  It has always been a challenge for researchers to efficiently and accurately
post process experimental data which is distorted by the noise. Superconducting
microwave devices e.g. resonators, directional filters, beam-splitters etc.
operate at frequency of several GHz to THz and temperatures well below critical
temperature (Tc) with few exceptions like transition edge sensors where devices
are operated at temperatures close to Tc. These devices are measured usually
with vector network analyser in terms of scattering parameters. Two kinds of
errors, systematic and drift can easily be removed from the measurements taken
with VNA. However, random errors are not easy to address and remove due to
their unpredictability and randomness. In this manuscript we will present an
algorithm to post process experimental data to cope with measurements that have
been corrupted or useful spectral response is buried in spurious signal. We
have developed a robust and efficient algorithm, implemented in MATLAB, to
detect peaks in spectral response, remove baseline and finally estimate
parameters of two-port superconductor resonator using an Improved Nelder-Mead
Method for unconstrained multidimensional least square minimization. The
algorithm has been successfully tested and verified by processing spectral
response of half wavelength microwave transmission-line resonator successfully
isolating resonator response from noisy background. We were able to compute
loaded quality factor, resonance frequency from response data with high
reproducibility even from those experimental data sets where resonance spikes
were hardly visible.


  The trajectory representation in the high energy limit (Bohr correspondence
principle) manifests a residual indeterminacy. This indeterminacy is compared
to the indeterminacy found in the classical limit (Planck's constant to 0)
[Int. J. Mod. Phys. A 15, 1363 (2000)] for particles in the classically allowed
region, the classically forbiden region, and near the WKB turning point. The
differences between Bohr's and Planck's principles for the trajectory
representation are compared with the differences between these correspondence
principles for the wave representation. The trajectory representation in the
high energy limit is shown to go to neither classical nor statistical
mechanics. The residual indeterminacy is contrasted to Heisenberg uncertainty.
The relationship between indeterminacy and 't Hooft's information loss and
equivalence classes is investigated.


  We present preliminary results from multidimensional numerical studies of
pair instability supernova (PSN), studying the fluid instabilities that occur
in multiple spatial dimensions. We use the new radiation-hydrodynamics code,
CASTRO, and introduce a new mapping procedure that defines the initial
conditions for the multidimensional runs in such a way that conservation of
physical quantities is guaranteed at any level of resolution.


  The next generation of observatories will facilitate the discovery of new
types of astrophysical transients. The detection of such phenomena, whose
characteristics are presently poorly constrained, will hinge on the ability to
perform blind searches. We present a new algorithm for this purpose, based on
deep learning. We incorporate two approaches, utilising anomaly detection and
classification techniques. The first is model-independent, avoiding the use of
background modelling and instrument simulations. The second method enables
targeted searches, relying on generic spectral and temporal patterns as input.
We compare our methodology with the existing approach to serendipitous
detection of gamma-ray transients. We use our framework to derive the detection
prospects of low-luminosity gamma-ray bursts with the upcoming Cherenkov
Telescope Array. Our method is an unbiased, data-driven approach for
multiwavelength and multi-messenger transient detection.


  We study the temporal intensity profile, or pulse shape, of cosmic ray ground
level enhancements (GLEs) by calculating the rise $(\tau_\mathrm{r})$ and decay
$(\tau_\mathrm{d})$ times for a small subset of all available events. Although
these quantities show very large inter-event variability, a linear dependence
of $\tau_\mathrm{d} \approx 3.5 \tau_\mathrm{r}$ is found. We interpret these
observational findings in terms of an interplanetary transport model, thereby
including the effects of scattering (in pitch-angle) as these particles
propagate from (near) the Sun to Earth. It is shown that such a model can
account for the observed trends in the pulse shape, illustrating that
interplanetary transport must be taken into account when studying GLE events,
especially their temporal profiles. Furthermore, depending on the model
parameters, the pulse shape of GLEs may be determined entirely by
interplanetary scattering, obscuring all information regarding the initial
acceleration process, and hence making a classification between impulsive and
gradual events, as is traditionally done, superfluous.


  IC 1613 is a Local Group dwarf irregular galaxy at a distance of 750 kpc. In
this work, we present an analysis of the star formation history (SFH) of a
field of $\sim200$ square arcmin in the central part of the galaxy. To this
aim, we use a novel method based on the resolved population of more highly
evolved stars. We identify 53 such stars, 8 of which are supergiants and the
remainder are long period variables (LPV), large amplitude variables (LAV) or
extreme Asymptotic Giant Branch (x-AGB) stars. Using stellar evolution models,
we find the age and birth mass of these stars and thus reconstruct the SFH. The
average rate of star formation during the last Gyr is $\sim3\times10^{-4}$
M$_\odot$ yr$^{-1}$ kpc$^{-2}$. The absence of a dominant epoch of star
formation over the past 5 Gyr, suggests that IC 1613 has evolved in isolation
for that long, spared harrassment by other Local Group galaxies (in particular
M 31 and the Milky Way). We confirm the radial age gradient, with star
formation currently concentrated in the central regions of IC 1613, and the
failure of recent star formation to have created the main HI supershell. Based
on the current rate of star formation at $(5.5\pm2)\times10^{-3}$ M$_\odot$
yr$^{-1}$, the interstellar gas mass of the galaxy of $9\times10^7$ M$_\odot$
and the gas production rate from AGB stars at $\sim6\times10^{-4}$ M$_\odot$
yr$^{-1}$, we conclude that the star formation activity of IC 1613 can continue
for $\sim18$ Gyr in a closed-box model, but is likely to cease much earlier
than that unless gas can be accreted from outside.


  In this survey, we give a friendly introduction from a graph theory
perspective to the q-state Potts model, an important statistical mechanics tool
for analyzing complex systems in which nearest neighbor interactions determine
the aggregate behavior of the system. We present the surprising equivalence of
the Potts model partition function and one of the most renowned graph
invariants, the Tutte polynomial, a relationship that has resulted in a
remarkable synergy between the two fields of study. We highlight some of these
interconnections, such as computational complexity results that have alternated
between the two fields. The Potts model captures the effect of temperature on
the system and plays an important role in the study of thermodynamic phase
transitions. We discuss the equivalence of the chromatic polynomial and the
zero-temperature antiferromagnetic partition function, and how this has led to
the study of the complex zeros of these functions. We also briefly describe
Monte Carlo simulations commonly used for Potts model analysis of complex
systems. The Potts model has applications as widely varied as magnetism, tumor
migration, foam behaviors, and social demographics, and we provide a sampling
of these that also demonstrates some variations of the Potts model. We conclude
with some current areas of investigation that emphasize graph theoretic
approaches.
  This paper is an elementary general audience survey, intended to popularize
the area and provide an accessible first point of entry for further
exploration.


  We introduce the use of superchiral surface waves for the all-optical
separation of chiral compounds. Using a combination of electrodynamics modeling
and analytical techniques, we show that the proposed approach provides chiral
optical forces two orders of magnitude larger than those obtained with
circularly polarized plane waves. Superchiral surface waves allow for
enantiomer separation on spatial, temporal and size scales than would not be
achievable with alternative techniques, thus representing a viable route
towards all-optical enantiomer separation.


  We prove the asymptotic of the logarithmic Bergman kernel. And as an
application, we calculate the conditional expectation of density of zeros of
Gaussian random sections of powers of a positive line bundle that vanish along
a fixed smooth subvariety.


  The notion of an e-value has been recently proposed as a possible alternative
to critical regions and p-values in statistical hypothesis testing. In this
paper we consider testing the nonparametric hypothesis of symmetry, introduce
analogues for e-values of three popular nonparametric tests, define an analogue
for e-values of Pitman's asymptotic relative efficiency, and apply it to the
three nonparametric tests. We discuss limitations of our simple definition of
asymptotic relative efficiency and list directions of further research.


  Let $b$ be an integer strictly greater than $1$. Each set of nonnegative
integers is represented in base $b$ by a language over $\{0, 1, \dots, b -
1\}$. The set is said to be $b$-recognisable if it is represented by a regular
language. It is known that ultimately periodic sets are $b$-recognisable, for
every base $b$, and Cobham's theorem implies the converse: no other set is
$b$-recognisable in every base $b$.
  We consider the following decision problem: let $S$ be a set of nonnegative
integers that is $b$-recognisable, given as a finite automaton over $\{0,1,
\dots, b - 1\}$, is $S$ periodic? Honkala showed in 1986 that this problem is
decidable. Later on, Leroux used in 2005 the convention to write number
representations with the least significant digit first (LSDF), and designed a
quadratic algorithm to solve a more general problem.
  We use here LSDF convention as well and give a structural description of the
minimal automata that accept periodic sets. Then, we show that it can be
verified in linear time if a minimal automaton meets this description. In
general, this yields a $O(b \log(n))$ procedure to decide whether an automaton
with $n$ states accepts an ultimately periodic set of nonnegative integers.


  Conventional sequential learning methods such as Recurrent Neural Networks
(RNNs) focus on interactions between consecutive inputs, i.e. first-order
Markovian dependency. However, most of sequential data, as seen with videos,
have complex temporal dependencies that imply variable-length semantic flows
and their compositions, and those are hard to be captured by conventional
methods. Here, we propose Temporal Dependency Networks (TDNs) for learning
video data by discovering these complex structures of the videos. The TDNs
represent video as a graph whose nodes and edges correspond to frames of the
video and their dependencies respectively. Via a parameterized kernel with
graph-cut and graph convolutions, the TDNs find compositional temporal
dependencies of the data in multilevel graph forms. We evaluate the proposed
method on the large-scale video dataset Youtube-8M. The experimental results
show that our model efficiently learns the complex semantic structure of video
data.


  The two long-baseline experiments T2K and NO$\nu$A have recently presented
new findings. T2K has shown the first $\bar \nu_e$ appearance data while
NO$\nu$A has released the first $\nu_e$ appearance results. These data are of
particular importance because they allow us to probe for the first time in a
direct (or manifest) way the leptonic CP-violation. In fact, it is the first
time that a hint of CP-violation arises from the comparison of the observations
of neutrinos and antineutrinos. We consider the implications of such new
results both for the standard 3-flavor framework and for the non-standard 3+1
scheme involving one sterile neutrino species. The 3-flavor analysis shows a
consolidation of the previous trends, namely a slight preference for $\sin
\delta <0$, disfavoring CP conservation ($\delta =0, \pi$) with a statistical
significance close to $90\%$ C.L., and a mild preference (at more than 68\%
C.L.) for the normal hierarchy. In a 3+1 framework, the data constrain two
CP-phases ($\delta_{13} \equiv \delta$ and $\delta_{14}$), which exhibit a
slight preference for the common value $\delta_{13} \simeq \delta_{14} \simeq
-\pi/2 $. Interestingly, in the enlarged four neutrino scheme the preference
for the normal hierarchy found within the 3-flavor framework completely
disappears. This indicates that light sterile neutrinos may constitute a
potential source of fragility in the capability of the two LBL experiments of
discriminating the neutrino mass hierarchy.


  Van der Waals heterostructures formed by stacking two-dimensional atomic
crystals are a unique platform for exploring new phenomena and functionalities.
Interlayer excitons, bound states of spatially separated electron-hole pairs in
van der Waals heterostructures, have demonstrated potential for rich valley
physics and optoelectronics applications, and been proposed to facilitate
high-temperature superfluidity. Here, we demonstrate highly tunable
layer-polarized excitons by an out-of-plane electric field in homo-bilayers of
transition metal dichalcogenides. Continuous tuning from negative to positive
layer polarity has been achieved, which is not possible in hetero-bilayers due
to the presence of large built-in interfacial electric fields. A large linear
field-induced redshift up to ~ 100 meV has been observed in the exciton
resonance energy. The Stark effect is accompanied by an enhancement of the
exciton recombination lifetime by more than two orders of magnitude to > 20 ns.
The long recombination lifetime has allowed the creation of a layer-polarized
exciton gas with density as large as 1.2*10^11 cm^-2 by moderate
continuous-wave optical pumping. Our results have paved the way for realization
of degenerate exciton gases in atomically thin semiconductors.


  One is interested here in the observation of dynamic processes starting from
the traces which they leave or those that one makes them produce. It is
considered here that it should be possible to make several observations
simultaneously, using a large variety of independently developed analyzers. For
this purpose, we introduce the original notion of ``full trace'' to capture the
idea that a process can be instrumented in such a way that it may broadcast all
information which could ever be requested by any kind of observer. Each
analyzer can then find in the full trace the data elements which it needs. This
approach uses what has been called a "tracer driver" which completes the tracer
and drives it to answer the requests of the analyzers. A tracer driver allows
to restrict the flow of information and makes this approach tractable. On the
other side, the potential size of a full trace seems to make the idea of full
trace unrealistic. In this work we explore the consequences of this notion in
term of potential efficiency, by analyzing the respective workloads between the
(full) tracer and many different analyzers, all being likely run in true
parallel environments. To illustrate this study, we use the example of the
observation of the resolution of constraints systems (proof-tree, search-tree
and propagation) using sophisticated visualization tools, as developed in the
project OADymPPaC (2001-2004). The processes considered here are computer
programs, but we believe the approach can be extended to many other kinds of
processes.


  Let G be a profinite group which is topologically finitely generated, p a
prime number and d an integer. We show that the functor from rigid analytic
spaces over Q_p to sets, which associates to a rigid space Y the set of
continuous d-dimensional pseudocharacters G -> O(Y), is representable by a
quasi-Stein rigid analytic space X, and we study its general properties. Our
main tool is a theory of "determinants" extending the one of pseudocharacters
but which works over an arbitrary base ring; an independent aim of this paper
is to expose the main facts of this theory. The moduli space X is constructed
as the generic fiber of the moduli formal scheme of continuous formal
determinants on G of dimension d. As an application to number theory, this
provides a framework to study the generic fibers of pseudodeformation rings
(e.g. of Galois representations), especially in the "residually reducible"
case, and including when p <= d.


  In recent years, Deep Neural Networks (DNN) based methods have achieved
remarkable performance in a wide range of tasks and have been among the most
powerful and widely used techniques in computer vision. However, DNN-based
methods are both computational-intensive and resource-consuming, which hinders
the application of these methods on embedded systems like smart phones. To
alleviate this problem, we introduce a novel Fixed-point Factorized Networks
(FFN) for pretrained models to reduce the computational complexity as well as
the storage requirement of networks. The resulting networks have only weights
of -1, 0 and 1, which significantly eliminates the most resource-consuming
multiply-accumulate operations (MACs). Extensive experiments on large-scale
ImageNet classification task show the proposed FFN only requires one-thousandth
of multiply operations with comparable accuracy.


  Analysis of speech for recognition of stress is important for identification
of emotional state of person. This can be done using 'Linear Techniques', which
has different parameters like pitch, vocal tract spectrum, formant frequencies,
Duration, MFCC etc. which are used for extraction of features from speech.
TEO-CB-Auto-Env is the method which is non-linear method of features
extraction. Analysis is done using TU-Berlin (Technical University of Berlin)
German database. Here emotion recognition is done for different emotions like
neutral, happy, disgust, sad, boredom and anger. Emotion recognition is used in
lie detector, database access systems, and in military for recognition of
soldiers' emotion identification during the war.


  Scaling relations between black hole (BH) masses and their host galaxy
properties have been studied extensively over the last two decades, and point
towards co-evolution of central massive BHs and their hosts. However, these
relations remain poorly constrained for BH masses below $\sim10^{6}$ M_sun.
Here we present optical and X-ray observations of the dwarf galaxy RGG 118
taken with the Magellan Echellette Spectrograph on the 6.5m Clay Telescope and
Chandra X-ray Observatory. Based on Sloan Digital Sky Survey spectroscopy, RGG
118 was identified as possessing narrow emission line ratios indicative of
photoionization partly due to an active galactic nucleus. Our higher resolution
spectroscopy clearly reveals broad H$\alpha$ emission in the spectrum of RGG
118. Using virial BH mass estimate techniques, we calculate a BH mass of
$\sim50,000$ \msun. We detect a nuclear X-ray point source in RGG 118,
suggesting a total accretion powered luminosity of $L=4\times10^{40}~{\rm
erg~s^{-1}}$, and an Eddington fraction of $\sim1$ per cent. The BH in RGG 118
is the smallest ever reported in a galaxy nucleus and we find that it lies on
the extrapolation of the $M_{\rm BH}-\sigma_{\ast}$ relation to the lowest
masses yet.


  We continue our study of the dynamics of the flavour sector of the
Sakai-Sugimoto model in the presence of an external magnetic field, uncovering
several features of the meson spectrum at high and low temperatures. We employ
both analytical and numerical methods to study the coupled non-linear equations
that result from the gravity dual.


  Based on a self-consistent $t$-matrix approximation, we explore the influence
of magnetic and nonmagnetic doping on the surface electronic states and
conductivity of topological insulators. We show that warping parameter has a
crucial impact on the density of states and dc conductivity of the doped
surfaces. As the warping strength is increased, the surface density of states
at high energies is suppressed and the resonant states induced by impurities in
the vicinity of the Dirac point gradually disappear. It is found that
nonmagnetic impurities break electron-hole symmetry at low warping strength,
while the symmetry remains unchanged when the surface is magnetically doped.
Our findings reveal that surface conductivity can be controlled by tuning the
doping, the direction of external magnetic field and that of impurity magnetic
moments. Also, the surface conductivity features in topological insulators with
warped energy dispersions are not significantly affected by the presence of
impurities compared to that of materials with circular energy contour.


  The least absolute shrinkage and selection operator (Lasso) is widely
recognized across various fields of mathematics and engineering. Its variant,
the generalized Lasso, finds extensive application in the fields of statistics,
machine learning, image science, and related areas. Among the optimization
techniques used to tackle this issue, saddle-point methods stand out, with the
primal-dual hybrid gradient (PDHG) algorithm emerging as a particularly popular
choice. However, the iterative behavior of PDHG remains poorly understood. In
this paper, we employ dimensional analysis to derive a system of
high-resolution ordinary differential equations (ODEs) tailored for PDHG. This
system effectively captures a key feature of PDHG, the coupled $x$-correction
and $y$-correction, distinguishing it from the proximal Arrow-Hurwicz
algorithm. The small but essential perturbation ensures that PDHG consistently
converges, bypassing the periodic behavior observed in the proximal
Arrow-Hurwicz algorithm. Through Lyapunov analysis, We investigate the
convergence behavior of the system of high-resolution ODEs and extend our
insights to the discrete PDHG algorithm. Our analysis indicates that numerical
errors resulting from the implicit scheme serve as a crucial factor affecting
the convergence rate and monotonicity of PDHG, showcasing a noteworthy pattern
also observed for the Alternating Direction Method of Multipliers (ADMM), as
identified in [Li and Shi, 2024]. In addition, we further discover that when
one component of the objective function is strongly convex, the iterative
average of PDHG converges strongly at a rate $O(1/N)$, where $N$ is the number
of iterations.


  The primary objective of simultaneous machine translation (SiMT) is to
minimize latency while preserving the quality of the final translation. Drawing
inspiration from CPU branch prediction techniques, we propose incorporating
branch prediction techniques in SiMT tasks to reduce translation latency.
Specifically, we utilize a language model as a branch predictor to predict
potential branch directions, namely, future source words. Subsequently, we
utilize the predicted source words to decode the output in advance. When the
actual source word deviates from the predicted source word, we use the real
source word to decode the output again, replacing the predicted output. To
further reduce computational costs, we share the parameters of the encoder and
the branch predictor, and utilize a pre-trained language model for
initialization. Our proposed method can be seamlessly integrated with any SiMT
model. Extensive experimental results demonstrate that our approach can improve
translation quality and latency at the same time. Our code is available at
https://github.com/YinAoXiong/simt_branch_predictor .


  Superconducting MgB2 thin films were fabricated on Al2O3(0001) substrates
under an ex-situ processing conditions. Boron thin films were deposited by
pulsed laser deposition followed by a post-annealing process. Resistance
measurements of the deposited MgB2 films show Tc of about 39K, while scanning
electron microscopy and x-ray diffraction analysis indicate that the films
consist of well-crystallized grains with a highly c-axis-oriented structure.


  It is proved that the random integral mappings (some type of functionals of
L\'evy processes) are always isomorphisms between convolution semigroups of
infinitely divisible measures. However, the inverse mappings are no longer of
the random integral form. Domains are characterized in may ways. Compositions
(iterated integrals) can be expressed as a single random integral mapping.
Finally, all obtained results are illustrated by examples.


  In recent years, researchers have proposed many deep learning (DL) methods
for various tasks, and particularly face recognition (FR) made an enormous leap
using these techniques. Deep FR systems benefit from the hierarchical
architecture of the DL methods to learn discriminative face representation.
Therefore, DL techniques significantly improve state-of-the-art performance on
FR systems and encourage diverse and efficient real-world applications. In this
paper, we present a comprehensive analysis of various FR systems that leverage
the different types of DL techniques, and for the study, we summarize 168
recent contributions from this area. We discuss the papers related to different
algorithms, architectures, loss functions, activation functions, datasets,
challenges, improvement ideas, current and future trends of DL-based FR
systems. We provide a detailed discussion of various DL methods to understand
the current state-of-the-art, and then we discuss various activation and loss
functions for the methods. Additionally, we summarize different datasets used
widely for FR tasks and discuss challenges related to illumination, expression,
pose variations, and occlusion. Finally, we discuss improvement ideas, current
and future trends of FR tasks.


  We present a multi-wavelength (X-ray, $\gamma$-ray, optical and radio) study
of HD 194816, a late O-type X-ray detected spectroscopic binary. X-ray spectra
are analyzed and the X-ray photon arrival times are checked for pulsation. In
addition, newly obtained optical spectroscopic monitoring data on HD 164816 are
presented. They are complemented by available radio data from several large
scale surveys as well as the \emph{FERMI} $\gamma$-ray data from its
\emph{Large Area Telescope}. We report the detection of a low energy excess in
the X-ray spectrum that can be described by a simple absorbed blackbody model
with a temperature of $\sim$ 50 eV as well as a 9.78 s pulsation of the X-ray
source. The soft X-ray excess, the X-ray pulsation, and the kinematical age
would all be consistent with a compact object like a neutron star as companion
to HD 164816. The size of the soft X-ray excess emitting area is consistent
with a circular region with a radius of about 7 km, typical for neutron stars,
while the emission measure of the remaining harder emission is typical for late
O-type single or binary stars. If HD 164816 includes a neutron star born in a
supernova, this supernova should have been very recent and should have given
the system a kick, which is consistent with the observation that the star HD
164816 has a significantly different radial velocity than the cluster mean. In
addition we confirm the binarity of HD 164816 itself by obtaining an orbital
period of 3.82 d, projected masses $m_1 {\rm sin}^{3} i$ = 2.355(69) M$_\odot$,
$m_2 {\rm sin}^{3} i$ = 2.103(62) M$_\odot$ apparently seen at low inclination
angle, determined from high-resolution optical spectra.


  Massive black-hole (MBH) binaries, which are expected to form following the
merger of their parent galaxies, produce gravitational waves which will be
detectable by Pulsar Timing Arrays at nanohertz frequencies (year periods).
While no confirmed, compact MBH binary systems have been seen in
electromagnetic observations, a large number of candidates have recently been
identified in optical surveys of AGN variability. Using a combination of
cosmological, hydrodynamic simulations; comprehensive, semi-analytic binary
merger models; and analytic AGN spectra and variability prescriptions; we
calculate the expected electromagnetic detection rates of MBH binaries as
periodically variable AGN. In particular, we consider two independent
variability models: (i) Doppler boosting due to large orbital velocities, and
(ii) hydrodynamic variability in which the fueling of MBH accretion disks is
periodically modulated by the companion. Our models predict that numerous MBH
binaries should be present and distinguishable in the existing data. In
particular, our fiducial models produce an expectation value of $0.2$ (Doppler)
and $5$ (hydrodynamic) binaries to be identifiable in CRTS, while $20$ and
$100$ are expected after five years of LSST observations. The brightness
variations in most systems are too small to be distinguishable, but almost
$1\%$ of AGN at redshifts $z \lesssim 0.6$ could be in massive binaries. We
analyze the predicted binary parameters of observable systems and their
selection biases, and include an extensive discussion of our model parameters
and uncertainties.


  Adversarial attacks reveal serious flaws in deep learning models. More
dangerously, these attacks preserve the original meaning and escape human
recognition. Existing methods for detecting these attacks need to be trained
using original/adversarial data. In this paper, we propose detection without
training by voting on hard labels from predictions of transformations, namely,
VoteTRANS. Specifically, VoteTRANS detects adversarial text by comparing the
hard labels of input text and its transformation. The evaluation demonstrates
that VoteTRANS effectively detects adversarial text across various
state-of-the-art attacks, models, and datasets.


  The problem of unambiguous state discrimination consists of determining which
of a set of known quantum states a particular system is in. One is allowed to
fail, but not to make a mistake. The optimal procedure is the one with the
lowest failure probability. This procedure has been extended to bipartite
states where the two parties, Alice and Bob, are allowed to manipulate their
particles locally and communicate classically in order to determine which of
two possible two-particle states they have been given. The failure probability
of this local procedure has been shown to be the same as if the particles were
together in the same location. Here we examine the effect of restricting the
classical communication between the parties, either allowing none or
eliminating the possibility that one party's measurement depends on the result
of the other party's. These issues are studied for two-qubit states, and
optimal procedures are found. In some cases the restrictions cause increases in
the failure probability, but in other cases they do not. Applications of these
procedures, in particular to secret sharing, are discussed.


  The explicit coordinate transformations which show the equivalence between a
four-dimensional spatially flat cosmology and an appropriate submanifold in the
flat five-dimensional Minkowski space-time are presented. Analogous procedure
is made for the case of five-dimensional warped brane world models. Several
examples are presented.


  Background: High-spin isomers at {\approx}9-MeV excitation energies have been
reported in several N = 83 isotones near Z = 64. Spin and parity assignments of
J{^\pi} = 49/2+ remain tentative for a number of these states in the odd-A
nuclei. Purpose: To study the decay of the (49/2 +) high-spin isomer in 145 Sm,
make firm spin and parity assignments to the isomer and states populated in its
decay, and investigate the structure of the nucleus. Methods: The 145Sm isomer
was populated in the 124Sn(26Mg,5n) reaction. Gamma-ray and conversion-electron
data were collected using the Solenogam array. Results: A revised lifetime of
t1/2 = 3.52(16) mus was measured for the high-spin isomer. Several new states
have been added to the level scheme, and a new state at 8815 keV is proposed as
the isomer, based on decay-property systematics, transition strengths, and spin
and parity assignments. Firm spin and parity assignments have been made to
states up to and including the isomer and the new level scheme is interpreted
using shell-model calculations performed with the KShell program. Conclusions:
The interpretation of the 49/2+ isomer as a deformed excitation of the core
neutrons remains unchanged, although there has been a significant revision of
the level scheme below the isomer, and hence significant reinterpretations of
the lower-lying states.


  In this paper, we propose a $p$-adic analog of Mellin amplitudes for scalar
operators, and present the computation of the general contact amplitude as well
as arbitrary-point tree-level amplitudes for bulk diagrams involving up to
three internal lines, and along the way obtain the $p$-adic version of the
split representation formula. These amplitudes share noteworthy similarities
with the usual (real) Mellin amplitudes for scalars, but are also significantly
simpler, admitting closed-form expressions where none are available over the
reals. The dramatic simplicity can be attributed to the absence of descendant
fields in the $p$-adic formulation.


  In this paper, we analyze the spectrum occupancy using different machine
learning techniques. Both supervised techniques (naive Bayesian classifier
(NBC), decision trees (DT), support vector machine (SVM), linear regression
(LR)) and unsupervised algorithm (hidden markov model (HMM)) are studied to
find the best technique with the highest classification accuracy (CA). A
detailed comparison of the supervised and unsupervised algorithms in terms of
the computational time and classification accuracy is performed. The classified
occupancy status is further utilized to evaluate the probability of secondary
user outage for the future time slots, which can be used by system designers to
define spectrum allocation and spectrum sharing policies. Numerical results
show that SVM is the best algorithm among all the supervised and unsupervised
classifiers. Based on this, we proposed a new SVM algorithm by combining it
with fire fly algorithm (FFA), which is shown to outperform all other
algorithms.


  A tool that could suggest new personalized research directions and ideas by
taking insights from the scientific literature could significantly accelerate
the progress of science. A field that might benefit from such an approach is
artificial intelligence (AI) research, where the number of scientific
publications has been growing exponentially over the last years, making it
challenging for human researchers to keep track of the progress. Here, we use
AI techniques to predict the future research directions of AI itself. We
develop a new graph-based benchmark based on real-world data -- the
Science4Cast benchmark, which aims to predict the future state of an evolving
semantic network of AI. For that, we use more than 100,000 research papers and
build up a knowledge network with more than 64,000 concept nodes. We then
present ten diverse methods to tackle this task, ranging from pure statistical
to pure learning methods. Surprisingly, the most powerful methods use a
carefully curated set of network features, rather than an end-to-end AI
approach. It indicates a great potential that can be unleashed for purely ML
approaches without human knowledge. Ultimately, better predictions of new
future research directions will be a crucial component of more advanced
research suggestion tools.


  This paper introduces the abstraction of max-plus linear (MPL) systems via
predicates. Predicates are automatically selected from system matrix, as well
as from the specifications under consideration. We focus on verifying
time-difference specifications, which encompass the relation between successive
events in MPL systems. We implement a bounded model checking (BMC) procedure
over a predicate abstraction of the given MPL system, to verify the
satisfaction of time-difference specifications. Our predicate abstractions are
experimentally shown to improve on existing MPL abstractions algorithms.
Furthermore, with focus on the BMC algorithm, we can provide an explicit upper
bound on the completeness threshold by means of the transient and the cyclicity
of the underlying MPL system.


  We consider the spectrum of the Sample Covariance matrix $\mathbf{A}_N:=
\frac{\mathbf{X}_N \mathbf{X}_N^*}{N}, $ where $\mathbf{X}_N$ is the $P\times
N$ matrix with i.i.d. half-heavy tailed entries and $\frac{P}{N}\to y>0$ (the
entries of the matrix have variance, but do not have the fourth moment). We
derive the Central Limit Theorem for the Stieltjes transform of the matrix
$\mathbf{A}_N$ and compute the covariance kernel. Apart from that, we derive
the Central Limit Theorem for the Stieltjes transform of overlapping Sample
Covariance matrices.


  The levitation of a cylindrical permanent magnet over a high-temperature
superconductor cooled by liquid nitrogen can be accompanied by spontaneous
oscillations and rotation. The reason for spontaneous rotation of the magnet is
magnetization inhomogeneity induced by the temperature gradient. An experiment
was carried out on the levitation of Nd-Fe-B magnets over a composite
high-temperature superconductor. The experimental results confirm that the
rotation frequency depends on the difference in the magnetization values in the
upper and lower halves of the magnet. Methods for controlling the rotation
frequency of a levitating magnet are proposed.


  In traditional neural network architectures, a multilayer perceptron (MLP) is
typically employed as a classification block following the feature extraction
stage. However, the Kolmogorov-Arnold Network (KAN) presents a promising
alternative to MLP, offering the potential to enhance prediction accuracy. In
this paper, we propose the replacement of linear and convolutional layers of
traditional networks with KAN-based counterparts. These modifications allowed
us to significantly increase the per-pixel classification accuracy for
hyperspectral remote-sensing images. We modified seven different neural network
architectures for hyperspectral image classification and observed a substantial
improvement in the classification accuracy across all the networks. The
architectures considered in the paper include baseline MLP, state-of-the-art 1D
(1DCNN) and 3D convolutional (two different 3DCNN, NM3DCNN), and transformer
(SSFTT) architectures, as well as newly proposed M1DCNN. The greatest effect
was achieved for convolutional networks working exclusively on spectral data,
and the best classification quality was achieved using a KAN-based transformer
architecture. All the experiments were conducted using seven openly available
hyperspectral datasets. Our code is available at
https://github.com/f-neumann77/HyperKAN.


  In the framework of research into radioactive waste disposal, it was decided
to investigate the thermally induce pore pressure occurring in the
Callovo-Oxfordian claystone, a possible host rock in which the ANDRA
underground laboratory of Bure (East of France) has been excavated. Thermal
pore pressures appear in low permeability soils and rocks because the thermal
expansion coefficient of water is significantly higher than that of the solid
grains (Campanella and Mitchell; 1968 [1], Ghabezloo and Sulem; 2009 [2]). This
phenomenon has clearly been observed in various in-situ heating tests conducted
in Opalinus claystone in the Mont-Terri Underground Research Laboratory (URL)
in Switzerland (HE-D test) and in Callovo-Oxfordian (COx) claystone in the Bure
URL in France (TER test, Wileveau and Su; 2007 [3]) The processes of coring,
transportation, storage and specimen trimming induce some desaturation in the
sample. Due to the very low permeability (10-20 m2) of the COx claystone, a
long period of time is necessary to properly resaturate the sample, a mandatory
condition to satisfactorily investigate thermal pressurisation. Particular
emphasis was hence put on the previous saturation procedure that was carried
out under in-situ effective stress condition. Thermal pressurization has been
investigated by performing undrained heating tests while measuring pore
pressures changes in a specially adapted thermal isotropic compression cell.
Special care was devoted to calibration procedures to account for the effects
of the system on the pore pressure measurements. The thermal pressurization
coefficient measured appeared to change with temperature, mainly because of the
changes with temperature of both the water thermal expansion coefficient of
water and the drained compression coefficient of the claystone.


  This paper studies high-dimensional canonical correlation analysis (CCA) with
an emphasis on the vectors that define canonical variables. The paper shows
that when two dimensions of data grow to infinity jointly and proportionally,
the classical CCA procedure for estimating those vectors fails to deliver a
consistent estimate. This provides the first result on the impossibility of
identification of canonical variables in the CCA procedure when all dimensions
are large. As a countermeasure, the paper derives the magnitude of the
estimation error, which can be used in practice to assess the precision of CCA
estimates. Applications of the results to cyclical vs. non-cyclical stocks and
to a limestone grassland data set are provided.


  The Boltzmann equation becomes invalid as the size of gas molecules is
comparable with the average intermolecular distance. A better description is
provided by the Enskog collision operator, which takes into account the finite
size of gas molecules. This extension implies non-local collisions as well as
an increase in collision frequency, making it computationally expensive to
solve. An approximation of the Enskog collision operator, denoted the
simplified Enskog collision operator, is used in this work to develop a
quadrature-based Lattice Boltzmann model for non-ideal monatomic dense gases.
The Shakhov collision term is implemented in order to fine-tune the Prandtl
number. This kinetic model is shown to be able to tackle non-equilibrium flow
problems of dense gases, namely the sound wave and the shock wave propagation.
The results are compared systematically with the results of the more accurate
but computationally intensive particle method of solving the Enskog equation.
The model introduced in this paper is shown to have good accuracy for small to
moderate denseness of the fluid (defined as the ratio of the molecular diameter
to the mean free path) and, due to the efficiency in terms of the computational
time, it is suitable for practical applications.


  We study the superconductivity of spin-polarized electrons in centrosymmetric
ferromagnetic metals. Due to the spin-polarization and the Fermi statistics of
electrons, the superconducting pairing function naturally has odd parity.
According to the parity formula proposed by Fu, Berg, and Sato, odd-parity
pairing leads to conventional first-order topological superconductivity when a
normal metal has an odd number of Fermi surfaces. Here, we derive generalized
parity formulae for the topological invariants characterizing higher-order
topology of centrosymmetric superconductors. Based on the formulae, we
systematically classify all possible band structures of ferromagnetic metals
that can induce inversion-protected higher-order topological superconductivity.
Among them, doped ferromagnetic nodal semimetals are identified as the most
promising normal state platform for higher-order topological superconductivity.
In two dimensions, we show that odd-parity pairing of doped Dirac semimetals
induces a second-order topological superconductor. In three dimensions,
odd-parity pairing of doped nodal line semimetals generates a nodal line
topological superconductor with monopole charges. On the other hand, odd-parity
pairing of doped monopole nodal line semimetals induces a three-dimensional
third-order topological superconductor. Our theory shows that the combination
of superconductivity and ferromagnetic nodal semimetals opens up a new avenue
for future topological quantum computations using Majorana zero modes.


  We construct a renormalization operator which acts on analytic circle maps
whose critical exponent $\alpha$ is not necessarily an odd integer $2n+1$,
$n\in\mathbb N$. When $\alpha=2n+1$, our definition generalizes cylinder
renormalization of analytic critical circle maps. In the case when $\alpha$ is
close to an odd integer, we prove hyperbolicity of renormalization for maps of
bounded type. We use it to prove universality and $C^{1+\alpha}$-rigidity for
such maps.


  Automated software testing is integral to the software development process,
streamlining workflows and ensuring product reliability. Visual testing within
this context, especially concerning user interface (UI) and user experience
(UX) validation, stands as one of crucial determinants of overall software
quality. Nevertheless, conventional methods like pixel-wise comparison and
region-based visual change detection fall short in capturing contextual
similarities, nuanced alterations, and understanding the spatial relationships
between UI elements. In this paper, we introduce a novel graph-based method for
visual change detection in software test automation. Leveraging a machine
learning model, our method accurately identifies UI controls from software
screenshots and constructs a graph representing contextual and spatial
relationships between the controls. This information is then used to find
correspondence between UI controls within screenshots of different versions of
a software. The resulting graph encapsulates the intricate layout of the UI and
underlying contextual relations, providing a holistic and context-aware model.
This model is finally used to detect and highlight visual regressions in the
UI. Comprehensive experiments on different datasets showed that our change
detector can accurately detect visual software changes in various simple and
complex test scenarios. Moreover, it outperformed pixel-wise comparison and
region-based baselines by a large margin in more complex testing scenarios.
This work not only contributes to the advancement of visual change detection
but also holds practical implications, offering a robust solution for
real-world software test automation challenges, enhancing reliability, and
ensuring the seamless evolution of software interfaces.


  A semiregular tree is a tree where all non-pendant vertices have the same
degree. Belardo et al. (MATCH Commun. Math. Chem. 61(2), pp. 503-515, 2009)
have shown that among all semiregular trees with a fixed order and degree, a
graph with index is a caterpillar. In this technical report we provide a
different proof for this theorem. Furthermore, we give counter examples that
show this result cannot be generalized to the class of trees with a given
(non-constant) degree sequence.


  We present a new possibility for the active frequency stabilization of a
laser using transitions in neutral praseodymium. Because of its five outer
electrons, this element shows a high density of energy levels leading to an
extremely line-rich excitation spectrum with more than 25000 known spectral
lines ranging from the UV to the infrared. We demonstrate the active frequency
stabilization of a diode laser on several praseodymium lines between 1105 and
1123 nm. The excitation signals were recorded in a hollow cathode lamp and
observed via laser-induced fluorescence. These signals are strong enough to
lock the diode laser onto most of the lines by using standard laser locking
techniques. In this way, the frequency drifts of the unlocked laser of more
than 30 MHz/h were eliminated and the laser frequency stabilized to within
1.4(1) MHz for averaging times >0.2 s. Frequency quadrupling the stabilized
diode laser can produce frequency-stable UV-light in the range from 276 to 281
nm. In particular, using a strong hyperfine component of the praseodymium
excitation line E = 16 502.616_7/2 cm^-1 -> E' = 25 442.742_9/2 cm^-1 at lambda
= 1118.5397(4) nm makes it possible - after frequency quadruplication - to
produce laser radiation at lambda/4 = 279.6349(1) nm, which can be used to
excite the D2 line in Mg^+.


  We investigate the dynamical fixed points of the zero temperature Glauber
dynamics in Ising-like models. The stability analysis of the fixed points in
the mean field calculation shows the existence of an exponent that depends on
the coordination number $z$ in the Ising model. For the generalised voter
model, a phase diagram is obtained based on this study. Numerical results for
the Ising model for both the mean field case and short ranged models on
lattices with different values of $z$ are also obtained. A related study is the
behaviour of the exit probability $E(x_0)$, defined as the probability that a
configuration ends up with all spins up starting with $x_0$ fraction of up
spins. An interesting result is $E(x_0) = x_0$ in the mean field approximation
when $z=2$, which is consistent with the conserved magnetisation in the system.
For larger values of $z$, $E(x_0)$ shows the usual finite size dependent non
linear behaviour both in the mean field model and in Ising model with nearest
neighbour interaction on different two dimensional lattices. For such a
behaviour, a data collapse of $E(x_0)$ is obtained using $y = \frac{(x_0 -
x_c)}{x_c}L^{1/\nu}$ as the scaling variable and $f(y)=\frac{1+\tanh(\lambda
y)}{2}$ appears as the scaling function. The universality of the exponent and
the scaling factor is investigated.


  A deep underwater Cherenkov telescope has been operating since 1993 in stages
of growing size at 1.1 km depth in Lake Baikal. The key component of the
telescope is the Optical Module (OM) which houses the highly sensitive
phototube QUASAR-370. We describe design and parameters of the QUASAR-370, the
layout of the optical module, the front-end electronics and the calibration
procedures, and present selected results from the five-year operation
underwater. Also, future developments with respect to a telescope consisting
from several thousand OMs are discussed.


  We evaluate the model-dependent piece of O(alpha) long-distance radiative
corrections to tau^- \to \pi^- \pi^0\nu_{\tau} decays by using a meson
dominance model. We find that these corrections to the di-pion invariant mass
spectrum are smaller than in previous calculations based on chiral perturbation
theory. The corresponding correction to the photon inclusive rate is tiny
(-0.15%) but it can be of relevance when new measurements reach better
precision.


  Galaxy clusters are the universe's largest objects in the universe kept
together by gravity. Most of their baryonic content is made of a magnetized
diffuse plasma. We investigate the impact of such magnetized environment on
ultra-high-energy-cosmic-ray (UHECR) propagation. The intracluster medium is
described according to the self-similar assumption, in which the gas density
and pressure profiles are fully determined by the cluster mass and redshift.
The magnetic field is scaled to the thermal components of the intracluster
medium under different assumptions. We model the propagation of UHECRs in the
intracluster medium using a modified version of the Monte Carlo code {\it
SimProp}, where hadronic processes and diffusion in the turbulent magnetic
field are implemented. We provide a universal parametrization that approximates
the UHECR fluxes escaping from the environment as a function of the most
relevant quantities, such as the mass of the cluster, the position of the
source with respect to the center of the cluster and the nature of the
accelerated particles. We show that galaxy clusters are an opaque environment
especially for UHECR nuclei. The role of the most massive nearby clusters in
the context of the emerging UHECR astronomy is finally discussed.


  We investigate the roughening of shear cracks running along the interface
between a thin film and a rigid substrate. We demonstrate that short-range
correlated fluctuations of the interface strength lead to self-affine
roughening of the crack front as the driving force (the applied shear
stress/stress intensity factor) increases towards a critical value. We
investigate the disorder-induced perturbations of the crack displacement field
and crack energy, and use the results to determine the crack pinning force and
to assess the shape of the critical crack. The analytical arguments are
validated by comparison with simulations of interface cracking.


  In this paper, inspired by the holographic dual of the entanglement entropy,
we consider the behaviour of extremal, codimension two, spacelike surfaces in
the background of three and four dimensional charged boson stars in
asymptotically anti-de Sitter spacetime. We find conditions for which families
of minimal area surfaces fail to contain the entire bulk spacetime and
construct a phase diagram showcasing the transition between regimes. In
addition, we use the relation between the star's mass and the central density
of the scalar field to argue for a possible instability of such hollow
solutions. Finally, we discuss the consequences of our findings for the study
of holographic duals of reduced density matrices.


  We investigate the prospects for reconstructing the mass, spin-independent
and spin-dependent cross-sections of Dark Matter particles with a combination
of future direct detection experiments such as XENON1T, and the IceCube
neutrino telescope in the 86-string configuration including the DeepCore array.
We quantify the degree of complementarity between the two experiments by
adopting realistic values for their exposure, energy threshold and resolution.
Starting from benchmark models arising from a supersymmetric model with 25 free
parameters, we show that despite the stringent constraints set by the run with
79 strings, IceCube can help break the degeneracies in the Dark Matter
cross-section parameter space, even in the unfortunate case where it fails to
discover high energy neutrinos from the Sun. We also discuss how the
reconstruction of the Dark Matter particle parameters from the combined data
sets is affected by the uncertainties associated with the nuclear structure of
the target material in case of spin-dependent scattering and those associated
with astrophysical quantities such as the Dark Matter density and velocity
distribution.


  We devise and experimentally realize a procedure capable of detecting and
distinguishing quantum discord and classical correlations as well the presence
of factorized states in a joint system-environment setting. Our scheme builds
on recent theoretical results showing how the distinguishability between two
reduced states of a quantum system in a bipartite setting can convey important
information about the correlations present in the bipartite state and the
interaction between the subsystems. The two addressed subsystems are the
polarization and spatial degrees of freedom of the signal beam generated by
parametric down-conversion, which are suitably prepared by the idler beam.
Different global and local operations allow for the detection of different
correlations by studying via state tomography the trace distance behavior
between suitable polarization subsystem states.


  Unmanned aerial vehicle (UAV) networks have grown rapidly in recent years and
become attractive for various emergence communications scenarios. In this
paper, we consider a UAV acting as a relay node to assist wireless
transmissions from a base station (BS) to a ground user (GUs). A closed-form
expression of outage probability for the BS-GUs transmission via UAV relaying
is derived over Rician fading channels. We then formulate an optimization
problem to minimize the outage probability of UAV relay communications with a
constraint on the total transit power of the BS and GUs. It is proved that our
formulated optimization problem is convex and an optimal power allocation
solution is found for the outage probability minimization. Simulation results
demonstrate that with an increasing power allocation factor, the outage
probability initially decreases and then starts to increase, showing the
existence of an optimal power allocation solution. Additionally, it is shown
that the proposed optimal power allocation scheme significantly outperforms the
conventional equal power allocation in terms of the outage probability.


  We show that suitably regulated multi-trace primary states in large N CFTs
behave like `in' and `out' scattering states in the flat-space limit of AdS.
Their transition matrix elements approach the exact scattering amplitudes for
the bulk theory, providing a natural CFT definition of the flat space S-Matrix.
We study corrections resulting from the AdS curvature and particle propagation
far from the center of AdS, and show that AdS simply provides an IR regulator
that disappears in the flat space limit.


  For an arbitrary infinite cardinal $\kappa$, we define classes of
coordinatewise $\kappa$-slender and tailwise $\kappa$-slender modules as well
as related classes of $h\kappa$-modules and initiate a study of these classes.


  Magnetic chains of hollow cobalt nanoparticles (450-750 nm) have been
synthesized by using poly(vinyl pyrrolidone) (PVP) as a template in an ethylene
glycol solution. In this process, CoCl2.6H2O was reduced by N2H4.H2O in the
presence of PVP. All of the Co nanoparticles are hollow with a shell of 40 nm
and self-assembled into a chainlike structure that is as long as ~10 micron. At
300 K, the branched hollow Co nanoparticle chains exhibit a saturation
magnetization of 37.5 emu/g, a remnant magnetization of approximately 1.55
emu/g, and a coercivity of about 66 Oe, which is more than an order of
magnitude larger than that of the bulk


  We point out some serious mistakes in the investigation of Zhukovskii et al.
Both the amplitude and the probability of the process were calculated wrongly,
that is, the problem of the neutrino pair production by a virtual photon in an
external magnetic field is still unsolved.


  Reverberation mapping of nearby active galactic nuclei has led to estimates
of broad-line-region (BLR) sizes and central-object masses for some 37 objects
to date. However, successful reverberation mapping has yet to be performed for
quasars of either high luminosity (above L_opt~10^{46} erg/s) or high redshift
(z>0.3). Over the past six years, we have carried out, at the Hobby-Eberly
Telescope, rest-frame-ultraviolet spectrophotometric monitoring of a sample of
six quasars at redshifts z=2.2--3.2, with luminosities of
L_opt~10^{46.4}--10^{47.6} erg/s, an order of magnitude greater than those of
previously mapped quasars. The six quasars, together with an additional five
having similar redshift and luminosity properties, were monitored
photometrically at the Wise Observatory during the past decade. All 11 quasars
monitored show significant continuum variations of order 10%--70%. This is
about a factor of two smaller variability than for lower luminosity quasars
monitored over the same rest-frame period. In the six objects which have been
spectrophotometrically monitored, significant variability is detected in the
CIV1550 broad emission line. In several cases the variations track the
continuum variations in the same quasar, with amplitudes comparable to, or even
greater than, those of the corresponding continua. In contrast, no significant
Ly\alpha variability is detected in any of the four objects in which it was
observed. Thus, UV lines may have different variability trends in
high-luminosity and low-luminosity AGNs. For one quasar, S5~0836+71 at z=2.172,
we measure a tentative delay of 595 days between CIV and UV-continuum
variations, corresponding to a rest-frame delay of 188 days and a central
black-hole mass of 2.6\times10^9 M_\odot.


  We investigate the nonparametric, composite hypothesis testing problem for
arbitrary unknown distributions in the asymptotic regime where both the sample
size and the number of hypotheses grow exponentially large. Such asymptotic
analysis is important in many practical problems, where the number of
variations that can exist within a family of distributions can be countably
infinite. We introduce the notion of \emph{discrimination capacity}, which
captures the largest exponential growth rate of the number of hypotheses
relative to the sample size so that there exists a test with asymptotically
vanishing probability of error. Our approach is based on various distributional
distance metrics in order to incorporate the generative model of the data. We
provide analyses of the error exponent using the maximum mean discrepancy (MMD)
and Kolmogorov-Smirnov (KS) distance and characterize the corresponding
discrimination rates, i.e., lower bounds on the discrimination capacity, for
these tests. Finally, an upper bound on the discrimination capacity based on
Fano's inequality is developed. Numerical results are presented to validate the
theoretical results.


  Aggregating successfully the choices regarding a given decision problem made
by the multiple collective members into a single solution is essential for
exploiting the collective's intelligence and for effective crowdsourcing. There
are various aggregation techniques, some of which come down to a simple and
sometimes effective deterministic aggregation rule. However, it has been shown
that the efficiency of those techniques is unstable under varying conditions
and within different domains. Other methods mainly rely on learning from the
decision-makers previous responses or the availability of additional
information about them. In this study, we present two one-shot
machine-learning-based aggregation approaches. The first predicts, given
multiple features about the collective's choices, including meta-cognitive
ones, which aggregation method will be best for a given case. The second
directly predicts which decision is optimal, given, among other things, the
selection made by each method. We offer a meta-cognitive feature-engineering
approach for characterizing a collective decision-making case in a
context-sensitive fashion. In addition, we offer a new aggregation method, the
Devil's-Advocate aggregator, to deal with cases in which standard aggregation
methods are predicted to fail. Experimental results show that using either of
our proposed approaches increases the percentage of successfully aggregated
cases (i.e., cases in which the correct answer is returned) significantly,
compared to the uniform application of each rule-based aggregation method. We
also demonstrate the importance of the Devil's Advocate aggregator.


  The triviality of the scalar sector of the standard one-doublet Higgs model
implies that this model is only an effective low-energy theory valid below some
cut-off scale $\Lambda$. The underlying high-energy theory must include flavor
dynamics at a scale of order $\Lambda$ or greater in order to give rise to the
different Yukawa couplings of the Higgs to ordinary fermions. This flavor
dynamics will generically produce flavor-changing neutral currents and
non-universal corrections to Z -> b b-bar. We show that the experimental
constraints on the neutral D-meson mass difference imply that $\Lambda$ must be
greater than of order 21 TeV. We also discuss bounds on $\Lambda$ from the
constraints on extra contributions to the K_L - K_S mass difference and to the
coupling of the Z boson to b-quarks. For theories defined about the
infrared-stable Gaussian fixed-point, we estimate that this lower bound on
$\Lambda$ yields an upper bound of approximately 460 GeV on the Higgs boson's
mass, independent of the regulator chosen to define the theory.


  In this paper we prove a new extremal property of the Reuleaux triangle: it
maximizes the Cheeger constant among all bodies of (same) constant width. The
proof relies on a fine analysis of the optimality conditions satisfied by an
optimal Reuleaux polygon together with an explicit upper bound for the inradius
of the optimal domain. As a possible perspective, we conjecture that this
maximal property of the Reuleaux triangle holds for the first eigenvalue of the
$p$-Laplacian for any $p\in (1,+\infty)$ (the current paper covers the case
$p=1$ whereas the case $p=+\infty$ was already known).


  The application of psychophysiologicy in human-computer interaction is a
growing field with significant potential for future smart personalised systems.
Working in this emerging field requires comprehension of an array of
physiological signals and analysis techniques.
  Human speech affords, alongside linguistic content, rich information in the
intonation, voice quality, prosody, and rhythmic variation of utterances,
allowing listeners to recognise numerous distinct emotional states in the
speaker. Several types of factors affect speech, ranging from emotions to
cognitive load and pathological conditions, providing a promising non-intrusive
source for online understanding of context and psychophysiological state.
  This paper aims to serve as a primer for the novice, enabling rapid
familiarisation with the latest core concepts. We put special emphasis on
everyday human-computer interface applications to distinguish from the more
common clinical or sports uses of psychophysiology.
  This paper is an extract from a comprehensive review of the entire field of
ambulatory psychophysiology, including 12 similar chapters, plus application
guidelines and systematic review. Thus any citation should be made using the
following reference:
  B. Cowley, M. Filetti, K. Lukander, J. Torniainen, A. Henelius, L. Ahonen, O.
Barral, I. Kosunen, T. Valtonen, M. Huotilainen, N. Ravaja, G. Jacucci. The
Psychophysiology Primer: a guide to methods and a broad review with a focus on
human-computer interaction. Foundations and Trends in Human-Computer
Interaction, vol. 9, no. 3-4, pp. 150--307, 2016.


  Whereas current cosmological observations suggest that the universe is
dominated by a positive cosmological constant ($\Lambda > 0$), the AdS/CFT
correspondence tells us that the case $\Lambda<0$ is still worthy of
consideration. In this paper we study the McVittie solution with $\Lambda<0$.
Following a related study, the solution is understood here by way of a
systematic construction of conformal diagrams based on detailed numerical
integrations of the null geodesic equations. As in the pure Robertson - Walker
case, we find that $\Lambda<0$ ensures collapse to a Big Crunch, a feature
which completely dominates the global structure.


  Two-dimensional carbides and nitrides of transition metals, known as MXenes,
are a fast-growing family of 2D materials that draw attention as energy storage
materials. So far, MXenes are mainly prepared from Al-containing MAX phases
(where A = Al) by Al dissolution in F-containing solution, but most other MAX
phases have not been explored. Here, a redox-controlled A-site-etching of MAX
phases in Lewis acidic melts is proposed and validated by the synthesis of
various MXenes from unconventional MAX phase precursors with A elements Si, Zn,
and Ga. A negative electrode of Ti3C2 MXene material obtained through this
molten salt synthesis method delivers a Li+ storage capacity up to 738 C g-1
(205 mAh g-1) with high-rate performance and pseudocapacitive-like
electrochemical signature in 1M LiPF6 carbonate-based electrolyte. MXene
prepared from this molten salt synthesis route offer opportunities as high-rate
negative electrode material for electrochemical energy storage applications.


  The combinatorics of certain osculating lattice paths is studied, and a
relationship with oscillating tableaux is obtained. More specifically, the
paths being considered have fixed start and end points on respectively the
lower and right boundaries of a rectangle in the square lattice, each path can
take only unit steps rightwards or upwards, and two different paths are
permitted to share lattice points, but not to cross or share lattice edges.
Such paths correspond to configurations of the six-vertex model of statistical
mechanics with appropriate boundary conditions, and they include cases which
correspond to alternating sign matrices and various subclasses thereof.
Referring to points of the rectangle through which no or two paths pass as
vacancies or osculations respectively, the case of primary interest is tuples
of paths with a fixed number $l$ of vacancies and osculations. It is then shown
that there exist natural bijections which map each such path tuple $P$ to a
pair $(t,\eta)$, where $\eta$ is an oscillating tableau of length $l$ (i.e., a
sequence of $l+1$ partitions, starting with the empty partition, in which the
Young diagrams of successive partitions differ by a single square), and $t$ is
a certain, compatible sequence of $l$ weakly increasing positive integers.
Furthermore, each vacancy or osculation of $P$ corresponds to a partition in
$\eta$ whose Young diagram is obtained from that of its predecessor by
respectively the addition or deletion of a square. These bijections lead to
enumeration formulae for osculating paths involving sums over oscillating
tableaux.


  Many macroscopic non-Fourier heat conduction models have been developed in
the past decades based on Chapman-Enskog, Hermite or other small perturbation
expansion methods. These macroscopic models have made great success on
capturing non-Fourier thermal behaviors in solid materials, but most of them
are limited by small Knudsen numbers and incapable of capturing highly
non-equilibrium or ballistic thermal transport. In this paper, we provide a new
strategy for constructing macroscopic non-Fourier heat conduction modeling,
that is, using data-driven deep learning methods combined with non-equilibrium
thermodynamics instead of small perturbation expansion. We present the
mechanism-data fusion method, an approach that seamlessly integrates the
rigorous framework of Conservation-Dissipation Formalism (CDF) with the
flexibility of machine learning to model non-Fourier heat conduction.
Leveraging the conservation-dissipation principle with dual-dissipative
variables, we derive an interpretable series of partial differential equations,
fine-tuned through a training strategy informed by data from the phonon
Boltzmann transport equation. Moreover, we also present the inner-step
operation to narrow the gap from the discrete form to the continuous system.
Through numerical tests, our model demonstrates excellent predictive
capabilities across various heat conduction regimes, including diffusive,
hydrodynamic and ballistic regimes, and displays its robustness and precision
even with discontinuous initial conditions.


  We show how the Dijkgraaf-Vafa matrix model proposal can be extended to
describe five-dimensional gauge theories compactified on a circle to four
dimensions. This involves solving a certain quantum mechanical matrix model. We
do this for the lift of the N=1* theory to five dimensions. We show that the
resulting expression for the superpotential in the confining vacuum is
identical with the elliptic superpotential approach based on Nekrasov's
five-dimensional generalization of Seiberg-Witten theory involving the
relativistic elliptic Calogero-Moser, or Ruijsenaars-Schneider, integrable
system.


  We suggest a method for an approximative solution of the two dimensional
Hubbard model close to half filling. It is based on partial bosonisation,
supplemented by an investigation of the functional renormalisation group flow.
The inclusion of both the fermionic and bosonic fluctuations leads in lowest
order to agreement with the Hartree-Fock result or Schwinger-Dyson equation and
cures the ambiguity of mean field theory . We compute the temperature
dependence of the antiferromagnetic order parameter and the gap below the
critical temperature. We argue that the Mermin-Wagner theorem is not
practically applicable for the spontaneous breaking of the continuous spin
symmetry in the antiferromagnetic state of the Hubbard model. The long distance
behavior close to and below the critical temperature is governed by the
renormalisation flow for the effective interactions of composite Goldstone
bosons and deviates strongly from the Hartree-Fock result.


  Gradients in the stellar populations (SP) of galaxies -- e.g., in age,
metallicity, stellar Initial Mass Function (IMF) -- can result in gradients in
the stellar mass to light ratio, $M_*/L$. Such gradients imply that the
distribution of the stellar mass and light are different. For old SPs, e.g., in
early-type galaxies at $z\sim 0$, the $M_*/L$ gradients are weak if driven by
variations in age and metallicity, but significantly larger if driven by the
IMF. A gradient which has larger $M_*/L$ in the center increases the estimated
total stellar mass ($M_*$) and reduces the scale which contains half this mass
($R_{e,*}$), compared to when the gradient is ignored. For the IMF gradients
inferred from fitting MILES simple SP models to the H$_\beta$,
$\langle$Fe$\rangle$, [MgFe] and TiO$_{\rm 2SDSS}$ absorption lines measured in
spatially resolved spectra of early-type galaxies in the MaNGA survey, the
fractional change in $R_{e,*}$ can be significantly larger than that in $M_*$,
especially when the light is more centrally concentrated. The $R_{e,*}-M_*$
correlation which results is offset by 0.3 dex to smaller sizes compared to
when these gradients are ignored. Comparisons with `quiescent' galaxies at
higher-$z$ must account for evolution in SP gradients (especially age and IMF)
and the light profile before drawing conclusions about how $R_{e,*}$ and $M_*$
evolve. The implied merging between higher-$z$ and the present is less
contrived if $R_{e,*}/R_e$ at $z\sim 0$ is closer to our IMF-driven gradient
calibration than to unity.


  Building on the concept of a smooth DG algebra we define the notion of a
smooth derived category. We the propose the definition of a categorical
resolution of singularities. Our main example is the derived category $D(X)$ of
quasi-coherent sheaves on a scheme $X$. We prove that $D(X)$ has a canonical
categorical resolution if the base field is perfect and $X$ is a separated
scheme of finite type with a dualizing complex.


  We evaluate the grand potential of a cluster of two molecular species,
equivalent to its free energy of formation from a binary vapour phase, using a
nonequilibrium molecular dynamics technique where guide particles, each
tethered to a molecule by a harmonic force, move apart to disassemble a cluster
into its components. The mechanical work performed in an ensemble of
trajectories is analysed using the Jarzynski equality to obtain a free energy
of disassembly, a contribution to the cluster grand potential. We study
clusters of sulphuric acid and water at 300 K, using a classical interaction
scheme, and contrast two modes of guided disassembly. In one, the cluster is
broken apart through simple pulling by the guide particles, but we find the
trajectories tend to be mechanically irreversible. In the second approach, the
guide motion and strength of tethering are modified in a way that prises the
cluster apart, a procedure that seems more reversible. We construct a surface
representing the cluster grand potential, and identify a critical cluster for
droplet nucleation under given vapour conditions. We compare the equilibrium
populations of clusters with calculations reported by Henschel et al. [J. Phys.
Chem. A 118, 2599 (2014)] based on optimised quantum chemical structures.


  Very high energy {\gamma}-rays are one of the most important messengers of
the non-thermal Universe. The major motivation of very high energy {\gamma}-ray
astronomy is to find sources of high energy cosmic rays. Several astrophysical
sources are known to accelerate cosmic rays to very high energies under extreme
conditions. Very high energy {\gamma}-rays are produced at these astrophysical
sites or near through interactions of cosmic rays in the surrounding medium
close to the sources. Gamma-rays, being neutral, travel in a straight line and
thus give us valuable information about the cosmic ray sources and their
surroundings. Additionally, very high energy {\gamma}-ray astronomy can probe
many fundamental physics questions. Ground-based {\gamma}-ray astronomy began
its journey in 1989 when Whipple telescope detected TeV {\gamma}-rays from the
Crab, a pulsar wind nebula in the Milky Way. In the last two decades,
technological improvements have facilitated the development of the latest
generation of very high energy detectors and telescopes which have delivered
exciting new results. Until now over two hundred very high energy {\gamma}-ray
sources, both galactic and extra-galactic has been detected. These observations
have provided a deeper insight into a large number of important questions in
high energy astrophysics and astroparticle physics. This review article is an
attempt to enumerate the most important results in the exciting and rapidly
developing field of very high energy {\gamma}-ray astronomy.


  This paper introduces a new method to solve the problem of the approximation
of the diagonal for face-coherent families of polytopes. We recover the
classical cases of the simplices and the cubes and we solve it for the
associahedra, also known as Stasheff polytopes. We show that it satisfies an
easy-to-state cellular formula. For the first time, we endow a family of
realizations of the associahedra (the Loday realizations) with a topological
and cellular operad structure; it is shown to be compatible with the diagonal
maps.


  In this work we consider Randall-Sundrum braneworld type scenarios, in which
the spacetime is described by a five-dimensional manifold with matter fields
confined in a domain wall or three-brane. We present the results of a
systematic analysis, using dynamical systems techniques, of the qualitative
behaviour of Friedmann-Lemaitre-Robertson-Walker type models, whose matter is
described by a scalar field with an exponential potential. We construct the
state spaces for these models and discuss how their structure changes with
respect to the general-relativistic case, in particular, what new critical
points appear and their nature and the occurrence of bifurcation.


  Weak affine light typing (WALT) assigns light affine linear formulae as types
to a subset of lambda-terms in System F. WALT is poly-time sound: if a
lambda-term M has type in WALT, M can be evaluated with a polynomial cost in
the dimension of the derivation that gives it a type. In particular, the
evaluation can proceed under any strategy of a rewriting relation, obtained as
a mix of both call-by-name/call-by-value beta-reductions. WALT is poly-time
complete since it can represent any poly-time Turing machine. WALT weakens,
namely generalizes, the notion of stratification of deductions common to some
Light Systems -- we call as such those logical systems, derived from Linear
logic, to characterize FP, the set of Polynomial functions -- . A weaker
stratification allows to define a compositional embedding of the Quasi-linear
fragment QlSRN of Safe recursion on notation (SRN) into WALT. QlSRN is SRN,
which is a recursive-theoretical system characterizing FP, where only the
composition scheme is restricted to linear safe variables. So, the expressivity
of WALT is stronger, as compared to the known Light Systems. In particular,
using the types, the embedding puts in evidence the stratification of normal
and safe arguments hidden in QlSRN: the less an argument is impredicative, the
deeper, in a formal, proof-theoretical sense, gets its representation in WALT.


  In this paper we study the development in Taylor series of the function
$f(x)=x^x$. First section establishes a recursive relationship among successive
derivatives of the function by using the coefficients defined therein. From
recursion between the derivatives you get one general description of them
(section 2). Finally, section 3 has the main result, the expansion series.
Section 4 deals with the coefficients: characterization, their relationship
with rencontre numbers and their emergence as coefficients in certain
polynomial families. The specific use of some of these polynomials allows
eventually go deeper in the description of the series.


  Mobile devices with embedded sensors for data collection and environmental
sensing create a basis for a cost-effective approach for data trading. For
example, these data can be related to pollution and gas emissions, which can be
used to check the compliance with national and international regulations. The
current approach for IoT data trading relies on a centralized third-party
entity to negotiate between data consumers and data providers, which is
inefficient and insecure on a large scale. In comparison, a decentralized
approach based on distributed ledger technologies (DLT) enables data trading
while ensuring trust, security, and privacy. However, due to the lack of
understanding of the communication efficiency between sellers and buyers, there
is still a significant gap in benchmarking the data trading protocols in IoT
environments. Motivated by this knowledge gap, we introduce a model for
DLT-based IoT data trading over the Narrowband Internet of Things (NB-IoT)
system, intended to support massive environmental sensing. We characterize the
communication efficiency of three basic DLT-based IoT data trading protocols
via NB-IoT connectivity in terms of latency and energy consumption. The model
and analyses of these protocols provide a benchmark for IoT data trading
applications.


  In this paper, we study the effect of ultra-high energy collisions of two
particles with different energies near the horizon of a 2+1 dimensional BTZ
black hole (BSW effect). We find that the particle with the critical angular
momentum could exist inside the outer horizon of BTZ black hole regardless of
the particle energy. Therefore, for the non-extremal BTZ black hole, the BSW
process is possible on the inner horizon with the fine tuning of parameters
which are characterized by the motion of particle. While for the extremal BTZ
black hole, the particle with the critical angular momentum could only exist on
the degenerate horizon, and the BSW process could also happen there.


  We generalize the maximum likelihood method to non-Gaussian distribution
functions by means of the multivariate Edgeworth expansion. We stress the
potential interest of this technique in all those cosmological problems in
which the determination of a non-Gaussian signature is relevant, e.g. in the
analysis of large scale structure and cosmic microwave background. A first
important result is that the asymptotic confidence limits on the parameters are
systematically widened when the higher order correlation functions are
positive, with respect to the Gaussian case.


  Transfer learning has recently attracted significant research attention, as
it simultaneously learns from different source domains, which have plenty of
labeled data, and transfers the relevant knowledge to the target domain with
limited labeled data to improve the prediction performance. We propose a
Bayesian transfer learning framework where the source and target domains are
related through the joint prior density of the model parameters. The modeling
of joint prior densities enables better understanding of the "transferability"
between domains. We define a joint Wishart density for the precision matrices
of the Gaussian feature-label distributions in the source and target domains to
act like a bridge that transfers the useful information of the source domain to
help classification in the target domain by improving the target posteriors.
Using several theorems in multivariate statistics, the posteriors and posterior
predictive densities are derived in closed forms with hypergeometric functions
of matrix argument, leading to our novel closed-form and fast Optimal Bayesian
Transfer Learning (OBTL) classifier. Experimental results on both synthetic and
real-world benchmark data confirm the superb performance of the OBTL compared
to the other state-of-the-art transfer learning and domain adaptation methods.


  We construct aligned and unaligned stationary perturbation configurations in
a composite system of stellar and coplanarly magnetized gaseous singular
isothermal discs (SIDs) coupled by gravity. In comparison with SID problems
studied earlier, there exist three possible classes of stationary solutions
allowed by more dynamic freedoms. Our exact global perturbation solutions and
critical points are valuable for testing numerical magnetohydrodynamic codes.
For galactic applications, our model analysis contains more realistic elements
and offer useful insights for structures and dynamics of disc galaxies
consisting of stars and magnetized gas.


  Random samples are lossy summaries which allow queries posed over the data to
be approximated by applying an appropriate estimator to the sample. The
effectiveness of sampling, however, hinges on estimator selection. The choice
of estimators is subjected to global requirements, such as unbiasedness and
range restrictions on the estimate value, and ideally, we seek estimators that
are both efficient to derive and apply and {\em admissible} (not dominated, in
terms of variance, by other estimators). Nevertheless, for a given data domain,
sampling scheme, and query, there are many admissible estimators. We study the
choice of admissible nonnegative and unbiased estimators for monotone sampling
schemes. Monotone sampling schemes are implicit in many applications of massive
data set analysis. Our main contribution is general derivations of admissible
estimators with desirable properties. We present a construction of {\em
order-optimal} estimators, which minimize variance according to {\em any}
specified priorities over the data domain. Order-optimality allows us to
customize the derivation to common patterns that we can learn or observe in the
data. When we prioritize lower values (e.g., more similar data sets when
estimating difference), we obtain the L$^*$ estimator, which is the unique
monotone admissible estimator. We show that the L$^*$ estimator is
4-competitive and dominates the classic Horvitz-Thompson estimator. These
properties make the L$^*$ estimator a natural default choice. We also present
the U$^*$ estimator, which prioritizes large values (e.g., less similar data
sets). Our estimator constructions are both easy to apply and possess desirable
properties, allowing us to make the most from our summarized data.


  Many hot Jupiters may experience orbital decays, which are manifested as
long-term transit timing variations. We have analyzed 7068 transits from the
Transiting Exoplanet Survey Satellite (TESS) for a sample of 326 hot Jupiters.
These new mid-transit time data allow us to update ephemerides for these
systems. By combining the new TESS transit timing data with archival data, we
search for possible long-term orbital period variations in these hot Jupiters
using a linear and a quadratic ephemeris model. We identified 26 candidates
that exhibit possible long-term orbital period variations, including 18
candidates with decreasing orbital periods and 8 candidates with increasing
orbital periods. Among them, 12 candidates have failed in our leave-one-out
cross-validation (LOOCV) test and thus should be considered as marginal
candidates. In addition to tidal interaction, alternative mechanisms such as
apsidal precession, R{\o}mer effect, and Applegate effect could also contribute
to the observed period variations. The ephemerides derived in this work are
useful for scheduling follow-up observations for these hot Jupiters in the
future. The Python code used to generate the ephemerides is made available
online.


  Off-road semantic segmentation with fine-grained labels is necessary for
autonomous vehicles to understand driving scenes, as the coarse-grained road
detection can not satisfy off-road vehicles with various mechanical properties.
Fine-grained semantic segmentation in off-road scenes usually has no unified
category definition due to ambiguous nature environments, and the cost of
pixel-wise labeling is extremely high. Furthermore, semantic properties of
off-road scenes can be very changeable due to various precipitations,
temperature, defoliation, etc. To address these challenges, this research
proposes an active and contrastive learning-based method that does not rely on
pixel-wise labels, but only on patch-based weak annotations for model learning.
There is no need for predefined semantic categories, the contrastive
learning-based feature representation and adaptive clustering will discover the
category model from scene data. In order to actively adapt to new scenes, a
risk evaluation method is proposed to discover and select hard frames with
high-risk predictions for supplemental labeling, so as to update the model
efficiently. Experiments conducted on our self-developed off-road dataset and
DeepScene dataset demonstrate that fine-grained semantic segmentation can be
learned with only dozens of weakly labeled frames, and the model can
efficiently adapt across scenes by weak supervision, while achieving almost the
same level of performance as typical fully supervised baselines.


  This paper is concerned with the accurate, conservative, and stable
imposition of boundary conditions and inter-element coupling for
multi-dimensional summation-by-parts (SBP) finite-difference operators. More
precisely, the focus is on diagonal-norm SBP operators that are not based on
tensor products and are applicable to unstructured grids composed of arbitrary
elements. We show how penalty terms --- simultaneous approximation terms (SATs)
--- can be adapted to discretizations based on multi-dimensional SBP operators
to enforce boundary and interface conditions. A general SAT framework is
presented that leads to conservative and stable discretizations of the
variable-coefficient advection equation. This framework includes the case where
there are no nodes on the boundary of the SBP element at which to apply
penalties directly. This is an important generalization, because elements
analogous to Legendre-Gauss collocation, \ie without boundary nodes, typically
have higher accuracy for the same number of degrees of freedom. Symmetric and
upwind examples of the general SAT framework are created using a decomposition
of the symmetric part of an SBP operator; these particular SATs enable the
pointwise imposition of boundary and inter-element conditions. We illustrate
the proposed SATs using triangular-element SBP operators with and without nodes
that lie on the boundary. The accuracy, conservation, and stability properties
of the resulting SBP-SAT discretizations are verified using linear advection
problems with spatially varying divergence-free velocity fields.


  Finding a basis matrix (dictionary) by which objective signals are
represented sparsely is of major relevance in various scientific and
technological fields. We consider a problem to learn a dictionary from a set of
training signals. We employ techniques of statistical mechanics of disordered
systems to evaluate the size of the training set necessary to typically succeed
in the dictionary learning. The results indicate that the necessary size is
much smaller than previously estimated, which theoretically supports and/or
encourages the use of dictionary learning in practical situations.


  The present article discusses the impact of liquid coolant subcooling on
multiphase fluid flow and boiling heat transfer in porous media with internal
heat generation. Although extremely relevant and important, only limited
studies are available in the open literature on the effects of coolant
subcooling in heat-generating porous media and hence, this requires a detailed
analysis. The analysis is carried out using a developed computational model of
multiphase fluid flow through clear fluid and porous media considering the
relevant heat transfer and mass transfer phenomena. Results suggest that the
qualitative nature of boiling heat transfer from the heat-generating porous
body, with subcooled coolants, remain similar to that observed with a saturated
coolant. Quantitative differences are, however, observed as a result of
solid-liquid convective heat transfer, and competing mechanisms of boiling and
condensation heat transfer, when subcooled liquid coolant is present. It is
observed that a substantially larger heating power density is required - as
coolant subcooling is increased - for achieving the same temperature rise in
the porous body. Dryout power density at different coolant subcooling is also
observed to follow a similar trend. The thermal energy removal limit is, hence,
observed to be substantially enhanced in presence of subcooled coolants.
Further, the dryout region within the porous body is observed to gradually
shift towards its interior as the coolant subcooling is increased.


  Large-scale quantum networks have been employed to overcome practical
constraints of transmissions and storage for single entangled systems. Our goal
in this article is to explore the strong entanglement distribution of quantum
networks. We firstly show any connected network consisting of generalized EPR
states and GHZ states satisfies strong CKW monogamy inequality in terms of
bipartite entanglement measure. This reveals interesting feature of
high-dimensional entanglement with local tensor decomposition going beyond
qubit entanglement. We then apply the new entanglement distribution relation in
entangled networks for getting quantum max-flow min-cut theorem in terms of von
Neumann entropy and R\'{e}nyi-$\alpha$ entropy. We finally classify entangled
quantum networks by distinguishing network configurations under local unitary
operations. These results provide new insights into characterizing quantum
networks in quantum information processing.


  Convex clustering, a convex relaxation of k-means clustering and hierarchical
clustering, has drawn recent attentions since it nicely addresses the
instability issue of traditional nonconvex clustering methods. Although its
computational and statistical properties have been recently studied, the
performance of convex clustering has not yet been investigated in the
high-dimensional clustering scenario, where the data contains a large number of
features and many of them carry no information about the clustering structure.
In this paper, we demonstrate that the performance of convex clustering could
be distorted when the uninformative features are included in the clustering. To
overcome it, we introduce a new clustering method, referred to as Sparse Convex
Clustering, to simultaneously cluster observations and conduct feature
selection. The key idea is to formulate convex clustering in a form of
regularization, with an adaptive group-lasso penalty term on cluster centers.
In order to optimally balance the tradeoff between the cluster fitting and
sparsity, a tuning criterion based on clustering stability is developed. In
theory, we provide an unbiased estimator for the degrees of freedom of the
proposed sparse convex clustering method. Finally, the effectiveness of the
sparse convex clustering is examined through a variety of numerical experiments
and a real data application.


  We give an algebraic geometric compactification of certain moduli spaces of
semistable E-pairs in the sense of Yokogawa. In particular, we obtain a
compactification of the moduli spaces of semistable Higgs pairs on a curve
which were constructed by N. Hitchin.


  In this paper we study a cross-diffusion system whose coefficient matrix is
non-symmetric and degenerate. The system arises in the study of tissue growth
with autophagy. The existence of a weak solution is established. We also
investigate the limiting behavior of solutions as the pressure gets stiff. The
so-called incompressible limit is a free boundary problem of Hele-Shaw type.
Our key new discovery is that the usual energy estimate still holds as long as
the time variable stays away from $0$.


  We propose a scotogenic model for generating neutrino masses through a
three-loop seesaw. It is a minimally extended inert doublet model with a
spontaneously broken global symmetry $U(1)'$ and a preserved $\mathbb{Z}_2$
symmetry. The three-loop suppression allows the new particles to have masses at
the TeV scale without fine-tuning the Yukawa couplings. The model leads to a
rich phenomenology while satisfying all the current constraints imposed by
neutrinoless double-beta decay, charged-lepton flavor violation, and
electroweak precision observables. The relatively large Yukawa couplings lead
to sizable rates for charged lepton flavor violation processes, well within
future experimental reach. The model could also successfully explain the $W$
mass anomaly and provides viable fermionic or scalar dark matter candidates.


  We conducted multi-epoch, multi-frequency parsec-scale studies on the
gigahertz-peaked spectrum quasar PKS 0858-279 with the Very Long Baseline Array
(VLBA). Our observations on 2005-11-26 elucidated a weak core, characterized by
an inverted spectrum, and a distinctly bent jet that exhibited a notable bright
feature in its Stokes I emission. Through comprehensive analysis of
polarization and spectral data, we inferred the formation of a shock wave
within this feature, stemming from interactions with a dense cloud in the
ambient medium. In this paper, VLBI-Gaia astrometry further reinforces the core
identification. With a deep analysis of six additional VLBA epochs spanning
from 2007 to 2018, we observed that while the quasar's parsec-scale structure
remained largely consistent, there were discernible flux density changes. These
variations strongly imply the recurrent ejection of plasma into the jet.
Complementing our VLBA data, RATAN-600 observations of the integrated spectra
suggested an interaction between standing and travelling shock waves in 2005.
Moreover, our multi-epoch polarization analysis revealed a drastic drop in
rotation measure values from 6000 rad/m^2 to 1000 rad/m^2 within a single year,
attributable to diminishing magnetic fields and particle density in an external
cloud. This change is likely instigated by a shock in the cloud, triggered by
the cloud's interaction with the jet, subsequently prompting its expansion.
Notably, we also observed a significant change in the magnetic field direction
of the jet, from being perpendicular post its observed bend to being
perpendicular prior to the bend - an alteration possibly induced by the
dynamics of shock waves.