  Unquantified sources of uncertainty in observational causal analyses can
break the integrity of the results. One would never want another analyst to
repeat a calculation with the same dataset, using a seemingly identical
procedure, only to find a different conclusion. However, as we show in this
work, there is a typical source of uncertainty that is essentially never
considered in observational causal studies: the choice of match assignment for
matched groups, that is, which unit is matched to which other unit before a
hypothesis test is conducted. The choice of match assignment is anything but
innocuous, and can have a surprisingly large influence on the causal
conclusions. Given that a vast number of causal inference studies test
hypotheses on treatment effects after treatment cases are matched with similar
control cases, we should find a way to quantify how much this extra source of
uncertainty impacts results. What we would really like to be able to report is
that \emph{no matter} which match assignment is made, as long as the match is
sufficiently good, then the hypothesis test result still holds. In this paper,
we provide methodology based on discrete optimization to create robust tests
that explicitly account for this possibility. We formulate robust tests for
binary and continuous data based on common test statistics as integer linear
programs solvable with common methodologies. We study the finite-sample
behavior of our test statistic in the discrete-data case. We apply our methods
to simulated and real-world datasets and show that they can produce useful
results in practical applied settings.


  In this paper, we present a solution for a five-dimensional boson star under
gravity with infinite tower of higher curvature corrections. We discover that
when the coupling constant exceeds a certain threshold, an alternative
configuration emerges, distinct from the conventional five-dimensional boson
star. This new structure is characterized by a broader frequency range, with
its minimum value approaching zero. At a truncation of $n=2$ for the correction
order, the solution and its scalar curvature diverge as the frequency
approaches zero. However, as the order of higher curvature corrections
increases, the singularity at the center vanishes, resulting in a globally
regular solution. Additionally, as the frequency approaches zero, the scalar
field's radial distribution becomes concentrated within the critical radius
$r_c$, forming what we term a ``frozen star". Beyond this radius, the metric of
the frozen star almost degenerates into that of an extreme black hole. The
solutions for such frozen stars offer a new avenue for exploring the enigmatic
interiors of compact celestial bodies, enhancing our understanding of the
internal structure of black holes under semi-classical conditions and
potentially addressing the series of paradoxes associated with information loss
due to singularities and horizons.


  Recently, many works studied the expressive power of graph neural networks
(GNNs) by linking it to the $1$-dimensional Weisfeiler--Leman algorithm
($1\text{-}\mathsf{WL}$). Here, the $1\text{-}\mathsf{WL}$ is a well-studied
heuristic for the graph isomorphism problem, which iteratively colors or
partitions a graph's vertex set. While this connection has led to significant
advances in understanding and enhancing GNNs' expressive power, it does not
provide insights into their generalization performance, i.e., their ability to
make meaningful predictions beyond the training set. In this paper, we study
GNNs' generalization ability through the lens of Vapnik--Chervonenkis (VC)
dimension theory in two settings, focusing on graph-level predictions. First,
when no upper bound on the graphs' order is known, we show that the bitlength
of GNNs' weights tightly bounds their VC dimension. Further, we derive an upper
bound for GNNs' VC dimension using the number of colors produced by the
$1\text{-}\mathsf{WL}$. Secondly, when an upper bound on the graphs' order is
known, we show a tight connection between the number of graphs distinguishable
by the $1\text{-}\mathsf{WL}$ and GNNs' VC dimension. Our empirical study
confirms the validity of our theoretical findings.


  Medical imaging provides many valuable clues involving anatomical structure
and pathological characteristics. However, image degradation is a common issue
in clinical practice, which can adversely impact the observation and diagnosis
by physicians and algorithms. Although extensive enhancement models have been
developed, these models require a well pre-training before deployment, while
failing to take advantage of the potential value of inference data after
deployment. In this paper, we raise an algorithm for source-free unsupervised
domain adaptive medical image enhancement (SAME), which adapts and optimizes
enhancement models using test data in the inference phase. A
structure-preserving enhancement network is first constructed to learn a robust
source model from synthesized training data. Then a teacher-student model is
initialized with the source model and conducts source-free unsupervised domain
adaptation (SFUDA) by knowledge distillation with the test data. Additionally,
a pseudo-label picker is developed to boost the knowledge distillation of
enhancement tasks. Experiments were implemented on ten datasets from three
medical image modalities to validate the advantage of the proposed algorithm,
and setting analysis and ablation studies were also carried out to interpret
the effectiveness of SAME. The remarkable enhancement performance and benefits
for downstream tasks demonstrate the potential and generalizability of SAME.
The code is available at
https://github.com/liamheng/Annotation-free-Medical-Image-Enhancement.


  We present an anomalous experimental observation on the rising speed of air
bubbles in a Hele-Shaw cell containing a suspension of spherical,
neutrally-buoyant, non-Brownian particles. Strikingly, bubbles rise faster in
suspensions as compared to particle-less liquids of the same effective
viscosity. By carefully measuring this bubble speed increase at various
particle volume fraction and via velocity field imaging, we demonstrate that
this strange bubble dynamics is linked to a reduction in the bulk dissipation
rate. A good match between our experimental data and computations based on
Suspension Balance Model illustrates that the underlying mechanism for this
dissipation-rate-deficit is related to a non-uniform particle distribution in
the direction perpendicular to the channel walls due to shear-induced particle
migration.


  The results of a search for gluino and squark pair production with the pairs
decaying via the lightest charginos into a final state consisting of two $W$
bosons, the lightest neutralinos ($\tilde\chi^0_1$), and quarks, are presented.
The signal is characterised by the presence of a single charged lepton
($e^{\pm}$ or $\mu^{\pm}$) from a $W$ boson decay, jets, and missing transverse
momentum. The analysis is performed using 139 fb$^{-1}$ of proton-proton
collision data taken at a centre-of-mass energy $\sqrt{s}=13$ TeV delivered by
the Large Hadron Collider and recorded by the ATLAS experiment. No
statistically significant excess of events above the Standard Model expectation
is found. Limits are set on the direct production of squarks and gluinos in
simplified models. Masses of gluino (squark) up to 2.2 TeV (1.4 TeV) are
excluded at 95% confidence level for a light $\tilde\chi^0_1$.


  Non-vanishing parity-odd correlators of gamma ray arrival directions observed
by Fermi-LAT indicate the existence of a helical intergalactic magnetic field
(Tashiro et al.2013). We successfully test this hypothesis using more stringent
cuts of the data, Monte Carlo simulations with Fermi-LAT time exposure
information, separate analyses for the northern and southern galactic
hemispheres, and confirm predictions made in Tashiro & Vachaspati (2014). With
some further technical assumptions, we show how to reconstruct the magnetic
helicity spectrum from the parity-odd correlators.


  We construct sequences of rapidly rotating neutron stars in dilatonic
Einstein-Gauss-Bonnet theory, employing two equations of state for the nuclear
matter. We analyze the dependence of the physical properties of these neutron
stars on the Gauss-Bonnet coupling strength. For a given equation of state we
determine the physically relevant domain of rapidly rotating neutron stars,
which is delimited by the set of neutron stars rotating at the Kepler limit,
the set of neutron stars along the secular instability line, and the set of
static neutron stars. As compared to Einstein gravity, the presence of the
Gauss-Bonnet term decreases this domain, leading to lower values for the
maximum mass as well as to smaller central densities. The quadrupole moment is
decreased by the Gauss-Bonnet term for rapidly rotating neutron stars, while it
is increased for slowly rotating neutron stars. The universal relation between
the quadrupole moment and the moment of inertia found in General Relativity
appears to extend to dilatonic Einstein-Gauss-Bonnet theory with very little
dependence on the coupling strength of the Gauss-Bonnet term. The neutron stars
carry a small dilaton charge.


  We have systematically investigated the structural property and electrical
structures of the transition metal intercalated titanium disulfide compound
Fe$_x$TiS$_2$ ($0\leq x\leq0.33$) from angle-resolved photoelectron
spectroscopy with tunable polarized synchrotron radiation. The effect of
intercalation on the energy bands and density of states of the host material
around two high-symmetry points ($\Gamma, L$) is studied. Charge transfer from
the Fe atoms to the dichalcogenide layers leads to the movement of the Ti
3d-derived electron pocket. Two hybridized states are observed around $\sim500$
meV and Fermi level. Knowledge of hybridization among the Fe $3d$, Ti $3d$, and
S $3p$ states is very important to understand the physical of Fe$_x$TiS$_2$
system.


  With stochastic transport simulations we study in detail central and
peripheral collisions at Fermi energies and suggest new observables, sensitive
to the symmetry energy below normal density. As such we identify on one hand
the isospin imbalance ratio, i.e. the relative amount of isospin equilibration
in binary, peripheral reactions of nuclei with different isospin, as a function
of the energy loss, which is sensitive to isospin diffusion; on the other hand
the isospin asymmetry of an intermediate mass fragment (IMF) in symmetric
collisions in ternary reactions, or more particularly, the ratio of the IMF to
the residue asymmetry, which is sensitive to isospin migration.


  We study a one-parameter generalization of the symmetric simple exclusion
process on a one dimensional lattice. In addition to the usual dynamics (where
particles can hop with equal rates to the left or to the right with an
exclusion constraint), annihilation and creation of pairs can occur. The system
is driven out of equilibrium by two reservoirs at the boundaries. In this
setting the model is still integrable: it is related to the open XXZ spin chain
through a gauge transformation. This allows us to compute the full spectrum of
the Markov matrix using Bethe equations. Then, we derive the spectral gap in
the thermodynamical limit. We also show that the stationary state can be
expressed in a matrix product form permitting to compute the multi-points
correlation functions as well as the mean value of the lattice current and of
the creation-annihilation current. Finally the variance of the lattice current
is exactly computed for a finite size system. In the thermodynamical limit, it
matches perfectly the value obtained from the associated macroscopic
fluctuation theory. It provides a confirmation of the macroscopic fluctuation
theory for dissipative system from a microscopic point of view.


  We give a topological characterization of the n-dimensional pseudo-boundary
of the (2n+1)-dimensional Euclidean space.


  We report the magneto-optical response of Gadolinium Gallium Garnet (GGG) and
Gadolinium Scandium Gallium Garnet (SGGG) at frequencies ranging from $300 \,
\mathrm{GHz}$ to $1 \, \mathrm{THz}$, and determine the material response
tensor. Within this frequency window, the materials exhibit nondispersive and
low-loss optical responses. At low temperatures, significant THz Faraday
rotations are found in the (S)GGG samples. Such strong gyroelectric response is
likely associated with the high-spin paramagnetic state of the Gd$^{3+}$ ions.
A model of the material response tensor is determined, together with the Verdet
and magneto-optic constants.


  We consider the motion of a particle in a uniform field in noncommutative
space which is rotationally invariant. On the basis of exact calculations it is
shown that there is an effect of coordinate noncommutativity on the mass of a
particle. A particular case of motion of a particle in a uniform gravitational
field is considered and the equivalence principle is studied. We propose the
way to solve the problem of violation of the equivalence principle in the
rotationally invariant noncommutative space.


  We study quantum systems of interacting electrons, magnetic monopoles, and
electromagnetic field. We formulate a convenient field theory, in which the
electron-photon, monopole-photon, and electron-monopole interactions take
simple forms.


  This paper describes our dialogue robot system, OSbot, developed for Dialogue
Robot Competition 2022. The dialogue flow is based on state transitions
described manually and the transition conditions use the results of keyword
extraction and sentiment analysis. The transitions can be easily viewed and
edited by managing them on a spreadsheet. The keyword extraction is based on
named entity extraction and our predefined keyword set. The sentiment analysis
is text-based and uses SVM, which was trained with the multimodal dialogue
corpus Hazumi. We quickly checked and edited a dialogue flow by using a logging
function. In the competition's preliminary round, our system ended up in third
place.


  The dependence of the sources and properties of the near-Earth solar wind on
solar cycle activity is an important issue in solar and space physics. We use
the improved two-step mapping procedure that takes into account the initial
acceleration processes to trace the near-Earth solar winds back to their source
regions from 1999 to 2020, covering solar cycles (SCs) 23 and 24. Then the
solar wind is categorized into coronal hole (CH), active region (AR), and quiet
Sun (QS) solar wind based on the source region types. We find that the
proportions of CH and AR (QS) wind during SC 23 are higher (lower) than those
during SC 24. During solar maximum and declining phases, the magnetic field
strength, speed, helium abundance (AHe), and charge states of all three types
of solar wind during SC 23 are generally higher than those during SC 24. During
solar minimum, these parameters of solar wind are generally lower during SC 23
than those during SC 24. There is a significant decrease in the charge states
of all three types of solar wind during the solar minimum of SC 23. The present
statistical results demonstrate that the sources and properties of the solar
wind are both influenced by solar cycle amplitude. The temperatures of AR, QS,
and CH regions exhibit significant difference at low altitudes, whereas they
are almost uniform at high altitudes.


  The neutrino-nucleus reaction cross sections of 4He and 12C are evaluated
using new shell model Hamiltonians. Branching ratios of various decay channels
are calculated to evaluate the yields of Li, Be, and B produced through the
nu-process in supernova explosions. The new cross sections enhance the yields
of 7Li and 11B produced during the supernova explosion of a 16.2 M_odot star
model compared to the case using the conventional cross sections by about 10%.
On the other hand, the yield of 10B decreases by a factor of two. The yields of
6Li, 9Be, and the radioactive nucleus 10Be are found at a level of 10^{-11}
M_odot. The temperature of nu_{mu,tau}- and bar{nu}_{mu,tau}-neutrinos inferred
from the supernova contribution of 11B in Galactic chemical evolution models is
constrained to the 4.3-6.5 MeV range. The increase in the 7Li and 11B yields
due to neutrino oscillations is demonstrated with the new cross sections.


  In this article, we study the Mahler measures of more than 500 families of
reciprocal polynomials defining genus 2 and genus 3 curves. We numerically find
relations between the Mahler measures of these polynomials with special values
of $L$-functions. We also numerically discover more than 100 identities between
Mahler measures involving different families of polynomials defining genus 2
and genus 3 curves. Furthermore, we study the Mahler measures of several
families of nonreciprocal polynomials defining genus 2 curves and numerically
find relations between the Mahler measures of these families and special values
of $L$-functions of elliptic curves. We also find identities between the Mahler
measures of these nonreciprocal families and tempered polynomials defining
genus 1 curves. We will explain these relations by considering the pushforward
and pullback of certain elements in $K_2$ of curves defined by these
polynomials and applying Beilinson's conjecture on $K_2$ of curves. We show
that there are two and three explicit linearly independent elements in $K_2$ of
certain families of genus 2 and genus 3 curves.


  We investigate the possibility to detect the integrated Sachs-Wolfe (ISW)
effect by cross-correlating 21-cm surveys at high redshifts with galaxies, in a
way similar to the usual CMB-galaxy cross-correlation. The high-redshift 21-cm
signal is dominated by CMB photons that travel freely without interacting with
the intervening matter, and hence its late-time ISW signature should correlate
extremely well with that of the CMB at its peak frequencies. Using the 21-cm
temperature brightness instead of the CMB would thus be a further check of the
detection of the ISW effect, measured by different instruments at different
frequencies and suffering from different systematics. We also study the ISW
effect on the photons that are scattered by HI clouds. We show that a detection
of the unscattered photons is achievable with planned radio arrays, while one
using scattered photons will require advanced radio interferometers, either an
extended version of the planned Square Kilometre Array or futuristic
experiments such as a lunar radio array.


  The hard X-ray twisted photons and relativistic massive particles with
orbital angular momentum -- vortex electrons, muons, protons, etc. -- have many
potential applications in high-energy and nuclear physics. However, such states
can be obtained so far mainly via diffraction techniques, not applicable for
relativistic energies. Here we show that the vortex states of different
particles, including hadrons, ions, and nuclei, can be generated in a large
class of processes with two final particles simply by altering a postselection
protocol. Thanks to entanglement and to the uncertainty relations, an evolved
state of a final particle becomes twisted if the momentum azimuthal angle of
the other particle is measured with a large uncertainty. We give several
examples, including Cherenkov and undulator radiation, particle collisions with
intense laser beams, $e\mu \to e\mu, ep \to ep$. This technique can be adapted
for ultrarelativistic lepton and hadron beams of linear colliders, and it can
also facilitate the development of sources of X-ray and $\gamma$-range twisted
photons at storage rings and free-electron lasers.


  The availability of large, high-quality datasets has been one of the main
drivers of recent progress in question answering (QA). Such annotated datasets
however are difficult and costly to collect, and rarely exist in languages
other than English, rendering QA technology inaccessible to underrepresented
languages. An alternative to building large monolingual training datasets is to
leverage pre-trained language models (PLMs) under a few-shot learning setting.
Our approach, QAmeleon, uses a PLM to automatically generate multilingual data
upon which QA models are trained, thus avoiding costly annotation. Prompt
tuning the PLM for data synthesis with only five examples per language delivers
accuracy superior to translation-based baselines, bridges nearly 60% of the gap
between an English-only baseline and a fully supervised upper bound trained on
almost 50,000 hand labeled examples, and always leads to substantial
improvements compared to fine-tuning a QA model directly on labeled examples in
low resource settings. Experiments on the TyDiQA-GoldP and MLQA benchmarks show
that few-shot prompt tuning for data synthesis scales across languages and is a
viable alternative to large-scale annotation.


  Modern hardware design starts with specifications provided in natural
language. These are then translated by hardware engineers into appropriate
Hardware Description Languages (HDLs) such as Verilog before synthesizing
circuit elements. Automating this translation could reduce sources of human
error from the engineering process. But, it is only recently that artificial
intelligence (AI) has demonstrated capabilities for machine-based end-to-end
design translations. Commercially-available instruction-tuned Large Language
Models (LLMs) such as OpenAI's ChatGPT and Google's Bard claim to be able to
produce code in a variety of programming languages; but studies examining them
for hardware are still lacking. In this work, we thus explore the challenges
faced and opportunities presented when leveraging these recent advances in LLMs
for hardware design. Given that these `conversational' LLMs perform best when
used interactively, we perform a case study where a hardware engineer
co-architects a novel 8-bit accumulator-based microprocessor architecture with
the LLM according to real-world hardware constraints. We then sent the
processor to tapeout in a Skywater 130nm shuttle, meaning that this `Chip-Chat'
resulted in what we believe to be the world's first wholly-AI-written HDL for
tapeout.


  The 1-norm is a good convex regularization for the recovery of sparse vectors
from under-determined linear measurements. No other convex regularization seems
to surpass its sparse recovery performance. How can this be explained? To
answer this question, we define several notions of "best" (convex)
regulariza-tion in the context of general low-dimensional recovery and show
that indeed the 1-norm is an optimal convex sparse regularization within this
framework.


  Many stellar configurations, including white dwarfs, neutron stars, black
holes, supermassive stars, and star clusters, rely on relativistic effects. The
Tolman-Oppenheimer-Volkoff (TOV) equation of the polytropic gas sphere is
ultimately a hydrostatic equilibrium equation developed from the general
relativity framework. In the modified Rieman Liouville (mRL) frame, we
formulate the fractional TOV (FTOV) equations and introduce an analytical
solution. Using power series expansions to solve the fractional TOV equations
yields a limited physical range to the convergent power series solution.
Therefore, the two techniques of Euler-Abel transformation and Pade
approximation have been combined to improve the convergence of the obtained
series solutions. For all possible values of the relativistic parameters
(\sigma), we calculated twenty fractional gas models for the polytropic indexes
n=0, 0.5, 1, 1.5, 2. Investigating the impacts of fractional and relativistic
parameters on the models revealed fascinating phenomena; the two effects for
n=0.5 are that the sphere's volume and mass decrease with increasing \sigma and
the fractional parameter (\alpha). For n=1, the volume decreases when
\sigma=0.1 and then increases when \sigma=0.2 and 0.3. The volume of the sphere
reduces as both \sigma and \alpha increase for n=1.5 and n=2. We calculated the
maximum mass and the corresponding minimum radius of the white dwarfs modeled
with polytropic index n=3 and several fractional and relativistic parameter
values. We obtained a mass limit for the white dwarfs somewhat near the
Chandrasekhar limit for the integer models with small relativistic parameters
(\alpha=1, \sigma=0.001). The situation is altered by lowering the fractional
parameter; the mass limit increases to Mlimit=1.63348 M at \alpha=0.95 and
\sigma=0.001.


  In this work, we examine the robustness of state-of-the-art semi-supervised
learning (SSL) algorithms when applied to morphological classification in
modern radio astronomy. We test whether SSL can achieve performance comparable
to the current supervised state of the art when using many fewer labelled data
points and if these results generalise to using truly unlabelled data. We find
that although SSL provides additional regularisation, its performance degrades
rapidly when using very few labels, and that using truly unlabelled data leads
to a significant drop in performance.


  We investigate emission line galaxies across cosmic time by combining the
modified L-Galaxies semi-analytical galaxy formation model with the JiuTian
cosmological simulation. We improve the tidal disruption model of satellite
galaxies in L-Galaxies to address the time dependence problem. We utilise the
public code CLOUDY to compute emission line ratios for a grid of HII region
models. The emission line models assume the same initial mass function as that
used to generate the spectral energy distribution of semi-analytical galaxies,
ensuring a coherent treatment for modelling the full galaxy spectrum. By
incorporating these emission line ratios with galaxy properties, we reproduce
observed luminosity functions for H$\alpha$, H$\beta$, [OII], and [OIII] in the
local Universe and at high redshifts. We also find good agreement between model
predictions and observations for auto-correlation and cross-correlation
functions of [OII]-selected galaxies, as well as their luminosity dependence.
The bias of emission line galaxies depends on both luminosity and redshift. At
lower redshifts, it remains constant with increasing luminosity up to around
$\sim 10^{42.5}\rm \, erg\,s^{-1}$ and then rises steeply for higher
luminosities. The transition luminosity increases with redshift and becomes
insignificant above $z$=1.5. Generally, galaxy bias shows an increasing trend
with redshift. However, for luminous galaxies, the bias is higher at low
redshifts, as the strong luminosity dependence observed at low redshifts
diminishes at higher redshifts. We provide a fitting formula for the bias of
emission line galaxies as a function of luminosity and redshift, which can be
utilised for large-scale structure studies with future galaxy surveys.


  We calculate the fully-differential rate of the decays $B\to P\tau(\to
\mu\bar\nu\nu)\bar\nu$ where $P = D,\pi$, which is a background to the
semimuonic decays $B\to P\mu\bar\nu$. The decays with a $3\nu$ final state can
have a sizeable impact on the experimental analyses of the ratios $R_D$ and
$R_\pi$, depending on the event selection in the analysis. We outline a
strategy which permits the extraction of $R_P \mathcal{B}(\tau \to
\mu\bar\nu\nu)$ from the neutrino-inclusive rate. Our analytic results can also
be used to test both existing and upcoming experimental analyses. We further
provide Monte Carlo samples of the 5D rate of the neutrino-inclusive decays
$B\to P\mu X_{\bar\nu}$.


  Diffusion induced by a thermal gradient in a granular binary mixture is
analyzed in the context of the (inelastic) Enskog equation. Although the Enskog
equation neglects velocity correlations among particles which are about to
collide, it retains spatial correlations arising from volume exclusion effects
and thus it is expected to apply to moderate densities. In the steady state
with gradients only along a given direction, a segregation criterion is
obtained from the thermal diffusion factor $\Lambda$ measuring the amount of
segregation parallel to the thermal gradient. As expected, the sign of the
factor $\Lambda$ provides a criterion for the transition between the Brazil-nut
effect (BNE) and the reverse Brazil-nut effect (RBNE) by varying the parameters
of the mixture (masses, sizes, concentration, solid volume fraction, and
coefficients of restitution). The form of the phase diagrams for the BNE/RBNE
transition is illustrated in detail for several systems, with special emphasis
on the significant role played by the inelasticity of collisions. In
particular, an effect already found in dilute gases (segregation in a binary
mixture of identical masses and sizes {\em but} different coefficients of
restitution) is extended to dense systems. A comparison with recent computer
simulation results shows a good qualitative agreement at the level of the
thermal diffusion factor. The present analysis generalizes to arbitrary
concentration previous theoretical results derived in the tracer limit case.


  Optical illumination of quantum-dot qubit devices at cryogenic temperatures,
while not well studied, is often used to recover operating conditions after
undesired shocking events or charge injection. Here, we demonstrate systematic
threshold voltage shifts in a dopant-free, Si/SiGe field effect transistor
using a near infrared (780 nm) laser diode. We find that illumination under an
applied gate voltage can be used to set a specific, stable, and reproducible
threshold voltage that, over a wide range in gate bias, is equal to that gate
bias. Outside this range, the threshold voltage can still be tuned, although
the resulting threshold voltage is no longer equal to the applied gate bias
during illumination. We present a simple and intuitive model that provides a
mechanism for the tunability in gate bias. The model presented also explains
why cryogenic illumination is successful at resetting quantum dot qubit devices
after undesired charging events.


  The massive scalar field theory and the chiral Schwinger model are quantized
on a Poincar\'e disk of radius $\rho$. The amplitudes are derived in terms of
hypergeometric functions. The behavior at long distances and near the boundary
of some of the relevant correlation functions is studied. The exact computation
of the chiral determinant appearing in the Schwinger model is obtained
exploiting perturbation theory. This calculation poses interesting mathematical
problems, as the Poincar\'e disk is a noncompact manifold with a metric tensor
which diverges approaching the boundary. The results presented in this paper
are very useful in view of possible extensions to general Riemann surfaces.
Moreover, they could also shed some light in the quantization of field theories
on manifolds with constant curvature scalars in higher dimensions.


  In this paper, we develop a general study of contributions at infinity of
Bochner-Weitzenb\"ock-type formulas on asymptotically flat manifolds, inspired
by Witten's proof of the positive mass theorem. As an application, we show that
similar proofs can be obtained in a much more general setting as any choice of
an irreducible natural bundle and a very large choice of first-order operators
may lead to a positive mass theorem along the same lines if the necessary
curvature conditions are satisfied.


  The cross section for vector leptoquark pair production in $e^+e^-$
annihilation is calculated for the case of finite anomalous gauge boson
couplings $\kappa_{\gamma, Z}$ and $\lambda_{\gamma, Z}$. The minimal cross
section is found to behave $\propto \beta^7$, leading to weaker mass bounds in
the threshold range than in models studied previously.


  Science is idolized as a cumulative process ("standing on the shoulders of
giants"), yet scientific knowledge is typically built on a patchwork of
research contributions without much coordination. This lack of efficiency has
specifically been addressed in clinical research by recommendations for living
systematic reviews and against research waste. We propose to further those
recommendations with ALL-IN meta-analysis: Anytime Live and Leading INterim
meta-analysis. ALL-IN provides statistical methodology for a meta-analysis that
can be updated at any time -- reanalyzing after each new observation while
retaining type-I error guarantees, live -- no need to prespecify the looks, and
leading -- in the decisions on whether individual studies should be initiated,
stopped or expanded, the meta-analysis can be the leading source of
information. We illustrate the method for time-to-event data, showing how
synthesizing data at interim stages of studies can increase efficiency when
studies are slow in themselves to provide the necessary number of events for
completion. The meta-analysis can be performed on interim data, but does not
have to. The analysis design requires no information about the number of
patients in trials or the number of trials eventually included. So it can
breathe life into living systematic reviews, through better and simpler
statistics, efficiency, collaboration and communication.


  It is well known that quantum theory forbids the exact copying of an unknown
quantum state. Therefore in broadcasting of classical information by a quantum
channel an additional contribution to the error in the decoding is expected. We
consider the optimal copying transformation which is adapted to classical
information transmission by two linearly independent quantum states, and show
that there is no additional contribution to the error. Instead the clones are
correlated, and this breaks their usefulness: The entanglement increases with
non-orthogonality of the states. The capacity of the corresponding quantum
channel is considered also.


  A low-cost measurement system using filtering of measurements for two-wheeled
balancing robot stabilisation purposes has been addressed in this paper. In
particular, a measurement system based on gyroscope, accelerometer, and encoder
has been considered. The measurements have been corrected for deterministic
disturbances and then filtered with Kalman, $\alpha$-$\beta$ type, and
complementary filters. A quantitative assessment of selected filters has been
given. As a result, the complete structure of a measurement system has been
obtained. The performance of the proposed measurement system has been validated
experimentally by using a dedicated research rig.


  Let R be a ring and let G be a group. We prove a rather curious necessary and
sufficient condition for the commutative group ring RG to be weakly nil-neat
only in terms of R,G and their sections. This somewhat expands three recent
results, namely those established by McGovern et al. in (J. Algebra Appl.,
2015), by Danchev-McGovern in (J. Algebra, 2015) and by the present authors in
(J. Taibah Univ. Sci., 2020), related to commutative nil-clean, weakly
nil-clean and nil-neat group rings, respectively.


  The density band model proposed by Kassam for robust hypothesis testing is
revisited in this paper. First, a novel criterion for the general
characterization of least favorable distributions is proposed, which unifies
existing results. This criterion is then used to derive an implicit definition
of the least favorable distributions under band uncertainties. In contrast to
the existing solution, it only requires two scalar values to be determined and
eliminates the need for case-by-case statements. Based on this definition, a
generic fixed-point algorithm is proposed that iteratively calculates the least
favorable distributions for arbitrary band specifications. Finally, three
different types of robust tests that emerge from band models are discussed and
a numerical example is presented to illustrate their potential use in practice.


  Accurate and safety-quantifiable localization is of great significance for
safety-critical autonomous systems, such as unmanned ground vehicles (UGV) and
unmanned aerial vehicles (UAV). The visual odometry-based method can provide
accurate positioning in a short period but is subjected to drift over time.
Moreover, the quantification of the safety of the localization solution (the
error is bounded by a certain value) is still a challenge. To fill the gaps,
this paper proposes a safety-quantifiable line feature-based visual
localization method with a prior map. The visual-inertial odometry provides a
high-frequency local pose estimation which serves as the initial guess for the
visual localization. By obtaining a visual line feature pair association, a
foot point-based constraint is proposed to construct the cost function between
the 2D lines extracted from the real-time image and the 3D lines extracted from
the high-precision prior 3D point cloud map. Moreover, a global navigation
satellite systems (GNSS) receiver autonomous integrity monitoring (RAIM)
inspired method is employed to quantify the safety of the derived localization
solution. Among that, an outlier rejection (also well-known as fault detection
and exclusion) strategy is employed via the weighted sum of squares residual
with a Chi-squared probability distribution. A protection level (PL) scheme
considering multiple outliers is derived and utilized to quantify the potential
error bound of the localization solution in both position and rotation domains.
The effectiveness of the proposed safety-quantifiable localization system is
verified using the datasets collected in the UAV indoor and UGV outdoor
environments.


  Top quark pair production in association with a $Z$-boson or a photon at the
LHC directly probes neutral top-quark couplings. We present predictions for
these two processes in the Standard Model (SM) Effective Field Theory (EFT) at
next-to-leading (NLO) order in QCD. We include the full set of CP-even
dimension-six operators that enter the top-quark interactions with the SM gauge
bosons. For comparison, we also present predictions in the SMEFT for top
loop-induced $HZ$ production at the LHC and for $t\bar{t}$ production at the
ILC at NLO in QCD. Results for total cross sections and differential
distributions are obtained and uncertainties coming from missing higher orders
in the strong coupling and in the EFT expansions are discussed. NLO results
matched to the parton shower are available, allowing for event generation to be
directly employed in an experimental analyses. Our framework provides a solid
basis for the interpretation of current and future measurements in the SMEFT,
with improved accuracy and precision.


  Canada recently lost its major supply of neutron beams with the closure of
the National Research Universal reactor at Chalk River Laboratories in March
2018. This issue is further exacerbated by the closure of other reactors
abroad, which also served as centers for neutron research. Consequently, there
is a demand for new sources, both for Canada and internationally, as the global
supply shrinks. Compact accelerator driven neutron sources provide an avenue to
realize an intense source of pulsed neutron beams, with a capital cost
significantly lower than spallation sources. In an effort to close the neutron
gap in Canada, a Prototype Canadian compact accelerator driven neutron source
(PC-CANS) is proposed for installation at the University of Windsor. The
PC-CANS is envisaged to serve two neutron science instruments, a boron neutron
capture therapy (BNCT) station and a beamline for fluorine-18 radioisotope
production for positive emission tomography. To serve these diverse
applications of neutron beams, a linear accelerator solution is selected to
provide 10 MeV protons with a peak current of 10 mA within a 5\% duty cycle.
The accelerator is based on an RFQ and a DTL with a post-DTL pulsed kicker
system to simultaneously deliver macro-pulses to each end-station. The neutron
production targets for both neutron science and BNCT will be made of beryllium
and engineered to handle the high beam power density. Conceptual studies of the
accelerator and benchmarking studies of neutron production and moderation with
FLUKA and MCNP, in support of the target-moderator-reflector design are
reported


  In this paper, we propose a novel framework with 3D convolutional networks
(ConvNets) for automated detection of pulmonary nodules from low-dose CT scans,
which is a challenging yet crucial task for lung cancer early diagnosis and
treatment. Different from previous standard ConvNets, we try to tackle the
severe hard/easy sample imbalance problem in medical datasets and explore the
benefits of localized annotations to regularize the learning, and hence boost
the performance of ConvNets to achieve more accurate detections. Our proposed
framework consists of two stages: 1) candidate screening, and 2) false positive
reduction. In the first stage, we establish a 3D fully convolutional network,
effectively trained with an online sample filtering scheme, to sensitively and
rapidly screen the nodule candidates. In the second stage, we design a
hybrid-loss residual network which harnesses the location and size information
as important cues to guide the nodule recognition procedure. Experimental
results on the public large-scale LUNA16 dataset demonstrate superior
performance of our proposed method compared with state-of-the-art approaches
for the pulmonary nodule detection task.


  Parallel Micro Hollow Cathodes Discharges are characterised by electrical and
spectroscopic studies and by using an ICCD camera. V-I curves present a
hysteresis cycle between Townsend and normal glow regimes. For I d =4-10mA, gas
temperature ranges between 300-500{\textdegree}C. When the diameter of a hole
decreases, a more significant discharge voltage is needed.


  The Lyman-alpha forest provides strong constraints on both cosmological
parameters and intergalactic medium astrophysics, which are forecast to improve
further with the next generation of surveys including eBOSS and DESI. As is
generic in cosmological inference, extracting this information requires a
likelihood to be computed throughout a high-dimensional parameter space.
Evaluating the likelihood requires a robust and accurate mapping between the
parameters and observables, in this case the 1D flux power spectrum.
Cosmological simulations enable such a mapping, but due to computational time
constraints can only be evaluated at a handful of sample points; "emulators"
are designed to interpolate between these. The problem then reduces to placing
the sample points such that an accurate mapping is obtained while minimising
the number of expensive simulations required. To address this, we introduce an
emulation procedure that employs Bayesian optimisation of the training set for
a Gaussian process interpolation scheme. Starting with a Latin hypercube
sampling (other schemes with good space-filling properties can be used), we
iteratively augment the training set with extra simulations at new parameter
positions which balance the need to reduce interpolation error while focussing
on regions of high likelihood. We show that smaller emulator error from the
Bayesian optimisation propagates to smaller widths on the posterior
distribution. Even with fewer simulations than a Latin hypercube, Bayesian
optimisation shrinks the 95% credible volume by 90% and, e.g., the 1 sigma
error on the amplitude of small-scale primordial fluctuations by 38%. This is
the first demonstration of Bayesian optimisation applied to large-scale
structure emulation, and we anticipate the technique will generalise to many
other probes such as galaxy clustering, weak lensing and 21cm.


  This paper links the field of potential theory -- i.e. the Dirichlet and
Neumann problems for the heat and Laplace equation -- to that of the Feynman
path integral, by postulating that the potential is equal to plus/minus the
Laplacian of the indicator of the domain D. The Laplacian of the indicator is a
generalized function: it is the d-dimensional analogue of the Dirac
delta'-function. This function has -- according to the author's best knowledge
-- not formally been defined before.
  We show, first, that the path integral's perturbation series (or Born series)
matches the classical single and double boundary layer series of potential
theory, thereby connecting two hitherto unrelated fields. Second, we show that
the perturbation series is valid for all domains D that allow Green's theorem
(i.e. with a finite number of corners, edges and cusps), thereby expanding the
classical applicability of boundary layers. Third, we show that the minus
(plus) in the potential holds for the Dirichlet (Neumann) boundary condition;
showing for the first time a particularly close connection between these two
classical problems. Fourth, we demonstrate that the perturbation series of the
path integral converges in a monotone/alternating fashion, depending on the
convexity/concavity of the domain.
  We also discuss the third boundary problem (which poses Robin boundary
conditions) and discuss an extension to moving domains.


  For the dramatic increase of Android malware and low efficiency of manual
check process, deep learning methods started to be an auxiliary means for
Android malware detection these years. However, these models are highly
dependent on the quality of datasets, and perform unsatisfactory results when
the quality of training data is not good enough. In the real world, the quality
of datasets without manually check cannot be guaranteed, even Google Play may
contain malicious applications, which will cause the trained model failure. To
address the challenge, we propose a robust Android malware detection approach
based on selective ensemble learning, trying to provide an effective solution
not that limited to the quality of datasets. The proposed model utilizes
genetic algorithm to help find the best combination of the component learners
and improve robustness of the model. Our results show that the proposed
approach achieves a more robust performance than other approaches in the same
area.


  The near-absence of compact massive quiescent galaxies in the local Universe
implies a size evolution since $z\sim2.5$. It is often theorised that such `red
nuggets' have evolved into today's elliptical (E) galaxies via an E-to-E
transformation. We examine an alternative scenario in which a red nugget
develops a rotational disc through mergers and accretion, say, at $1\lesssim
z\lesssim2$, thereby cloaking the nugget as the extant bulge/spheroid component
of a larger, now old, galaxy. We have performed detailed, physically-motivated,
multi-component decompositions of a volume-limited sample of 103 massive
($M_*/\rm M_{\odot} \gtrsim 1\times 10^{11}$) galaxies within 110\,Mpc. Among
our 28 galaxies with existing elliptical classifications, we found that 18 have
large-scale discs, and two have intermediate-scale discs, and are reclassified
here as lenticulars (S0) and elliculars (ES). The local spheroid stellar mass
function, size-mass diagram and bulge-to-total ($B/T$) flux ratio are
presented. We report lower-limits for the volume number density of compact
massive spheroids, $n_\mathrm{c,Sph}\sim (0.17$-$1.2) \times 10^{-4}\,\rm
Mpc^{-3}$, based on different definitions of `red nuggets' in the literature.
Similar number densities of local compact massive bulges were reported by de la
Rosa et al. using automated two-component decompositions and their existence is
now abundantly clear with our multi-component decompositions. We find
disc-cloaking to be a salient alternative for galaxy evolution. In particular,
instead of an E-to-E process, disc growth is the dominant evolutionary pathway
for at least low-mass ($1\times10^{10}<M_*/\rm M_{\odot} \lessapprox 4 \times
10^{10}$) red nuggets, while our current lower-limits are within an alluring
factor of a few of the peak abundance of high-mass red nuggets at $1\lesssim
z\lesssim2$.


  We prove a quenched central limit theorem for random walks in i.i.d. weakly
elliptic random environments in the ballistic regime. Such theorems have been
proved recently by Rassoul-Agha and Sepp\"al\"ainen in [10] and Berger and
Zeitouni in [2] under the assumption of large finite moments for the
regeneration time. In this paper, with the extra $(T)_{\gamma}$ condition of
Sznitman we reduce the moment condition to ${\Bbb E}(\tau^2(\ln
\tau)^{1+m})<+\infty$ for $m>1+1/\gamma$, which allows the inclusion of new
non-uniformly elliptic examples such as Dirichlet random environments.


  We present an XML-based simulation authoring environment. The proposed
description language allows to describe mathematical objects such as systems of
ordinary differential equations, partial differential equations in two
dimensions, or simple curves and surfaces. It also allows to describe the
parameters on which these objects depend. This language is independent of the
target software and allows to ensure the perennity of author's work, as well as
collaborative work and content reuse. The actual implementation of XMLlab
allows to run the generated simulations within the open source mathematical
software Scilab, either locally when Scilab is installed on the client
machines, or on thin clients running a simple web browser, when XMLlab and
Scilab are installed on a distant server running a standard HTTP server.


  The origin of ferromagnetism in nanoparticles of nonmagnetic oxides is an
interesting area of research. In the present work, transport properties of
niobium thin films, with beta-Ga2O3 nanoparticles embedded within them, are
presented. Nanoparticles of beta-Ga2O3 embedded in a Nb matrix were prepared at
room temperature by radio frequency co-sputtering technique on Si (100) and
glass substrates held at room temperature. The thin films deposited on Si
substrates were subjected to Ar annealing at a temperature range of 600-650 C
for 1 hour. Films were characterized by X-ray diffraction (XRD), Micro-Raman
and elemental identification was performed with an Energy Dispersive X-ray
Spectroscopy (EDS). Transport measurements were performed down to liquid helium
temperatures by four-probe contact technique, showed characteristics analogous
to those observed in the context of a Kondo system. A comparison of the
experimental data with the theoretical formalism of Kondo and Hamann is
presented. It is suggested that this behavior arises from the existence of
magnetic moments associated with the oxygen vacancy defects in the
nanoparticles of the nonmagnetic oxide Ga2O3.


  We have modelled the high-energy astrophysics of the inner 200 pc of the
Galaxy with a view to explaining the diffuse, broad-band (radio continuum to
TeV gamma-ray), non-thermal signal detected from this region. Our modelling
pins down the ISM parameters for the environment wherein cosmic ray (CR)
electrons and ions reside in the Galactic centre (GC). We find that the
magnetic field in this region is 100-300 microG, the gas density < 60 cm^-3,
and that a powerful (> 200 km/s) 'super'-wind acts to remove > 95% of the
cosmic rays accelerated in the region before they have time to lose their
energy in situ. The ~ 10^39 erg/s carried away by the GC cosmic ray protons is
precisely enough to energise the ~GeV gamma-ray emission from the Fermi
'bubbles' recently found to extend north and south of the GC out to distances
of ~10 kpc, provided that the bubbles constitute thick targets to the GC
protons and that the situation has reached steady state. In such a situation of
'saturation' the hard, uniform spectrum of the bubbles are explained and
secondary electron synchrotron explains the non-thermal microwave emission
found in WMAP data mirroring the bubbles. Given the very low density of the
bubble plasma (<0.01 cm^-3), the pp loss time in the Bubbles is > 5 Gyr. Our
scenario thus has the startling implication that a GC source of nonthermal
particles of time-averaged power 10^39 erg/s has persisted since the youth of
the Galaxy.


  Graham et al. (2015) have detected a 5.2 year periodic optical variability of
the quasar PG 1302-102 at redshift $z=0.3$, which they interpret as the
redshifted orbital period $(1+z)t_{\rm bin}$ of a putative supermassive black
hole binary (SMBHB). Here we consider the implications of a $3-8$ times shorter
orbital period, suggested by hydrodynamical simulations of circumbinary discs
(CBDs) with nearly equal--mass SMBHBs ($q\equiv M_2/M_1\gtrsim 0.3$). With the
corresponding $2-4$ times tighter binary separation, PG 1302 would be
undergoing gravitational wave dominated inspiral, and serve as a proof that the
BHs can be fueled and produce bright emission even in this late stage of the
merger. The expected fraction of binaries with the shorter $t_{\rm bin}$, among
bright quasars, would be reduced by 1-2 orders of magnitude, compared to the
5.2 year period, in better agreement with the rarity of candidates reported by
Graham et al. (2015). Finally, shorter periods would imply higher binary
speeds, possibly imprinting periodicity on the light curves from relativistic
beaming, as well as measurable relativistic effects on the Fe K$\alpha$ line.
The CBD model predicts additional periodic variability on timescales of $t_{\rm
bin}$ and $\approx 0.5 t_{\rm bin}$, as well as periodic variation of broad
line widths and offsets relative to the narrow lines, which are consistent with
the observations. Future observations will be able to test these predictions
and hence the binary+CBD hypothesis for PG 1302.


  The Casimir force between parallel lines in a theory describing condensed
vortices in a plane is determined. We make use of the relation between a
Chern-Simons-Higgs model and its dualized version, which is expressed in terms
of a dual gauge field and a vortex field. The dual model can have a phase of
condensed vortices and, in this phase, there is a mapping to a model of two
non-interacting massive scalar fields from which the Casimir force can readily
be obtained. The choice of boundary conditions required for the mapped scalar
fields and their association with those for the vectorial field and the issues
involved are discussed. We also briefly discuss the implications of our results
for experiments related to the Casimir effect when vortices can be present.


  Multilevel self-assembly involving small structured groups of nano-particles
provides new routes to development of functional materials with a sophisticated
architecture. Apart from the inter-particle forces, the geometrical shapes and
compatibility of the building blocks are decisive factors in each phase of
growth. Therefore, a comprehensive understanding of these processes is
essential for the design of large assemblies of desired properties. Here, we
introduce a computational model for cooperative self-assembly with simultaneous
attachment of structured groups of particles, which can be described by
simplexes (connected pairs, triangles, tetrahedrons and higher order cliques)
to a growing network, starting from a small seed. The model incorporates
geometric rules that provide suitable nesting spaces for the new group and the
chemical affinity $\nu$ of the system to accepting an excess number of
particles. For varying chemical affinity, we grow different classes of
assemblies by binding the cliques of distributed sizes. Furthermore, to
characterise the emergent large-scale structures, we use the metrics of graph
theory and algebraic topology of graphs, and 4-point test for the intrinsic
hyperbolicity of the networks. Our results show that higher Q-connectedness of
the appearing simplicial complexes can arise due to only geometrical factors,
i.e., for $\nu = 0$, and that it can be effectively modulated by changing the
chemical potential and the polydispersity of the size of binding simplexes. For
certain parameters in the model we obtain networks of mono-dispersed clicks,
triangles and tetrahedrons, which represent the geometrical descriptors that
are relevant in quantum physics and frequently occurring chemical clusters.


  This contribution presents a method of obtaining the optimal power and
subcarrier allocations that maximize the energy-efficiency (EE) of a
multi-user, multi-relay, orthogonal frequency division multiple access (OFDMA)
cellular network. Initially, the objective function (OF) is formulated as the
ratio of the spectral-efficiency (SE) over the power consumption of the
network. This OF is shown to be quasi-concave, thus Dinkelbach's method can be
employed for solving it as a series of parameterized concave problems. We
characterize the performance of the aforementioned method by comparing the
optimal solutions obtained to those found using an exhaustive search.
Additionally, we explore the relationship between the achievable SE and EE in
the cellular network upon increasing the number of active users. In general,
increasing the number of users supported by the system benefits both the SE and
EE, and higher SE values may be obtained at the cost of EE, when an increased
power may be allocated.


  We show that there exist K\"ahler-Einstein metrics on two exceptional
Pasquier's two-orbits varieties. As an application, we will provide a new
example of K-unstable Fano manifold with Picard number one.


  Krylov complexity has been proposed as a diagnostic of chaos in
non-integrable lattice and quantum mechanical systems, and if the system is
chaotic, Krylov complexity grows exponentially with time. However, when Krylov
complexity is applied to quantum field theories, even in free theory, it grows
exponentially with time. This exponential growth in free theory is simply due
to continuous momentum in non-compact space and has nothing to do with the mass
spectrum of theories. Thus by compactifying space sufficiently, exponential
growth of Krylov complexity due to continuous momentum can be avoided. In this
paper, we propose that the Krylov complexity of operators such as
$\mathcal{O}=\textrm{Tr}[F_{\mu\nu}F^{\mu\nu}]$ can be an order parameter of
confinement/deconfinement transitions in large $N$ quantum field theories on
such a compactified space. We explicitly give a prescription of the
compactification at finite temperature to distinguish the continuity of
spectrum due to momentum and mass spectrum. We then calculate the Krylov
complexity of $\mathcal{N}=4, 0$ $SU(N)$ Yang-Mills theories in the large $N$
limit by using holographic analysis of the spectrum and show that the behavior
of Krylov complexity reflects the confinement/deconfinement phase transitions
through the continuity of mass spectrum.


  The development of wavelet theory has in recent years spawned applications in
signal processing, in fast algorithms for integral transforms, and in image and
function representation methods. This last application has stimulated interest
in wavelet applications to statistics and to the analysis of experimental data,
with many successes in the efficient analysis, processing, and compression of
noisy signals and images. This is a selective review article that attempts to
synthesize some recent work on ``nonlinear'' wavelet methods in nonparametric
curve estimation and their role on a variety of applications. After a short
introduction to wavelet theory, we discuss in detail several wavelet shrinkage
and wavelet thresholding estimators, scattered in the literature and developed,
under more or less standard settings, for density estimation from i.i.d.
observations or to denoise data modeled as observations of a signal with
additive noise. Most of these methods are fitted into the general concept of
regularization with appropriately chosen penalty functions. A narrow range of
applications in major areas of statistics is also discussed such as partial
linear regression models and functional index models. The usefulness of all
these methods are illustrated by means of simulations and practical examples.


  The integration of sensing and communication enables wireless communication
systems to serve environment-aware applications. In this paper, we propose to
leverage sensing to enhance physical layer security (PLS) in multiuser
communication systems in the presence of a suspicious target. To this end, we
develop a two-phase framework to first estimate the location of the potential
eavesdropper by sensing and then utilize the estimated information to enhance
PLS for communication. In particular, in the first phase, a dual-functional
radar and communication (DFRC) base station (BS) exploits a sensing signal to
mitigate the sensing information uncertainty of the potential eavesdropper.
Then, in the second phase, to facilitate joint sensing and secure
communication, the DFRC BS employs beamforming and artificial noise to enhance
secure communication. The design objective is to maximize the system sum rate
while alleviating the information leakage by jointly optimizing the time
allocation and beamforming policy. Capitalizing on monotonic optimization
theory, we develop a two-layer globally optimal algorithm to reveal the
performance upper bound of the considered system. Simulation results show that
the proposed scheme achieves a significant sum rate gain over two baseline
schemes that adopt existing techniques. Moreover, our results unveil that ISAC
is a promising paradigm for enhancing secure communication in wireless
networks.


  Literature analyzes the way in which Newton's second law can be used when
non-inertial rotating systems are used. However, the treatment of the work and
energy theorem in rotating systems is not considered in textbooks. In this
paper, we show that the work and energy theorem can still be applied to a
closed system of particles in a rotating system, as long as the work of
fictitious forces is properly included in the formalism. The coriolis force
does not contribute to the work coming from fictitious forces. It worths
remarking that real forces that do not do work in an inertial reference frame
can do work in the rotating reference frame and viceversa. The combined effects
of acceleration of the origin and rotation of the non-inertial system are also
studied.


  There is a canonical unitary transformation from $L^2(\R)$ onto the Fock
space $F^2$, called the Bargmann transform. We study the action of the Bargmann
transform on several classical integral operators on $L^2(\R)$, including the
fractional Fourier transform, the fractional Hilbert transform, and the wavelet
transform.


  This short paper examines some of the ongoing research at the UMB Data and
Society Lab hosted at the Faculty of Political Science and International
Relations at Matej Bel University. It begins with an introduction on the
necessity of security threat identification on social networking services
(SNSs), done by states. The paper follows with a general overview of selected
projects of the Lab in this field, and afterwards it introduces a use case
study focused on the announcement of the UK snap general election 2017. The
main aim of this paper is to demonstrate some of the possibilities of social
networking services analysis in the field of international relations, with an
emphasis on disinformation and the necessity of identifying novel digital
actors in Slovakia. We also outline an easy custom system tasked to collect
social media data, and afterwards process it using various cognitive analytic
methods.


  The jamming transition characterizes athermal systems of particles
interacting via finite range repulsive potentials, and occurs on increasing the
density when particles cannot avoid making contacts with those of their first
coordination shell. We have recently shown [M. Pica Ciamarra and P. Sollich,
arXiv:1209.3334] that the same systems are also characterized by a series of
jamming crossovers. These occur at higher volume fractions as particles are
forced to make contact with those of subsequent coordination shells. At finite
temperature, the crossovers give rise to dynamic and thermodynamic density
anomalies, including a diffusivity anomaly and a negative thermal expansion
coefficient. Density anomalies may therefore be related to structural changes
occurring at the jamming crossovers. Here we elucidate these structural
changes, investigating the evolution of the structure and of the mechanical
properties of a jammed system as its volume fraction varies from the jamming
transition to and beyond the first jamming crossover. We show that the first
jamming crossover occurs at a well defined volume fraction, and that it induces
a rearrangement of the force network causing a softening of the system. It also
causes qualitative changes in the normal mode density of states and the spatial
properties of the normal mode vectors.


  Starting from the existence of a weakly compact cardinal, we build a generic
extension of the universe in which $GCH$ holds and all $\aleph_2$-Aronszajn
trees are special and hence there are no $\aleph_2$-Souslin trees. This result
answers a well-known open question from the 1970's.


  Decision trees have been widely used in machine learning. However, due to
some reasons, data collecting in real world contains a fuzzy and uncertain
form. The decision tree should be able to handle such fuzzy data. This paper
presents a method to construct fuzzy decision tree. It proposes a fuzzy
decision tree induction method in iris flower data set, obtaining the entropy
from the distance between an average value and a particular value. It also
presents an experiment result that shows the accuracy compared to former ID3.


  Current advancements in reinforcement learning (RL) have predominantly
focused on learning step-based policies that generate actions for each
perceived state. While these methods efficiently leverage step information from
environmental interaction, they often ignore the temporal correlation between
actions, resulting in inefficient exploration and unsmooth trajectories that
are challenging to implement on real hardware. Episodic RL (ERL) seeks to
overcome these challenges by exploring in parameters space that capture the
correlation of actions. However, these approaches typically compromise data
efficiency, as they treat trajectories as opaque \emph{black boxes}. In this
work, we introduce a novel ERL algorithm, Temporally-Correlated Episodic RL
(TCE), which effectively utilizes step information in episodic policy updates,
opening the 'black box' in existing ERL methods while retaining the smooth and
consistent exploration in parameter space. TCE synergistically combines the
advantages of step-based and episodic RL, achieving comparable performance to
recent ERL methods while maintaining data efficiency akin to state-of-the-art
(SoTA) step-based RL.


  In this paper, we derive optimality conditions (Chebyshev approximation) for
multivariate functions. The theory of Chebyshev (uniform) approximation for
univariate functions is very elegant. The optimality conditions are based on
the notion of alternance (maximal deviation points with alternating deviation
signs). It is not very straightforward, however, how to extend the notion of
alternance to the case of multivariate functions. There have been several
attempts to extend the theory of Chebyshev approximation to the case of
multivariate functions. We propose an alternative approach, which is based on
the notion of convexity and nonsmooth analysis.


  We study a continuous quasi-two-dimensional order-disorder phase transition
that occurs in a simple model of a material that is inhomogeneously strained
due to the presence of dislocation lines. Performing Monte Carlo simulations of
different system sizes and using finite size scaling, we measure critical
exponents describing the transition of beta=0.18\pm0.02, gamma=1.0\pm0.1, and
alpha=0.10\pm0.02. Comparable exponents have been reported in a variety of
physical systems. These systems undergo a range of different types of phase
transitions, including structural transitions, exciton percolation, and
magnetic ordering. In particular, similar exponents have been found to describe
the development of magnetic order at the onset of the pseudogap transition in
high-temperature superconductors. Their common universal critical exponents
suggest that the essential physics of the transition in all of these physical
systems is the same as in our model. We argue that the nature of the transition
in our model is related to surface transitions, although our model has no free
surface.


  Purely rotational transitions of a molecule, such as HCCCN, embedded in large
$^4$He clusters have been detected for the first time. The saturation behavior
shows that, in contrast to previous expectations, the microwave line profiles
are dominated by inhomogeneous broadening mechanisms. Spectral holes and peaks
produced by microwave-microwave double resonance have widths comparable to
those of single resonance lines, indicating that relaxation occurs among
quantum states of the inhomogeneous distribution of each rotational level, at a
rate approximately 10 times faster than rotationally inelastic relaxation.


  Let G be a discrete group and let X be a G-finite, proper G-CW-complex. We
prove that Kasparov's equivariant K-homology groups KK^G(C_0(X),\C) are
isomorphic to the geometric equivariant K-homology groups of X that are
obtained by making the geometric K-homology theory of Baum and Douglas
equivariant in the natural way. This reconciles the original and current
formulations of the Baum-Connes conjecture for discrete groups.


  Precise physical properties of the known transiting exoplanets are essential
for their precise atmospheric characterization using modern and upcoming
instruments. Leveraging the large volume of high SNR photometric follow-up data
from TESS, highly precise physical properties can be estimated for these
systems, especially for those discovered using ground-based instruments prior
to the TESS mission. In this work, I have used the publicly available TESS
follow-up data for 28 transiting systems with 10 $<$ V$_{mag}$ $<$ 10.5, with
an aim to update their known physical properties. The observed lightcurves have
been analysed by implementing a state-of-the-art critical noise treatment
algorithm to effectively reduce both time-correlated and un-correlated noise
components, using sophisticated techniques like wavelet denoising and
Gaussian-process regression. Compared with the previous studies, the estimated
transit parameters are found to be more precise for most of the targets,
including a few cases where a larger space-based instrument like Spitzer,
Kepler or CHEOPS has been used in the previous study. The large volume of
transit observations used for each target has also resulted in a more accurate
estimation of the physical properties, as this overcomes any error in parameter
estimations from bias present in a smaller volume of data. Thus, comparing with
the literature values, statistically significant improvements in the known
physical properties of several targeted systems have been reported from this
work. The large volume of transit timing information from the analyses was also
used to search for Transit Timing Variation trends in these targets, which has
resulted in no significant detection.


  The present study examined 4698 Indian Coronary Artery Disease research
publications, as indexed in Web of Science database during 1990-2019, with a
view to understand their growth rate, global share, citation impact,
international collaborative papers, distribution of publications by broad
subjects, productivity and citation profile of top organizations and authors,
and preferred media of communication. The Indian publications registered an
annual average growth rate of 11.47%, global share of 1.14%, international
collaborative publications share of 38.89% and its citation impact averaged to
25.58 citations per paper. Among broad subjects, Cardiovascular System &
Cardiology contributed the largest publications share of 19.14% in Indian
coronary artery disease output, followed by Neurosciences & Neurology (14.94%),
Pharmacology & Pharmacy (8.51%), etc. during 1990-2019. Among various
organizations and authors contributing to Indian coronary artery disease
research, the top 20 organizations and top 30 authors together contributed
40.70% and 37.29% respectively as their share of Indian publication output and
38.36% and 33.13% respectively as their share of Indian citation output during
1990-2019. Among 1222 contributing journals in Indian coronary artery disease
research, the top 30 journals registered 30.80% share during 1990-2019. There
is an urgent need to increase the publication output, improve research quality
and improve international collaboration. Indian government also needs to come
up with a policy for identification, screening, diagnosis and treatment of
coronary artery disease patients, besides curriculum reform in teaching,
capacity building, patient education and political support are badly needed.


  An optical ring resonator with third-order, or Kerr, nonlinearity will
exhibit symmetry breaking between the two counterpropagating circulating powers
when pumped with sufficient power in both the clockwise and counterclockwise
directions. This is due to the effects of self- and cross-phase modulation on
the resonance frequencies in the two directions. The critical point of this
symmetry breaking exhibits universal behaviors including divergent responsivity
to external perturbations, critical slowing down, and scaling invariance. Here
we derive a model for the critical dynamics of this system, first for a
symmetrically-pumped resonator and then for the general case of asymmetric
pumping conditions and self- and cross-phase modulation coefficients. This
theory not only provides a detailed understanding of the dynamical response of
critical-point-enhanced optical gyroscopes and near-field sensors, but is also
applicable to nonlinear critical points in a wide range of systems.


  The paper presents results of a numerical simulation of the stochastic
dynamics of magnetic flux in an RF SQUID loop with a Josephson point (ScS)
contact driven by a mix of band-limited Gaussian noise and low-frequency
small-amplitude sine signal, at finite temperatures $0<T<T_{c}$. A change in
the gain of the stochastically amplified weak signal with the temperature rise
is shown to be due to smearing of the energy barrier between adjacent
metastable current states of the loop. A comparison to an RF SQUID with a
tunnel (SIS) junction is done.


  Visual patterns represent the discernible regularity in the visual world.
They capture the essential nature of visual objects or scenes. Understanding
and modeling visual patterns is a fundamental problem in visual recognition
that has wide ranging applications. In this paper, we study the problem of
visual pattern mining and propose a novel deep neural network architecture
called PatternNet for discovering these patterns that are both discriminative
and representative. The proposed PatternNet leverages the filters in the last
convolution layer of a convolutional neural network to find locally consistent
visual patches, and by combining these filters we can effectively discover
unique visual patterns. In addition, PatternNet can discover visual patterns
efficiently without performing expensive image patch sampling, and this
advantage provides an order of magnitude speedup compared to most other
approaches. We evaluate the proposed PatternNet subjectively by showing
randomly selected visual patterns which are discovered by our method and
quantitatively by performing image classification with the identified visual
patterns and comparing our performance with the current state-of-the-art. We
also directly evaluate the quality of the discovered visual patterns by
leveraging the identified patterns as proposed objects in an image and compare
with other relevant methods. Our proposed network and procedure, PatterNet, is
able to outperform competing methods for the tasks described.


  In this work we establish long-time existence of the normalized Yamabe flow
with positive Yamabe constant on a class of manifolds that includes spaces with
incomplete cone-edge singularities. We formulate our results axiomatically, so
that our results extend to general stratified spaces as well, provided certain
parabolic Schauder estimates hold. The central analytic tool is a parabolic
Moser iteration, which yields uniform upper and lower bounds on both the
solution and the scalar curvature.


  Accurately generating ground truth (GT) trajectories is essential for
Simultaneous Localization and Mapping (SLAM) evaluation, particularly under
varying environmental conditions. This study introduces a systematic approach
employing a prior map-assisted framework for generating dense
six-degree-of-freedom (6-DoF) GT poses for the first time, enhancing the
fidelity of both indoor and outdoor SLAM datasets. Our method excels in
handling degenerate and stationary conditions frequently encountered in SLAM
datasets, thereby increasing robustness and precision. A significant aspect of
our approach is the detailed derivation of covariances within the factor graph,
enabling an in-depth analysis of pose uncertainty propagation. This analysis
crucially contributes to demonstrating specific pose uncertainties and
enhancing trajectory reliability from both theoretical and empirical
perspectives. Additionally, we provide an open-source toolbox
(https://github.com/JokerJohn/Cloud_Map_Evaluation) for map evaluation
criteria, facilitating the indirect assessment of overall trajectory precision.
Experimental results show at least a 30\% improvement in map accuracy and a
20\% increase in direct trajectory accuracy compared to the Iterative Closest
Point (ICP) \cite{sharp2002icp} algorithm across diverse campus environments,
with substantially enhanced robustness. Our open-source solution
(https://github.com/JokerJohn/PALoc), extensively applied in the
FusionPortable\cite{Jiao2022Mar} dataset, is geared towards SLAM benchmark
dataset augmentation and represents a significant advancement in SLAM
evaluations.


  We present observations of radio recombination lines from the starburst
galaxy Arp 220 at 1.4, 8.1, 84, 96 and 207 GHz (sensitive upper limit for the
1.4 GHz line and firm detections at the other frequencies), and the radio
continuum spectrum between 330 MHz and 207 GHz.
  We show that a model with three components of ionized gas with different
densities and area covering factors can consistently explain both RRL and
continuum data. The total mass of ionized gas in the three components is 3 x
10^7 M_sun requiring 3 x 10^5 O5 stars with a total Lyman continuum production
rate (NLyc) of 1.3 x 10^{55} photons /s. These values imply a dust extinction
A_V ~ 45 magnitudes and an SFR of ~240 M_sun/yr. The NLyc of ~3% associated
with the high density HII regions implies similar SFR at recent epochs. The
data is also consistent with multiple starbursts of very high SFR and short
durations. The derived value of 24 for the IR-excess favours a starburst rather
than an AGN as the origin of the observed FIR luminosity.
  (the abstract has been abridged)


  Single-scale Feynman diagrams yield integrals that are periods, namely
projective integrals of rational functions of Schwinger parameters. Algebraic
geometry may therefore inform us of the types of number to which these
integrals evaluate. We give examples at 3, 4 and 6 loops of massive Feynman
diagrams that evaluate to Dirichlet $L$-series of modular forms and examples at
6, 7 and 8 loops of counterterms that evaluate to multiple zeta values or
polylogarithms of the sixth root of unity. At 8 loops and beyond, algebraic
geometry informs us that polylogs are insufficient for the evaluation of terms
in the beta-function of $\phi^4$ theory. Here, modular forms appear as
obstructions to polylogarithmic evaluation.


  Vision algorithms capable of interpreting scenes from a real-time video
stream are necessary for computer-assisted surgery systems to achieve
context-aware behavior. In laparoscopic procedures one particular algorithm
needed for such systems is the identification of surgical phases, for which the
current state of the art is a model based on a CNN-LSTM. A number of previous
works using models of this kind have trained them in a fully supervised manner,
requiring a fully annotated dataset. Instead, our work confronts the problem of
learning surgical phase recognition in scenarios presenting scarce amounts of
annotated data (under 25% of all available video recordings). We propose a
teacher/student type of approach, where a strong predictor called the teacher,
trained beforehand on a small dataset of ground truth-annotated videos,
generates synthetic annotations for a larger dataset, which another model - the
student - learns from. In our case, the teacher features a novel CNN-biLSTM-CRF
architecture, designed for offline inference only. The student, on the other
hand, is a CNN-LSTM capable of making real-time predictions. Results for
various amounts of manually annotated videos demonstrate the superiority of the
new CNN-biLSTM-CRF predictor as well as improved performance from the CNN-LSTM
trained using synthetic labels generated for unannotated videos. For both
offline and online surgical phase recognition with very few annotated
recordings available, this new teacher/student strategy provides a valuable
performance improvement by efficiently leveraging the unannotated data.


  Most observations of the solar corona beyond 2 Rs consist of broadband
visible light imagery from coronagraphs. The associated diagnostics mainly
consist of kinematics and derivations of the electron number density. While the
measurement of the properties of emission lines can provide crucial additional
diagnostics of the coronal plasma (temperatures, velocities, abundances, etc.),
these observations are comparatively rare. In visible wavelengths, observations
at these heights are limited to total eclipses. In the VUV range, very few
additional observations have been achieved since the pioneering results of
UVCS. One of the objectives of the Full Sun Imager (FSI) channel of the EUI
telescope on board the Solar Orbiter mission has been to provide very wide
field-of-view EUV diagnostics of the morphology and dynamics of the solar
atmosphere in temperature regimes that are typical of the lower transition
region and of the corona. FSI carries out observations in two narrowbands of
the EUV spectrum centered on 17.4 nm and 30.4 nm that are dominated,
respectively, by lines of Fe IX/X (formed in the corona around 1 MK) and by the
resonance line of He II (formed around 80 kK in the lower transition region).
Unlike previous EUV imagers, FSI includes a moveable occulting disk that can be
inserted in the optical path to reduce the amount of instrumental stray light
to a minimum. FSI detects signals at 17.4 nm up to the edge of its FOV (7~Rs),
which is about twice further than was previously possible. Comparisons with
observations by the LASCO and Metis coronagraphs confirm the presence of
morphological similarities and differences between the broadband visible light
and EUV emissions, as documented on the basis of prior eclipse and space-based
observations. The very-wide-field observations of FSI are paving the way for
future dedicated instruments.


  Let $P$ be a bounded convex subset of $\mathbb R^n$ of positive volume.
Denote the smallest degree of a polynomial $p(X_1,\dots,X_n)$ vanishing on
$P\cap\mathbb Z^n$ by $r_P$ and denote the smallest number $u\geq0$ such that
every function on $P\cap\mathbb Z^n$ can be interpolated by a polynomial of
degree at most $u$ by $s_P$. We show that the values $(r_{d\cdot P}-1)/d$ and
$s_{d\cdot P}/d$ for dilates $d\cdot P$ converge from below to some numbers
$v_P,w_P>0$ as $d$ goes to infinity. The limits satisfy $v_P^{n-1}w_P \leq
n!\cdot\operatorname{vol}(P)$. When $P$ is a triangle in the plane, we show
equality: $v_Pw_P = 2\operatorname{vol}(P)$. These results are obtained by
looking at the set of standard monomials of the vanishing ideal of $d\cdot
P\cap\mathbb Z^n$ and by applying the Bernstein--Kushnirenko theorem. Finally,
we study irreducible Laurent polynomials that vanish with large multiplicity at
a point. This work is inspired by questions about Seshadri constants.


  One of the main obstacles to broad application of reinforcement learning
methods is the parameter sensitivity of our core learning algorithms. In many
large-scale applications, online computation and function approximation
represent key strategies in scaling up reinforcement learning algorithms. In
this setting, we have effective and reasonably well understood algorithms for
adapting the learning-rate parameter, online during learning. Such
meta-learning approaches can improve robustness of learning and enable
specialization to current task, improving learning speed. For
temporal-difference learning algorithms which we study here, there is yet
another parameter, $\lambda$, that similarly impacts learning speed and
stability in practice. Unfortunately, unlike the learning-rate parameter,
$\lambda$ parametrizes the objective function that temporal-difference methods
optimize. Different choices of $\lambda$ produce different fixed-point
solutions, and thus adapting $\lambda$ online and characterizing the
optimization is substantially more complex than adapting the learning-rate
parameter. There are no meta-learning method for $\lambda$ that can achieve (1)
incremental updating, (2) compatibility with function approximation, and (3)
maintain stability of learning under both on and off-policy sampling. In this
paper we contribute a novel objective function for optimizing $\lambda$ as a
function of state rather than time. We derive a new incremental, linear
complexity $\lambda$-adaption algorithm that does not require offline batch
updating or access to a model of the world, and present a suite of experiments
illustrating the practicality of our new algorithm in three different settings.
Taken together, our contributions represent a concrete step towards black-box
application of temporal-difference learning methods in real world problems.


  Space weather indices are used commonly to drive forecasts of thermosphere
density, which directly affects objects in low-Earth orbit (LEO) through
atmospheric drag. One of the most commonly used space weather proxies, $F_{10.7
cm}$, correlates well with solar extreme ultra-violet (EUV) energy deposition
into the thermosphere. Currently, the USAF contracts Space Environment
Technologies (SET), which uses a linear algorithm to forecast $F_{10.7 cm}$. In
this work, we introduce methods using neural network ensembles with multi-layer
perceptrons (MLPs) and long-short term memory (LSTMs) to improve on the SET
predictions. We make predictions only from historical $F_{10.7 cm}$ values, but
also investigate data manipulation to improve forecasting. We investigate data
manipulation methods (backwards averaging and lookback) as well as multi step
and dynamic forecasting. This work shows an improvement over the baseline when
using ensemble methods. The best models found in this work are ensemble
approaches using multi step or a combination of multi step and dynamic
predictions. Nearly all approaches offer an improvement, with the best models
improving between 45 and 55\% on relative MSE. Other relative error metrics
were shown to improve greatly when ensembles methods were used. We were also
able to leverage the ensemble approach to provide a distribution of predicted
values; allowing an investigation into forecast uncertainty. Our work found
models that produced less biased predictions at elevated and high solar
activity levels. Uncertainty was also investigated through the use of a
calibration error score metric (CES), our best ensemble reached similar CES as
other work.


  The energy eigenvalues of the superintegrable chiral Potts model are
determined by the zeros of special polynomials which define finite
representations of Onsager's algebra. The polynomials determining the
low-sector eigenvalues have been given by Baxter in 1988. In the Z_3-case they
satisfy 4-term recursion relations and so cannot form orthogonal sequences.
However, we show that they are closely related to Jacobi polynomials and
satisfy a special "partial orthogonality" with respect to a Jacobi weight
function.


  Within the framework of the homogeneous non-linear Boltzmann equation, we
present a new analytic method, without the intrinsic limitations of existing
methods, for obtaining asymptotic solutions. This method permits extension of
existing results for Maxwell molecules and hard spheres to large classes of
particle interactions, from very hard spheres to softer than Maxwell molecules,
as well as to more general forcing mechanisms, beyond free cooling and white
noise driving. By combining this method with numerical solutions, obtained from
the Direct Simulation Monte Carlo (DSMC) method, we study a broad class of
models relevant for the dynamics of dissipative fluids, including granular
gases. We establish a criterion connecting the stability of the non-equilibrium
steady state to an exponentially bound form for the velocity distribution $F$,
which varies depending on the forcing mechanism. Power laws arise in marginal
stability cases, of which several new cases are reported. Our results provide a
minimal framework for interpreting large classes of experiments on driven
granular gases.


  We reply to a Comment on our Letter [Phys. Rev. Lett. 118, 232501 (2017),
arXiv:1612.01502] by A. Deltuva and R. Lazauskas [Phys. Rev. Lett 123, 069201
(2019), arXiv:1904.00925].


  This paper presents a novel framework, named Global-Local Correspondence
Framework (GLCF), for visual anomaly detection with logical constraints. Visual
anomaly detection has become an active research area in various real-world
applications, such as industrial anomaly detection and medical disease
diagnosis. However, most existing methods focus on identifying local structural
degeneration anomalies and often fail to detect high-level functional anomalies
that involve logical constraints. To address this issue, we propose a
two-branch approach that consists of a local branch for detecting structural
anomalies and a global branch for detecting logical anomalies. To facilitate
local-global feature correspondence, we introduce a novel semantic bottleneck
enabled by the visual Transformer. Moreover, we develop feature estimation
networks for each branch separately to detect anomalies. Our proposed framework
is validated using various benchmarks, including industrial datasets, Mvtec AD,
Mvtec Loco AD, and the Retinal-OCT medical dataset. Experimental results show
that our method outperforms existing methods, particularly in detecting logical
anomalies.


  A terahertz metamaterial consisting of radiative slot antennas and subradiant
complementary split-ring resonators exhibits plasmon induced opacity in a
narrow spectral range due to the destructive interference between the bright
and dark modes of the coupled oscillators. Femtosecond optical excitations
instantly quench the mode coupling and plasmon oscillations, injecting
photocarriers into the metamaterial. The plasmon resonances in the coupled
metamaterial are transiently restored by intense terahertz pulses. The strong
terahertz fields induce intervalley scattering and interband tunneling of the
photocarries, and achieve significant reduction of the photocarrier mobility.
The ultrafast dynamics of the nonlinear THz interactions reveals intricate
interplay between photocarriers and plasmon oscillations. The high-field THz
control of the plasmon oscillations implies potential applications to
ultrahigh-speed plasmonics.


  A ternary ring of operators (TRO) in finite dimensions is a diagonal sum of
spaces of rectangular matrices. TRO as operator space corresponds to quantum
channels that are diagonal sums of partial traces, which we call TRO channels.
TRO channels admits simple, single-letter capacity formula. Using operator
space and complex interpolation techniques, we give perturbative capacities
estimates for a wider class of quantum channels by comparison to TRO channels.
Our estimates applies mainly for quantum and private capacity and also strong
converse rates. The examples includes random unitary from group representations
which in general are non-degradable channels.


  Observations of nearby galaxies and galaxy clusters have reported an
unexpected X-ray emission line around 3.5 kilo-electron volts (keV). Proposals
to explain this line include decaying dark matter$-$in particular, that the
decay of sterile neutrinos with a mass around 7 keV could match the available
data. If this interpretation is correct, the 3.5 keV line should also be
emitted by dark matter in the halo of the Milky Way. We used more than 30
megaseconds of XMM-Newton (X-ray Multi-Mirror Mission) blank-sky observations
to test this hypothesis, finding no evidence of the 3.5-keV line emission from
the Milky Way halo. We set an upper limit on the decay rate of dark matter in
this mass range, which is inconsistent with the possibility that the 3.5-keV
line originates from dark matter decay.


  This paper proposes a method to quickly and efficiently compute the voltage
and current along a transmission line which can be "damaged"; that is its
electrical properties can be unevenly distributed. The method approximates a
transmission line by a self-similar circuit network and leverages our previous
work regarding the frequency response for that class of networks. The main
motivation arises from the research for railway track circuit systems where
transmission line models are often employed. However, in contrast to real
transmission lines, railway track circuits are more likely to be damaged due to
its scale and environmental uncertainties; furthermore, changes in circuit
properties due to a train occupying a segment of the track also is of great
interest as a means to ensure safety. As a result, an accurate and quick
simulation of damaged track circuits is necessary and can contribute to the
corresponding health monitoring research area in the future.


  Computational methods for assessing the likely impacts of mutations, known as
variant effect predictors (VEPs), are widely used in the assessment and
interpretation of human genetic variation, as well as in other applications
like protein engineering. Many different VEPs have been released to date, and
there is tremendous variability in their underlying algorithms and outputs, and
in the ways in which the methodologies and predictions are shared. This leads
to considerable challenges for end users in knowing which VEPs to use and how
to use them. Here, to address these issues, we provide guidelines and
recommendations for the release of novel VEPs. Emphasising open-source
availability, transparent methodologies, clear variant effect score
interpretations, standardised scales, accessible predictions, and rigorous
training data disclosure, we aim to improve the usability and interpretability
of VEPs, and promote their integration into analysis and evaluation pipelines.
We also provide a large, categorised list of currently available VEPs, aiming
to facilitate the discovery and encourage the usage of novel methods within the
scientific community.


  Recently, deep neural networks (DNN) have been widely used in speaker
recognition area. In order to achieve fast response time and high accuracy, the
requirements for hardware resources increase rapidly. However, as the speaker
recognition application is often implemented on mobile devices, it is necessary
to maintain a low computational cost while keeping high accuracy in far-field
condition. In this paper, we apply structural sparsification on time-delay
neural networks (TDNN) to remove redundant structures and accelerate the
execution. On our targeted hardware, our model can remove 60% of parameters and
only slightly increasing equal error rate (EER) by 0.18% while our structural
sparse model can achieve more than 1.5x speedup.


  We generalize a theorem by J. Choe on capillary surfaces for arbitrary
3-dimensional spaces of constant curvature. The main tools in this paper are an
extension of a theorem of H. Hopf due to S.-S. Chern and two index lemmas by J.
Choe.


  CLOVER is an experiment which aims to detect the signature of gravitational
waves from inflation by measuring the B-mode polarization of the cosmic
microwave background. CLOVER consists of three telescopes operating at 97, 150,
and 220 GHz. The 97-GHz telescope has 160 feedhorns in its focal plane while
the 150 and 220-GHz telescopes have 256 horns each. The horns are arranged in a
hexagonal array and feed a polarimeter which uses finline-coupled TES
bolometers as detectors. To detect the two polarizations the 97-GHz telescope
has 320 detectors while the 150 and 220-GHz telescopes have 512 detectors each.
To achieve the target NEPs (1.5, 2.5, and 4.5x10^-17 W/rtHz) the detectors are
cooled to 100 mK for the 97 and 150-GHz polarimeters and 230 mK for the 220-GHz
polarimeter. Each detector is fabricated as a single chip to ensure a 100%
operational focal plane. The detectors are contained in linear modules made of
copper which form split-block waveguides. The detector modules contain 16 or 20
detectors each for compatibility with the hexagonal arrays of horns in the
telescopes' focal planes. Each detector module contains a time-division SQUID
multiplexer to read out the detectors. Further amplification of the multiplexed
signals is provided by SQUID series arrays. The first prototype detectors for
CLOVER operate with a bath temperature of 230 mK and are used to validate the
detector design as well as the polarimeter technology. We describe the design
of the CLOVER detectors, detector blocks, and readout, and present preliminary
measurements of the prototype detectors performance.


  In this work we apply reduced basis methods for parametric PDEs to an
isogeometric formulation based on NURBS. The motivation for this work is an
integrated and complete work pipeline from CAD to parametrization of domain
geometry, then from full order to certified reduced basis solution.
IsoGeometric Analysis (IGA) is a growing research theme in scientific computing
and computational mechanics, as well as reduced basis methods for parametric
PDEs. Their combination enhances the solution of some class of problems,
especially the ones characterized by parametrized geometries we introduced in
this work. This work wants to demonstrate that it is also possible for some
class of problems to deal with affine geometrical parametrization combined with
a NURBS IGA formulation. This is what this work brings as original ingredients
with respect to other works dealing with reduced order methods and IGA. In this
work we show a certification of accuracy and a complete integration between IGA
formulation and parametric certified greedy RB formulation.


  We present a 8.4 GHz VLA survey of 50 optical counterparts to 46 ROSAT
All-Sky survey (RASS) X-ray sources south of the Taurus-Auriga star-forming
region. This survey detected 3 sources with a sensitivity limit of ~ 0.12 mJy.
Merging our sample with other radio observations of sources in and around
Taurus-Auriga, we find scarce radio emission among RASS sources south of the
Taurus-Auriga molecular clouds. Our data support the evidence that these
sources are older than weak-lined T Tauri stars, most of them probably being
close to the ZAMS.