  In this paper, we prove that the kappa distribution is the stationary
solution of the Vlasov-Poisson system in an inhomogeneous plasma under the
polytropic equation of state and an assumption restricting the local velocity
distribution to a specific mathematical form. The profiles of density,
temperature, and electric potential are obtained theoretically. The kappa index
can be determined if the initial state is known. In order to verify the theory,
particle-in-cell simulations are made and the results show excellent agreement
with the theoretical predictions for density, temperature, and velocity
distributions of electrons. It is shown that the electron velocity distribution
of spatially inhomogeneous plasma evolves from an initial Maxwellian to the
final kappa distribution. It is also found that the value of kappa index in the
final stationary state depends on the initial state of plasma.


  Spontaneous-fission properties of 256Fm, 258Fm, and 260Fm isotopes are
studied within the Skyrme-Hartree-Fock+BCS framework. In the particle-hole
channel we take the Skyrme SkM* effective force, while in the particle-particle
channel we employ the seniority pairing interaction. Three static fission paths
for all investigated heavy fermium isotopes are found. The analysis of these
fission modes allows to describe observed asymmetric fission of 256Fm, as well
as bimodal fission of 258Fm and symmetric fission in 260Fm.


  We study a realistic Floquet topological superconductor, a periodically
driven nanowire proximitized to an equilibrium s-wave superconductor. Due to
both strong energy and density fluctuations caused from the superconducting
proximity effect, the Floquet Majorana wire becomes dissipative. We show that
the Floquet band structure is still preserved in this dissipative system. In
particular, we find that both the Floquet Majorana zero and pi modes can no
longer be simply described by the Floquet topological band theory. We also
propose an effective model to simplify the calculation of the lifetime of these
Floquet Majoranas, and find that the lifetime can be engineered by the external
driving field.


  Despite their extraordinary properties, electrides are still a relatively
unexplored class of materials with only a few compounds grown experimentally.
Especially for layered electrides, the current researches mainly focus on
several isostructures of Ca2N with similar interlayer two-dimensional (2D)
anionic electrons. An extensive screening for different layered electrides is
still missing. Here, by screening materials with anionic electrons for the
structures in Materials Project, we uncover 12 existing materials as new
layered electrides. Remarkably, these layered electrides demonstrate completely
different properties from Ca2N. For example, unusual fully spin-polarized
zero-dimensional (0D) anionic electrons are shown in metal halides with
MoS2-like structures; unique one-dimensional (1D) anionic electrons are
confined within the tubes of the quasi-1D structures; a coexistence of magnetic
and non-magnetic anionic electrons is found in ZrCl-like structures and a new
ternary Ba2LiN with both 0D and 1D anionic electrons. These materials not only
significantly increase the pool of experimentally synthesizable layered
electrides but also are promising to be exfoliated into advanced 2D materials.


  We show that the topology of pointwise convergence on scattered spaces is
compatible with the group structure of their homeomorphism group. We then
establish a few topological properties of the homeomorphism group of the first
uncountable ordinal, such as amenability and Roelcke-precompactness.


  The greybody factor and the Hawking temperature for a class of asymptotically
flat charged dilatonic black holes are computed by using the low-energy
dynamics of scalar fields in the black hole background. In the semi-classical
approximation, the Hawking temperature has a good agreement with the one
computed from the surface gravity, which is independent of the magnetic (or
electric) charges of dilatonic black holes.


  We demonstrate the coupling between the fundamental and second flexural mode
of a microcantilever. A mechanical analogue of cavity-optomechanics is then
employed, where the mechanical cavity is formed by the second vibrational mode
of the same cantilever, coupled to the fundamental mode via the geometric
nonlinearity. By exciting the cantilever at the sum and difference frequencies
between fundamental and second flexural mode, the motion of the fundamental
mode of the cantilever is amplified and damped. This concept makes it possible
to enhance or suppress the Q-factor over a wide range.


  Ultra-hot Jupiters are tidally locked gas giants with dayside temperatures
high enough to dissociate hydrogen and other molecules. Their atmospheres are
vastly non-uniform in terms of chemistry, temperature and dynamics, and this
makes their high-resolution transmission spectra and cross-correlation signal
difficult to interpret. In this work, we use the SPARC/MITgcm global
circulation model to simulate the atmosphere of the ultra-hot Jupiter WASP-76b
under different conditions, such as atmospheric drag and the absence of TiO and
VO. We then employ a 3D Monte-Carlo radiative transfer code, HIRES-MCRT, to
self-consistently model high-resolution transmission spectra with iron (Fe I)
lines at different phases during the transit. To untangle the structure of the
resulting cross-correlation map, we decompose the limb of the planet into four
sectors, and we analyse each of their contributions separately. Our experiments
demonstrate that the cross-correlation signal of an ultra-hot Jupiter is
primarily driven by its temperature structure, rotation and dynamics, while
being less sensitive to the precise distribution of iron across the atmosphere.
We also show that the previously published iron signal of WASP-76b can be
reproduced by a model featuring iron condensation on the leading limb.
Alternatively, the signal may be explained by a substantial temperature
asymmetry between the trailing and leading limb, where iron condensation is not
strictly required to match the data. Finally, we compute the $K_{p}-V_{sys}$
maps of the simulated WASP-76b atmospheres, and we show that rotation and
dynamics can lead to multiple peaks that are displaced from zero in the
planetary rest frame.


  We construct a new Monte Carlo generator of events for neutrino interactions.
The dynamical models for quasi-elastic reactions, $\Delta$ excitation and more
inelastic events described by the DIS formalism with the PDFs modified
according to recent JLab data are used. We describe in detail single pion
production channels, which combine the $\Delta$ excitation and DIS
contribution. Many comparisons of the outcome of simulations with experimental
data are presented.


  The brain tumor is the most aggressive kind of tumor and can cause low life
expectancy if diagnosed at the later stages. Manual identification of brain
tumors is tedious and prone to errors. Misdiagnosis can lead to false treatment
and thus reduce the chances of survival for the patient. Medical resonance
imaging (MRI) is the conventional method used to diagnose brain tumors and
their types. This paper attempts to eliminate the manual process from the
diagnosis process and use machine learning instead. We proposed the use of
pretrained convolutional neural networks (CNN) for the diagnosis and
classification of brain tumors. Three types of tumors were classified with one
class of non-tumor MRI images. Networks that has been used are ResNet50,
EfficientNetB1, EfficientNetB7, EfficientNetV2B1. EfficientNet has shown
promising results due to its scalable nature. EfficientNetB1 showed the best
results with training and validation accuracy of 87.67% and 89.55%,
respectively.


  Even though speech-emotion recognition (SER) has been receiving much
attention as research topic, there are still some disputes about which vocal
features can identify certain emotion. Emotion expression is also known to be
differed according to the cultural backgrounds that make it important to study
SER specific to the culture where the language belongs to. Furthermore, only a
few studies addresses the SER in Indonesian which what this study attempts to
explore. In this study, we extract simple features from 3420 voice data
gathered from 38 participants. The features are compared by means of linear
mixed effect model which shows that people who are in emotional and
non-emotional state can be differentiated by their speech duration. Using SVM
and speech duration as input feature, we achieve 76.84% average accuracy in
classifying emotional and non-emotional speech.


  It is well known that the electron transfer (ET) reaction in
donor-bridge-acceptor systems is much influenced by the bridge conformational
changes. However, the importance of the dynamical contribution to the ET rate
is still under debate. In this study, we investigate the significance of the
dynamical non-Condon effect on the ET rate in the coherent resonant tunneling
regime. To deal with the resonance, we generalize the time-dependent Fermi's
golden rule expression for the ET rate in the $T$-matrix framework. The
dynamical effect is thereby expressed in terms of the time correlation function
of the non-Hermitian $T$-matrix. We discuss the property of the quantum time
correlation function of the non-Hermitian $T$-matrix and construct the
semiclassical approximation. Then we perform a computational study using
classical equilibrium molecular dynamics simulations and quantum chemical
calculations. It is shown that the ET rate can be significantly enhanced by the
dynamical non-Condon effects.


  The high cost associated with the evaluation of Hartree-Fock exchange (HFX)
makes hybrid functionals computationally challenging for large systems. In this
work, we present an efficient way to accelerate HFX calculations with numerical
atomic basis sets. Our approach is based on the recently proposed interpolative
separable density fitting (ISDF) decomposition to construct a low rank
approximation of HFX matrix, which avoids explicit calculations of the electron
repulsion integrals (ERIs) and significantly reduces the computational cost. We
implement the ISDF method for hybrid functional (PBE0) calculations in the
HONPAS package. We take benzene and polycyclic aromatic hydrocarbons molecules
as examples and demonstrate that hybrid functionals with ISDF yields quite
promising results at a significantly reduced computational cost. Especially,
the ISDF approach reduces the total cost for evaluating HFX matrix by nearly 2
orders of magnitude compared to conventional approaches of direct evaluation of
ERIs.


  We examine the Meissner state nonlinear electrodynamic effects on the field
and angular dependence of the low temperature penetration depth, $\lambda$, of
superconductors in several kinds of unconventional pairing states, with nodes
or deep minima (``quasinodes'') in the energy gap. Our calculations are
prompted by the fact that, for typical unconventional superconducting material
parameters, the predicted size of these effects for $\lambda$ exceeds the
available experimental precision for this quantity by a much larger factor than
for others. We obtain expressions for the nonlinear component of the
penetration depth, $\Delta\lambda$, for different two- and three- dimensional
nodal or quasinodal structures. Each case has a characteristic signature as to
its dependence on the size and orientation of the applied magnetic field. This
shows that $\Delta\lambda$ measurements can be used to elucidate the nodal or
quasinodal structure of the energy gap. For nodal lines we find that
$\Delta\lambda$ is linear in the applied field, while the dependence is
quadratic for point nodes. For layered materials with
$\rm{YBa_2Cu_3O_{7-\delta}}$ (YBCO) type anisotropy, our results for the
angular dependence of $\Delta\lambda$ differ greatly from those for tetragonal
materials and are in agreement with experiment. For the two- and three-
dimensional quasinodal cases, $\Delta\lambda$ is no longer proportional to a
power of the field and the field and angular dependences are not separable,
with a suppression of the overall signal as the node is filled in.


  We study 4d simplicial quantum gravity in the dynamical triangulation
approach with a non-trivial class of measures. We find that the measure
contribution plays an important role, influencing the phase diagram and the
nature of the (possible) critical theory. We discuss how the lattice theory
could be used to fix the quantum measure in a non-ambiguous way.


  Let \[Y_j=f_*(X_j)+\xi_j,\qquad j=1,...,n,\] where $X,X_1,...,X_n$ are i.i.d.
random variables in a measurable space $(S,\mathcal{A})$ with distribution
$\Pi$ and $\xi,\xi_1,... ,\xi_n$ are i.i.d. random variables with
${\mathbb{E}}\xi=0$ independent of $(X_1,...,X_n).$ Given a dictionary
$h_1,...,h_N:S\mapsto{\mathbb{R}},$ let
$f_{\lambda}:=\sum_{j=1}^N\lambda_jh_j$,
$\lambda=(\lambda_1,...,\lambda_N)\in{\mathbb{R}}^N.$ Given $\varepsilon>0,$
define \[\hat{\Lambda}_{\varepsilon}:=\Biggl\{\lam
bda\in{\mathbb{R}}^N:\max_{1\leq k\leq N}\Biggl|n^{-1}\sum_{j=1}^n\big
l(f_{\lambda}(X_j)-Y_j\bigr)h_k(X_j)\Biggr|\leq\varepsilon \Biggr\}\] and
\[\hat{\lambda}:=\hat{\lambda}^{\varepsilon}\in \operatorname {Arg
min}\limits_{\lambda\in\hat{\Lambda}_{\varepsilon}}\|\lambda\|_{\ell_1}.\] In
the case where $f_*:=f_{\lambda^*},\lambda^*\in {\mathbb{R}}^N,$ Candes and Tao
[Ann. Statist. 35 (2007) 2313--2351] suggested using $\hat{\lambda}$ as an
estimator of $\lambda^*.$ They called this estimator ``the Dantzig selector''.
We study the properties of $f_{\hat{\lambda}}$ as an estimator of $f_*$ for
regression models with random design, extending some of the results of Candes
and Tao (and providing alternative proofs of these results).


  We consider estimation and inference in a single index regression model with
an unknown convex link function. We introduce a convex and Lipschitz
constrained least squares estimator (CLSE) for both the parametric and the
nonparametric components given independent and identically distributed
observations. We prove the consistency and find the rates of convergence of the
CLSE when the errors are assumed to have only $q \ge 2$ moments and are allowed
to depend on the covariates. When $q\ge 5$, we establish $n^{-1/2}$-rate of
convergence and asymptotic normality of the estimator of the parametric
component. Moreover, the CLSE is proved to be semiparametrically efficient if
the errors happen to be homoscedastic. {We develop and implement a numerically
stable and computationally fast algorithm to compute our proposed estimator in
the R package~\texttt{simest}}. We illustrate our methodology through extensive
simulations and data analysis. Finally, our proof of efficiency is geometric
and provides a general framework that can be used to prove efficiency of
estimators in a wide variety of semiparametric models even when they do not
satisfy the efficient score equation directly.


  This work is a methodical study on hybrid reconstruction techniques for
hybrid imaging/timing Cherenkov observations. This type of hybrid array is to
be realized at the gamma-observatory TAIGA intended for very high energy
gamma-ray astronomy (>30 TeV). It aims at combining the cost-effective
timing-array technique with imaging telescopes. Hybrid operation of both of
these techniques can lead to a relatively cheap way of development of a large
area array. The joint approach of gamma event selection was investigated on
both types of simulated data: the image parameters from the telescopes, and the
shower parameters reconstructed from the timing array. The optimal set of
imaging parameters and shower parameters to be combined is revealed. The cosmic
ray background suppression factor depending on distance and energy is
calculated. The optimal selection technique leads to cosmic ray background
suppression of about 2 orders of magnitude on distances up to 450 m for
energies greater than 50 TeV.


  We present reduced images and catalogues of photometric and emission line
data ($\sim$230,000 and $\sim$8,000 sources, respectively) for the WFC3
Infrared Spectroscopic Parallel (WISP) Survey. These data are made publicly
available on the Mikulski Archive for Space Telescopes (MAST) and include
reduced images from various facilities: ground-based $ugri$, HST WFC3, and
Spitzer IRAC (Infrared Array Camera). Coverage in at least one additional
filter beyond the WFC3/IR data are available for roughly half of the fields
(227 out of 483), with $\sim$20% (86) having coverage in six or more filters
from $u$-band to IRAC 3.6$\mu$m (0.35-3.6$\mu$m). For the lower spatial
resolution (and shallower) ground-based and IRAC data, we perform PSF-matched,
prior-based, deconfusion photometry (i.e., forced-photometry) using the TPHOT
software to optimally extract measurements or upper limits. We present the
methodology and software used for the WISP emission line detection and visual
inspection. The former adopts a continuous wavelet transformation that
significantly reduces the number of spurious sources as candidates before the
visual inspection stage. We combine both WISP catalogues and perform SED
fitting on galaxies with reliable spectroscopic redshifts and multi-band
photometry to measure their stellar masses. We stack WISP spectra as functions
of stellar mass and redshift and measure average emission line fluxes and
ratios. We find that WISP emission line sources are typically `normal'
star-forming galaxies based on the Mass-Excitation diagram ([OIII]/H$\beta$ vs.
$M_\star$; $0.74<z_\mathrm{grism}<2.31$), the galaxy main sequence (SFR vs.
$M_\star$; $0.30<z_\mathrm{grism}<1.45$), $S_{32}$ ratio vs. $M_\star$
($0.30<z_\mathrm{grism}<0.73$), and $O_{32}$ and $R_{23}$ ratios vs. $M_\star$
($1.27<z_\mathrm{grism}<1.45$).


  We illustrate the use of the R-package "rstiefel" for matrix-variate data
analysis in the context of two examples. The first example considers estimation
of a reduced-rank mean matrix in the presence of normally distributed noise.
The second example considers the modeling of a social network of friendships
among teenagers. Bayesian estimation for these models requires the ability to
simulate from the matrix-variate von Mises-Fisher distributions and the
matrix-variate Bingham distributions on the Stiefel manifold.


  Associated to each complex-valued random variable satisfying appropriate
integrability conditions, we introduce a different generalization of the
Stirling numbers of the second kind. Various equivalent definitions are
provided. Attention, however, is focused on applications. Indeed, such numbers
describe the moments of sums of i.i.d. random variables, determining their
precise asymptotic behavior without making use of the central limit theorem.
Such numbers also allow us to obtain explicit and simple Edgeworth expansions.
Applications to L\'{e}vy processes and cumulants are discussed, as well.


  We present measurements of the spatial intensity distribution of localized
modes in a two-dimensional open microwave cavity randomly filled with
cylindrical dielectric scatterers. We show that each of these modes displays a
range of localization lengths and successfully relate the largest value to the
measured leakage rate at the boundary. These results constitute unambiguous
signatures of the existence of strongly localized electromagnetic modes in
two-dimensionnal open random media.


  Using atomistic electronic structure calculations and Boltzmann
semi-classical transport we compute the thermoelectric power factor of
ultra-thin-body p-type Si layers of thicknesses from W=3nm up to 10nm. We show
that the power factor for channels in [110] transport orientation and strong
(110) surface confinement largely outperforms all differently oriented channels
by more than 2X. Furthermore, the power factor in this channel increases by
~40% with layer thickness reduction. This increase, together with the large
confinement effective mass of the (110) surface, make this particular channel
less affected by the detrimental effects of enhanced surface roughness
scattering and distortion at the nanoscale. Our results, therefore, point
towards the optimal geometrical features regarding orientation and length scale
for power factor improvement in 2D thin-layers of zincblende semiconductors.


  Neutron stars and stellar-mass black holes are the remnants of massive star
explosions. Most massive stars reside in close binary systems, and the
interplay between the companion star and the newly formed compact object has
been theoretically explored, but signatures for binarity or evidence for the
formation of a compact object during a supernova explosion are still lacking.
Here we report a stripped-envelope supernova, SN 2022jli, which shows 12.4-day
periodic undulations during the declining light curve. Narrow H$\alpha$
emission is detected in late-time spectra with concordant periodic velocity
shifts, likely arising from hydrogen gas stripped from a companion and accreted
onto the compact remnant. A new Fermi/LAT $\gamma$-ray source is temporally and
positionally consistent with SN 2022jli. The observed properties of SN 2022jli,
including periodic undulations in the optical light curve, coherent H$\alpha$
emission shifting, and evidence for association with a $\gamma$-ray source,
point to the explosion of a massive star in a binary system leaving behind a
bound compact remnant. Mass accretion from the companion star onto the compact
object powers the light curve of the supernova and generates the $\gamma$-ray
emission.


  Materials undergoing antiferroelectric/ferroelectric (AFE/FE) transitions are
studied for possible applications that exploit the large volume, charge and
entropy differences between the two states, such as electrocaloric cooling,
energy storage, electromechanical actuators. Though certain compositions of PZT
codoped with La and Sn may withstand millions of electrically induced AFE/FE
cycles, in other cases few thermally induced cycles and room temperature aging
may cause noticeable changes in the material properties. This is particularly
evident in the elastic moduli, which at room temperature can become as much as
four times softer. In order to get more insight into the mechanisms involved in
such elastic aging and full recovering with mild annealing at 600-800 K, the
effect of La doping on PZT with 4.6% Ti is studied with anelastic measurements.
Complete suppression of the time dependent phenomena is found after the
transformation of the intermediate FE phase into incommensurate AFE by 2% La
doping. This is discussed in terms of disappearance of the stress and electric
fields at the FE/AFE interfaces, in the light of the thermally activated
anelastic relaxation processes that are observed at high temperature, and are
due to mobile defects, presumably O vacancies.


  As one of the key properties of the Higgs boson, the Higgs total width is
sensitive to global profile of the Higgs boson couplings, and thus new physics
would modify the Higgs width. We investigate the total width in various new
physics models, including various scalar extension, composite Higgs models, and
fraternal twin Higgs model. Typically the Higgs width is smaller than the
standard model value due to mixture with other scalar if the Higgs is
elementary, or curved Higgs field space for the composite Higgs. On the other
hand, except the possible invisible decay mode, the enhanced Yukawa coupling in
the two Higgs doublet model or the exotic fermion embeddings in the composite
Higgs, could enhance the Higgs width greatly. The precision measurement of the
Higgs total width at the high-luminosity LHC can be used to discriminate
certain new physics models.


  We extend recent results on affine Volterra processes to the inhomogeneous
case. This includes moment bounds of solutions of Volterra equations driven by
a Brownian motion with an inhomogeneous kernel $K(t,s)$ and inhomogeneous drift
and diffusion coefficients $b(s,X_s)$ and $\sigma(s,X_s)$. In the case of
affine $b$ and $\sigma \sigma^T$ we show how the conditional Fourier-Laplace
functional can be represented by a solution of an inhomogeneous
Riccati-Volterra integral equation. For a kernel of convolution type
$K(t,s)=\overline{K}(t-s)$ we establish existence of a solution to the
stochastic inhomogeneous Volterra equation. If in addition $b$ and $\sigma
\sigma^T$ are affine, we prove that the conditional Fourier-Laplace functional
is exponential-affine in the past path. Finally, we apply these results to an
inhomogeneous extension of the rough Heston model used in mathematical finance.


  X-ray scattering measurements are performed in order to verify % that the
mechanism leading to the DC electric field induced $C_{2v}^{19} \to
C_{2v}^{\neq 19}$ phase transition in KDP crystal at 119 K is the changing of
the local sites symmetries of phosphate group from $C_2$ in the $C_{2v}^{19}$
phase to $C_s$ in the $C_{2v}^{\neq 19}$ phase. It is shown by analyzing the
integrated intensity of the (800) and (080) reflections that under DC electric
field the density of oxygen atoms lying on these plane changes indicating that
phosphate group rotates around the [010] direction relative to the orthorhombic
$C_{2v}^{19}$ structure. Some Raman results are also discussed.


  We summarize the perspectives on quarkonium, in particular charmonium,
detection at the LHC, both for proton-proton and heavy-ion collisions. We give
a review of the experimental capabilities of the four LHC detectors: ALICE,
ATLAS, CMS and LHCb.


  This work presents a data-driven method for learning low-dimensional
time-dependent physics-based surrogate models whose predictions are endowed
with uncertainty estimates. We use the operator inference approach to model
reduction that poses the problem of learning low-dimensional model terms as a
regression of state space data and corresponding time derivatives by minimizing
the residual of reduced system equations. Standard operator inference models
perform well with accurate training data that are dense in time, but producing
stable and accurate models when the state data are noisy and/or sparse in time
remains a challenge. Another challenge is the lack of uncertainty estimation
for the predictions from the operator inference models. Our approach addresses
these challenges by incorporating Gaussian process surrogates into the operator
inference framework to (1) probabilistically describe uncertainties in the
state predictions and (2) procure analytical time derivative estimates with
quantified uncertainties. The formulation leads to a generalized least-squares
regression and, ultimately, reduced-order models that are described
probabilistically with a closed-form expression for the posterior distribution
of the operators. The resulting probabilistic surrogate model propagates
uncertainties from the observed state data to reduced-order predictions. We
demonstrate the method is effective for constructing low-dimensional models of
two nonlinear partial differential equations representing a compressible flow
and a nonlinear diffusion-reaction process, as well as for estimating the
parameters of a low-dimensional system of nonlinear ordinary differential
equations representing compartmental models in epidemiology.


  We try to identify the nature of high redshift long Gamma-Ray Bursts (LGRBs)
host galaxies by comparing the observed abundance ratios in the interstellar
medium with detailed chemical evolution models accounting for the presence of
dust. We compared measured abundance data from LGRB afterglow spectra to
abundance patterns as predicted by our models for different galaxy types. We
analysed in particular [X/Fe] abundance ratios (where X is C, N, O, Mg, Si, S,
Ni, Zn) as functions of [Fe/H]. Different galaxies (irregulars, spirals,
ellipticals) are, in fact, characterised by different star formation histories,
which produce different [X/Fe] ratios ("time-delay model"). This allows us to
identify the morphology of the hosts and to infer their age (i.e. the time
elapsed from the beginning of star formation) at the time of the GRB events, as
well as other important parameters. Relative to previous works, we use newer
models in which we adopt updated stellar yields and prescriptions for dust
production, accretion and destruction. We have considered a sample of seven
LGRB host galaxies. Our results have suggested that two of them (GRB 050820,
GRB 120815A) are ellipticals, two (GRB 081008, GRB 161023A) are spirals and
three (GRB 050730, GRB 090926A, GRB 120327A) are irregulars. We also found that
in some cases changing the initial mass function can give better agreement with
the observed data. The calculated ages of the host galaxies span from the order
of 10 Myr to little more than 1 Gyr.


  The dynamics of metastable magnetic domain walls in straight ferromagnetic
nanowires under spin waves, external magnetic fields, and current induced spin
transfer torque are studied by micromagnetic simulations. It is found that in
contrast to a stable wall, it is possible to displace a metastable domain wall
in the absence of any external excitation. In addition, independent of the
domain wall excitation method, the velocity of a metastable wall is much
smaller than a stable wall and their displacement direction could be different
from the stable wall depending on the structure of metastable walls. Under the
current induced spin transfer torque excitation, the direction of domain wall
displacement is directly related to the intensity of nonadiabatic spin transfer
torque. In a rough nanowire, it is found that the displacement of a metastable
wall could happen much below the critical excitation of a stable wall.
Furthermore, we show that it is possible to have either a forward or backward
displacement of a metastable domain wall by changing the pulse width of the
excitation.


  Let $R$ be a unitary commutative real algebra and $K\subseteq
Hom(R,\mathbb{R})$, closed with respect to the product topology. We consider
$R$ endowed with the topology $\mathcal{T}_K$, induced by the family of
seminorms $\rho_{\alpha}(a):=|\alpha(a)|$, for $\alpha\in K$ and $a\in R$. In
case $K$ is compact, we also consider the topology induced by
$\|a\|_K:=\sup_{\alpha\in K}|\alpha(a)|$ for $a\in R$. If $K$ is Zariski dense,
then those topologies are Hausdorff. In this paper we prove that the closure of
the cone of sums of 2d-powers, $\sum R^{2d}$, with respect to those two
topologies is equal to $Psd(K):=\{a\in R:\alpha(a)\geq 0,\textrm{for
all}\alpha\in K\}$. In particular, any continuous linear functional $L$ on the
polynomial ring $R=\mathbb{R}[X_1,...,X_n]$ with $L(h^{2d})\ge0$ for each $h\in
R$ is integration with respect to a positive Borel measure supported on $K$.
Finally we give necessary and sufficient conditions to ensure the continuity of
a linear functional with respect to those two topologies.


  A non-autonomous stochastic dynamical model approach is developed to describe
the seasonal to interannual variability of the El Ni\~no-Southern Oscillation
(ENSO). We determine the model coefficients by systematic statistical
estimations using partial observations involving only sea surface temperature
data. Our approach reproduces the observed seasonal phase locking and its
uncertainty, as well as the highly non-Gaussian statistics of ENSO. Finally, we
recover the intermittent time series of the hidden processes, including the
thermocline depth and the wind bursts.


  The majority of High Mass X-ray Binaries (HMXBs) behave as X-ray pulsars,
revealing that they contain a magnetised neutron star. Among the four HMXBs not
showing pulsations, and that do not show the characteristics of accreting black
holes, there is the unusual HMXB 4U 2206+54. Here we present contemporaneous
high-energy and radio observations of this system conducted with INTEGRAL and
the VLA in order to unveil its nature. The high-energy spectra show clear
indications of the presence of an absorption feature at ~32 keV. This is the
third high-energy observatory which reveals marginal evidence of this feature,
giving strong support to the existence of a cyclotron resonance scattering
feature, which implies a magnetic field of 3.6 x 10^12 G. On the other hand,
the source is not detected at centimetre radio wavelengths with a 3 sigma upper
limit of 0.039 mJy. The expected radio emission for an accreting black hole in
the low/hard state, inferred from X-ray flux measurements, would be at least 60
times greater than the measured upper limit. Both results firmly indicate that,
in spite of the absence of pulsations, 4U 2206+54 hosts a magnetic accreting
neutron star, the first one not to be observed as an X-ray pulsar.


  Quantum electrodynamics (QED) with self-conjugated equations with spinor wave
functions for fermion fields is considered. In the low order of the
perturbation theory, matrix elements of some of QED physical processes are
calculated. The final results coincide with cross-sections calculated in the
standard QED. The self-energy of an electron and amplitudes of processes
associated with determination of the anomalous magnetic moment of an electron
and Lamb shift are calculated. These results agree with the results in the
standard QED.Distinctive feature of the developed theory is the fact that only
states with positive energies are present in the intermediate virtual states in
the calculations of the electron self-energy, anomalous magnetic moment of an
electron and Lamb shift. Besides, in equations, masses of particle and
anti-particles have the opposite signs.


  Monte Carlo methods use random sampling to estimate numerical quantities
which are hard to compute deterministically. One important example is the use
in statistical physics of rapidly mixing Markov chains to approximately compute
partition functions. In this work we describe a quantum algorithm which can
accelerate Monte Carlo methods in a very general setting. The algorithm
estimates the expected output value of an arbitrary randomised or quantum
subroutine with bounded variance, achieving a near-quadratic speedup over the
best possible classical algorithm. Combining the algorithm with the use of
quantum walks gives a quantum speedup of the fastest known classical algorithms
with rigorous performance bounds for computing partition functions, which use
multiple-stage Markov chain Monte Carlo techniques. The quantum algorithm can
also be used to estimate the total variation distance between probability
distributions efficiently.


  Initial offset placement in p2p streaming systems is studied in this paper.
Proportional placement (PP) scheme is proposed. In this scheme, peer places the
initial offset as the offset reported by other reference peer with a shift
proportional to the buffer width or offset lag of this reference peer. This
will introduce a stable placement that supports larger buffer width for peers
and small buffer width for tracker. Real deployed placement method in PPLive is
studied through measurement. It shows that, instead of based on offset lag, the
placement is based on buffer width of the reference peer to facilitate the
initial chunk fetching. We will prove that, such a PP scheme may not be stable
under arbitrary buffer occupation in the reference peer. The required average
buffer width then is derived. A simple good peer selection mechanism to check
the buffer occupation of reference peer is proposed for a stable PP scheme
based on buffer width


  We propose to apply Ramsey's method of separated oscillating fields to the
spectroscopy of the quantum states in the gravity potential above a vertical
mirror. This method allows a precise measurement of quantum mechanical
phaseshifts of a Schr\"odinger wave packet bouncing off a hard surface in the
gravitational field of the earth. Measurements with ultra-cold neutrons will
offer a sensitivity to Newton's law or hypothetical short-ranged interactions,
which is about 21 orders of magnitude below the energy scale of
electromagnetism.


  This paper presents an automated peer-to-peer negotiation strategy for
settling energy contracts among prosumers in a Residential Energy Cooperative
considering heterogeneity prosumer preferences. The heterogeneity arises from
prosumers' evaluation of energy contracts through multiple societal and
environmental criteria and the prosumers' private preferences over those
criteria. The prosumers engage in bilateral negotiations with peers to mutually
agree on periodical energy contracts/loans consisting of the energy volume to
be exchanged at that period and the return time of the exchanged energy. The
negotiating prosumers navigate through a common negotiation domain consisting
of potential energy contracts and evaluate those contracts from their
valuations on the entailed criteria against a utility function that is robust
against generation and demand uncertainty. From the repeated interactions, a
prosumer gradually learns about the compatibility of its peers in reaching
energy contracts that are closer to Nash solutions. Empirical evaluation on
real demand, generation and storage profiles -- in multiple system scales --
illustrates that the proposed negotiation based strategy can increase the
system efficiency (measured by utilitarian social welfare) and fairness
(measured by Nash social welfare) over a baseline strategy and an individual
flexibility control strategy representing the status quo strategy. We thus
elicit system benefits from peer-to-peer flexibility exchange already without
any central coordination and market operator, providing a simple yet flexible
and effective paradigm that complements existing markets.


  We estimate the mass of the Large Magellanic Cloud (LMC) using the kinematics
of 30 LMC globular clusters (GCs). We combine proper motions (PMs) measured
with HST, Gaia, or a combination of the two, from a recent study by Bennet et
al. (2022) with literature line-of-sight velocities (LOSVs) to give 3
components of motion. With these, we derive a 3D velocity dispersion anisotropy
$\beta = -0.72 ^{+0.62} _{-1.07}$, consistent with the GCs forming a flattened
system with significant azimuthal motion. We then apply a tracer mass estimator
and measure an enclosed mass $M (<13.2 \mathrm{kpc})= 2.66^{+0.42} _{-0.36}
\times 10^{10} \mathrm{M}_\odot$. This is broadly consistent with results from
previous studies of the LOSVs of GCs and other luminous tracers. Assuming a
cosmologically-constrained NFW distribution for the dark matter, this implies a
virial mass $M_\mathrm{virial} = 1.80^{+1.05} _{-0.54} \times 10^{11}
\mathrm{M}_\odot$. Despite being an extrapolation by almost an order of
magnitude in radius, this result is consistent with published estimates from
other methods that are directly sensitive to the LMC's total mass. Our results
support the conclusion that the LMC is approximately 17$^{+10}_{-6}$% of the
Milky Way's mass, making it a significant contributor to the Local Group (LG)
potential.


  What is physics? What are the limits of what physics can say about the world?
In seeking ever-broader theoretical `umbrellas' for physical phenomena, we are
seeking unifying principles. Emergent phenomena have turned out to be some of
the most difficult to explain, causing a `clash of umbrellas' so-to-speak, at
the interface between the quantum and classical domains. This essay explores
the role of articulation in this particularly vexing problem and ultimately
addresses the question of whether the language and mathematics we use to
describe the universe is sufficient in its present form and application.


  The room-temperature longitudinal piezoresistance of n-type and p-type
crystalline silicon along selected crystal axes is investigated under uniaxial
compressive stresses up to 3 GPa. While the conductance ($G$) of n-type silicon
eventually saturates at $\approx 45%$ of its zero-stress value ($G_0$) in
accordance with the charge transfer model, in p-type material $G/G_0$ increases
above a predicted limit of $\approx 4.5$ without any significant saturation,
even at 3 GPa. Calculation of $G/G_0$ using \textit{ab-initio} density
functional theory reveals that neither $G$ nor the mobility, when properly
averaged over the hole distribution, saturate at stresses lower than 3 GPa. The
lack of saturation has important consequences for strained silicon
technologies.


  HST images of NGC 3379 show that the V and I luminosity profiles in the inner
13 arcsec of this E1 galaxy are represented by two different components: a
stellar bulge following a Sersic Law with exponent n = 2.36, and a central core
(r < 0.7 arcsec) with a characteristic "cuspy" profile. Subtraction of the
underlying stellar component represented by the fitted Sersic profile revealed
the presence of a small (r ~ 105 pc) dust disk of about 150 solar masses,
oriented at PA = 125 degrees and inclined ~ 77 degrees with respect to the line
of sight. The same absorption structure is detected in the color-index (V-I)
image. The stellar rotation in the inner 20 arcsec is well represented by a
parametric planar disk model, inclined ~ 26 degrees relative to the plane of
the sky, and apparent major axis along PA ~ 67 degrees. The gas velocity curves
in the inner 5 arcsec show a steep gradient, indicating that the gas rotates
much faster than the stars, although in the same direction. The velocity field
of the gaseous system, however, is not consistent with the simple model of
Keplerian rotation sustained by the large (7 x 10E9 solar masses within a
radius of ~ 90 pc) central mass implied by the maximum velocity observed, but
the available data precludes a more detailed analysis.


  Seeking tighter relaxations of combinatorial optimization problems,
semidefinite programming is a generalization of linear programming that offers
better bounds and is still polynomially solvable. Yet, in practice, a
semidefinite program is still significantly harder to solve than a similar-size
Linear Program (LP). It is well-known that a semidefinite program can be
written as an LP with infinitely-many cuts that could be solved by repeated
separation in a Cutting-Planes scheme; this approach is likely to end up in
failure. We proposed in [Projective Cutting-Planes, Daniel Porumbel, Siam
Journal on Optimization, 2020] the Projective Cutting-Planes method that
upgrades t he well-known separation sub-problem to the projection sub-problem:
given a feasible $y$ inside a polytope $P$ and a direction $d$, find the
maximum $t^*$ so that $y+t^*d\in P$. Using this new sub-problem, one can
generate a sequence of both inner and outer solutions that converge to the
optimum over $P$. This paper shows that the projection sub-problem can be
solved very efficiently in a semidefinite programming context, enabling the
resulting method to compete very well with state-of-the-art semidefinite
optimization software (refined over decades). Results suggest it may the
fastest method for matrix sizes larger than $2000\times 2000$.


  We propose here a fully backaction-evading scheme for the measurement of the
entanglement between two nanomechanical resonators. The system, which consists
of two mechanical oscillators, coupled to a single mode of an electromagnetic
resonant cavity through a radiation-pressure interaction term, is driven by two
pump tones and four detection tones. As previously discussed in the literature,
the former induce entanglement between the two mechanical oscillators, while we
show here that a specific choice of phase and amplitude of the detection tones
allows for direct pairwise reconstruction of the collective quadrature
fluctuations of the mechanical oscillators belonging to quantum-mechanics-free
subspaces, thereby providing direct evidence of the entanglement properties of
the two mechanical resonators.


  Cyber physical ecosystem connects different intelligent devices over
heterogeneous networks. Various operations are performed on smart objects to
ensure efficiency and to support automation in smart environments. An Activity
(defined by Gupta and Sandhu) reflects the current state of an object, which
changes in response to requested operations. Due to multiple running activities
on different objects, it is critical to secure collaborative systems
considering run-time decisions impacted due to related activities (and other
parameters) supporting active enforcement of access control decision. Recently,
Gupta and Sandhu proposed Activity-Centric Access Control (ACAC) and discussed
the notion of activity as a prime abstraction for access control in
collaborative systems. The model provides an active security approach that
considers activity decision factors such as authorizations, obligations,
conditions, and dependencies among related device activities. This paper takes
a step forward and presents the core components of an ACAC model and compares
with other security models differentiating novel properties of ACAC. We
highlight how existing models do not (or in limited scope) support `active'
decision and enforcement of authorization in collaborative systems. We propose
a hierarchical structure for a family of ACAC models by gradually adding the
properties related to notion of activity and discuss states of an activity. We
highlight the convergence of ACAC with Zero Trust tenets to reflect how ACAC
supports necessary security posture of distributed and connected smart
ecosystems. This paper aims to gain a better understanding of ACAC in
collaborative systems supporting novel abstractions, properties and
requirements.


  Knowledge-based Visual Question Answering (KVQA) requires external knowledge
beyond the visible content to answer questions about an image. This ability is
challenging but indispensable to achieve general VQA. One limitation of
existing KVQA solutions is that they jointly embed all kinds of information
without fine-grained selection, which introduces unexpected noises for
reasoning the correct answer. How to capture the question-oriented and
information-complementary evidence remains a key challenge to solve the
problem. Inspired by the human cognition theory, in this paper, we depict an
image by multiple knowledge graphs from the visual, semantic and factual views.
Thereinto, the visual graph and semantic graph are regarded as
image-conditioned instantiation of the factual graph. On top of these new
representations, we re-formulate Knowledge-based Visual Question Answering as a
recurrent reasoning process for obtaining complementary evidence from
multimodal information. To this end, we decompose the model into a series of
memory-based reasoning steps, each performed by a G raph-based R ead, U pdate,
and C ontrol ( GRUC ) module that conducts parallel reasoning over both visual
and semantic information. By stacking the modules multiple times, our model
performs transitive reasoning and obtains question-oriented concept
representations under the constrain of different modalities. Finally, we
perform graph neural networks to infer the global-optimal answer by jointly
considering all the concepts. We achieve a new state-of-the-art performance on
three popular benchmark datasets, including FVQA, Visual7W-KB and OK-VQA, and
demonstrate the effectiveness and interpretability of our model with extensive
experiments.


  The role played by non extensive thermodynamics in physical systems has been
under intense debate for the last decades. With many applications in several
areas, the Tsallis statistics has been discussed in details in many works and
triggered an interesting discussion on the most deep meaning of entropy and its
role in complex systems. Some possible mechanisms that could give rise to non
extensive statistics have been formulated along the last several years, in
particular a fractal structure in thermodynamics functions was recently
proposed as a possible origin for non extensive statistics in physical systems.
In the present work we investigate the properties of such fractal
thermodynamical system and propose a diagrammatic method for calculations of
relevant quantities related to such system. It is shown that a system with the
fractal structure described here presents temperature fluctuation following an
Euler Gamma Function, in accordance with previous works that evidenced the
connections between those fluctuations and Tsallis statistics. Finally, the
fractal scale invariance is discussed in terms of the Callan-Symanzik Equation.


  We study first-passage percolation in two dimensions, using measures mu on
passage times with b:=inf supp(mu) >0 and mu({b})=p \geq p_c, the threshold for
oriented percolation. We first show that for each such mu, the boundary of the
limit shape for mu is differentiable at the endpoints of flat edges in the
so-called percolation cone. We then conclude that the limit shape must be
non-polygonal for all of these measures. Furthermore, the associated
Richardson-type growth model admits infinite coexistence and if mu is not
purely atomic the graph of infection has infinitely many ends. We go on to show
that lower bounds for fluctuations of the passage time given by Newman-Piza
extend to these measures. We establish a lower bound for the variance of the
passage time to distance n of order log n in any direction outside the
percolation cone under a condition of finite exponential moments for mu. This
result confirms a prediction of Newman-Piza and Zhang. Under the assumption of
finite radius of curvature for the limit shape in these directions, we obtain a
power-law lower bound for the variance and an inequality between the exponents
chi and xi.


  Intersecting D-brane models and their T-dual magnetic compactifications yield
attractive models of particle physics where magnetic flux plays a twofold role,
being the source of fermion chirality as well as supersymmetry breaking. A
potential problem of these models is the appearance of tachyons which can only
be avoided in certain regions of moduli space and in the presence of Wilson
lines. We study the effective four-dimensional field theory for an orientifold
compactification of type IIA string theory and the corresponding toroidal
compactification of type I string theory. After determining the Kaluza-Klein
and Landau-level towers of massive states in different sectors of the model, we
evaluate their contributions to the one-loop effective potential, summing over
all massive states, and we relate the result to the corresponding string
partition functions. We find that the Wilson-line effective potential has only
saddle points, and the theory is therefore driven to the tachyonic regime.
There tachyon condensation takes place and chiral fermions acquire a mass of
the order of the compactification scale. We also find evidence for a tachyonic
behaviour of the volume moduli. More work on tachyon condensation is needed to
clarify the connection between supersymmetry breaking, a chiral fermion
spectrum and vacuum stability.


  A Lorentz violating vector "{\ae}ther" field in five dimensional spacetime
can give rise to an inflation model in which the eta problem can be avoided. By
identifying the inflaton field with the moduli field describing the radius of
the extra dimension, we demonstrate that non-vanishing vacuum expectation value
of the {\ae}ther field can suppress contributions from operators that usually
destroy the flatness of the inflaton potential. We also show that the latest
observational data can put the lower bound on the vacuum expectation value of
the {\ae}ther field.


  Our extensive real measurements over Amazon EC2 show that the virtual
instances often have different computing speeds even if they share the same
configurations. This motivates us to study heterogeneous Coded Storage Elastic
Computing (CSEC) systems where machines, with different computing speeds, join
and leave the network arbitrarily over different computing steps. In CSEC
systems, a Maximum Distance Separable (MDS) code is used for coded storage such
that the file placement does not have to be redefined with each elastic event.
Computation assignment algorithms are used to minimize the computation time
given computation speeds of different machines. While previous studies of
heterogeneous CSEC do not include stragglers-the slow machines during the
computation, we develop a new framework in heterogeneous CSEC that introduces
straggler tolerance. Based on this framework, we design a novel algorithm using
our previously proposed approach for heterogeneous CSEC such that the system
can handle any subset of stragglers of a specified size while minimizing the
computation time. Furthermore, we establish a trade-off in computation time and
straggler tolerance. Another major limitation of existing CSEC designs is the
lack of practical evaluations using real applications. In this paper, we
evaluate the performance of our designs on Amazon EC2 for applications of the
power iteration and linear regression. Evaluation results show that the
proposed heterogeneous CSEC algorithms outperform the state-of-the-art designs
by more than 30%.


  In a decade-and-a-half old experiment, Raabe et al.(Nature 431 (2004) 823),
had studied fusion of an incoming beam of halo nucleus $^{6}He$ with the target
nucleus $^{238}U$. We extract a new interpretation of the experiment, different
from the one that has been inferred so far. We show that their experiment is
actually able to discriminate between the structures of the target nucleus
(behaving as standard nucleus with density distribution described with
canonical RMS radius r = $r_0 A^{\frac{1}{3}}$ with $r_0$ = 1.2 fm), and the
{\bf "core"} of the halo nucleus, which surprisingly, does not follow the
standard density distribution with the above RMS radius. In fact the core has
the structure of a tennis-ball (bubble) like nucleus, with a "hole" at the
centre of the density distribution. This novel interpretation of the fusion
experiment provides an unambigous support to an almost two decades old model
(Abbas, Mod. Phys. Lett. A 16 (2001) 755), of the halo nuclei. This Quantum
Chromodyanamics based model, succeeds in identifyng all known halo nuclei and
makes clear-cut and unique predictions for new and heavier halo nuclei. This
model supports the existence of tennis-ball (bubble) like core, of even the
giant-neutron halo nuclei. This should prove beneficial to the
experimentalists, to go forward more confidently, in their study of exotic
nuclei.


  Wirelessly connected devices can collaborately train a machine learning model
using federated learning, where the aggregation of model updates occurs using
over-the-air computation. Carrier frequency offset caused by imprecise clocks
in devices will cause the phase of the over-the-air channel to drift randomly,
such that late symbols in a coherence block are transmitted with lower quality
than early symbols. To mitigate the effect of degrading symbol quality, we
propose a scheme where one of the permutations Roll, Flip and Sort are applied
on gradients before transmission. Through simulations we show that the
permutations can both improve and degrade learning performance. Furthermore, we
derive the expectation and variance of the gradient estimate, which is shown to
grow exponentially with the number of symbols in a coherence block.


  The Central Molecular Zone (CMZ) spans the inner ~450 pc (3 degrees) of our
Galaxy. This region is defined by its enhanced molecular emission and contains
5% of the entire Galaxy's molecular gas mass. However, the number of detected
star forming sites towards the CMZ may be low for the amount of molecular gas
that is present, and improved surveys of star formation indicators can help
clarify this. With the Karl G Jansky Very Large Array (VLA), we conducted a
blind survey of 6.7 GHz methanol masers spanning the inner 3deg x 40arcmin (450
pc x 100 pc) of the Galaxy. We detected 43 methanol masers towards 28
locations, 16 of which are new detections. The velocities of most of these
masers are consistent with being located within the CMZ. A majority of the
detected methanol masers are distributed towards positive Galactic longitudes,
similar to 2/3 of the molecular gas mass distributed at positive Galactic
longitudes. The 6.7 GHz methanol maser is an excellent indicator of high mass
(>8 solar mass) star formation, with new detections indicating sites of massive
star formation in the CMZ.


  High-quality reconstruction of MRI images from under-sampled `k-space' data,
which is in the Fourier domain, is crucial for shortening MRI acquisition times
and ensuring superior temporal resolution. Over recent years, a wealth of deep
neural network (DNN) methods have emerged, aiming to tackle the complex,
ill-posed inverse problem linked to this process. However, their instability
against variations in the acquisition process and anatomical distribution
exposes a deficiency in the generalization of relevant physical models within
these DNN architectures. The goal of our work is to enhance the generalization
capabilities of DNN methods for k-space interpolation by introducing
`MA-RECON', an innovative mask-aware DNN architecture and associated training
method. Unlike preceding approaches, our `MA-RECON' architecture encodes not
only the observed data but also the under-sampling mask within the model
structure. It implements a tailored training approach that leverages data
generated with a variety of under-sampling masks to stimulate the model's
generalization of the under-sampled MRI reconstruction problem. Therefore,
effectively represents the associated inverse problem, akin to the classical
compressed sensing approach. The benefits of our MA-RECON approach were
affirmed through rigorous testing with the widely accessible fastMRI dataset.
Compared to standard DNN methods and DNNs trained with under-sampling mask
augmentation, our approach demonstrated superior generalization capabilities.
This resulted in a considerable improvement in robustness against variations in
both the acquisition process and anatomical distribution, especially in regions
with pathology. In conclusion, our mask-aware strategy holds promise for
enhancing the generalization capacity and robustness of DNN-based methodologies
for MRI reconstruction from undersampled k-space data.


  As demonstrated by GPT-3 and T5, transformers grow in capability as parameter
spaces become larger and larger. However, for tasks that require a large amount
of knowledge, non-parametric memory allows models to grow dramatically with a
sub-linear increase in computational cost and GPU memory requirements. Recent
models such as RAG and REALM have introduced retrieval into conditional
generation. These models incorporate neural initial retrieval from a corpus of
passages. We build on this line of research, proposing Re2G, which combines
both neural initial retrieval and reranking into a BART-based
sequence-to-sequence generation. Our reranking approach also permits merging
retrieval results from sources with incomparable scores, enabling an ensemble
of BM25 and neural initial retrieval. To train our system end-to-end, we
introduce a novel variation of knowledge distillation to train the initial
retrieval, reranker, and generation using only ground truth on the target
sequence output. We find large gains in four diverse tasks: zero-shot slot
filling, question answering, fact-checking, and dialog, with relative gains of
9% to 34% over the previous state-of-the-art on the KILT leaderboard. We make
our code available as open source at
https://github.com/IBM/kgi-slot-filling/tree/re2g.


  A gas of $N$ Bogomol'nyi vortices in the Abelian Higgs model is studied on a
compact Riemann surface of genus $g$ and area $A$. The volume of the moduli
space is computed and found to depend on $N, g$ and $A$, but not on other
details of the shape of the surface. The volume is then used to find the
thermodynamic partition function and it is shown that the thermodynamical
properties of such a gas do not depend on the genus of the Riemann surface.


  We discuss the Bloch-state solutions of the stationary Gross-Pitaevskii
equation and of the Bogoliubov equations for a Bose-Einstein condensate in the
presence of a one-dimensional optical lattice. The results for the
compressibility, effective mass and velocity of sound are analysed as a
function of the lattice depth and of the strength of the two-body interaction.
The band structure of the spectrum of elementary excitations is compared with
the one exhibited by the stationary solutions (``Bloch bands''). Moreover, the
numerical calculations are compared with the analytic predictions of the tight
binding approximation. We also discuss the role of quantum fluctuations and
show that the condensate exhibits 3D, 2D or 1D features depending on the
lattice depth and on the number of particles occupying each potential well. We
finally show how, using a local density approximation, our results can be
applied to study the behaviour of the gas in the presence of harmonic trapping.


  Progress in human behavior modeling involves understanding both implicit,
early-stage perceptual behavior such as human attention and explicit,
later-stage behavior such as subjective preferences/likes. Yet, most prior
research has focused on modeling implicit and explicit human behavior in
isolation; and often limited to a specific type of visual content. Can we build
a unified model of human attention and preference behavior that works reliably
across diverse types of visual content? Such a model would enable predicting
subjective feedback such as satisfaction or aesthetic quality, along with the
underlying human attention or interaction heatmaps and viewing order, enabling
designers and content-creation models to optimize their creation for
human-centric improvements. In this paper, we propose ALOHA -- a unified model
for understanding human responses from attention to likes, across diverse
visual content. ALOHA leverages a multimodal transformer % featuring distinct
prediction heads for each facet, and predicts different human responses such as
attention heatmaps, scanpath or viewing order, as well as subjective
rating/preference. We train ALOHA on diverse public datasets spanning natural
images, webpages and graphic designs, and achieve SOTA performance on multiple
benchmarks across different image domains and various behavior modeling tasks.
Potential applications include providing instant feedback on the effectiveness
of UIs/designs/images, and serving as a reward model to further optimize
visual-content creation.


  Presented in this work is a framework for the data-driven determination of
multi-scale porous media parametrizations. Simulations of flow and transport in
a porous medium at the REV scale, although efficient, require well defined
parameters that represent pore-scale phenomena to maintain their accuracy.
Determining the optimal parameters for this often require expensive pore-scale
calculations. This work outlines a series of four steps where these parameters
can be calculated from pore scale data, their solutions generalized with a
convolutional neural network, and their content better understood with
descriptive pore metrics.


  In this paper, we present a novel artificial intelligence (AI) program that
identifies pulsars from recent surveys using image pattern recognition with
deep neural nets---the PICS (Pulsar Image-based Classification System) AI. The
AI mimics human experts and distinguishes pulsars from noise and interferences
by looking for patterns from candidate. The information from each pulsar
candidate is synthesized in four diagnostic plots, which consist of up to
thousands pixel of image data. The AI takes these data from each candidate as
its input and uses thousands of such candidates to train its ~9000 neurons.
Different from other pulsar selection programs which use pre-designed patterns,
the PICS AI teaches itself the salient features of different pulsars from a set
of human-labeled candidates through machine learning. The deep neural networks
in this AI system grant it superior ability in recognizing various types of
pulsars as well as their harmonic signals. The trained AI's performance has
been validated with a large set of candidates different from the training set.
In this completely independent test, PICS ranked 264 out of 277 pulsar-related
candidates, including all 56 previously known pulsars, to the top 961 (1%) of
90008 test candidates, missing only 13 harmonics. The first non-pulsar
candidate appears at rank 187, following 45 pulsars and 141 harmonics. In other
words, 100% of the pulsars were ranked in the top 1% of all candidates, while
80% were ranked higher than any noise or interference. The performance of this
system can be improved over time as more training data are accumulated. This AI
system has been integrated into the PALFA survey pipeline and has discovered
six new pulsars to date.


  We study an ideal Bose gas of N atoms contained in a box formed by two
identical planar and parallel surfaces S, enclosed by a mantle of height a
perpendicular to them. Calling r0 the mean atomic distance, we assume S >> r0^2
while a may be comparable to r0. In the bidimensional limit (a/r0 << 1) we find
a macroscopic number of atoms in the condensate at temperatures T ~1/log(N);
therefore, condensation cannot be described in terms of intensive quantities;
in addition, it occurs at temperatures not too low in comparison to the
tridimensional case. When condensation is present we also find a macroscopic
occupation of the low--lying excited states. In addition, the condensation
phenomenon is sensitive to the shape of S. The former two effects are
significant for a nanoscopic system. The tridimensional limit is slowly
attained for increasing (a/r0), roughly at (a/r0) ~ 10^{2}-10^{3}.


  Biologists use random transposon mutagenesis to construct knockout libraries
for bacteria. Random mutagenesis offers cost and efficiency benefits over the
standard site directed mutagenesis, but one can no longer ensure that all the
nonessential genes will appear in the library. In random libraries for haploid
organisms, there is always a class of genes for which knockout clones have not
been made, and the members of this class are either essential or nonessential.
One requires statistical methods to estimate the number of essential genes. Two
groups of researchers, Blades and Broman and Jacobs et al., independently and
simultaneously developed methods to do this. Blades and Broman used a Gibbs
sampler and Jacobs et al. used a parametric bootstrap. We compare the
performance of these two methods and find that they both depend on having an
accurate probabilistic model for transposon insertion or on having a library
with a large number of clones. At this point, we do not have good enough
probabilistic models so we must build libraries that have at least five clones
per open reading frame to accurately estimate the number of essential genes.


  Canonical Feynman integrals are of great interest in the study of scattering
amplitudes at the multi-loop level. We propose to construct $d\log$-form
integrals of the hypergeometric type, treat them as a representation of Feynman
integrals, and project them into master integrals using intersection theory.
This provides a constructive way to build canonical master integrals whose
differential equations can be solved easily. We use our method to investigate
both the maximally cut integrals and the uncut ones at one and two loops, and
demonstrate its applicability in problems with multiple scales.


  Background: Fluctuating hearing loss is characteristic of Meniere's Disease
(MD) during acute episodes. However, no reliable audiometric hallmarks are
available for counselling the hearing recovery possibility. Aims/Objectives: To
find parameters for predicting MD hearing outcomes. Material and Methods: We
applied machine learning techniques to analyse transient-evoked otoacoustic
emission (TEOAE) signals recorded from patients with MD. Thirty unilateral MD
patients were recruited prospectively after onset of acute cochleo-vestibular
symptoms. Serial TEOAE and pure-tone audiogram (PTA) data were recorded
longitudinally. Denoised TEOAE signals were projected onto the three most
prominent principal directions through a linear transformation. Binary
classification was performed using a support vector machine (SVM). TEOAE signal
parameters, including signal energy and group delay, were compared between
improved and nonimproved groups using Welchs t-test. Results: Signal energy did
not differ (p = 0.64) but a significant difference in 1-kHz (p = 0.045) group
delay was recorded between improved and nonimproved groups. The SVM achieved a
cross-validated accuracy of >80% in predicting hearing outcomes. Conclusions
and Significance: This study revealed that baseline TEOAE parameters obtained
during acute MD episodes, when processed through machine learning technology,
may provide information on outer hair cell function to predict hearing
recovery.


  Prediction the conversion to early-stage dementia is critical for mitigating
its progression but remains challenging due to subtle cognitive impairments and
structural brain changes. Traditional T1-weighted magnetic resonance imaging
(T1-MRI) research focus on identifying brain atrophy regions but often fails to
address the intricate connectivity between them. This limitation underscores
the necessity of focuing on inter-regional connectivity for a comprehensive
understand of the brain's complex network. Moreover, there is a pressing demand
for methods that adaptively preserve and extract critical information,
particularly specialized subgraph mining techniques for brain networks. These
are essential for developing high-quality feature representations that reveal
critical spatial impacts of structural brain changes and its topology. In this
paper, we propose Brain-SubGNN, a novel graph representation network to mine
and enhance critical subgraphs based on T1-MRI. This network provides a
subgraph-level interpretation, enhancing interpretability and insights for
graph analysis. The process begins by extracting node features and a
correlation matrix between nodes to construct a task-oriented brain network.
Brain-SubGNN then adaptively identifies and enhances critical subgraphs,
capturing both loop and neighbor subgraphs. This method reflects the loop
topology and local changes, indicative of long-range connections, and maintains
local and global brain attributes. Extensive experiments validate the
effectiveness and advantages of Brain-SubGNN, demonstrating its potential as a
powerful tool for understanding and diagnosing early-stage dementia. Source
code is available at https://github.com/Leng-10/Brain-SubGNN.


  Numerical studies of the two-dimensional Hubbard model have shown that it
exhibits the basic phenomena seen in the cuprate materials. At half-filling one
finds an antiferromagnetic Mott-Hubbard groundstate. When it is doped, a
pseudogap appears and at low temperature d-wave pairing and striped states are
seen. In addition, there is a delicate balance between these various phases.
Here we review evidence for this and then discuss what numerical studies tell
us about the structure of the interaction which is responsible for pairing in
this model.


  The time development of the reduced density matrix for a quantum oscillator
damped by coupling it to an ohmic environment is calculated via an identity of
the Debye-Waller form. Results obtained some years ago by Hakim and the author
in the free particle limit [1] are thus recovered. The evolution of a free
particle in a prepared initial state is examined, and a previously published
exchange [2,3] is illuminated with figures showing no decoherence without
dissipation.
  [1] V. Hakim and V. Ambegaokar, Phys. Rev. A 32, 423 (1985). [2] G.W. Ford
and R.F. O'Connell, Phys. Rev. A 70, 026102 (2004). [3] D. Gobert, J. von
Delft, and V. Ambegaokar, Phys. Rev. A 70, 0261001 (2004).


  DRAM is the prevalent main memory technology, but its long access latency can
limit the performance of many workloads. Although prior works provide DRAM
designs that reduce DRAM access latency, their reduced storage capacities
hinder the performance of workloads that need large memory capacity. Because
the capacity-latency trade-off is fixed at design time, previous works cannot
achieve maximum performance under very different and dynamic workload demands.
  This paper proposes Capacity-Latency-Reconfigurable DRAM (CLR-DRAM), a new
DRAM architecture that enables dynamic capacity-latency trade-off at low cost.
CLR-DRAM allows dynamic reconfiguration of any DRAM row to switch between two
operating modes: 1) max-capacity mode, where every DRAM cell operates
individually to achieve approximately the same storage density as a
density-optimized commodity DRAM chip and 2) high-performance mode, where two
adjacent DRAM cells in a DRAM row and their sense amplifiers are coupled to
operate as a single low-latency logical cell driven by a single logical sense
amplifier.
  We implement CLR-DRAM by adding isolation transistors in each DRAM subarray.
Our evaluations show that CLR-DRAM can improve system performance and DRAM
energy consumption by 18.6% and 29.7% on average with four-core multiprogrammed
workloads. We believe that CLR-DRAM opens new research directions for a system
to adapt to the diverse and dynamically changing memory capacity and access
latency demands of workloads.


  Scanning tunneling microscopy permits to image the Kondo resonance of a
single magnetic atom adsorbed on a metallic surface. When the magnetic impurity
is placed at the focus of an elliptical quantum corral, a Kondo resonance has
been recently observed both on top of the impurity and on top of the focus
where no magnetic impurity is present. This projection of the Kondo resonance
to a remote point on the surface is referred to as quantum mirage. We present a
quantum mechanical theory for the quantum mirage inside an ideal quantum corral
and predict that the mirage will occur in corrals with shapes other than
elliptical.


  In this paper we show that the polynomial structure of the topological string
partition function found by Yamaguchi and Yau for the quintic holds for an
arbitrary Calabi-Yau manifold with any number of moduli. Furthermore, we
generalize these results to the open topological string partition function as
discussed recently by Walcher and reproduce his results for the real quintic.


  Based on the scheme of variational Monte Carlo sampling, we develop an
accurate and efficient two-dimensional tensor-network algorithm to simulate
quantum lattice models. We find that Monte Carlo sampling shows huge advantages
in dealing with finite projected entangled pair states, which allows
significantly enlarged system size and improves the accuracy of tensor network
simulation. We demonstrate our method on the square-lattice antiferromagnetic
Heisenberg model up to $32 \times 32$ sites, as well as a highly frustrated
$J_1-J_2$ model up to $24\times 24$ sites. The results, including ground state
energy and spin correlations, are in excellent agreement with those of the
available quantum Monte Carlo or density matrix renormalization group methods.
Therefore, our method substantially advances the calculation of 2D tensor
networks for finite systems, and potentially opens a new door towards resolving
many challenging strongly correlated quantum many-body problems.


  In this article, we study the change of genus zero Gromov-Witten invariants
under Type II extremal transitions in degree 4.


  In axion gauge field inflation an axion-like particle driving cosmic
inflation is coupled to the Chern-Simons density of an Abelian or non-Abelian
gauge group. In the case of a non-Abelian gauge group, this can lead to the
formation of a stable, homogeneous and isotropic gauge field background. We
study the dynamics of the inflaton and gauge fields in terms of the two
effective coupling parameters: the gauge coupling and the axion decay constant.
Starting from the Bunch-Davies vacuum in the far past, we find that the
non-trivial gauge field background arises only significantly after the cosmic
microwave background (CMB) scales have left the horizon. At these scales, the
model thus closely resembles Abelian axion inflation, thereby naturally
reconciling the tension of non-Abelian axion gauge field inflation with the
latest CMB observations. We further consider two exemplary UV-completions of
this setup: multiple Peccei-Quinn axions and axion monodromy in string theory.
In both cases we find that the majority of the parameter space is excluded by
theoretical or observational constraints. The remaining parameter space can be
divided into three regimes. (i) For small gauge couplings we recover natural
inflation. For large gauge couplings the non-Abelian gauge theory either (ii)
mimics the Abelian theory or (iii) non-linear interactions prohibit a linear
analysis of the gauge field perturbations.


  Existing scene text spotters are designed to locate and transcribe texts from
images. However, it is challenging for a spotter to achieve precise detection
and recognition of scene texts simultaneously. Inspired by the glimpse-focus
spotting pipeline of human beings and impressive performances of Pre-trained
Language Models (PLMs) on visual tasks, we ask: 1) "Can machines spot texts
without precise detection just like human beings?", and if yes, 2) "Is text
block another alternative for scene text spotting other than word or
character?" To this end, our proposed scene text spotter leverages advanced
PLMs to enhance performance without fine-grained detection. Specifically, we
first use a simple detector for block-level text detection to obtain rough
positional information. Then, we finetune a PLM using a large-scale OCR dataset
to achieve accurate recognition. Benefiting from the comprehensive language
knowledge gained during the pre-training phase, the PLM-based recognition
module effectively handles complex scenarios, including multi-line, reversed,
occluded, and incomplete-detection texts. Taking advantage of the fine-tuned
language model on scene recognition benchmarks and the paradigm of text block
detection, extensive experiments demonstrate the superior performance of our
scene text spotter across multiple public benchmarks. Additionally, we attempt
to spot texts directly from an entire scene image to demonstrate the potential
of PLMs, even Large Language Models (LLMs).


  The smart grid spawns many innovative ideas, but many of them cannot be
easily integrated into the existing power system due to power system
constraints, such as the lack of capacity to transport renewable energy in
remote areas to the urban centers. An energy delivery system can be built upon
the traffic network and electric vehicles (EVs) utilized as energy carriers to
transport energy over a large geographical region. A generalized architecture
called the vehicular energy network (VEN) is constructed and a mathematically
tractable framework is developed. Dynamic wireless (dis)charging allows
electric energy, as an energy packet, to be added and subtracted from EV
batteries seamlessly. With proper routing, energy can be transported from the
sources to destinations through EVs along appropriate vehicular routes. This
paper gives a preliminary study of VEN. Models are developed to study its
operational and economic feasibilities with real traffic data in the United
Kingdom. Our study shows that a substantial amount of renewable energy can be
transported from some remote wind farms to London under some reasonable
settings and VEN is likely to be profitable in the near future. VEN can
complement the power network and enhance its power delivery capability.


  Normally, the focal length of a conventional lens is fixed. Scientists have
made much effort in modulating it into bifocal, which is very important for
virtual reality (VR) and argument reality (AR) 3D display. It is even much more
difficult for a lens to realize both convex and concave functions with one
geometric structure, that is, a concave/convex switchable lens, which can tune
3D real-images and virtual-images in AR and VR, corresponding to long depth of
view in 3D display. Based on the tunable refractive indexes of phase-change
materials, here we propose a series of concave/convex switchable lenses. When
the phase-change material is in different states, one switchable lens is able
to perform a negative or positive focal length, or perform negative and
positive focal lengths simultaneously. The lenses can be either cylindrical,
spherical or other types. For the superior characteristics, these switchable
lenses can be employed in various optical fields.


  A novel parallel technique for Fourier-Galerkin pseudo-spectral methods with
applications to two-dimensional Navier-Stokes equations and inviscid Boussinesq
approximation equations is presented. It takes the advantage of the programming
structure of the phase-shift de-aliased scheme for pseudo-spectral codes, and
combines the task-distribution strategy [Yin, Clercx and Montgomery, Comput.
Fluids, 33, 509 (2004)] and parallelized Fast Fourier Transform scheme. The
performances of the resulting MPI Fortran90 codes with the new procedure on SGI
3800 are reported. For fixed resolution of the same problem, the peak speed of
the new scheme can be twice as fast as the old parallel methods. The
parallelized codes are used to solve some challenging numerical problems
governed by the Navier-Stokes equations and the Boussinesq equations. Two
interesting physical problems, namely, the double-valued $\omega$-$\psi $
structure in two-dimensional decaying turbulence and the collapse of the bubble
cap in the Boussinesq simulation, are solved by using the proposed parallel
algorithms.


  Representation learning has recently been successfully used to create vector
representations of entities in language learning, recommender systems and in
similarity learning. Graph embeddings exploit the locality structure of a graph
and generate embeddings for nodes which could be words in a language, products
of a retail website; and the nodes are connected based on a context window. In
this paper, we consider graph embeddings with an error-free associative
learning update rule, which models the embedding vector of node as a non-convex
Gaussian mixture of the embeddings of the nodes in its immediate vicinity with
some constant variance that is reduced as iterations progress. It is very easy
to parallelize our algorithm without any form of shared memory, which makes it
possible to use it on very large graphs with a much higher dimensionality of
the embeddings. We study the efficacy of proposed method on several benchmark
data sets and favorably compare with state of the art methods. Further,
proposed method is applied to generate relevant recommendations for a large
retailer.


  Modern agricultural applications rely more and more on deep learning
solutions. However, training well-performing deep networks requires a large
amount of annotated data that may not be available and in the case of 3D
annotation may not even be feasible for human annotators. In this work, we
develop a deep learning approach to segment mushrooms and estimate their pose
on 3D data, in the form of point clouds acquired by depth sensors. To
circumvent the annotation problem, we create a synthetic dataset of mushroom
scenes, where we are fully aware of 3D information, such as the pose of each
mushroom. The proposed network has a fully convolutional backbone, that parses
sparse 3D data, and predicts pose information that implicitly defines both
instance segmentation and pose estimation task. We have validated the
effectiveness of the proposed implicit-based approach for a synthetic test set,
as well as provided qualitative results for a small set of real acquired point
clouds with depth sensors. Code is publicly available at
https://github.com/georgeretsi/mushroom-pose.


  We calculate the complete tadpoles and self-energies at the two-loop order
for scalars in general renormalisable theories, a crucial component for
calculating two-loop electroweak corrections to Higgs-boson masses or for any
scalar beyond the Standard Model. We renormalise the amplitudes using
mass-independent renormalisation schemes, based on both dimensional
regularisation and dimensional reduction. The results are presented here in
Feynman gauge, with expressions for all 121 self-energy and 25 tadpole diagrams
given in terms of scalar and tensor integrals with the complete set of rules to
reduce them to a minimal basis of scalar integrals for any physical kinematic
configuration. In addition, we simplify the results to a set of only 16 tadpole
and 58 self-energy topologies using relations in order to substitute the ghost
and Goldstone-boson couplings that we derive. To facilitate their application,
we also provide our results in electronic form as a new code TLDR. We test our
results by applying them to the Standard Model and compare with analytic
expressions in the literature.


  I would like to attract attention of the LHC high-energy physics community to
non-accelerator, low-energy experiments, that are also very sensitive to new
physics. My example concerns search for supersymmetric dark matter particles.
It is shown that non-observation of the SUSY dark matter candidates with a
high-accuracy detector can exclude large domains of the MSSM parameter space
and, in particular, can make especially desirable collider search for light
SUSY charged Higgs boson.


  Wireless local area networks (WLANs) are the most popular kind of wireless
Internet connection because of their simplicity of deployment and operation. As
a result, the number of devices accessing the Internet through WLANs such as
laptops, smartphones, or wearables, is increasing drastically at the same time
that applications' throughput requirements do. To cope with these challenges,
channel bonding (CB) techniques are used for enabling higher data rates by
transmitting in wider channels, thus increasing spectrum efficiency. However,
important issues like higher potential co-channel and adjacent channel
interference arise when bonding channels. This may harm the performance of the
carrier sense multiple access (CSMA) protocol because of recurrent backoff
freezing while making nodes more sensitive to hidden node effects. In this
paper, we address the following point at issue: is it convenient for
high-density (HD) WLANs to use wider channels and potentially overlap in the
spectrum? First, we highlight key aspects of DCB in toy scenarios through a
continuous time Markov network (CTMN) model. Then, by means of extensive
simulations covering a wide range of traffic loads and access point (AP)
densities, we show that dynamic channel bonding (DCB) - which adapts the
channel bandwidth on a per-packet transmission - significantly outperforms
traditional single-channel on average. Nevertheless, results also corroborate
that DCB is more prone to generate unfair situations where WLANs may starve.
Contrary to most of the current thoughts pushing towards non-overlapping
channels in HD deployments, we highlight the benefits of allocating channels as
wider as possible to WLANs altogether with implementing adaptive access
policies to cope with the unfairness situations that may appear.


  Current-voltage characteristics of Au~/~YBa$_2$Cu$_3$O$_{7-\delta}$
interfaces (Au/YBCO), built on optimally-doped YBCO thin films, grown by pulsed
laser deposition, were measured as a function of temperature in the 50 K to 270
K range, for two different resistance states. A non-trivial equivalent circuit
model is proposed, which reveals the existence of a highly inhomogeneous
scenario composed by two complex layers: one presenting both a non-linear
Poole-Frenkel conduction as well as Variable Range Hopping localization effects
(probably associated with YBa$_2$Cu$_3$O$_{6}$) mixed with a minor metallic
phase, while the other is also composed by a mixture of YBCO with different
oxygen contents, where a metallic ohmic phase still percolates. A microscopic
description of the effects produced by the resistance switching is given,
showing the evolution of carrier traps, localization effects and dielectric
behavior for each state. The dielectric behavior is interpreted in terms of a
Maxwell-Wagner scenario.


  In this paper, we study contact structures on any open 3-manifold V which is
the interior of a compact 3-manifold. To do this, we introduce proper contact
isotopy invariants called the slope at infinity and the division number at
infinity. We first prove several classification theorems for T^2 x [0, \infty),
T^2 x R, and S^1 x R^2 using these concepts. This investigation yields
infinitely many tight contact structures on T^2 x [0,\infty), T^2 x R, and S^1
x R^2 which admit no precompact embedding into another tight contact structure
on the same space. Finally, we show that if V is irreducible and has an end of
nonzero genus, then there are uncountably many tight contact structures on V
that are not contactomorphic, yet are isotopic. Similarly, there are
uncountably many overtwisted contact structures on V that are not
contactomorphic, yet are isotopic.


  Topological Chern indices are related to the number of rotational states in
each molecular vibrational band. Modification of the indices is associated to
the appearance of ``band degeneracies'', and exchange of rotational states
between two consecutive bands. The topological dynamical origin of these
indices is proven through a semi-classical approach, and their values are
computed in two examples. The relation with the integer quantum Hall effect is
briefly discussed.


  This paper investigates the problem of reconstructing hyperspectral (HS)
images from single RGB images captured by commercial cameras, \textbf{without}
using paired HS and RGB images during training. To tackle this challenge, we
propose a new lightweight and end-to-end learning-based framework.
Specifically, on the basis of the intrinsic imaging degradation model of RGB
images from HS images, we progressively spread the differences between input
RGB images and re-projected RGB images from recovered HS images via effective
unsupervised camera spectral response function estimation. To enable the
learning without paired ground-truth HS images as supervision, we adopt the
adversarial learning manner and boost it with a simple yet effective
$\mathcal{L}_1$ gradient clipping scheme. Besides, we embed the semantic
information of input RGB images to locally regularize the unsupervised
learning, which is expected to promote pixels with identical semantics to have
consistent spectral signatures. In addition to conducting quantitative
experiments over two widely-used datasets for HS image reconstruction from
synthetic RGB images, we also evaluate our method by applying recovered HS
images from real RGB images to HS-based visual tracking. Extensive results show
that our method significantly outperforms state-of-the-art unsupervised methods
and even exceeds the latest supervised method under some settings. The source
code is public available at
https://github.com/zbzhzhy/Unsupervised-Spectral-Reconstruction.


  In this paper, we established some new inequalities via s-convex and
s-concave functions.


  Critical intermittency stands for a type of intermittent dynamics in iterated
function systems, caused by an interplay of a superstable fixed point and a
repelling fixed point. We consider critical intermittency for iterated function
systems of interval maps and demonstrate the existence of a phase transition
when varying probabilities, where the absolutely continuous stationary measure
changes between finite and infinite. We discuss further properties of this
stationary measure and show that its density is not in $L^q$ for any $q > 1$.
This provides a theory of critical intermittency alongside the theory for the
well studied Manneville-Pomeau maps, where the intermittency is caused by a
neutral fixed point.


  We reinterpret the OSV formula for the on-shell action/entropy function of
asymptotically flat BPS black holes as a fixed point formula that is formally
equivalent to a recent gluing proposal for asymptotically AdS$_4$ black holes.
This prompts a conjecture that the complete perturbative answer for the most
general gravitational building block of 4d $\mathcal{N}=2$ supergravity at a
single fixed point takes the form of a Nekrasov-like partition function with
equivariant parameters related to the higher-derivative expansion of the
prepotential. In turn this leads to a simple localization-like proposal for a
set of supersymmetric partition functions in (UV completed) 4d $\mathcal{N}=2$
supergravity theories. The conjecture is shown to be in agreement with a number
of available results for different BPS backgrounds with both Minkowski and AdS
asymptotics. In particular, it follows that the OSV formula comes from the
unrefined limit of the general expression including only the so-called
$\mathbb{W}$ tower of higher derivatives, while the on-shell action of pure
(Euclidean) AdS$_4$ with round S$^3$ boundary comes from the NS limit that
includes only the $\mathbb{T}$ tower. Backgrounds preserving less
supersymmetry, such as the under-rotating black holes in flat space, the
holographic squashed S$^3$, and the static/rotating twisted and non-twisted
Kerr-Newman-like black holes in AdS$_4$ lead to a more general refined version
of the corresponding gravitational blocks as dictated by the supersymmetric
gluing rules.


  About 15% of the world population is affected by a disability in some form,
amongst whom only 31% perform the recommended exercises without intervention.
We are working on developing a motivating and effective way to encourage
people. In our work, we leverage the fact that repetitive exercises can help
people with motor disabilities due to the robust plasticity of the pre-frontal
cognitive control system in the brain. We investigate the role of repetitive
activities for neurorehabilitation with the help of a brain computer interface,
formulated using immersive game design with Kinect v2.0 and Unity 3D. We also
introduce a game design paradigm for adaptive learning for the patients.


  The triple alpha rate is obtained from the three-body bound and continuum
states computed in a large box. The results from this genuine full three-body
calculation are compared with standard reference rates obtained by two
sequential two-body processes. The fairly good agreement relies on two
different assumptions about the lowest $2^+$ resonance energy. With the same
$2^+$ energy the rates from the full three-body calculation are smaller than
those of the standard reference. We discuss the rate dependence on the
experimentally unknown $2^+$ energy. Substantial deviations from previous
results appear for temperatures above 3 GK.


  A thorough investigation of the relaxational dynamics in the recently
discovered multiferroic CdCr2S4 showing a colossal magnetocapacitive effect has
been performed. Broadband dielectric measurements without and with external
magnetic fields up to 10 T provide clear evidence that the observed
magnetocapacitive effect stems from enormous changes of the relaxation dynamics
induced by the development of magnetic order.


  We present a parallelizable SSOR preconditioning scheme for Krylov subspace
iterative solvers which proves to be efficient in lattice QCD applications
involving Wilson fermions. Our preconditioner is based on a locally
lexicographic ordering of the lattice points. In actual hybrid Monte Carlo
applications with the bi-conjugate gradient stabilized method BiCGstab, we
achieve a gain factor of about 2 in the number of iterations compared to
conventional odd-even preconditioning. Whether this translates into similar
reductions in run time will depend on the parallel computer in use. We discuss
implementation issues using the `Eisenstat-trick' and machine specific
advantages of the method for the APE100/Quadrics parallel computer. In a full
QCD simulation with Wilson fermions on a 512-processor Quadrics QH4 we find a
gain in cpu-time of a factor of 1.7 over odd-even preconditioning for a 24^3 x
40 lattice.


  In 1996/7, J. Bernstein observed that smooth or analytic supermanifolds that
mathematicians study are real or (almost) complex ones, while Minkowski
superspaces are completely different objects. They are what we call almost
real-complex supermanifolds, i.e., real supermanifolds with a non-integrable
distribution, the collection of subspaces of the tangent space, and in every
subspace a complex structure is given.
  An almost complex structure on a real supermanifold can be given by an even
or odd operator; it is complex (without "always") if the suitable superization
of the Nijenhuis tensor vanishes. On almost real-complex supermanifolds, we
define the circumcised analog of the Nijenhuis tensor. We compute it for the
Minkowski superspaces and superstrings. The space of values of the circumcised
Nijenhuis tensor splits into (indecomposable, generally) components whose
irreducible constituents are similar to those of Riemann or Penrose tensors.
The Nijenhuis tensor vanishes identically only on superstrings of
superdimension 1|1 and, besides, the superstring is endowed with a contact
structure. We also prove that all real forms of complex Grassmann algebras are
isomorphic although singled out by manifestly different anti-involutions.


  Light field image quality assessment (LF-IQA) plays a significant role due to
its guidance to Light Field (LF) contents acquisition, processing and
application. The LF can be represented as 4-D signal, and its quality depends
on both angular consistency and spatial quality. However, few existing LF-IQA
methods concentrate on effects caused by angular inconsistency. Especially,
no-reference methods lack effective utilization of 2-D angular information. In
this paper, we focus on measuring the 2-D angular consistency for LF-IQA. The
Micro-Lens Image (MLI) refers to the angular domain of the LF image, which can
simultaneously record the angular information in both horizontal and vertical
directions. Since the MLI contains 2-D angular information, we propose a
No-Reference Light Field image Quality assessment model based on MLI (LF-QMLI).
Specifically, we first utilize Global Entropy Distribution (GED) and Uniform
Local Binary Pattern descriptor (ULBP) to extract features from the MLI, and
then pool them together to measure angular consistency. In addition, the
information entropy of Sub-Aperture Image (SAI) is adopted to measure spatial
quality. Extensive experimental results show that LF-QMLI achieves the
state-of-the-art performance.