  The unique electronic and crystal structures driven by external pressure in
transition metal chalcogenides (TMCs) can host emergent quantum states. Here we
report pressure-induced metallization, nontrivial $Z_2$ band topology and
superconductivity in TMC $\mathrm{Ta}_2 \mathrm{Ni}_3 \mathrm{Te}_5$. Our
electrical transport measurements show that the metallization emerges at 3.3
GPa, followed by appearance of the superconductivity at $P_{\mathrm{c}}$ = 21.3
GPa with $T_{\mathrm{c}} \sim$ 0.4 K. Room-temperature synchrotron x-ray
diffraction experiments demonstrate the stability of the pristine orthorhombic
structure upon compression. Our first-principles calculations further reveal a
topological phase transition (from $Z_2=0$ to $Z_2=1$), which occurs after
$\mathrm{Ta}_2 \mathrm{Ni}_3 \mathrm{Te}_5$ is turned into an electron-hole
compensated semimetal by pressure. The pressure-induced superconductivity at
$P_{\mathrm{c}}$ could be attributed to the abruptly enhanced density of states
at the Fermi level. These findings demonstrate that $\mathrm{Ta}_2
\mathrm{Ni}_3 \mathrm{Te}_5$ is a new platform for realizing exotic quantum
phenomena in TMCs, as well as exploring the interplay between topological
property and superconductivity.


  A fundamental tool in topological data analysis is persistent homology, which
allows extraction of information from complex datasets in a robust way.
Persistent homology assigns a module over a principal ideal domain to a
one-parameter family of spaces obtained from the data. In applications data
often depend on several parameters, and in this case one is interested in
studying the persistent homology of a multiparameter family of spaces
associated to the data. While the theory of persistent homology for
one-parameter families is well-understood, the situation for multiparameter
families is more delicate. Following Carlsson and Zomorodian we recast the
problem in the setting of multigraded algebra, and we propose multigraded
Hilbert series, multigraded associated primes and local cohomology as
invariants for studying multiparameter persistent homology. Multigraded
associated primes provide a stratification of the region where a multigraded
module does not vanish, while multigraded Hilbert series and local cohomology
give a measure of the size of components of the module supported on different
strata. These invariants generalize in a suitable sense the invariant for the
one-parameter case.


  Significant work has been done on advancing localization and mapping in
underwater environments. Still, state-of-the-art methods are challenged by
low-texture environments, which is common for underwater settings. This makes
it difficult to use existing methods in diverse, real-world scenes. In this
paper, we present TURTLMap, a novel solution that focuses on textureless
underwater environments through a real-time localization and mapping method. We
show that this method is low-cost, and capable of tracking the robot
accurately, while constructing a dense map of a low-textured environment in
real-time. We evaluate the proposed method using real-world data collected in
an indoor water tank with a motion capture system and ground truth reference
map. Qualitative and quantitative results validate the proposed system achieves
accurate and robust localization and precise dense mapping, even when subject
to wave conditions. The project page for TURTLMap is
https://umfieldrobotics.github.io/TURTLMap.


  The very high energy (VHE) gamma ray spectral index of high energy peaked
blazars correlates strongly with its corresponding redshift whereas no such
correlation is observed in the X-ray or the GeV bands. We attribute this
correlation to a result of photon-photon absorption of TeV photons with the
extragalactic background light (EBL) and utilizing this, we compute the allowed
flux range for the EBL, which is independent of previous estimates. The
observed VHE spectrum of the sources in our sample can be well approximated by
a power-law, and if the de-absorbed spectrum is also assumed to be a power law,
then we show that the spectral shape of EBL will be $\epsilon n(\epsilon) \sim
k log(\frac{\epsilon}{\epsilon_p}) $. We estimate the range of values for the
parameters defining the EBL spectrum, $k$ and $\epsilon_p$, such that the
correlation of the intrinsic VHE spectrum with redshift is nullified. The
estimated EBL depends only on the observed correlation and the assumption of a
power law source spectrum. Specifically, it does not depend on the spectral
modeling or radiative mechanism of the sources, nor does it depend on any
theoretical shape of the EBL spectrum obtained through cosmological
calculations. The estimated EBL spectrum is consistent with the upper and lower
limits imposed by different observations. Moreover, it also agrees closely with
the theoretical estimates obtained through cosmological evolution models.


  In multiple scientific and technological applications we face the problem of
having low dimensional data to be justified by a linear model defined in a high
dimensional parameter space. The difference in dimensionality makes the problem
ill-defined: the model is consistent with the data for many values of its
parameters. The objective is to find the probability distribution of parameter
values consistent with the data, a problem that can be cast as the exploration
of a high dimensional convex polytope. In this work we introduce a novel
algorithm to solve this problem efficiently. It provides results that are
statistically indistinguishable from currently used numerical techniques while
its running time scales linearly with the system size. We show that the
algorithm performs robustly in many abstract and practical applications. As
working examples we simulate the effects of restricting reaction fluxes on the
space of feasible phenotypes of a {\em genome} scale E. Coli metabolic network
and infer the traffic flow between origin and destination nodes in a real
communication network.


  It is well known that the Milgrom's MOND (modified Newtonian dynamics)
explains well the mass discrepancy problem in galaxy rotation curves. The MOND
predicts a universal acceleration scale below which the Newtonian dynamics is
invalid yet. The universal acceleration scale we got from the SPARC dataset is
$g_{\dag}=1.02\times10^{-10} \rm m~s^{-2}$. Milgrom suggested that the
acceleration scale may be a fingerprint of cosmology on local dynamics and
related with the Hubble constant $g_{\dag}\sim cH_0$. In this paper, we use the
hemisphere comparison method with the SPARC dataset to investigate the spatial
anisotropy on the acceleration scale. We find that the hemisphere of the
maximum acceleration scale is in the direction $(l,b) =
({175.5^\circ}^{+6^\circ}_{-10^\circ}, {-6.5^\circ}^{+8^\circ}_{-3^\circ})$
with $g_{\dag,max}=1.10\times10^{-10} \rm m~s^{-2}$, while the hemisphere of
the minimum acceleration scale is in the opposite direction $(l,b) =
({355.5^\circ}^{+6^\circ}_{-10^\circ}, {6.5^\circ}^{+3^\circ}_{-8^\circ})$ with
$g_{\dag,min}=0.76\times10^{-10} \rm m~s^{-2}$. The maximum anisotropy level
reaches up to $0.37\pm0.04$. Robust tests present that such a level of
anisotropy can't be reproduced by a statistically isotropic data. In addition,
we show that the spatial anisotropy on the acceleration scale has little
correlation with the non-uniform distribution of the SPARC data points in sky.
We also find that the maximum anisotropy direction is close with other
cosmological preferred directions, especially the direction of the "Australia
dipole" for the fine structure constant.


  We propose a generalization of the best arm identification problem in
stochastic multi-armed bandits (MAB) to the setting where every pull of an arm
is associated with delayed feedback. The delay in feedback increases the
effective sample complexity of standard algorithms, but can be offset if we
have access to partial feedback received before a pull is completed. We propose
a general framework to model the relationship between partial and delayed
feedback, and as a special case we introduce efficient algorithms for settings
where the partial feedback are biased or unbiased estimators of the delayed
feedback. Additionally, we propose a novel extension of the algorithms to the
parallel MAB setting where an agent can control a batch of arms. Our
experiments in real-world settings, involving policy search and hyperparameter
optimization in computational sustainability domains for fast charging of
batteries and wildlife corridor construction, demonstrate that exploiting the
structure of partial feedback can lead to significant improvements over
baselines in both sequential and parallel MAB.


  In the classification of real singularities by Arnold et al. (1985), normal
forms, as representatives of equivalence classes under right equivalence, are
not always uniquely determined. We describe the complete structure of the
equivalence classes of the unimodal real singularities of corank 2. In other
words, we explicitly answer the question which normal forms of different type
are equivalent, and how a normal form can be transformed within the same
equivalence class by changing the value of the parameter. This provides new
theoretical insights into these singularities and has important consequences
for their algorithmic classification.


  Interconnection and damping assignment passivity-based control scheme has
been used to stabilize many physical systems such as underactuated mechanical
systems through total energy shaping. In this method, some partial differential
equations (PDEs) arisen by kinetic and potential energy shaping, shall be
solved analytically. Finding a suitable desired inertia matrix as the solution
of nonlinear PDEs related to kinetic energy shaping is a challenging problem.
  In this paper, a systematic approach to solve this matching equation for
systems with one degree of underactuation is proposed. A special structure for
desired inertia matrix is proposed to simplify the solution of the
corresponding PDE. It is shown that the proposed method is more general than
that of some reported methods in the literature. In order to derive a suitable
desired inertia matrix, a necessary condition is also derived. The proposed
method is applied to three examples, including VTOL aircraft, pendubot and 2D
SpiderCrane system.


  We conducted a study on the X-ray polarization properties of MGC-5-23-16 by
analyzing long-term monitoring data from NuSTAR jointly with IXPE observations
made in May and November 2022. The re-analysis of IXPE data gives
model-dependent polarization degree, PD (\%)=$1.08\pm0.66$ in the energy band
$2-8$ keV, which agrees with previous studies within error bars. The
model-independent analysis of PD poses an upper limit of $\leq3.8$ ($1\sigma$
level) for the same energy band. The observed upper limit of PD, along with
broadband spectral analysis ($2-79$ keV) using an accretion-ejection based
model, allowed us to derive the corona geometry (i.e. radius and height) and
the accretion disk inclination ($\sim 33^\circ$). Additional {\it NuSTAR}
observations were also analyzed to gain insights into the accretion flow
properties of the source and to estimate the expected polarization during those
epochs with PD $\sim 4.3\%$. The radius and height of the corona varies between
$28.2\pm3.1 - 39.8\pm4.6$ r$_s$ and $14.3\pm1.7-21.4\pm1.9$ r$_s$ respectively,
with a mass outflow rate from the corona measuring $0.14\pm0.03-0.2\pm0.03$
Eddington rate ($\dot m_{\rm Edd}$). The spectral analysis further provided an
estimate for the mass of the central black hole $\sim 2\times 10^7$ M$_\odot$
and the velocity of the outflowing gas $\sim 0.16-0.19c$. A comparative
broadband spectral study using reflection-based models estimates the disk
inclination between $\sim 31^\circ\pm8^\circ-45^\circ\pm7^\circ$, and yields an
expected PD of 3.4-6.0\%. We also found a weak reflection fraction and a less
ionized distant reflecting medium. Our modeling of the disk-corona-outflows and
polarization connection can be extended and validated with data from the
recently launched XPoSat, India's first X-ray Polarimeter Satellite, offering
potential applications to other sources.


  We determine the phase diagram of complete asymptotically free SU($N_c$)
gauge theories featuring $N_s$ complex scalars and $N_f$ Dirac quarks
transforming according to the fundamental representation of the gauge group.
The analysis is performed at the maximum known order in perturbation theory. We
unveil a very rich dynamics and associated phase structure. Intriguingly we
discover that the complete asymptotically free conditions guarantee that the
infrared dynamics displays long distance conformality, and in a regime when
perturbation theory is applicable. We conclude our analysis by determining the
quantum corrected potential of the theory and summarising the possible patterns
of radiative symmetry breaking. These theories are of potential
phenomenological interest as either elementary or composite ultraviolet finite
extensions of the Standard Model.


  The theory of screws clarifies many analogies between apparently unrelated
notions in mechanics, including the duality between forces and angular
velocities. It is known that the real 6-dimensional space of screws can be
endowed with an operator E, E^2 = 0, that converts it into a rank 3 free module
over the dual numbers. In this paper we prove the converse, namely, given a
rank 3 free module over the dual numbers, endowed with orientation and a
suitable scalar product (D-module geometry), we show that it is possible to
define, in a canonical way, a Euclidean space so that each element of the
module is represented by a screw vector field over it. The new approach has the
effectiveness of motor calculus while being independent of any reduction point.
It gives insights into the transference principle by showing that affine space
geometry is basically vector space geometry over the dual numbers. The main
results of screw theory are then recovered by using this point of view.


  The quest to realize novel phases of matter with topological order is an
important pursuit with implications for strongly correlated physics and quantum
information. Utilizing ideas from state-of-the-art coherent control of
artificial quantum systems such as superconducting circuits, we present a
proposal to realize bosonic fractional quantum Hall physics on small lattices
by creating nearly flat topological bands using staggered flux patterns.
Fingerprints of fractionalization through charge pumping can be observed with
nearly perfect quantization using as few as 24 lattice sites (two photons). We
suggest an implementation using a finite lattice of superconducting qubits with
cylindrical connectivity on both triangular and square lattices.


  We perform optimization of Q-factor in the system of freestanding
three/four/five/six coaxial subwavelength dielectric disks over all scales.
Each parameter contributes almost one order of magnitude of the Q-factor due to
multiple avoided crossings of resonances to give totally the unprecedented
values for the Q-factors: $6.6\cdot10^4$ for the three, $4.8\cdot10^6$ for
four, $8.5\cdot10^7$ for five and one billion for six freestanding silicon
disks. By multipole analysis of the resulting hybridized resonant mode we
observe that such extremely large values of the $Q$-factor are attributed to
strong redistribution of radiation that originates from almost exact
destructive interference of dominating complex multipole radiation amplitudes.


  We extend properties of the weak order on finite Coxeter groups to Weyl
groupoids admitting a finite root system. In particular, we determine the
topological structure of intervals with respect to weak order, and show that
the set of morphisms with fixed target object forms an ortho-complemented meet
semilattice. We define the Coxeter complex of a Weyl groupoid with finite root
system and show that it coincides with the triangulation of a sphere cut out by
a simplicial hyperplane arrangement. As a consequence, one obtains an algebraic
interpretation of many hyperplane arrangements that are not reflection
arrangements.


  We present a measurement of the accretion disk size of the quadruple lensed
quasar HE 0435-1223 from well-sampled 13-yr COSMOGRAIL optical light curves.
Using accurate time delays for the images A, B, C, and D, we modeled and
removed the intrinsic quasar variability, and found microlensing events of
amplitude up to 0.6, 0.4, and 0.5 mag in the images A, C and D respectively.
From the statistics of microlensing magnifications in these images we use
Bayesian methods to estimate the size of the quasar accretion disk. We have
inferred the half-light radius for the accretion disk using two different
methods, $R_{1/2} = 7.6_{-1.1}^{+12.0} \sqrt{M/0.3M_{\odot}}$ light-days
(histogram product) and $R_{1/2} = 7.7_{-1.2}^{+7.0} \sqrt{M/0.3M_{\odot}}$
light-days ($\chi^2$ criterion). The results are self-consistent and in good
agreement with the continuum size predicted by single-epoch spectroscopy and
previous studies making use of narrow-band photometry of HE 0435-1223.


  In this paper, we investigate the code-switching detection performance of a
code-switching (CS) automatic speech recognition (ASR) system with
data-augmented acoustic and language models. We focus on the recognition of
Frisian-Dutch radio broadcasts where one of the mixed languages, namely
Frisian, is under-resourced. Recently, we have explored how the acoustic
modeling (AM) can benefit from monolingual speech data belonging to the
high-resourced mixed language. For this purpose, we have trained
state-of-the-art AMs on a significantly increased amount of CS speech by
applying automatic transcription and monolingual Dutch speech. Moreover, we
have improved the language model (LM) by creating CS text in various ways
including text generation using recurrent LMs trained on existing CS text.
Motivated by the significantly improved CS ASR performance, we delve into the
CS detection performance of the same ASR system in this work by reporting CS
detection accuracies together with a detailed detection error analysis.


  Self-supervised pre-training paradigms have been extensively explored in the
field of skeleton-based action recognition. In particular, methods based on
masked prediction have pushed the performance of pre-training to a new height.
However, these methods take low-level features, such as raw joint coordinates
or temporal motion, as prediction targets for the masked regions, which is
suboptimal. In this paper, we show that using high-level contextualized
features as prediction targets can achieve superior performance. Specifically,
we propose Skeleton2vec, a simple and efficient self-supervised 3D action
representation learning framework, which utilizes a transformer-based teacher
encoder taking unmasked training samples as input to create latent
contextualized representations as prediction targets. Benefiting from the
self-attention mechanism, the latent representations generated by the teacher
encoder can incorporate the global context of the entire training samples,
leading to a richer training task. Additionally, considering the high temporal
correlations in skeleton sequences, we propose a motion-aware tube masking
strategy which divides the skeleton sequence into several tubes and performs
persistent masking within each tube based on motion priors, thus forcing the
model to build long-range spatio-temporal connections and focus on
action-semantic richer regions. Extensive experiments on NTU-60, NTU-120, and
PKU-MMD datasets demonstrate that our proposed Skeleton2vec outperforms
previous methods and achieves state-of-the-art results.


  We obtain sharp estimates for certain trilinear oscillatory integrals. In
particular, we extend Phong and Stein's seminal result to a trilinear setting.
This result partially answers a question raised by Christ, Li, Tao and Thiele
concerning the sharp estimates for certain multilinear oscillatory integrals.
The method in this paper relies on a self-contained algorithm of resolution of
singularities in $\mathbb R^2$, which may be of independent interest.


  We present an experimental and numerical study that describes the motion of
flexible blades, scaled to be dynamically similar to natural aquatic
vegetation, forced by wave-induced oscillatory flows. For the conditions
tested, blade motion is governed primarily by two dimensionless variables: (i)
the Cauchy number, $Ca$, which represents the ratio of the hydrodynamic forcing
to the restoring force due to blade stiffness, and (ii) the ratio of the blade
length to the wave orbital excursion, $L$. For flexible blades with $Ca \gg 1$,
the relationship between drag and velocity can be described by two different
scaling laws at the large- and small-excursion limits. For large excursions ($L
\ll 1$), the flow resembles a unidirectional current and the scaling laws
developed for steady-flow reconfiguration studies hold. For small excursions
($L \gg 1$), the beam equations may be linearized and a different scaling law
for drag applies. The experimental force measurements suggest that the
small-excursion scaling applies even for intermediate cases with $L \sim O(1)$.
The numerical model employs the well-known Morison force formulation, and
adequately reproduces the observed blade dynamics and measured hydrodynamic
forces without the use of any fitted parameters. For $Ca \gg 1$, the movement
of the flexible blades reduces the measured and modeled hydrodynamic drag
relative to a rigid blade of the same morphology. However, in some cases with
$Ca \sim O(1)$, the measured hydrodynamic forces generated by the flexible
blades exceed those generated by rigid blades, but this is not reproduced in
the model. Observations of blade motion suggest that this unusual behavior is
related to an unsteady vortex shedding event, which the simple numerical model
cannot reproduce. Finally, we also discuss implications for the modeling of
wave energy dissipation over canopies of natural aquatic vegetation.


  We have introduced coherent state neutral pion into Antisymmetrized Molecular
Dynamics. With the aid of coherent state technique, it becomes possible to
calculate transition matrix elements of the pion field operator and to study
excited states containing pions. For large pion-nucleon coupling fpiN > 1.6,
pions have a finite expectation value and bring large energy gain in 12C. We
discuss two aspects of pionic effects in spectroscopy; the LS interaction like
effect and the mixing of different nucleon parity states, which would modify
low energy nuclear levels.


  We report the results of a search for pair production of scalar bottom quarks
(sbottom) and scalar third-generation leptoquarks in 5.2 fb-1 of ppbar
collisions at the D0 experiment of the Fermilab Tevatron Collider. Scalar
bottom quarks are assumed to decay to a neutralino and a $b$ quark, and we set
95% C.L. lower limits on their production in the (m_sbottom, m_neutralino) mass
plane such as m_sbottom>247 GeV for m_neutralino=0 and m_neutralino>110 GeV for
160<m_sbottom<200 GeV. The leptoquarks are assumed to decay to a tau neutrino
and a $b$ quark, and we set a 95% C.L. lower limit of 247 GeV on the mass of a
charge-1/3 third-generation scalar leptoquark.


  We obtain an asymptotic formula for all moments of Dirichlet $L$-functions
$L(1,\chi)$ modulo $p$ when averaged over a subgroup of characters $\chi$ of
size $(p-1)/d$ with $\varphi(d)=o(\log p)$. Assuming the infinitude of Mersenne
primes, the range of our result is optimal and improves and generalises the
previous result of S. Louboutin and M. Munsch (2022) for second moments. We
also use our ideas to get an asymptotic formula for the second moment of
$L(1/2,\chi)$ over subgroups of characters of similar size. This leads to
non-vanishing results in this family where the proportion obtained depends on
the height of the smallest rational number lying in the dual group.
Additionally, we prove that, in both cases, we can take much smaller subgroups
for almost all primes $p$. Our method relies on pointwise and average estimates
on small solutions of linear congruences which in turn leads us to use and
modify some results for product sets of Farey fractions.


  Public-key quantum money is a cryptographic protocol in which a bank can
create quantum states which anyone can verify but no one except possibly the
bank can clone or forge. There are no secure public-key quantum money schemes
in the literature; as we show in this paper, the only previously published
scheme [1] is insecure. We introduce a category of quantum money protocols
which we call collision-free. For these protocols, even the bank cannot prepare
multiple identical-looking pieces of quantum money. We present a blueprint for
how such a protocol might work as well as a concrete example which we believe
may be insecure.


  In adiabatic rapid passage, the Bloch vector of a qubit is inverted by slowly
inverting an external field to which it is coupled, and along which it is
initially aligned. In non-adiabatic twisted rapid passage, the external field
is allowed to twist around its initial direction with azimuthal angle \phi(t)
at the same time that it is non-adiabatically inverted. For polynomial twist,
\phi(t) \sim Bt^{n}. We show that for n \ge 3, multiple qubit resonances can
occur during a single inversion of the external field, producing strong
interference effects in the qubit transition probability. The character of the
interference is controllable through variation of the twist strength B.
Constructive and destructive interference are possible, greatly enhancing or
suppressing qubit transitions. Experimental confirmation of these controllable
interference effects has already occurred. Application of this interference
mechanism to the construction of fast fault-tolerant quantum CNOT and NOT gates
is discussed.


  With the advent and popularity of big data mining and huge text analysis in
modern times, automated text summarization became prominent for extracting and
retrieving important information from documents. This research investigates
aspects of automatic text summarization from the perspectives of single and
multiple documents. Summarization is a task of condensing huge text articles
into short, summarized versions. The text is reduced in size for summarization
purpose but preserving key vital information and retaining the meaning of the
original document. This study presents the Latent Dirichlet Allocation (LDA)
approach used to perform topic modelling from summarised medical science
journal articles with topics related to genes and diseases. In this study,
PyLDAvis web-based interactive visualization tool was used to visualise the
selected topics. The visualisation provides an overarching view of the main
topics while allowing and attributing deep meaning to the prevalence individual
topic. This study presents a novel approach to summarization of single and
multiple documents. The results suggest the terms ranked purely by considering
their probability of the topic prevalence within the processed document using
extractive summarization technique. PyLDAvis visualization describes the
flexibility of exploring the terms of the topics' association to the fitted LDA
model. The topic modelling result shows prevalence within topics 1 and 2. This
association reveals that there is similarity between the terms in topic 1 and 2
in this study. The efficacy of the LDA and the extractive summarization methods
were measured using Latent Semantic Analysis (LSA) and Recall-Oriented
Understudy for Gisting Evaluation (ROUGE) metrics to evaluate the reliability
and validity of the model.


  It is shown that the multiplicative anomaly in the vector-axial-vector model,
which apparently has nothing to do with the breaking of classical current
symmetries, nevertheless is strictly related to the well known consistent and
covariant anomalies.


  Human intelligence is characterized not only by the capacity to learn complex
skills, but the ability to rapidly adapt and acquire new skills within an
ever-changing environment. In this work we study how the learning of modular
solutions can allow for effective generalization to both unseen and potentially
differently distributed data. Our main postulate is that the combination of
task segmentation, modular learning and memory-based ensembling can give rise
to generalization on an exponentially growing number of unseen tasks. We
provide a concrete instantiation of this idea using a combination of: (1) the
Forget-Me-Not Process, for task segmentation and memory based ensembling; and
(2) Gated Linear Networks, which in contrast to contemporary deep learning
techniques use a modular and local learning mechanism. We demonstrate that this
system exhibits a number of desirable continual learning properties: robustness
to catastrophic forgetting, no negative transfer and increasing levels of
positive transfer as more tasks are seen. We show competitive performance
against both offline and online methods on standard continual learning
benchmarks.


  In this short supplement to [1], we discuss the uplift of half-flat six-folds
to Spin(7) eight-folds by fibration of the former over a product of two
intervals. We show that the same can be done in two ways - one, such that the
required Spin(7) eight-fold is a double G_2 seven-fold fibration over an
interval, the G_2 seven-fold itself being the half-flat six-fold fibered over
the other interval, and second, by simply considering the fibration of the
half-flat six-fold over a product of two intervals. The flow equations one gets
are an obvious generalization of the Hitchin's flow equations (to obtain
seven-folds of G_2 holonomy from half-flat six-folds [2]). We explicitly show
the uplift of the Iwasawa using both methods, thereby proposing the form of the
new Spin(7) metrics. We give a plausibility argument ruling out the uplift of
the Iwasawa manifold to a Spin(7) eight fold at the "edge", using the second
method. For $Spin(7)$ eight-folds of the type $X_7\times S^1$, $X_7$ being a
seven-fold of SU(3) structure, we motivate the possibility of including
elliptic functions into the "shape deformation" functions of seven-folds of
SU(3) structure of [1] via some connections between elliptic functions, the
Heisenberg group, theta functions, the already known $D7$-brane metric [3] and
hyper-K\"{a}hler metrics obtained in twistor spaces by deformations of
Atiyah-Hitchin manifolds by a Legendre transform in [4].


  We show that unitary groups of II$_1$ factors and of properly infinite von
Neumann algebras have strong uncountable cofinality. In particular, we obtain a
short alternative proof for the strong uncountable cofinality of
$U(\ell^2(\mathbb{N}))$, which was first proven by Ricard and Rosendal.


  We establish quasi-two-dimensional thin films of iron-based superconductors
(FeSCs) as a new high-temperature platform for hosting intrinsic
time-reversal-invariant helical topological superconductivity (TSC). Based on
the combination of Dirac surface state and bulk extended $s$-wave pairing, our
theory should be directly applicable to a large class of experimentally
established FeSCs, opening a new TSC paradigm. In particular, an applied
electric field serves as a "topological switch" for helical Majorana edge modes
in FeSC thin films, allowing for an experimentally feasible design of
gate-controlled helical Majorana circuits. Applying an in-plane magnetic field
drives the helical TSC phase into a higher-order TSC carrying corner-localized
Majorana zero modes. Our proposal should enable the experimental realization of
helical Majorana fermions.


  Supercooling of water can be easily studied with a simple apparatus suitable
for the student laboratory. We describe such an apparatus and its capabilities.
The parameters influencing supercooling include the initial temperature of the
water and the temperature and the type of chilling medium. We correlate the
occurrence of supercooling with the ability of the chilling medium to promptly
nucleate ice; if it nucleates promptly, the layer of ice crystals formed on the
boundary will initiate freezing of the bulk water without supercooling. If the
chilling medium is unable to nucleate ice promptly, ice nucleation is delayed
and the water supercools. Students can study and compare supercooling of
distilled and natural water. Even quite dirty river water may be supercooled by
as much as 5 C.


  We propose a method to assess the intrinsic risk carried by a financial
position $X$ when the agent faces uncertainty about the pricing rule assigning
its present value. Our approach is inspired by a new interpretation of the
quasiconvex duality in a Knightian setting, where a family of probability
measures replaces the single reference probability and is then applied to value
financial positions.
  Diametrically, our construction of Value\&Risk measures is based on the
selection of a basket of claims to test the reliability of models. We compare a
random payoff $X$ with a given class of derivatives written on $X$ , and use
these derivatives to \textquotedblleft test\textquotedblright\ the pricing
measures.
  We further introduce and study a general class of Value\&Risk measures $%
R(p,X,\mathbb{P})$ that describes the additional capital that is required to
make $X$ acceptable under a probability $\mathbb{P}$ and given the initial
price $p$ paid to acquire $X$.


  We study a model for microscopic segregation in a homogeneous system of
particles moving on a one-dimensional lattice. Particles tend to separate from
each other, and evolution ceases when at least one empty site is found between
any two particles. Motion is a mixture of diffusion to nearest-neighbour sites
and long-range jumps, known as annealed small-world propagation. The long-range
jump probability plays the role of the small-world disorder. We show that there
is an optimal value of this probability, for which the segregation process is
fastest. Moreover, above a critical probability, the time needed to reach a
fully segregated state diverges for asymptotically large systems. These special
values of the long-range jump probability depend crucially on the particle
density. Our system is a novel example of the rare dynamical processes with
critical behaviour at a finite value of the small-world disorder.


  Recognizing group activities is challenging due to the difficulties in
isolating individual entities, finding the respective roles played by the
individuals and representing the complex interactions among the participants.
Individual actions and group activities in videos can be represented in a
common framework as they share the following common feature: both are composed
of a set of low-level features describing motions, e.g., optical flow for each
pixel or a trajectory for each feature point, according to a set of composition
constraints in both temporal and spatial dimensions. In this paper, we present
a unified model to assess the similarity between two given individual or group
activities. Our approach avoids explicit extraction of individual actors,
identifying and representing the inter-person interactions. With the proposed
approach, retrieval from a video database can be performed through
Query-by-Example; and activities can be recognized by querying videos
containing known activities. The suggested video matching process can be
performed in an unsupervised manner. We demonstrate the performance of our
approach by recognizing a set of human actions and football plays.


  Designing effective architectures is one of the key factors behind the
success of deep neural networks. Existing deep architectures are either
manually designed or automatically searched by some Neural Architecture Search
(NAS) methods. However, even a well-searched architecture may still contain
many non-significant or redundant modules or operations (e.g., convolution or
pooling), which may not only incur substantial memory consumption and
computation cost but also deteriorate the performance. Thus, it is necessary to
optimize the operations inside an architecture to improve the performance
without introducing extra computation cost. Unfortunately, such a constrained
optimization problem is NP-hard. To make the problem feasible, we cast the
optimization problem into a Markov decision process (MDP) and seek to learn a
Neural Architecture Transformer (NAT) to replace the redundant operations with
the more computationally efficient ones (e.g., skip connection or directly
removing the connection). Based on MDP, we learn NAT by exploiting
reinforcement learning to obtain the optimization policies w.r.t. different
architectures. To verify the effectiveness of the proposed strategies, we apply
NAT on both hand-crafted architectures and NAS based architectures. Extensive
experiments on two benchmark datasets, i.e., CIFAR-10 and ImageNet, demonstrate
that the transformed architecture by NAT significantly outperforms both its
original form and those architectures optimized by existing methods.


  We present a continuous-variable experimental analysis of a two-photon Fock
state of free-propagating light. This state is obtained from a pulsed
non-degenerate parametric amplifier, which produces two intensity-correlated
twin beams. Counting two photons in one beam projects the other beam in the
desired two-photon Fock state, which is analyzed by using a pulsed homodyne
detection. The Wigner function of the measured state is clearly negative. We
developed a detailed analytic model which allows a fast and efficient analysis
of the experimental results.


  We show that diagram groups can be viewed as fundamental groups of spaces of
positive paths on directed 2-complexes (these spaces of paths turn out to be
classifying spaces). Thus diagram groups are analogs of second homotopy groups,
although diagram groups are as a rule non-Abelian. Part of the paper is a
review of the previous results from this point of view. In particular, we show
that the so called rigidity of the R.Thompson's group $F$ and some other groups
is similar to the flat torus theorem. We find several finitely presented
diagram groups (even of type F_\infty) each of which contains all countable
diagram groups. We show how to compute minimal presentations and homology
groups of a large class of diagram groups. We show that the Poincare series of
these groups are rational functions. We prove that all integer homology groups
of all diagram groups are free Abelian.


  We investigate the coherent population trapping resonance induced by a
polychromatic optical field with an asymmetric spectrum, i.e., whose sidebands
equidistant from the carrier have unequal powers. A situation is considered
where a modulation is used to provide the so-called in-phase and quadrature
signals in the optical field's transmission, which are used for stabilization
of the local oscillator's frequency in chip-scale atomic clocks. In a general
case, the frequencies of the signals nonlinearly depend on the optical field's
intensity due to the asymmetry of the resonance. In this work, we demonstrate
a) that this effect stems from a multi-peak structure of the resonance; b) a
linear dependence of the frequency on the optical field's intensity at high
modulation values; c) that the regime of the resolved structure has more
advantages than suppression of the frequency shift due to the spectrum
asymmetry.


  Deep Inelastic Scattering and Drell-Yan experiments have measured a light
flavor asymmetry in the proton sea. The excess of dbar over ubar quarks can be
understood in many models, but the ratio dbar(x)/ubar(x) measured by Fermilab
E866 has not been successfully described. Fermilab E-906 will probe the
kinematic dependence of this ratio with better resolution and extend it to
higher x. We have developed a hybrid model that includes both perturbative and
non-perturbative contributions to the proton sea. A meson cloud formalism is
used to represent the non-perturbative fluctuation of the proton into
meson-baryon states. We include perturbative processes by using a statistical
model that uses Fock states of quarks, antiquarks and gluons to represent the
parton distributions of the 'bare' hadrons in the meson cloud. We compare our
results to the E866 data.


  The "water-filling" solution for the quadratic rate-distortion function of a
stationary Gaussian source is given in terms of its power spectrum. This
formula naturally lends itself to a frequency domain "test-channel"
realization. We provide an alternative time-domain realization for the
rate-distortion function, based on linear prediction. This solution has some
interesting implications, including the optimality at all distortion levels of
pre/post filtered vector-quantized differential pulse code modulation (DPCM),
and a duality relationship with decision-feedback equalization (DFE) for
inter-symbol interference (ISI) channels.


  For any group $G$ and any set $A$, a cellular automaton (CA) is a
transformation of the configuration space $A^G$ defined via a finite memory set
and a local function. Let $\text{CA}(G;A)$ be the monoid of all CA over $A^G$.
In this paper, we investigate a generalisation of the inverse of a CA from the
semigroup-theoretic perspective. An element $\tau \in \text{CA}(G;A)$ is von
Neumann regular (or simply regular) if there exists $\sigma \in \text{CA}(G;A)$
such that $\tau \circ \sigma \circ \tau = \tau$ and $\sigma \circ \tau \circ
\sigma = \sigma$, where $\circ$ is the composition of functions. Such an
element $\sigma$ is called a generalised inverse of $\tau$. The monoid
$\text{CA}(G;A)$ itself is regular if all its elements are regular. We
establish that $\text{CA}(G;A)$ is regular if and only if $\vert G \vert = 1$
or $\vert A \vert = 1$, and we characterise all regular elements in
$\text{CA}(G;A)$ when $G$ and $A$ are both finite. Furthermore, we study
regular linear CA when $A= V$ is a vector space over a field $\mathbb{F}$; in
particular, we show that every regular linear CA is invertible when $G$ is
torsion-free elementary amenable (e.g. when $G=\mathbb{Z}^d, \ d \in
\mathbb{N}$) and $V=\mathbb{F}$, and that every linear CA is regular when $V$
is finite-dimensional and $G$ is locally finite with $\text{Char}(\mathbb{F})
\nmid o(g)$ for all $g \in G$.


  Although statistical evidence is not overwhelming, possible support for an
approximately 35 million year periodicity in the crater record on Earth could
indicate a nonrandom underlying enhancement of meteorite impacts at regular
intervals. A proposed explanation in terms of tidal effects on Oort cloud comet
perturbations as the Solar System passes through the galactic midplane is
hampered by lack of an underlying cause for sufficiently enhanced gravitational
effects over a sufficiently short time interval and by the time frame between
such possible enhancements. We show that a smooth dark disk in the galactic
midplane would address both these issues and create a periodic enhancement of
the sort that has potentially been observed. Such a disk is motivated by a
novel dark matter component with dissipative cooling that we considered in
earlier work. We show how to evaluate the statistical evidence for periodicity
by input of appropriate measured priors from the galactic model, justifying or
ruling out periodic cratering with more confidence than by evaluating the data
without an underlying model. We find that, marginalizing over astrophysical
uncertainties, the likelihood ratio for such a model relative to one with a
constant cratering rate is 3.0, which moderately favors the dark disk model.
Our analysis furthermore yields a posterior distribution that, based on current
crater data, singles out a dark matter disk surface density of approximately 10
solar masses per square parsec. The geological record thereby motivates a
particular model of dark matter that will be probed in the near future.


  The boundary conditions (BCs) have shown great potential in requirements
engineering because a BC captures the particular combination of circumstances,
i.e., divergence, in which the goals of the requirement cannot be satisfied as
a whole. Existing researches have attempted to automatically identify lots of
BCs. Unfortunately, a large number of identified BCs make assessing and
resolving divergences expensive. Existing methods adopt a coarse-grained
metric, generality, to filter out less general BCs. However, the results still
retain a large number of redundant BCs since a general BC potentially captures
redundant circumstances that do not lead to a divergence. Furthermore, the
likelihood of BC can be misled by redundant BCs resulting in costly repeatedly
assessing and resolving divergences.
  In this paper, we present a fine-grained metric to filter out the redundant
BCs. We first introduce the concept of contrasty of BC. Intuitively, if two BCs
are contrastive, they capture different divergences. We argue that a set of
contrastive BCs should be recommended to engineers, rather than a set of
general BCs that potentially only indicates the same divergence. Then we design
a post-processing framework (PPAc) to produce a set of contrastive BCs after
identifying BCs. Experimental results show that the contrasty metric
dramatically reduces the number of BCs recommended to engineers. Results also
demonstrate that lots of BCs identified by the state-of-the-art method are
redundant in most cases. Besides, to improve efficiency, we propose a joint
framework (JAc) to interleave assessing based on the contrasty metric with
identifying BCs. The primary intuition behind JAc is that it considers the
search bias toward contrastive BCs during identifying BCs, thereby pruning the
BCs capturing the same divergence. Experiments confirm the improvements of JAc
in identifying contrastive BCs.


  Nanoparticle formation by femtosecond laser ablation is investigated for
different experimental conditions. Dynamics of the laser plume expansion is
visualized based on numerical simulations and the possibility of primary
nanoparticle formation is analyzed. Calculations are performed for metals under
different background conditions. The effects of the background environment are
considered, including initial expansion stage of the ejected atoms and
clusters, nucleation, collisions, and longer scale nanoparticle evolution.
Nanoparticle size distribution is shown to evolve from a decreasing function to
the commonly observed bell-shaped distribution as early as at 50 microseconds
after the beginning of the laser pulse. However, at the end of the initial
plume expansion stage, the supersaturation parameter can be sufficiently high
for a much longer diffusion-driven nanoparticle growth to enter into play. In
this case, the major nanoparticle formation process is based on the so-called
catastrophic nucleation accompanied by collisional growth, which determines the
final size distribution. The obtained calculation results explain numerous
experimental findings and help to predict both nanoparticle plume evolution and
changes in its size distribution.


  The Regge symmetry is a set of remarkable relations between two tetrahedra
whose edge lengths are related in a simple fashion. It was first discovered as
a consequence of an asymptotic formula in mathematical physics. Here we give a
simple geometric proof of Regge symmetries in Euclidean, spherical, and
hyperbolic geometry.


  The size-Ramsey number $\hat{R}(F,r)$ of a graph $F$ is the smallest integer
$m$ such that there exists a graph $G$ on $m$ edges with the property that any
colouring of the edges of $G$ with $r$ colours yields a monochromatic copy of
$F$. In this short note, we give an alternative proof of the recent result of
Krivelevich that $\hat{R}(P_n,r) = O((\log r)r^2 n)$. This upper bound is
nearly optimal, since it is also known that $\hat{R}(P_n,r) = \Omega(r^2 n)$.


  Scanamorphos is public software initially developed to post-process scan
observations performed with the Herschel photometer arrays. This
post-processing mainly consists in subtracting the total low-frequency noise
(both its thermal and non-thermal components), masking cosmic ray hits, and
projecting the data onto a map. Building upon the results obtained for
P-ArT\'eMiS (the prototype of ArT\'eMiS), Herschel and then NIKA2 (a resident
camera of the IRAM 30-m telescope operating at 1.25 and 2 mm), it has now been
tailored to the ArT\'eMiS camera, an ESO and OSO P.I. instrument installed at
the APEX 12-m telescope, demonstrating our initial claim that the software
principles were directly transposable to scan observations made with other
instruments, including from the ground, provided they entail sufficient
redundancy. This document explains how the algorithm was modified to cope with
the specificities of ArT\'eMiS observations and with the atmospheric emission
at 350 and 450 $\mu$m, far dominating the instrumental drifts that were the
only low-frequency noise component in Herschel data. Like in the original
software, this was accomplished without assuming any noise model and without
applying any Fourier-space filtering, by exploiting the redundancy built in the
observations - taking advantage of the fact that each portion of the sky is
sampled at multiple times by multiple bolometers. It remains an interactive
software in the sense that the user is allowed to optionally visualize and
control results at each intermediate step, but the processing is fully
automated. It has been grafted onto the ArT\'eMiS pipeline, in charge of the
formatting, calibration and projection of the data, that is described
elsewhere.


  The aim of this work is to study the spectral statistics of the asymmetric
rotor model (triaxial rigid rotator). The asymmetric top is classically
integrable and, according to the Berry-Tabor theory, its spectral statistics
should be Poissonian. Surprisingly, our numerical results show that the nearest
neighbor spacing distribution $P(s)$ and the spectral rigidity $\Delta_3(L)$ do
not follow Poisson statistics. In particular, $P(s)$ shows a sharp peak at
$s=1$ while $\Delta_3(L)$ for small values of $L$ follows the Poissonian
predictions and asymptotically it shows large fluctuations around its mean
value. Finally, we analyze the information entropy, which shows a dissolution
of quantum numbers by breaking the axial symmetry of the rigid rotator.


  Integral Field Spectroscopy (IFS) with JWST NIRSpec will significantly
improve our understanding of the first quasars, by providing spatially
resolved, infrared spectroscopic capabilities which cover key rest-frame
optical emission lines that have been previously unobservable. Here we present
our results from the first two z>6 quasars observed as a part of the Galaxy
Assembly with NIRSpec IFS (GA-NIFS) GTO program, DELS J0411-0907 at z=6.82 and
VDES J0020-3653 at z=6.86. By observing the H$\beta$, [OIII], and H$\alpha$
emission lines in these high-z quasars for the first time, we measure accurate
black hole masses, $M_{\rm{BH}}=1.85e9$ and $2.9e9$M$_\odot$, corresponding to
Eddington ratios of $\lambda_{\rm{Edd}}=0.8$ and 0.4 for DELS J0411-0907 and
VDES J0020-3653 respectively. These provide a key comparison for existing
estimates from the more uncertain MgII line. We perform quasar-host
decomposition using models of the quasars' broad lines to measure the
underlying host galaxies. We also discover multiple emission line regions
surrounding each of the host galaxies, which are likely companion galaxies
undergoing mergers with these hosts. We measure the star formation rates,
excitation mechanisms, and dynamical masses of the hosts and companions,
measuring the $M_{\rm{BH}}/M_{\rm{dyn}}$ ratios at high-z using these
estimators for the first time. DELS J0411-0907 and VDES J0020-3653 both lie
above the local black hole--host mass relation, and are consistent with the
existing observations of $z\gtrsim6$ quasar host galaxies with ALMA. We detect
ionized outflows in [OIII] and H$\beta$ from both quasars, with mass outflow
rates of 58 and 525 M$_{\odot}$/yr for DELS J0411-0907 and VDES J0020-3653,
much larger than their host star formation rates of <33 and <54 M$_\odot$/yr.
This work highlights the exceptional capabilities of the JWST NIRSpec IFU for
observing quasars in the early Universe.


  Magnetic Resonance Imaging (MRI) is considered today the golden-standard
modality for soft tissues. The long acquisition times, however, make it more
prone to motion artifacts as well as contribute to the relatively high costs of
this examination. Over the years, multiple studies concentrated on designing
reduced measurement schemes and image reconstruction schemes for MRI, however,
these problems have been so far addressed separately. On the other hand, recent
works in optical computational imaging have demonstrated growing success of the
simultaneous learning-based design of the acquisition and reconstruction
schemes manifesting significant improvement in the reconstruction quality with
a constrained time budget. Inspired by these successes, in this work, we
propose to learn accelerated MR acquisition schemes (in the form of Cartesian
trajectories) jointly with the image reconstruction operator. To this end, we
propose an algorithm for training the combined acquisition-reconstruction
pipeline end-to-end in a differentiable way. We demonstrate the significance of
using the learned Cartesian trajectories at different speed up rates.


  We present the results of high-resolution mid-infrared observations of the
source NGC 3576 IRS 1. Near diffraction-limited images were taken at the Gemini
South Observatory through OSCIR's filters N, 7.9, 9.8, 12.5 and IHW18. The
source IRS 1 was resolved into 3 sources for the first time at mid-infrared
wavelengths. For each source we constructed the SED from 1.25 to 18 microns, as
well the color temperature and the spatial distribution of the dust in the
region. The optical depth of the silicate absorption feature at 9.8 microns is
presented also.


  Quantum memory management is becoming a pressing problem, especially given
the recent research effort to develop new and more complex quantum algorithms.
The only existing automatic method for quantum states clean-up relies on the
availability of many extra resources. In this work, we propose an automatic
tool for quantum memory management. We show how this problem exactly matches
the reversible pebbling game. Based on that, we develop a SAT-based algorithm
that returns a valid clean-up strategy, taking the limitations of the quantum
hardware into account. The developed tool empowers the designer with the
flexibility required to explore the trade-off between memory resources and
number of operations. We present three show-cases to prove the validity of our
approach. First, we apply the algorithm to straight-line programs, widely used
in cryptographic applications. Second, we perform a comparison with the
existing approach, showing an average improvement of 52.77%. Finally, we show
the advantage of using the tool when synthesizing a quantum circuit on a
constrained near-term quantum device.


  The observed characteristics of the nebulosity surrounding the SMC High Mass
X-ray Binary XTE J0111.2-7317 are examined in the context of three possible
nebular types: SNR, bowshock and HII region. Observational evidence is
presented which appears to support the interpretation that the nebulosity
surrounding XTE J0111.2-7317 is an HII region. The source therefore appears to
be a normal SMC Be X-ray binary (BeXRB) embedded in a locally enhanced ISM
which it has photoionised to create an HII region. This is supported by
observations of the X-ray outburst seen with BATSE and RXTE in 1998-1999. It
exhibited characteristics typical of a giant or type II outburst in a BeXRB
including large spin-up rates, Lx~10E38 erg/sq.cm-s, and a correlation between
spin-up rate and pulsed flux. However, the temporal profile of the outburst was
unusual, consisting of two similar intensity peaks, with the first peak of
shorter duration than the second.


  Using a model comprising a 2-level bridge connecting free electron reservoirs
we show that coupling of a molecular bridge to electron-hole excitations in the
leads can markedly effect the source-drain current through a molecular
junction.In some cases, e.g. molecules that exhibit strong charge transfer
transitions, the contribution from electron-hole excitations can exceed the
Landauer elastic current and dominate the observed conduction.


  Optimal transport (OT) has become a widely used tool in the machine learning
field to measure the discrepancy between probability distributions. For
instance, OT is a popular loss function that quantifies the discrepancy between
an empirical distribution and a parametric model. Recently, an entropic penalty
term and the celebrated Sinkhorn algorithm have been commonly used to
approximate the original OT in a computationally efficient way. However, since
the Sinkhorn algorithm runs a projection associated with the Kullback-Leibler
divergence, it is often vulnerable to outliers. To overcome this problem, we
propose regularizing OT with the \beta-potential term associated with the
so-called $\beta$-divergence, which was developed in robust statistics. Our
theoretical analysis reveals that the $\beta$-potential can prevent the mass
from being transported to outliers. We experimentally demonstrate that the
transport matrix computed with our algorithm helps estimate a probability
distribution robustly even in the presence of outliers. In addition, our
proposed method can successfully detect outliers from a contaminated dataset


  This study presents the morphological and biochemical findings on human downy
arm hairs using 3-D quantitative phase imaging techniques. 3-D refractive index
tomograms and high-resolution 2-D synthetic aperture images of individual downy
arm hairs were measured using a Mach-Zehnder laser interferometric microscopy
equipped with a two-axis galvanometer mirror. From the measured quantitative
images, the biochemical and morphological parameters of downy hairs were
non-invasively quantified including the mean refractive index, volume,
cylinder, and effective radius of individual hairs. In addition, the effects of
hydrogen peroxide on individual downy hairs were investigated.


  In this paper we describe how to give a particular global category of rings
and modules the structure of a relaxed multi category, and we describe an
algebra in this relaxed multi category such that vertex algebras appear as such
algebras.


  We present time-resolved spectroscopy of the eclipsing, short period
cataclysmic variable CTCV J1300-3052. Using absorption features from the
secondary star, we determine the radial velocity semi-amplitude of the
secondary star to be K2 = 378 \pm 6 km/s, and its projected rotational velocity
to be v sin i = 125 \pm 7 km/s. Using these parameters and Monte Carlo
techniques, we obtain masses of M1 = 0.79 \pm 0.05 MSun for the white dwarf
primary and M2 = 0.198 \pm 0.029 MSun for the M-type secondary star. These
parameters are found to be in excellent agreement with previous mass
determinations found via photometric fitting techniques, supporting the
accuracy and validity of photometric mass determinations in short period CVs.


  Bosonic thermal transport through a two-level system is analyzed at
temperatures below and comparable to the two-level energy splitting. It is
shown that in the low-temperature regime transport is dominated by correlated
two-boson processes analogous to electron cotunneling in quantum dots under
Coulomb blockade. We present a detailed analysis of the sequential-cotunneling
crossover and obtain essentially an analytic description of the transport
problem. Perturbative analysis is complemented by employing scaling properties
of the Ohmic spin-boson model, allowing us to extract an anomalous low
temperature scaling of thermal conductance.


  Drones have been widely used in various services, such as delivery and
surveillance. Authentication forms the foundation of the security of these
services. However, drones are expensive and may carry important payloads. To
avoid being captured by attackers, drones should keep a safe distance from the
verifier before authentication succeeds. This makes authentication methods that
only work in very close proximity not applicable. Our work leverages drone
noises for authentication. While using sounds for authentication is highly
usable, how to handle various attacks that manipulate sounds is an
\emph{unresolved challenge}. It is also unclear how to ensure robustness under
various environmental sounds. Being the first in the literature, we address the
two major challenges by exploiting unique characteristics of drone noises. We
thereby build an authentication system that does \emph{not} rely on any drone
sound fingerprints, keeps resilient to attacks, and is robust under
environmental sounds. An extensive evaluation demonstrates its security and
usability.


  For a Fermat quasi-homogeneous polynomial $W$, we study a family of
K-theoretic quantum invariants parametrized by a positive rational number
$\epsilon$. We prove a wall-crossing formula by showing the generating
functions lie on the Lagrangian cone of the permutation-equivariant K-theoretic
FJRW theory of $W$.


  The gaussoid axioms are conditional independence inference rules which
characterize regular Gaussian CI structures over a three-element ground set. It
is known that no finite set of inference rules completely describes regular
Gaussian CI as the ground set grows. In this article we show that the gaussoid
axioms logically imply every inference rule of at most two antecedents which is
valid for regular Gaussians over any ground set. The proof is accomplished by
exhibiting for each inclusion-minimal gaussoid extension of at most two CI
statements a regular Gaussian realization. Moreover we prove that all those
gaussoids have rational positive-definite realizations inside every
$\varepsilon$-ball around the identity matrix. For the proof we introduce the
concept of algebraic Gaussians over arbitrary fields and of positive Gaussians
over ordered fields and obtain the same two-antecedental completeness of the
gaussoid axioms for algebraic and positive Gaussians over all fields of
characteristic zero as a byproduct.


  We study Hopf bifurcation from traveling-front solutions in the Cahn-Hilliard
equation. The primary front is induced by a moving source term. Models of this
form have been used to study a variety of physical phenomena, including pattern
formation in chemical deposition and precipitation processes. Technically, we
study bifurcation in the presence of essential spectrum. We contribute a simple
and direct functional analytic method and determine bifurcation coefficients
explicitly. Our approach uses exponential weights to recover Fredholm
properties and spectral flow ideas to compute Fredholm indices. Simple mass
conservation helps compensate for negative indices. We also construct an
explicit, prototypical example, prove the existence of a bifurcating front, and
determine the direction of bifurcation.


  The basic $\kappa$-color box-ball (BBS) system is an integrable cellular
automaton on one dimensional lattice whose local states take
$\{0,1,\cdots,\kappa \}$ with $0$ regarded as an empty box. The time evolution
is defined by a combinatorial rule of quantum group theoretical origin, and the
complete set of conserved quantities is given by a $\kappa$-tuple of Young
diagrams. In the randomized BBS, a probability distribution on
$\{0,1,\cdots,\kappa \}$ to independently fill the consecutive $n$ sites in the
initial state induces a highly nontrivial probability measure on the
$\kappa$-tuple of those invariant Young diagrams. In a recent work
\cite{kuniba2018randomized}, their large $n$ `equilibrium shape' has been
determined in terms of Schur polynomials by a Markov chain method and also by a
very different approach of Thermodynamic Bethe Ansatz (TBA). In this paper, we
establish a large deviations principle for the row lengths of the invariant
Young diagrams. As a corollary, they are shown to converge almost surely to the
equilibrium shape at an exponential rate. We also refine the TBA analysis and
obtain the exact scaling form of the vacancy, the row length and the column
multiplicity, which exhibit nontrivial factorization in a one-parameter
specialization.


  Recently, Neural Topic Models (NTM), inspired by variational autoencoders,
have attracted a lot of research interest; however, these methods have limited
applications in the real world due to the challenge of incorporating human
knowledge. This work presents a semi-supervised neural topic modeling method,
vONTSS, which uses von Mises-Fisher (vMF) based variational autoencoders and
optimal transport. When a few keywords per topic are provided, vONTSS in the
semi-supervised setting generates potential topics and optimizes topic-keyword
quality and topic classification. Experiments show that vONTSS outperforms
existing semi-supervised topic modeling methods in classification accuracy and
diversity. vONTSS also supports unsupervised topic modeling. Quantitative and
qualitative experiments show that vONTSS in the unsupervised setting
outperforms recent NTMs on multiple aspects: vONTSS discovers highly clustered
and coherent topics on benchmark datasets. It is also much faster than the
state-of-the-art weakly supervised text classification method while achieving
similar classification performance. We further prove the equivalence of optimal
transport loss and cross-entropy loss at the global minimum.


  We propose an approach to study non-Abelian Iwasawa theory, using the idea of
Johnson homomorphisms in low dimensional topology. We introduce arithmetic
analogues of Johnson homomorphisms/maps, called the p-Johnson
homomorphisms/maps, associated to the Zassenhaus filtration of a pro-p Galois
group over a Z_p-extension of a number field. We give their cohomological
interpretation in terms of Massey products in Galois cohomology.


  A new general route for siRNA delivery is presented combining porous
core-shell silica nanocarriers with a modularly designed multifunctional block
copolymer. Specifically, the internal storage and release of siRNA from
mesoporous silica nanoparticles (MSN) with orthogonal core-shell surface
chemistry was investigated as a function of pore-size, pore morphology, surface
properties and pH. Very high siRNA loading capacities of up to 380 microg/mg
MSN were obtained with charge-matched amino-functionalized mesoporous cores,
and release profiles show up to 80% siRNA elution after 24 h. We demonstrate
that adsorption and desorption of siRNA is mainly driven by electrostatic
interactions, which allow for high loading capacities even in medium-sized
mesopores with pore diameters down to 4 nm in a stellate pore morphology. The
negatively charged MSN shell enabled the association with a block copolymer
containing positively charged artificial amino acids and oleic acid blocks,
which acts simultaneously as capping function and endosomal release agent. The
potential of this multifunctional delivery platform is demonstrated by highly
effective cell transfection and siRNA delivery into KB-cells. A luciferase
reporter gene knock-down of up to 90% was possible using extremely low cell
exposures with only 2.5 microg MSN containing 32 pM siRNA per 100 microL well.


  A theory of electron-phonon mediated superconductivity requires the knowledge
of full phonon spectrum to calculate superconducting transition temperature,
T$_c$. However, there is no experimental technique which can measure phonon
spectrum in highly-compressed near-room-temperature (NRT) superconductors to
date. In this paper we propose to advance McMillan's approach (1968 Phys Rev
167 331) which utilizes the Debye temperature, T$_{\theta}$ (an integrated
parameter of full phonon spectrum), that we deduced by the fit of
experimentally measured temperature-dependent resistance data, R(T), to
Bloch-Gruneisen equation for highly-compressed black phosphorous, boron, GeAs,
SiH$_4$, H$_x$S, D$_y$S, LaH$_x$ and LaD$_y$. By utilizing relations between
T$_c$, Debye temperature and electron-phonon coupling strength constant,
$\lambda_{e-ph}$ it is possible to affirm/disprove the electron-phonon
mechanism in given superconductors. We show that computed $\lambda_{e-ph}$ for
highly-compressed black phosphorous, boron, GeAs, SiH$_4$ and for one sample of
LaH$_{10}$ are in a good agreement with $\lambda_{e-ph}$ values deduced from
experimental data. It is also found remarkable constancy of Debye temperature
in H$_3$S at different ageing stages. We also show that if phonon spectra of
two isotopic counterparts have similar shape then within electron-phonon
phenomenology these materials should obey the relation of
T$_{c,1}$/T$_{c,2}$=T$_{\theta,1}$/T$_{\theta,2}$ (where 1 and 2 designate
isotopes). We report that these ratios for H$_3$S-D$_3$S predicted by
electron-phonon phenomenology are largely different from ratios deduced from
experiment. This alludes that NRT superconductivity in H3S-D3S system is
originated from more than one mechanism, where the electron-phonon coupling
lifts T$_c$ in H$_3$S vs D$_3$S, but primary origin for NRT background of in
both H$_3$S and D$_3$S remains to be discovered.


  Exclusive photoproduction cross sections have been measured for the process
$\gamma p \rightarrow p\pi^0(e^+e^-(\gamma))$ with the Dalitz decay final state
using tagged photon energies in the range of $E_{\gamma} = 1.275-5.425$ GeV.
The complete angular distribution of the final state $\pi^0$, for the entire
photon energy range up to large values of $t$ and $u$, has been measured for
the first time. The data obtained show that the cross section $d\sigma/dt$, at
mid to large angles, decreases with energy as $s^{-6.89\pm 0.26} $. This is in
agreement with the perturbative QCD quark counting rule prediction of $s^{-7}
$. Paradoxically, the size of angular distribution of measured cross sections
is greatly underestimated by the QCD based Generalized Parton Distribution
mechanism at highest available invariant energy $s=11$ GeV$^2$. At the same
time, the Regge exchange based models for $\pi^0$ photoproduction are more
consistent with experimental data.


  This work describes Cosmic Microwave Background (CMB) data analysis
algorithms and their implementations, developed to produce a pixelized map of
the sky and a corresponding pixel-pixel noise correlation matrix from time
ordered data for a CMB mapping experiment. We discuss in turn algorithms for
estimating noise properties from the time ordered data, techniques for
manipulating the time ordered data, and a number of variants of the maximum
likelihood map-making procedure. We pay particular attention to issues
pertinent to real CMB data, and present ways of incorporating them within the
framework of maximum likelihood map-making. Making a map of the sky is shown to
be not only an intermediate step rendering an image of the sky, but also an
important diagnostic stage, when tests for and/or removal of systematic effects
can efficiently be performed. The case under study is the MAXIMA data set.
However, the methods discussed are expected to be applicable to the analysis of
other current and forthcoming CMB experiments.


  Effective range is a quantity to characterize the energy dependence in
two-body scattering strength, and is widely used in cold atomic systems
especially across narrow resonances. Here we show that the effective range can
significantly modify the magnetic property of one-dimensional (1D) spin-$1/2$
fermions in the strongly repulsive regime. In particular, the effective range
breaks the large spin degeneracy in the hard-core limit, and induces a
Heisenberg exchange term in the spin chain that is much more sensitive to the
local density than that induced by the bare coupling. With an external harmonic
trap, this leads to a very rich magnetic pattern where the anti-ferromagnetic
(AFM) and ferromagnetic (FM) correlations can coexist and distribute in highly
tunable regions across the trap. Finally, we propose to detect the
range-induced magnetic order in the tunneling experiment. Our results can be
directly tested in 1D Fermi gases across narrow resonance, and suggest a
convenient route towards the local manipulation of quantum magnetism in cold
atoms.


  Understanding and explaining the predictions of Graph Neural Networks (GNNs),
is crucial for enhancing their safety and trustworthiness. Subgraph-level
explanations are gaining attention for their intuitive appeal. However, most
existing subgraph-level explainers face efficiency challenges in explaining
GNNs due to complex search processes. The key challenge is to find a balance
between intuitiveness and efficiency while ensuring transparency. Additionally,
these explainers usually induce subgraphs by nodes, which may introduce
less-intuitive disconnected nodes in the subgraph-level explanations or omit
many important subgraph structures. In this paper, we reveal that inducing
subgraph explanations by edges is more comprehensive than other subgraph
inducing techniques. We also emphasize the need of determining the subgraph
explanation size for each data instance, as different data instances may
involve different important substructures. Building upon these considerations,
we introduce a training-free approach, named EiG-Search. We employ an efficient
linear-time search algorithm over the edge-induced subgraphs, where the edges
are ranked by an enhanced gradient-based importance. We conduct extensive
experiments on a total of seven datasets, demonstrating its superior
performance and efficiency both quantitatively and qualitatively over the
leading baselines.


  There is a proper countable support iteration of length $\omega$ adding no
new reals at finite stages and adding a Sacks real in the limit.


  Tin-containing nanocrystals, embedded in silicon, have been fabricated by
growing an epitaxial layer of Si_{1-x-y}Sn_{x}C_{y}, where x = 1.6 % and y =
0.04 %, followed by annealing at various temperatures ranging from 650 to 900
degrees C. The nanocrystal density and average diameters are determined by
scanning transmission-electron microscopy to ~ 10^{17} cm^{-3} and ~ 5 nm,
respectively. Photoluminescence spectroscopy demonstrates that the light
emission is very pronounced for samples annealed at 725 degrees C, and
Rutherford back-scattering spectrometry shows that the nanocrystals are
predominantly in the diamond-structured phase at this particular annealing
temperature. The origin of the light emission is discussed.


  This paper gives an overview of a transaction level modeling (TLM) design
flow for straightforward embedded system design with SystemC. The goal is to
systematically develop both application-specific HW and SW components of an
embedded system using the TLM approach, thus allowing for fast communication
architecture exploration, rapid prototyping and early embedded SW development.
To this end, we specify the lightweight transaction-based communication
protocol SHIP and present a methodology for automatic mapping of the
communication part of a system to a given architecture, including HW/SW
interfaces.


  We study a general class of quadratic capacitated $p$-location problems
facility location problems with single assignment where a non-separable,
non-convex, quadratic term is introduced in the objective function to account
for the interaction cost between facilities and customer assignments. This
problem has many applications in the field of transportation and logistics
where its most well-known special case is the single-allocation hub location
problem and its many variants. The non-convex, binary quadratic program is
linearized by applying a reformulation-linearization technique and the
resulting continuous auxiliary variables are projected out using Benders
decomposition. The obtained Benders reformulation is then solved using an exact
branch-and-cut algorithm that exploits the underlying network flow structure of
the decomposed separation subproblems to efficiently generate strong
Pareto-optimal Benders cuts. Additional enhancements such as a matheuristic, a
partial enumeration procedure, and variable elimination tests are also embedded
in the proposed algorithmic framework. Extensive computational experiments on
benchmark instances (with up to 500 nodes) and on a new set of instances (with
up to 1,000 nodes) of four variants of single-allocation hub location problems
confirm the algorithm's ability to scale to large-scale instances.


  Deviations from the standard $\Lambda$CDM model motivate an interpretation of
early universe cosmology using the Scalar-Tensor-Vector-Gravity (STVG) theory.
A constraint analysis carried out by Valentino, Melchiorri and Silk, revealed
deviations from the growth of structure predicted by General Relativity, and a
lensing anomaly in the angular CMB power spectrum data. The modified gravity
(MOG) theory resolves the lensing deviation from the standard model and
provides an explanation of the CMB and structure growth data.


  This paper studies quadcopters obstacle avoidance trajectory control (OATC)
problem for express delivery. A new nonlinear adaptive learning controller that
is low-cost and portable to different wheelbase sizes is proposed to adapt to
large-angle maneuvers and load changes in UAV delivery missions. The controller
consists of a nonlinear variable gain (NLVG) function and an extreme value
search (ES) algorithm to reduce overshoot and settling time. Finally,
simulations were conducted on a quadcopter to verify the effectiveness of the
proposed control scheme under two typical collision-free trajectories.


  In this paper we consider the hyperplane arrangement in $\mathbb{R}^n$ whose
hyperplanes are $\{x_i + x_j = 1\mid 1\leq i < j\leq n\}\cup \{x_i=0,1\mid
1\leq i\leq n\}$. We call it the \emph{boxed threshold arrangement} since we
show that the bounded regions of this arrangement are contained in an $n$-cube
and are in one-to-one correspondence with the labeled threshold graphs on $n$
vertices. The problem of counting regions of this arrangement was studied
earlier by Joungmin Song. He determined the characteristic polynomial of this
arrangement by relating its coefficients to the count of certain graphs. Here,
we provide bijective arguments to determine the number of regions. In
particular, we construct certain signed partitions of the set $\{-n,\dots,
n\}\setminus\{0\}$ and also construct colored threshold graphs on $n$ vertices
and show that both these objects are in bijection with the regions of the boxed
threshold arrangement. We independently count these objects and provide closed
form formula for the number of regions.


  The performance of repository-level code completion depends upon the
effective leverage of both general and repository-specific knowledge. Despite
the impressive capability of code LLMs in general code completion tasks, they
often exhibit less satisfactory performance on repository-level completion due
to the lack of repository-specific knowledge in these LLMs. To address this
problem, we propose GraphCoder, a retrieval-augmented code completion framework
that leverages LLMs' general code knowledge and the repository-specific
knowledge via a graph-based retrieval-generation process. In particular,
GraphCoder captures the context of completion target more accurately through
code context graph (CCG) that consists of control-flow, data- and
control-dependence between code statements, a more structured way to capture
the completion target context than the sequence-based context used in existing
retrieval-augmented approaches; based on CCG, GraphCoder further employs a
coarse-to-fine retrieval process to locate context-similar code snippets with
the completion target from the current repository. Experimental results
demonstrate both the effectiveness and efficiency of GraphCoder: Compared to
baseline retrieval-augmented methods, GraphCoder achieves higher exact match
(EM) on average, with increases of +6.06 in code match and +6.23 in identifier
match, while using less time and space.


  Observational cosmology has become an important laboratory for testing
General Relativity, with searches for modified gravity forming a significant
portion of the science case for existing and future surveys. In this paper, we
illustrate how future measurements of the Cosmic Microwave Background (CMB)
temperature and polarization anisotropies can be combined with large galaxy
surveys to improve constraints on modified gravity using the technique of
Sunyaev Zel'dovich (SZ) tomography. SZ tomography uses the correlations between
the kinetic/polarized SZ contributions to the small-angular scale CMB and the
distribution of structure measured in a galaxy redshift survey to reconstruct
the remote dipole and quadrupole fields, e.g. the CMB dipole and quadrupole
observed throughout the Universe. We compute the effect of a class of
modifications of gravity on the remote dipole and quadrupole fields,
illustrating that these observables combine a number of the desirable features
of existing probes. We then perform a fisher forecast of constraints on a
two-parameter class of modifications of gravity for next-generation CMB
experiments and galaxy surveys. By incorporating information from the
reconstructed remote dipole and quadrupole fields, we find that it is possible
to improve the constraints on this model by a factor of $\sim 2$ beyond what is
possible with a galaxy survey alone. We conclude that SZ tomography is a
promising method for testing gravity with future cosmological datasets.


  We consider a branching-selection particle system on the real line,
introduced by Brunet and Derrida. In this model the size of the population is
fixed to a constant $N$. At each step individuals in the population reproduce
independently, making children around their current position. Only the $N$
rightmost children survive to reproduce at the next step. B\'erard and
Gou\'er\'e studied the speed at which the cloud of individuals drifts, assuming
the tails of the displacement decays at exponential rate; B\'erard and Maillard
took interest in the case of heavy tail displacements. We take interest in an
intermediate model, considering branching random walks in which the critical
spine behaves as an $\alpha$-stable random walk.


  Long-term periodicity in the rate of flares is observed for two repeating
sources of fast radio bursts (FRBs). In this paper We present a hydrodynamical
modeling of a massive binary consisting of a magnetar and an early-type star.
We model the interaction of the pulsar wind from the magnetar with an intense
stellar wind. It is shown that only during a fraction of the orbital period
radio emission can escape the system. This explains the duty cycle of the two
repeating FRB sources with periodic activity. The width of the transparency
window depends on the eccentricity, stellar wind properties, and the viewing
angle. To describe properties of the known sources it is necessary to assume
large eccentricities $\gtrsim 0.5$. We apply the maser cyclotron mechanism of
the radio emission generation to model spectral properties of the sources. The
produced spectrum is not wide: $\Delta \nu/\nu \sim 0.2$ and the typical
frequency depends on the radius of the shock where the emission is generated.
The shock radius changes along the orbit. This, together with changing
parameters of the medium, allows us to explain the frequency drift during the
phase of visibility. Frequency dependence of the degree of polarization at few
GHz can be a consequence of a small scale turbulence in the shocked stellar
wind. It is much more difficult to explain huge ($\sim 10^5$ [rad/m$^2$]) and
variable value of the rotation measure observed for FRB 121102. We suggest that
this can be explained if the supernova explosion which produced the magnetar
happened near a dense interstellar cloud with $n \sim100$ cm$^{-3}$.


  We consider a supersymmetric model in which gaugino masses are generated by
the anomaly-mediation mechanism while scalar masses are from tree-level
supergravity interaction. In such a model, scalar fermions as well as Higgsinos
become as heavy as O(10-100TeV) and hence only the gauginos are superparticles
kinematically accessible to the LHC. We study how and how well the properties
of gauginos can be studied. We also discuss the strategy to test the
anomaly-mediation model at the LHC.


  We systematically test a quasi-classical spin model of a large spin-lattice
in energy space, with a tunable, reversible Hamiltonian and effective
long-range interactions. The system is simulated by a weakly interacting Fermi
gas undergoing perturbed quantum rewinding using radio-frequency(RF) pulses.
The model reported here is found to be in a quantitative agreement with
measurements of the ensemble-averaged energy-resolved spin density. This work
elucidates the effects of RF detunings on the system and measurements, pointing
the way to new correlation measurement methods.


  In order to overcome the limitations of the original expression of the
probability distribution appearing in literature of Incomplete Statistics, a
new expression of the probability distribution is derived, where the Lagrange
multiplier %B% introduced here is proved to be identical with that introduced
in the second and third choices for the internal energy constraint in Tsallis'
statistics and to be just equal to the physical inverse temperature. It is
expounded that the probability distribution described by the new expression is
invariant through uniform translation of the energy spectrum. Moreover, several
fundamental thermodynamic relations are given and the relationship between the
new and the original expressions of the probability distribution is discussed.


  Direct imaging and spectroscopy is the likely means by which we will someday
identify, confirm, and characterize an Earth-like planet around a nearby
Sun-like star. This Chapter summarizes the current state of knowledge regarding
discovering and characterizing exoplanets by direct imaging and spectroscopy.
We detail instruments and software needed for direct imaging detections and
summarize the current inventory of confirmed and candidate directly-imaged
exoplanets. Direct imaging and spectroscopy in the past decade has provided key
insights into jovian planet atmospheres, probed the demographics of the
outskirts of planetary systems, and shed light on gas giant planet formation.
We forecast the new tools and future facilities on the ground and in space that
will enhance our capabilities for exoplanet imaging and will likely image
habitable zone rocky planets around the nearest stars.


  Model smoothing is of central importance for obtaining a reliable teacher
model in the student-teacher framework, where the teacher generates surrogate
supervision signals to train the student. A popular model smoothing method is
the Temporal Moving Average (TMA), which continuously averages the teacher
parameters with the up-to-date student parameters. In this paper, we propose
"Spatial Ensemble", a novel model smoothing mechanism in parallel with TMA.
Spatial Ensemble randomly picks up a small fragment of the student model to
directly replace the corresponding fragment of the teacher model.
Consequentially, it stitches different fragments of historical student models
into a unity, yielding the "Spatial Ensemble" effect. Spatial Ensemble obtains
comparable student-teacher learning performance by itself and demonstrates
valuable complementarity with temporal moving average. Their integration, named
Spatial-Temporal Smoothing, brings general (sometimes significant) improvement
to the student-teacher learning framework on a variety of state-of-the-art
methods. For example, based on the self-supervised method BYOL, it yields +0.9%
top-1 accuracy improvement on ImageNet, while based on the semi-supervised
approach FixMatch, it increases the top-1 accuracy by around +6% on CIFAR-10
when only few training labels are available. Codes and models are available at:
https://github.com/tengteng95/Spatial_Ensemble.


  The increase in academic dishonesty cases among college students has raised
concern, particularly due to the shift towards online learning caused by the
pandemic. We aim to develop and implement a method capable of generating
tailored questions for each student. The use of Automatic Question Generation
(AQG) is a possible solution. Previous studies have investigated AQG frameworks
in education, which include validity, user-defined difficulty, and personalized
problem generation. Our new AQG approach produces logical equivalence problems
for Discrete Mathematics, which is a core course for year-one computer science
students. This approach utilizes a syntactic grammar and a semantic attribute
system through top-down parsing and syntax tree transformations. Our
experiments show that the difficulty level of questions generated by our AQG
approach is similar to the questions presented to students in the textbook [1].
These results confirm the practicality of our AQG approach for automated
question generation in education, with the potential to significantly enhance
learning experiences.


  We describe the cohomology ring $H^*(J_2;\mathbb{F}_3)$ both as subring of
$H^*(3^{1+2}_+;\mathbb{F}_3)$ and with an abstract presentation. We also give
its Poincar\'{e} series. We use as tool a spectral sequence for the strongly
closed $3$-subgroup of $J_2$. This method might be used to compute the
cohomology of any finite simple group with a strongly closed $p$-subgroup.


  In this work we are analyzing scalability of the heuristic algorithm we used
in the past to discover knowledge from multi-valued symbolic attributes in
fuzzy databases. The non-atomic descriptors, characterizing a single attribute
of a database record, are commonly used in fuzzy databases to reflect
uncertainty about the recorded observation. In this paper, we present
implementation details and scalability tests of the algorithm, which we
developed to precisely interpret such non-atomic values and to transfer (i.e.
defuzzify) the fuzzy tuples to the forms acceptable for many regular (i.e.
atomic values based) data mining algorithms. Important advantages of our
approach are: (1) its linear scalability, and (2) its unique capability of
incorporating background knowledge, implicitly stored in the fuzzy database
models in the form of fuzzy similarity hierarchy, into the
interpretation/defuzzification process.


  Current drone delivery systems mostly focus on point-to-point package
delivery. We present a multi-stop drone service system to deliver packages
anywhere anytime within a specified geographic area. We define a skyway network
which takes into account flying regulations, including restricted areas and
no-fly zones. The skyway nodes typically represent building rooftops which may
act as both recharging stations and delivery destinations. A heuristic-based A*
algorithm is used to compute an optimal path from source to destination taking
into account a number of constraints, including delivery time, availability of
recharging stations, etc. We deploy our drone delivery system in an indoor
testbed environment using a 3D model of Sydney CBD. We describe a graphical
user interface to monitor the real-time package delivery in the skyway network.


  We investigate a well-known phenomenon of variational approaches in image
processing, where typically the best image quality is achieved when the
gradient flow process is stopped before converging to a stationary point. This
paradox originates from a tradeoff between optimization and modelling errors of
the underlying variational model and holds true even if deep learning methods
are used to learn highly expressive regularizers from data. In this paper, we
take advantage of this paradox and introduce an optimal stopping time into the
gradient flow process, which in turn is learned from data by means of an
optimal control approach. As a result, we obtain highly efficient numerical
schemes that achieve competitive results for image denoising and image
deblurring. A nonlinear spectral analysis of the gradient of the learned
regularizer gives enlightening insights about the different regularization
properties.


  Single-photon sources are at the heart of quantum-optical networks, with
their uniquely quantum emission and phenomenon of two-photon interference
allowing for the generation and transfer of nonclassical states. Although a few
analytical methods have been briefly investigated for describing pulsed
single-photon sources, these methods apply only to either perfectly ideal or at
least extremely idealized sources. Here, we present the first complete picture
of pulsed single-photon sources by elaborating how to numerically and fully
characterize non-ideal single-photon sources operating in a pulsed regime. In
order to achieve this result, we make the connection between quantum
Monte--Carlo simulations, experimental characterizations, and an extended form
of the quantum regression theorem. We elaborate on how an ideal pulsed
single-photon source is connected to its photocount distribution and its
measured degree of second- and first-order optical coherence. By doing so, we
provide a description of the relationship between instantaneous source
correlations and the typical experimental interferometers (Hanbury-Brown and
Twiss, Hong-Ou-Mandel, and Mach-Zehnder) used to characterize such sources.
Then, we use these techniques to explore several prototypical quantum systems
and their non-ideal behaviors. As an example numerical result, we show that for
the most popular single-photon source---a resonantly excited two-level
system---its error probability is directly related to its excitation pulse
length. We believe that the intuition gained from these representative systems
and characters can be used to interpret future results with more complicated
source Hamiltonians and behaviors. Finally, we have thoroughly documented our
simulation methods with contributions to the Quantum Optics Toolbox in Python
(QuTiP) in order to make our work easily accessible to other scientists and
engineers.


  The application of knowledge from quantum physics to computer science, which
we call \doubleq{quantum informatics}, is driving the development of new
technologies, such as quantum computing and quantum key distribution.
Researchers in physics education have recognized the promise and significance
of teaching quantum informatics in schools, and various teaching methods are
being developed, researched and applied. Although quantum informatics is
equally relevant to computer science education, little research has been done
on how to teach it with a focus on computer science concepts and knowledge. In
this study, we position quantum informatics within Denning's Great Principles
of Computing and propose Quantum Informatics Standards for secondary schools.


  Deep Convolutional Neural Networks (CNN) has achieved significant success in
computer vision field. However, the high computational cost of the deep complex
models prevents the deployment on edge devices with limited memory and
computational resource. In this paper, we proposed a novel filter pruning for
convolutional neural networks compression, namely spectral clustering filter
pruning with soft self-adaption manners (SCSP). We first apply spectral
clustering on filters layer by layer to explore their intrinsic connections and
only count on efficient groups. By self-adaption manners, the pruning
operations can be done in few epochs to let the network gradually choose
meaningful groups. According to this strategy, we not only achieve model
compression while keeping considerable performance, but also find a novel angle
to interpret the model compression process.


  Electromagnetic field in nuclear matter and nuclei are studied. In the
nuclear matter, because the expectation value of the electric charge density
operator is not zero, different in vacuum, the U(1) local gauge symmetry of
electric charge is spontaneously broken, and consequently, the photon gains an
effective mass through the Higgs mechanism. An alternative way to study the
effective mass of photon is to calculate the self-energy of photon
perturbatively. It shows that the effective mass of photon is about $5.42MeV$
in the symmetric nuclear matter at the saturation density $\rho_0 =
0.16fm^{-3}$ and about $2.0MeV$ at the surface of ${}^{238}U$. It seems that
the two-body decay of a massive photon causes the sharp lines of
electron-positron pairs in the low energy heavy ion collision experiments of
${}^{238}U+{}^{232}Th$ .


  Discourse relation classification is an especially difficult task without
explicit context markers (Prasad et al., 2008). Current approaches to implicit
relation prediction solely rely on two neighboring sentences being targeted,
ignoring the broader context of their surrounding environments (Atwell et al.,
2021). In this research, we propose three new methods in which to incorporate
context in the task of sentence relation prediction: (1) Direct Neighbors
(DNs), (2) Expanded Window Neighbors (EWNs), and (3) Part-Smart Random
Neighbors (PSRNs). Our findings indicate that the inclusion of context beyond
one discourse unit is harmful in the task of discourse relation classification.


  Popular culture has contemplated societies of thinking machines for
generations, envisioning futures from utopian to dystopian. These futures are,
arguably, here now-we find ourselves at the doorstep of technology that can at
least simulate the appearance of thinking, acting, and feeling. The real
question is: now what?


  Kirillov's orbit theory provides a powerful tool for the investigation of
irreducible unitary representations of many classes of Lie groups. In a
previous paper we used a modification hereof, called monomial linearisation, to
construct a monomial basis of the regular representation of $p$-Sylow subgroups
$U$ of the finite classical groups of untwisted type. In this sequel to this
article we determine the stabilizers of special orbit generators and show, that
for the groups of Lie type ${\mathfrak{B}}_n$ and ${\mathfrak{D}}_n$ a subclass
of the orbit modules decompose the $U$-modules affording the Andr\'{e}-Neto
supercharacters into a direct sum of submodules. Moreover these special orbit
modules are either isomorphic or have no irreducible constituent in common, and
each irreducible $U$ module is up to isomorphism constituent of precisely one
of these.