  Medical image segmentation is one of the major challenges addressed by
machine learning methods. Yet, deep learning methods profoundly depend on a
large amount of annotated data, which is time-consuming and costly. Though,
semi-supervised learning methods approach this problem by leveraging an
abundant amount of unlabeled data along with a small amount of labeled data in
the training process. Recently, MixUp regularizer has been successfully
introduced to semi-supervised learning methods showing superior performance.
MixUp augments the model with new data points through linear interpolation of
the data at the input space. We argue that this option is limited. Instead, we
propose ROAM, a RandOm lAyer Mixup, which encourages the network to be less
confident for interpolated data points at randomly selected space. ROAM
generates more data points that have never seen before, and hence it avoids
over-fitting and enhances the generalization ability. We conduct extensive
experiments to validate our method on three publicly available datasets on
whole-brain image segmentation. ROAM achieves state-of-the-art (SOTA) results
in fully supervised (89.5%) and semi-supervised (87.0%) settings with a
relative improvement of up to 2.40% and 16.50%, respectively for the
whole-brain segmentation.


  Class I protostars are thought to represent an early stage in the lifetime of
protoplanetary disks, when they are still embedded in their natal envelope.
Here we measure the disk masses of 10 Class I protostars in the Taurus
Molecular Cloud to constrain the initial mass budget for forming planets in
disks. We use radiative transfer modeling to produce synthetic protostar
observations and fit the models to a multi-wavelength dataset using a Markov
Chain Monte Carlo fitting procedure. We fit these models simultaneously to our
new CARMA 1.3 mm observations that are sensitive to the wide range of spatial
scales that are expected from protostellar disks and envelopes so as to be able
to distinguish each component, as well as broadband spectral energy
distributions compiled from the literature. We find a median disk mass of 0.018
M$_{\odot}$ on average, more massive than the Taurus Class II disks, which have
median disk mass of $\sim$0.0025 M$_{\odot}$. This decrease in disk mass can be
explained if dust grains have grown by a factor of 75 in grain size, indicating
that by the Class II stage, at a few Myr, a significant amount of dust grain
processing has occurred. However, there is evidence that significant dust
processing has occurred even during the Class I stage, so it is likely that the
initial mass budget is higher than the value quoted here.


  Given an $\mathcal{N}=2$ superconformal field theory, we reconsider the Schur
index $\mathcal{I}_L(q)$ in the presence of a half line defect $L$. Recently
Cordova-Gaiotto-Shao found that $\mathcal{I}_L(q)$ admits an expansion in terms
of characters of the chiral algebra $\mathcal{A}$ introduced by Beem et al.,
with simple coefficients $v_{L,\beta}(q)$. We report a puzzling new feature of
this expansion: the $q \to 1$ limit of the coefficients $v_{L_,\beta}(q)$ is
linearly related to the vacuum expectation values $\langle L \rangle$ in
$U(1)_r$-invariant vacua of the theory compactified on $S^1$. This relation can
be expressed algebraically as a commutative diagram involving three algebras:
the algebra generated by line defects, the algebra of functions on
$U(1)_r$-invariant vacua, and a Verlinde-like algebra associated to
$\mathcal{A}$. Our evidence is experimental, by direct computation in the
Argyres-Douglas theories of type $(A_1,A_2)$, $(A_1,A_4)$, $(A_1, A_6)$, $(A_1,
D_3)$ and $(A_1, D_5)$. In the latter two theories, which have flavor
symmetries, the Verlinde-like algebra which appears is a new deformation of
algebras previously considered.


  Matrix completion is one of the key problems in signal processing and machine
learning. In recent years, deep-learning-based models have achieved
state-of-the-art results in matrix completion. Nevertheless, they suffer from
two drawbacks: (i) they can not be extended easily to rows or columns unseen
during training; and (ii) their results are often degraded in case discrete
predictions are required. This paper addresses these two drawbacks by
presenting a deep matrix factorization model and a generic method to allow
joint training of the factorization model and the discretization operator.
Experiments on a real movie rating dataset show the efficacy of the proposed
models.


  We give various characterizations for a positive unital Tr-preserving map on
a matrix algebra to preserve the von Neumann entropy of a state. Among others,
it is given by that the map behaves as a *-automorphism. This is also
equivalent to that the entropy of the stochastic matrix arising from the map
and the state is zero.


  MRI-guidance techniques that dynamically adapt radiation beams to follow
tumor motion in real-time will lead to more accurate cancer treatments and
reduced collateral healthy tissue damage. The gold-standard for reconstruction
of undersampled MR data is compressed sensing (CS) which is computationally
slow and limits the rate that images can be available for real-time adaptation.
Here, we demonstrate the use of automated transform by manifold approximation
(AUTOMAP), a generalized framework that maps raw MR signal to the target image
domain, to rapidly reconstruct images from undersampled radial k-space data.
The AUTOMAP neural network was trained to reconstruct images from a
golden-angle radial acquisition, a benchmark for motion-sensitive imaging, on
lung cancer patient data and generic images from ImageNet. Model training was
subsequently augmented with motion-encoded k-space data derived from videos in
the YouTube-8M dataset to encourage motion robust reconstruction. We find that
AUTOMAP-reconstructed radial k-space has equivalent accuracy to CS but with
much shorter processing times after initial fine-tuning on retrospectively
acquired lung cancer patient data. Validation of motion-trained models with a
virtual dynamic lung tumor phantom showed that the generalized motion
properties learned from YouTube lead to improved target tracking accuracy. Our
work shows that AUTOMAP can achieve real-time, accurate reconstruction of
radial data. These findings imply that neural-network-based reconstruction is
potentially superior to existing approaches for real-time image guidance
applications.


  This paper studies the exponential stability of primal-dual gradient dynamics
(PDGD) for solving convex optimization problems where constraints are in the
form of Ax+By= d and the objective is min f(x)+g(y) with strongly convex smooth
f but only convex smooth g. We show that when g is a quadratic function or when
g and matrix B together satisfy an inequality condition, the PDGD can achieve
global exponential stability given that matrix A is of full row rank. These
results indicate that the PDGD is locally exponentially stable with respect to
any convex smooth g under a regularity condition. To prove the exponential
stability, two quadratic Lyapunov functions are designed. Lastly, numerical
experiments further complement the theoretical analysis.


  We investigate the degree of dust obscured star formation in 49 massive
(${\rm log}_{10}(M_{\star}/{\rm M}_{\odot})>9$) Lyman-break galaxies (LBGs) at
$z = 6.5$-$8$ observed as part of the ALMA Reionization Era Bright Emission
Line Survey (REBELS) large program. By creating deep stacks of the photometric
data and the REBELS ALMA measurements we determine the average rest-frame UV,
optical and far-infrared (FIR) properties which reveal a significant fraction
($f_{\rm obs} = 0.4$-$0.7$) of obscured star formation, consistent with
previous studies. From measurements of the rest-frame UV slope, we find that
the brightest LBGs at these redshifts show bluer ($\beta \simeq -2.2$) colours
than expected from an extrapolation of the colour-magnitude relation found at
fainter magnitudes. Assuming a modified blackbody spectral-energy distribution
(SED) in the FIR (with dust temperature of $T_{\rm d} = 46\,{\rm K}$ and
$\beta_{\rm d} = 2.0$), we find that the REBELS sources are in agreement with
the local ''Calzetti-like'' starburst Infrared-excess (IRX)-$\beta$ relation.
By reanalysing the data available for 108 galaxies at $z \simeq 4$-$6$ from the
ALPINE ALMA large program using a consistent methodology and assumed FIR SED,
we show that from $z \simeq 4$-$8$, massive galaxies selected in the rest-frame
UV have no appreciable evolution in their derived IRX-$\beta$ relation. When
comparing the IRX-$M_{\star}$ relation derived from the combined ALPINE and
REBELS sample to relations established at $z < 4$, we find a deficit in the
IRX, indicating that at $z > 4$ the proportion of obscured star formation is
lower by a factor of $\gtrsim 3$ at a given a $M_{\star}$. Our IRX-$\beta$
results are in good agreement with the high-redshift predictions of simulations
and semi-analytic models for $z \simeq 7$ galaxies with similar stellar masses
and SFRs.


  The Standard Model (SM) is augmented with a $\mathrm{U}(1)_{B-3L_\mu} $ gauge
symmetry spontaneously broken above the TeV scale when an SM-singlet scalar
condenses. Scalar leptoquarks $S_{1(3)} = (\overline{\mathbf{3}},\, \mathbf{1}
(\mathbf{3}),\, ^1\!/_3)$ charged under $\mathrm{U}(1)_{B-3L_\mu} $ mediate the
intriguing effects observed in muon $(g-2)$, $R_{K^{(*)}}$ and $b \to s \mu^+
\mu^-$, while generically evading all other phenomenological constraints. The
fermionic sector is minimally extended with three right-handed neutrinos, and a
successful type-I seesaw mechanism is realized. Charged lepton flavor violation
is effectively suppressed, and proton decay - a common prediction of
leptoquarks - is postponed to the dimension-6 effective Lagrangian. Unavoidable
radiative corrections in the Higgs mass and muon Yukawa favor leptoquark masses
interesting for collider searches. The parameters of the model are radiatively
stable and can be evolved by the renormalization group to the Planck scale
without inconsistencies. Alternative lepton-flavored gauge extensions of the
SM, under which leptoquarks become muoquarks, are proposed for comparison.


  T\^atonnement is a simple, intuitive market process where prices are
iteratively adjusted based on the difference between demand and supply. Many
variants under different market assumptions have been studied and shown to
converge to a market equilibrium, in some cases at a fast rate. However, the
classical case of linear Fisher markets have long eluded the analyses, and it
remains unclear whether t\^atonnement converges in this case. We show that, for
a sufficiently small step size, the prices given by the t\^atonnement process
are guaranteed to converge to equilibrium prices, up to a small approximation
radius that depends on the stepsize. To achieve this, we consider the dual
Eisenberg-Gale convex program in the price space, view t\^atonnement as
subgradient descent on this convex program, and utilize novel last-iterate
convergence results for subgradient descent under error bound conditions. In
doing so, we show that the convex program satisfies a particular error bound
condition, the quadratic growth condition, and that the price sequence
generated by t\^atonnement is bounded above and away from zero. We also show
that a similar convergence result holds for t\^atonnement in quasi-linear
Fisher markets. Numerical experiments are conducted to demonstrate that the
theoretical linear convergence aligns with empirical observations.


  The MEDLEY setup based at The Svedberg Laboratory (TSL), Uppsala, Sweden has
previously been used to measure double-differential cross-sections for elastic
nd scattering, as well as light ion production reactions for various nuclei in
the interaction with neutrons around 95 MeV. When moved to the new beam line,
the first experimental campaign was on light-ion production from Ca at 94 MeV
in February 2005.
  These data sets have been analyzed for proton production in forward and
backward angles. The Delta E - Delta E - E technique have been used to identify
protons, and a cutoff as low as 2.5 MeV is achieved. Suppression of events
induced by neutrons in the low-energy tail of the neutron field is achieved by
time-of-flight techniques. The data are normalized relative to elastic np
scattering measured in the 20-degree telescope. Results from an accepted
neutron spectrum are presented and some methods to correct for events from low
energy neutrons are presented and evaluated. The data are compared with
calculations using the nuclear code TALYS. It was found that TALYS
systematically overestimates the compound part, and underestimates the
pre-equilibrium part of the cross-section.


  Nuclear state densities are important inputs to statistical models of
compound-nucleus reactions. State densities are often calculated with
self-consistent mean-field approximations that do not include important
correlations and have to be augmented with empirical collective enhancement
factors. Here, we benchmark the static-path plus random-phase approximation
(SPA+RPA) to the state density in a chain of samarium isotopes $^{148-155}$Sm
against exact results (up to statistical errors) obtained with the shell model
Monte Carlo (SMMC) method. The SPA+RPA method incorporates all static
fluctuations beyond the mean field together with small-amplitude quantal
fluctuations around each static fluctuation. Using a pairing plus quadrupole
interaction, we show that the SPA+RPA state densities agree well with the exact
SMMC densities for both the even- and odd-mass isotopes. For the even-mass
isotopes, we also compare our results with mean-field state densities
calculated with the finite-temperature Hartree-Fock-Bogoliubov (HFB)
approximation. We find that the SPA+RPA repairs the deficiencies of the
mean-field approximation associated with broken rotational symmetry in deformed
nuclei and the violation of particle-number conservation in the pairing
condensate. In particular, in deformed nuclei the SPA+RPA reproduces the
rotational enhancement of the state density relative to the mean-field state
density.


  Ocean scientists have been collecting visual data to study marine organisms
for decades. These images and videos are extremely valuable both for basic
science and environmental monitoring tasks. There are tools for automatically
processing these data, but none that are capable of handling the extreme
variability in sample populations, image quality, and habitat characteristics
that are common in visual sampling of the ocean. Such distribution shifts can
occur over very short physical distances and in narrow time windows. Creating
models that are able to recognize when an image or video sequence contains a
new organism, an unusual collection of animals, or is otherwise out-of-sample
is critical to fully leverage visual data in the ocean. The FathomNet2023
competition dataset presents a realistic scenario where the set of animals in
the target data differs from the training data. The challenge is both to
identify the organisms in a target image and assess whether it is
out-of-sample.


  Autoregressive moving average and generalized autoregressive moving average
are often used in statistical modeling. This study uses this method because the
method uses data from the previous period to model the data for the current
period. In addition, the technique is often used in data prediction. The
familiar data used is count data. Count data is the data that most often cause
data not to spread usually. Therefore, time series modeling, one of which is
through arithmetic series, was developed. This study aims to obtain the best
modeling results from positive confirmed cases of Covid 19 in Indonesia. They
were getting the results from the best modeling for positive confirmed cases of
Covid 19 in Indonesia based on the smallest Aikake information criterion value.


  We propose a subspace-accelerated Bregman method for the linearly constrained
minimization of functions of the form $f(\mathbf{u})+\tau_1 \|\mathbf{u}\|_1 +
\tau_2 \|D\,\mathbf{u}\|_1$, where $f$ is a smooth convex function and $D$
represents a linear operator, e.g. a finite difference operator, as in
anisotropic Total Variation and fused-lasso regularizations. Problems of this
type arise in a wide variety of applications, including portfolio optimization
and learning of predictive models from functional Magnetic Resonance Imaging
(fMRI) data, and source detection problems in electroencephalography. The use
of $\|D\,\mathbf{u}\|_1$ is aimed at encouraging structured sparsity in the
solution. The subspaces where the acceleration is performed are selected so
that the restriction of the objective function is a smooth function in a
neighborhood of the current iterate. Numerical experiments on multi-period
portfolio selection problems using real datasets show the effectiveness of the
proposed method.


  Since the discovery of topological insulators a lot of research effort has
been devoted to magnetic topological materials, in which non-trivial spin
properties can be controlled by magnetic fields, culminating in a wealth of
fundamental phenomena and possible applications. The main focus was on
ferromagnetic materials that can host Weyl fermions and therefore spin textured
Fermi arcs. The recent discovery of Fermi arcs and new magnetic bands splitting
in antiferromagnet (AFM) NdBi has opened up new avenues for exploration. Here
we show that these uncharted effects are not restricted to this specific
compound, but rather emerge in CeBi, NdBi, and NdSb when they undergo
paramagnetic to AFM transition. Our data show that the Fermi arcs in NdSb have
2-fold symmetry, leading to strong anisotropy that may enhance effects of spin
textures on transport properties. Our findings thus demonstrate that the RBi
and RSb series are materials that host magnetic Fermi arcs and may be a
potential platform for modern spintronics.


  The effect of irradiation by 30-keV Ga+ and 35-keV He+ ions (in relatively
small doses) on the excitonic reflectivity spectra of single InGaAs/GaAs
quantum-well structures is studied. It is found that the irradiation results in
decreasing intensity and broadening of the excitonic resonances in the
reflectivity spectra for all the doses. It is shown that these changes are not
related to a decrease of the exciton transition oscillator strength and,
therefore, to the irradiation-induced destruction of the excitonic states, but
can be rather ascribed to a common cause, namely, to inhomogeneous broadening
of the excitonic resonances proportional to the exposure dose.A tentative model
of the irradiation-induced broadening is considered, with the mechanism of the
process being a consequence of scat-tering of the 2D excitons by structural
defects associated with Ga(In) and As vacancies arising upon collisions of the
high-energy ions with regular atoms of the crystal structure. The model is used
to compare experimental dependence of efficiency of the Ga+-ion-induced
broad-ening on distance of the quantum well from the irradiated surface with a
similar dependence calculated using the Monte-Carlo technique. A discrepancy
between the results of simulation and experimental data is discussed.


  (Abridged) The Large Synoptic Survey Telescope (LSST) is currently by far the
most ambitious proposed ground-based optical survey. Solar System mapping is
one of the four key scientific design drivers, with emphasis on efficient
Near-Earth Object (NEO) and Potentially Hazardous Asteroid (PHA) detection,
orbit determination, and characterization. In a continuous observing campaign
of pairs of 15 second exposures of its 3,200 megapixel camera, LSST will cover
the entire available sky every three nights in two photometric bands to a depth
of V=25 per visit (two exposures), with exquisitely accurate astrometry and
photometry. Over the proposed survey lifetime of 10 years, each sky location
would be visited about 1000 times. The baseline design satisfies strong
constraints on the cadence of observations mandated by PHAs such as closely
spaced pairs of observations to link different detections and short exposures
to avoid trailing losses. Equally important, due to frequent repeat visits LSST
will effectively provide its own follow-up to derive orbits for detected moving
objects. Detailed modeling of LSST operations, incorporating real historical
weather and seeing data from LSST site at Cerro Pachon, shows that LSST using
its baseline design cadence could find 90% of the PHAs with diameters larger
than 250 m, and 75% of those greater than 140 m within ten years. However, by
optimizing sky coverage, the ongoing simulations suggest that the LSST system,
with its first light in 2013, can reach the Congressional mandate of cataloging
90% of PHAs larger than 140m by 2020.


  We continue the study of the Hochschild structure of a smooth space that we
began in our previous paper, examining implications of the
Hochschild-Kostant-Rosenberg theorem. The main contributions of the present
paper are:
  -- we introduce a generalization of the usual Mukai pairing on differential
forms that applies to arbitrary manifolds;
  -- we give a proof of the fact that the natural Chern character map $K_0(X)
\to HH_0(X)$ becomes, after the HKR isomorphism, the usual one $K_0(X) \to
\bigoplus H^i(X, \Omega_X^i)$; and
  -- we present a conjecture that relates the Hochschild and harmonic
structures of a smooth space.


  Most recent works in device-to-device (D2D) underlay communications focus on
the optimization of either power or channel allocation to improve the spectral
efficiency, and typically consider uplink and downlink separately. Further,
several of them also assume perfect knowledge of channel-stateinformation
(CSI). In this paper, we formulate a joint uplink and downlink resource
allocation scheme, which assigns both power and channel resources to D2D pairs
and cellular users in an underlay network scenario. The objective is to
maximize the overall network rate while maintaining fairness among the D2D
pairs. In addition, we also consider imperfect CSI, where we guarantee a
certain outage probability to maintain the desired quality-of-service (QoS).
The resulting problem is a mixed integer non-convex optimization problem and we
propose both centralized and decentralized algorithms to solve it, using convex
relaxation, fractional programming, and alternating optimization. In the
decentralized setting, the computational load is distributed among the D2D
pairs and the base station, keeping also a low communication overhead.
Moreover, we also provide a theoretical convergence analysis, including also
the rate of convergence to stationary points. The proposed algorithms have been
experimentally tested in a simulation environment, showing their favorable
performance, as compared with the state-of-the-art alternatives.


  We propose a minimal model where a dark sector, odd under a $\mathcal{Z}_2$
discrete symmetry, is the seed of lepton number violation in the neutrino
sector at the loop level, in the context of the linear seesaw mechanism.
Neutrino mass suppression stems from a naturally small scalar potential
coupling which breaks the lepton number symmetry softly. The fact that we
consider (dark) Dirac vector-like neutral leptons, prevents the appearance of
other mass terms that could contribute to neutrino masses via alternative
mechanisms. We study the dark-matter phenomenology of the model, focusing on
the case in which the stable particle is the lightest neutral scalar arising
from the dark scalar sector. Prospects for testing our framework with the
results of current and future lepton flavour violation searches are also
discussed.


  Discriminative Dictionary Learning (DL) methods have been widely advocated
for image classification problems. To further sharpen their discriminative
capabilities, most state-of-the-art DL methods have additional constraints
included in the learning stages. These various constraints, however, lead to
additional computational complexity. We hence propose an efficient
Discriminative Convolutional Analysis Dictionary Learning (DCADL) method, as a
lower cost Discriminative DL framework, to both characterize the image
structures and refine the interclass structure representations. The proposed
DCADL jointly learns a convolutional analysis dictionary and a universal
classifier, while greatly reducing the time complexity in both training and
testing phases, and achieving a competitive accuracy, thus demonstrating great
performance in many experiments with standard databases.


  Pigou's problem has many applications in real life scenarios like traffic
networks, graph theory, data transfer in internet networks, etc. The two player
classical Pigou's network has an unique Nash equilibrium with the Price of
Stability and Price of Anarchy agreeing with each other. The situation changes
for the $k-$person classical Pigou's network with $n$ being the total number of
people. If we fix the behaviour of $(n-2)$ people and assume that $k-$persons
take path $P_2$ where $k<(n-2)$ and the remaining take path $P_1$, the minimum
cost of Nash equilibrium becomes $k$ dependent and we find a particular $k$ for
which the cost is an absolute minimum. In contrast to the two person classical
Pigou's network, the quantum two qubit Pigou's network with maximal
entanglement gives a lower cost for the Nash equilibrium, while in contrast to
$k-$person classical Pigou's network, it's quantum version gives reduced cost
for the Nash equilibrium strategy. This has major implications for information
transfer in both classical as well as quantum data networks. By employing
entanglement and quantum strategies, one can significantly reduce congestion
costs in quantum data networks.


  A multi-channel algebraic scattering theory, to find solutions of
coupled-channel scattering problems with interactions determined by collective
models, has been structured to ensure that the Pauli principle is not violated.
Positive (scattering) and negative (sub-threshold) solutions can be found to
predict both the compound nucleus sub-threshold spectrum and all resonances due
to coupled channel effects that occur on a smooth energy varying background.


  For bouncing cosmologies such as the ekpyrotic/cyclic scenarios we show that
it is possible to make predictions for density perturbations which are
independent of the details of the bouncing phase. This can be achieved, as in
inflationary cosmology, thanks to the existence of a dynamical attractor, which
makes local observables equal to the unperturbed solution up to exponentially
small terms. Assuming that the physics of the bounce is not extremely sensitive
to these corrections, perturbations can be evolved even at non-linear level.
The resulting spectrum is not scale invariant and thus incompatible with
experimental data. This can be explicitly shown in synchronous gauge where,
contrary to what happens in the commonly used Newtonian gauge, all
perturbations remain small going towards the bounce and the existence of the
attractor is manifest.


  Using a fully quantum mechanical post-form finite-range distorted-wave Born
approximation theory of Coulomb breakup, I study the parallel momentum
distribution of the core in the Coulomb breakup of suggested excited-state
one-neutron halo nuclei considered in their different bound excited states.
Narrow momentum distributions obtained in the present calculations for some
cases indicate the possibilities of the excited-state halo structure in the
nuclei under consideration and therefore favor the previous predictions.


  We study pairs of structures, such as the Poisson-Nijenhuis structures, on
the tangent bundle of a manifold or, more generally, on a Lie algebroid or a
Courant algebroid. These composite structures are defined by two of the
following, a closed 2-form, a Poisson bivector or a Nijenhuis tensor, with
suitable compatibility assumptions. We establish the relationships between such
composite structures. We then show that the non-degenerate Monge-Amp\`ere
structures on 2-dimensional manifolds satisfying an integrability condition
provide numerous examples of such structures, while in the case of
3-dimensional manifolds, such Monge-Amp\`ere operators give rise to generalized
complex structures or generalized product structures on the cotangent bundle of
the manifold.


  We study the one dimensional periodic derivative nonlinear Schr\"odinger
(DNLS) equation. This is known to be a completely integrable system, in the
sense that there is an infinite sequence of formal integrals of motion $\int
h_k$, $k\in \mathbb{Z}_{+}$. In each $\int h_{2k}$ the term with the highest
regularity involves the Sobolev norm $\dot H^{k}(\mathbb{T})$ of the solution
of the DNLS equation. We show that a functional measure on $L^2(\mathbb{T})$,
absolutely continuous w.r.t. the Gaussian measure with covariance
$(\mathbb{I}+(-\Delta)^{k})^{-1}$, is associated to each integral of motion
$\int h_{2k}$, $k\geq1$.


  The Type II seesaw model requires that its scalar doublet $H$ and triplet
$\Delta$ get specific patterns of VEVs $\langle H\rangle\propto(0,1)^{T}$ and
$\langle\Delta\rangle\propto(0,0,1)^{T}$ to accommodate neutrino masses.
However, other types of minima could also exist in the scalar potential, which
may strongly contradict to experimental observations. This paper studies when
the minimum at $\langle H\rangle\propto(0,1)^{T}$ and
$\langle\Delta\rangle\propto(0,0,1)^{T}$ will be global and finds the necessary
and sufficient condition for that, assuming that the lepton number violating
term $H^{2}\Delta$ in the potential is perturbatively small.


  The string landscape suggests that the supersymmetry breaking scale can be
high, and then the simplest low energy effective theory is the Standard Model
(SM). Considering grand unification scale supersymmetry breaking, we show that
gauge coupling unification can be achieved at about 10^{16-17} GeV in the SM
with suitable normalizations of the U(1)_Y, and we predict that the Higgs mass
range is 127 GeV to 165 GeV, with the precise value strongly correlated with
the top quark mass m_t and SU(3)_C gauge coupling. For example, if m_t=178\pm1
GeV, the Higgs boson mass is predicted to be between 141 GeV and 154 GeV. We
also point out that gauge coupling unification in the Minimal Supersymmetric
Standard Model (MSSM) does not imply the canonical U(1)_Y normalization. In
addition, we present 7-dimensional orbifold grand unified theories (GUTs) in
which such normalizations for the U(1)_Y and charge quantization can be
realized. The supersymmetry can be broken at the grand unification scale by the
Scherk--Schwarz mechanism. We briefly comment on a non-canonical U(1)_Y
normalization due to the brane localized gauge kinetic terms in orbifold GUTs.


  Recent work has shown that Binarized Neural Networks (BNNs) are able to
greatly reduce computational costs and memory footprints, facilitating model
deployment on resource-constrained devices. However, in comparison to their
full-precision counterparts, BNNs suffer from severe accuracy degradation.
Research aiming to reduce this accuracy gap has thus far largely focused on
specific network architectures with few or no 1x1 convolutional layers, for
which standard binarization methods do not work well. Because 1x1 convolutions
are common in the design of modern architectures (e.g. GoogleNet, ResNet,
DenseNet), it is crucial to develop a method to binarize them effectively for
BNNs to be more widely adopted. In this work, we propose an "Elastic-Link" (EL)
module to enrich information flow within a BNN by adaptively adding real-valued
input features to the subsequent convolutional output features. The proposed EL
module is easily implemented and can be used in conjunction with other methods
for BNNs. We demonstrate that adding EL to BNNs produces a significant
improvement on the challenging large-scale ImageNet dataset. For example, we
raise the top-1 accuracy of binarized ResNet26 from 57.9% to 64.0%. EL also
aids convergence in the training of binarized MobileNet, for which a top-1
accuracy of 56.4% is achieved. Finally, with the integration of ReActNet, it
yields a new state-of-the-art result of 71.9% top-1 accuracy.


  We present measurements of branching fractions for four previously unobserved
B-meson decays with an eta or eta' meson in the final state. The data sample
corresponds to 182 million B-Bbar pairs produced from e+e- annihilation at the
Upsilon(4S) resonance. We measure the following branching fractions in units of
10^-6: BF(B0 -> eta omega) = 1.2 +/- 0.6 +/- 0.2 (<2.3 at 90% C.L.), BF(B0 ->
eta K0) = 2.5 +/-0.8 +/- 0.1, BF(B+ -> eta rho+) = 8.6 +/- 2.2 +/- 1.1, and
BF(B+ -> eta' pi+) = 4.2 +/- 1.0 +/- 0.5, where the first error quoted is
statistical and the second systematic. The charge asymmetries are A_ch(B+ ->
eta rho+) = (7 +/- 19 +/- 2)% and A_ch(B+ -> eta' pi+) = (24 +/- 19 +/- 1)%.
All results are preliminary.


  Outflows up to 1500 km/s of atomic neutral hydrogen are detected in a growing
number of radio galaxies. Outflows with similar velocities are also detected in
ionized gas, suggesting a common origin for the extreme kinematics of these two
phases of the gas. The high detection rate ofsuch outflows in young (or
restarted) radio sources appears to be related to the existence of a dense ISM
around these objects. Such a dense ISM can have important consequences for the
evolution of the radio source and the galaxy as a whole. Here we summarize the
recent results obtained and the characteristics derived so far for these
outflows. We also discuss possible mechanisms (e.g. interaction between the
radio plasma and the ISM and adiabatically expanding broad emission lines
clouds) that can be at the origin of these phenomena.


  We introduce a class of numerical schemes for optimal control problems based
on a novel Markov chain approximation, which uses, in turn, a piecewise
constant policy approximation, Euler-Maruyama time stepping, and a
Gauss-Hermite approximation of the Gaussian increments. We provide lower error
bounds of order arbitrarily close to 1/2 in time and 1/3 in space for Lipschitz
viscosity solutions, coupling probabilistic arguments with regularization
techniques as introduced by Krylov. The corresponding order of the upper bounds
is 1/4 in time and 1/5 in space. For sufficiently regular solutions, the order
is 1 in both time and space for both bounds. Finally, we propose techniques for
further improving the accuracy of the individual components of the
approximation.


  The instability of the fully polarized ferromagnetic state (Nagaoka state)
with respect to single spin flips is re-examined for the Hubbard model on the
square lattice with a large family of variational wave functions which include
correlation effects of the majority spins in the vicinity of the flipped spin.
We find a critical hole density of $\delta_{cr} = 0.251$ for $U = \infty$ and a
critical coupling of $U_{cr} = 77.7 t$. Both values improve previous
variational results considerably.


  We study the different ways of introducing light-matter interaction in
first-principle tight-binding (TB) models. The standard way of describing
optical properties is the velocity gauge, defined by linear coupling to the
vector potential.
  In finite systems a transformation to represent the electromagnetic radiation
by the electric field instead is possible, albeit subtleties arise in periodic
systems. The resulting dipole gauge is a multi-orbital generalization of
Peierl's substitution. In this work, we investigate accuracy of both pathways,
with particular emphasis on gauge invariance, for TB models constructed from
maximally localized Wannier functions. Focusing on paradigmatic two-dimensional
materials, we construct first-principle models and calculate the response to
electromagnetic fields in linear response and for strong excitations.
Benchmarks against fully converged first-principle calculations allow for
ascertaining the accuracy of the TB models. We find that the dipole gauge
provides a more accurate description than the velocity gauge in all cases. The
main deficiency of the velocity gauge is an imperfect cancellation of
paramagnetic and diamagnetic current. Formulating a corresponding sum rule
however provides a way to explicitly enforce this cancellation. This procedure
corrects the TB models in the velocity gauge, yielding excellent agreement with
dipole gauge and thus gauge invariance.


  A current bias dc SQUID behaves as an anharmonic quantum oscillator
controlled by a bias current and an applied magnetic flux. We consider here its
two level limit consisting of the two lower energy states $| 0 \right>$ and $|
1 \right>$. We have measured energy relaxation times and microwave absorption
for different bias currents and fluxes in the low microwave power limit.
Decoherence times are extracted. The low frequency flux and current noise have
been measured independently by analyzing the probability of current switching
from the superconducting to the finite voltage state, as a function of applied
flux. The high frequency part of the current noise is derived from the
electromagnetic environment of the circuit. The decoherence of this quantum
circuit can be fully accounted by these current and flux noise sources.


  Bibliometrics provides accurate, cheap and simple descriptions of research
systems and should lay the foundations for research policy. However,
disconnections between bibliometric knowledge and research policy frequently
misguide the research policy in many countries. A way of correcting these
disconnections might come from the use of simple indicators of research
performance. One such simple indicator is the number of highly cited
researchers, which can be used under the assumption that a research system that
produces and employs many highly cited researchers will be more successful than
others with fewer of them. Here, we validate the use of the number of highly
cited researchers (Ioannidis et al. 2020; PLoS Biol 18(10): e3000918) for
research assessment at the country level and determine a country ranking of
research success. We also demonstrate that the number of highly cited
researchers reported by Clarivate Analytics is also an indicator of the
research success of countries. The formal difference between the numbers of
highly cited researchers according to Ionannidis et al. and Clarivate Analytics
is that evaluations based on these two lists of highly cited researchers are
approximately equivalent to evaluations based on the top 5% and 0.05% of highly
cited papers, respectively. Moreover, the Clarivate Analytics indicator is
flawed in some countries.


  We study radial behavior of harmonic functions in the unit disk belonging to
the Korenblum class. We prove that functions which admit two-sided Korenblum
estimate either oscillate or have slow growth along almost all radii.


  We calculate the time delays of neutrinos emitted in gamma ray bursts due to
the effects of neutrino mass and quantum gravity using a time dependent Hubble
constant which can significantly change the naive results presented hitherto in
the literature for large redshifts, and gives some sensitivity to the details
of dark energy. We show that the effects of neutrino mass, quantum gravity and
dark energy may be disentangled by using low energy neutrinos to study neutrino
mass, high energy neutrinos to study quantum gravity, and large redshifts to
study dark energy. From low energy neutrinos one may obtain direct limits on
neutrino masses of order 10^{-3} eV, and distinguish a neutrino mass hierarchy
from an inverted mass hierarchy. From ultra-high energy neutrinos the
sensitivity to the scale of quantum gravity can be pushed up to E_{QG} ~ 5
times 10^{30} GeV. By studying neutrinos from GRBs at large redshifts a
cosmological constant could be distinguished from quintessence.


  We investigate non-vanishing properties of $L(f,s)$ on the real line, when
$f$ is a Hecke eigenform of half-integral weight $k+{1\over 2}$ on
$\Gamma_0(4).$


  The Effective Average Action is a form of effective action which depends on a
cutoff scale suppressing the contribution of low momentum modes in the
functional integral. It reduces to the ordinary effective action when the
cutoff scale goes to zero. We derive the modifications of the scale Ward
identity due to this cutoff and show how the resulting identity then intimately
relates the trace anomaly to the Wilsonian realisation of the renormalization
group.


  Dealing with non-stationarity in environments (e.g., in the transition
dynamics) and objectives (e.g., in the reward functions) is a challenging
problem that is crucial in real-world applications of reinforcement learning
(RL). While most current approaches model the changes as a single shared
embedding vector, we leverage insights from the recent causality literature to
model non-stationarity in terms of individual latent change factors, and causal
graphs across different environments. In particular, we propose Factored
Adaptation for Non-Stationary RL (FANS-RL), a factored adaption approach that
learns jointly both the causal structure in terms of a factored MDP, and a
factored representation of the individual time-varying change factors. We prove
that under standard assumptions, we can completely recover the causal graph
representing the factored transition and reward function, as well as a partial
structure between the individual change factors and the state components.
Through our general framework, we can consider general non-stationary scenarios
with different function types and changing frequency, including changes across
episodes and within episodes. Experimental results demonstrate that FANS-RL
outperforms existing approaches in terms of return, compactness of the latent
state representation, and robustness to varying degrees of non-stationarity.


  We describe simple quantum-mechanical approach to calculating equilibrium
particle current along the edge of a system with non-trivial band spectrum
topology. The approach does not require any a priori knowledge of the band
topology and, as a matter of fact, treats topological and non-topological
contributions to the edge currents on the same footing. We illustrate its
usefulness by demonstrating the existence of `topologically non-trivial'
particle currents along the edges of three different physical systems:
two-dimensional electron gas with spin-orbit coupling and Zeeman magnetic
field, surface state of a topological insulator, and kagome antiferromagnet
with Dzyaloshinskii-Moriya interaction. We describe relation of our results to
the notion of orbital magnetization.


  Compact finite-difference (FD) schemes specify derivative approximations
implicitly, thus to achieve parallelism with domain-decomposition suitable
partitioning of linear systems is required. Consistent order of accuracy,
dispersion, and dissipation is crucial to maintain in wave propagation problems
such that deformation of the associated spectra of the discretized problems is
not too severe. In this work we consider numerically tuning spectral error, at
fixed formal order of accuracy to automatically devise new compact FD schemes.
Grid convergence tests indicate error reduction of at least an order of
magnitude over standard FD. A proposed hybrid matching-communication strategy
maintains the aforementioned properties under domain-decomposition. Under
evolution of linear wave-propagation problems utilizing exponential integration
or explicit Runge-Kutta methods improvement is found to remain robust. A first
demonstration that compact FD methods may be applied to the Z4c formulation of
numerical relativity is provided where we couple our header-only, templated C++
implementation to the highly performant GR-Athena++ code. Evolving Z4c on
test-bed problems shows at least an order in magnitude reduction in phase error
compared to FD for propagated metric components. Stable binary-black-hole
evolution utilizing compact FD together with improved convergence is also
demonstrated.


  We present that by predicting the spectrum in discrete space from the phase
shift in continuous space, the neural network can remarkably reproduce the
numerical L\"uscher's formula to a high precision. The model-independent
property of the L\"uscher's formula is naturally realized by the
generalizability of the neural network. This exhibits the great potential of
the neural network to extract model-independent relation between
model-dependent quantities, and this data-driven approach could greatly
facilitate the discovery of the physical principles underneath the intricate
data.


  The development of fully autonomous vehicles (AVs) can potentially eliminate
drivers and introduce unprecedented seating design. However, highly flexible
seat configurations may lead to occupants' unconventional poses and actions.
Understanding occupant behaviors and prioritize safety features become
eye-catching topics in the AV research frontier. Visual sensors have the
advantages of cost-efficiency and high-fidelity imaging and become more widely
applied for in-car sensing purposes. Occlusion is one big concern for this type
of system in crowded car cabins. It is important but largely unknown about how
a visual-sensing framework will look like to support 2-D and 3-D human pose
tracking towards highly configurable seats. As one of the first studies to
touch this topic, we peek into the future camera-based sensing framework via a
simulation experiment. Constructed representative car-cabin, seat layouts, and
occupant sizes, camera coverage from different angles and positions is
simulated and calculated. The comprehensive coverage data are synthesized
through an optimization process to determine the camera layout and overall
occupant coverage. The results show the needs and design of a different number
of cameras to fully or partially cover all the occupants with changeable
configurations of up to six seats.


  We study reflection of optically spin-oriented hot electrons as a means to
probe the semiconductor crystal symmetry and its intimate relation with the
spin-orbit coupling. The symmetry breaking by reflection manifests itself by
tipping the net-spin vector of the photoexcited electrons out of the light
propagation direction. The tipping angle and the pointing direction of the
net-spin vector are set by the crystal-induced spin precession, momentum
alignment and spin-momentum correlation of the initial photoexcited electron
population. We examine non-magnetic semiconductor heterostructures and
semiconductor/ferromagnet systems and show the unique signatures of these
effects.


  The ferrimagnet Mn3Si2Te6 attracts attention because of a recently discovered
colossal magnetoresistance (CMR) with unique magnetic field properties. An
improved magnetic structure for the material has emerged from a neutron
diffraction study linked to understanding the CMR. A deeper theoretical
investigation of the magnetic structure has now revealed anapole, chiral and
orbital states of manganese ions not previously mentioned. Moreover, it is
shown that existence of these states in the low temperature form of Mn3Si2Te6,
with a magnetic field applied, can be tested by neutron and resonant x-ray
diffraction.


  We investigate the production of spin-3/2 hyperon pairs, $\Omega^-
\bar{\Omega}^+$, in electron-positron annihilation within the helicity
amplitude formalism. A refined selection of helicity basis matrices is proposed
to relate polarization expansion coefficients and spin density matrix elements
and to illuminate their inherent physical interpretations and symmetrical
properties. With a novel parametrization scheme of helicity amplitudes, we
perform an analysis of polarization correlation coefficients for double-tag
$\Omega^- \bar{\Omega}^+$ pairs. We present three sets of expressions to
describe the decay of $\Omega^{-}$ hyperons, and further address the existing
tension in the measurements of its decay parameters, particularly
$\phi_{\Omega}$. The method and the framework developed in this paper can also
be applied to studies of the production and decay mechanisms of other spin-3/2
particles.


  In a superconducting quantum processor with nearest neighbor coupling, the
dispersive interaction between adjacent qubits can result in an effective
next-nearest-neighbor coupling whose strength depends on the state of the
intermediary qubit. Here, we theoretically explore the possibility of
engineering this next-nearest-neighbor coupling to implement controlled
two-qubit operations where the intermediary qubit controls the operation on the
next-nearest neighbor pair of qubits. In particular, in a system comprising two
types of superconducting qubits with anharmonicities of opposite-sign arranged
in an -A-B-A- pattern, where the unwanted static ZZ coupling between adjacent
qubits could be heavily suppressed, a switchable coupling between the
next-nearest-neighbor qubits can be achieved via the intermediary qubit, the
qubit state of which functions as an on/off switch for this coupling.
Therefore, depending on the adopted activating scheme, various controlled
two-qubit operations such as controlled-iSWAP gate can be realized, potentially
enabling circuit depth reductions as to a standard decomposition approach for
implementing generic quantum algorithms.


  We prove weighted $L^2$ estimates for the Klein-Gordon equation perturbed
with singular potentials such as the inverse-square potential. We then deduce
the well-posedness of the Cauchy problem for this equation with small
perturbations, and go on to discuss local smoothing and Strichartz estimates
which improve previously known ones.


  In this work we investigate theoretically the influence of a Fermi velocity
modulation in the electronic and transport properties of magnetic graphene
superlattices. We solve the effective Dirac equation for graphene with a
position dependent vector potential and Fermi velocity and use the transfer
matrix method to obtain the transmission coefficient for the finite cases and
the dispersion relation for a periodic superlattice. Our results reveals that
the Fermi velocity modulation can control the resonance peaks of the
transmittance and also works as a switch, turning on/off the transmission
through the magnetic barriers. The results obtained here can be used for the
fabrication of graphene-based electronic devices.


  Extended theories of gravity have gathered a lot of attention over the last
years, for they not only provide an excellent framework to describe the
inflationary era but also yields an alternative to the elusive and mysterious
dark energy. Among the different extended theories of gravity, on this work we
focus on metric $f(R)$ theories. In addition, it is well known that if the
late-time acceleration of the universe is stronger than the one induced by a
cosmological constant then some future cosmic singularities might arise, being
the Big Rip the most virulent one. Following this reasoning, on this work, we
analyse the Big Rip singularity in the framework of $f(R)$ quantum
geometrodynamics. Invoking the DeWitt criterium, i. e. that the wave function
vanishes at the classical singularity, we proof that a class of solutions to
the Wheeler-DeWitt equation fulfilling this condition can be found. Therefore,
this result hints towards the avoidance of the Big Rip in metric $f(R)$
theories of gravity.


  The X-ray magnetic circular dichroism (XMCD) has been measured at the Co K
edge in Co-hcp and R-Co compounds (R=La, Tb, Dy). The structure of the
experimental XMCD spectra in the near-edge region has been observed to be
highly sensitive to the magnetic environment of the absorbing site.
Calculations of the XMCD have been carried out at the Co K edge in Co metal,
LaCo$_5$ and TbCo$_5$ within the multiple-scattering framework including the
spin-orbit coupling. In the three systems, the XMCD spectra in the near-edge
region are well reproduced. The possibility to separate and quantitatively
estimate the local effects from those due to the neighboring atoms in the XMCD
cross section makes possible a more physical understanding of the spectra. The
present results emphasize the major role played by the $d$ states of the Tb
ions in the XMCD spectrum at the Co K edge in the TbCo$_5$ compound.


  To ensure unbiased and ethical automated predictions, fairness must be a core
principle in machine learning applications. Fairness in machine learning aims
to mitigate biases present in the training data and model imperfections that
could lead to discriminatory outcomes. This is achieved by preventing the model
from making decisions based on sensitive characteristics like ethnicity or
sexual orientation. A fundamental assumption in machine learning is the
independence of observations. However, this assumption often does not hold true
for data describing social phenomena, where data points are often clustered
based. Hence, if the machine learning models do not account for the cluster
correlations, the results may be biased. Especially high is the bias in cases
where the cluster assignment is correlated to the variable of interest. We
present a fair mixed effects support vector machine algorithm that can handle
both problems simultaneously. With a reproducible simulation study we
demonstrate the impact of clustered data on the quality of fair machine
learning predictions.


  In this work, we consider scattering amplitudes relevant for high-precision
Large Hadron Collider (LHC) phenomenology. We analyse the general structure of
amplitudes, and we review state-of-the-art methods for computing them. We
discuss advantages and shortcomings of these methods, and we point out the
bottlenecks in modern amplitude computations. As a practical illustration, we
present frontier applications relevant for multi-loop multi-scale processes. We
compute the helicity amplitudes for diphoton production in gluon fusion and
photon+jet production in proton scattering in three-loop massless Quantum
Chromodynamics (QCD). We have adopted a new projector-based prescription to
compute helicity amplitudes in the 't Hooft-Veltman scheme. We also rederived
the minimal set of independent Feynman integrals for this problem using the
differential equations method, and we confirmed their intricate analytic
properties. By employing modern methods for integral reduction, we provide the
final results in a compact form, which is appropriate for efficient numerical
evaluation. Beyond QCD, we have computed the two-loop mixed QCD-electroweak
amplitudes for Z+jet production in proton scattering in light-quark-initiated
channels, without closed fermion loops. This process provides important insight
into the high-precision studies of the Standard Model, as well as into Dark
Matter searches at the LHC. We have employed a numerical approach based on
high-precision evaluation of Feynman integrals with the modern Auxiliary Mass
Flow method. The obtained numerical results in all relevant partonic channels
are evaluated on a two-dimensional grid appropriate for further
phenomenological applications.


  We investigate first- and second-order quantum phase transitions of the
anisotropic quantum Rabi model, in which the rotating- and counter-rotating
terms are allowed to have different coupling strength. The model interpolates
between two known limits with distinct universal properties. Through a
combination of analytic and numerical approaches we extract the phase diagram,
scaling functions, and critical exponents, which allows us to establish that
the universality class at finite? anisotropy is the same as the isotropic
limit. We also reveal other interesting features, including a
superradiance-induced freezing of the effective mass and discontinuous scaling
functions in the Jaynes-Cummings limit. Our findings are relevant in a variety
of systems able to realize strong coupling between light and matter, such as
circuit QED setups where a finite anisotropy appears quite naturally.


  The multi-color photometric light curves for a contact binary AV Puppis (AV
Pup) in $VR_cI_c$ bandpasses are presented, and they are analyzed by using of
the 2013 version of the Wilson-Devinney (W-D) code. The solutions suggest that
AV Pup is a peculiar A-subtype W UMa contact binary with a high mass ratio
($q=m_2/m_1= 0.896$) and a fill-out factor ($f=10\%$). Combining with our newly
determined times of minimum with those collected from literatures, the orbital
period changes of this system are investigated. The $O-C$ analysis shows that
the orbital period of AV Pup is increasing at a rate of
$\mathrm{d}P/\mathrm{d}t=4.83 \times 10^{-7} \ \mathrm{days \ yr^{-1}}$, which
can be explained by mass transfer from the less massive component to the more
massive one.


  This study explores the promising potential of integrating sensing
capabilities into multiple-input multiple-output (MIMO)-orthogonal frequency
division multiplexing (OFDM)-based networks through innovative multi-sensor
fusion techniques, tracking algorithms, and resource management. A novel data
fusion technique is proposed within the MIMO-OFDM system, which promotes
cooperative sensing among monostatic joint sensing and communication (JSC) base
stations by sharing range-angle maps with a central fusion center. To manage
data sharing and control network overhead introduced by cooperation, an
excision filter is introduced at each base station. After data fusion, the
framework employs a three-step clustering procedure combined with a tracking
algorithm to effectively handle point-like and extended targets. Delving into
the sensing/communication trade-off, resources such as transmit power,
frequency, and time are varied, providing valuable insights into their impact
on the overall system performance. Additionally, a sophisticated channel model
is proposed, accounting for complex urban propagation scenarios and addressing
multipath effects and multiple reflection points for extended targets like
vehicles. Evaluation metrics, including optimal sub-pattern assignment (OSPA),
downlink sum rate, and bit rate, offer a comprehensive assessment of the
system's localization and communication capabilities, as well as network
overhead.


  In this paper we investigate the impact of news to predict extreme financial
returns using high frequency data. We consider several model specifications
differing for the dynamic property of the underlying stochastic process as well
as for the innovation process. Since news are essentially qualitative measures,
they are firstly transformed into quantitative measures which are subsequently
introduced as exogenous regressors into the conditional volatility dynamics.
Three basic sentiment indexes are constructed starting from three list of words
defined by historical market news response and by a discriminant analysis.
Models are evaluated in terms of their predictive accuracy to forecast
out-of-sample Value-at-Risk of the STOXX Europe 600 sectors at different
confidence levels using several statistic tests and the Model Confidence Set
procedure of Hansen et al. (2011). Since the Hansen's procedure usually
delivers a set of models having the same VaR predictive ability, we propose a
new forecasting combination technique that dynamically weights the VaR
predictions obtained by the models belonging to the optimal final set. Our
results confirms that the inclusion of exogenous information as well as the
right specification of the returns' conditional distribution significantly
decrease the number of actual versus expected VaR violations towards one, as
this is especially true for higher confidence levels.


  As part of an ongoing effort to characterize the high temperature phase of
QCD, we measure the quark baryon density in the vicinity of a fixed test quark
and compare it with similar measurements at low temperature and at the
crossover temperature. Such an observable has also been studied by the Vienna
group. We find an extremely weak correlation at high temperature, suggesting
that small color singlet clusters are unimportant in the thermal ensemble. We
also find that at T = 0.75 T_c the induced quark number shows a surprisingly
large component attributable to baryonic screening. A simulation of a simple
flux tube model produces results that suggest a plausible scenario: As the
crossover temperature is approached from below, baryonic states proliferate.
Above the crossover temperature the mean size of color singlet clusters grows
explosively, resulting in an effective electrostatic deconfinement.


  In the framework of an incomplete financial market where the stock price
dynamics are modeled by a continuous semimartingale (not necessarily Markovian)
an explicit second-order expansion formula for the power investor's value
function - seen as a function of the underlying market price of risk process -
is provided. This allows us to provide first-order approximations of the
optimal primal and dual controls. Two specific calibrated numerical examples
illustrating the accuracy of the method are also given.


  Motivation: One of the branches of Systems Biology is focused on a deep
understanding of underlying regulatory networks through the analysis of the
biomolecules oscillations and their interplay. Synthetic Biology exploits gene
or/and protein regulatory networks towards the design of oscillatory networks
for producing useful compounds. Therefore, at different levels of application
and for different purposes, the study of biomolecular oscillations can lead to
different clues about the mechanisms underlying living cells. It is known that
network-level interactions involve more than one type of biomolecule as well as
biological processes operating at multiple omic levels. Combining
network/pathway-level information with genetic information it is possible to
describe well-understood or unknown bacterial mechanisms and organism-specific
dynamics. Results: Network multi-omic integration has led to the discovery of
interesting oscillatory signals. Following the methodologies used in signal
processing and communication engineering, a new methodology is introduced to
identify and quantify the extent of the multi-omic oscillations of the signal.
New signal metrics are designed to allow further biotechnological explanations
and provide important clues about the oscillatory nature of the pathways and
their regulatory circuits. Our algorithms designed for the analysis of
multi-omic signals are tested and validated on 11 different bacteria for
thousands of multi-omic signals perturbed at the network level by different
experimental conditions. Information on the order of genes, codon usage, gene
expression, and protein molecular weight is integrated at three different
functional levels. Oscillations show interesting evidence that network-level
multi-omic signals present a synchronized response to perturbations and
evolutionary relations along with taxa.


  We study the dynamics of the renormalization operator acting on the space of
pairs (v,t), where v is a diffeomorphism and t belongs to [0,1], interpreted as
unimodal maps x-->v(q_t(x)), where q_t(x)=-2t|x|^a+2t-1. We prove the so called
complex bounds for sufficiently renormalizable pairs with bounded
combinatorics. This allows us to show that if the critical exponent a is close
to an even number then the renormalization operator has a unique fixed point.
Furthermore this fixed point is hyperbolic and its codimension one stable
manifold contains all infinitely renormalizable pairs.


  We study the large-time behavior of solutions to the compressible
Navier-Stokes equations for a viscous and heat-conductive gases in
$\mathbb{R}^3$. More precisely, under a suitable additional condition involving
only the low frequencies of the initial data, we exhibit the optimal time decay
rates for the constructed global solutions. The proof relies on some new energy
arguments developed by Xin and Xu [39] for the compressible Navier-Stokes
equations.


  In this paper, we show that the Alperin-McKay conjecture holds for 2-blocks
of maximal defect. A major step in the proof is the verification of the
inductive Alperin-McKay condition for the principal 2-block of groups of Lie
type in odd characteristic.


  A loop $(Q,\cdot,\backslash,/)$ is called a middle Bol loop if it obeys the
identity $x(yz\backslash x)=(x/z)(y\backslash x)$. In this paper, some new
algebraic properties of a middle Bol loop are established. Four bi-variate
mappings $f_i,g_i,~i=1,2$ and four $j$-variate mappings
$\alpha_j,\beta_j,\phi_j,\psi_j,~j\in\mathbb{N}$ are introduced and some
interesting properties of the former are found. Neccessary and sufficient
conditons in terms of $f_i,g_i,~i=1,2$, for a middle Bol loop to have the
elasticity property, RIP, LIP, right alternative property (RAP) and left
alternative property (LAP) are establsihed. Also, neccessary and sufficient
conditons in terms of $\alpha_j,\beta_j,\phi_j,\psi_j,~j\in\mathbb{N}$, for a
middle Bol loop to have power RAP and power LAP are establsihed. Neccessary and
sufficient conditons in terms of $f_i,g_i,~i=1,2$ and
$\alpha_j,\beta_j,\phi_j,\psi_j,~j\in\mathbb{N}$, for a middle Bol loop to be a
group, Moufang loop or extra loop are established. A middle Bol loop is shown
to belong to some classes of loops whose identiites are of the J.D. Phillips'
RIF-loop and WRIF-loop (generalizations of Moufang and Steiner loops) and WIP
power associative conjugacy closed loop types if and only if some identities
defined by $g_1$ and $g_2$ are obeyed.


  We study the tunneling dynamics in a time-periodically modulated two-mode
Bose-Hubbard model using Floquet theory. We consider situations where the
system is in the self-trapping regime and either the tunneling amplitude, the
interaction strength, or the energy difference between the modes is modulated.
In the former two cases, the tunneling is enhanced in a wide range of
modulation frequencies, while in the latter case the resonance is narrow. We
explain this difference with the help of Floquet analysis. If the modulation
amplitude is weak, the locations of the resonances can be found using the
spectrum of the non-modulated Hamiltonian. Furthermore, we use Floquet analysis
to explain the coherent destruction of tunneling (CDT) occurring in a
large-amplitude modulated system. Finally, we present two ways to create a NOON
state (a superposition of $N$ particles in mode 1 with zero particles in mode 2
and vice versa). One is based on a coherent oscillation caused by detuning from
a partial CDT. The other makes use of an adiabatic variation of the modulation
frequency. This results in a Landau-Zener type of transition between the ground
state and a NOON-like state.


  We compute the change in the Lorentz force integrated over the outer solar
atmosphere implied by observed changes in vector magnetograms that occur during
large, eruptive solar flares. This force perturbation should be balanced by an
equal and opposite force perturbation acting on the solar photosphere and solar
interior. The resulting expression for the estimated force change in the solar
interior generalizes the earlier expression presented by Hudson, Fisher and
Welsch (CS-383, ASP, 221, 2008), providing horizontal as well as vertical force
components, and provides a more accurate result for the vertical component of
the perturbed force. We show that magnetic eruptions should result in the
magnetic field at the photosphere becoming more horizontal, and hence should
result in a downward (towards the solar interior) force change acting on the
photosphere and solar interior, as recently argued from an analysis of
magnetogram data by Wang and Liu (Astrophys. J. Lett. 716, L195, 2010). We
suggest the existence of an observational relationship between the force change
computed from changes in the vector magnetograms, the outward momentum carried
by the ejecta from the flare, and the properties of the helioseismic
disturbance driven by the downward force change. We use the impulse driven by
the Lorentz-force change in the outer solar atmosphere to derive an upper limit
to the mass of erupting plasma that can escape from the Sun. Finally, we
compare the expected Lorentz-force change at the photosphere with simple
estimates from flare-driven gasdynamic disturbances and from an estimate of the
perturbed pressure from radiative backwarming of the photosphere in flaring
conditions.


  Curved A-infinity algebras appear in nature as deformations of dg algebras.
We develop the basic theory of curved A-infinity algebras and, in particular,
curved dg algebras. We investigate their link with a suitable class of dg
coalgebras via the bar construction and produce Quillen model structures on
their module categories. We define the analogue of the relative derived
category for a curved dg algebra.


  Machine-to-Machine (M2M) networks are an emerging technology with
applications in numerous areas including smart grids, smart cities, vehicular
telematics, and healthcare. In this paper, we design two estimation protocols
for rapidly obtaining separate estimates of the number of active nodes of each
traffic type in a heterogeneous M2M network with $T$ types of M2M nodes (e.g.,
those that send emergency, periodic, normal type data etc), where $T \geq 2$ is
an arbitrary integer. One of these protocols, Method I, is a simple scheme, and
the other, Method II, is more sophisticated and performs better than Method I.
Also, we design a medium access control (MAC) protocol that supports
multi-channel operation for a heterogeneous M2M network with an arbitrary
number of types of M2M nodes, operating as a secondary network using Cognitive
Radio technology. Our Cognitive MAC protocol uses the proposed node cardinality
estimation protocols to rapidly estimate the number of active nodes of each
type in every time frame; these estimates are used to find the optimal
contention probabilities to be used in the MAC protocol. We compute a closed
form expression for the expected number of time slots required by Method I to
execute as well as a simple upper bound on it. Also, we mathematically analyze
the performance of the Cognitive MAC protocol and obtain expressions for the
expected number of successful contentions per frame and the expected amount of
energy consumed. Finally, we evaluate the performances of our proposed
estimation protocols and Cognitive MAC protocol using simulations.


  We consider a non-polynomial cubic spline to develop the classes of methods
for the numerical solution of singularly perturbed two-point boundary value
problems. The proposed methods are second and fourth order accurate and
applicable to problems both in singular and non-singular cases. Numerical
results are given to illustrate the efficiency of our methods and compared with
the methods given by different authors.


  We propose to use Vavilov-Cherenkov (VC) and transition radiations as a
source of twisted photons in a wide range of energies. The experimental setup
to observe the orbital angular momentum of photons constituting those
radiations is described. The radiation produced by relativistic electrons and
ions in passing dielectric plates is considered. The experimental verification
of the addition rule for the total angular momentum in the radiation of twisted
photons by helically microbunched beams is proposed for VC and transition
radiations. The parameters of the experiments are presented.


  We devise the fast adjoint response algorithm for the gradient of physical
measures (long-time-average statistics) of discrete-time hyperbolic chaos with
respect to many system parameters. Its cost is independent of the number of
parameters. The algorithm transforms our new theoretical tools, the adjoint
shadowing lemma and the equivariant divergence formula, into the form of
progressively computing $u$ many bounded vectors on one orbit. Here $u$ is the
unstable dimension. We demonstrate our algorithm on an example difficult for
previous methods, a system with random noise, and a system of a discontinuous
map. We also give a short formal proof of the equivariant divergence formula.
  Compared to the better-known finite-element method, our algorithm is not
cursed by dimensionality of the phase space (typical real-life systems have
very high dimensions), since it samples by one orbit. Compared to the
ensemble/stochastic method, our algorithm is not cursed by the butterfly
effect, since the recursive relations in our algorithm is bounded.


  Many-body systems with chiral fermions exhibit anomalous transport phenomena
originated from quantum anomalies. Based on quantum field theory, we derive the
kinetic theory for chiral fermions interacting with an external electromagnetic
field and a background curved geometry. The resultant framework respects the
covariance under the U(1) gauge, local Lorentz, and diffeomorphic
transformations. It is particularly useful to study the gravitational or
non-inertial effects for chiral systems. As the first application, we study the
chiral dynamics in a rotating coordinate and clarify the roles of the Coriolis
force and spin-vorticity coupling in generating the chiral vortical effect
(CVE). We also show that the CVE is an intrinsic phenomenon of a rotating
chiral fluid, and thus independent of observer's frame.


  Exploiting the explicit mass formulae for logarithmic potential model of
Quigg and Rosner it is shown that at least on the level of mass-relations this
model reproduces the naive quark model relations and generalizes the last one
in case of highly non-trivial potential. Generalization includes the relations
for higher values of orbital quantum numbers. In particular, preditions for
recently discovered atom-like P-states are no worse than for any other
potential models.The advantage consists in simplicity of approach.


  This paper deals with modelling the effect of wear on the dynamics of the
shrouded bladed disk with frictional contacts at the shrouds and the contact
interface evolution. Prediction of fretting wear commonly occurring at the
contacts of turbomachinery components, and its impact on the dynamics is
increasingly researched due to the components subjected to their structural
limits for performance and operating at high loading conditions. Over a
lifetime, the fretting wear at these contacts could alter the global dynamic
response of these bladed disks from the designed operating point and could lead
to high vibration amplitudes. This study implements a coupled static/dynamic
harmonic balance method (HBM) with wear energy approach and an adaptive wear
logic to study the impact on the steady-state nonlinear dynamic response.
Firstly, the methodology is applied to a cantilever beam with a contact patch
and then to a shrouded bladed disk with shroud contacts using cyclic symmetry
boundary conditions. The novelty of the paper is to show the effect of wear on
the dynamics with the changing contact pre-load using a coupled approach. A
wear acceleration $v_{w,max}$ parameter is defined, and the impact of the
choice of this parameter is discussed in detail on the computation time and the
accuracy of results. The test cases demonstrate the impact of wear on the
dynamic nonlinear response curves and the contact interface evolution for
various scenarios.


  The higher order moments of the fluctuations for the thermodynamical systems
in the presence of fields are investigated in the framework of a theoretical
method. The metod uses a generalized statistical ensemble consonant with the
adequate expression for the generalized internal energy. The applications refer
to the case of a system in magnetoquasistatic field. In the case of linear
magnetic media one finds that for the description of the magnetic induction
fluctuations the Gaussian approximation is good enough. For nonlinear media the
coresponding fluctuations are non-Gaussian, they having a non-null asymmetry.
Aditionally the respective fluctuations have characteristics of leptokurtic,
mesokurtic and platykurtic type, depending of the value of the magnetic field
strength comparatively with a scaling factor of the magnetization curve.


  We go back to the roots of enriched category theory and study categories
enriched in chain complexes; that is, we deal with differential graded
categories (DG-categories for short). In particular, we recall weighted
colimits and provide examples. We solve the 50 year old question of how to
characterize Cauchy complete DG-categories in terms of existence of some
specific finite absolute colimits. As well as the interactions between absolute
weighted colimits, we also examine the total complex of a chain complex in a
DG-category as a non-absolute weighted colimit.


  Manufacturing is using Virtual Reality tools to enhance the product life
cycle. Their definitions are still in flux and it is necessary to define their
connections. Thus, firstly, we will introduce more closely some definitions
where we will find that, if the Virtual manufacturing concepts originate from
machining operations and evolve in this manufacturing area, there exist a lot
of applications in different fields such as casting, forging, sheet
metalworking and robotics (mechanisms). From the recent projects in Europe or
in USA, we notice that the human perception or the simulation of mannequin is
more and more needed in both fields. In this context, we have isolated some
applications as ergonomic studies, assembly and maintenance simulation, design
or training where the virtual reality tools can be applied. Thus, we find out a
family of applications where the virtual reality tools give the engineers the
main role in the optimization process. We will illustrate our paper by several
examples where virtual reality interfaces are used and combined with
optimization tools as multi-agent systems.


  The Hanbury Brown and Twiss effect (HBT) is described by numerical and
analytical modeling, as well as experimentally, using sound waves and easily
available instrumentation. An interesting phenomenon that has often been
considered too difficult to be included in standard physics studies at bachelor
and master level, can now be introduced even for second year bachelor students
and up. In the original Hanbury Brown and Twiss effect the angular size of the
source (the star Sirius) was calculated by determining the distance between two
detectors that lead to a drop in the cross-correlations in the signals from the
detectors. We find that this principle works equally well by sound waves from a
waterfall. This is remarkable, since we use a completely different kind of
waves from the HBT case, the frequency of the waves differ by a factor $\sim
10^{12}$ and the wavelength as well as the angular extension of the source seen
from the observer's position differ by a factor $\sim 10^{7}$. The original HBT
papers were based on measurements of \emph{intensity} fluctuations recorded by
two detectors and correlations between these signals. The starting point for
the theory that explained the effect was therefore intensity fluctuations per
see, and the theory is not easy to understand, at least not for an
undergraduate physics student. Our starting point is descriptions of broadband
waves at the amplitude level (not at intensity level) by numerical modeling.
Important properties of broadband waves can easily be revealed and understood
by numerical modeling, and time-resolved frequency analysis (TFA) based on
Morlet wavelets turns out to be a very useful tool. In fact, we think it has
been far too little attention to broadband waves in physics education hitherto,
but the growth of use of numerical methods in basic physics courses opens up
new possibilities.


  Science-AI Symbiotic Group at Seven Square Academy, Naigaon was formed in
2023 with the purpose of bringing school students to the forefronts of science
research by involving them in hands on research. In October 2023 a new project
was started with the goal of studying the lunar surface by real-time
observations and open source data. Twelve students/members from grades 8, 9, 10
participated in this research attempt wherein each student filled an
observation metric by observing the Moon on various days with a Bresser Messier
150mm/1200mm reflector Newtonian telescope. After the observations were done,
the members were assigned various zones on the lunar near side for analysis of
geological features. Then a data analysis metric was filled by each of students
with the help of Lunar Reconnaissance Orbiter Camera's/ LROC's quickmap open
access data hosted by Arizona State University. In this short paper a brief
overview of this project is given. One example each of observation metric and
data analysis metric is presented. This kind of project has high impact for
school science education with minimal costs. This project can also serve as an
interesting science outreach program for organisations looking forward to
popularise planetary sciences research at school level.


  In this investigation we perform a systematic computational search for
potential singularities in 3D Navier-Stokes flows based on the
Ladyzhenskaya-Prodi-Serrin conditions. They assert that if the quantity
$\int_0^T \| \mathbf{u}(t) \|_{L^q(\Omega)}^p \, dt$, where $2/p+3/q \le 1$, $q
> 3$, is bounded, then the solution $\mathbf{u}(t)$ of the Navier-Stokes system
is smooth on the interval $[0,T]$. In other words, if a singularity should
occur at some time $t \in [0,T]$, then this quantity must be unbounded. We have
probed this condition by studying a family of variational PDE optimization
problems where initial conditions $\mathbf{u}_0$ are sought to maximize
$\int_0^T \| \mathbf{u}(t) \|_{L^4(\Omega)}^8 \, dt$ for different $T$ subject
to suitable constraints. These problems are solved numerically using a
large-scale adjoint-based gradient approach. Even in the flows corresponding to
the optimal initial conditions determined in this way no evidence has been
found for singularity formation, which would be manifested by unbounded growth
of $\| \mathbf{u}(t) \|_{L^4(\Omega)}$. However, the maximum enstrophy attained
in these extreme flows scales in proportion to $\mathcal{E}_0^{3/2}$, the same
as found by Kang et al. (2020) when maximizing the finite-time growth of
enstrophy. In addition, we also consider sharpness of an a priori estimate on
the time evolution of $\| \mathbf{u}(t) \|_{L^4(\Omega)}$ by solving another
PDE optimization problem and demonstrate that the upper bound in this estimate
could be improved.


  This paper investigates the issue of how to exploit target location
distribution for multiple input multiple output (MIMO) radar waveform design.
We consider a MIMO radar aiming to estimate the unknown and random angular
location parameters of a point target, whose distribution information can be
exploited by the radar. First, we establish the models of the MIMO radar system
and the target location distribution. Based on the considered models, we
propose the first category of target location distribution exploitation methods
by analyzing the radar direction-of-angle (DoA) estimation performance and
deriving a general form of posterior Cramer-Rao bound (PCRB) as the lower bound
of the mean square error of DoA estimation. Following this, to explore more
insights, we proposed the second category of target location distribution
exploitation methods by introducing a novel radar metric, probability scaled
beampattern (PSBP), from the perspective of radar beampattern. To compare the
two methods, we formulate the PCRB and PSBP oriented radar waveform design
problems and propose corresponding low-complexity and convergence-guaranteed
algorithms to tackle them. Finally, numerical simulations are conducted in
different scenarios to provide a comprehensive evaluation and comparison of the
radar performance.


  Recent theoretical and experimental work demonstrated that nonlinear optics
of ferrofluid-based hyperbolic metamaterials exhibits very unusual
2+2-dimensional spatiotemporal dynamics. Here we report a detailed microscopic
study of mutual interactions of individual self-focused optical filaments
inside this metamaterial. In agreement with theoretical expectations, the
observed mutual interactions of individual filaments exhibit strong
similarities with general relativity in 2+1 dimensions. This observation is
very important since 2+1-dimensional gravity is an exactly solvable theory even
in the quantum gravity limit.


  Hierarchical text classification, which aims to classify text documents into
a given hierarchy, is an important task in many real-world applications.
Recently, deep neural models are gaining increasing popularity for text
classification due to their expressive power and minimum requirement for
feature engineering. However, applying deep neural networks for hierarchical
text classification remains challenging, because they heavily rely on a large
amount of training data and meanwhile cannot easily determine appropriate
levels of documents in the hierarchical setting. In this paper, we propose a
weakly-supervised neural method for hierarchical text classification. Our
method does not require a large amount of training data but requires only
easy-to-provide weak supervision signals such as a few class-related documents
or keywords. Our method effectively leverages such weak supervision signals to
generate pseudo documents for model pre-training, and then performs
self-training on real unlabeled data to iteratively refine the model. During
the training process, our model features a hierarchical neural structure, which
mimics the given hierarchy and is capable of determining the proper levels for
documents with a blocking mechanism. Experiments on three datasets from
different domains demonstrate the efficacy of our method compared with a
comprehensive set of baselines.


  The Internet plays a key role in society and is vital to economic
development. Due to the pressure of competition, most technology companies,
including Internet finance companies, continue to explore new markets and new
business. Funding subsidies and resource inputs have led to significant
business income tendencies in financial statements. This tendency of business
income is often manifested as part of the business loss or long-term
unprofitability. We propose a risk change indicator (RFR) and compare the risk
indicator of fourteen representative companies. This model combines extreme
risk value with slope, and the combination method is simple and effective. The
results of experiment show the potential of this model. The risk volatility of
technology enterprises including Internet finance enterprises is highly
cyclical, and the risk volatility of emerging Internet fintech companies is
much higher than that of other technology companies.


  We study two aspects of the CFL phase of QCD in the NJL approximation. The
first one is the issue of the dependence on \mu of the ultraviolet cutoff in
the gap equation, which is solved allowing a running coupling constant. The
second one is the dependence of the gap on the strange quark mass; using the
high density effective theory we perform an expansion in the parameter
(m_s/\mu)^2 after checking that its numerical validity is very good already at
first order.


  We address the problem of the minus sign sampling for two electron systems
using the path integral approach. We show that this problem can be reexpressed
as one of computing free energy differences and sampling the tails of
statistical distributions. Using Metadynamics, a realistic problem like that of
two electrons confined in a quantum dot can be solved. We believe that this is
a strategy that can possibly be extended to more complex systems.


  Generalised uncorrelated Wishart matrices are formed out of rectangular
standard Gaussian data matrices with a certain pattern of zero entries.
Development of the theory in the real and complex cases has proceeded along
separate line. For example, emphasis in the real case has been placed on the
Bellman and Riesz distributions, while that in the complex case has been shown
to be closely related to the Muttalib-Borodin model. In this work, as well as
uniting the lines of development, a tie in with matrix spherical functions is
identified in the context of deducing the eigenvalue probability density
function from the joint element probability density function.


  In this paper, we introduce a method for segmenting time series data using
tools from Bayesian nonparametrics. We consider the task of temporal
segmentation of a set of time series data into representative stationary
segments. We use Gaussian process (GP) priors to impose our knowledge about the
characteristics of the underlying stationary segments, and use a nonparametric
distribution to partition the sequences into such segments, formulated in terms
of a prior distribution on segment length. Given the segmentation, the model
can be viewed as a variant of a Gaussian mixture model where the mixture
components are described using the covariance function of a GP. We demonstrate
the effectiveness of our model on synthetic data as well as on real time-series
data of heartbeats where the task is to segment the indicative types of beats
and to classify the heartbeat recordings into classes that correspond to
healthy and abnormal heart sounds.


  In 2004 the second author of the present paper proved that a point set in
$[0,1]^d$ which has star-discrepancy at most $\varepsilon$ must necessarily
consist of at least $c_{abs} d \varepsilon^{-1}$ points. Equivalently, every
set of $n$ points in $[0,1]^d$ must have star-discrepancy at least $c_{abs} d
n^{-1}$. The original proof of this result uses methods from
Vapnik--Chervonenkis theory and from metric entropy theory. In the present
paper we give an elementary combinatorial proof for the same result, which is
based on identifying a sub-box of $[0,1]^d$ which has approximately $d$
elements of the point set on its boundary. Furthermore, we show that a point
set for which no such box exists is rather irregular, and must necessarily have
a large star-discrepancy.


  We consider a nanomachining process of hard, abrasive particles grinding on
the rough surface of a polycrystalline ferritic work piece. Using extensive
large-scale molecular dynamics (MD) simulations, we show that the mode of
thermostatting, i.e., the way that the heat generated through deformation and
friction is removed from the system, has crucial impact on tribological and
materials related phenomena. By adopting an electron-phonon coupling approach
to parametrize the thermostat of the system, thus including the electronic
contribution to the thermal conductivity of iron, we can reproduce the
experimentally measured values that yield realistic temperature gradients in
the work piece. We compare these results to those obtained by assuming the two
extreme cases of only phononic heat conduction and instantaneous removal of the
heat generated in the machining interface. Our discussion of the differences
between these three cases reveals that although the average shear stress is
virtually temperature independent up to a normal pressure of approximately 1
GPa, the grain and chip morphology as well as most relevant quantities depend
heavily on the mode of thermostatting beyond a normal pressure of 0.4 GPa.
These pronounced differences can be explained by the thermally activated
processes that guide the reaction of the Fe lattice to the external mechanical
and thermal loads caused by nanomachining.


  This paper addresses the problem of quickest detection of a change in the
maximal coherence between columns of a $n\times p$ random matrix based on a
sequence of matrix observations having a single unknown change point. The
random matrix is assumed to have identically distributed rows and the maximal
coherence is defined as the largest of the $p \choose 2$ correlation
coefficients associated with any row. Likewise, the $k$ nearest neighbor (kNN)
coherence is defined as the $k$-th largest of these correlation coefficients.
The forms of the pre- and post-change distributions of the observed matrices
are assumed to belong to the family of elliptically contoured densities with
sparse dispersion matrices but are otherwise unknown. A non-parametric stopping
rule is proposed that is based on the maximal k-nearest neighbor sample
coherence between columns of each observed random matrix. This is a summary
statistic that is related to a test of the existence of a hub vertex in a
sample correlation graph having a degree at least $k$. Performance bounds on
the delay and false alarm performance of the proposed stopping rule are
obtained in the purely high dimensional regime where $p\rightarrow \infty$ and
$n$ is fixed. When the pre-change dispersion matrix is diagonal it is shown
that, among all functions of the proposed summary statistic, the proposed
stopping rule is asymptotically optimal under a minimax quickest change
detection (QCD) model as the stopping threshold approaches infinity. The theory
developed also applies to sequential hypothesis testing and fixed sample size
tests.


  Owing to the richness of symmetry and well-established knowledge on the bulk
superfluidity, the superfluid $^3$He has offered a prototypical system to study
intertwining of topology and symmetry. This article reviews recent progress in
understanding the topological superfluidity of $^3$He in a multifaceted manner,
including symmetry consideration, the Jackiw-Rebbi's index theorem, and the
quasiclassical theory. Special focus is placed on the symmetry protected
topological superfuidity of the $^3$He-B confined in a slab geometry. The
$^3$He-B under a magnetic field is separated to two different sub-phases: The
symmetry protected topological phase and non-topological phase. The former
phase is characterized by the existence of symmetry protected Majorana
fermions. The topological phase transition between them is triggered off by the
spontaneous breaking of a hidden discrete symmetry. The critical field is
quantitatively determined from the microscopic calculation that takes account
of magnetic dipole interaction of $^3$He nucleus. It is also demonstrated that
odd-frequency even-parity Cooper pair amplitudes are emergent in low-lying
quasiparticles. The key ingredients, symmetry protected Majorana fermions and
odd-frequency pairing, bring an important consequence that the coupling of the
surface states to an applied field is prohibited by the hidden discrete
symmetry, while the topological phase transition with the spontaneous symmetry
breaking is accompanied by anomalous enhancement and anisotropic quantum
criticality of surface spin susceptibility. We also illustrate common
topological features between topological crystalline superconductors and
symmetry protected topological superfluids, taking UPt$_3$ and Rashba
superconductors as examples.


  Second moment beam widths are commonly used in paraxial optics to define the
focal extent of beams. However, second moments of arbitrary beams are not
guaranteed to be finite. I propose the focal concentration area as a measure of
beam focal area, defined to be the ratio of total radial intensity to radial
intensity regulated by a unit area Gaussian distribution. I use the Dirac delta
limit of this distribution to establish a rigorous upper bound on the peak
intensity of non-paraxial beams of a given total intensity, and show that this
is achieved by the recently proposed `proto- beam' solution. I discuss the
generalisation to electromagnetic beams, and find the same lower bound as the
scalar case. This bound cannot be achieved for any physical beam, and as such
the physical lower bound must be higher.


  At thermal equilibrium, intensive quantities like temperature and pressure
have to be uniform throughout the system, restricting inhomogeneous systems
composed of different phases. The paradigmatic example is the coexistence of
vapor and liquid, a state that can also be observed for active Brownian
particles steadily driven away from equilibrium. Recently, a strategy has been
proposed that allows to predict phase equilibria of active particles [Phys.
Rev. E \textbf{97}, 020602(R)(2018)]. Here we elaborate on this strategy and
formulate it in the framework of a van der Waals theory for active discs. For a
given equation of state, we derive the effective free energy analytically and
show that it yields coexisting densities in very good agreement with numerical
results. We discuss the interfacial tension and the relation to Cahn-Hilliard
models.


  A careful study of thermodynamic and transport properties of a high quality
single crystal of MnSi at ambient pressure suggests that its transition to a
helical magnetic state near 29 K is weakly first order. The heat capacity,
temperature derivatives of resistivity, thermal expansion and magnetic
susceptibility exhibit a specific structure around the phase transition point,
interpreted as a combination of first and second order features. Conclusions
drawn from these experiments question prevailing views on the phase diagram of
MnSi and propose that the phase transition under study becomes second order at
high pressure and low temperature.


  In this paper, we propose an extension to graph-based sentiment lexicon
induction methods by incorporating distributed and semantic word
representations in building the similarity graph to expand a three-dimensional
sentiment lexicon. We also implemented and evaluated the label propagation
using four different word representations and similarity metrics. Our
comprehensive evaluation of the four approaches was performed on a single data
set, demonstrating that all four methods can generate a significant number of
new sentiment assignments with high accuracy. The highest correlations
(tau=0.51) and the lowest error (mean absolute error < 1.1%), obtained by
combining both the semantic and the distributional features, outperformed the
distributional-based and semantic-based label-propagation models and approached
a supervised algorithm.


  The adoption of Artificial Intelligence (AI) based Virtual Network Functions
(VNFs) has witnessed significant growth, posing a critical challenge in
orchestrating AI models within next-generation 6G networks. Finding optimal AI
model placement is significantly more challenging than placing traditional
software-based VNFs, due to the introduction of numerous uncertain factors by
AI models, such as varying computing resource consumption, dynamic storage
requirements, and changing model performance. To address the AI model placement
problem under uncertainties, this paper presents a novel approach employing a
sequence-to-sequence (S2S) neural network which considers uncertainty
estimations. The S2S model, characterized by its encoding-decoding
architecture, is designed to take the service chain with a number of AI models
as input and produce the corresponding placement of each AI model. To address
the introduced uncertainties, our methodology incorporates the orthonormal
certificate module for uncertainty estimation and utilizes fuzzy logic for
uncertainty representation, thereby enhancing the capabilities of the S2S
model. Experiments demonstrate that the proposed method achieves competitive
results across diverse AI model profiles, network environments, and service
chain requests.