  We prove the convergence of geodesic distance during the quantization of the
space of K\"ahler potentials. As applications, this provides alternative proofs
of certain inequalities about the K-energy functional in the projective case.


  Software engineering (SE) is full of abstract concepts that are crucial for
both researchers and practitioners, such as programming experience, team
productivity, code comprehension, and system security. Secondary studies aimed
at summarizing research on the influences and consequences of such concepts
would therefore be of great value.
  However, the inability to measure abstract concepts directly poses a
challenge for secondary studies: primary studies in SE can operationalize such
concepts in many ways. Standardized measurement instruments are rarely
available, and even if they are, many researchers do not use them or do not
even provide a definition for the studied concept. SE researchers conducting
secondary studies therefore have to decide a) which primary studies intended to
measure the same construct, and b) how to compare and aggregate vastly
different measurements for the same construct.
  In this experience report, we discuss the challenge of study selection in SE
secondary research on latent variables. We report on two instances where we
found it particularly challenging to decide which primary studies should be
included for comparison and synthesis, so as not to end up comparing apples
with oranges. Our report aims to spark a conversation about developing
strategies to address this issue systematically and pave the way for more
efficient and rigorous secondary studies in software engineering.


  Nonlocal correlation represents the key feature of quantum mechanics, which
is exploited as a resource in quantum information processing. However, the
loophole issues hamper the practical applications. We report the first
demonstration of steering nonlocality with detection loophole closed at
telecommunication wavelengths. In this endeavour, we design and fabricate a
low-loss silicon chip for efficient entanglement generation, and further apply
the direct modulation technique to its optical pump to eliminate phase-encoding
loss at the steering side. The newly proposed phase-encoding measurement
setting adapts to an ultra-fast modulation rate (GHz). Consequently, we build a
fiber-optic setup that can overcome the detection efficiency that is required
by quantum steering with multiple measurement settings. Our setup provides an
immediate platform for exploring applications based on steering nonlocality,
especially for quantum communication.


  As the responsivity (R) of a thin film photo detector is proportional to the
product of the photo-induced carrier density (n) and mobility (u) (Z. Sun, Z.
Liu, J. Li, G.-a. Tai, S. Lau and F. Yan, Adv. Mater., 24, 5878, 2012), which
of the types is conducive to photo detection, choosing between
layer-heterojunction (LH) and bulk-heterojunction (BH) field effect
phototransistors (FEpTs), is still unknown. A comparison study is performed
based on an MEH-PPV and PbS QDs hybrid.


  A classical general relativistic theory possessing magnetic currents, as well
electric ones and admitting massive photons was built up. As the geometric
basis serves a space with Weylian non-metricity and torsion. The theory is
coordinate covariant as well Weyl-gauge covariant. In the limit one obtains the
ordinary Einstein-Maxwell theory.


  We present an experimental study of the fluctuations of Coulomb blockade peak
positions of a quantum dot. The dot is defined by patterning the
two-dimensional electron gas of a silicon MOSFET structure using stacked gates.
This permits variation of the number of electrons on the quantum dot without
significant shape distortion. The ratio of charging energy to single particle
energy is considerably larger than in comparable GaAs/AlGaAs quantum dots. The
statistical distribution of the conductance peak spacings in the Coulomb
blockade regime was found to be unimodal and does not follow the Wigner
surmise. The fluctuations of the spacings are much larger than the typical
single particle level spacing and thus clearly contradict the expectation of
random matrix theory.


  We extend the Lieb-Schupp theorem to the Heisenberg models with higher order
interactions on non-frustrated or frustrated finite lattices. These lattices
are constructed by even numbered rings with or without crossing bonds and have
reflection symmetry. The results show that all ground states have total spin
zero in wide interaction parameters region which is not covered with the
results of the Marshall-Lieb-Mattis type arguments.


  We show a proof of principle for warping, a method to interpret the inner
working of neural networks in the context of gene expression analysis. Warping
is an efficient way to gain insight to the inner workings of neural nets and
make them more interpretable. We demonstrate the ability of warping to recover
meaningful information for a given class on a samplespecific individual basis.
We found warping works well in both linearly and nonlinearly separable
datasets. These encouraging results show that warping has a potential to be the
answer to neural networks interpretability in computational biology.


  A summary of recent results obtained for higher-order corrections to
precision observables in the Standard Model and the Minimal Supersymmetric
Standard Model is given. In the Standard Model electroweak two-loop results
valid for arbitrary values of the masses of the top quark, the Higgs boson and
the gauge bosons are discussed. For the example of two specific diagrams the
exact two-loop result is compared with the result of an expansion in the
top-quark mass up to next-to-leading order. Furthermore the Higgs-mass
dependence of the two-loop corrections to the relation between the gauge-boson
masses is analyzed. In the MSSM the exact gluonic corrections to $\Delta r$ are
derived. They are compared with the leading contribution entering via the
$\rho$ parameter.


  Online social as an extension of traditional life plays an important role in
our daily lives. Users often seek out new friends that have significant
similarities such as interests and habits, motivating us to exploit such online
information to suggest friends to users. In this work, we focus on friend
suggestion in online game platforms because in-game social quality
significantly correlates with player engagement, determining game experience.
Unlike a typical recommendation system that depends on item-user interactions,
in our setting, user-user interactions do not depend on each other. Meanwhile,
user preferences change rapidly due to fast changing game environment. There
has been little work on designing friend suggestion when facing these
difficulties, and for the first time we aim to tackle this in large scale
online games. Motivated by the fast changing online game environment, we
formulate this problem as friend ranking by modeling the evolution of
similarity among users, exploiting the long-term and short-term feature of
users in games. Our experiments on large-scale game datasets with several
million users demonstrate that our proposed model achieves superior performance
over other competing baselines.


  The aim of the presented research is to give a rigorous mathematical approach
to Feynman path integrals based on strong (pathwise) approximations based on
simple random walks.


  The statement that Maxwell's electrodynamics in vacuum is already covariant
under Lorentz transformations is commonplace in the literature. We analyse the
actual meaning of that statement and demonstrate that Maxwell's equations are
perfectly fit to be Lorentz-covariant; they become Lorentz-covariant if we
construct to be so, by postulating certain transformation properties of field
functions. In Aristotelian terms, the covariance is a plain potentiality, but
not necessarily entelechy.


  The Photometric LSST Astronomical Time Series Classification Challenge
(PLAsTiCC) is an open data challenge to classify simulated astronomical
time-series data in preparation for observations from the Large Synoptic Survey
Telescope (LSST), which will achieve first light in 2019 and commence its
10-year main survey in 2022. LSST will revolutionize our understanding of the
changing sky, discovering and measuring millions of time-varying objects.
  In this challenge, we pose the question: how well can we classify objects in
the sky that vary in brightness from simulated LSST time-series data, with all
its challenges of non-representativity? In this note we explain the need for a
data challenge to help classify such astronomical sources and describe the
PLAsTiCC data set and Kaggle data challenge, noting that while the references
are provided for context, they are not needed to participate in the challenge.


  With the advancement of sensing technology, multivariate time series
classification (MTSC) has recently received considerable attention. Existing
deep learning-based MTSC techniques, which mostly rely on convolutional or
recurrent neural networks, are primarily concerned with the temporal dependency
of single time series. As a result, they struggle to express pairwise
dependencies among multivariate variables directly. Furthermore, current
spatial-temporal modeling (e.g., graph classification) methodologies based on
Graph Neural Networks (GNNs) are inherently flat and cannot aggregate hub data
in a hierarchical manner. To address these limitations, we propose a novel
graph pooling-based framework MTPool to obtain the expressive global
representation of MTS. We first convert MTS slices to graphs by utilizing
interactions of variables via graph structure learning module and attain the
spatial-temporal graph node features via temporal convolutional module. To get
global graph-level representation, we design an "encoder-decoder" based
variational graph pooling module for creating adaptive centroids for cluster
assignments. Then we combine GNNs and our proposed variational graph pooling
layers for joint graph representation learning and graph coarsening, after
which the graph is progressively coarsened to one node. At last, a
differentiable classifier takes this coarsened representation to get the final
predicted class. Experiments on ten benchmark datasets exhibit MTPool
outperforms state-of-the-art strategies in the MTSC task.


  Generative, ML-driven interactive systems have the potential to change how
people interact with computers in creative processes - turning tools into
co-creators. However, it is still unclear how we might achieve effective
human-AI collaboration in open-ended task domains. There are several known
challenges around communication in the interaction with ML-driven systems. An
overlooked aspect in the design of co-creative systems is how users can be
better supported in learning to collaborate with such systems. Here we reframe
human-AI collaboration as a learning problem: Inspired by research on team
learning, we hypothesize that similar learning strategies that apply to
human-human teams might also increase the collaboration effectiveness and
quality of humans working with co-creative generative systems. In this position
paper, we aim to promote team learning as a lens for designing more effective
co-creative human-AI collaboration and emphasize collaboration process quality
as a goal for co-creative systems. Furthermore, we outline a preliminary
schematic framework for embedding team learning support in co-creative AI
systems. We conclude by proposing a research agenda and posing open questions
for further study on supporting people in learning to collaborate with
generative AI systems.


  We study the relation between the internal structures of 10 benchmark
main-sequence F-stars and their rotational properties. Stellar rotation of
main-sequence F-type stars can be characterised by two distinct rotational
regimes. Early-type F-stars are usually rapid rotators with periods typically
below 10 days, whereas later-type F-stars have longer rotation periods.
Specifically, and since the two rotational regimes are tightly connected to the
effective temperatures of the stars, we investigate in detail the
characteristics of the partial ionisation zones in the outer convective
envelopes of these stars, which in turn, depend on the internal temperature
profiles. Our study shows that the two rotational regimes might be
distinguished by the relative locations of the partial ionisation region of
heavy elements and the base of the convective zone. Since in all these stars is
expected a dynamo-driven magnetic field where the shear layer between
convective and radiative zones (tachocline) plays an important role, this
result suggests that the magnetic field may be related to the combined
properties of convection and ionisation.


  Model interpretation is essential in data mining and knowledge discovery. It
can help understand the intrinsic model working mechanism and check if the
model has undesired characteristics. A popular way of performing model
interpretation is Instance-wise Feature Selection (IFS), which provides an
importance score of each feature representing the data samples to explain how
the model generates the specific output. In this paper, we propose a
Model-agnostic Effective Efficient Direct (MEED) IFS framework for model
interpretation, mitigating concerns about sanity, combinatorial shortcuts,
model identifiability, and information transmission. Also, we focus on the
following setting: using selected features to directly predict the output of
the given model, which serves as a primary evaluation metric for
model-interpretation methods. Apart from the features, we involve the output of
the given model as an additional input to learn an explainer based on more
accurate information. To learn the explainer, besides fidelity, we propose an
Adversarial Infidelity Learning (AIL) mechanism to boost the explanation
learning by screening relatively unimportant features. Through theoretical and
experimental analysis, we show that our AIL mechanism can help learn the
desired conditional distribution between selected features and targets.
Moreover, we extend our framework by integrating efficient interpretation
methods as proper priors to provide a warm start. Comprehensive empirical
evaluation results are provided by quantitative metrics and human evaluation to
demonstrate the effectiveness and superiority of our proposed method. Our code
is publicly available online at https://github.com/langlrsw/MEED.


  Using a spin-polarized full-potential linear muffin-tin orbital method we
present calculations of the nonlinear magneto-optical Kerr effect for thin bcc
Fe films within a slab geometry. Film layer dependent contributions to the Kerr
spectrum are determined. Thus, we calculate the magneto-optical Kerr spectra
for the linear and nonlinear case. Our results show clearly that the Kerr
spectra of thin films are characteristicly different from those at surfaces of
bulk materials. In the case of linear Kerr spectra of Au/Fe(bcc)/Au(001) films
our theoretical results are in good agreement with observed frequency- and
thickness-dependent spectra.


  This paper proposes a fully distributed termination method for distributed
optimization algorithms solved by multiple agents. The proposed method
guarantees terminating a distributed optimization algorithm after satisfying
the global termination criterion using information from local computations and
neighboring agents. The proposed method requires additional iterations after
satisfying the global terminating criterion to communicate the termination
status. The number of additional iterations is bounded by the diameter of the
communication network. This paper also proposes a fault-tolerant extension of
this termination method that prevents early termination due to faulty agents or
communication errors. We provide a proof of the method's correctness and
demonstrate the proposed method by solving the optimal power flow problem for
electric power grids using the alternating direction method of multipliers.


  Given recent algorithm, software, and hardware innovation, computing has
enabled a plethora of new applications. As computing becomes increasingly
ubiquitous, however, so does its environmental impact. This paper brings the
issue to the attention of computer-systems researchers. Our analysis, built on
industry-reported characterization, quantifies the environmental effects of
computing in terms of carbon emissions. Broadly, carbon emissions have two
sources: operational energy consumption, and hardware manufacturing and
infrastructure. Although carbon emissions from the former are decreasing thanks
to algorithmic, software, and hardware innovations that boost performance and
power efficiency, the overall carbon footprint of computer systems continues to
grow. This work quantifies the carbon output of computer systems to show that
most emissions related to modern mobile and data-center equipment come from
hardware manufacturing and infrastructure. We therefore outline future
directions for minimizing the environmental impact of computing systems.


  To address thermal stability issues for spintronic devices with a reduced
size, we investigate spin-orbit torque in Co/Pt multilayers with strong
perpendicular magnetic anisotropy. Note that the spin-orbit torque arises from
the global imbalance of the spin currents from the top and bottom interfaces
for each Co layer. By inserting Ta or Cu layers to strengthen the top-down
asymmetry, the spin-orbit torque efficiency can be greatly modified without
compromised perpendicular magnetic anisotropy. Above all, the efficiency builds
up as the number of layers increases, realizing robust thermal stability and
high spin-orbit-torque efficiency simultaneously in the multilayers structure.


  Let $A$ and $B$ be unital semisimple commutative Banach algebras and $T$ a
map from the invertible group $A^{-1}$ onto $B^{-1}$. Linearity and
multiplicativity of the map are not assumed. We consider the hypotheses on $T$:
(1) $\sigma (TfTg)=\sigma (fg)$; (2) $\sigma_{\pi}(TfTg-\alpha)\cap
\sigma_{\pi}(fg-\alpha)\ne \emptyset$; (3) $\mathrm{r} (TfTg-\alpha
)=\mathrm{r}(fg-\alpha)$ hold for some non-zero complex number $\alpha$ and for
every $f, g\in A^{-1}$, where $\sigma (\cdot)$ (resp. $\sigma_{\pi}(\cdot)$)
denotes the (resp. peripheral) spectrum and $\rr(\cdot)$ denotes the spectral
radius. Under each of the hypotheses we show representations for $T$ and under
additional assumptions we show that $T$ is extended to an algebra isomorphism.
In particular, if $T$ is a surjective group homomorphism such that $T$
preserves the spectrum or $T$ is a surjective isometry with respect to the
spectral radius, then $T$ is extended to an algebra isomorphism. Similar
results holds for maps from $A$ onto $B$.


  We calculate an $s$-wave amplitude matrix for all the possible 2--to--2 body
scalar boson elastic scatterings in models with three scalar doublets,
including contributions from the longitudinal component of weak gauge bosons
via the Equivalence Theorem Approximation. Specifically, we concentrate on the
two cases with two[one] active plus one[two] inert doublet fields, referred to
as I(1+2)HDM[I(2+1)HDM], under CP conservation. We obtain three analytically
irreducible sub-matrices with the $3\times 3$ form and eighteen eigenvalues for
the amplitude matrix as an independent set, where the same formula can be
applied to both models. By requiring a perturbative unitarity condition, we can
constrain the magnitude of quartic coupling constants in the Higgs potential.
This constraint, in particular in the I(1+2)HDM, can be translated into a bound
on masses of extra active scalar bosons. Furthermore, when Standard Model-like
Higgs boson couplings with weak gauge bosons are deviated from the Standard
Model predictions, the unitarity condition provides an upper limit on the
masses. We find that stronger upper bounds on the masses of the active CP-even
and CP-odd Higgs bosons are obtained under the constraints from the unitarity
and vacuum stability conditions, as well as the electroweak $S$, $T$ and $U$
parameters, as compared to those in 2-Higgs Doublet Models with a softly-broken
$Z_2$ symmetry.


  We investigate the memory properties of discrete sequences built upon a
finite number of states. We find that the block entropy can reliably determine
the memory for systems modeled as Markov chains of arbitrary finite order.
Further, we provide an entropy estimator that remarkably gives accurate results
when correlations are present. To illustrate our findings, we calculate the
memory of daily precipitation series at different locations. Our results are in
agreement with existing methods being at the same time valid in the
undersampled regime and independent of model selection.


  Motion planning is a key element of robotics since it empowers a robot to
navigate autonomously. Particle Swarm Optimization is a simple, yet a very
powerful optimization technique which has been effectively used in many complex
multi-dimensional optimization problems. This paper proposes a path planning
algorithm based on particle swarm optimization for computing a shortest
collision-free path for a mobile robot in environments populated with static
convex obstacles. The proposed algorithm finds the optimal path by performing
random sampling on grid lines generated between the robot start and goal
positions. Functionality of the proposed algorithm is illustrated via
simulation results for different scenarios.


  We proceed to generalize the Yang-Baxter (YB) deformation of
Wess-Zumino-Witten (WZW) model to the Lie supergroups case. This generalization
enables us to utilize various kinds of solutions of the (modified) graded
classical Yang-Baxter equation ((m)GCYBE) to classify the YB deformations of
WZW models based on the Lie supergroups. We obtain the inequivalent solutions
(classical r-matrices) of the (m)GCYBE for the $gl(1|1)$ and $({\cal C}^3
+{\cal A})$ Lie superalgebras in the non-standard basis, in such a way that the
corresponding automorphism transformations are employed. Then, the YB
deformations of the WZW models based on the $GL(1|1)$ and $(C^3 + A)$ Lie
supergroups are specified by skew-supersymmetric classical r-matrices
satisfying (m)GCYBE. In some cases for both families of deformed models, the
metrics remain invariant under the deformation, while the components of
$B$-fields are changed. After checking the conformal invariance of the models
up to one-loop order, it is concluded that the $GL(1|1)$ and $(C^3 + A)$ WZW
models are conformal theories within the classes of the YB deformations
preserving the conformal invariance. However, our results are interesting in
themselves, but at a constructive level, may prompt many new insights into
(generalized) supergravity solutions.


  We present an extension of our search for Extremely Inverted Spectrum
Extragalactic Radio Sources (EISERS) to the northern celestial hemisphere. With
an inverted radio spectrum of slope $\alpha$ > +2.5, these rare sources would
either require a non-standard particle acceleration mechanism (in the framework
of synchrotron self-absorption hypothesis), or a severe free-free absorption
which attenuates practically all of their synchrotron radiation at metre
wavelengths. By applying a sequence of selection filters, a list of 15 EISERS
candidates is extracted out by comparing two large-sky radio surveys, WENSS
(325 MHz) and TGSS-ADR1 (150 MHz), which overlap across 1.03$\pi$ steradian of
the sky. Here we report quasi-simultaneous GMRT observations of these 15 EISERS
candidates at 150 MHz and 325 MHz, in an attempt to accurately define their
spectra below the turnover frequency. Out of the 15 candidates observed, two
are confirmed as EISERS, since the slope of the inverted spectrum between these
two frequencies is found to be significantly larger than the critical value
$\alpha_c$ = +2.5: the theoretical limit for the standard case of synchrotron
self-absorption (SSA). For another 3 sources, the spectral slope is close to,
or just above the critical value $\alpha_c$. Nine of the sources have GPS type
radio spectra. The parsec-scale radio structural information available for the
sample is also summarised.


  We present and test an effective model for N -body simulations that aims at
mimicking the impact of supernova (SN) feedback on the dark matter (DM)
distribution of isolated halos hosting dwarf galaxies. Although the model is
physically decoupled from the cosmological history of both the DM halo and the
dwarf galaxy, it allows us to study the impact of different macroscopic
parameters such as galaxy concentration, feedback energy and energy injection
time in the process of SN-driven core formation in a physically clear way.
Using our effective model in a suite of N-body simulations of an isolated halo
with different SN feedback parameters, we find that whether or not a DM core
forms is determined by the total amount of SN feedback energy that is
transferred to the DM particles. At a fixed injected energy, the amount of
transferred energy is bigger - and the size of the DM core is larger - the
faster the energy injection occurs and the more compact the dwarf galaxy is.
Analyzing the orbital evolution of kinematic tracers, we demonstrate that a
core forms through SN feedback only if the energy injection is impulsive
relative to the dynamical timescale of particles in the inner halo. However,
there is no fundamental link between the total amount of injected energy and
the injection rate. Consequently, the presence of signatures of impulsive
changes of the gravitational potential is not a sufficient condition for
dwarf-size halos to have cored density profiles.


  Following Smale, we study simple symmetric mechanical systems of $n$ point
particles in the plane. In particular, we address the question of the linear
and spectral stability properties of relative equilibria, which are special
solutions of the equations of motion. Our main result is a sufficient condition
to detect spectral (hence linear) instability. Namely, we prove that if the
Morse index of an equilibrium point with even nullity is odd, then the
associated relative equilibrium is spectrally unstable. The proof is based on
some refined formul\ae\ for computing the spectral flow. As a notable
application of our theorem, we examine two important classes of singular
potentials: the $\alpha$-homogeneous one, with $\alpha \in (0, 2)$, which
includes the gravitational case, and the logarithmic one. We also establish,
for the $\alpha$-homogeneous potential, an inequality which is useful to test
the spectral instability of the associated relative equilibrium.


  We present new Very Long Baseline Interferometry observations of the LINER
galaxy NGC 4278. The observations were taken with the Very Long Baseline Array
(VLBA) and a single antenna of the Very Large Array (VLA) at 5 GHz and 8.4 GHz
and have a linear resolution of <0.1 pc. Our radio data reveal a two sided
structure, with symmetric S-shaped jets emerging from a flat spectrum core. We
fit the jet brightness with gaussian components, which we identify from a
previous observation taken five years before. By comparing the positions of the
components in the two epochs, we measure motions between 0.45 +/- 0.14 and 3.76
+/- 0.65 mas, corresponding to apparent velocities < 0.2c, and to ages in the
range 8.3 - 65.8 years. Assuming that the radio morphology is intrinsically
symmetric and its appearance is governed by Doppler beaming effects, we find
that NGC4278 has mildly relativistic jets (beta ~ 0.75), closely aligned to the
line-of-sight (2 degrees < theta < 4 degrees). Alternatively, the source could
be oriented at a larger angle and asymmetries could be related to the jet
interaction with the surrounding medium. We also present new simultaneous VLA
observations between 1.4 and 43 GHz, and a 5 GHz light curve between 1972 and
2003. The radio spectrum can be fit by a relatively steep power-law (alpha =
0.54). We find significant variability at 5 GHz. All these arguments indicate
that the radiation from NGC 4278 is emitted via the synchrotron process by
relativistic particles accelerated by a supermassive black hole. Despite a much
lower power, this is the same process that takes place in ordinary radio loud
AGNs.


  The multiconfiguration Dirac-Hartree-Fock theory (MCDHF) has been employed to
calculate the electric dipole moment of the 7s6d 3D2 state of radium induced by
the nuclear Schiff moment. The results are dominated by valence and
core-valence electron correlation effects. We show that the correlation effects
can be evaluated in a converged series of multiconfiguration expansions.


  Reliable and efficient Visual Place Recognition is a major building block of
modern SLAM systems. Leveraging on our prior work, in this paper we present a
Hamming Distance embedding Binary Search Tree (HBST) approach for binary
Descriptor Matching and Image Retrieval. HBST allows for descriptor Search and
Insertion in logarithmic time by exploiting particular properties of binary
Feature descriptors. We support the idea behind our search structure with a
thorough analysis on the exploited descriptor properties and their effects on
completeness and complexity of search and insertion. To validate our claims we
conducted comparative experiments for HBST and several state-of-the-art methods
on a broad range of publicly available datasets. HBST is available as a compact
open-source C++ header-only library.


  The goal of the DARPA Automated Rapid Certification Of Software (ARCOS)
program is to "automate the evaluation of software assurance evidence to enable
certifiers to determine rapidly that system risk is acceptable." As part of
this program, the DesCert project focuses on the assurance-driven development
of new software. The DesCert team consists of SRI International, Honeywell
Research, and the University of Washington. We have adopted a formal,
tool-based approach to the construction of software artifacts that are
supported by rigorous evidence. The DesCert workflow integrates evidence
generation into a design process that goes from requirements capture and
analysis to the decomposition of the high-level software requirements into
architecture properties and software components with assertional contracts, and
on to software that can be analyzed both dynamically and statically. The
generated evidence is organized by means of an assurance ontology and
integrated into the RACK knowledge base.


  Scene recovery is a fundamental imaging task for several practical
applications, e.g., video surveillance and autonomous vehicles, etc. To improve
visual quality under different weather/imaging conditions, we propose a
real-time light correction method to recover the degraded scenes in the cases
of sandstorms, underwater, and haze. The heart of our work is that we propose
an intensity projection strategy to estimate the transmission. This strategy is
motivated by a straightforward rank-one transmission prior. The complexity of
transmission estimation is $O(N)$ where $N$ is the size of the single image.
Then we can recover the scene in real-time. Comprehensive experiments on
different types of weather/imaging conditions illustrate that our method
outperforms competitively several state-of-the-art imaging methods in terms of
efficiency and robustness.


  Suzaku observation of an ultraluminous X-ray source, NGC 2403 Source 3,
performed on 2006 March 16--17, is reported. The Suzaku XIS spectrum of Source
3 was described with a multi-color black-body-like emission from an optically
thick accretion disk. The innermost temperature and radius of the accretion
disk was measured to be $T_{\rm in} = 1.08_{-0.03}^{+0.02} $ keV and $R_{\rm
in} = 122.1_{-6.8}^{+7.7} \alpha^{1/2}$ km, respectively, where $\alpha = (\cos
60^\circ /\cos i)$ with $i$ being the disk inclination. The bolometric
luminosity of the source was estimated to be $L_{\rm bol} = 1.82 \times 10^{39}
\alpha $ ergs s$^{-1}$. Archival Chandra and XMM-Newton data of the source were
analyzed for long-term spectral variations. In almost all observations, the
source showed multi-color black-body-like X-ray spectra with parameters similar
to those in the Suzaku observation. In only one Chandra observation, however,
Source 3 was found to exhibit a power-law-like spectrum, with a photon index of
$\Gamma = 2.37 \pm 0.08$, when it was fainter by about $\sim 15 %$ than in the
Suzaku observation. The spectral behavior is naturally explained in terms of a
transition between the slim disk state and the "very high" states, both found
in Galactic black hole binaries when their luminosity approach the Eddington
limit. These results are utilized to argue that ultraluminous X-ray sources
generally have significantly higher black-hole masses than ordinary
stellar-mass black holes.


  We introduce a new strategy in solving the truncated complex moment problem.
To this aim we investigate recursive doubly indexed sequences and their
characteristic polynomials. A characterization of recursive doubly indexed
\emph{moment} sequences is provided. As a simple application, we obtain a
computable solution to the complex moment problem for cubic harmonic
characteristic polynomials of the form $z^3+az+b\overline{z}$, where $a$ and
$b$ are arbitrary real numbers. We also recapture a recent result due to
Curto-Yoo given for cubic column relations in $M(3)$ of the form
$Z^3=itZ+u\overline{Z}$ with $t,u$ real numbers satisfying some suitable
inequalities. Furthermore, we solve the truncated complex moment problem with
column dependence relations of the form $Z^{k+1}= \sum\limits_{0\leq n+ m \leq
k} a_{nm} \overline{Z}^n Z^m$ ($a_{nm} \in \mathbb{C}$).


  In this paper, we implement non-stiff interface tracking methods for the
evolution of 2-D curves that follow Airy flow, a curvature-dependent dispersive
geometric evolution law. The curvature of the curve satisfies the modified
Korteweg-de Vries equation, a dispersive non-linear soliton equation. We
present a fully discrete space-time analysis of the equations (proof of
convergence) and numerical evidence that confirms the accuracy, convergence,
efficiency, and stability of the methods.


  We calculate extensions between certain irreducible admissible
representations of p-adic groups.


  Over-parameterization and adaptive methods have played a crucial role in the
success of deep learning in the last decade. The widespread use of
over-parameterization has forced us to rethink generalization by bringing forth
new phenomena, such as implicit regularization of optimization algorithms and
double descent with training progression. A series of recent works have started
to shed light on these areas in the quest to understand -- why do neural
networks generalize well? The setting of over-parameterized linear regression
has provided key insights into understanding this mysterious behavior of neural
networks.
  In this paper, we aim to characterize the performance of adaptive methods in
the over-parameterized linear regression setting. First, we focus on two
sub-classes of adaptive methods depending on their generalization performance.
For the first class of adaptive methods, the parameter vector remains in the
span of the data and converges to the minimum norm solution like gradient
descent (GD). On the other hand, for the second class of adaptive methods, the
gradient rotation caused by the pre-conditioner matrix results in an in-span
component of the parameter vector that converges to the minimum norm solution
and the out-of-span component that saturates. Our experiments on
over-parameterized linear regression and deep neural networks support this
theory.


  This paper is devoted to a consumer-preferred community-level energy
management system (CEMS), in which a system manager allows consumers their
selfish decisions of power-saving while regulating the overall demand-supply
imbalance. The key structure of the system is to weakly control consumers: the
controller sends the allowable range of the power-saving amount to each
consumer, which is modeled by a set-valued control signal. Then, the consumers
decide the amount in the range based on their private preference. In this
paper, we address the design problem of the controller that generates the
set-valued control signals. The controller structure is based on internal model
control, which plays the essential role of guaranteeing the
consumer-independent stability and the worst-case control performance of the
overall CEMS. Finally, a numerical experiment of the consumer-preferred CEMS is
performed to demonstrate the design procedure of the controller and to show its
effectiveness.


  Disaster management demands a near real-time information dissemina-tion so
that the emergency services can be provided to the right people at the right
time. Recent advances in information and communication technologies enable
collection of real-time information from various sources. For example, sensors
deployed in the fields collect data about the environment. Similarly, social
networks like Twitter and Facebook can help to collect data from people in the
disaster zone. On one hand, inadequate situation awareness in disasters has
been identified as one of the primary factors in human errors with grave
consequences such as loss of lives and destruction of critical infrastructure.
On the other hand, the growing ubiquity of social media and mobile devices, and
pervasive nature of the Internet-of-Things means that there are more sources of
outbound traffic, which ultimately results in the creation of a data deluge,
beginning shortly after the onset of disaster events, leading to the problem of
information tsunami. In addition, security and privacy has crucial role to
overcome the misuse of the system for either intrusions into data or overcome
the misuse of the information that was meant for a specified purpose. .... In
this chapter, we provide such a situation aware application to support disaster
management data lifecycle, i.e. from data ingestion and processing to alert
dissemination. We utilize cloud computing, Internet of Things and social
computing technologies to achieve a scalable, effi-cient, and usable
situation-aware application called Cloud4BigData.


  Remote user authentication is desirable for a Telecare medicine information
system (TMIS) to verify the correctness of remote users. In 2013, Jiang et al.
proposed privacy preserving authentication scheme for TMIS. Recently, Wu and Xu
analyzed Jiang's scheme and identify serious security flaws in their scheme,
namely, user impersonation attack, DoS attack and off-line password guessing
attack. In this article, we analyze Wu and Xu's scheme and show that their
scheme is also vulnerable to off-line password guessing attack and does not
protect user anonymity. Moreover, we identify the inefficiency of incorrect
input detection of the login phase in Wu and Xu's scheme, where the smart card
executes the login session in-spite of wrong input.


  We present a one-parameter family of deformation functions $f(\phi)$ which
have novel properties. Firstly, the deformation function is its own inverse. We
show that a class of potentials remains invariant under this deformation.
Further, when applied to a certain class of kink bearing potentials, one
obtains potentials which are not bounded from below. Besides, we show that
there is a wide class of potentials having power law kink tails which are
connected by this deformation but the corresponding kink tails of the two
potentials have different asymptotic behavior. Finally, we show that when this
deformation function is applied to an appropriate one-parameter family of
potentials having two kink solutions, it creates new potentials with an
arbitrary even number ($2n$) of kink solutions. Conversely, by starting from
this potential with $2n$ kink solutions the deformation function annihilates
$2n-2$ kinks and we get back the potential which we started with having only
two kink solutions.


  If Supersymmetry (SUSY) has a compressed spectrum the current limits from the
LHC can be drastically reduced. We take possible `worst case' scenarios where
combinations of the stop, squark and gluino masses are degenerate with the mass
of the lightest SUSY particle. To accurately derive limits in the model, care
must be taken when describing QCD radiation and we examine this in detail.
Lower mass bounds are then produced by considering all the 7 TeV hadronic SUSY
and monojet searches. The evolution of the limits as the mass splitting is
varied is also presented.


  Granular superconductivity occurs when microscopic superconducting grains are
separated by non-superconducting regions through which they communicate by
Josephson tunneling to establish the macroscopic superconducting state [1].
Although crystals of the cuprate high-Tc superconductors are not granular in a
structural sense, theory indicates that at low hole densities the holes can
become concentrated at some locations resulting in hole-rich superconducting
domains [2-5]. Granular superconductivity due to Josephson tunneling through
'undoped' regions between such domains would represent a new paradigm for the
underdoped cuprates. Here we report studies of the spatial interrelationships
between STM tunneling spectra in underdoped Bi2Sr2CaCu2O8+x. They reveal an
apparent spatial segregation of the electronic structure into ~3nm diameter
domains (with superconducting characteristics and local energy gap delta<50
meV) in an electronically distinct background. To explore whether this
represents nanoscale segregation of two distinct electronic phases, we employ
scattering-resonances at Ni impurity atoms [6] as 'markers' for the local
existence of superconductivity [7-9]. No Ni-resonances are detected in any
regions where delta>50(2.5) meV. These observations suggest that underdoped
Bi2Sr2CaCu2O8+x is a mixture of two different short-range electronic orders
with the long-range characteristics of a granular superconductor.


  A comparative first principles pseudopotential study of the adsorption and
migration profiles of single Pt and Au atoms on the stoichiometric and reduced
TiO2 rutile (110) surfaces is presented. Pt and Au behave similarly with
respect to (i) most favorable adsorption sites, which are found to be the
hollow and substitutional sites on the stoichiometric and reduced surfaces,
respectively, (ii) the large increase in their binding energy (by ~1.7 eV) when
the surface is reduced, and (iii) their low migration barrier near 0.15 eV on
the stoichiometric surface. Pt, on the other hand, binds more strongly (by ~2
eV) to both surfaces. On the stoichiometric surface, Pt migration pattern is
expected to be one-dimensional, which is primarily influenced by interactions
with O atoms. Au migration is expected to be two-dimensional, with Au-Ti
interactions playing a more important role. On the reduced surface, the
migration barrier for Pt diffusion is significantly larger compared to Au.


  Parameters for linac-ring type e^{-}e^{+} colliders with sqrt(s)=1020 MeV
(phi factory), 3770 MeV (charm factory) and 4.2 GeV (tau factory) are
discussed. It is shown that luminosities of the order of 10^{34} cm^{-2}s^{-1}
can be achieved.


  In this paper we prove that generic small partial smoothings of
Kahler-Einstein (KE) Del Pezzo orbifolds with only nodal singularities, and
with no non-zero holomorphic vector fields, admit orbifold KE metrics which are
close in the Gromov-Hausdorff sense to the original KE metric.


  The Energetic X-ray Imaging Survey Telescope (EXIST) is designed to i) use
the birth of stellar mass black holes, as revealed by cosmic Gamma-Ray Bursts
(GRBs), as probes of the very first stars and galaxies to exist in the
Universe. Both their extreme luminosity (~104 times larger than the most
luminous quasars) and their hard X-ray detectability over the full sky with
wide-field imaging make them ideal "back-lights" to measure cosmic structure
with X-ray, optical and near-IR (nIR) spectra over many sight lines to high
redshift. The full-sky imaging detection and rapid followup narrow-field
imaging and spectroscopy allow two additional primary science objectives: ii)
novel surveys of supermassive black holes (SMBHs) accreting as very luminous
but rare quasars, which can trace the birth and growth of the first SMBHs as
well as quiescent SMBHs (non-accreting) which reveal their presence by X-ray
flares from the tidal disruption of passing field stars; and iii) a
multiwavelength Time Domain Astrophysics (TDA) survey to measure the temporal
variability and physics of a wide range of objects, from birth to death of
stars and from the thermal to non-thermal Universe. These science objectives
are achieved with the telescopes and mission as proposed for EXIST described
here.


  We relate the moduli space of Yang-Mills instantons to quaternionic
manifolds. For instanton number one, the Wolf spaces play an important role. We
apply these ideas to instanton calculations in N=4 SYM theory.


  The notion of the electron Fermi surface is one of the characteristic
concepts in the field of condensed matter physics, and it plays a crucial role
in the understanding of the physical properties of doped Mott insulators. Based
on the t-J model, we study the nature of the electron Fermi surface in the
cobaltates, and qualitatively reproduce the essential feature of the evolution
of the electron Fermi surface with doping. It is shown that the underlying
hexagonal electron Fermi surface obeys Luttinger's theorem. The theory also
predicts a Fermi-arc phenomenon at the low-doped regime, where the region of
the hexagonal electron Fermi surface along the \Gamma-K direction is suppressed
by the electron self-energy, and then six disconnected Fermi arcs located at
the region of the hexagonal electron Fermi surface along the \Gamma-M direction
emerge. However, this Fermi-arc phenomenon at the low-doped regime weakens with
the increase of doping.


  The existence of hot, two-temperature accretion flows is essential to the
recent discussions of the low luminosity, hard X-ray emission from accreting
neutron stars and black holes in Galactic binaries and massive black holes in
low luminosity galactic nuclei. In these flows, protons are essentially
virialized and relativistic energies for non-thermal protons are likely.
Observational confirmation of the energetic protons' presence could further
support the two-temperature accretion flow models. We point out that
synchrotron emission from nonthermal relativistic protons could provide an
observational signature in strongly magnetized neutron star systems. The
self-absorbed synchrotron emission from an accreting neutron star with the
magnetic moment $\sim 10^{30} G cm^3$ is expected to exhibit a spectrum \nu
I_{\nu}~ \nu**2 with the luminosity ~ a few x 10^**33 (L_x/10**36 erg/s)**0.4
erg/s at \nu~10**15 Hz where L_x is the X-ray luminosity from the neutron star
surface. The detection of the expected synchrotron signature in optical and UV
bands during the low luminosity state of the pulsar systems such as 4U 1626-67
and GX 1+4 could prove the existence of the hot, two-temperature accretion
flows during their spin-down episodes. The detected optical emission in 4U
1626-67 has a spectral shape and luminosity level very close to our
predictions.


  Task-oriented dialogue systems often assist users with personal or
confidential matters. For this reason, the developers of such a system are
generally prohibited from observing actual usage. So how can they know where
the system is failing and needs more training data or new functionality? In
this work, we study ways in which realistic user utterances can be generated
synthetically, to help increase the linguistic and functional coverage of the
system, without compromising the privacy of actual users. To this end, we
propose a two-stage Differentially Private (DP) generation method which first
generates latent semantic parses, and then generates utterances based on the
parses. Our proposed approach improves MAUVE by 2.5$\times$ and parse tree
function type overlap by 1.3$\times$ relative to current approaches for private
synthetic data generation, improving both on fluency and semantic coverage. We
further validate our approach on a realistic domain adaptation task of adding
new functionality from private user data to a semantic parser, and show overall
gains of 8.5% points in accuracy with the new feature.


  Deep learning (DL) has been used in the automatic diagnosis of Mild Cognitive
Impairment (MCI) and Alzheimer's Disease (AD) with brain imaging data. However,
previous methods have not fully exploited the relation between brain image and
clinical information that is widely adopted by experts in practice. To exploit
the heterogeneous features from imaging and tabular data simultaneously, we
propose the Visual-Attribute Prompt Learning-based Transformer (VAP-Former), a
transformer-based network that efficiently extracts and fuses the multi-modal
features with prompt fine-tuning. Furthermore, we propose a Prompt fine-Tuning
(PT) scheme to transfer the knowledge from AD prediction task for progressive
MCI (pMCI) diagnosis. In details, we first pre-train the VAP-Former without
prompts on the AD diagnosis task and then fine-tune the model on the pMCI
detection task with PT, which only needs to optimize a small amount of
parameters while keeping the backbone frozen. Next, we propose a novel global
prompt token for the visual prompts to provide global guidance to the
multi-modal representations. Extensive experiments not only show the
superiority of our method compared with the state-of-the-art methods in pMCI
prediction but also demonstrate that the global prompt can make the prompt
learning process more effective and stable. Interestingly, the proposed prompt
learning model even outperforms the fully fine-tuning baseline on transferring
the knowledge from AD to pMCI.


  In this paper, we discuss returning arrows with respect to the Nakayama
translation appearing in the quivers of some important algebras when we
construct extensions. When constructing twisted trivial extensions for a graded
self-injective algebra, we show that the returning arrows appear in the quiver,
that the complexity increases by 1 in Koszul cases, and the representation
dimension also increases by 1 under certain additional conditions. By applying
Koszul duality, for each Koszul Artin-Schelter regular algebra of global
dimension l and Gelfand-Kirilov dimension $c$, we construct a family of Koszul
Artin-Schelter regular algebras of global dimension $l+1$ and Gelfand-Kirilov
dimension $c+1$, among them one is central extension and one is
$l+1$-Calabi-Yau.


  In this work we study the cosmology of the general f(T) gravity theory. We
express the modified Einstein equations using covariant quantities, and derive
the gauge-invariant perturbation equations in covariant form. We consider a
specific choice of f(T), designed to explain the observed late-time
accelerating cosmic expansion without including an exotic dark energy
component. Our numerical solution shows that the extra degree of freedom of
such f(T) gravity models generally decays as one goes to smaller scales, and
consequently its effects on scales such as galaxies and galaxies clusters are
small. But on large scales, this degree of freedom can produce large deviations
from the standard LCDM scenario, leading to severe constraints on the f(T)
gravity models as an explanation to the cosmic acceleration.


  Which mechanism(s) are mainly driving nuclear activity in the centres of
galaxies is a major unsettled question. In this study, we investigate the
statistical relevance of galaxy mergers for fuelling gas onto the central few
kpc of a galaxy, potentially resulting in an active galactic nucleus (AGN). To
robustly address that, we employ large-scale cosmological hydrodynamic
simulations from the Magneticum Pathfinder set, including models for BH
accretion and AGN feedback. Our simulations predict that for luminous AGN
($L_{\rm AGN} > 10^{45} {\rm erg/s}$) at $z = 2$, more than 50 per cent of
their host galaxies have experienced a merger in the last 0.5~Gyr. These high
merger fractions, however, merely reflect the intrinsically high merger
fractions of massive galaxies at $z=2$, in which luminous AGN preferentially
occur. Apart from that, our simulations suggest that merger events are not the
statistically dominant fuelling mechanism for nuclear activity over a redshift
range $z=0-2$: irrespective of AGN luminosity, less than 20 per cent of AGN
hosts have on average undergone a recent merger, in agreement with a number of
observational studies. The central ISM conditions required for inducing AGN
activity can be, but are not necessarily caused by a merger. Despite the
statistically minor relevance of mergers, at a given AGN luminosity and stellar
mass, the merger fractions of AGN hosts can be by up to three times higher than
that of inactive galaxies. Such elevated merger fractions still point towards
an intrinsic connection between AGN and mergers, consistent with our
traditional expectation.


  In this paper, we derive first-order Pontryagin optimality conditions for
risk-averse stochastic optimal control problems subject to final time
inequality constraints, and whose costs are general, possibly non-smooth finite
coherent risk measures. Unlike preexisting contributions covering this
situation, our analysis holds for classical stochastic differential equations
driven by standard Brownian motions. In addition, it presents the advantages of
neither involving second-order adjoint equations, nor leading to the so-called
weak version of the PMP, in which the maximization condition with respect to
the control variable is replaced by the stationarity of the Hamiltonian.


  We report a $^{35}$Cl nuclear magnetic resonance study in the honeycomb
lattice, $\alpha$-RuCl$_3$, a material that has been suggested to potentially
realize a Kitaev quantum spin liquid (QSL) ground state. Our results provide
direct evidence that $\alpha$-RuCl$_3$ exhibits a magnetic field-induced QSL.
For fields larger than $\sim 10$ T a spin-gap opens up while resonance lines
remain sharp, evidencing that spins are quantum disordered and locally
fluctuating. The spin gap increases linearly with increasing magnetic field,
reaching $\sim50$ K at 15 T, and is nearly isotropic with respect to the field
direction. The unusual rapid increase of the spin gap with increasing field and
its isotropic nature are incompatible with conventional magnetic ordering and
in particular exclude that the ground state is a fully polarized ferromagnet.
The presence of such a field-induced, gapped QSL phase has indeed been
predicted in the Kitaev model.


  Network systems are commonly used in various fields, such as power grid,
Internet of Things (IoT), and gas networks. Reliability redundancy allocation
problem (RRAP) is a well-known reliability design tool, which needs to be
developed when the system is extended from the series-parallel structure to a
more general network structure. Therefore, this study proposes a novel RRAP
called General RRAP (GRRAP) to be applied to network systems. The Binary
Addition Tree Algorithm (BAT) is used to solve the network reliability. Since
GRRAP is an NP-hard problem, a new algorithm called Binary-addition simplified
swarm optimization (BSSO) is also proposed in this study. BSSO combines the
accuracy of the BAT with the efficiency of SSO, which can effectively reduce
the solution space and speed up the time to find high-quality solutions. The
experimental results show that BSSO outperforms three well-known algorithms,
Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Swarm
Optimization (SSO), on six network benchmarks.


  This is primarily an expository piece and the first sentence of the
introduction pretty much sums it up: This article is aimed at people who
already know what mixed Hodge structures are and what they are good for, but
who are not sure how to construct them.


  We present the experimental observation of scalar multi-pole solitons in
highly nonlocal nonlinear media, including dipole-, tri-pole, quadru-pole, and
necklace-type solitons, organized as arrays of out-of-phase bright spots. These
complex solitons are meta-stable, but with a large parameters range where the
instability is weak, enabling their experimental observation.


  The general one-dimensional ``log-sine'' gas is defined by restricting the
positive and negative charges of a two-dimensional Coulomb gas to live on a
circle. Depending on charge constraints, this problem is equivalent to
different boundary field theories. We study the electrically neutral case,
which is equivalent to a two-dimensional free boson with an impurity cosine
potential. We use two different methods: a perturbative one based on Jack
symmetric functions, and a non-perturbative one based on the thermodynamic
Bethe ansatz and functional relations. The first method allows us to compute
explicitly all coefficients in the virial expansion of the free energy and the
experimentally-measurable conductance. Some results for correlation functions
are also presented. The second method provides in particular a surprising
fluctuation-dissipation relation between the free energy and the conductance.


  This article describes a fast iterative algorithm for image denoising and
deconvolution with signal-dependent observation noise. We use an optimization
strategy based on variable splitting that adapts traditional Gaussian
noise-based restoration algorithms to account for the observed image being
corrupted by mixed Poisson-Gaussian noise and quantization errors.


  A multivariate density forecast model based on deep learning is designed in
this paper to forecast the joint cumulative distribution functions (JCDFs) of
multiple security margins in power systems. Differing from existing
multivariate density forecast models, the proposed method requires no a priori
hypotheses on the distribution of forecasting targets. In addition, based on
the universal approximation capability of neural networks, the value domain of
the proposed approach has been proven to include all continuous JCDFs. The
forecasted JCDF is further employed to calculate the deterministic security
assessment index evaluating the security level of future power system
operations. Numerical tests verify the superiority of the proposed method over
current multivariate density forecast models. The deterministic security
assessment index is demonstrated to be more informative for operators than
security margins as well.


  We give the explicit expression of the infinite volume limit for the random
overlap structures appearing in the mean field spin glass model. These
structures have the expected factorization property for the cavity fields, and
enjoy invariance with respect to a large class of random transformations. These
properties are typical of the Parisi Ansatz.


  We give necessary and sufficient conditions for the boundedness of the
maximal commutators $M_{b}$, the commutators of the maximal operator $[b, M]$
and the commutators of the sharp maximal operator $[b, M^{\sharp}]$ in Orlicz
spaces $L^{\Phi}(\mathbb{G})$ on any stratified Lie group $\mathbb{G}$ when $b$
belongs to Lipschitz spaces $\dot{\Lambda}_{\beta}(\mathbb{G})$. We obtain some
new characterizations for certain subclasses of Lipschitz spaces
$\dot{\Lambda}_{\beta}(\mathbb{G})$.


  We present the Voigt profile analysis of 132 intervening CIV+CIII components
associated with optically-thin HI absorbers at 2.1 < z < 3.4 in the 19
high-quality UVES/VLT and HIRES/Keck QSO spectra. For log N(CIV) = [11.7,
14.1], N(CIII) is proportional to N(CIV) with an exponent (1.42 +- 0.11) and <
N(CIII)/N(CIV) > = 1.0 +- 0.3 with a negligible redshift evolution. For 54 CIV
components tied (aligned) with HI at log N(HI) = [12.2, 16.0] and log N(CIV) =
[11.8, 13.8], the gas temperature T_b estimated from absorption line widths is
well-approximated to a Gaussian peaking at log T_b ~ 4.4 +- 0.3 for log T_b =
[3.5, 5.5], with a negligible non-thermal contribution. For 32 of 54 tied
HI+CIV pairs, also tied with CIII at log N(CIII) = [11.7, 13.8], we ran both
photoionisation equilibrium (PIE) and non-PIE (using a fixed temperature T_b)
Cloudy models for the Haardt-Madau QSO+galaxy 2012 UV background. We find
evidence of bimodality in observed and derived physical properties.
High-metallicity branch absorbers have a carbon abundance [C/H]_temp > -1.0, a
line-of-sight length L_temp < 20 kpc, and a total (neutral and ionised)
hydrogen volume density log n(H, temp) = [-4.5, -3.3] and and log T_b = [3.9,
4.5]. Low-metallicity branch absorbers have [C/H]_temp < -1.0, L_temp = [20,
480] kpc and log n(H, temp) = [-5.2, -4.3] and log T_b ~ 4.5. High-metallicity
branch absorbers seem to be originated from extended disks, inner halos or
outflowing gas of intervening galaxies, while low-metallicity absorbers are
produced by galactic halos or the surrounding IGM filament.


  Recently, prompt tuning \cite{lester2021power} has gradually become a new
paradigm for NLP, which only depends on the representation of the words by
freezing the parameters of pre-trained language models (PLMs) to obtain
remarkable performance on downstream tasks. It maintains the consistency of
Masked Language Model (MLM) \cite{devlin2018bert} task in the process of
pre-training, and avoids some issues that may happened during fine-tuning.
Naturally, we consider that the "[MASK]" tokens carry more useful information
than other tokens because the model combines with context to predict the masked
tokens. Among the current prompt tuning methods, there will be a serious
problem of random composition of the answer tokens in prediction when they
predict multiple words so that they have to map tokens to labels with the help
verbalizer. In response to the above issue, we propose a new \textbf{Pro}mpt
\textbf{Tu}ning based on "[\textbf{M}ASK]" (\textbf{Protum}) method in this
paper, which constructs a classification task through the information carried
by the hidden layer of "[MASK]" tokens and then predicts the labels directly
rather than the answer tokens. At the same time, we explore how different
hidden layers under "[MASK]" impact on our classification model on many
different data sets. Finally, we find that our \textbf{Protum} can achieve much
better performance than fine-tuning after continuous pre-training with less
time consumption. Our model facilitates the practical application of large
models in NLP.


  We consider theoretically a current flowing perpendicular to interfaces of a
spin-valve type ferromagnetic metallic junction. For the first time an
effective approach is investigated to calculate a simultaneous action of the
two current effects, namely, the nonequilibrium longitudinal spin injection and
the transversal spin-transfer surface torque. Dispersion relation for
fluctuations is derived and solved. Nonlinear problem is solved about steady
state arising due to instability for a thick enough free layer.


  A systematic investigation on muon anomalous magnetic moment and related
lepton flavor-violating process such as $\m\to e\g$, $\t\to e\g$ and $\t\to
\m\g$ is made at two loop level in the models with flavor-changing scalar
interactions. The two loop diagrams with double scalar exchanges are studied
and their contributions are found to be compatible with the ones from Barr-Zee
diagram. By comparing with the latest data, the allowed ranges for the relevant
Yukawa couplings $Y_{ij}$ in lepton sector are obtained. The results show a
hierarchical structure of $Y_{\m e, \t e} \ll Y_{\m \t} \simeq Y_{\m\m}$ in the
physical basis if $\Delta a_{\mu}$ is found to be $>50\times 10^{-11}$. It
deviates from the widely used ansatz in which the off diagonal elements are
proportional to the square root of the products of related fermion masses. An
alternative Yukawa coupling matrix in the lepton sector is suggested to
understand the current data. With such a reasonable Yukawa coupling ansatz, the
decay rate of $\t\to \m\g$ is found to be near the current experiment upper
bound.


  String matching algorithms in the presence of abbreviations, such as in Stock
Keeping Unit (SKU) product catalogs, remains a relatively unexplored topic. In
this paper, we present a unified architecture for SKU search that provides both
a real-time suggestion system (based on a Trie data structure) as well as a
lower latency search system (making use of character level TF-IDF in
combination with language model vector embeddings) where users initiate the
search process explicitly. We carry out ablation studies that justify designing
a complex search system composed of multiple components to address the delicate
trade-off between speed and accuracy. Using SKU search in the Dynamics CRM as
an example, we show how our system vastly outperforms, in all aspects, the
results provided by the default search engine. Finally, we show how SKU
descriptions may be enhanced via generative text models (using gpt-3.5-turbo)
so that the consumers of the search results may get more context and a
generally better experience when presented with the results of their SKU
search.


  X-ray resonant scattering has been used to measure the magnetic order of the
Dy ions below 40K in multiferroic DyMn$_{2}$O$_{5}$. The magnetic order has a
complex behaviour. There are several different ordering wavevectors, both
incommensurate and commensurate, as the temperature is varied. In addition a
non-magnetic signal at twice the wavevector of one of the commensurate signals
is observed, the maximum intensity of which occurs at the same temperature as a
local maximum in the ferroelectric polarisation. Some of the results, which
bear resemblence to the behaviour of other members of the RMn$_{2}$O$_{5}$
family of multiferroic materials, may be explained by a theory based on
so-called acentric spin-density waves.


  Deep neural networks (DNNs) have achieved great success in various machine
learning tasks. However, most existing powerful DNN models are computationally
expensive and memory demanding, hindering their deployment in devices with low
memory and computational resources or in applications with strict latency
requirements. Thus, several resource-adaptable or flexible approaches were
recently proposed that train at the same time a big model and several
resource-specific sub-models. Inplace knowledge distillation (IPKD) became a
popular method to train those models and consists in distilling the knowledge
from a larger model (teacher) to all other sub-models (students). In this work
a novel generic training method called IPKD with teacher assistant (IPKD-TA) is
introduced, where sub-models themselves become teacher assistants teaching
smaller sub-models. We evaluated the proposed IPKD-TA training method using two
state-of-the-art flexible models (MSDNet and Slimmable MobileNet-V1) with two
popular image classification benchmarks (CIFAR-10 and CIFAR-100). Our results
demonstrate that the IPKD-TA is on par with the existing state of the art while
improving it in most cases.


  We show that a St\"{u}ckelberg interferometer made of two massive Dirac cones
can reveal information on band eigenstates such as the chirality and mass sign
of the cones. For a given spectrum with two gapped cones, we propose several
low-energy Hamiltonians differing by their eigenstates properties. The
corresponding inter-band transition probability is affected by such differences
in its interference fringes being shifted by a new phase of geometrical origin.
This phase can be a useful bulk probe for topological band structures realized
with artificial crystals.


  Why we do not see large macroscopic objects in entangled states? There are
two ways to approach this question. The first is dynamic: the coupling of a
large object to its environment cause any entanglement to decrease
considerably. The second approach, which is discussed in this paper, puts the
stress on the difficulty to observe a large scale entanglement. As the number
of particles n grows we need an ever more precise knowledge of the state, and
an ever more carefully designed experiment, in order to recognize entanglement.
To develop this point we consider a family of observables, called witnesses,
which are designed to detect entanglement. A witness W distinguishes all the
separable (unentangled) states from some entangled states. If we normalize the
witness W to satisfy |tr(W\rho)| \leq 1 for all separable states \rho, then the
efficiency of W depends on the size of its maximal eigenvalue in absolute
value; that is, its operator norm ||W||. It is known that there are witnesses
on the space of n qbits for which ||W|| is exponential in n. However, we
conjecture that for a large majority of n-qbit witnesses ||W|| \leq O(\sqrt{n
logn}). Thus, in a non ideal measurement, which includes errors, the largest
eigenvalue of a typical witness lies below the threshold of detection. We prove
this conjecture for the family of extremal witnesses introduced by Werner and
Wolf (Phys. Rev. A 64, 032112 (2001)).


  Understanding dependence structure among extreme values plays an important
role in risk assessment in environmental studies. In this work we propose the
$\chi$ network and the annual extremal network for exploring the extremal
dependence structure of environmental processes. A $\chi$ network is
constructed by connecting pairs whose estimated upper tail dependence
coefficient, $\hat \chi$, exceeds a prescribed threshold. We develop an initial
$\chi$ network estimator and we use a spatial block bootstrap to assess both
the bias and variance of our estimator. We then develop a method to correct the
bias of the initial estimator by incorporating the spatial structure in $\chi$.
In addition to the $\chi$ network, which assesses spatial extremal dependence
over an extended period of time, we further introduce an annual extremal
network to explore the year-to-year temporal variation of extremal connections.
We illustrate the $\chi$ and the annual extremal networks by analyzing the
hurricane season maximum precipitation at the US Gulf Coast and surrounding
area. Analysis suggests there exists long distance extremal dependence for
precipitation extremes in the study region and the strength of the extremal
dependence may depend on some regional scale meteorological conditions, for
example, sea surface temperature.


  We discuss the asymptotics of the nonparametric maximum likelihood estimator
(NPMLE) in the normal mixture model. We then prove the convergence rate of the
NPMLE decision in the empirical Bayes problem with normal observations. We
point to (and heavily use) the connection between the NPMLE decision and Stein
unbiased risk estimator (\sure).
  Next, we prove that the same solution is optimal in the compound decision
problem where the unobserved parameters are not assumed to be random.
  Similar results are usually claimed using an oracle-based argument. However,
we contend that the standard oracle argument is not valid. It was only
partially proved that it can be fixed, and the existing proofs of these partial
results are tedious. Our approach, on the other hand, is straightforward and
short.


  We derive scaling relations for the extension statistics and the confinement
free energy for a semi-flexible polymer confined to a channel with a
rectangular cross-section. Our motivation are recent numerical results [Gupta
{\em et al.}, JCP {\bf 140} (2014) 214901] indicating that extensional
fluctuations are quite different in rectangular channels compared to square
channels. Our results are of direct relevance for interpreting current
experiments on DNA molecules confined to nano-channels, as many experiments are
performed for rectangular channels with large aspect ratios while theoretical
and simulation results are usually obtained for square channels.


  Fault analysis is a powerful attack to stream ciphers. Up to now, the major
idea of fault analysis is to simplify the cipher system by injecting some soft
faults. We call it soft fault analysis. As a hardware-oriented stream cipher,
Trivium is weak under soft fault analysis.
  In this paper we consider another type of fault analysis of stream cipher,
which is to simplify the cipher system by injecting some hard faults. We call
it hard fault analysis. We present the following results about such attack to
Trivium. In Case 1 with the probability not smaller than 0.2396, the attacker
can obtain 69 bits of 80-bits-key. In Case 2 with the probability not smaller
than 0.2291, the attacker can obtain all of 80-bits-key. In Case 3 with the
probability not smaller than 0.2291, the attacker can partially solve the key.
In Case 4 with non-neglectable probability, the attacker can obtain a
simplified cipher, with smaller number of state bits and slower
non-linearization procedure. In Case 5 with non-neglectable probability, the
attacker can obtain another simplified cipher. Besides, these 5 cases can be
checked out by observing the key-stream.


  We define a general class of random systems of horizontal and vertical
weighted broken lines on the quarter plane whose distribution are proved to be
translation invariant. This invariance stems from a reversibility property of
the model. This class of systems generalizes several classical processes of the
same kind, such as Hammersley's broken line processes involved in Last Passage
Percolation theory or such as the six-vertex model for some special sets of
parameters. The novelty comes here from the introduction of a weight associated
with each line. The lines are initially generated by spatially homogeneous
weighted Poisson Point Process and their evolution (turn, split, crossing) are
ruled by a Markovian dynamics which preserves Kirchhoff's node law for the line
weights at each intersection. Among others, we derive some new explicit
invariant measures for some bullet models as well as new reversible properties
for some six-vertex models with an external electromagnetic field.


  A dynamical theory for the evolution of the genetic code is presented, which
accounts for its universality and optimality. The central concept is that a
variety of collective, but non-Darwinian, mechanisms likely to be present in
early communal life generically lead to refinement and selection of
innovation-sharing protocols, such as the genetic code. Our proposal is
illustrated using a simplified computer model, and placed within the context of
a sequence of transitions that early life may have made, prior to the emergence
of vertical descent.


  PHENIX has measured the two particle azimuth correlation in Au + Au at
$\sqrt{s}$ = 200 GeV. Jet shape and yield at the away side are found to be
strongly modified at intermediate and low $p_T$. The modifications vary
dramatically with $p_T$ and centrality. At high $p_T$, away side jet peak
reappears but the yield is suppressed. Similar jet strength is found for Au +
Au and Cu + Cu collisions with similar number of participant nucleons.


  A flat of a matroid is cyclic if it is a union of circuits. The cyclic flats
of a matroid form a lattice under inclusion. We study these lattices and
explore matroids from the perspective of cyclic flats. In particular, we show
that every lattice is isomorphic to the lattice of cyclic flats of a matroid.
We give a necessary and sufficient condition for a lattice Z of sets and a
function r on Z to be the lattice of cyclic flats of a matroid and the
restriction of the corresponding rank function to Z. We define cyclic width and
show that this concept gives rise to minor-closed, dual-closed classes of
matroids, two of which contain only transversal matroids.


  Robust Principal Component Analysis (RPCA) via rank minimization is a
powerful tool for recovering underlying low-rank structure of clean data
corrupted with sparse noise/outliers. In many low-level vision problems, not
only it is known that the underlying structure of clean data is low-rank, but
the exact rank of clean data is also known. Yet, when applying conventional
rank minimization for those problems, the objective function is formulated in a
way that does not fully utilize a priori target rank information about the
problems. This observation motivates us to investigate whether there is a
better alternative solution when using rank minimization. In this paper,
instead of minimizing the nuclear norm, we propose to minimize the partial sum
of singular values, which implicitly encourages the target rank constraint. Our
experimental analyses show that, when the number of samples is deficient, our
approach leads to a higher success rate than conventional rank minimization,
while the solutions obtained by the two approaches are almost identical when
the number of samples is more than sufficient. We apply our approach to various
low-level vision problems, e.g. high dynamic range imaging, motion edge
detection, photometric stereo, image alignment and recovery, and show that our
results outperform those obtained by the conventional nuclear norm rank
minimization method.


  On-body devices are an intrinsic part of the Internet-of-Things (IoT) vision
to provide human-centric services. These on-body IoT devices are largely
embedded devices that lack a sophisticated user interface to facilitate
traditional Pre-Shared Key based security protocols. Motivated by this
real-world security vulnerability, this paper proposes SecureTag, a system
designed to add defense in depth against active attacks by integrating physical
layer (PHY) information with upper-layer protocols. The underpinning of
SecureTag is a signal processing technique that extracts the peculiar
propagation characteristics of creeping waves to discern on-body devices. Upon
overhearing a suspicious transmission, SecureTag initiates a PHY-based
challenge-response protocol to mitigate attacks. We implement our system on
different commercial off-the-shelf (COTS) wearables and a smartphone. Extensive
experiments are conducted in a lab, apartments, malls, and outdoor areas,
involving 12 volunteer subjects of different age groups, to demonstrate the
robustness of our system. Results show that our system can mitigate 96.13% of
active attack attempts while triggering false alarms on merely 5.64% of
legitimate traffic.


  Mirror symmetric twisted trilayer graphene (tTLG) is composed of even parity
twisted bilayer graphene (tBLG)-like bands and odd parity Dirac-like bands.
Here, we study the mirror-symmetric and mirror-asymmetric Hofstadter-Moir\'{e}
(HM) fractal bands of tTLG. A novel quantum parity Hall state is identified in
mirror-symmetric tTLG at experimentally accessible charge densities. This
mirror symmetry-protected topological phase exhibits simultaneous quantized
Hall and longitudinal resistances. The effects of the displacement field on the
HM fractal bands of tTLG and topological phase transitions are also studied.
The application of an electric displacement field results in an emergent weakly
dispersive band at the charge neutrality point for a range of twist angles.
This zero-energy state resides in the middle layer. It is isolated from the HM
spectrum by an energy gap that scales proportional to the applied displacement
field, making it a prime candidate to host correlated topological states.


  Conformal many-body mechanics in Newton-Hooke spacetime is studied within the
framework of the Lagrangian formalism. Global symmetries and Noether charges
are given in a form convenient for analyzing the flat space limit. N=2
superconformal extension is built and a new class on N=2 models related to
simple Lie algebras is presented. A decoupling similarity transformation on N=2
quantum mechanics in Newton-Hooke spacetime is discussed.


  In this paper, we prove that if a metric measure space satisfies the volume
doubling condition and the Gagliardo-Nirenberg inequality with the same
exponent $n$ $(n\geq 2)$, then it has exactly the $n$-dimensional volume
growth. Besides, two interesting applications have also been given. The one is
that we show that if a complete $n$-dimensional Finsler manifold of nonnegative
$n$-Ricci curvature satisfies the Gagliardo-Nirenberg inequality with the sharp
constant, then its flag curvature is identically zero. The other one is that we
give an alternative proof to Mao's main result in [23] for smooth metric
measure spaces with nonnegative weighted Ricci curvature.


  The usual formulation of the BCS ansatz for superconductivity in the grand
canonical ensemble makes the handling of the Pauli exclusion principle between
paired electrons straightforward. It however tends to mask that many-body
effects between Cooper pairs interacting through the reduced BCS potential are
entirely controlled by this exclusion. To show it up, one has to work in the
canonical ensemble. Pauli blocking between a fixed number of composite bosons
is however known to be difficult to handle. To do it, we here develop a
commutator formalism for Cooper pairs, along the line we used for excitons. We
then rederive, within the $N$-pair subspace, a few results of BCS
superconductivity commonly derived in the grand canonical ensemble, to evidence
their Pauli blocking origin. We end by discussing what should be called "Cooper
pair wave function".


  The capability to generate responses with diversity and faithfulness using
factual knowledge is paramount for creating a human-like, trustworthy dialogue
system. Common strategies either adopt a two-step paradigm, which optimizes
knowledge selection and response generation separately, and may overlook the
inherent correlation between these two tasks, or leverage conditional
variational method to jointly optimize knowledge selection and response
generation by employing an inference network. In this paper, we present an
end-to-end learning framework, termed Sequential Posterior Inference (SPI),
capable of selecting knowledge and generating dialogues by approximately
sampling from the posterior distribution. Unlike other methods, SPI does not
require the inference network or assume a simple geometry of the posterior
distribution. This straightforward and intuitive inference procedure of SPI
directly queries the response generation model, allowing for accurate knowledge
selection and generation of faithful responses. In addition to modeling
contributions, our experimental results on two common dialogue datasets (Wizard
of Wikipedia and Holl-E) demonstrate that SPI outperforms previous strong
baselines according to both automatic and human evaluation metrics.


  In this paper, we examine linear programming (LP) relaxations based on
Bernstein polynomials for polynomial optimization problems (POPs). We present a
progression of increasingly more precise LP relaxations based on expressing the
given polynomial in its Bernstein form, as a linear combination of Bernstein
polynomials. The well-known bounds on Bernstein polynomials over the unit box
combined with linear inter-relationships between Bernstein polynomials help us
formulate "Bernstein inequalities" which yield tighter lower bounds for POPs in
bounded rectangular domains. The results can be easily extended to optimization
over polyhedral and semi-algebraic domains. We also examine techniques to
increase the precision of these relaxations by considering higher degree
relaxations, and a branch-and-cut scheme


  The optimality of Bayesian filtering relies on the completeness of prior
models, while deep learning holds a distinct advantage in learning models from
offline data. Nevertheless, the current fusion of these two methodologies
remains largely ad hoc, lacking a theoretical foundation. This paper presents a
novel solution, namely an explainable gated Bayesian recurrent neural network
specifically designed to state estimation under model mismatches. Firstly, we
transform the non-Markov state-space model into an equivalent first-order
Markov model with memory. It is a generalized transformation that overcomes the
limitations of the first-order Markov property and enables recursive filtering.
Secondly, by deriving a data-assisted joint state-memory-mismatch Bayesian
filtering, we design a Bayesian gated framework that includes a memory update
gate for capturing the temporal regularities in state evolution, a state
prediction gate with the evolution mismatch compensation, and a state update
gate with the observation mismatch compensation. The Gaussian approximation
implementation of the filtering process within the gated framework is derived,
taking into account the computational efficiency. Finally, the corresponding
internal neural network structures and end-to-end training methods are
designed. The Bayesian filtering theory enhances the interpretability of the
proposed gated network, enabling the effective integration of offline data and
prior models within functionally explicit gated units. In comprehensive
experiments, including simulations and real-world datasets, the proposed gated
network demonstrates superior estimation performance compared to benchmark
filters and state-of-the-art deep learning filtering methods.


  Measurements of the longitudinal structure function FL at the ep collider
HERA are presented. They are derived from inclusive deep inelastic neutral
current e+p scattering cross section measurements based on data collected in
2007 with the H1 and ZEUS detectors at a positron beam energy of 27.5 GeV and
proton beam energies of 920, 575 and 460 GeV. Employing the energy dependence
of the cross sections, FL(x,Q2) is measured in the range of negative
four-momentum transfer squared from 12 to 800 GeV2 and low Bjorken-x from
0.00028 to 0.0353. The measured longitudinal structure function is compared
with higher order QCD predictions.


  We revisit the topic of polynomial kernels for Vertex Cover relative to
structural parameters. Our starting point is a recent paper due to Fomin and
Str{\o}mme [WG 2016] who gave a kernel with $\mathcal{O}(|X|^{12})$ vertices
when $X$ is a vertex set such that each connected component of $G-X$ contains
at most one cycle, i.e., $X$ is a modulator to a pseudoforest. We strongly
generalize this result by using modulators to $d$-quasi-forests, i.e., graphs
where each connected component has a feedback vertex set of size at most $d$,
and obtain kernels with $\mathcal{O}(|X|^{3d+9})$ vertices. Our result relies
on proving that minimal blocking sets in a $d$-quasi-forest have size at most
$d+2$. This bound is tight and there is a related lower bound of
$\mathcal{O}(|X|^{d+2-\epsilon})$ on the bit size of kernels.
  In fact, we also get bounds for minimal blocking sets of more general graph
classes: For $d$-quasi-bipartite graphs, where each connected component can be
made bipartite by deleting at most $d$ vertices, we get the same tight bound of
$d+2$ vertices. For graphs whose connected components each have a vertex cover
of cost at most $d$ more than the best fractional vertex cover, which we call
$d$-quasi-integral, we show that minimal blocking sets have size at most
$2d+2$, which is also tight. Combined with existing randomized polynomial
kernelizations this leads to randomized polynomial kernelizations for
modulators to $d$-quasi-bipartite and $d$-quasi-integral graphs. There are
lower bounds of $\mathcal{O}(|X|^{d+2-\epsilon})$ and
$\mathcal{O}(|X|^{2d+2-\epsilon})$ for the bit size of such kernels.


  in the recent paper [Journal of Physics A, 43474-0288 (2011)], B. Helffer and
R. Purice compute the second term of a semi-classical trace formula for a
Schr\"odinger operator with magnetic field. We show how to recover their
formula by using the methods developped by the geometers in the seventies for
the heat expansions.


  Quantum key distribution (QKD) is a technique to establish a secret key
between two parties through a quantum channel. Several QKD protocols have been
proposed and implemented over optical fibers or free space links. The main
challenge of operating QKD protocols over a free space link is atmospheric
losses. In this paper, we have studied and compared the performance of single
and entangled photon based QKD protocols by evaluating the quantum bit error
rate and secure key rate for terrestrial free-space quantum communication by
considering different free-space losses, such as geometrical losses,
atmospheric losses as well as device imperfections.


  The giant spiral galaxy M101 is host to the nearest recent Type Ia Supernova
(SN 2011fe) and thus has been extensively monitored in the near-infrared to
study the late-time lightcurve of the supernova. Leveraging this existing
baseline of observations, we derive the first Mira-based distance to M101 by
discovering and classifying a sample of 211 Miras with periods ranging from 240
to 400 days in the supernova field. Combined with new HST WFC3/IR channel
observations, our dataset totals 11 epochs of F110W (HST $YJ$) and 13 epochs of
F160W (HST $H$) data spanning $\sim$2900 days. We adopt absolute calibrations
of the Mira Period-Luminosity Relation based on geometric distances to the
Large Magellanic Cloud and the water megamaser host galaxy NGC 4258, and find
$\mu_{\rm M101} = $ 29.10 $\pm$ 0.06 mag. This distance is in 1$\sigma$
agreement with most other recent Cepheid and Tip of the Red Giant Branch
distance measurements to M101. Including the previous Mira-SNIa host, NGC 1559
and SN 2005df, we determine the fiducial SN Ia peak luminosity, $M^0_B = -19.27
\pm 0.09$ mag. With the Hubble diagram of SNe Ia, we derive $H_0 = 72.37 \pm
2.97 $ km s$^{-1}$Mpc$^{-1}$, a $4.1\%$ measurement of $H_0$ using Miras. We
find excellent agreement with recent Cepheid distance ladder measurements of
$H_0$ and confirm previous indications that the local universe value of $H_0$
is higher than the early-universe value at $\sim$ $95\%$ confidence. Currently,
the Mira-based $H_0$ measurement is still dominated by the statistical
uncertainty in the SN Ia peak magnitude.


  Too little mathematics has been written in prose. Thus we prove here, via a
fantasy novellette, that a locally L-bilipschitz mapping $f \colon X \to Y$
between uniformly Ahlfors $q$-regular, complete and locally compact path-metric
spaces $X$ and $Y$ is an $L$-bilipschitz map when $Y$ is simply connected. The
motivation for such a result arises from studying the asymptotic values of
BLD-mappings with an empty branch set; see e.g. [L17].
  As far as the author is aware, the result is new, even though it would not be
hard for specialists in the field to prove. The proof is essentially a modest
extension of the ideas in [L17] in a more general setting when the branch set
is empty.


  We present a new approach to realizing the Dimopoulos-Wiczek mechanism for
doublet-triplet splitting in supersymmetric SO(10). The method can be used to
achieve strong suppression of proton decay in a straightforward manner and is
relatively economical; in particular the particle spectrum required for its
implementation is consistent with the constraints from string compactification
at Kac-Moody level two. We construct two examples of realistic models based on
this idea. These models have no unwanted flat directions and do not make use of
any nonrenormalizable operators to stabilize the vacuum thereby maintaining
coupling constant unification as a prediction. The first model is characterised
by a simple discrete symmetry that guarantees naturalness at the renormalizable
level while also eliminating all potentially dangerous R-parity violating
effects. The second model predicts the strong suppression of Higgsino mediated
proton decay. In both models the light MSSM doublets emerge from two different
{\bf 10}'s so that there is considerable flexibility in constructing realistic
fermion masses and CKM angles.


  The zeta functions for the Schr\"odinger equation with a triangular potential
are investigated. Values of the zeta functions are computed using both the
Weierstrass factorization theorem and analytic continuation via contour
integration. The results were found to be consistent where the domains of the
two methods overlap. Analytic continuation is used to compute values of the
zeta functions at zero and the negative integers, explore the pole structure
(and residue values), as well as the value of the slopes at the origin. Those
results are used for the computation of the trace and determinant of the
associated Hamiltonians.


  A recent model for monodisperse granular suspensions is used to analyze
transport properties in spatially inhomogeneous states close to the simple (or
uniform) shear flow. The kinetic equation is based on the inelastic Boltzmann
(for low density gases) with the presence of a viscous drag force that models
the influence of the interstitial gas phase on the dynamics of grains. A normal
solution is obtained via a Chapman-Enskog-like expansion around a (local) shear
flow distribution which retains all the hydrodynamic orders in the shear rate.
To first-order in the expansion, the transport coefficients characterizing
momentum and heat transport around shear flow are given in terms of the
solutions of a set of coupled linear integral equations which are approximately
solved by using a kinetic model of the Boltzmann equation. To simplify the
analysis, the steady-state conditions when viscous heating is compensated by
the cooling terms arising from viscous friction and collisional dissipation are
considered to get the explicit forms of the set of generalized transport
coefficients. The shear-rate dependence of some of the transport coefficients
of the set is illustrated for several values of the coefficient of restitution.


  In contrast to the many examples of convex divisible domains in real
projective space, we prove that up to projective isomorphism there is only one
convex divisible domain in the Grassmannian of $p$-planes in $\mathbb{R}^{2p}$
when $p > 1$. Moreover, this convex divisible domain is a model of the
symmetric space associated to the simple Lie group SO$(p, p)$.


  WD 0145+234 is a white dwarf that is accreting metals from a circumstellar
disc of planetary material. It has exhibited a substantial and sustained
increase in 3-5 micron flux since 2018. Follow-up Spitzer photometry reveals
that emission from the disc had begun to decrease by late 2019. Stochastic
brightening events superimposed on the decline in brightness suggest the
liberation of dust during collisional evolution of the circumstellar solids. A
simple model is used to show that the observations are indeed consistent with
ongoing collisions. Rare emission lines from circumstellar gas have been
detected at this system, supporting the emerging picture of white dwarf debris
discs as sites of collisional gas and dust production.


  In this paper, we exhibit two matrix representations of the rational roots of
generalized Fibonacci polynomials (GFPs) under convolution product, in terms of
determinants and permanents, respectively. The underlying root formulas for
GFPs and for weighted isobaric polynomials (WIPs), which appeared in an earlier
paper by MacHenry and Tudose, make use of two types of operators. These
operators are derived from the generating functions for Stirling numbers of the
first kind and second kind. Hence we call them Stirling operators. To construct
matrix representations of the roots of GFPs, we use the Stirling operators of
the first kind. We give explicit examples to show how the Stirling operators of
the second kind appear in the low degree cases for the WIP-roots. As a
consequence of the matrix construction, we have matrix representations of
multiplicative arithmetic functions under the Dirichlet product into its
divisible closure.


  Although there has been a dramatically reduction on the prices of capturing
devices and an increase on computing power in the last decade, it seems that
biometric systems are still far from massive adoption for civilian
applications. This paper deals with the causes of this phenomenon, as well as
some misconceptions regarding biometric identification.


  We have analyzed the dynamics of a single hole doped in a canted
antiferromagnet using the t-J model. Within the self consistent Born
approximation we have found that the hole propagates at two different energy
scales along the antiferromagnetic and the ferromagnetic components of the
canted order, respectively. While the many body quasiparticle excitation has
its origin in the coherent coupling of the hole with the magnon excitations of
the antiferromagnetic component, the ferromagnetic component gives rise to a
free like hole motion at higher energies. We have found a non-trivial behavior
of the hole spectral function with the canting angle. In particular, in the
strong coupling regime, the quasiparticle weight strongly depends on the
momenta, vanishing inside the magnetic Brillouin zone for a canting angle
greater than 60 degree.


  : From the epoch of recombination $(z\approx 10^3)$ till today, the typical
density contrasts have grown by a factor of about $10^6$ in a Friedmann
universe with $\Omega=1$. However, during the same epoch the typical
gravitational potential has grown only by a factor of order unity. We present
theoretical arguments explaining the origin of this approximate constancy of
gravitational potential. This fact can be exploited to provide a new, powerful,
approximation scheme to study the formation of nonlinear structures in the
universe. The essential idea of this method is to evolve the initial
distribution of particles using a gravitational potential frozen in time
(Frozen Potential Approximation). This approximation provides valuable insight
into understanding various features of nonlinear evolution; for example, it
provides a simple explanation as to why pancakes remain thin during the
evolution even in the absence of any artificial, adhesion-like, damping terms.
We compare the trajectories of particles in various approximations. We also
discuss a few applications of the frozen potential approximation.