  Given a fine abelian group grading on a finite dimensional simple Lie algebra
over an algebraically closed field of characteristic zero, with universal
grading group $G$, it is shown that the induced grading by the free group
$G/\tor(G)$ is a grading by a (not necessarily reduced) root system.
  Some consequences for the classification of fine gradings on the exceptional
simple Lie algebras are drawn.


  Comparison is carried out of the long term variation of the year averaged
solar wind speed and interplanetary scintillation index with the variations of
Wolf's numbers and A_P indexes of geomagnetic activity for the data of 20-24
solar activity cycles. It is shown that the slow non-monotonous trend in the
scintillation parameters at middle and high heliolatitudes exists with the
typical scale of order of century cycle. Correlation between the variations of
Wolf's numbers and anomalies of the air temperature is analyzed for long data
series from 1610 up to the present time. Possible application of the results to
the global climate problem is discussed.


  Line-driven wind instability is expected to cause small-scale wind
inhomogeneities, X-ray emission, and wind line profile variability. The
instability can already develop around the sonic point if it is initiated close
to the photosphere due to stochastic turbulent motions. In such cases, it may
leave its imprint on the light curve as a result of wind blanketing. We study
the photometric signatures of the line-driven wind instability. We used
line-driven wind instability simulations to determine the wind variability
close to the star. We applied two types of boundary perturbations: a sinusoidal
one that enables us to study in detail the development of the instability and a
stochastic one given by a Langevin process that provides a more realistic
boundary perturbation. We estimated the photometric variability from the
resulting mass-flux variations. The variability was simulated assuming that the
wind consists of a large number of independent conical wind sectors. We
compared the simulated light curves with TESS light curves of OB stars that
show stochastic variability. We find two typical signatures of line-driven wind
instability in photometric data: a knee in the power spectrum of magnitude
fluctuations, which appears due to engulfment of small-scale structure by
larger structures, and a negative skewness of the distribution of fluctuations,
which is the result of spatial dominance of rarefied regions. These features
endure even when combining the light curves from independent wind sectors. The
stochastic photometric variability of OB stars bears certain signatures of the
line-driven wind instability. The distribution function of observed photometric
data shows negative skewness and the power spectra of a fraction of light
curves exhibit a knee. This can be explained as a result of the line-driven
wind instability triggered by stochastic base perturbations.


  Neuromorphic circuits mimic partial functionalities of brain in a
bio-inspired information processing sense in order to achieve similar
efficiencies as biological systems. While there are common mathematical models
for neurons, which can be realized as nonlinear oscillating circuits, an
electrical representation of the synaptic coupling between those is more
challenging. Since this coupling strength depends on the learning procedure in
the past, it should include a kind of memory. We believe that memristive
devices, which are essentially nonlinear resistors with memory, are potential
candidates for replacing synapses in neuromorphic circuits. Due to a huge
number of synapses in a complex neuromorphic circuit, pre-investigations based
on simulations of such systems can be very inefficient and time-consuming.
Flexible and real-time capable memristive emulators, which can directly be
incorporated into real circuits, can overcome this problem. In our approach, we
introduce a generic memristive emulator based on wave digital principles. The
proposed emulator is flexible, robust, efficient, and it preserves the
passivity of the real device. This, in turn, also makes it reusable independent
of a particular application. In the presented work, the emulation of different
mathematical models as well as of a real device based on physical models are
listed.


  In this paper the certain 4-dimensional algebra in 4-dimensional
pseudo-Riemannian space with signature (1, -1, -1, -1) is constructed. On the
basis of this algebra the elements of the analysis, i.e. the theory of
4-dimensional functions of the 4-dimensional variable are built up. In the
process of designing the analysistwo additional assumptions aboutthe properties
of functions are made. Obtained under different assumptionsabout the properties
of functions, the Cauchy-Riemann equations are solved in flat, spherically and
cylindrically symmetric cases. In the cylindrically symmetric case the wave
solutions were obtained both for the metric tensor and for the 4-dimensional
function. Both waves spreadwith the speed equal to unity along null geodesics.
The properties of the metric tensor as physically real object are postulated,
but the idea what the 4-dimensional function (vector field) is remains unclear.


  Doping of semiconductors by impurity atoms enabled their widespread
technological application in micro and opto-electronics. For colloidal
semiconductor nanocrystals, an emerging family of materials where size,
composition and shape-control offer widely tunable optical and electronic
properties, doping has proven elusive. This arises both from the synthetic
challenge of how to introduce single impurities and from a lack of fundamental
understanding of this heavily doped limit under strong quantum confinement. We
develop a method to dope semiconductor nanocrystals with metal impurities
providing control of the band gap and Fermi energy. A combination of optical
measurements, scanning tunneling spectroscopy and theory revealed the emergence
of a confined impurity band and band-tailing. Successful control of doping and
its understanding provide n- and p-doped semiconductor nanocrystals which
greatly enhance the potential application of such materials in solar cells,
thin-film transistors, and optoelectronic devices.


  We derive the 60\mu local LF to sensitivity levels 10 times deeper than
before, to investigate evolutionary effects up to a redshift of 0.37, and,
using the 60/15\mu bi-variate method, the poorly known 15\mu local LF of
galaxies. We exploited our ISOCAM observations of the IRAS Deep Survey (IDS)
fields (Hacking and Houck 1987), to correct the $60mu fluxes for confusion
effects and observational biases. We find indications of a significant
incompleteness of the IDS sample, still one of the deepest far-IR selected
galaxy samples, below \simeq 80mJy (Mazzei et al. 2001). We have reliable
identifications and spectroscopic redshifts for 100% of a complete subsample
comprising 56 sources with S(60mu(m))> 80mJy. With our spectroscopic coverage
we construct the 60mu LF for a sample complete down to 80 mJy. This LF extends
over three orders of magnitude in luminosity, from 9 up to more than 12 in
log(L_(60)/L_(\odot)). Despite the fact that the redshift range of our sample
exceeds z=0.3, the V/V_{max} test gives <V/V_{max}>=0.51\pm 0.06, consistent
with a uniform distribution of sources. A more direct test, whereby the LF was
measured in each of four different redshift intervals, does not point out any
signature of evolution. On the other hand, the rest--frame 15\mu local LF we
derive, extends up to log(L_{15}/L_{\odot})=12 and predicts 10 times more
sources at log(L_{15}/L_{\odot})=11 than are seen by Pozzi et al. (2004).


  The content of this paper includes studying holographical and thermodynamical
aspects of dyonic black holes in the presence of massive gravity. For the first
part of paper, thermodynamical properties of the bulk which includes black
holes are studied and the main focus is on critical behavior. It will be shown
that the existence of massive gravitons introduces remnant for temperature
after evaporation of black holes, van der Waals phase transition for
non-spherical black holes and etc. The consistency of different thermodynamical
approaches toward critical behavior of the black holes is presented and the
physical properties near the region of thermal instability are given. Next part
of the paper studies holographical aspects of the boundary theory.
Magnetization and susceptibility of the boundary are extracted and the
conditions for having diamagnetic and paramagnetic behaviors are investigated.
It will be shown that generalization to massive gravity results into the
existence of diamagnetic/paramagnetic phases in phase structure of the
hyperbolic and horizon flat of boundary conformal field theory.


  In the Majorana or stellar representation of quantum states, an arbitrary
(pure) state of a spin-1 system is represented by a pair of points on the unit
sphere or, equivalently, by a pair of unit vectors. This paper presents an
expression for the squared modulus of the inner product of two spin-1 states in
terms of their Majorana vectors and uses it to give a geometrical construction
of the MUBs and SIC-POVMs of a spin-1 system. The results are not new and
duplicate those obtained earlier by other methods, but the Majorana approach
nevertheless illuminates them from an unusual point of view. In particular, it
reveals the MUBs and SICs as symmetrical collections of vectors in ordinary
three-dimensional space, rather than as rays in a projective Hilbert space.
While it does not appear feasible to extend this treatment to higher spin
systems, the spin-1 case exhibits sufficient subtlety and complexity to be
worth spelling out for its pedagogical and historical interest.


  The two-dimensional Jacobian Conjecture says that a Keller map $f: (x,y)
\mapsto (p,q) \in k[x,y]^2$ having an invertible Jacobian is an automorphism of
$k[x,y]$. We prove that there is no Keller map with $[k(x,y): k(p,q)]$ prime.


  We describe a new technique for computing lower-bounds on the minimum energy
configuration of a planar Markov Random Field (MRF). Our method successively
adds large numbers of constraints and enforces consistency over binary
projections of the original problem state space. These constraints are
represented in terms of subproblems in a dual-decomposition framework that is
optimized using subgradient techniques. The complete set of constraints we
consider enforces cycle consistency over the original graph. In practice we
find that the method converges quickly on most problems with the addition of a
few subproblems and outperforms existing methods for some interesting classes
of hard potentials.


  Let $L$ be a finite lattice and $\mathcal{E}(L)$ be the set of join
endomorphisms of $L$. We consider the problem of given $L$ and $f,g \in
\mathcal{E}(L)$, finding the greatest lower bound $f \sqcap_{{\scriptsize
\mathcal{E}(L)}} g$ in the lattice $\mathcal{E}(L)$. (1) We show that if $L$ is
distributive, the problem can be solved in time $O(n)$ where $n=| L |$. The
previous upper bound was $O(n^2)$. (2) We provide new algorithms for arbitrary
lattices and give experimental evidence that they are significantly faster than
the existing algorithm. (3) We characterize the standard notion of distributed
knowledge of a group as the greatest lower bound of the join-endomorphisms
representing the knowledge of each member of the group. (4) We show that
deciding whether an agent has the distributed knowledge of two other agents can
be computed in time $O(n^2)$ where $n$ is the size of the underlying set of
states. (5) For the special case of $S5$ knowledge, we show that it can be
decided in time $O(n\alpha_{n})$ where $\alpha_{n}$ is the inverse of the
Ackermann function.


  A detection or non detection of primordial non--Gaussianity (NG) by using the
cosmic microwave background radiation (CMB) is a possible way to break the
degeneracy of early universe models. Since a single statistical estimator
hardly can be sensitive to all possible forms of NG which may be present in the
data, it is important to use different statistical estimators to study NG in
CMB. Recently, two new large-angle NG indicators based on skewness and kurtosis
of spherical caps or spherical cells of CMB sky have been proposed and used in
both CMB data and simulated maps. Here, we make a comparative study of these
two different procedures by examining the NG in the WMAP seven years ILC map.
We show that the spherical cells procedure detects a higher level of NG than
that obtained by the method with overlapping spherical caps.


  In this paper, we develop the kinetic and hydrodynamic theories of the
convective mesoscale flows driven by the spatially inhomogeneous electrostatic
ion cyclotron parametric microturbulence in the pedestal plasma with a sheared
poloidal flow. The developed kinetic theory predicts the generation of the
sheared poloidal convective flow, and of the radial compressed flow with radial
flow velocity gradient. The developed hydrodynamic theory of the convective
flows reveals the radial compressed convective flow as the dominant factor in
the formation of the steep pedestal density profile with density gradient
exponentially growing with time. This gradient density growth is limited by the
formation of the radial oscillating with time ion outflow of pedestal plasma to
scrape-off layer.


  The scalar-vector-tensor theories with second-order equations of motion can
accommodate both Horndeski and generalized Proca theories as specific cases. In
the presence of a perfect fluid, we study the cosmology in such a most general
scheme of scalar-vector-tensor theories with parity invariance by paying
particular attention to the application to dark energy. We obtain a closed-form
expression of the background equations of motion by using coefficients
appearing in the second-order action of scalar perturbations. We also derive
conditions for the absence of ghost and Laplacian instabilities of tensor and
vector perturbations and show that the existence of matter does not
substantially modify the stabilities of dynamical degrees of freedom in the
small-scale limit. On the other hand, the sound speed of scalar perturbations
is affected by the presence of matter. Employing the quasi-static approximation
for scalar perturbations deep inside the sound horizon, we derive analytic
expressions of Newtonian and weak lensing gravitational potentials as well as
two scalar perturbations arising from the scalar and vector fields. We apply
our general framework to dark energy theories with the tensor propagation speed
equivalent to the speed of light and show that the observables associated with
the growth of matter perturbations and weak lensing potentials are generally
affected by intrinsic vector modes and by interactions between scalar and
vector fields.


  This research presents a novel framework for the compression and
decompression of medical images utilizing the Latent Diffusion Model (LDM). The
LDM represents advancement over the denoising diffusion probabilistic model
(DDPM) with a potential to yield superior image quality while requiring fewer
computational resources in the image decompression process. A possible
application of LDM and Torchvision for image upscaling has been explored using
medical image data, serving as an alternative to traditional image compression
and decompression algorithms. The experimental outcomes demonstrate that this
approach surpasses a conventional file compression algorithm, and convolutional
neural network (CNN) models trained with decompressed files perform comparably
to those trained with original image files. This approach also significantly
reduces dataset size so that it can be distributed with a smaller size, and
medical images take up much less space in medical devices. The research
implications extend to noise reduction in lossy compression algorithms and
substitute for complex wavelet-based lossless algorithms.


  Hybrid multiple-antenna transceivers, which combine large-dimensional analog
pre/postprocessing with lower-dimensional digital processing, are the most
promising approach for reducing the hardware cost and training overhead in
massive MIMO systems. This paper provides a comprehensive survey of the various
incarnations of such structures that have been proposed in the literature. We
provide a taxonomy in terms of the required channel state information (CSI),
namely whether the processing adapts to the instantaneous or the average
(second-order) CSI; while the former provides somewhat better signal-to-noise
and interference ratio (SNIR), the latter has much lower overhead for CSI
acquisition. We furthermore distinguish hardware structures of different
complexities. Finally, we point out the special design aspects for operation at
millimeter-wave frequencies.


  Transitioning from fossil-fuel power generation to renewable energy
generation and energy storage in remote locations has the potential to reduce
both carbon emissions and cost. This study presents a techno-economic analysis
for implementation of a hybrid renewable energy system at the South Pole in
Antarctica, which currently hosts several high-energy physics experiments with
nontrivial power needs. A tailored model of resource availability and economics
for solar photovoltaics, wind turbine generators, lithium-ion energy storage,
and long-duration energy storage at this site is explored in different
combinations with and without existing diesel energy generation. The Renewable
Energy Integration and Optimization (REopt) platform is used to determine the
optimal system component sizing and the associated system economics and
environmental benefit. We find that the least-cost system includes all three
energy generation sources and lithium-ion energy storage. For an example
steady-state load of 170 kW, this hybrid system includes 180 kW-DC of
photovoltaic panels, 570 kW of wind turbines, and a 3.4 MWh lithium-ion battery
energy storage system. This system reduces diesel consumption by 95% compared
to an all-diesel configuration, resulting in approximately 1200 metric tons of
carbon footprint avoided annually. Over the course of a 15-year analysis period
the reduced diesel usage leads to a net savings of 57 million United States
dollars, with a time to payback of approximately two years. All the scenarios
modeled show that the transition to renewables is highly cost effective under
the unique economics and constraints of this extremely remote site.


  We present high spatial resolution radio and near-infrared hydrogen
recombination line observations of the southern massive star-forming region
G333.6-0.2. The 3.4-cm continuum peak is found slightly offset from the
infrared source. The H90alpha spectra show for the first time a double peak
profile at some positions. The complex velocity structure may be accounted for
by champagne outflows, which may also explain the offset between the radio and
infrared sources. The 2.17-um Brackett gamma image and H90alpha map are
combined to construct an extinction map which shows a trend probably set by the
blister nature of the HII region. The total number of Lyman continuum photons
in the central 50-arcsec is estimated to be equivalent to that emitted by up to
19 O7V stars.


  Fix $k\geq 2$, choose $\frac{\log n}{n^{(k-1)/k}}\leq p\leq
1-\Omega(\frac{\log^4 n}{n})$, and consider $G\sim G(n,p)$. For any pair of
vertices $v,w\in V(G)$, we give a simple and precise formula for the expected
number of steps that a random walk on $G$ starting at $w$ needs to first arrive
at $v$. The formula only depends on basic structural properties of $G$. This
improves and extends recent results of Ottolini and Steinerberger, as well as
Ottolini, who considered this problem for constant as well as for mildly
vanishing $p$.


  If the gravitational field is quantized, then a solution of Einstein's field
equations is a valid cosmological model only if it corresponds to a classical
limit of a quantum cosmology. To determine which solutions are valid requires
looking at quantum cosmology in a particular way. Because we infer the geometry
by measurements on matter, we can represent the amplitude for any measurement
in terms of the amplitude for the matter fields, allowing us to integrate out
the gravitational degrees of freedom. Combining that result with a
path-integral representation for quantum cosmology leads to an integration over
4-geometries. Even when a semiclassical approximation for the propagator is
valid, the amplitude for any measurement includes an integral over the
gravitational degrees of freedom. The conditions for a solution of the field
equations to be a classical limit of a quantum cosmology are: (1) The effect of
the classical action dominates the integration, (2) the action is stationary
with respect to variation of the gravitational degrees of freedom, and (3) only
one saddlepoint contributes significantly to each integration.


  In a previous work by the author it was shown that every finite dimensional
algebraic structure over an algebraically closed field of characteristic zero K
gives rise to a character $K[X]_{aug}\to K$, where $K[X]_aug$ is a commutative
Hopf algebra that encodes scalar invariants of structures. This enabled us to
think of some characters $K[X]_{aug}\to K$ as algebraic structures with closed
orbit. In this paper we study structures in general symmetric monoidal
categories, and not only in $Vec_K$. We show that every character $\chi :
K[X]_{aug}\to K$ arises from such a structure, by constructing a category
$C_{\chi}$ that is analogous to the universal construction from TQFT. We then
give necessary and sufficient conditions for a given character to arise from a
structure in an abelian category with finite dimensional hom-spaces. We call
such characters good characters. We show that if $\chi$ is good then $C_{\chi}$
is abelian and semisimple, and that the set of good characters forms a
K-algebra. This gives us a way to interpolate algebraic structures, and also
symmetric monoidal categories, in a way that generalizes Deligne's categories
$Rep(S_t)$, $Rep(GL_t(K))$, $Rep(O_t)$, and also some of the symmetric monoidal
categories introduced by Knop. We also explain how one can recover the recent
construction of 2 dimensional TQFT of Khovanov, Ostrik, and Kononov, by the
methods presented here. We give new examples, of interpolations of the
categories $Rep(Aut_{O}(M))$ where $O$ is a discrete valuation ring with a
finite residue field, and M is a finite module over it. We also generalize the
construction of wreath products with $S_t$, which was introduced by Knop.


  By interpreting the product of the Principal Component Analysis, that is the
covariance matrix, as a sequence of nested subspaces naturally coming with
weights according to the level of approximation they provide, we are able to
embed all $d$--dimensional Grassmannians into a stratified space of covariance
matrices. We observe that Grassmannians constitute the lowest dimensional
skeleton of the stratification while it is possible to define a Riemaniann
metric on the highest dimensional and dense stratum, such a metric being
compatible with the global stratification. With such a Riemaniann metric at
hand, it is possible to look for geodesics between two linear subspaces of
different dimensions that do not go through higher dimensional linear subspaces
as would euclidean geodesics. Building upon the proposed embedding of
Grassmannians into the stratified space of covariance matrices, we generalize
the concept of varifolds to what we call flagfolds in order to model
multi-dimensional shapes.


  We have investigated the anomalous quartic couplings defined by the
dimension-8 operators in semileptonic decay channel of the
$e^{+}e^{-}\rightarrow \nu_{e} W^{-} W^{+} \bar{\nu}_{e}$ process for
unpolarized and polarized electron (positron) beam at the Compact Linear
Collider. We give the $95\%$ confidence level bounds on anomalous
$\frac{f_{S0}}{\Lambda^{4}}$, $\frac{f_{S1}}{\Lambda^{4}}$ and
$\frac{f_{T0}}{\Lambda^{4}}$ couplings for various values of the integrated
luminosities and center-of-mass energies. The best sensitivities obtained on
anomalous $\frac{f_{S0}}{\Lambda^{4}}$, $\frac{f_{S1}}{\Lambda^{4}}$ and
$\frac{f_{T0}}{\Lambda^{4}}$ couplings through the process
$e^{+}e^{-}\rightarrow \nu_{e} W^{-} W^{+} \bar{\nu}_{e}$ with beam
polarization at $\sqrt{s}=3$ TeV and an integrated luminosity of $L_{int}=2000$
fb$^{-1}$ are $[-4.05;\, 3.67]\times 10^{-12}$ GeV$^{-4}$, $[-3.08; \,
2.12]\times 10^{-12}$ GeV$^{-4}$, $[-1.98; \, 0.64]\times 10^{-13}$ GeV$^{-4}$,
which show improvement over the current bounds.


  In this note we consider a heat trace expansion on a manifold with wedge-like
singularity. We show that there are two terms in the expansion that contain
information about the presence of the singularity, namely the logarithmic term
$ct^{-1/2}\log t$ and the half power term $bt^{-1/2}$. We also give a geometric
expression for $c$.


  This paper reports the findings of a 606-participant study where we analyzed
the perception and engagement effects of COVID-19 vaccine rumours on Twitter
pertaining to (a) vaccine efficacy; and (b) mass immunization efforts in the
United States. Misperceptions regarding vaccine efficacy were successfully
induced through simple content alterations and the addition of popular anti
COVID-19 hashtags to otherwise valid Twitter content. Twitter's misinformation
contextual tags caused a "backfire effect" for the skeptic, vaccine-hesitant
reinforcing their opposition stance. While the majority of the participants
staunchly refrain from engaging with the COVID-19 rumours, the skeptic,
vaccine-hesitant ones were open to comment, re-tweet, like and share the
vaccine efficacy rumors. We discuss the implications of our results in the
context of broadening the effort for dispelling rumors about COVID-19 on social
media.


  In this paper, we study generalized constant ratio (GCR) hypersurfaces in
Euclidean spaces. We mainly focus on the hypersurfaces in $\mathbb E^4$. First,
we deal with $\delta(2)$-ideal GCR hypersurfaces. Then, we study on
hypersurfaces with constant (first) mean curvature. Finally, we obtain the
complete classification of GCR hypersurfaces with vanishing Gauss-Kronecker
curvature. We also give some explicit examples.
  Keywords: Generalized constant ratio submanifolds, $\delta(r)$-invariant
hypersurfaces, constant mean curvature, Gauss-Kronecker curvature


  The equidistribution of the double sign patterns of the Liouville function
$\lambda$ is proved unconditionally. As application, it is shown that the
computable real number $$\sum_{n \geq 1} \frac{1+\lambda(n)}{2^{n}}$$ is a
simply normal number in base 4.


  The applicability of nuclear {\it ab initio} calculations has rapidly
extended over the past decades. However, starting research projects is still
challenging due to the required numerical expertise in the generation of
underlying nuclear interaction matrix elements and many-body calculations. To
ease the first issue, in this paper we introduce the numerical code
\texttt{NuHamil} to generate the nucleon-nucleon (NN) and three-nucleon (3N)
matrix elements expressed in a spherical harmonic-oscillator basis, inputs of
many-body calculations. The ground-state energies for the selected doubly
closed shell nuclei are calculated with the no-core shell-model (NCSM) and
in-medium similarity renormalization group (IMSRG). The code is written in
modern Fortran, and OpenMP+MPI hybrid parallelization is available for the 3N
matrix-element calculations.


  We present a detailed analysis of the evolution of a simulated isolated disc
galaxy. The simulation includes stars, gas, star formation and simple chemical
yields. Stellar particles are split in two populations: the old one is present
at the beginning of the simulation and is calibrated according to various ages
and metallicities; the new population borns in the course of the simulation and
inherits the metallicity of the gas particles. The results have been calibrated
in four wavebands with the spectro-photometric evolutionary model GISSEL2000
(Bruzual & Charlot 1993). Dust extinction has also been taken into account. A
rest-frame morphological and bidimensional photometric analysis has been
performed on simulated images, with the same tools as for observations. The
effects of the stellar bar formation and the linked star formation episode on
the global properties of the galaxy (mass and luminosity distribution, colours,
isophotal radii) have been analysed. In particular, we have disentangled the
effects of stellar evolution from dynamic evolution to explain the cause of the
isophotal radii variations. We show that the dynamic properties (e.g. mass) of
the area enclosed by any isophotal radius depends on the waveband and on the
level of star formation activity. It is also shown that the bar isophotes
remain thinner than mass isodensities a long time (> 0.7 Gyr) after the maximum
of star formation rate. We show that bar ellipticity is very wavelength
dependent as suggested by real observations. Effects of dust extinction on
photometric and morphological measurements are systematically quantified.


  Hydrogen bonds are a common feature in protein folding and aggregation. Due
to their chemical peculiarities in terms of strength and directionality, a
particular attention must be paid to the definition of the hydrogen bond
potential itself. This global target has been tackled through a computational
approach based on a minimalist description of the protein and the proper design
of algorithms, mainly using Monte Carlo and Kinetic Monte Carlo methods. We
have designed a hydrogen bond potential, see J. Chem. Phys. 132, 235102 (2010).
We have been performed a complete study of sequenceless peptide systems under
different conditions, such as temperature and concentration. To carry out full
protein studies, we need additional potentials to describe tertiary
interactions. We have discussed two different points of view. The first one is
the combination of the hydrogen bond potential with a structure-based one. We
have evaluated the implications of a proper definition of hydrogen bonds in the
thermodynamic and dynamic aspects of protein folding. See Biophys. J. 101,
1474-1482 (2011). We have undertaken a second strategy, combining a generic
hydrophobic model with the hydrogen bond one. The two main factors of folding
and aggregation are merged, then, to create a simple but complete potential.
Thanks to it, we have re-studied peptide aggregation including sequence.
Besides, we have simulated complete proteins with different folded shapes. We
have analyzed the competition between folding and aggregation, and how sequence
and hydrogen bonds can influence the interplay between them. See J. Chem. Phys.
136, 215103 (2012).


  We model relativistically colliding plasma by PIC simulations in one and two
spatial dimensions, taking an ion-to-electron mass ratio of 400. Energy
dissipation by a wave precursor of mixed polarity and different densities of
the colliding plasma slabs results in a relativistic forward shock forming on
millisecond timescales. The forward shock accelerates electrons to
ultrarelativistic energies and reflects upstream ions, which drag the electrons
along to preserve the plasma quasi-neutrality. No reverse shock forms. The
shock may be representative for internal gamma ray burst shocks.


  We report here discovery of the existence of two different accretion modes in
the high mass X-ray binary pulsar Cen X-3 during its high states. The multiband
X-ray light curves of Cen X-3 lasting for more than 3400 days obtained with the
All Sky Monitor (ASM) onboard the Rossi X-ray Timing Explorer (RXTE) shows many
episodes of high and low X-ray intensities. The high intensity phases last
between a few to upto 110 days and the separation between two high intensity
phases also varies widely. One remarkable feature deduced from the RXTE-ASM
light curves is that during these high intensity phases, Cen X-3 manifests in
two very distinct spectral states. When the source makes a transition from the
low intensity phase to the high intensity phase, it adopts one of these two
spectral states and during the entire high intensity phase remains in that
particular spectral state. During December 2000 to April 2004, all the high
intensity episodes showed a hardness ratio which is significantly larger than
the same during all the high states prior to and subsequent to this period. It
is also found that most of the soft outbursts reach a nearly constant peak flux
in the 5--12 keV band. For comparison, similar analysis was carried out on the
long term X-ray light curves of three other X-ray binary pulsars Her X-1, Vela
X-1, and SMC X-1. Results obtained with these sources are also presented here
and we found that none of the other sources show such a behaviour. From these
observations, we suggest that Cen X-3 has two different accretion modes and in
the course of nine years it has exhibited two switch overs between these.


  We show that a small conducting object, such as a nanosphere or a nanoring,
embedded into or placed in the vicinity of the two-dimensional electron liquid
(2DEL) and subjected to a circularly polarized electromagnetic radiation
induces ``twisted'' plasmonic oscillations in the adjacent 2DEL. The
oscillations are rectified due to the hydrodynamic nonlinearities leading to
the helicity sensitive circular dc current and to a magnetic moment. This
hydrodynamic inverse Faraday effect (HIFE) can be observed at room temperature
in different materials. The HIFE is dramatically enhanced in a periodic array
of the nanospheres forming a resonant plasmonic coupler. Such a coupler exposed
to a circularly polarized wave converts the entire 2DEL into a vortex state.
Hence, the twisted plasmonic modes support resonant plasmonic-enhanced
gate-tunable optical magnetization. Due to the interference of the plasmonic
and Drude contributions, the resonances have an asymmetric Fano-like shape.
These resonances present a signature of the 2DEL properties not affected by
contacts and interconnects and, therefore, providing the most accurate
information about the 2DEL properties. In particular, the widths of the
resonances encode direct information about the momentum relaxation time and
viscosity of the 2DEL.


  We study how to introduce locality mechanisms into vision transformers. The
transformer network originates from machine translation and is particularly
good at modelling long-range dependencies within a long sequence. Although the
global interaction between the token embeddings could be well modelled by the
self-attention mechanism of transformers, what is lacking a locality mechanism
for information exchange within a local region. Yet, locality is essential for
images since it pertains to structures like lines, edges, shapes, and even
objects.
  We add locality to vision transformers by introducing depth-wise convolution
into the feed-forward network. This seemingly simple solution is inspired by
the comparison between feed-forward networks and inverted residual blocks. The
importance of locality mechanisms is validated in two ways: 1) A wide range of
design choices (activation function, layer placement, expansion ratio) are
available for incorporating locality mechanisms and all proper choices can lead
to a performance gain over the baseline, and 2) The same locality mechanism is
successfully applied to 4 vision transformers, which shows the generalization
of the locality concept. In particular, for ImageNet2012 classification, the
locality-enhanced transformers outperform the baselines DeiT-T and PVT-T by
2.6\% and 3.1\% with a negligible increase in the number of parameters and
computational effort. Code is available at
\url{https://github.com/ofsoundof/LocalViT}.


  Popular generative model learning methods such as Generative Adversarial
Networks (GANs), and Variational Autoencoders (VAE) enforce the latent
representation to follow simple distributions such as isotropic Gaussian. In
this paper, we argue that learning a complicated distribution over the latent
space of an auto-encoder enables more accurate modeling of complicated data
distributions. Based on this observation, we propose a two stage optimization
procedure which maximizes an approximate implicit density model. We
experimentally verify that our method outperforms GANs and VAEs on two image
datasets (MNIST, CELEB-A). We also show that our approach is amenable to
learning generative model for sequential data, by learning to generate speech
and music.


  Effect of the electric quadrupole moment, $Q$, is studied for positron-atom
bound systems. It is demonstrated that for $Q >50$ a.u. the electric quadrupole
potential is sufficiently strong to bind positron (or electron) even in the
absence of the dipole polarization potential. Such large values of $Q$ are not
known for atomic ground states, however, they exist in molecules and excited
atoms. In the state $2s2p~^3P^o_2$ of beryllium, the quadrupole contribution
makes difference between stable bound state and decay to Be$^+$ ion and
positronium. In a majority of atoms the quadrupole contribution is small and
can be neglected.


  Computing based on biochemical processes is a newest rapidly developing field
of unconventional information and signal processing. In this paper we present
results of our research in the field of biochemical computing and summarize the
obtained numerical and experimental data for implementations of the standard
two-input OR and AND gates with double-sigmoid shape of the output signal. This
form of response was obtained as a function of the two inputs in each of the
realized biochemical systems. The enzymatic gate processes in the first system
were activated with two chemical inputs and resulted in optically detected
chromogen oxidation, which happens when either one or both of the inputs are
present. In this case, the biochemical system is functioning as the OR gate. We
demonstrate that the addition of a "filtering" biocatalytic process leads to a
considerable reduction of the noise transmission factor and the resulting gate
response has sigmoid shape in both inputs. The second system was developed for
functioning as an AND gate, where the output signal was activated only by a
simultaneous action of two enzymatic biomarkers. This response can be used as
an indicator of liver damage, but only if both of these of the inputs are
present at their elevated, pathophysiological values of concentrations. A
kinetic numerical model was developed and used to estimate the range of
parameters for which the experimentally realized logic gate is close to
optimal. We also analyzed the system to evaluate its noise-handling properties.


  We study the zeta determinant of global boundary problems of APS-type through
a general theory for relative spectral invariants. In particular, we compute
the zeta determinant for Dirac-Laplacian boundary problems in terms of a
scattering Fredholm determinant over the boundary.


  An attempt is made in order to clarify the so called regular black holes
issue. It is revisited that if one works within General Relativity minimally
coupled with non linear source, mainly of electromagnetic origin, and within a
static spherically symmetric ansatz for the metric, there is still room for
singular contribution to the black hole solution. A reconstruction method is
proposed and several examples are discussed, including new ones. A possible way
to obtain a non singular black hole is introduced, and in this case, several
known examples are re-discussed, and new ones are provided.


  Identity recognition in a car cabin is a critical task nowadays and offers a
great field of applications ranging from personalizing intelligent cars to suit
drivers physical and behavioral needs to increasing safety and security.
However, the performance and applicability of published approaches are still
not suitable for use in series cars and need to be improved. In this paper, we
investigate Human Identity Recognition in a car cabin with Time Series
Classification (TSC) and deep neural networks. We use gas and brake pedal
pressure as input to our models. This data is easily collectable during driving
in everyday situations. Since our classifiers have very little memory
requirements and do not require any input data preproccesing, we were able to
train on one Intel i5-3210M processor only. Our classification approach is
based on a combination of LSTM and ResNet. The network trained on a subset of
NUDrive outperforms the ResNet and LSTM models trained solely by 35.9 % and
53.85 % accuracy respectively. We reach a final accuracy of 79.49 % on a
10-drivers subset of NUDrive and 96.90 % on a 5-drivers subset of UTDrive.


  A natural oriented (2k+2)-chain in CP^{2k+1} with boundary twice RP^{2k+1},
its complex shade, is constructed. Via intersection numbers with the shade, a
new invariant, the shade number of k-dimensional subvarieties with normal
vector fields along their real part, is introduced. For an even-dimensional
real variety, the shade number and the Euler number of the complement of the
normal vector field in the real normal bundle of its real part agree. For an
odd- dimensional orientable real variety, a linear combination of the shade
number and the wrapping number (self-linking number) of its real part is
independent of the normal vector field and equals the encomplexed writhe as
defined by Viro. Shade numbers of varieties without real points and encomplexed
writhes of odd- dimensional real varieties are, in a sense, Vassiliev
invariants of degree 1.
  Complex shades of odd-dimensional spheres are constructed. Shade numbers of
real subvarieties in spheres have properties analogous to those of their
projective counterparts.


  In this paper we study the properties of an electron trapped on a torus
surface. We consider the influence of surface curvature on the spectrum and the
behaviour of the wave function. In addition, the effects of external electric
and magnetic fields were explored. Two toroid configurations were considered,
namely, a torus-like configuration and a ring-like configuration both in
agreement with experimental data. We also obtain the bound states and discuss
the role of geometry on the Landau levels.


  During the Precambrian era, Earth's decelerating rotation would have passed a
21-hour period that would have been resonant with the semidiurnal atmospheric
thermal tide. Near this point, the atmospheric torque would have been
maximized, being comparable in magnitude but opposite in direction to the lunar
torque, halting Earth's rotational deceleration, maintaining a constant day
length, as detailed by Zahnle and Walker (1987). We develop a computational
model to determine necessary conditions for formation and breakage of this
resonant effect. Our simulations show the resonance to be resilient to
atmospheric thermal noise but suggest a sudden atmospheric temperature increase
like the deglaciation period following a possible "snowball Earth" near the end
of the Precambrian would break this resonance; the Marinoan and Sturtian
glaciations seem the most likely candidates for this event. Our model provides
a simulated day length over time that resembles existing paleorotational data,
though further data is needed to verify this hypothesis.


  Audio/visual recognition and retrieval applications have recently garnered
significant attention within Internet-of-Things (IoT) oriented services, given
that video cameras and audio processing chipsets are now ubiquitous even in
low-end embedded systems. In the most typical scenario for such services, each
device extracts audio/visual features and compacts them into feature
descriptors, which comprise media queries. These queries are uploaded to a
remote cloud computing service that performs content matching for
classification or retrieval applications. Two of the most crucial aspects for
such services are: (i) controlling the device energy consumption when using the
service; (ii) reducing the billing cost incurred from the cloud infrastructure
provider. In this paper we derive analytic conditions for the optimal coupling
between the device energy consumption and the incurred cloud infrastructure
billing. Our framework encapsulates: the energy consumption to produce and
transmit audio/visual queries, the billing rates of the cloud infrastructure,
the number of devices concurrently connected to the same cloud server, {the
query volume constraint of each cluster of devices,} and the statistics of the
query data production volume per device. Our analytic results are validated via
a deployment with: (i) the device side comprising compact image descriptors
(queries) computed on Beaglebone Linux embedded platforms and transmitted to
Amazon Web Services (AWS) Simple Storage Service; (ii) the cloud side carrying
out image similarity detection via AWS Elastic Compute Cloud (EC2) instances,
with the AWS Auto Scaling being used to control the number of instances
according to the demand.


  Saliency prediction refers to the computational task of modeling overt
attention. Social cues greatly influence our attention, consequently altering
our eye movements and behavior. To emphasize the efficacy of such features, we
present a neural model for integrating social cues and weighting their
influences. Our model consists of two stages. During the first stage, we detect
two social cues by following gaze, estimating gaze direction, and recognizing
affect. These features are then transformed into spatiotemporal maps through
image processing operations. The transformed representations are propagated to
the second stage (GASP) where we explore various techniques of late fusion for
integrating social cues and introduce two sub-networks for directing attention
to relevant stimuli. Our experiments indicate that fusion approaches achieve
better results for static integration methods, whereas non-fusion approaches
for which the influence of each modality is unknown, result in better outcomes
when coupled with recurrent models for dynamic saliency prediction. We show
that gaze direction and affective representations contribute a prediction to
ground-truth correspondence improvement of at least 5% compared to dynamic
saliency models without social cues. Furthermore, affective representations
improve GASP, supporting the necessity of considering affect-biased attention
in predicting saliency.


  Optical sensors and learning algorithms for autonomous vehicles have
dramatically advanced in the past few years. Nonetheless, the reliability of
today's autonomous vehicles is hindered by the limited line-of-sight sensing
capability and the brittleness of data-driven methods in handling extreme
situations. With recent developments of telecommunication technologies,
cooperative perception with vehicle-to-vehicle communications has become a
promising paradigm to enhance autonomous driving in dangerous or emergency
situations. We introduce COOPERNAUT, an end-to-end learning model that uses
cross-vehicle perception for vision-based cooperative driving. Our model
encodes LiDAR information into compact point-based representations that can be
transmitted as messages between vehicles via realistic wireless channels. To
evaluate our model, we develop AutoCastSim, a network-augmented driving
simulation framework with example accident-prone scenarios. Our experiments on
AutoCastSim suggest that our cooperative perception driving models lead to a
40% improvement in average success rate over egocentric driving models in these
challenging driving situations and a 5 times smaller bandwidth requirement than
prior work V2VNet. COOPERNAUT and AUTOCASTSIM are available at
https://ut-austin-rpl.github.io/Coopernaut/.


  The spin-charge gauge approach to consider the metal-insulator crossover
(MIC) and other anomalous transport properties in High-T$_c$ cuprates is
briefly reviewed. A U(1) field gauging the global charge symmetry and an SU(2)
field gauging the global spin-rotational symmetry are introduced to study the
two-dimensional $t-J$ model in the limit $t\gg J$. The MIC as a clue to the
understanding of the ``pseudogap'' (PG) phase, is attributed to the competition
between the short-range antiferromagnetic order and dissipative motion of
charge carriers coupled to the slave-particle gauge field. The composite
particle formed by binding the charge carrier (holon) and spin excitation
(spinon) via the slave particle gauge field exhibits a number of peculiar
properties, and the calculated results are in good agreement with experimental
data for both PG and ``strange metal'' phases. Connections to other gauge field
approaches in studying the strong correlation problem are also briefly
outlined.


  Many camera sensors use a dual-pixel (DP) design that operates as a
rudimentary light field providing two sub-aperture views of a scene in a single
capture. The DP sensor was developed to improve how cameras perform autofocus.
Since the DP sensor's introduction, researchers have found additional uses for
the DP data, such as depth estimation, reflection removal, and defocus
deblurring. We are interested in the latter task of defocus deblurring. In
particular, we propose a single-image deblurring network that incorporates the
two sub-aperture views into a multi-task framework. Specifically, we show that
jointly learning to predict the two DP views from a single blurry input image
improves the network's ability to learn to deblur the image. Our experiments
show this multi-task strategy achieves +1dB PSNR improvement over
state-of-the-art defocus deblurring methods. In addition, our multi-task
framework allows accurate DP-view synthesis (e.g., ~39dB PSNR) from the single
input image. These high-quality DP views can be used for other DP-based
applications, such as reflection removal. As part of this effort, we have
captured a new dataset of 7,059 high-quality images to support our training for
the DP-view synthesis task. Our dataset, code, and trained models are publicly
available at
https://github.com/Abdullah-Abuolaim/multi-task-defocus-deblurring-dual-pixel-nimat.


  We analyze atomic structures of plasma embedded aluminum (Al) atom and its
ions in the weakly and strongly coupling regimes. The plasma screening effects
in these atomic systems are accounted for using the Debye and ion sphere (IS)
potentials for the weakly coupling and strongly coupling plasmas, respectively.
Within the Debye model, special attention is given to investigate the spherical
and non-spherical plasma-screening effects considering in the electron-electron
interaction potential. The relativistic coupled-cluster (RCC) method has been
employed to describe the relativistic and electronic correlation effects in the
above atomic systems. The variation in the ionization potentials (IPs) and
excitation energies (EEs) of the plasma embedded Al ions are presented. It is
found that the atomic systems exhibit more stability when the exact screening
effects are taken into account. It is also showed that in the presence of
strongly coupled plasma environment, the highly ionized Al ions show blue and
red shifts in the spectral lines of the transitions between the states with
same and different principal quantum numbers, respectively. Comparison among
the results obtained from the Debye and IS models are also carried out
considering similar plasma conditions.


  We generalize the Quantum Approximate Optimization Algorithm (QAOA) of Farhi
et al. (2014) to allow for arbitrary separable initial states with
corresponding mixers such that the starting state is the most excited state of
the mixing Hamiltonian. We demonstrate this version of QAOA, which we call
QAOA-warmest, by simulating Max-Cut on weighted graphs. We initialize the
starting state as a warm-start using $2$ and $3$-dimensional approximations
obtained using randomized projections of solutions to Max-Cut's semi-definite
program, and define a warm-start dependent custom mixer. We show that these
warm-starts initialize the QAOA circuit with constant-factor approximations of
$0.658$ for $2$-dimensional and $0.585$ for $3$-dimensional warm-starts for
graphs with non-negative edge weights, improving upon previously known trivial
(i.e., $0.5$ for standard initialization) worst-case bounds at $p=0$. These
factors in fact lower bound the approximation achieved for Max-Cut at higher
circuit depths, since we also show that QAOA-warmest with any separable initial
state converges to Max-Cut under the adiabatic limit as $p\rightarrow \infty$.
However, the choice of warm-starts significantly impacts the rate of
convergence to Max-Cut, and we show empirically that our warm-starts achieve a
faster convergence compared to existing approaches. Additionally, our numerical
simulations show higher quality cuts compared to standard QAOA, the classical
Goemans-Williamson algorithm, and a warm-started QAOA without custom mixers for
an instance library of $1148$ graphs (upto $11$ nodes) and depth $p=8$. We
further show that QAOA-warmest outperforms the standard QAOA of Farhi et al. in
experiments on current IBM-Q and Quantinuum hardware.


  AIMS: We analyse the photometric properties of the early-type Fornax cluster
dwarf galaxy population, based on a wide field imaging study of the central
cluster area in V and I. We used the instrument/telescope combination
IMACS/Magellan at Las Campanas Observatory, providing much larger light
collecting area and better image resolution than previous surveys.
  METHODS: We create a fiducial sample of Fornax cluster dwarf ellipticals
(dEs) in the following three steps: (1) To verify cluster membership, we
measured I-band surface brightness fluctuation (SBF) distances to candidate
dEs; (2) We re-assessed morphological classifications for candidate dEs too
faint for SBF detection; and (3) We searched for new candidate dEs in the
size-luminosity regime close to the resolution limit of previous surveys.
  RESULTS: (1) We confirm cluster membership for 28 candidate dEs in the range
-16.6<M_V<-10.1 mag by means of SBF distances. We find no SBF background
galaxy. (2) Of 51 further candidate dEs in the range -13.2<M_V<-8.6 mag, 2/3
are confirmed as probable cluster members by morphological re-assessment, while
1/3 are re-classified as probable background objects. (3) We find 12 new dE
candidates in the range -12.3<M_V<-8.8 mag.
  The surface brightness-magnitude relation defined by the resulting fiducial
dE sample shows that Fornax dEs are about 40% larger than Local Group dEs. The
Fornax dE sample furthermore defines a colour-magnitude relation that appears
slightly shallower than that of Local Group dEs. The early-type dwarf galaxy
luminosity function in Fornax has a faint end slope alpha = -1.1 +/- 0.1.
  We discuss these findings in the context of structure formation theories.
(ABRIDGED)


  Spin liquid is a state of electron spins in which quantum fluctuation breaks
magnetic ordering while maintaining spin correlation. It has been a central
topic in magnetism because of its relevance to high-Tc superconductivity and
topological states. However, utilizing spin liquid has been quite difficult.
Typical spin liquid states are realized in one-dimensional spin systems, called
quantum spin chains. Here, we show that a spin liquid in a spin-1/2 quantum
chain generates and carries spin current via its long-range spin fluctuation.
This is demonstrated by observing an anisotropic negative spin Seebeck effect
along the spin chains in Sr2CuO3. The results show that spin current can flow
even in an atomic channel owing the spin liquid state, which can be used for
atomic spin-current wiring.


  We study arrays of parallel doped semiconductor nanowires in a temperature
range where the electrons propagate through the nanowires by phonon assisted
hops between localized states. By solving the Random Resistor Network problem,
we compute the thermopower $S$, the electrical conductance $G$, and the
electronic thermal conductance $K^e$ of the device. We investigate how those
quantities depend on the position -- which can be tuned with a back gate -- of
the nanowire impurity band with respect to the equilibrium electrochemical
potential. We show that large power factors can be reached near the band edges,
when $S$ self-averages to large values while $G$ is small but scales with the
number of wires. Calculating the amount of heat exchanged locally between the
electrons inside the nanowires and the phonons of the environment, we show that
phonons are mainly absorbed near one electrode and emitted near the other when
a charge current is driven through the nanowires near their band edges. This
phenomenon could be exploited for a field control of the heat exchange between
the phonons and the electrons at submicron scales in electronic circuits. It
could be also used for cooling hot spots.


  Predicting the traffic incident duration is a hard problem to solve due to
the stochastic nature of incident occurrence in space and time, a lack of
information at the beginning of a reported traffic disruption, and lack of
advanced methods in transport engineering to derive insights from past
accidents. This paper proposes a new fusion framework for predicting the
incident duration from limited information by using an integration of machine
learning with traffic flow/speed and incident description as features, encoded
via several Deep Learning methods (ANN autoencoder and character-level LSTM-ANN
sentiment classifier). The paper constructs a cross-disciplinary modelling
approach in transport and data science. The approach improves the incident
duration prediction accuracy over the top-performing ML models applied to
baseline incident reports. Results show that our proposed method can improve
the accuracy by $60\%$ when compared to standard linear or support vector
regression models, and a further $7\%$ improvement with respect to the hybrid
deep learning auto-encoded GBDT model which seems to outperform all other
models. The application area is the city of San Francisco, rich in both traffic
incident logs (Countrywide Traffic Accident Data set) and past historical
traffic congestion information (5-minute precision measurements from Caltrans
Performance Measurement System).


  We show that technique of Dyson equation in wave multiple scattering by
spatially disordered discrete medium statistical theory leads directly to a
dielectric permittivity tensor, which is characterized by spatial dispersion
and obeys the generalized Lorentz-Lorenz formula. Introduced via this spatial
dispersion an effective magnetic permeability demonstrates the diamagnetic
property in limit of independent strongly reflected non-magnetic small
spherical particles in accordance with earlier intuitive predictions. The
revealed physical nature of the effective diamagnetic property consists in that
electric and magnetic dipoles induced in a particle by wave scattering give
different contribution into the transverse and longitudinal components of the
effective dielectric permittivity. Besides as appeared the diamagnetism under
study is enhanced by appearance of additional effective dielectric displacement
current in the medium.


  We present multi-adaptive versions of the standard continuous and
discontinuous Galerkin methods for ODEs. Taking adaptivity one step further, we
allow for individual time-steps, order and quadrature, so that in particular
each individual component has its own time-step sequence. This paper contains a
description of the methods, an analysis of their basic properties, and a
posteriori error analysis. In the accompanying paper [A. Logg, SIAM J. Sci.
Comput., 27 (2003), pp. 741-758], we present adaptive algorithms for
time-stepping and global error control based on the results of the current
paper.


  Submillimetre galaxies (SMGs) are bright sources at submillimetre
wavelengths. Made up of mostly of high-z galaxies, SMGs are amongst the most
luminous dusty galaxies in the Universe. Studying their environments and
clustering strength is thus important to put these galaxies in a cosmological
context. We present an environmental study of a sample of 116 SMGs in 96 ALMA
observation fields, which were initially discovered with the AzTEC camera on
ASTE and identified with high-resolution ALMA imaging within the COSMOS survey
field, having either spectroscopic or unambiguous photometric redshift. We
analysed their environments making use of the latest release of the COSMOS
photometric catalogue, COSMOS2015, a catalogue that contains precise
photometric redshifts for more than half a million objects over the 2deg2
COSMOS field. We searched for dense galaxy environments computing the so-called
overdensity parameter as a function of distance within a radius of 5 arcmin
from the SMG. We validated this approach spectroscopically for those SMGs for
which spectroscopic redshift is available. As an additional test, we searched
for extended X-ray emission as a proxy for the hot intracluster medium,
performing an X-ray stacking analysis in the 0.5-2 keV band with a 32 arcsec
aperture and our SMG position using all available XMM-Newton and Chandra X-ray
observations of the COSMOS field. We find that 27% (31 out of 116) of the SMGs
in our sample are located in a galactic dense environment; a fraction that is
similar to previous studies. The spectroscopic redshift is known for 15 of
these 31 sources, thus this photometric approach is tested using spectroscopy.
We are able to confirm that 7 out of 15 SMGs lie in high-density peaks.
However, the search for associated extended X-ray emission via an X-ray
stacking analysis leads to a detection that is not statistically significant.


  The search for time-harmonic solutions of nonlinear Maxwell equations in the
absence of charges and currents leads to the elliptic equation
$$\nabla\times\left(\mu(x)^{-1} \nabla\times u\right) - \omega^2\varepsilon(x)u
= f(x,u)$$ for the field $u:\Omega\to\mathbb{R}^3$ in a domain
$\Omega\subset\mathbb{R}^3$. Here $\varepsilon(x) \in \mathbb{R}^{3\times3}$ is
the (linear) permittivity tensor of the material, and $\mu(x) \in
\mathbb{R}^{3\times3}$ denotes the magnetic permeability tensor. The
nonlinearity $f:\Omega\times\mathbb{R}^3\to\mathbb{R}^3$ comes from the
nonlinear polarization. If $f=\nabla_uF$ is a gradient then this equation has a
variational structure. The goal of this paper is to give an introduction to the
problem and the variational approach, and to survey recent results on ground
and bound state solutions. It also contains refinements of known results and
some new results.


  The aim of this paper is to present a kinetic numerical scheme for the
computations of transient pressurised flows in closed water pipes with variable
sections. Firstly, we detail the derivation of the mathematical model in
curvilinear coordinates under some hypothesis and we performe a formal
asymptotic analysis. Then the obtained system is written as a conservative
hyperbolic partial differential system of equations, and we recall how to
obtain the corresponding kinetic formulation based on an upwinding of the
source term due to the "pseudo topography" performed in a close manner
described by Perthame and al.


  Considering the nucleon as a system of confined valence quarks surrounded by
pions we derive a Galster-like parameterization of the neutron electric form
factor $G_E^n$. Furthermore, we show that the proposed parameterization can be
linked to properties of the pion cloud. By this, the high quality data for the
pion form factor can be used in predictions of $G_E^n$ in the low $Q^2$ region,
where the direct double polarization measurements are not available.


  We consider optimal control of the scalar wave equation where the control
enters as a coefficient in the principal part. Adding a total variation penalty
allows showing existence of optimal controls, which requires continuity results
for the coefficient-to-solution mapping for discontinuous coefficients. We
additionally consider a so-called "multi-bang" penalty that promotes controls
taking on values pointwise almost everywhere from a specified discrete set.
Under additional assumptions on the data, we derive an improved regularity
result for the state, leading to optimality conditions that can be interpreted
in an appropriate pointwise fashion. The numerical solution makes use of a
stabilized finite element method and a nonlinear primal-dual proximal splitting
algorithm.


  The impact of the strongly attractive electromagnetic field of heavy nuclei
on electrons in quasi-elastic (e,e') scattering is often accounted for by the
effective momentum approximation. This method is a plane wave Born
approximation which takes the twofold effect of the attractive nucleus on
initial and final state electrons into account, namely the modification of the
electron momentum in the vicinity of the nucleus, and the focusing of electrons
towards the nuclear region leading to an enhancement of the corresponding wave
function amplitudes. The focusing effect due to the attractive Coulomb field of
a homogeneously charged sphere on a classical ensemble of charged particles
incident on the field is calculated in the highly relativistic limit and
compared to results obtained from exact solutions of the Dirac equation. The
result is relevant for the theoretical foundation of the effective momentum
approximation and describes the high energy behavior of the amplitude of
continuum Dirac waves in the potential of a homogeneously charged sphere. Our
findings indicate that the effective momentum approximation is a useful
approximation for the calculation of Coulomb corrections in (e,e') scattering
off heavy nuclei for sufficiently high electron energies and momentum transfer.


  Spectral imaging has received enormous interest in the field of medical
imaging modalities. It provides a powerful tool for the analysis of different
organs and non-invasive tissues. Therefore, significant amount of research has
been conducted to explore the possibility of using spectral imaging in
biomedical applications. To observe spectral image information in real time
during surgery and monitor the temporal changes in the organs and tissues is a
demanding task. Available spectral imaging devices are not sufficient to
accomplish this task with an acceptable spatial and spectral resolution. A
solution to this problem is to estimate the spectral video from RGB video and
perform visualization with the most prominent spectral bands. In this research,
we propose a framework to generate neurosurgery spectral video from RGB video.
A spectral estimation technique is applied on each RGB video frames. The RGB
video is captured using a digital camera connected with an operational
microscope dedicated to neurosurgery. A database of neurosurgery spectral
images is used to collect training data and evaluate the estimation accuracy. A
searching technique is used to identify the best training set. Five different
spectrum estimation techniques are experimented to indentify the best method.
Although this framework is established for neurosurgery spectral video
generation, however, the methodology outlined here would also be applicable to
other similar research.


  Empirical evidence is presented which contradicts the established
interpretation of the intraspecific genetic variability as the adaptive
potential of the species: the uniform evolutionary tempo across the life
kingdom, species discreteness, and absence of correlation between genetic
variability and prosperity of extant species testify against the ruling
paradigm. Consistent interpretation of the nature of intraspecific genetic
variability is based on recognizing the limited sensitivity of stabilising
selection, which allows for accumulation and persistence in the population of a
considerable amount of mutational substitutions, which, to some degree, erase
the meaningful genetic information of the species. The proposed interpretation
also provides solution to the inbreeding paradox in the invasive species.


  We propose an improved scheme to do the time dependent variational principle
(TDVP) in finite matrix product states (MPS) for two-dimensional systems or
one-dimensional systems with long range interactions. We present a method to
represent the time-evolving state in a MPS with its basis enriched by
state-averaging with global Krylov vectors. We show that the projection error
is significantly reduced so that precise time evolution can still be obtained
even if a larger time step is used. Combined with the one-site TDVP, our
approach provides a way to dynamically increase the bond dimension while still
preserving unitarity for real time evolution. Our method can be more accurate
and exhibit slower bond dimension growth than the conventional two-site TDVP.


  The mixing of magmas is a fundamental process in the Earth system causing
extreme compositional variations in igneous rocks. This process can develop
with different intensities both in space and time, making the interpretation of
compositional patterns in igneous rocks a petrological challenge. As a
time-dependent process, magma mixing has been suggested to preserve information
about the time elapsed between the injection of a new magma into sub-volcanic
magma chambers and eruptions. This allowed the use of magma mixing as an
additional volcanological tool to infer the mixing-to-eruption timescales. In
spite of the potential of magma mixing processes to provide information about
the timing of volcanic eruptions its statistical robustness is not yet
established. This represents a prerequisite to apply reliably this conceptual
model. Here, new chaotic magma mixing experiments were performed at different
times using natural melts. The degree of reproducibility of experimental
results was tested repeating one experiment at the same starting conditions and
comparing the compositional variability. We further tested the robustness of
the statistical analysis by randomly removing from the analysed dataset a
progressively increasing number of samples. Results highlight the robustness of
the method to derive empirical relationships linking the efficiency of chemical
exchanges and mixing time. These empirical relationships remain valid by
removing up to 80% of the analytical determinations. Experimental results were
applied to constrain the homogenization time of chemical heterogeneities in
natural magmatic system during mixing. The calculations show that, when the
mixing dynamics generate millimetre thick filaments, homogenization timescales
of the order of a few minutes are to be expected.


  This work proposes an open interoperable data portal that offers access to a
Web-wide climate domain knowledge graph created for Ireland and England's NOAA
climate daily data. There are three main components contributing to this data
portal: the first is the upper layer schema of the knowledge graph -- the
climate analysis (CA) ontology -- the second is an ad hoc SPARQL server by
which to store the graph data and provide public Web access, the last is a
dereferencing engine deployed to resolve URIs for entity information. Our
knowledge graph form of NOAA climate data facilitates the supply of semantic
climate information to researchers and offers a variety of semantic
applications that can be built on top of it.


  We investigate the process of an ultrarelativistic fluid accreted onto
axisymmetric Kerr-Sen black holes in Einstein-Maxwell-dilaton-axion theory. We
obtain the solution describing the velocity potential of a stationary
irrotational fluid with a stiff equation of state and the solution for the
streamlined diagram of the quadrupolar flow. We also investigate how the
solution's coefficients and the stagnation points are affected by the
parameters. The injection rate, the ejection rate, and the critical angle are
discussed in detail. We find that with an increasing dilaton parameter the
ratio of the ejection rate to the injection rate increases and that the
radiative efficiency is larger, while the redshift is lower, compared to the
Kerr black hole.


  We study the existence of ferromagnetism in one dimensional Hubbard Model
with on site interaction and nearest and next nearest neighbor hopping. Using
the Hubbard I approximation, a self consistent equation for $<n_\sigma>$, the
particle density with spin $\sigma$ is obtained. We find that ferromagnetism
exists over a wide range of values of the next nearest neighbor hopping element
t' when on-site interaction U is large. The phase diagram as a function of t'/t
and the electron density $<n>$ has been obtained by numerically solving the
self consistent equation. It is found that the maximum density at which
ferromagnetism can occur, peaks around $t'/t \approx -0.53$. We show that this
phenomenon is a result of the peculiar way in which the t' term modifies the
density of states.


  Ionization injection in a plasma wakefield accelerator was investigated
experimentally using two lithium plasma sources of different lengths. The
ionization of the helium gas, used to confine the lithium, injects electrons in
the wake. After acceleration, these injected electrons were observed as a
distinct group from the drive beam on the energy spectrometer. They typically
have a charge of tens of pC, an energy spread of a few GeV, and a maximum
energy of up to 30 GeV. The emittance of this group of electrons can be many
times smaller than the initial emittance of the drive beam. The energy scaling
for the trapped charge from one plasma length to the other is consistent with
the blowout theory of the plasma wakefield.


  Clustering coefficient is one of the most important metrics to understand the
complex structure of networks. This paper addresses the estimation of
clustering coefficient in network streams. There have been substantial work in
this area, most of conducting empirical comparisons of various algorithms. The
variance and the bias of the estimators have not been quantified. Starting with
a simple yet powerful streaming algorithm, we derived the variance and bias for
the estimator, and the estimators for the variances and bias. More importantly,
we simplify the estimators so that it can be used in practice. The variance and
bias estimators are verified extensively on 49 real networks.


  We consider second order divergence form elliptic operators with $W^{1,1}$
coefficients, in a uniform domain $\Omega$ with Ahlfors regular boundary. We
show that the $A_\infty$ property of the elliptic measure associated to any
such operator implies that $\Omega$ is a set of locally finite perimeter whose
boundary, $\partial\Omega$, is rectifiable. As a corollary we show that for
this type of operators, absolute continuity of the surface measure with respect
to the elliptic measure is enough to guarantee rectifiability of the boundary.
In the case that the coefficients are continuous we obtain additional
information about $\Omega$.


  Among the major remaining challenges for single image super resolution (SISR)
is the capacity to recover coherent images with global shapes and local details
conforming to human vision system. Recent generative adversarial network (GAN)
based SISR methods have yielded overall realistic SR images, however, there are
always unpleasant textures accompanied with structural distortions in local
regions. To target these issues, we introduce the gradient branch into the
generator to preserve structural information by restoring high-resolution
gradient maps in SR process. In addition, we utilize a U-net based
discriminator to consider both the whole image and the detailed per-pixel
authenticity, which could encourage the generator to maintain overall coherence
of the reconstructed images. Moreover, we have studied objective functions and
LPIPS perceptual loss is added to generate more realistic and natural details.
Experimental results show that our proposed method outperforms state-of-the-art
perceptual-driven SR methods in perception index (PI), and obtains more
geometrically consistent and visually pleasing textures in natural image
restoration.


  Based on the conjecture for the exact eigenvalue of the transfer matrix of
the higher half-integer spin XXZ chain at the Razumov-Stroganov point, we
evaluate the corresponding Baxter's Q operator in closed form by solving the TQ
equation. The combination of the Q operators on the "right side" and the "wrong
side" is shown to produce the hierarchy of functional relations.


  Financial crime detection using graph learning improves financial safety and
efficiency. However, criminals may commit financial crimes across different
institutions to avoid detection, which increases the difficulty of detection
for financial institutions which use local data for graph learning. As most
financial institutions are subject to strict regulations in regards to data
privacy protection, the training data is often isolated and conventional
learning technology cannot handle the problem. Federated learning (FL) allows
multiple institutions to train a model without revealing their datasets to each
other, hence ensuring data privacy protection. In this paper, we proposes a
novel two-stage approach to federated graph learning (2SFGL): The first stage
of 2SFGL involves the virtual fusion of multiparty graphs, and the second
involves model training and inference on the virtual graph. We evaluate our
framework on a conventional fraud detection task based on the
FraudAmazonDataset and FraudYelpDataset. Experimental results show that
integrating and applying a GCN (Graph Convolutional Network) with our 2SFGL
framework to the same task results in a 17.6\%-30.2\% increase in performance
on several typical metrics compared to the case only using FedAvg, while
integrating GraphSAGE with 2SFGL results in a 6\%-16.2\% increase in
performance compared to the case only using FedAvg. We conclude that our
proposed framework is a robust and simple protocol which can be simply
integrated to pre-existing graph-based fraud detection methods.


  Robots need lightweight legs for agile locomotion, and intrinsic series
elastic compliance has proven to be a major ingredient for energy-efficient
locomotion and robust locomotion control. Animals' anatomy and locomotion
capabilities emphasize the importance of that lightweight legs and integrated,
compact, series elastically actuated for distal leg joints. But unlike robots,
animals achieve series elastic actuation by their muscle-tendon units. So far
no designs are available that feature all characteristics of a perfect distal
legged locomotion actuator; a low-weight and low-inertia design, with high
mechanical efficiency, no stick and sliding friction, low mechanical
complexity, high-power output while being easy to mount. Ideally, such an
actuator can be controlled directly and without mechanical cross-coupling, for
example remotely. With this goal in mind, we propose a low-friction,
lightweight Series ELastic Diaphragm distal Actuator (SELDA) which meets many,
although not all, of the above requirements. We develop, implement, and
characterize a bioinspired robot leg that features a SELDA-actuated foot
segment. We compare two leg configurations controlled by a central pattern
generator that both feature agile forward hopping. By tuning SELDA's activation
timing, we effectively adjust the robot's hopping height by 11% and its forward
velocity by 14%, even with comparatively low power injection to the distal
joint.


  Vehicular fog computing (VFC) pushes the cloud computing capability to the
distributed fog nodes at the edge of the Internet, enabling compute-intensive
and latency-sensitive computing services for vehicles through task offloading.
However, a heterogeneous mobility environment introduces uncertainties in terms
of resource supply and demand, which are inevitable bottlenecks for the optimal
offloading decision. Also, these uncertainties bring extra challenges to task
offloading under the oblivious adversary attack and data privacy risks. In this
article, we develop a new adversarial online learning algorithm with bandit
feedback based on the adversarial multi-armed bandit theory, to enable scalable
and low-complexity offloading decision making. Specifically, we focus on
optimizing fog node selection with the aim of minimizing the offloading service
costs in terms of delay and energy. The key is to implicitly tune the
exploration bonus in the selection process and the assessment rules of the
designed algorithm, taking into account volatile resource supply and demand. We
theoretically prove that the input-size dependent selection rule allows to
choose a suitable fog node without exploring the sub-optimal actions, and also
an appropriate score patching rule allows to quickly adapt to evolving
circumstances, which reduce variance and bias simultaneously, thereby achieving
a better exploitation-exploration balance. Simulation results verify the
effectiveness and robustness of the proposed algorithm.


  We find periodic I-band variability in two ultracool dwarfs, TVLM 513-46546
and 2MASS J00361617+1821104, on either side of the M/L dwarf boundary. Both of
these targets are short-period radio transients, with the detected I-band
periods matching those found at radio wavelengths (P=1.96 hr for TVLM
513-46546, and P=3 hr for 2MASS J00361617+1821104). We attribute the detected
I-band periodicities to the periods of rotation of the dwarfs, supported by
radius estimates and measured $v$ sin $i$ values for the objects. Based on the
detected period of rotation of TVLM 513-46546 (M9) in the I-band, along with
confirmation of strong magnetic fields from recent radio observations, we argue
for magnetically induced spots as the cause of this periodic variability. The
I-band rotational modulation of L3.5 dwarf 2MASS J00361617+1821104 appeared to
vary in amplitude with time. We conclude that the most likely cause of the
I-band variability for this object is magnetic spots, possibly coupled with
time-evolving features such as dust clouds.


  An $S=1/2$ ferromagnetic (F) - antiferromagnetic (AF) random alternating
Heisenberg quantum spin chain model is investigated in connection to its
realization compound: (CH$_3$)$_2$CHNH$_3$Cu(Cl$_x$Br$_{1-x}$)$_3$. The
exchange interaction bonds have alternating strong F-AF random bonds and weak
uniform AF bonds. Using the quantum Monte Carlo method we have found that the
excitation energy gap closes and the uniform AF order becomes critical in the
intermediate concentration region. This finding explains the experimental
observation of the magnetic transition by considering weak interchain
interactions. Present results suggest that the uniform AF order survives even
in the presence of randomly located ferromagnetic bonds. This may be a new
quantum effect.


  In the absence of coalescence, coarsening of emulsions (and foams) is
controlled by molecular diffusion of dispersed phase between droplets/bubbles.
Studies of dilute emulsions have shown how the osmotic pressure of a trapped
species within droplets can ``osmotically stabilise'' the emulsion. Webster and
Cates (Langmuir, 1998, 14, 2068-2079) gave rigorous criteria for osmotic
stabilisation of mono- and polydisperse emulsions, in the dilute regime. We
consider here whether analogous criteria exist for the osmotic stabilisation of
mono- and polydisperse concentrated emulsions and foams, and suggest that the
pressure differences driving coarsening are small compared to the mean Laplace
pressure. An exact calculation confirms this for a monodisperse 2D model,
finding a bubble's pressure as P_i = P + Pi + P_i^G, with P, Pi the atmospheric
and osmotic pressures, and P_i^G a ``geometric pressure'' that reduces to the
Laplace pressure only for a spherical bubble. For Princen's 2D emulsion model,
P_i^G is only 5% larger in the dry limit than the dilute limit. We conclude
that osmotic stabilisation of dense systems typically requires a pressure of
trapped molecules in each droplet that is comparable to the Laplace pressures
the same droplets would have if spherical, as opposed to the much larger
Laplace pressures present in the system. We study coarsening of foams and
concentrated emulsions when there is insufficient of the trapped species
present. Rate-limiting mechanisms are considered, their applicability and
associated droplet growth rates discussed. In a concentrated foam or emulsion,
a finite yield threshold for droplet rearrangement may be enough to prevent
coarsening of the remainder.


  Unmanned aerial vehicles (UAVs) are becoming largely ubiquitous with an
increasing demand for aerial data. Accurate navigation and localization,
required for precise data collection in many industrial applications, often
relies on RTK GNSS. These systems, able of centimeter-level accuracy, require a
setup and calibration process and are relatively expensive. This paper
addresses the problem of accurate positioning and navigation of UAVs through
cooperative localization. Inexpensive ultra-wideband (UWB) transceivers
installed on both the UAV and a support ground robot enable centimeter-level
relative positioning. With fast deployment and wide setup flexibility, the
proposed system is able to accommodate different environments and can also be
utilized in GNSS-denied environments. Through extensive simulations and test
fields, we evaluate the accuracy of the system and compare it to GNSS in urban
environments where multipath transmission degrades accuracy. For completeness,
we include visual-inertial odometry in the experiments and compare the
performance with the UWB-based cooperative localization.


  The shape invariance condition is the integrability condition in
supersymmetric quantum mechanics (SUSYQM). It is a difference-differential
equation connecting the superpotential W and its derivative at two different
values of parameters. We show that this difference equation is equivalent to a
non-linear partial differential equation whose solutions are translational
shape invariant superpotentials. In lieu of trial and error, this method
provides the first ab initio technique for generating shape invariant
superpotentials.


  We present precision radial velocities and stellar population parameters for
77 star clusters in the Local Group galaxy M33. Our GTC and WHT observations
sample both young, massive clusters and known/candidate globular clusters,
spanning ages ~ 10^6 - 10^10 yr, and metallicities, [M/H] ~-1.7 to solar. The
cluster system exhibits an age-metallicity relation; the youngest clusters are
the most metal-rich. When compared to HI data, clusters with [M/H] ~ -1.0 and
younger than ~ 4 Gyr are clearly identified as a disc population. The clusters
show evidence for strong time evolution in the disc radial metallicity gradient
(d[M/H]dt / dR = 0.03 dex/kpc/Gyr). The oldest clusters have stronger, more
negative gradients than the youngest clusters in M33. The clusters also show a
clear age-velocity dispersion relation. The line of sight velocity dispersions
of the clusters increases with age similar to Milky Way open clusters and
stars. The general shape of the relation is reproduced by disc heating
simulations, and the similarity between the relations in M33 and the Milky Way
suggests that heating by substructure, and cooling of the ISM both play a role
in shaping this relation. We identify 12 "classical" GCs, six of which are
newly identified GC candidates. The GCs are more metal-rich than Milky Way halo
clusters, and show weak rotation. The inner (R < 4.5 kpc) GCs exhibit a steep
radial metallicity gradient (d[M/H]/dR = -0.29+-0.11 dex/kpc) and an
exponential-like surface density profile. We argue that these inner GCs are
thick disc rather than halo objects.


  This paper studies the Kalman decomposition for linear quantum systems.
Contrary to the classical case, the coordinate transformation used for the
decomposition must belong to a specific class of transformations as a
consequence of the laws of quantum mechanics. We propose a construction method
for such transformations that put the system in a Kalman canonical form.
Furthermore, we uncover an interesting structure for the obtained
decomposition. In the case of passive systems, it is shown that there exist
only controllable/observable and uncontrollable/unobservable subsystems. In the
general case, controllable/unobservable and uncontrollable/observable
subsystems may also be present, but their respective system variables must be
conjugate variables of each other. This decomposition naturally exposes
decoherence-free modes, quantum-nondemolition modes, quantum-mechanics-free
subsystems, and back-action evasion measurements in the quantum system, which
are useful resources for quantum information processing, and quantum
measurements. The theory developed is applied to physical examples.


  The continuing process of software systems enlargement in size and complexity
becomes system design extremely important for software production. In this way,
the role of software architecture is significantly important in software
development. It serves as an evaluation and implementation plan for software
development and software evaluation. Consequently, choosing the correct
architecture is a critical issue in software engineering domain.
Moreover,software architecture selection is a multicriteria decision-making
problem in which different goals and objectives must be taken into
consideration. In this paper, more precise and suitable decisions in selection
of architecture styles have been presented by using ANP inference to support
decisions of software architects in order to exploit properties of styles in
the best way to optimize the design of software architecture.


  We report the observation of 823+/-57 B^0 and 970+/-65 B^+ decays to doubly
charmed final states Dbar^(*)D^(*)K, where Dbar^(*) and D^(*) are fully
reconstructed and K is either a K+/- or a K^0. We use a sample of 82.3+/- 0.9
million BBbar events collected between 1999 and 2002 with the babar detector at
the PEP-II storage ring at the Stanford Linear Accelerator Center. The 22
possible B decays to Dbar^(*)D^(*)K are reconstructed exclusively and the
corresponding branching fractions or limits are determined. The branching
fractions of the B^0 and of the B^+ to Dbar^(*)D^(*)K$ are found to be BR(B^0
--> Dbar^(*) D^(*) K) = (4.3 +/- 0.3 stat +/- 0.6 syst)%, BR(B^+ --> Dbar^(*)
D^(*) K) = (3.5 +/- 0.3 stat +/- 0.5 syst)%.
  A search for decays to orbitally excited $D_s$ states, B --> Dbar^(*)
D_{sJ}^{+} (D_{sJ}^{+} --> D^(*)0K^+) is also performed.


  Low- and intermediate mass Higgs bosons decay preferably into fermion pairs.
The one-loop electroweak corrections to the respective decay rates are
dominated by a flavour-independent term of ${\cal O}(G_Fm_t^2)$. We calculate
the two-loop gluon correction to this term. It turns out that this correction
screens the leading high-$m_t$ behaviour of the one-loop result by roughly
10\%. We also present the two-loop QCD correction to the contribution induced
by a pair of fourth-generation quarks with arbitrary masses. As expected, the
inclusion of the QCD correction considerably reduces the renormalization-scheme
dependence of the prediction.


  We consider the calculation of amplitudes for processes that take place in a
constant background magnetic field, first using the standard method for the
calculation of an amplitude in an external field, and second utilizing the
Schwinger propagator for charged particles in a magnetic field. We show that
there are processes for which the Schwinger propagator method does not yield
the total amplitude. We explain why the two methods yield equivalent results in
some cases and indicate when we can expect the equivalence to hold. We show
these results in fairly general terms and illustrate them with specific
examples as well.


  The Self-Consistent Field theory of brushes of neutral water-soluble polymers
described by two-state models is formulated in terms of the effective Flory
interaction parameter $\chi_{eff}(T,\phi)$ that depends on both temperature, T
and the monomer volume fraction, $\phi$. The concentration profiles,
distribution of free ends and compression force profiles are obtained in the
presence and in the absence of a vertical phase separation. A vertical phase
separation within the layer leads to a distinctive compression force profile
and a minimum in the plot of the moments of the concentration profile vs. the
grafting density. The analysis is applied explicitly to the Karalstrom model.
The relevance to brushes of Poly(N-isopropylacrylamide)(PNIPAM) is discussed.


  Deep learning for tabular data has garnered increasing attention in recent
years, yet employing deep models for structured data remains challenging. While
these models excel with unstructured data, their efficacy with structured data
has been limited. Recent research has introduced retrieval-augmented models to
address this gap, demonstrating promising results in supervised tasks such as
classification and regression. In this work, we investigate using
retrieval-augmented models for anomaly detection on tabular data. We propose a
reconstruction-based approach in which a transformer model learns to
reconstruct masked features of \textit{normal} samples. We test the
effectiveness of KNN-based and attention-based modules to select relevant
samples to help in the reconstruction process of the target sample. Our
experiments on a benchmark of 31 tabular datasets reveal that augmenting this
reconstruction-based anomaly detection (AD) method with sample-sample
dependencies via retrieval modules significantly boosts performance. The
present work supports the idea that retrieval module are useful to augment any
deep AD method to enhance anomaly detection on tabular data.


  Three-dimensional Chern-Simons vector models display an approximate higher
spin symmetry in the large $N$ limit. Their single-trace operators consist of a
tower of weakly broken currents, as well as a scalar $\sigma$ of approximate
twist $1$ or $2$. We study the consequences of crossing symmetry for the
four-point correlator of $\sigma$ in a $1/N$ expansion, using analytic
bootstrap techniques. To order $1/N$ we show that crossing symmetry fixes the
contribution from the tower of currents, providing an alternative derivation of
well-known results by Maldacena and Zhiboedov. When $\sigma$ has twist $1$ its
OPE receives a contribution from the exchange of $\sigma$ itself with an
arbitrary coefficient, due to the existence of a marginal sextic coupling. We
develop the machinery to determine the corrections to the OPE data of
double-trace operators due to this, and to similar exchanges. This in turns
allows us to fix completely the correlator up to three known truncated
solutions to crossing. We then proceed to study the problem to order $1/N^2$.
We find that crossing implies the appearance of odd-twist double-trace
operators, and calculate their OPE coefficients in a large spin expansion.
Also, surprisingly, crossing at order $1/N^2$, implies non-trivial $O(1/N)$
anomalous dimensions for even-twist double-trace operators, even though such
contributions do not appear in the four-point function at order $1/N$ (in the
case where there is no scalar exchange). We argue that this phenomenon arises
due to operator mixing. Finally, we analyse the bosonic vector model with a
sextic coupling without gauge interactions, and determine the order $1/N^2$
corrections to the dimensions of twist-$2$ double-trace operators.


  Doped lithium lanthanum zirconium oxide (LLZO) is a promising class of solid
electrolytes for lithium-ion batteries due to their good electrochemical
stability and compatibility with Li metal anodes. Ionic diffusivity in these
ceramics is known to occur via correlated, vacancy mediated, jumps of Li+
between alternating tetrahedral and octahedral sites. Aliovalent doping at the
Zr-site increases the concentration of vacancies in the Li+ sublattice and
cation diffusivity, but such an increase is universally followed by a decrease
for Li+ concentration lower than 6.3 - 6.5 Li molar content. Molecular dynamics
simulations based on density functional theory show that the maximum in
diffusivity originates from competing effects between the increased vacancy
concentration and the increasing occupancy of the low-energy tetrahedral sites
by Li+, which increases the overall activation energy associated with
diffusion. For the relatively high temperatures of our simulations, Li+
concentration plays a dominant role in transport as compared to dopant
chemistry.


  Beams injected into a linear focusing channel typically have some degree of
space-charge nonuniformity. In general, injected particle distributions with
systematic charge nonuniformities are not equilibria of the focusing channel
and launch a broad spectrum of collective modes. These modes can phase-mix and
have nonlinear wave-wave interactions which, at high space-charge intensities,
results in a relaxation to a more thermal-like distribution characterized by a
uniform density profile. This thermalization can transfer self-field energy
from the initial space-charge nonuniformity to the local particle temperature,
thereby increasing beam phase space area (emittance growth). In this paper, we
employ a simple kinetic model of a continuous focusing channel and build on
previous work that applied system energy and charge conservation quantify
emittance growth associated with the collective thermalization of an initial
azimuthally symmetric, rms matched beam with a radial density profile that is
hollowed or peaked. This emittance growth is shown to be surprisingly modest
even for high beam intensities with significant radial structure in the initial
density profile.


  This review discusses the structure and evolution of the Milky Way, in the
context of opportunities provided by asteroseismology of red giants. The review
is structured according to the main Galactic components: the thin disk, thick
disk, stellar halo, and the Galactic bar/bulge. The review concludes with an
overview of Galactic archaeology and chemical tagging, and a brief account of
the upcoming HERMES survey with the AAT.


  We consider a system of d non-linear stochastic heat equations in spatial
dimension 1 driven by d-dimensional space-time white noise. The non-linearities
appear both as additive drift terms and as multipliers of the noise. Using
techniques of Malliavin calculus, we establish upper and lower bounds on the
one-point density of the solution u(t,x), and upper bounds of Gaussian-type on
the two-point density of (u(s,y),u(t,x)). In particular, this estimate
quantifies how this density degenerates as (s,y) converges to (t,x). From these
results, we deduce upper and lower bounds on hitting probabilities of the
process {u(t,x)}_{t \in \mathbb{R}_+, x \in [0,1]}, in terms of respectively
Hausdorff measure and Newtonian capacity. These estimates make it possible to
show that points are polar when d >6 and are not polar when d<6. We also show
that the Hausdorff dimension of the range of the process is 6 when d>6, and
give analogous results for the processes t \mapsto u(t,x) and x \mapsto u(t,x).
Finally, we obtain the values of the Hausdorff dimensions of the level sets of
these processes.