmetry $A_{SL}$,
untagged single muon charge asymmetry $A_{SL}^s$, and lifetime difference in
untagged $B_s \to J/\psi\phi$ decay. The latter two approaches provide an
alternative avenue, if not crosscheck, to the usual mixing-decay interference
study in modes such as $B_s \to J/\psi\phi$. Prospects at the Tevatron, LHC and
(Super) B factories are discussed.


  The low energy behavior of the Adler function D(Q^2) is studied by employing
recently derived integral representation for the latter. This representation
embodies the nonperturbative constraints on D(Q^2), in particular, it retains
the effects due to the nonvanishing pion mass. The Adler function is calculated
within the developed approach by making use of its perturbative approximation
as the only additional input. The obtained result agrees with the experimental
prediction for the Adler function in the entire energy range and possesses
remarkable stability with respect to the higher loop corrections.


  We present a family of kernels for analysis of data generated by dynamical
systems. These so-called cone kernels feature an explicit dependence on the
dynamical vector field operating in the phase-space manifold, estimated
empirically through finite-differences of time-ordered data samples. In
particular, cone kernels assign strong affinity to pairs of samples whose
relative displacement vector lies within a narrow cone aligned with the
dynamical vector field. As a result, in a suitable asymptotic limit, the
associated diffusion operator generates diffusions along the dynamical flow,
and is invariant under a weakly restrictive class of transformations of the
data, which includes conformal transformations. Moreover, the corresponding
Dirichlet form is governed by the directional derivative of functions along the
dynamical vector field. The latter feature is metric-independent. The diffusion
eigenfunctions obtained via cone kernels are therefore adapted to the dynamics
in that they vary predominantly in directions transverse to the flow. We
demonstrate the utility of cone kernels in nonlinear flows on the 2-torus and
North Pacific sea surface temperature data generated by a comprehensive climate
model.


  We calculate the charm quark contribution to the rare decay K+ -> pi+ nu
anti-nu in the next-to-next-to-leading order of QCD. This new contribution
reduces the theoretical uncertainty in the relevant parameter Pc from +/- 10.1%
down to +/- 2.4%, corresponding to scale uncertainties of +/- 1.3%, +/- 1.0%,
+/- 0.006 and +/- 1.2 degrees in BR(K+ -> pi+ nu anti-nu) and in |V_td|, sin(2
beta) and gamma extracted from the K -> pi nu anti-nu system. The error in Pc =
0.37 +/- 0.04 is now fully dominated by the current uncertainty of +/- 3.8% in
the charm quark mass mc. We find BR(K+ -> pi+ nu anti-nu) = (8.0 +/- 1.1) *
10^-11, where the quoted error stems almost entirely from the present
uncertainties in mc and the Cabibbo-Kobayashi-Maskawa elements.


  Motivated by applications to bond markets, we propose a multivariate
framework for discrete time financial markets with proportional transaction
costs and a countable infinite number of tradable assets. We show that the
no-arbitrage of second kind property (NA2 in short), recently introduced by
Rasonyi for finite-dimensional markets, allows us to provide a closure property
for the set of attainable claims in a very natural way, under a suitable
efficient friction condition. We also extend to this context the equivalence
between NA2 and the existence of many (strictly) consistent price systems.


  An efficient entailment proof system is essential to compositional
verification using separation logic. Unfortunately, existing decision
procedures are either inexpressive or inefficient. For example, Smallfoot is an
efficient procedure but only works with hardwired lists and trees. Other
procedures that can support general inductive predicates run exponentially in
time as their proof search requires back-tracking to deal with a disjunction in
the consequent.
  This paper presents a decision procedure to derive cyclic entailment proofs
for general inductive predicates in polynomial time. Our procedure is efficient
and does not require back-tracking; it uses normalisation rules that help avoid
the introduction of disjunction in the consequent. Moreover, our decidable
fragment is sufficiently expressive: It is based on compositional predicates
and can capture a wide range of data structures, including sorted and nested
list segments, skip lists with fast forward pointers, and binary search trees.
We have implemented the proposal in a prototype tool and evaluated it over
challenging problems taken from a recent separation logic competition. The
experimental results confirm the efficiency of the proposed system.


  Given a set of pairwise disjoint polygonal obstacles in the plane, finding an
obstacle-avoiding Euclidean shortest path between two points is a classical
problem in computational geometry and has been studied extensively. The
previous best algorithm was given by Hershberger and Suri [FOCS 1993, SIAM J.
Comput. 1999] and the algorithm runs in $O(n\log n)$ time and $O(n\log n)$
space, where $n$ is the total number of vertices of all obstacles. The
algorithm is time-optimal because $\Omega(n\log n)$ is a lower bound. It has
been an open problem for over two decades whether the space can be reduced to
$O(n)$. In this paper, we settle it by solving the problem in $O(n\log n)$ time
and $O(n)$ space, which is optimal in both time and space; we achieve this by
modifying the algorithm of Hershberger and Suri. Like their original algorithm,
our new algorithm can build a shortest path map for a source point $s$ in
$O(n\log n)$ time and $O(n)$ space, such that given any query point $t$, the
length of a shortest path from $s$ to $t$ can be computed in $O(\log n)$ time
and a shortest path can be produced in additional time linear in the number of
edges of the path.


  With the escalated demand of human-machine interfaces for intelligent
systems, development of gaze controlled system have become a necessity. Gaze,
being the non-intrusive form of human interaction, is one of the best suited
approach. Appearance based deep learning models are the most widely used for
gaze estimation. But the performance of these models is entirely influenced by
the size of labeled gaze dataset and in effect affects generalization in
performance. This paper aims to develop a semi-supervised contrastive learning
framework for estimation of gaze direction. With a small labeled gaze dataset,
the framework is able to find a generalized solution even for unseen face
images. In this paper, we have proposed a new contrastive loss paradigm that
maximizes the similarity agreement between similar images and at the same time
reduces the redundancy in embedding representations. Our contrastive regression
framework shows good performance in comparison to several state of the art
contrastive learning techniques used for gaze estimation.


  We introduce in this paper the generalized virtual braid group on n strands
GVB_n, generalizing simultaneously the braid groups and their virtual versions.
A Mastumoto-Tits type section lifting shuffles in a symmetric group S_n to the
monoid associated to GVB_n is constructed, which is then applied to
characterize the quantum quasi-shuffle product. A family of representations of
GVB_n is constructed using quantum groups.


  The Brenke type generating functions are the polynomial generating functions
of the form $$\sum_{n=0}^{\infty}{P_n(x )\over n!}t^n=A(t)B(xt), $$ where $A$
and $B$ are two formal power series subject to the conditions
$A(0)\;B^{(k)}(0)\neq0,\, k=0,1,2\ldots$.\\ In this work, we determine all
Brenke-type polynomials when they are also $2$-orthogonal polynomial sets, that
is to say, polynomials satisfying one standard four-term recurrence relation.
That allows us, on one hand, to obtain new 2-orthogonal sequences generalizing
known orthogonal families of polynomials, and on the other hand, to recover
particular cases of polynomial sequences discovered in the context of
$d$-orthogonality.\\ The classification is based on the resolution of a
three-order difference equation induced by the four-term recurrence relation
satisfied by the considered polynomials. This study is motivated by the work of
Chihara who gave all pairs $(A (t), B(t))$ for which $\{P_n(x)\}_{n\geq 0}$ is
an orthogonal polynomial sequence.


  In this paper, we study complete oriented $f$-minimal hypersurfaces properly
immersed in a cylinder shrinking soliton $(\mathbb{S}^n\times \mathbb{R},
\bar{g}, f)$. We prove that such hypersurface with $L_f$-index one must be
either $\mathbb{S}^n\times\{0\}$ or $\mathbb{S}^{n-1}\times\mathbb{R}$, where
$\mathbb{S}^{n-1}$ denotes the sphere in $\mathbb{S}^{n}$ of the same radius.
Also we prove a pinching theorem for them.


  We study some of the roles for unparticles in an inflationary universe.
Unparticles by themselves are not appropriate for generating the primordial
perturbations since their power spectrum does not match what has been inferred
from observations. In fact, when the scaling dimension for the unparticles
exceeds three-halves, the unparticle power spectrum diverges. However, when a
unparticle couples to an ordinary inflaton particle, loop corrections can
produce a slight enhancement of the inflaton's power spectrum at longer
wavelengths. We examine these loop corrections from unparticles in some detail
to learn how they scale in the wavelength of a perturbation and how they depend
on the scaling dimension of the field.


  This paper deals with numerical solutions of maximizing expected utility from
terminal wealth under a non-bankruptcy constraint. The wealth process is
subject to shocks produced by a general marked point process. The problem of
the agent is to derive the optimal insurance strategy which allows "lowering"
the level of the shocks. This optimization problem is related to a suitable
dual stochastic control problem in which the delicate boundary constraints
disappear. In Mnif \cite{mnif10}, the dual value function is characterized as
the unique viscosity solution of the corresponding Hamilton Jacobi Bellman
Variational Inequality (HJBVI in short). We characterize the optimal insurance
strategy by the solution of the variational inequality which we solve
numerically by using an algorithm based on policy iterations.


  Mass-angle distribution (MAD) measurement of heavy and superheavy element
fragmentation reactions is one of the powerful tools for investigating the
mechanism of fission and fusion process. MAD shows a strong correlation between
mass and angle when the quasi-fission event is dominant. It has characteristic
that appears diagonal correlation as long as the quasi-fission event is
dominant. This diagonal correlation could not be reproduced in previous our
model before the introduction of the parameters. In this study, we
systematically evaluate the unknown model parameters contained in our model and
clarify those model parameters to reproduce the diagonal correlation that
appears in MAD. Using a dynamical model based on the fluctuation dissipation
theorem that employs Langevin equations, we calculate MADs of two reaction
systems $^{48}$Ti+$^{186}$W and $^{34}$S+$^{232}$Th which are dominated by
quasi-fission. We were able to clarify the effects of unknown model parameters
on the MAD. In addition, we identified the values of model parameters that can
reproduce the correlation between mass and angle. As a result, it was found
that the balance of tangential friction and moment of inertia values is
important for the correlation between mass and angle.


  This paper develops a framework to extend the strategic form analysis of
cursed equilibrium (CE) developed by Eyster and Rabin (2005) to multi-stage
games. The approach uses behavioral strategies rather than normal form mixed
strategies, and imposes sequential rationality. We define cursed sequential
equilibrium (CSE) and compare it to sequential equilibrium and standard
normal-form CE. We provide a general characterization of CSE and establish its
properties. We apply CSE to five applications in economics and political
science. These applications illustrate a wide range of differences between CSE
and Bayesian Nash equilibrium or CE: in signaling games; games with preplay
communication; reputation building; sequential voting; and the dirty faces game
where higher order beliefs play a key role. A common theme in several of these
applications is showing how and why CSE implies systematically different
behavior than Bayesian Nash equilibrium in dynamic games of incomplete
information with private values, while CE coincides with Bayesian Nash
equilibrium for such games.


  This paper discusses the challenge when evaluating multi-objective
optimisation algorithms under noise, and argues that decision maker preferences
need to be taken into account. It demonstrates that commonly used performance
metrics are problematic when there is noise, and proposes two alternative
performance metrics that capture selection error of the decision maker due to
mis-estimating a solution's true fitness values.


  The existence of adversarial examples has led to considerable uncertainty
regarding the trust one can justifiably put in predictions produced by
automated systems. This uncertainty has, in turn, lead to considerable research
effort in understanding adversarial robustness. In this work, we take first
steps towards separating robustness analysis from the choice of robustness
threshold and norm. We propose robustness curves as a more general view of the
robustness behavior of a model and investigate under which circumstances they
can qualitatively depend on the chosen norm.


  Cascades from high-energy particles produce a brief current and associated
magnetic fields. Even sub-nanosecond duration magnetic fields can be detected
with a relatively low bandwidth system by latching image currents on a
capacitor. At accelerators, this technique is employed routinely by
beam-current monitors, which work for pulses even as fast as femtoseconds. We
discuss scaling up these instruments in size, to 100 meters and beyond, to
serve as a new kind of ground- and space-based high-energy particle detector
which can instrument large areas relatively inexpensively. This new technique
may be used to detect and/or veto ultra-high energy cosmic-ray showers above
100 PeV. It may also be applied to searches for hypothetical highly charged
particles. In addition, these detectors may serve to search for extremely short
magnetic field pulses of any origin, faster than other detectors by orders of
magnitude.


  Existing networks directly learn feature representations on 3D point clouds
for shape analysis. We argue that 3D point clouds are highly redundant and hold
irregular (permutation-invariant) structure, which makes it difficult to
achieve inter-class discrimination efficiently. In this paper, we propose a
two-faceted solution to this problem that is seamlessly integrated in a single
`Blended Convolution and Synthesis' layer. This fully differentiable layer
performs two critical tasks in succession. In the first step, it projects the
input 3D point clouds into a latent 3D space to synthesize a highly compact and
more inter-class discriminative point cloud representation. Since, 3D point
clouds do not follow a Euclidean topology, standard 2/3D Convolutional Neural
Networks offer limited representation capability. Therefore, in the second
step, it uses a novel 3D convolution operator functioning inside the unit ball
($\mathbb{B}^3$) to extract useful volumetric features. We extensively derive
formulae to achieve both translation and rotation of our novel convolution
kernels. Finally, using the proposed techniques we present an extremely
light-weight, end-to-end architecture that achieves compelling results on 3D
shape recognition and retrieval.


  A proposal for fast-switching broadband frequency-shifting technology making
use of frequency conversion in a nonlinear crystal is set forth, whereby the
shifting is imparted to the converted photons by creating a bank of
frequency-displaced pump modes that can be selected by a photonic switch and
directed to the nonlinear crystal. Proof-of-principle results show that the
expected frequency-shifting operation can be achieved. Even though the
dimensions of the currently employed crystal and significant excess loss in the
experimental setup prevented conversion of single-photon-level inputs, thorough
experimental and theoretical analysis of the noise contribution allowed for the
estimation of the system performance in an optimized scenario, where the
expected signal-to-noise ratio for single-photon conversion and
frequency-shifting can reach up to 25 dB with proper narrow-band filtering and
state-of-the-art devices. The proposed frequency-shifting solution figures as a
promising candidate for applications in frequency-multiplexed quantum repeater
architectures with 25 dB output SNR (with 20\% conversion efficiency) and
capacity for 16 channels spread around a 100 GHz spectral region.


  Graphs (networks) are ubiquitous and allow us to model entities (nodes) and
the dependencies (edges) between them. Learning a useful feature representation
from graph data lies at the heart and success of many machine learning tasks
such as classification, anomaly detection, link prediction, among many others.
Many existing techniques use random walks as a basis for learning features or
estimating the parameters of a graph model for a downstream prediction task.
Examples include recent node embedding methods such as DeepWalk, node2vec, as
well as graph-based deep learning algorithms. However, the simple random walk
used by these methods is fundamentally tied to the identity of the node. This
has three main disadvantages. First, these approaches are inherently
transductive and do not generalize to unseen nodes and other graphs. Second,
they are not space-efficient as a feature vector is learned for each node which
is impractical for large graphs. Third, most of these approaches lack support
for attributed graphs.
  To make these methods more generally applicable, we propose a framework for
inductive network representation learning based on the notion of attributed
random walk that is not tied to node identity and is instead based on learning
a function $\Phi : \mathrm{\rm \bf x} \rightarrow w$ that maps a node attribute
vector $\mathrm{\rm \bf x}$ to a type $w$. This framework serves as a basis for
generalizing existing methods such as DeepWalk, node2vec, and many other
previous methods that leverage traditional random walks.


  Let G=(V,E) be a graph. A set S is independent if no two vertices from S are
adjacent, alpha(G) is the size of a maximum independent set, and core(G) is the
intersection of all maximum independent sets. The number d(X)=|X|-|N(X)| is the
difference of the set X, and d_{c}(G)=max{d(I):I is an independent set} is
called the critical difference of G. A set X is critical if d(X)=d_{c}(G). For
a graph G we define ker(G) as the intersection of all critical independent
sets, while diadem(G) is the union of all critical independent sets. For a
bipartite graph G=(A,B,E), with bipartition {A,B}, Ore defined delta(X)=d(X)
for every subset X of A, while delta_0(A)=max{delta(X):X is a subset of A}.
Similarly is defined delta_0(B). In this paper we prove that for every
bipartite graph G=(A,B,E) the following assertions hold:
d_{c}(G)=delta_0(A)+delta_0(B); ker(G)=core(G);
|ker(G)|+|diadem(G)|=2*alpha(G).


  Visual Reinforcement Learning is a popular and powerful framework that takes
full advantage of the Deep Learning breakthrough. However, it is also known
that variations in the input (e.g., different colors of the panorama due to the
season of the year) or the task (e.g., changing the speed limit for a car to
respect) could require complete retraining of the agents. In this work, we
leverage recent developments in unifying latent representations to demonstrate
that it is possible to combine the components of an agent, rather than retrain
it from scratch. We build upon the recent relative representations framework
and adapt it for Visual RL. This allows us to create completely new agents
capable of handling environment-task combinations never seen during training.
Our work paves the road toward a more accessible and flexible use of
reinforcement learning.


  We combine Freedman's topology with Eliashberg's holomorphic theory to
construct Stein neighborhood systems in complex surfaces, and use these to
study various notions of convexity and concavity. Every tame, topologically
embedded 2-complex K in a complex surface, after C^0-small topological ambient
isotopy, is the intersection of an uncountable nested family of Stein regular
neighborhoods that are all topologically ambiently isotopic rel K, but
frequently realize uncountably many diffeomorphism types. These arise from the
Cantor set levels of a topological mapping cylinder. The boundaries of the
neighborhoods are 3-manifolds that are only topologically embedded, but still
satisfy a notion of pseudoconvexity. Such 3-manifolds share some basic
properties of hypersurfaces that are strictly pseudoconvex in the usual smooth
sense, but they are far more common. The complementary notion of topological
pseudoconcavity is realized by uncountably many diffeomorphism types
homeomorphic to R^4.


  We derived chemical abundances for C, N, O, Na, Mg and Al in 20 asymptotic
giant branch (AGB) stars in the globular cluster NGC 6752. All these elements
(but Mg) show intrinsic star-to-star variations and statistically significant
correlations or anticorrelations analogous to those commonly observed in red
giant stars of globular clusters hosting multiple populations. This
demonstrates that, at odds with previous findings, both first and second
generation stars populate the AGB of NGC 6752. The comparison with the Na
abundances of red giant branch stars in the same cluster reveals that second
generation stars (with mild Na and He enrichment) do reach the AGB phase. The
only objects that are not observed along the AGB of NGC 6752 are stars with
extreme Na enhancement. This is also consistent with standard stellar evolution
models, showing that highly Na and He enriched stars populate the bluest
portion of the horizontal branch and, because of their low stellar masses,
evolve directly to the white dwarf cooling sequence, skipping the AGB phase.


  Pattern dynamics triggered by fixing a boundary is investigated. By
considering a reaction-diffusion equation that has a unique spatially-uniform
and limit cycle attractor under a periodic or Neumann boundary condition, and
then by choosing a fixed boundary condition, we found three novel phases
depending on the ratio of diffusion constants of activator to inhibitor:
transformation of temporally periodic oscillation into a spatially-periodic
fixed pattern, travelling wave emitted from the boundary, and aperiodic
spatiotemporal dynamics. The transformation into a fixed, periodic pattern is
analyzed by crossing of local nullclines at each spatial point, shifted by
diffusion terms. A spatial map, then, is introduced, whose temporal sequence
can reproduce the spatially periodic pattern, by replacing the time with space.
The generality of the boundary-induced pattern formation as well as its
relevance to biological morphogenesis is discussed.


  The frequencies and damping times due to gravitational radiation are
calculated for self-bound quark star models. The results are compared with
those for neutron star models. They are markedly contrasted in less
relativistic cases. The distinction derived here from a simple model of quark
stars may be relevant to the future theoretical and observational studies,
since the oscillation properties essentially depend on the mass and radius of
an equilibrium star.


  In this paper we propose a method of proving impossibility results based on
applying strong data-processing inequalities to estimate mutual information
between sets of variables forming certain Markov random fields. The end result
is that mutual information between two "far away" (as measured by the graph
distance) variables is bounded by the probability of the existence of an open
path in a bond-percolation problem on the same graph. Furthermore, stronger
bounds can be obtained by establishing mutual information comparison results
with an erasure model on the same graph, with erasure probabilities given by
the contraction coefficients.
  As applications, we show that our method gives sharp threshold for partially
recovering a rank-one perturbation of a random Gaussian matrix (spiked Wigner
model), yields the best known upper bound on the noise level for group
synchronization (obtained concurrently by Abbe and Boix), and establishes new
impossibility result for community detection on the stochastic block model with
$k$ communities.


  This paper proves the convergence of the narrow wedge solutions of the KPZ
equation to the Airy sheet and the directed landscape. This is the first
convergence result to the Airy sheet and the directed landscape established for
a positive temperature model.


  We consider a particle bound to a two-dimensional plane and a double well
potential, subject to a perpendicular uniform magnetic field . The energy
difference between the lowest two eigenvalues--the eigenvalue splitting--is
related to the tunneling probability between the two wells. We obtain upper and
lower bounds on this splitting in the regime where both the magnetic field
strength and the depth of the wells are large. The main step is a lower bound
on the hopping probability between the wells, a key parameter in tight binding
models of solid state physics.


  Strongly-interacting Rydberg atomic ensembles have shown intense collective
excitation effects due to the inclusion of single Rydberg excitation shared by
multiple atoms in the ensemble. In this paper we investigate a
counter-intuitive Rydberg excitation facilitation with a strongly-interacting
atomic ensemble in the strong probe-field regime, which is enabled by the role
of a control atom nearby. Differing from the case of a single ensemble, we show
that, the control atom's excitation adds to a second two-photon transition onto
the doubly-excited Rydberg state, arising an excitation facilitation for the
ensemble atoms. Our numerical studies depending on the method of quantum Monte
Carlo wavefunction, exhibit the observation constraints of this excitation
facilitation effect under practical experimental conditions. The results
obtained can provide a flexible control for the excitation of Rydberg atomic
ensembles and participate further uses in developing mesoscopic Rydberg gates
for multiqubit quantum computation.


  Embodied scene understanding serves as the cornerstone for autonomous agents
to perceive, interpret, and respond to open driving scenarios. Such
understanding is typically founded upon Vision-Language Models (VLMs).
Nevertheless, existing VLMs are restricted to the 2D domain, devoid of spatial
awareness and long-horizon extrapolation proficiencies. We revisit the key
aspects of autonomous driving and formulate appropriate rubrics. Hereby, we
introduce the Embodied Language Model (ELM), a comprehensive framework tailored
for agents' understanding of driving scenes with large spatial and temporal
spans. ELM incorporates space-aware pre-training to endow the agent with robust
spatial localization capabilities. Besides, the model employs time-aware token
selection to accurately inquire about temporal cues. We instantiate ELM on the
reformulated multi-faced benchmark, and it surpasses previous state-of-the-art
approaches in all aspects. All code, data, and models will be publicly shared.


  We experimentally demonstrate a method to control the relative amount of
quantum and classical energy correlations between two photons from a pair
emitted by spontaneous parametric downconversion. Decoherence in the energy
basis is achieved by applying random spectral phases on the photons. As a
consequence a diverging temporal second order correlation function is observed
and is explained by a mixture between an energy entangled pure state and a
fully classically correlated mixte state.


  We show that the poset of shuffles introduced by Greene in 1988 is
flag-symmetric, and we describe a "local" permutation action of the symmetric
group on the maximal chains which is closely related to the flag symmetric
function of the poset. A key tool is provided by a new labeling of the maximal
chains of a poset of shuffles, which is also used to give bijective proofs of
enumerative properties originally obtained by Greene. In addition we define a
monoid of multiplicative functions on all posets of shuffles and describe this
monoid in terms of a new operation on power series in two variables.


  The Doherty power amplifier (DPA) has been extensively explored in the past
and has become one of the most widely used power amplifier (PA) architectures
in cellular base stations. The classical DPA suffers intrinsic bandwidth
constrains which limit its application in future 5G wireless transmitters. In
this paper, we present a comprehensive review of the DPA bandwidth enhancement
techniques proposed in literature in order to provide a thorough understanding
of the DPA's broadband design for high-efficiency 5G wireless transmitters. We
elaborate on the main bandwidth limitation sources and provide circuit design
insights. We then follow with an overview of bandwidth enhancement techniques
developed for the DPA, including modified load-modulation networks, frequency
response optimization, parasitic compensation, post-matching, as well as
distributed DPA, dual-input digital DPA, transformer-based power-combining PA,
and transformer-less load modulated PA architectures. Furthermore, challenges
and design techniques for integrated circuit (IC) implementation of broadband
DPAs are discussed, including a review of circuits developed in CMOS, SiGe, and
GaN processes, and operating in RF and mm-Wave frequencies.


  We present a framework to deal with a range of large scale compressive
sensing problems using a quantum subroutine. We apply a quantum approximate
optimization algorithm (QAOA) to support detection in a sparse signal
reconstruction algorithm: matching pursuit. The constrained optimization
required in this algorithm is difficult to handle when the size of the problem
is large and constraints are given by unstructured patterns. Our framework
utilizes specially designed structured constraints that are easy to manipulate
and reduce the optimization problem to the solution of an Ising model which can
be found using Ising solvers. In this research, we test the performance of QAOA
for this purpose on a simulator of quantum computer. We observe that our method
can outperform reference classical methods. Our results explore a promising
path of applying quantum computers in the compressive sensing field.


  We say that a linear code C over a field F is triangular representable if
there exists a two dimensional simplicial complex $\Delta$ such that C is a
punctured code of the kernel ker $\Delta$ of the incidence matrix of $\Delta$
over F and there is a linear mapping between C and ker $\Delta$ which is a
bijection and maps minimal codewords to minimal codewords. We show that the
linear codes over rationals and over GF(p), where p is a prime, are triangular
representable. In the case of finite fields, we show that this representation
determines the weight enumerator of C. We present one application of this
result to the partition function of the Potts model.
  On the other hand, we show that there exist linear codes over any field
different from rationals and GF(p), p prime, that are not triangular
representable. We show that every construction of triangular representation
fails on a very weak condition that a linear code and its triangular
representation have to have the same dimension.


  Recovering low-rank structures via eigenvector perturbation analysis is a
common problem in statistical machine learning, such as in factor analysis,
community detection, ranking, matrix completion, among others. While a large
variety of bounds are available for average errors between empirical and
population statistics of eigenvectors, few results are tight for entrywise
analyses, which are critical for a number of problems such as community
detection.
  This paper investigates entrywise behaviors of eigenvectors for a large class
of random matrices whose expectations are low-rank, which helps settle the
conjecture in Abbe et al. (2014b) that the spectral algorithm achieves exact
recovery in the stochastic block model without any trimming or cleaning steps.
The key is a first-order approximation of eigenvectors under the $\ell_\infty$
norm: $$u_k \approx \frac{A u_k^*}{\lambda_k^*},$$ where $\{u_k\}$ and
$\{u_k^*\}$ are eigenvectors of a random matrix $A$ and its expectation
$\mathbb{E} A$, respectively. The fact that the approximation is both tight and
linear in $A$ facilitates sharp comparisons between $u_k$ and $u_k^*$. In
particular, it allows for comparing the signs of $u_k$ and $u_k^*$ even if $\|
u_k - u_k^*\|_{\infty}$ is large. The results are further extended to
perturbations of eigenspaces, yielding new $\ell_\infty$-type bounds for
synchronization ($\mathbb{Z}_2$-spiked Wigner model) and noisy matrix
completion.


  We propose a novel method for learning representations of poses for 3D
deformable objects, which specializes in 1) disentangling pose information from
the object's identity, 2) facilitating the learning of pose variations, and 3)
transferring pose information to other object identities. Based on these
properties, our method enables the generation of 3D deformable objects with
diversity in both identities and poses, using variations of a single object. It
does not require explicit shape parameterization such as skeletons or joints,
point-level or shape-level correspondence supervision, or variations of the
target object for pose transfer. To achieve pose disentanglement, compactness
for generative models, and transferability, we first design the pose extractor
to represent the pose as a keypoint-based hybrid representation and the pose
applier to learn an implicit deformation field. To better distill pose
information from the object's geometry, we propose the implicit pose applier to
output an intrinsic mesh property, the face Jacobian. Once the extracted pose
information is transferred to the target object, the pose applier is fine-tuned
in a self-supervised manner to better describe the target object's shapes with
pose variations. The extracted poses are also used to train a cascaded
diffusion model to enable the generation of novel poses. Our experiments with
the DeformThings4D and Human datasets demonstrate state-of-the-art performance
in pose transfer and the ability to generate diverse deformed shapes with
various objects and poses.


  For any given real number $\alpha$ with bounded partial quotients, we
construct explicitly continuum many real numbers $\beta$ with bounded partial
quotients for which the pair $(\alpha, \beta)$ satisfies a strong form of the
Littlewood conjecture. Our proof is elementary and rests on the basic theory of
continued fractions.


  We apply the variational approach to solitons in highly nonlocal nonlinear
media in $D=1,2,3$ dimensions. We compare results obtained by the variational
approach with those obtained in the accessible soliton approximation, by
considering the same system of equations in the same spatial region and under
the same boundary conditions. To assess the accuracy of these approximations,
we also compare them with the numerical solution of the equations. We discover
that the variational highly nonlocal approximation provides more accurate
results and as such is more appropriate solution than the accessible soliton
approximation.


  Cross-domain alignment between two sets of entities (e.g., objects in an
image, words in a sentence) is fundamental to both computer vision and natural
language processing. Existing methods mainly focus on designing advanced
attention mechanisms to simulate soft alignment, with no training signals to
explicitly encourage alignment. The learned attention matrices are also dense
and lacks interpretability. We propose Graph Optimal Transport (GOT), a
principled framework that germinates from recent advances in Optimal Transport
(OT). In GOT, cross-domain alignment is formulated as a graph matching problem,
by representing entities into a dynamically-constructed graph. Two types of OT
distances are considered: (i) Wasserstein distance (WD) for node (entity)
matching; and (ii) Gromov-Wasserstein distance (GWD) for edge (structure)
matching. Both WD and GWD can be incorporated into existing neural network
models, effectively acting as a drop-in regularizer. The inferred transport
plan also yields sparse and self-normalized alignment, enhancing the
interpretability of the learned model. Experiments show consistent
outperformance of GOT over baselines across a wide range of tasks, including
image-text retrieval, visual question answering, image captioning, machine
translation, and text summarization.


  Molecular Property Prediction (MPP) task involves predicting biochemical
properties based on molecular features, such as molecular graph structures,
contributing to the discovery of lead compounds in drug development. To address
data scarcity and imbalance in MPP, some studies have adopted Graph Neural
Networks (GNN) as an encoder to extract commonalities from molecular graphs.
However, these approaches often use a separate predictor for each task,
neglecting the shared characteristics among predictors corresponding to
different tasks. In response to this limitation, we introduce the GNN-MoCE
architecture. It employs the Mixture of Collaborative Experts (MoCE) as
predictors, exploiting task commonalities while confronting the homogeneity
issue in the expert pool and the decision dominance dilemma within the expert
group. To enhance expert diversity for collaboration among all experts, the
Expert-Specific Projection method is proposed to assign a unique projection
perspective to each expert. To balance decision-making influence for
collaboration within the expert group, the Expert-Specific Loss is presented to
integrate individual expert loss into the weighted decision loss of the group
for more equitable training. Benefiting from the enhancements of MoCE in expert
creation, dynamic expert group formation, and experts' collaboration, our model
demonstrates superior performance over traditional methods on 24 MPP datasets,
especially in tasks with limited data or high imbalance.


  The analysis of electromagnetic scattering has long been performed on a
discrete representation of the geometry. This representation is typically
continuous but {\em not} differentiable. The need to define physical quantities
on this geometric representation has led to development of sets of basis
functions that need to satisfy constraints at the boundaries of the
elements/tesselations (viz., continuity of normal or tangential components
across element boundaries). For electromagnetics, these result in either
curl/div-conforming basis sets. The geometric representation used for analysis
is in stark contrast with that used for design, wherein the surface
representation is higher order differentiable. Using this representation for
{\em both} geometry and physics on geometry has several advantages, and is
eludicated in Hughes et al., Isogeometric analysis: CAD, finite elements,
NURBS, exact geometry and mesh refinement, Computer Methods in Applied
Mechanics and Engineering 194 (39-41) (2005). Until now, a bulk of the
literature on isogeometric methods have been limited to solid mechanics, with
some effort to create NURBS based basis functions for electromagnetic analysis.
In this paper, we present the first complete isogeometry solution methodology
for the electric field integral equation as applied to simply connected
structures. This paper systematically proceeds through surface representation
using subdivision, definition of vector basis functions on this surface, to
fidelity in the solution of integral equations. We also present techniques to
stabilize the solution at low frequencies, and impose a Calder\'{o}n
preconditioner. Several results presented serve to validate the proposed
approach as well as demonstrate some of its capabilities.


  In this paper we apply topology optimization to micro-structured
superhydrophobic surfaces for the first time. It has been experimentally
observed that a droplet suspended on a brush of micrometric posts shows a high
static contact angle and low roll-off angle. To keep the fluid from penetrating
the space between the posts, we search for an optimal post cross section, that
minimizes the vertical displacement of the liquid-air interface at the base of
the drop when a pressure difference is applied. Topology optimisation proves
effective in this framework, showing that posts with a branching cross-section
are optimal, which is consistent with several biologic strategies to achieve
superhydrophobicity. Through a filtering technique, we can also control the
characteristic length scale of the optimal design, thus obtaining feasible
geometries


  Beyond assigning the correct class, an activity recognition model should also
be able to determine, how certain it is in its predictions. We present the
first study of how welthe confidence values of modern action recognition
architectures indeed reflect the probability of the correct outcome and propose
a learning-based approach for improving it. First, we extend two popular action
recognition datasets with a reliability benchmark in form of the expected
calibration error and reliability diagrams. Since our evaluation highlights
that confidence values of standard action recognition architectures do not
represent the uncertainty well, we introduce a new approach which learns to
transform the model output into realistic confidence estimates through an
additional calibration network. The main idea of our Calibrated Action
Recognition with Input Guidance (CARING) model is to learn an optimal scaling
parameter depending on the video representation. We compare our model with the
native action recognition networks and the temperature scaling approach - a
wide spread calibration method utilized in image classification. While
temperature scaling alone drastically improves the reliability of the
confidence values, our CARING method consistently leads to the best uncertainty
estimates in all benchmark settings.


  A coding theorem is proved for a class of stationary channels with feedback
in which the output Y_n = f(X_{n-m}^n, Z_{n-m}^n) is the function of the
current and past m symbols from the channel input X_n and the stationary
ergodic channel noise Z_n. In particular, it is shown that the feedback
capacity is equal to $$ \limp_{n\to\infty} \sup_{p(x^n||y^{n-1})} \frac{1}{n}
I(X^n \to Y^n), $$ where I(X^n \to Y^n) = \sum_{i=1}^n I(X^i; Y_i|Y^{i-1})
denotes the Massey directed information from the channel input to the output,
and the supremum is taken over all causally conditioned distributions
p(x^n||y^{n-1}) = \prod_{i=1}^n p(x_i|x^{i-1},y^{i-1}). The main ideas of the
proof are the Shannon strategy for coding with side information and a new
elementary coding technique for the given channel model without feedback, which
is in a sense dual to Gallager's lossy coding of stationary ergodic sources. A
similar approach gives a simple alternative proof of coding theorems for finite
state channels by Yang-Kavcic-Tatikonda, Chen-Berger, and
Permuter-Weissman-Goldsmith.


  The additivity principle of the extreme shell model stipulates that an
average value of a one-body operator be equal to the sum of the core
contribution and effective contributions of valence (particle or hole)
nucleons. For quadrupole moment and angular momentum operators, we test this
principle for highly and superdeformed rotational bands in the A~130 nuclei.
Calculations are done in the self-consistent cranked non-relativistic
Hartree-Fock and relativistic Hartree mean-field approaches. Results indicate
that the additivity principle is a valid concept that justifies the use of an
extreme single-particle model in an unpaired regime typical of high angular
momenta.


  Machine translation (MT) is one of the main tasks in natural language
processing whose objective is to translate texts automatically from one natural
language to another. Nowadays, using deep neural networks for MT tasks has
received great attention. These networks require lots of data to learn abstract
representations of the input and store it in continuous vectors. This paper
presents the first relatively large-scale Amharic-English parallel sentence
dataset. Using these compiled data, we build bi-directional Amharic-English
translation models by fine-tuning the existing Facebook M2M100 pre-trained
model achieving a BLEU score of 37.79 in Amharic-English 32.74 in
English-Amharic translation. Additionally, we explore the effects of Amharic
homophone normalization on the machine translation task. The results show that
the normalization of Amharic homophone characters increases the performance of
Amharic-English machine translation in both directions.


  We prove that the global minimum of the backpropagation (BP) training problem
of neural networks with an arbitrary nonlinear activation is given by the
ridgelet transform. A series of computational experiments show that there
exists an interesting similarity between the scatter plot of hidden parameters
in a shallow neural network after the BP training and the spectrum of the
ridgelet transform. By introducing a continuous model of neural networks, we
reduce the training problem to a convex optimization in an infinite dimensional
Hilbert space, and obtain the explicit expression of the global optimizer via
the ridgelet transform.


  In recent research it was found that the fundamental shear-localizing
instability of amorphous solids under external strain, which eventually results
in a shear band and failure, consists of a highly correlated array of Eshelby
quadrupoles all having the same orientation and some density $\rho$. In this
paper we calculate analytically the energy $E(\rho,\gamma)$ associated with
such highly correlated structures as a function of the density $\rho$ and the
external strain $\gamma$. We show that for strains smaller than a
characteristic strain $\gamma_Y$ the total strain energy initially increases as
the quadrupole density increases, but that for strains larger than $\gamma_Y$
the energy monotonically decreases with quadrupole density. We identify
$\gamma_Y$ as the yield strain. Its value, derived from values of the qudrupole
strength based on the atomistic model, agrees with that from the computed
stress-strain curves and broadly with experimental results.


  In addition to revealing spherical harmonic degrees, $\ell$, of excited
modes, pulsational amplitudes and phases from multicolour photometry and radial
velocity data yield a valuable constraints on stellar atmospheric parameters
and on subphotospheric convection. Multiperiodic pulsators are of particular
interest because each mode yields independent constraints. We present an
analysis of data on twelve modes observed in FG Vir star.


  Let $K$ be a $p$-adic local field. In this work we study a special kind of
$p$-adic Galois representations of it. These representations are similar to the
Galois representations occurred in the exceptional zero conjecture for modular
forms. In particular, we verify that a formula of Colmez can be generalized to
our case. We also include a degenerated version of Colmez's formula.


  Starting with a theory of the discrete space-time at the Planck scale, we
developed a Gravi-Weak Unification (GWU) - a $Spin(4,4)$-invariant model
unified gravity with weak $SU(2)$ gauge and Higgs fields in the visible and
invisible sectors of the Universe. Considering the Gravi-Weak symmetry
breaking, we showed that the obtained sub-algebras contain the self-dual
left-handed gravity in the OW, and the anti-self-dual right-handed gravity in
the MW. Finally, at the low energy limit, we have only the Standard Model (SM)
and the Einstein-Hilbert's gravity. The Froggatt-Nielsen's prediction of the
top-quark and Higgs masses was given in the assumption that there exist two
degenerate vacua in the SM. This prediction was improved by the next order
calculations. We have developed a model of the Higgs Inflation using the GWU
action. According to this inflationary model, a scalar field (inflaton) starts
trapped from the "false vacuum" of the Universe at the Higgs field's VEV $v_2
\sim 10^{18}$ GeV. The interaction between the ordinary and mirror Higgs fields
$\phi$ and $\widetilde{\phi}$ generates a Hybrid model by A.~Linde of the Higgs
Inflation in our Universe.


  We study the thermopower of a disordered nanowire in the field effect
transistor configuration. After a first paper devoted to the elastic coherent
regime (Bosisio R., Fleury G. and Pichard J.-L. 2014 \textit{New J. Phys.}
\textbf{16} 035004), we consider here the inelastic activated regime taking
place at higher temperatures. In the case where charge transport is thermally
assisted by phonons (Mott Variable Range Hopping regime), we use the
Miller-Abrahams random resistor network model as recently adapted by Jiang et
al. for thermoelectric transport. This approach previously used to study the
bulk of the nanowire impurity band is extended for studying its edges. In this
limit, we show that the typical thermopower is largely enhanced, attaining
values larger that $10\, k_B/e \sim 1\, \mathrm{mV\,K}^{-1}$ and exhibiting a
non-trivial behaviour as a function of the temperature. A percolation theory by
Zvyagin extended to disordered nanowires allows us to account for the main
observed edge behaviours of the thermopower.


  We present arguments that the low density surface region of self-bounded
superfluid $^4$He systems is an inhomogeneous dilute Bose gas, with almost all
of the atoms occupying the same single-particle state at $T = 0$. Numerical
evidence for this complete Bose-Einstein condensation was first given by the
many-body variational calculations of $^4$He droplets by Lewart, Pandharipande
and Pieper in 1988. We show that the low density surface region can be treated
rigorously using a generalized Gross-Pitaevskii equation for the Bose order
parameter.


  We describe a method for calculating the counting statistics of electronic
transport through nanoscale devices with both sequential and cotunneling
contributions. The method is based upon a perturbative expansion of the von
Neumann equation in Liouvillian space, with current cumulants calculated from
the resulting nonMarkovian master equation without further approximation. As
application, we consider transport through a single quantum dot and discuss the
effects of cotunneling on noise and skewness, as well as the properties of
various approximation schemes.


  We report the discovery of a molecular wind signature from a massive
intensely star-forming clump of a few $10^9$ Msun, in the strongly
gravitationally lensed submillimeter galaxy "the Emerald" (PLCK_G165.7+49.0) at
z=2.236. The Emerald is amongst the brightest high-redshift galaxies on the
submillimeter sky, and was initially discovered with the Planck satellite. The
system contains two magnificient structures with projected lengths of 28.5" and
21" formed by multiple, near-infrared arcs, falling behind a massive galaxy
cluster at z=0.35, as well as an adjacent filament that has so far escaped
discovery in other wavebands. We used HST/WFC3 and CFHT optical and
near-infrared imaging together with IRAM and SMA interferometry of the CO(4-3)
line and 850 $\mu$m dust emission to characterize the foreground lensing mass
distribution, construct a lens model with Lenstool, and calculate gravitational
magnification factors between 20 and 50 in most of the source. The majority of
the star formation takes place within two massive star-forming clumps which are
marginally gravitationally bound and embedded in a $9 \times 10^{10}$ Msun,
fragmented disk with 20% gas fraction. One of the clumps shows a pronounced
blue wing in the CO(4-3) line profile, which we interpret as a wind signature.
The mass outflow rates are high enough for us to suspect that the clump might
become unbound within a few tens of Myr, unless the outflowing gas can be
replenished by gas accretion from the surrounding disk. The velocity offset of
-200 km s$^{-1}$ is above the escape velocity of the clump, but not that of the
galaxy overall, suggesting that much of this material might ultimately rain
back onto the galaxy and contribute to fueling subsequent star formation.


  Molecular dynamics (MD) simulation is a widely used technique to simulate
molecular systems, most commonly at the all-atom resolution where equations of
motion are integrated with timesteps on the order of femtoseconds
($1\textrm{fs}=10^{-15}\textrm{s}$). MD is often used to compute equilibrium
properties, which requires sampling from an equilibrium distribution such as
the Boltzmann distribution. However, many important processes, such as binding
and folding, occur over timescales of milliseconds or beyond, and cannot be
efficiently sampled with conventional MD. Furthermore, new MD simulations need
to be performed for each molecular system studied. We present Timewarp, an
enhanced sampling method which uses a normalising flow as a proposal
distribution in a Markov chain Monte Carlo method targeting the Boltzmann
distribution. The flow is trained offline on MD trajectories and learns to make
large steps in time, simulating the molecular dynamics of $10^{5} -
10^{6}\:\textrm{fs}$. Crucially, Timewarp is transferable between molecular
systems: once trained, we show that it generalises to unseen small peptides
(2-4 amino acids) at all-atom resolution, exploring their metastable states and
providing wall-clock acceleration of sampling compared to standard MD. Our
method constitutes an important step towards general, transferable algorithms
for accelerating MD.


  Abductive reasoning starts from some observations and aims at finding the
most plausible explanation for these observations. To perform abduction, humans
often make use of temporal and causal inferences, and knowledge about how some
hypothetical situation can result in different outcomes. This work offers the
first study of how such knowledge impacts the Abductive NLI task -- which
consists in choosing the more likely explanation for given observations. We
train a specialized language model LMI that is tasked to generate what could
happen next from a hypothetical scenario that evolves from a given event. We
then propose a multi-task model MTL to solve the Abductive NLI task, which
predicts a plausible explanation by a) considering different possible events
emerging from candidate hypotheses -- events generated by LMI -- and b)
selecting the one that is most similar to the observed outcome. We show that
our MTL model improves over prior vanilla pre-trained LMs fine-tuned on
Abductive NLI. Our manual evaluation and analysis suggest that learning about
possible next events from different hypothetical scenarios supports abductive
inference.