  Training a neural network-based biomedical named entity recognition (BioNER)
model usually requires extensive and costly human annotations. While several
studies have employed multi-task learning with multiple BioNER datasets to
reduce human effort, this approach does not consistently yield performance
improvements and may introduce label ambiguity in different biomedical corpora.
We aim to tackle those challenges through transfer learning from easily
accessible resources with fewer concept overlaps with biomedical datasets. In
this paper, we proposed GERBERA, a simple-yet-effective method that utilized a
general-domain NER dataset for training. Specifically, we performed multi-task
learning to train a pre-trained biomedical language model with both the target
BioNER dataset and the general-domain dataset. Subsequently, we fine-tuned the
models specifically for the BioNER dataset. We systematically evaluated GERBERA
on five datasets of eight entity types, collectively consisting of 81,410
instances. Despite using fewer biomedical resources, our models demonstrated
superior performance compared to baseline models trained with multiple
additional BioNER datasets. Specifically, our models consistently outperformed
the baselines in six out of eight entity types, achieving an average
improvement of 0.9% over the best baseline performance across eight biomedical
entity types sourced from five different corpora. Our method was especially
effective in amplifying performance on BioNER datasets characterized by limited
data, with a 4.7% improvement in F1 scores on the JNLPBA-RNA dataset.


  When medical researchers conduct a systematic review (SR), screening studies
is the most time-consuming process: researchers read several thousands of
medical literature and manually label them relevant or irrelevant. Screening
prioritization (ie., document ranking) is an approach for assisting researchers
by providing document rankings where relevant documents are ranked higher than
irrelevant ones. Seed-driven document ranking (SDR) uses a known relevant
document (ie., seed) as a query and generates such rankings. Previous work on
SDR seeks ways to identify different term weights in a query document and
utilizes them in a retrieval model to compute ranking scores. Alternatively, we
formulate the SDR task as finding similar documents to a query document and
produce rankings based on similarity scores. We propose a document matching
measure named Mirror Matching, which calculates matching scores between medical
abstract texts by incorporating common writing patterns, such as background,
method, result, and conclusion in order. We conduct experiments on CLEF 2019
eHealth Task 2 TAR dataset, and the empirical results show this simple approach
achieves the higher performance than traditional and neural retrieval models on
Average Precision and Precision-focused metrics.


  Regular two-dimensional lattices of evanescently coupled waveguides may
provide in the near future photonic components capable of combining
interferometrically and simultaneously a large number of telescopes, thus
easing the imaging capabilities of optical interferometers. In this paper, the
theoretical modeling of the so-called Discrete Beam Combiners (DBC) is
described and compared to the conventional model used for photonic beam
combiners for astronomical interferometry. The performance of DBCs as compared
to an ideal ABCD beam combiner is discussed and applications to astronomical
instrumentation analyzed.


  Let $sigma$ be a complete simplicial fan in finite dimensional real Euclidean
space $V$, and let $G$ be a cyclic subgroup of $GL(V)$ which acts properly on
$\sigma$. We show that the representation of $G$ carried by the cohomology of
$X_{sigma}$, the toric variety associated to $sigma$, is a permutation
representation.


  We present new photometric and spectral observations of the newly discovered
nova-like eclipsing star 2MASS J01074282+4845188. To obtain a light curve
solution we used model of a nova-like star whose emission sources are a white
dwarf surrounded by an accretion disk, a secondary star filling its Roche lobe,
a hot spot and a hot line. 2MASS J01074282+4845188 shows the deepest permanent
eclipse among the known nova-like stars. It is reproduced by covering the very
bright accretion disk by the secondary component. The luminosity of the disk is
much bigger than that of the rest light sources. The determined high
temperature of the disk is typical for that observed during the outbursts of
CVs. The primary of 2MASS J01074282+4845188 is one of the hottest white dwarfs
in CVs. The temperature of 5090 K of its secondary is also quite high and more
appropriate for a long-period SW Sex star. It might be explained by the intense
heating from the hot white dwarf and the hot accretion disk of the target. The
high mass accretion rate $\dot{M} = 8\times 10^{-9} M_{\sun}$ yr$^{-1}$, the
broad and single-peaked H$\alpha$ emission profile, and the presence of an
S-wave are sure signs for the SW Sex classification of 2MASS J01074282+4845188.
The obtained flat temperature distribution along the disk radius as well as the
deviation of the energy distribution from the black-body law are evidence of
the non-steady emission of the disk. The close values of the disk temperature
and the parameter $\alpha_{g}$ of 2MASS J01074282+4845188 and those of the
cataclysmic stars at eruptions might be considered as an additional argument
for the permanent active state of nova-like stars.


  Experiments with reactor neutrinos continue to shed light on our
understanding of neutrino oscillations. We review some of the early decisive
experiments. We then turn to the recent long baseline oscillation experiments
at Palo Verde and Chooz which are leading to the conclusion that the
atmospheric neutrino anomaly if attributed to oscillations does not involve an
appreciable mixing with the $\bar\nu_e$ . The very long baseline KamLAND
experiment is now in the planning stages. Its goal is to help explore the large
mixing angle solar solution. A review of the $\bar\nu_e + d$ experiment at
Bugey and an outline of the $\bar\nu_e$ magnetic moment studies complete this
chapter.


  This paper presents a critical, qualitative study of the social role of
algorithmic bias in the context of the Chicago crime prediction algorithm, a
predictive policing tool that forecasts when and where in the city crime is
most likely to occur. Through interviews with 18 Chicago-area community
organizations, academic researchers, and public sector actors, we show that
stakeholders from different groups articulate diverse problem diagnoses of the
tool's algorithmic bias, strategically using it as evidence to advance criminal
justice interventions that align with stakeholders' positionality and political
ends. Drawing inspiration from Catherine D'Ignazio's taxonomy of "refusing and
using" data, we find that stakeholders use evidence of algorithmic bias to
reform the policies around police patrol allocation; reject algorithm-based
policing interventions; reframe crime as a structural rather than interpersonal
problem; reveal data on authority figures in an effort to subvert their power;
repair and heal families and communities; and, in the case of more powerful
actors, to reaffirm their own authority or existing power structures. We
identify the implicit assumptions and scope of these varied uses of algorithmic
bias as evidence, showing that they require different (and sometimes
conflicting) values about policing and AI. This divergence reflects
long-standing tensions in the criminal justice reform landscape between the
values of liberation and healing often centered by system-impacted communities
and the values of surveillance and deterrence often instantiated in data-driven
reform measures. We advocate for centering the interests and experiential
knowledge of communities impacted by incarceration to ensure that evidence of
algorithmic bias can serve as a device to challenge the status quo.


  IGR J17544-2619 belongs to a distinct group of at least seven fast X-ray
transients that cannot readily be associated with nearby flare stars or
pre-main sequence stars and most probably are X-ray binaries with wind
accretion. Sofar, the nature of the accretor has been determined in only one
case (SAX J1819.3-2525/V4641 Sgr). We carried out a 20 ks Chandra ACIS-S
observation of IGR J17544-2619 which shows the source in quiescence going into
outburst. The Chandra position confirms the previous tentative identification
of the optical counterpart, a blue O9Ib supergiant at 3 to 4 kpc (Pellizza,
Chaty & Negueruela, in prep.). This is the first detection of a fast X-ray
transient in quiescence. The quiescent spectrum is very soft. The photon index
of 5.9+/-1.2 (90% confidence error margin) is much softer than 6 quiescent
black hole candidates that were observed with Chandra ACIS-S (Kong et al. 2002;
Tomsick et al. 2003). Assuming that a significant fraction of the quiescent
photons comes from the accretor and not the donor star, we infer that the
accretor probably is a neutron star. A fit to the quiescent spectrum of the
neutron star atmosphere model developed by Pavlov et al. (1992) and Zavlin et
al. (1996) implies an unabsorbed quiescent 0.5--10 keV luminosity of
(5.2+/-1.3) x 10^32 erg/s. We speculate on the nature of the brief outbursts.


  This work investigates the secrecy capacity of the Wiretap Broadcast Channel
(WBC) with an external eavesdropper where a source wishes to communicate two
private messages over a Broadcast Channel (BC) while keeping them secret from
the eavesdropper. We derive a non-trivial outer bound on the secrecy capacity
region of this channel which, in absence of security constraints, reduces to
the best known outer bound to the capacity of the standard BC. An inner bound
is also derived which follows the behavior of both the best known inner bound
for the BC and the Wiretap Channel. These bounds are shown to be tight for the
deterministic BC with a general eavesdropper, the semi-deterministic BC with a
more-noisy eavesdropper and the Wiretap BC where users exhibit a less-noisiness
order between them. Finally, by rewriting our outer bound to encompass the
characteristics of parallel channels, we also derive the secrecy capacity
region of the product of two inversely less-noisy BCs with a more-noisy
eavesdropper. We illustrate our results by studying the impact of security
constraints on the capacity of the WBC with binary erasure (BEC) and binary
symmetric (BSC) components.


  It is expected that energetic solar flares releasing large amount of energy
at the photosphere may be able to excite the acoustic ($p$-) modes of
oscillations. We have determined the characteristic properties of mode
parameters by applying the ring diagram technique to 3-D power spectra obtained
for solar active region NOAA 10486 during the long duration energetic X17.2/4B
flare of October 28, 2003. Strong evidence of substantial increase in mode
amplitude and systematic variations in sub-surface flows, i.e., meridional and
zonal components of velocity, kinetic helicity, vorticity, is found from
comparison of the pre- to the post-flare phases.


  We consider the strong Ramsey-type game $\mathcal{R}^{(k)}(\mathcal{H},
\aleph_0)$, played on the edge set of the infinite complete $k$-uniform
hypergraph $K^k_{\mathbb{N}}$. Two players, called FP (the first player) and SP
(the second player), take turns claiming edges of $K^k_{\mathbb{N}}$ with the
goal of building a copy of some finite predetermined $k$-uniform hypergraph
$\mathcal{H}$. The first player to build a copy of $\mathcal{H}$ wins. If no
player has a strategy to ensure his win in finitely many moves, then the game
is declared a draw.
  In this paper, we construct a $5$-uniform hypergraph $\mathcal{H}$ such that
$\mathcal{R}^{(5)}(\mathcal{H}, \aleph_0)$ is a draw. This is in stark contrast
to the corresponding finite game $\mathcal{R}^{(5)}(\mathcal{H}, n)$, played on
the edge set of $K^5_n$. Indeed, using a classical game-theoretic argument
known as \emph{strategy stealing} and a Ramsey-type argument, one can show that
for every $k$-uniform hypergraph $\mathcal{G}$, there exists an integer $n_0$
such that FP has a winning strategy for $\mathcal{R}^{(k)}(\mathcal{G}, n)$ for
every $n \geq n_0$.


  We develop a general framework for identifying phase reduced equations for
finite populations of coupled oscillators that is valid far beyond the weak
coupling approximation. This strategy represents a general extension of the
theory from [Wilson and Ermentrout, Phys. Rev. Lett 123, 164101 (2019)] and
yields coupling functions that are valid to higher-order accuracy in the
coupling strength for arbitrary types of coupling (e.g., diffusive,
gap-junction, chemical synaptic). These coupling functions can be used to
understand the behavior of potentially high-dimensional, nonlinear oscillators
in terms of their phase differences. The proposed formulation accurately
replicates nonlinear bifurcations that emerge as the coupling strength
increases and is valid in regimes well beyond those that can be considered
using classic weak coupling assumptions. We demonstrate the performance of our
approach through two examples. First, we use diffusively coupled complex
Ginzburg-Landau (CGL) model and demonstrate that our theory accurately predicts
bifurcations far beyond the range of existing coupling theory. Second, we use a
realistic conductance-based model of a thalamic neuron and show that our theory
correctly predicts asymptotic phase differences for non-weak synaptic coupling.
In both examples, our theory accurately captures model behaviors that weak
coupling theories can not.


  Recently, a new class of non-convex optimization problems motivated by the
statistical problem of learning an acyclic directed graphical model from data
has attracted significant interest. While existing work uses standard
first-order optimization schemes to solve this problem, proving the global
optimality of such approaches has proven elusive. The difficulty lies in the
fact that unlike other non-convex problems in the literature, this problem is
not "benign", and possesses multiple spurious solutions that standard
approaches can easily get trapped in. In this paper, we prove that a simple
path-following optimization scheme globally converges to the global minimum of
the population loss in the bivariate setting.


  Replacing positivity constraints by an entropy barrier is popular to
approximate solutions of linear programs. In the special case of the optimal
transport problem, this technique dates back to the early work of
Schr\"odinger. This approach has recently been used successfully to solve
optimal transport related problems in several applied fields such as imaging
sciences, machine learning and social sciences. The main reason for this
success is that, in contrast to linear programming solvers, the resulting
algorithms are highly parallelizable and take advantage of the geometry of the
computational grid (e.g. an image or a triangulated mesh). The first
contribution of this article is the proof of the $\Gamma$-convergence of the
entropic regularized optimal transport problem towards the Monge-Kantorovich
problem for the squared Euclidean norm cost function. This implies in
particular the convergence of the optimal entropic regularized transport plan
towards an optimal transport plan as the entropy vanishes. Optimal transport
distances are also useful to define gradient flows as a limit of implicit Euler
steps according to the transportation distance. Our second contribution is a
proof that implicit steps according to the entropic regularized distance
converge towards the original gradient flow when both the step size and the
entropic penalty vanish (in some controlled way).


  For an arbitrary field $K$ and $K$-variety $V$, we introduce the \'etale-open
topology on the set $V(K)$ of $K$-points of $V$. This topology agrees with the
Zariski topology, Euclidean topology, or valuation topology when $K$ is
separably closed, real closed, or $p$-adically closed, respectively.
Topological properties of the \'etale-open topology corresponds to algebraic
properties of $K$. For example, the \'etale-open topology on $\mathbb{A}^1(K)$
is not discrete if and only if $K$ is large. As an application, we show that a
large stable field is separably closed.


  We give a conformal representation for indefinite improper affine spheres
which solve the Cauchy problem for their Hessian equation. As consequences, we
can characterize their geodesics and obtain a generalized symmetry principle.
Then, we classify the helicoidal indefinite improper affine spheres and find a
new family with complete non flat affine metric. Moreover, we present
interesting examples with singular curves and isolated singularities.


  We present UV imaging polarimetry data of the Seyfert 2 galaxy Mrk 3 taken by
the Hubble Space Telescope. The polarized flux is found to be extended to ~1
kpc from the nucleus, and the position angles of polarization are
centrosymmetric, confirming that the polarization is caused by scattering. We
determine the location of the hidden nucleus as the center of this
centrosymmetric pattern. From the polarization images taken in two broad bands,
we have obtained the color distribution of the polarized flux. Some regions
have blue polarized flux, consistent with optically-thin dust scattering, but
some bright knots have a color similar to that of Seyfert 1 nucleus. Also, the
recent Chandra X-ray observation suggests that the ratio of scattered UV flux
to scattered X-ray flux is rather similar to the intrinsic UV/X-ray ratio in a
Seyfert 1 nucleus, if the observed extended X-ray continuum is scattered light.
While the scattered X-ray would be essentially from electron scattering, the UV
slope and UV/X-ray ratio both being similar to Seyfert 1's would lead to two
possibilities as to the nature of the UV scatterers. One is that the UV may
also be scattered by electrons, in which case the scattering gas is somehow
dust-free. The other is that the UV is scattered by dust grains, but the
wavelength-independent UV scattering with low efficiency indicated by the UV
slope and UV/X-ray ratio would suggest that the grains reside in UV-opaque
clouds, or the dust might be mainly composed of large grains and lacks
small-grain population.


  Most of existing salient object detection models have achieved great progress
by aggregating multi-level features extracted from convolutional neural
networks. However, because of the different receptive fields of different
convolutional layers, there exists big differences between features generated
by these layers. Common feature fusion strategies (addition or concatenation)
ignore these differences and may cause suboptimal solutions. In this paper, we
propose the F3Net to solve above problem, which mainly consists of cross
feature module (CFM) and cascaded feedback decoder (CFD) trained by minimizing
a new pixel position aware loss (PPA). Specifically, CFM aims to selectively
aggregate multi-level features. Different from addition and concatenation, CFM
adaptively selects complementary components from input features before fusion,
which can effectively avoid introducing too much redundant information that may
destroy the original features. Besides, CFD adopts a multi-stage feedback
mechanism, where features closed to supervision will be introduced to the
output of previous layers to supplement them and eliminate the differences
between features. These refined features will go through multiple similar
iterations before generating the final saliency maps. Furthermore, different
from binary cross entropy, the proposed PPA loss doesn't treat pixels equally,
which can synthesize the local structure information of a pixel to guide the
network to focus more on local details. Hard pixels from boundaries or
error-prone parts will be given more attention to emphasize their importance.
F3Net is able to segment salient object regions accurately and provide clear
local details. Comprehensive experiments on five benchmark datasets demonstrate
that F3Net outperforms state-of-the-art approaches on six evaluation metrics.


  Under certain restrictions we describe the set of all pointwise multipliers
in case of Sobolev and Besov spaces of dominating mixed smoothness. In addition
we shall give necessary and sufficient conditions for the case that these
spaces form algebras with respect to pointwise multiplication.


  The default quantisation algorithms in the state-of-the-art High Efficiency
Video Coding (HEVC) standard, namely Uniform Reconstruction Quantisation (URQ)
and Rate-Distortion Optimised Quantisation (RDOQ), do not take into account the
perceptual relevance of individual transform coefficients. In this paper, a
Frequency-Dependent Perceptual Quantisation (FDPQ) technique for HEVC is
proposed. FDPQ exploits the well-established Modulation Transfer Function (MTF)
characteristics of the linear transformation basis functions by taking into
account the Euclidean distance of an AC transform coefficient from the DC
coefficient. As such, in luma and chroma Cb and Cr Transform Blocks (TBs), FDPQ
quantises more coarsely the least perceptually relevant transform coefficients
(i.e., the high frequency AC coefficients). Conversely, FDPQ preserves the
integrity of the DC coefficient and the very low frequency AC coefficients.
Compared with RDOQ, which is the most widely used transform coefficient-level
quantisation technique in video coding, FDPQ successfully achieves bitrate
reductions of up to 41%. Furthermore, the subjective evaluations confirm that
the FDPQ-coded video data is perceptually indistinguishable (i.e., visually
lossless) from the raw video data for a given Quantisation Parameter (QP).


  We prove that a biseparating map between spaces B(E), and some other Banach
algebras, is automatically continuous and an algebra isomorphism.


  This article describe globular weak $(n,\infty)$-transformations
($n\in\mathbb{N}$) in the sense of Grothendieck, i.e for each $n\in\mathbb{N}$
we build a coherator $\Theta^{\infty}_{\mathbb{M}^n}$ which sets models are
globular weak $(n,\infty)$-transformations. A natural globular filtration
emerges from these coherators.


  Motivated by recent measurements on CuGeO$_3$ the spectrum of magnetic
excitations of an antiferromagnetic $S=\frac{1}{2}$ chain with alternating
coupling strength is investigated. Wave vector dependent magnons and a
continuum with square root behavior at the band edges are found. The spectral
density of the continua is calculated. Spin rotation symmetry fixes the gap of
the continuum to be twice the elementary magnon gap. This is in excellent
agreement with experimental results. In addition, the existence of bound states
of two magnons is predicted: below the continuum a singlet and a triplet, above
the continuum an ``anti-bound'' quintuplet. The results are based on field
theoretic arguments, RPA calculations, and consideration of the limit of strong
alternation.


  The \textit{nowhere dense ideal} $\mathcal{NC}$ is introduced. It is a
coanalytic ideal of $\omega\times\omega$ whose defining characteristic is that
the sets of the form $X\times Y$, where $X,Y$ are infinite subsets of $\omega$,
are dense in the quotient $\mathcal{P}(\omega\times\omega)\slash\mathcal{NC}$.
This quotient has countable partitions and consistently has partitions of size
$\omega_1$ while $\mathfrak{a}>\omega_1$. This represents a huge contrast with
other definable quotients. Other combinatorial features of this ideal are
presented, as well as some results on a family of similar, higher dimensional
ideals.


  Magnesium diboride has been thermally treated in the presence of Rb, Cs, and
Ba. Magnetic susceptibility shows onsets of superconductivity in the resulting
samples at 52K (Rb), 58K (Cs) and 45K (Ba). Room-temperature 11B NMR indicates
to cubic symmetry of the electric field gradient at boron site for the samples
reacted with Rb and Cs, in contrast to the axial symmetry in the initial MgB2
and in the sample treated with Ba.


  We investigate the open spin chain describing the scalar sector of the Y=0
giant graviton brane at weak coupling. We provide a direct proof of
integrability in the SU(2) and SU(3) sectors by constructing the transfer
matrices. We determine the eigenvalues of these transfer matrices in terms of
roots of the corresponding Bethe ansatz equations (BAEs). Based on these
results, we propose BAEs for the full SO(6) sector. We find that, in the
weak-coupling limit, the recently-proposed all-loop BAEs essentially agree with
those proposed in the present work.


  While several techniques for detecting trace-level anomalies in event logs in
offline settings have appeared recently in the literature, such techniques are
currently lacking for online settings. Event log anomaly detection in online
settings can be crucial for discovering anomalies in process execution as soon
as they occur and, consequently, allowing to promptly take early corrective
actions. This paper describes a novel approach to event log anomaly detection
on event streams that uses statistical leverage. Leverage has been used
extensively in statistics to develop measures to identify outliers and it has
been adapted in this paper to the specific scenario of event stream data. The
proposed approach has been evaluated on both artificial and real event streams.


  As an indispensable personalized service within Location-Based Social
Networks (LBSNs), the Point-of-Interest (POI) recommendation aims to assist
individuals in discovering attractive and engaging places. However, the
accurate recommendation capability relies on the powerful server collecting a
vast amount of users' historical check-in data, posing significant risks of
privacy breaches. Although several collaborative learning (CL) frameworks for
POI recommendation enhance recommendation resilience and allow users to keep
personal data on-device, they still share personal knowledge to improve
recommendation performance, thus leaving vulnerabilities for potential
attackers. Given this, we design a new Physical Trajectory Inference Attack
(PTIA) to expose users' historical trajectories. Specifically, for each user,
we identify the set of interacted POIs by analyzing the aggregated information
from the target POIs and their correlated POIs. We evaluate the effectiveness
of PTIA on two real-world datasets across two types of decentralized CL
frameworks for POI recommendation. Empirical results demonstrate that PTIA
poses a significant threat to users' historical trajectories. Furthermore,
Local Differential Privacy (LDP), the traditional privacy-preserving method for
CL frameworks, has also been proven ineffective against PTIA. In light of this,
we propose a novel defense mechanism (AGD) against PTIA based on an adversarial
game to eliminate sensitive POIs and their information in correlated POIs.
After conducting intensive experiments, AGD has been proven precise and
practical, with minimal impact on recommendation performance.


  This study compares thirteen natural and industrial samples of supplementary
or emerging supplementary cementitious materials (SCMs): slag, fly ashes,
pozzolan, obsidian, silica fume, and recycled glass. These materials are used
or are under consideration for decarbonization in cement plants. XRF, XRD and
Raman microspectroscopy were used in order to achieve a deeper understanding of
the structural characterization of SCMs. The changes in position and shape of
the XRD diffuse halos were compared. Raman spectroscopy was used to study the
glass part of the SCM families, to better understand their structure in terms
of depolymerization degree, angle, ring size and incorporations into the
glass.The chemical composition of each glassy part was also estimated using
reverse Bogue calculations. The hump position is correlated with the Raman
shift, and with the XRF bulk or with the calculated glass chemical composition
of SCMs, in terms of CaO/(SiO2+Al2O3) or network modifiers to formers ratios.


  Program semantics can often be expressed as a (many-sorted) first-order
theory S, and program properties as sentences $\varphi$ which are intended to
hold in the canonical model of such a theory, which is often incomputable.
Recently, we have shown that properties $\varphi$ expressed as the existential
closure of a boolean combination of atoms can be disproved by just finding a
model of S and the negation $\neg\varphi$ of $\varphi$. Furthermore, this idea
works quite well in practice due to the existence of powerful tools for the
automatic generation of models for (many-sorted) first-order theories. In this
paper we extend our previous result to arbitrary properties, expressed as
sentences without any special restriction. Consequently, one can prove a
program property $\varphi$ by just finding a model of an appropriate theory
(including S and possibly something else) and an appropriate first-order
formula related to $\varphi$. Beyond its possible theoretical interest, we show
that our results can also be of practical use in several respects.


  Double inclusive cross-sections for gluon production in collision of two
nucleons with two nucleons are studied in the BFKL approach. Various
contributions include emission from the pomerons attached to the participants,
from the BFKL interactions between these pomerons and from the intermediate BKP
state. The last contribution may be observable provided the growth with energy
of the pomeron contribution is tamed in accordance with unitarity. Possibility
of long-range azimuthal correlations due to the BKP state are discussed.


  New reverberation mapping (RM) measurements, combined with accurate
luminosities and line ratios, provide strong constraints on the location of the
line emitting gas in the broad line region (BLR) of active galactic nuclei
(AGN). In this paper I present new calculations of radiation pressure and
magnetic pressure confined clouds and apply them to a ``generic AGN'' and to
NGC 5548. The new calculations are in good agreement with the observed lags of
all broad emission lines, and with the luminosities of La, Civ 1549, Ovi 1035
and Heii 1640. They are also in reasonable agreement with the luminosities of
Mgii 2798 and the 1990A blend of Ciii] and Siiii] lines for high metallicity
gas. They explain the changes in time-lag following an increase in continuum
luminosity and their dependencies on the inner and outer boundaries of the BLR.
They also predict very strong Balmer and Paschen continua with important
implications to continuum RM experiments. However, the calculated Balmer and
Paschen line luminosities are too weak, by factors of 2-5. This ``Balmer line
crisis'' was noted in several earlier works and is now confirmed and
constrained by RM measurements that were not available in the past. It seems
that present photoionization codes that use the escape probability formalism,
fail to correctly compute the Balmer line luminosities in high density, large
optical depth gas.


  We present a short introduction to the M{\o}ller gravity theory and study the
Schwarzschild solution and self-consistent spontaneous compactification
solutions in Kaluza-Klein theories, which can be obtained in the framework of
this interesting generalization of General Relativity.


  The Web publishing paradigm of Linked Data has been gaining traction in the
cultural heritage sector: libraries, archives and museums. At first glance, the
principles of Linked Data seem simple enough. However experienced Web
developers, designers and architects who attempt to put these ideas into
practice often find themselves having to digest and understand debates about
Web architecture, the semantic web, artificial intelligence and the
philosophical nature of identity. In this paper I will discuss some of the
reasons why Linked Data is of interest to the cultural heritage community, what
some of the pain points are for deploying it, and characterize some pragmatic
ways for cultural heritage organizations to realize the goals of Linked Data
with examples from the Web we have today.


  Foaming metals modulates their physical properties, enabling attractive
applications where lightweight, low thermal conductivity or acoustic isolation
are desirable. Adjusting the size of the bubbles in the foams is particularly
relevant for targeted applications. Here we provide a method with a detailed
theoretical understanding how to tune the size of the bubbles in aluminium
melts in-situ via acoustic pressure. Our description is in full agreement with
the high-rate three-dimensional X-Ray radioscopy of the bubble formation. We
complement our study with the intriguing results on the effect of foaming on
electrical resistivity, Seebeck coefficient and thermal conductivity from
cryogenic to room temperature. Compared to bulk materials the investigated foam
shows an enhancement in the thermoelectric figure of merit. These results
herald promising application of foaming in thermoelectrics materials and
devices for thermal energy conversion.


  In this article we study surface subgroups of groups acting simply
transitively on vertex sets of certain hyperbolic triangular buildings.
Kangaslampi and Vdovina have constructed and classified all groups acting
simply transitively on the vertices of hyperbolic triangular buildings of the
smallest non-trivial thickness. These groups gave the first examples of
cocompact lattices acting simply transitively on vertices of hyperbolic
triangular Kac-Moody buildings that are not right-angled. The hyperbolic
buildings arise as universal covers of polyhedra glued together from with 15
triangular faces with words written on the boundary. Later they proved, that
most of the obtained 23 torsion free groups do not admit periodic planes of
genus 2. Here we take another approach to study surface subgroups of these
groups. Namely, we consider first 2-cycles in the 1-vertex polyhedron defined
by the triangles, then in the 3-vertex cover of this polyhedron. As a result we
find surface subgroups in three of the 23 torsion free groups.


  In this work, by using Levi's parametrix method we first construct the
fundamental solution of the critical non-local operator perturbed by gradient.
Then, we use the obtained estimates to prove the existence and uniqueness of
strong solutions for stochastic differential equation driven by Markov process
with irregular coefficients, whose generator is a non-local and non-symmetric
L\'evy type operator.


  We apply the BRST approach, previously developed for higher spin field
theories, to gauge invariant Lagrangian construction for antisymmetric massive
and massless bosonic fields in arbitrary d-dimensional curved space. The
obtained theories are reducible gauge models both in massless and massive cases
and the order of reducibility grows with the value of the rank of the
antisymmetric field. In both the cases the Lagrangians contain the sets of
auxiliary fields and possess more rich gauge symmetry in comparison with
standard Lagrangian formulation for the antisymmetric fields. This serves
additional demonstration of universality of the BRST approach for Lagrangian
constructions in various field models.


  Ultrasound (US) imaging is widely used in diagnosing and staging abdominal
diseases due to its lack of non-ionizing radiation and prevalent availability.
However, significant inter-operator variability and inconsistent image
acquisition hinder the widespread adoption of extensive screening programs.
Robotic ultrasound systems have emerged as a promising solution, offering
standardized acquisition protocols and the possibility of automated
acquisition. Additionally, these systems enable access to 3D data via robotic
tracking, enhancing volumetric reconstruction for improved ultrasound
interpretation and precise disease diagnosis. However, the interpretability of
3D US reconstruction of abdominal images can be affected by the patient's
breathing motion. This study introduces a method to compensate for breathing
motion in 3D US compounding by leveraging implicit neural representations. Our
approach employs a robotic ultrasound system for automated screenings. To
demonstrate the method's effectiveness, we evaluate our proposed method for the
diagnosis and monitoring of abdominal aorta aneurysms as a representative use
case. Our experiments demonstrate that our proposed pipeline facilitates robust
automated robotic acquisition, mitigating artifacts from breathing motion, and
yields smoother 3D reconstructions for enhanced screening and medical
diagnosis.


  In this paper we consider a complete connected noncompact Riemannian manifold
M with bounded geometry and spectral gap. We prove that the imaginary powers of
the Laplacian and the Riesz transform are bounded from the Hardy space X^1(M),
introduced in previous work of the authors, to L^1(M).


  ParaMonte::Python (standing for Parallel Monte Carlo in Python) is a serial
and MPI-parallelized library of (Markov Chain) Monte Carlo (MCMC) routines for
sampling mathematical objective functions, in particular, the posterior
distributions of parameters in Bayesian modeling and analysis in data science,
Machine Learning, and scientific inference in general. In addition to providing
access to fast high-performance serial/parallel Monte Carlo and MCMC sampling
routines, the ParaMonte::Python library provides extensive post-processing and
visualization tools that aim to automate and streamline the process of model
calibration and uncertainty quantification in Bayesian data analysis.
Furthermore, the automatically-enabled restart functionality of
ParaMonte::Python samplers ensure seamless fully-deterministic into-the-future
restart of Monte Carlo simulations, should any interruptions happen. The
ParaMonte::Python library is MIT-licensed and is permanently maintained on
GitHub at
https://github.com/cdslaborg/paramonte/tree/master/src/interface/Python.


  The magnetical field tuned superconductor-insulator transition in amorphous
thin films, e.g., Ta and InO, exhibits a range of yet unexplained curious
phenomena, such as a putative low-resistance metallic phase intervening the
superconducting and the insulating phase, and a huge peak in the
magnetoresistance at large magnetic field. Qualitatively, the phenomena can be
explained equally well within several significantly different pictures,
particularly the condensation of quantum vortex liquid, and the percolation of
superconducting islands embedded in normal region. Recently, we proposed and
analyzed a new measurement that should be able to decisively point to the
correct picture: a drag resistance measurement in an amorphous thin-film
bilayer setup. Neglecting interlayer tunneling, we found that the drag
resistance within the vortex paradigm has opposite sign and is orders of
magnitude larger than that in competing paradigms. For example, two identical
films as in Sambandamurthy et al. 2004 with 25 nm layer separation at 0.07 K
would produce a drag resistance $\sim10^{-4}\Omega$ according the vortex
theory, but only $\sim10^{-12}\Omega$ for the percolation theory. We provide
details of our theoretical analysis of the drag resistance within both
paradigms, and report some new results as well.


  The initial mass function of the first, Population III (Pop III), stars plays
a vital role in shaping galaxy formation and evolution in the early Universe.
One key remaining issue is the final fate of secondary protostars formed in the
accretion disc, specifically whether they merge or survive. We perform a suite
of hydrodynamic simulations of the complex interplay between fragmentation,
protostellar accretion, and merging inside dark matter minihaloes. Instead of
the traditional sink particle method, we employ a stiff equation of state
approach, so that we can more robustly ascertain the viscous transport inside
the disc. The simulations show inside-out fragmentation because the gas
collapses faster in the central region. Fragments migrate on the viscous
timescale, over which angular momentum is lost, enabling them to move towards
the disc centre, where merging with the primary protostar can occur. This
process depends on the fragmentation scale, such that there is a maximum scale
of $(1 - 5) \times 10^4$ au, inside which fragments can migrate to the primary
protostar. Viscous transport is active until radiative feedback from the
primary protostar destroys the accretion disc. The final mass spectrum and
multiplicity thus crucially depends on the effect of viscosity in the disc. The
entire disc is subjected to efficient viscous transport in the primordial case
with viscous parameter $\alpha \le 1$. An important aspect of this question is
the survival probability of Pop III binary systems, possible gravitational wave
sources to be probed with the Advanced LIGO detectors.


  A model Hamiltonian is proposed in order to understand the
localization-delocalization transition in a quantum dot, where there are two
gate voltages: top and side. Considering energetically favorable degrees of
freedom only, we achieve a finite dimensional Hilbert space. As a result, exact
diagonalization is performed to find the ground state energy of the system. It
is the purpose to explain the peculiar pattern of the electron addition energy
measured in the dot of two gate voltages.


  In this paper we study the semiclassical limit $\hslash\to 0$ of a completely
solvable model in quantum field theory: the van Hove model, describing a scalar
field created and annihilated by an immovable source. Despite its simplicity,
the van Hove model possesses many characterizing features of quantum fields,
especially in the infrared region. In particular, the existence of non-Fock
ground and equilibrium states in the presence of infrared singular sources
makes a representation-independent algebraic approach of utmost importance. We
make use of recent representation-independent techniques of infinite
dimensional semiclassical analysis to establish the Bohr correspondence
principle for the dynamics, equilibrium states, and long-time asymptotics in
the van Hove model.


  The R package frailtySurv for simulating and fitting semi-parametric shared
frailty models is introduced. Package frailtySurv implements semi-parametric
consistent estimators for a variety of frailty distributions, including gamma,
log-normal, inverse Gaussian and power variance function, and provides
consistent estimators of the standard errors of the parameters' estimators. The
parameters' estimators are asymptotically normally distributed, and therefore
statistical inference based on the results of this package, such as hypothesis
testing and confidence intervals, can be performed using the normal
distribution. Extensive simulations demonstrate the flexibility and correct
implementation of the estimator. Two case studies performed with publicly
available datasets demonstrate applicability of the package. In the Diabetic
Retinopathy Study, the onset of blindness is clustered by patient, and in a
large hard drive failure dataset, failure times are thought to be clustered by
the hard drive manufacturer and model.


  In this paper we investigate the existence of positive solution for a class
of quasilinear problem on an Orlicz-Sobolev space that can be nonreflexive
  $$- \Delta_{\Phi} u +V(x)\phi(|u|)u= K(x)f(u)\mbox{ in } \mathbb{R}^{N}$$
  where $N\geq2$, $V,K$ are nonnegative continuous functions and $f$ is a
continuous function with a quasicritical growth. Here we extend the Hardy-type
inequalities presented in \cite{AlvesandMarco} to nonreflexive Orlicz spaces.
Through inequalities together with a variational method for non-differentiable
functionals we will obtain a ground state solution. We analyze also the problem
with $V=0$.


  Low-intensity Transcranial Ultrasonic Stimulation (TUS) is a non-invasive
brain stimulation technique enabling cortical and deep brain targeting with
unprecedented spatial accuracy. Given the high rate of adoption by new users
with varying levels of expertise and interdisciplinary backgrounds, practical
guidelines are needed to ensure state-of-the-art TUS application and
reproducible outcomes. Therefore, the International Transcranial Ultrasonic
Stimulation Safety and Standards (ITRUSST) consortium has formed a
subcommittee, endorsed by the International Federation of Clinical
Neurophysiology (IFCN), to develop recommendations for best practice in TUS
applications in humans. The practical guide presented here provides a brief
introduction into ultrasound physics and sonication parameters. It explains the
requirements of TUS lab equipment and transducer selection and discusses
experimental design and procedures alongside potential confounds and control
conditions. Finally, the guide elaborates on essential steps of application
planning for stimulation safety and efficacy, as well as considerations when
combining TUS with neuroimaging, electrophysiology, or other brain stimulation
techniques. We hope that this practical guide to TUS will assist both novice
and experienced users in planning and conducting high-quality studies and
provide a solid foundation for further advancements in this promising field.


  Robot evaluations in language-guided, real world settings are time-consuming
and often sample only a small space of potential instructions across complex
scenes. In this work, we introduce contrast sets for robotics as an approach to
make small, but specific, perturbations to otherwise independent, identically
distributed (i.i.d.) test instances. We investigate the relationship between
experimenter effort to carry out an evaluation and the resulting estimated test
performance as well as the insights that can be drawn from performance on
perturbed instances. We use contrast sets to characterize policies at reduced
experimenter effort in both a simulated manipulation task and a physical robot
vision-and-language navigation task. We encourage the use of contrast set
evaluations as a more informative alternative to small scale, i.i.d.
demonstrations on physical robots, and as a scalable alternative to
industry-scale real world evaluations.


  Objective: To conduct a proof-of-concept study of the detection of two
synthetic models of severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) using polarimetric imaging. Methods: Two SARS-CoV-2 models were
prepared as engineered lentiviruses pseudotyped with the G protein of the
vesicular stomatitis virus, and with the characteristic Spike protein of
SARS-CoV-2. Samples were preparations in two biofluids (saline solution and
artificial saliva), in four concentrations, and deposited as 5-{\mu}L droplets
on a supporting plate. The angles of maximal degree of linear polarization
(DLP) of light diffusely scattered from dry residues were determined using
Mueller polarimetry of 87 samples at 405 nm and 514 nm. A polarimetric camera
was used for simultaneous imaging of several samples under 380-420 nm
illumination at angles similar to those of maximal DLP. A per-pixel image
analysis included quantification and combination of polarization feature
descriptors in other 475 samples. Results: The angles (from sample surface) of
maximal DLP were 3 degrees for 405 nm and 6 degrees for 514 nm. Similar viral
particles that differ only in the characteristic spike protein of the
SARS-CoV-2, their corresponding negative controls, fluids, and the sample
holder were discerned from polarimetric image analysis at 10-degree and
15-degree configurations. Conclusion: Polarimetric imaging in the visible
spectrum has the potential for non-contact, reagent-free detection of viruses
in multiple dry fluid residues simultaneously. Further analysis including real
SARS-CoV-2 in human samples -- particularly, fresh saliva -- are required.
Significance: Polarimetric imaging under visible light could contribute to
fast, cost-effective screening of SARS-CoV-2 and other pathogens.


  In addition to inherent computational challenges, the absence of a canonical
method for quantifying `persistence' in multi-parameter persistent homology
remains a hurdle in its application.
  One of the best known quantifications of persistence for multi-parameter
persistent homology is the rank invariant, which has recently evolved into the
generalized rank invariant (GRI) by naturally extending its domain. This
extension enables us to quantify persistence across a broader range of regions
in the indexing poset compared to the rank invariant. However, the size of the
domain of the GRI is generally formidable, making it desirable to restrict its
domain to a more manageable subset for computational purposes. The foremost
questions regarding such a restriction of the domain are: (1) How to restrict,
if possible, the domain of the GRI without any loss of information? (2) When
can we more compactly encode the GRI as a `persistence diagram'? (3) What is
the trade-off between computational efficiency and the discriminating power of
the GRI as the amount of the restriction on the domain varies? (4) What proxies
exist for persistence diagrams in the multi-parameter setting that can be
derived from the GRI? To address the first three questions, we generalize and
axiomatize the classic fundamental lemma of persistent homology via the notion
of M\"obius invertibility of the GRI which we propose. This extension also
contextualizes known results regarding the (generalized) rank invariant within
the classical theory of M\"obius inversion. We conduct a comprehensive
comparison between M\"obius invertibility and other existing concepts related
to the structural simplicity of persistence modules.
  We address the fourth question through the notion of motivic invariants. We
demonstrate that many invariants from the literature can be both derived from
the GRI and recast as motivic invariants.


  Macroscopic modelling of ferroelectric properties refers usually to
Landau-Ginzburg-Devonshire theory. This paper questions the meaningfulness of
this term, discussing contributions of the three authors to what is supposed to
be a theory. The limitations of every contribution are analyzed. In the main
text and, to more extent in the Supplementary Material, the Landau theory is
presented from an unusual perspective starting from simple mechanical models of
spontaneous symmetry breaking and finishing by the Ising model. The aim of the
presentation is to emphasize along with the qualitative breakthroughs the
approximate character of macroscopic modelling associated with the above three
authors.


  We introduce and investigate new invariants on the pair of modules $M$ and
$N$ over quantum affine algebras $U_q'(\mathfrak{g})$ by analyzing their
associated R-matrices. From new invariants, we provide a criterion for a
monoidal category of finite-dimensional integrable $U_q'(\mathfrak{g})$-modules
to become a monoidal categorification of a cluster algebra.


  Clusters of galaxies are sites of acceleration of charged particles and
sources of non-thermal radiation. We report on new constraints on the
population of cosmic rays in the Intra Cluster Medium (ICM) obtained via radio
observations of a fairly large sample of massive, X-ray luminous, galaxy
clusters in the redshift interval 0.2--0.4. The bulk of the observed galaxy
clusters does not show any hint of Mpc scale synchrotron radio emission at the
cluster center (Radio Halo). We obtained solid upper limits to the diffuse
radio emission and discuss their implications for the models for the origin of
Radio Halos. Our measurements allow us to derive also a limit to the content of
cosmic ray protons in the ICM. Assuming spectral indices of these protons delta
=2.1-2.4 and microG level magnetic fields, as from Rotation Measures, these
limits are one order of magnitude deeper than present EGRET upper limits, while
they are less stringent for steeper spectra and lower magnetic fields.


  Photons propagating in a plasma acquire an effective mass $\mu$, which is
given by the plasma frequency and which scales with the square root of the
plasma density. As noted previously by Conlon and Herdeiro, for electron number
densities $n_e\sim 10^{-3}$ cm$^{-3}$ (such as those measured in the
interstellar medium) the effective mass induced by the plasma is $\mu \sim
10^{-12}$ eV. This would cause superradiant instabilities for spinning black
holes of a few tens of solar masses. An obvious problem with this picture is
that densities in the vicinity of black holes are much higher than in the
interstellar medium because of accretion. We have conducted numerical
simulations of the superradiant instability in spinning black holes surrounded
by a plasma with density increasing closer to the black hole, in order to mimic
the effect of accretion. While we confirm that superradiant instabilities
appear for plasma densities that are sufficiently low near the black hole, we
find that astrophysically realistic accretion disks are unlikely to trigger
such instabilities.


  We present a systematic study of the 3.0 um H2O ice and the 3.4 um aliphatic
carbon absorption features toward 48 local ultraluminous infrared galaxies
(ULIRGs) using spectra obtained by the AKARI Infrared Camera to investigate the
UV environment in their star-forming regions. All the ULIRGs in our sample
exhibit a ratio of optical depth of H2O ice to silicate dust (tau3.0/tau9.7)
that is lower than that in the Taurus dark cloud. This implies that ULIRGs
cannot be described as an ensemble of low-mass star-forming regions and that a
significant amount of high-mass star-forming regions contribute to star-forming
clouds in local ULIRGs. The results also show that the ratios of optical depth
of aliphatic carbon to silicate dust, tau3.4/tau9.7, exhibit diverse values. We
investigate two effects that can affect this ratio: the geometric temperature
gradient (which increases the ratio) and the intense UV environment (which
decreases it). The geometric temperature gradient is typically considered as a
sign of active galactic nuclei (AGN). ULIRGs with AGN signs (optical
classification, NIR color, and a PAH emission strength of 3.3 um) indeed tend
to exhibit a large tau3.4/tau9.7 ratio. However, we find that the presence of
buried AGN is not the only cause of the geometric temperature gradient, because
the enhancement of the ratio is also evident in pure starburst-like ULIRGs
without these AGN signs. Regarding the intense UV environment in star-forming
regions, the correlation between the aliphatic carbon ratio and the ratio of
the [C II] 158 um line luminosity to the far-infrared luminosity (L[CII]/LFIR),
which represents the UV environment in photodissociation regions, implies that
the intense UV environment causes the decrease of the aliphatic carbon ratio.
We find that an intense UV environment (G/nH > 3) in star-forming regions is
needed for the aliphatic carbon ratio to be suppressed.


  Goldston and Montgomery [3] proved that the Strong Pair Correlation
Conjecture and two second moments of primes in short intervals are equivalent
to each other under Riemann Hypothesis. In this paper, we get the second main
terms for each of the above and show that they are almost equivalent to each
other.


  The correction of the aberration of transcranial focused ultrasounds is a
relevant issue for enhancing various non-invasive medical treatments. Emission
through multi-element phased arrays has been the most widely accepted method to
reduce aberrations in recent years; however, a new disruptive technology, based
on 3D printed acoustic lenses, has recently been proposed with lower cost and
comparable accuracy. The number and size of transducers in phased array
configurations was a bottleneck limiting the focusing accuracy, but once the
submillimeter precision of the latest generation 3D printers has overcome this
limitation, the challenge is now to improve the accuracy of the numerical
simulations needed to design the lens. This study introduces and evaluates two
improvements to the numerical model applied in previous works that proposed 3D
lenses, which consist in the direct calculation of the phase pattern from the
propagation of oscillating magnitudes in complex form, and in the introduction
of the absorption phenomenon into the set of equations that describe the
dynamics of the wave in both fluid and solid media. Numerical experiments are
performed analysing the quality of the aberrated-corrected focus in different
configurations. The results obtained show that the inclusion of absorption
significantly improves focusing, especially where the thickness of the skull is
more irregular.


  The extravagances of quantum mechanics never fail to enrich daily the debate
around natural philosophy. Entanglement, non-locality, collapse, many worlds,
many minds, and subjectivism have challenged generations of thinkers. Its
approach can perhaps be placed in the stream of quantum logic, in which the
"strangeness" of quantum mechanics is "measured" through the violation of
Bell's inequalities and, from there, attempts an interpretative path that
preserves realism yet ends up overturning it, restating the fundamental
mechanisms of QM as a logical necessity for a strong realism.


  Solving math word problems is the task that analyses the relation of
quantities and requires an accurate understanding of contextual natural
language information. Recent studies show that current models rely on shallow
heuristics to predict solutions and could be easily misled by small textual
perturbations. To address this problem, we propose a Textual Enhanced
Contrastive Learning framework, which enforces the models to distinguish
semantically similar examples while holding different mathematical logic. We
adopt a self-supervised manner strategy to enrich examples with subtle textual
variance by textual reordering or problem re-construction. We then retrieve the
hardest to differentiate samples from both equation and textual perspectives
and guide the model to learn their representations. Experimental results show
that our method achieves state-of-the-art on both widely used benchmark
datasets and also exquisitely designed challenge datasets in English and
Chinese. \footnote{Our code and data is available at
\url{https://github.com/yiyunya/Textual_CL_MWP}


  Evidence is presented for the first time that the sharp and continuous
spectral changes observed in PbZr0.52Ti0.48O3 (PZT) between 350 and 10 K with
the 647.1 nm wavelength are due to a resonance Raman effect. Such a phenomenon
can be explained by means of a self-trapped exciton emission oxygen deficient
complex (TiTi' - VO-) of PZT powder whose energy is close to the radiation line
of the laser. This kind of approach should also be very useful to distinguish
the phase transition sequence for other related ferro/ piezoelectric systems.


  Cultivation experiments often produce sparse and irregular time series.
Classical approaches based on mechanistic models, like Maximum Likelihood
fitting or Monte-Carlo Markov chain sampling, can easily account for sparsity
and time-grid irregularities, but most statistical and Machine Learning tools
are not designed for handling sparse data out-of-the-box. Among popular
approaches there are various schemes for filling missing values (imputation)
and interpolation into a regular grid (alignment). However, such methods
transfer the biases of the interpolation or imputation models to the target
model. We show that Deep Set Neural Networks equipped with triplet encoding of
the input data can successfully handle bio-process data without any need for
imputation or alignment procedures. The method is agnostic to the particular
nature of the time series and can be adapted for any task, for example, online
monitoring, predictive control, design of experiments, etc. In this work, we
focus on forecasting. We argue that such an approach is especially suitable for
typical cultivation processes, demonstrate the performance of the method on
several forecasting tasks using data generated from macrokinetic growth models
under realistic conditions, and compare the method to a conventional fitting
procedure and methods based on imputation and alignment.


  I provide a broad framework to embed gradient flow equations in
non-relativistic field theory models that exhibit anisotropic scaling. The
prime example is the heat equation arising from a Lifshitz scalar field theory;
other examples include the Allen-Cahn equation that models the evolution of
phase boundaries. Then, I review recent results reported in arXiv:1002.0062
describing instantons of Horava-Lifshitz gravity as eternal solutions of
certain geometric flow equations on 3-manifolds. These instanton solutions are
in general chiral when the anisotropic scaling exponent is z=3. Some general
connections with the Onsager-Machlup theory of non-equilibrium processes are
also briefly discussed in this context. Thus, theories of Lifshitz type in d+1
dimensions can be used as off-shell toy models for dynamical vacuum selection
of relativistic field theories in d dimensions.


  Using the low energy limit of type IIB superstring theory, we obtain the
non-extremal limit of deformed conifold geometry which is dual to the IR limit
of large N thermal QCD.At low temperatures, the extremal geometry without black
hole is favored while at high temperatures, the field theory is described by
non-extremal black hole geometry. We compute the ten dimensional on shell
action for extremal and non-extremal geometries and demonstrate that at a
critical temperature $T_c$ there is a first order confinement to deconfinement
phase transition. We compute $T_c$ as a function of 'tHooft coupling and study
the thermodynamics of the dual gauge theory by evaluating the free energy and
entropy of the ten dimensional geometry. We find agreement with the conformal
limit while thermodynamics of non-conformal strongly coupled gauge theories is
explored using the black hole geometries in non-AdS space.


  We have compared different time profiles for the trajectory of the centre of
a quadrupole magnetic trap designed for the transport of cold sodium atoms. Our
experimental observations show that a smooth profile characterized by an
analytical expression involving the error function minimizes the transport
duration while limiting atom losses and heating of the trapped gas. Using
numerical calculations of single atom classical trajectories within the trap,
we show that this observation can be qualitatively interpreted as a trade-off
between two types of losses: finite depth of the confinement and Majorana spin
flips.


  The causal mechanism between climate and political violence is fraught with
complex mechanisms. Current quantitative causal models rely on one or more
assumptions: (1) the climate drivers persistently generate conflict, (2) the
causal mechanisms have a linear relationship with the conflict generation
parameter, and/or (3) there is sufficient data to inform the prior
distribution. Yet, we know conflict drivers often excite a social
transformation process which leads to violence (e.g., drought forces
agricultural producers to join urban militia), but further climate effects do
not necessarily contribute to further violence. Therefore, not only is this
bifurcation relationship highly non-linear, there is also often a lack of data
to support prior assumptions for high resolution modeling. Here, we aim to
overcome the aforementioned causal modeling challenges by proposing a neural
forward-intensity Poisson process (NFIPP) model. The NFIPP is designed to
capture the potential non-linear causal mechanism in climate induced political
violence, whilst being robust to sparse and timing-uncertain data. Our results
span 20 recent years and reveal an excitation-based causal link between extreme
climate events and political violence across diverse countries. Our
climate-induced conflict model results are cross-validated against qualitative
climate vulnerability indices. Furthermore, we label historical events that
either improve or reduce our predictability gain, demonstrating the importance
of domain expertise in informing interpretation.


  We explored the clustering properties of Lyman Break Galaxies (LBGs) at z=4
and 5 with an angular two-point correlation function on the basis of the very
deep and wide Subaru Deep Field data. We found an apparent dependence of the
correlation function slope on UV luminosity for LBGs at both z=4 and 5. More
luminous LBGs have a steeper correlation function. To compare these
observational results, we constructed numerical mock LBG catalogs based on a
semianalytic model of hierarchical clustering combined with high-resolution
N-body simulation, carefully mimicking the observational selection effects. The
luminosity functions for LBGs predicted by this mock catalog were found to be
almost consistent with the observation. Moreover, the overall correlation
functions of LBGs were reproduced reasonably well. The observed dependence of
the clustering on UV luminosity was not reproduced by the model, unless
subsamples of distinct halo mass were considered. That is, LBGs belonging to
more massive dark haloes had steeper and larger-amplitude correlation
functions. With this model, we found that LBG multiplicity in massive dark
halos amplifies the clustering strength at small scales, which steepens the
slope of the correlation function. The hierarchical clustering model could
therefore be reconciled with the observed luminosity-dependence of the angular
correlation function, if there is a tight correlation between UV luminosity and
halo mass. Our finding that the slope of the correlation function depends on
luminosity could be an indication that massive dark halos hosted multiple
bright LBGs (abridged).


  Let $G$ be a connected graph with $V(G)=\{1,\dotsc,n\}$. Then the resistance
distance between any two vertices $i$ and $j$ is given by
$r_{ij}:=l_{ii}^{\dag} + l_{jj}^{\dag}-2 l_{ij}^{\dag}$, where $l_{ij}^\dag$ is
the $(i,j)^{\rm th}$ entry of the Moore-Penrose inverse of the Laplacian matrix
of $G$.
  For the resistance matrix $R:=[r_{ij}]$, there is an elegant formula to
compute the inverse of $R$. This says that \[R^{-1}=-\frac{1}{2}L +
\frac{1}{\tau' R \tau} \tau \tau', \] where
\[\tau:=(\tau_1,\dotsc,\tau_n)'~~\mbox{and}~~ \tau_{i}:=2- \sum_{\{j \in
V(G):(i,j) \in E(G)\}} r_{ij}~~~i=1,\dotsc,n. \] A far reaching generalization
of this result that gives an inverse formula for a generalized resistance
matrix of a strongly connected and matrix weighted balanced directed graph is
obtained in this paper. When the weights are scalars, it is shown that the
generalized resistance is a non-negative real number. We also obtain a
perturbation result involving resistance matrices of connected graphs and
Laplacians of digraphs.


  Consider a particle diffusing in a confined volume which is divided into two
equal regions. In one region the diffusion coefficient is twice the value of
the diffusion coefficient in the other region. Will the particle spend equal
proportions of time in the two regions in the long term? Statistical mechanics
would suggest yes, since the number of accessible states in each region is
presumably the same. However, another line of reasoning suggests that the
particle should spend less time in the region with faster diffusion, since it
will exit that region more quickly. We demonstrate with a simple microscopic
model system that both predictions are consistent with the information given.
Thus, specifying the diffusion rate as a function of position is not enough to
characterize the behaviour of a system, even assuming the absence of external
forces. We propose an alternative framework for modelling diffusive dynamics in
which both the diffusion rate and equilibrium probability density for the
position of the particle are specified by the modeller. We introduce a
numerical method for simulating dynamics in our framework that samples from the
equilibrium probability density exactly and is suitable for discontinuous
diffusion coefficients.


  Pirashvili exhibited a small subcomplex of the Leibniz complex $(T(s
\mathfrak{g}), d_{\mathrm{Leib}})$ of a Leibniz algebra $\mathfrak{g}$. The
main result of this paper generalizes this result to show that the primitive
filtration of $T(s\mathfrak{g})$ provides an increasing, exhaustive filtration
of the Leibniz complex by subcomplexes, thus establishing a conjecture due to
Loday. The associated spectral sequence is used to give a new proof of
Pirashvili's conjecture that, when $\mathfrak{g}$ is a free Leibniz algebra,
the homology of the Pirashvili complex is zero except in degree one.
  This result is then used to show that the desuspension of the Pirashvili
complex carries a natural $L_\infty$-structure that induces the natural Lie
algebra structure on the homology of the complex in degree zero.


  We show that families of Pryms of abelian Galois covers of $\mathbb{P}^1$ in
$A_{g-1}$ (resp. $A_g$) do not give rise to high dimensional Shimura
subvareties.


  In this paper, we consider maximal and irreducible quartic orders which arise
from integral binary quartic forms, via the construction of Birch and Merriman,
and whose field of fractions is a quartic $D_4$-field. By a theorem of M. Wood,
such quartic orders may be regarded as quartic $D_4$-fields whose ring of
integers has a monogenic cubic resolvent. We shall give the asymptotic number
of such objects when ordered by conductor, as well as estimate the asymptotic
number of such objects when ordered by discriminant.


  For the Small Size League of RoboCup 2018, Team ZJUNLict has won the champion
and therefore, this paper thoroughly described the devotion which ZJUNLict has
devoted and the effort that ZJUNLict has contributed. There are three mean
optimizations for the mechanical part which accounted for most of our
incredible goals, they are "Touching Point Optimization", "Damping System
Optimization", and "Dribbler Optimization". For the electrical part, we
realized "Direct Torque Control", "Efficient Radio Communication Protocol"
which will be credited for stabilizing the dribbler and a more secure
communication between robots and the computer. Our software group contributed
as much as our hardware group with the effort of "Vision Lost Compensation" to
predict the movement by kalman filter, and "Interception Prediction Algorithm"
to achieve some skills and improve our ball possession rate.


  In-time and accurate assessments of on-road vehicle emissions play a central
role in urban air quality and health policymaking. However, official insight is
hampered by the Inspection/Maintenance (I/M) procedure conducted in the
laboratory annually. It not only has a large gap to real-world situations
(e.g., meteorological conditions) but also is incapable of regular supervision.
Here we build a unique dataset including 103831 light-duty gasoline vehicles,
in which on-road remote sensing (ORRS) measurements are linked to the I/M
records based on the vehicle identification numbers and license plates. On this
basis, we develop an ensemble model framework that integrates three machining
learning algorithms, including neural network (NN), extreme gradient boosting
(XGBoost), and random forest (RF). We demonstrate that this ensemble model
could rapidly assess the vehicle-specific emissions (i.e., CO, HC, and NO). In
particular, the model performs quite well for the passing vehicles under normal
conditions (i.e., lower VSP (< 18 kw/t), temperature (6 ~ 32 {\deg}C), relative
humidity (< 80%), and wind speed (< 5m/s)). Together with the current emission
standard, we identify a large number of the dirty (2.33%) or clean (74.92%)
vehicles in the real world. Our results show that the ORRS measurements,
assisted by the machine-learning-based ensemble model developed here, can
realize day-to-day supervision of on-road vehicle-specific emissions. This
approach framework provides a valuable opportunity to reform the I/M procedures
globally and mitigate urban air pollution deeply.


  Research in scene graph generation (SGG) usually considers two-stage models,
that is, detecting a set of entities, followed by combining them and labeling
all possible relationships. While showing promising results, the pipeline
structure induces large parameter and computation overhead, and typically
hinders end-to-end optimizations. To address this, recent research attempts to
train single-stage models that are computationally efficient. With the advent
of DETR, a set based detection model, one-stage models attempt to predict a set
of subject-predicate-object triplets directly in a single shot. However, SGG is
inherently a multi-task learning problem that requires modeling entity and
predicate distributions simultaneously. In this paper, we propose Transformers
with conditional queries for SGG, namely, TraCQ with a new formulation for SGG
that avoids the multi-task learning problem and the combinatorial entity pair
distribution. We employ a DETR-based encoder-decoder design and leverage
conditional queries to significantly reduce the entity label space as well,
which leads to 20% fewer parameters compared to state-of-the-art single-stage
models. Experimental results show that TraCQ not only outperforms existing
single-stage scene graph generation methods, it also beats many
state-of-the-art two-stage methods on the Visual Genome dataset, yet is capable
of end-to-end training and faster inference.


  We present a method to gradually compute a smaller and smaller unsatisfiable
core of a propositional formula by minimizing proofs of unsatisfiability. The
goal is to compute a minimal unsatisfiable core that is relatively small
compared to other minimal unsatisfiable cores of the same formula. We try to
achieve this goal by postponing deletion of arbitrary clauses from the formula
as long as possible---in contrast to existing minimal unsatisfiable core
algorithms. We applied this method to reduce the smallest known unit-distance
graph with chromatic number 5 from 553 vertices and 2720 edges to 529 vertices
and 2670 edges.


  A matrix $P$ is said to be a nontrivial generalized reflection matrix over
the real quaternion algebra $\mathbb{H}$ if $P^{\ast }=P\neq I$ and $P^{2}=I$
where $\ast$ means conjugate and transpose. We say that
$A\in\mathbb{H}^{n\times n}$ is generalized reflexive (or generalized
antireflexive) with respect to the matrix pair $(P,Q)$ if $A=PAQ$ $($or
$A=-PAQ)$ where $P$ and $Q$ are two nontrivial generalized reflection matrices
of demension $n$. Let ${\large \varphi}$ be one of the following subsets of
$\mathbb{H}^{n\times n}$ : (i) generalized reflexive matrix; (ii)reflexive
matrix; (iii) generalized antireflexive matrix; (iiii) antireflexive matrix.
Let $Z\in\mathbb{H}^{n\times m}$ with rank$\left( Z\right) =m$ and $\Lambda=$
diag$\left( \lambda_{1},...,\lambda_{m}\right) .$ The inverse eigenproblem is
to find a\ matrix $A$ such that the set ${\large \varphi }\left(
Z,\Lambda\right) =\left\{ A\in{\large \varphi}\text{ }|\text{
}AZ=Z\Lambda\right\} $ nonempty and find the general expression of $A.$\newline
In this paper, we investigate the inverse eigenproblem ${\large \varphi}\left(
Z,\Lambda\right) $. Moreover, the approximation problem: $\underset{A\in{\large
\varphi}}{\min\left\Vert A-E\right\Vert _{F}}$ is studied, where $E$ is a given
matrix over $\mathbb{H}$\ and $\parallel \cdot\parallel_{F}$ is the Frobenius
norm.


  Multivariate time-series modeling and forecasting is an important problem
with numerous applications. Traditional approaches such as VAR (vector
auto-regressive) models and more recent approaches such as RNNs (recurrent
neural networks) are indispensable tools in modeling time-series data. In many
multivariate time series modeling problems, there is usually a significant
linear dependency component, for which VARs are suitable, and a nonlinear
component, for which RNNs are suitable. Modeling such times series with only
VAR or only RNNs can lead to poor predictive performance or complex models with
large training times. In this work, we propose a hybrid model called R2N2
(Residual RNN), which first models the time series with a simple linear model
(like VAR) and then models its residual errors using RNNs. R2N2s can be trained
using existing algorithms for VARs and RNNs. Through an extensive empirical
evaluation on two real world datasets (aviation and climate domains), we show
that R2N2 is competitive, usually better than VAR or RNN, used alone. We also
show that R2N2 is faster to train as compared to an RNN, while requiring less
number of hidden units.


  Autonomous vehicles need to accomplish their tasks while interacting with
human drivers in traffic. It is thus crucial to equip autonomous vehicles with
artificial reasoning to better comprehend the intentions of the surrounding
traffic, thereby facilitating the accomplishments of the tasks. In this work,
we propose a behavioral model that encodes drivers' interacting intentions into
latent social-psychological parameters. Leveraging a Bayesian filter, we
develop a receding-horizon optimization-based controller for autonomous vehicle
decision-making which accounts for the uncertainties in the interacting
drivers' intentions. For online deployment, we design a neural network
architecture based on the attention mechanism which imitates the behavioral
model with online estimated parameter priors. We also propose a decision tree
search algorithm to solve the decision-making problem online. The proposed
behavioral model is then evaluated in terms of its capabilities for real-world
trajectory prediction. We further conduct extensive evaluations of the proposed
decision-making module, in forced highway merging scenarios, using both
simulated environments and real-world traffic datasets. The results demonstrate
that our algorithms can complete the forced merging tasks in various traffic
conditions while ensuring driving safety.


  Despite quantum computing's rapid development, current systems remain limited
in practical applications due to their limited qubit count and quality. Various
technologies, such as superconducting, trapped ions, and neutral atom quantum
computing technologies are progressing towards a fault tolerant era, however
they all face a diverse set of challenges in scalability and control. Recent
efforts have focused on multi-node quantum systems that connect multiple
smaller quantum devices to execute larger circuits. Future demonstrations hope
to use quantum channels to couple systems, however current demonstrations can
leverage classical communication with circuit cutting techniques. This involves
cutting large circuits into smaller subcircuits and reconstructing them
post-execution. However, existing cutting methods are hindered by lengthy
search times as the number of qubits and gates increases. Additionally, they
often fail to effectively utilize the resources of various worker
configurations in a multi-node system. To address these challenges, we
introduce FitCut, a novel approach that transforms quantum circuits into
weighted graphs and utilizes a community-based, bottom-up approach to cut
circuits according to resource constraints, e.g., qubit counts, on each worker.
FitCut also includes a scheduling algorithm that optimizes resource utilization
across workers. Implemented with Qiskit and evaluated extensively, FitCut
significantly outperforms the Qiskit Circuit Knitting Toolbox, reducing time
costs by factors ranging from 3 to 2000 and improving resource utilization
rates by up to 3.88 times on the worker side, achieving a system-wide
improvement of 2.86 times.


  The non-stationary nature of data streams strongly challenges traditional
machine learning techniques. Although some solutions have been proposed to
extend traditional machine learning techniques for handling data streams, these
approaches either require an initial label set or rely on specialized design
parameters. The overlap among classes and the labeling of data streams
constitute other major challenges for classifying data streams. In this paper,
we proposed a clustering-based data stream classification framework to handle
non-stationary data streams without utilizing an initial label set. A
density-based stream clustering procedure is used to capture novel concepts
with a dynamic threshold and an effective active label querying strategy is
introduced to continuously learn the new concepts from the data streams. The
sub-cluster structure of each cluster is explored to handle the overlap among
classes. Experimental results and quantitative comparison studies reveal that
the proposed method provides statistically better or comparable performance
than the existing methods.


  The response of a quantum dot coupled with one normal lead and a
superconductor lead driven by a step-like pulse bias $V_L$ is studied using the
non-equilibrium Green function method. In the linear pulse bias regime, the
responses of the upwards and downwards bias are symmetric. In this regime the
turn-on time and turn-off time are much slower than that of the normal system
due to the Andreev reflection. On the other hand, for the large pulse bias
$V_L$, the instantaneous current exhibits oscillatory behaviors with the
frequency $\hbar\Omega =qV_L$. The turn on/off times are in (or shorter than)
the scale of $1/V_L$, so they are faster for the larger bias $V_L$. In
addition, the responses for the upwards and downwards bias are asymmetric at
large $V_L$. The turn-on time is larger than the turn-off time but the
relaxation time \cite{note1} depends only on the coupling strength $\Gamma$ and
it is much smaller than the turn-on/off times for the large bias $V_L$.


  Let P be a selfadjoint elliptic operator of order m>0 acting on the sections
of a Hermitian vector bundle over a compact Riemannian manifold of dimension n.
General arguments show that its zeta and eta functions may have poles only at
points of the form s=k/m, where k ranges over all non-zero integers less than
or equal to n. In this paper, we construct elementary and explicit examples of
perturbations of P which make the zeta and eta functions be singular at all the
points at which they are allowed to have singularities. We proceed within three
classes of operators: Dirac-type operators, selfadjoint first-order
differential operators, and selfadjoint elliptic pseudodifferential operators.
As a result, we obtain genericity results for the singularities of the zeta and
eta functions in those settings. In particular, in the setting of Dirac-type
operators we obtain a new proof of a well known result of Branson-Gilkey, which
was obtained by means of Riemannian invariant theory. As it turns out, the
results of this paper contradict Theorem 6.3 of the third author's paper [Po1].
Corrections to that statement are given in Appendix B.


  The quantum renormalization group method is applied to study the quantum
criticality and entanglement entropy of the ground state of the Ising chain in
the presence of antisymmetric anisotropic couplings and alternating exchange
interactions. The quantum phase transitions can be characterized by the
discontinuity in the second derivative of the energy of renormalized ground
state. The phase diagram is obtained by the critical boundary line. The first
derivative of entanglement entropy also diverges at the same critical points
after enough iterations of the renormalization of coupling constants. The
antisymmetric anisotropy and alternating interaction can enhance the
renormalized entanglement via the creation of quantum fluctuations. The scaling
behavior of the derivative of the entropy around the critical points manifest
the logarithm dependence on the size of the spin system.


  An action in which the Ricci scalar is nonminimally coupled with a scalar
field and contains higher order curvature invariant terms carries a conserved
current under certain conditions that decouples geometric part from the scalar
field. The conserved current relates the pair of arbitrary coupling parameters
$f(\phi)$ and $\omega(\phi)$ with the gravitational field variable, where
$\omega(\phi)$ is the Brans-Dicke coupling parameter. The existence of such
conserved current may be helpful to sketch the cosmological evolution from its
early age till date in a single frame.


  First principles pseudopotential calculations have been performed for the
first time to investigate the phonon dispersion, thermodynamic and optical
properties including charge density, Fermi surface, Mulliken population
analysis, theoretical Vickers hardness of predicted MAX phase Sc2InC. We
revisited the structural, elastic and electronic properties of the compound
which assessed the reliability of our calculations. The analysis of the elastic
constants and the phonon dispersion along with phonon density of states
indicates the mechanical stability and dynamical stability of the MAX phase.
The Helmholtz free energy, internal energy, entropy specific heat capacity and
Debye temperature have also been calculated from the phonon density of states.
Mulliken population analysis indicates the existence of prominent covalency in
chemical bonding of Sc2InC. The electronic charge density mapping shows a
combination of ionic, covalent and metallic bonding in the compound. The Fermi
surface is comprised due to the low dispersive Sc 3d and C 2p states from the
[ScC] blocks. The phase is expected to be a soft material and easily mechinable
due to its low Vicker hardness value. Furthermore, the analysis of various
optical properties (such as dielectric function, refractive index,
photoconductivity, absorption coefficients, loss function and reflectivity)
suggests that the nanolaminate Sc2InC is a promising candidate for
optoelectronic devices in the visible and ultraviolet energy regions and as a
coating material to avoid solar heating.


  We are concerned with the study of the existence and multiplicity of
solutions for Dirichlet boundary value problems involving the (p(x),
q(x))-equation and the nonlinearity is superlinear but does not satisfy the
usual Ambrossetti-Rabinowitz condition in the framework of Sobolev spaces with
variable exponents in Complete manifolds. The main results are established by
means of the mountain pass theorem and Fountain theorem with Cerami condition.
Moreover, we are giving an example of a (p(x), q(x)) equation that verifies all
our demonstrated results.


  Flow fluctuations in ultra-relativistic heavy-ion collision can be probed by
studying the momentum dependent correlations or the factorization-breaking
coefficients between flow harmonics in separate kinematic bins (transverse
momentum or pseudorapidity). We study such factorization-breaking coefficients
for collisions of deformed U+U nuclei to see the effect of the nuclear
deformation on momentum dependent coefficients. We also study momentum
dependent mixed-flow correlations for the isobar collision system : Ru+Ru and
Zr+Zr, which have the same mass number but different nuclear structure, thus
providing the ideal scenario to study nuclear deformation effect on such
observables. We use the TRENTO + MUSIC model for simulations and event-by-event
analysis of those observables. We find that these momentum dependent
correlation coefficients are not only excellent candidates to probe the
fluctuation in heavy-ion collision, but also show significant sensitivity to
the nuclear deformation.


  In this paper, motivated by Chen--Ruan's stringy orbifold theory on almost
complex orbifolds, we construct a new cohomology ring $\mathscr
H^\ast_{G,cs}(X)$ for an equivariant almost complex pair $(X,G)$, where $X$ is
a compact connected almost complex manifold, $G$ is a connected compact Lie
group which acts on $X$ and preserves the almost complex structure.


  We develop and analyze a procedure for gradient-based optimization that we
refer to as stochastically controlled stochastic gradient (SCSG). As a member
of the SVRG family of algorithms, SCSG makes use of gradient estimates at two
scales, with the number of updates at the faster scale being governed by a
geometric random variable. Unlike most existing algorithms in this family, both
the computation cost and the communication cost of SCSG do not necessarily
scale linearly with the sample size $n$; indeed, these costs are independent of
$n$ when the target accuracy is low. An experimental evaluation on real
datasets confirms the effectiveness of SCSG.


  We apply a formulation of Einstein's general relativity with only cubic
interactions for deriving the metric of a Schwarzschild black hole to all
orders in perturbation theory. This cubic interactions formulation coupled to
effective worldline action of a massive point particle allows to derive a
recursion relation for the form factors of the off-shell graviton emission
current. The unique solution to the recursion relation leads to the
Schwarzschild black-hole solution in four dimensions. This provides the first
derivation of the black hole metric from a matter source to all orders in
perturbation theory from an amplitude approach.


  We present a detailed study of the Flambaum-Ginges radiative potential method
which enables the accurate inclusion of quantum electrodynamics (QED) radiative
corrections in a simple manner in atoms, ions, and molecules over the range
10<=Z<=120, where Z is the nuclear charge. Calculations are performed for
binding energy shifts to the lowest valence s, p, and d waves over the series
of alkali atoms Na to E119. The high accuracy of the radiative potential method
is demonstrated by comparison with rigorous QED calculations in frozen atomic
potentials, with deviations on the level of 1%. The many-body effects of core
relaxation and second- and higher-order perturbation theory on the interaction
of the valence electron with the core are calculated. The inclusion of
many-body effects tends to increase the size of the shifts, with the
enhancement particularly significant for d waves; for K to E119, the
self-energy shifts for d waves are only an order of magnitude smaller than the
s-wave shifts. It is shown that account of many-body effects is essential for
an accurate description of the Lamb shift.


  This is a compact overview of Higgs sectors in extensions of the MSSM. The
focus is on the summary of the main features of models with additional singlets
and triplets as well as of models with Dirac gauginos. In addition, also
important aspects of models with an extended gauge sector are shown. Finally, I
comment on available tools which can be used for an adequate study of
non-minimal SUSY models.


  Antibacterial photodynamic therapy (a-PDT) has emerged as a promising
non-invasive therapeutic modality that utilizes the combination of a
photosensitive agent, molecular oxygen, and excitation light to generate
reactive oxygen species (ROS), demonstrating remarkable activity against
multidrug-resistant bacterial infections. However, the effective use of
conventional photosensitizers is significantly limited by a number of their
shortcomings, namely, poor water solubility and low selectivity. Herein, we
present a novel biocompatible water-soluble nanocomposite based on hydrophobic
tetraphenylporphyrin (TPP) molecules and hydrophilic ternary AgInS$_2$/ZnS
quantum dots incorporated into a chitosan matrix as an improved photosensitizer
for a-PDT. We demonstrated that TPP molecules could be successfully transferred
into chitosan solution while remaining primarily in the form of monomers, which
are capable of singlet oxygen generation. We performed a detailed analysis of
the F\"orster resonance energy transfer (FRET) between quantum dots and TPP
molecules within the nanocomposite and proposed the mechanism of the singlet
oxygen efficiency enhancement via FRET.


  We examine the reliability of the theoretical methods presently used for
analysis and prediction of neutrino oscillation phenomena. Of particular
interest are the limitations imposed by branch points when expansions in one of
the small parameters of the Standard Neutrino Model (SNM) are made to obtain
tractable results. Our evaluation compares the approximate oscillation
probabilities for flavor-changing transitions in the $(\nu_e , \nu_\mu)$ sector
to exact results obtained from of a recently-developed exact analytical
representation of neutrino oscillations in matter within the SNM. From our
numerical comparisons, we are able to identify regions where the existing
approaches can be improved to take full advantage of the higher quality data
expected at future neutrino facilities.


  In this paper, we present a probabilistic self-balancing dictionary data
structure for massive data sets, and prove expected amortized I/O-optimal
bounds on the dictionary operations. We show how to use the structure as an
I/O-optimal priority queue. The data structure, which we call as the random
buffer tree, abstracts the properties of the random treap and the buffer tree
and has the same expected I/O-bounds as the buffer tree.


  Imaging companions to main-sequence stars often allows to detect a projected
orbital motion. MCMC has become very popular in for fitting their orbits. Some
of these companions appear to move on very eccentric, possibly unbound orbits.
This is the case for the exoplanet Fomalhaut b and the brown dwarf companion PZ
Tel B. For such orbits, standard MCMC codes assuming only bound orbits may be
inappropriate. We develop a new MCMC implementation able to handle bound and
unbound orbits as well in a continuous manner, and we apply it to the cases of
Fomalhaut b and PZ Tel B.
  This code is based on universal Keplerian variables and Stumpff functions
formalism. We present two versions of this code, the second one using a
different set of angular variables designed to avoid degeneracies arising when
the projected orbital motion is quasi-radial, as it is the case for PZ Tel B.
We also present additional observations of PZ Tel B.
  The code is applied to Fomalhaut b and PZ Tel B. Concerning Fomalhaut b, we
confirm previous results, but we show that open orbital solutions are also
possible. The eccentricity distribution nevertheless peaks around ~0.9 in the
bound regime. We present a first successful orbital fit of PZ Tel B, showing in
particular that the eccentricity distribution presents a sharp peak very close
to e=1, meaning a quasi-parabolic orbit.
  It was recently suggested that unseen inner companions may lead orbital
fitting algorithms to artificially give high eccentricities. We show that this
caveat is unlikely to apply to Fomalhaut b. Concerning PZ Tel B, an inner ~12
MJup companion would mimic a e=1 orbit despite a real eccentricity around 0.7,
but a dynamical analysis reveals that such a system would not be stable. We
conclude that our orbital fit is robust.


  In the study of discrete dynamical systems, we typically start with a
function from a space into itself, and ask questions about the properties of
sequences of iterates of the function. In this paper we reverse the direction
of this study. In particular, restricting to the complex plane, we start with a
sequence of complex numbers and study the functions (if any) for which this
sequence is an orbit under iteration. This gives rise to questions of existence
and of uniqueness. We resolve some questions, and show that these issues can be
quite delicate.


  Conducting pairwise comparisons is a widely used approach in curating human
perceptual preference data. Typically raters are instructed to make their
choices according to a specific set of rules that address certain dimensions of
image quality and aesthetics. The outcome of this process is a dataset of
sampled image pairs with their associated empirical preference probabilities.
Training a model on these pairwise preferences is a common deep learning
approach. However, optimizing by gradient descent through mini-batch learning
means that the "global" ranking of the images is not explicitly taken into
account. In other words, each step of the gradient descent relies only on a
limited number of pairwise comparisons. In this work, we demonstrate that
regularizing the pairwise empirical probabilities with aggregated rankwise
probabilities leads to a more reliable training loss. We show that training a
deep image quality assessment model with our rank-smoothed loss consistently
improves the accuracy of predicting human preferences.


  In this paper we examine the use of matching theory for user association in
millimeter wave (mmWave)-enabled cellular heterogeneous networks (HetNets). In
a mmWave system, the channel variations can be fast and unpredictable,
rendering centralized user association potentially inefficient. We propose an
efficient distributed matching algorithm, called early acceptance (EA),
tailored for user association in 5G HetNets. The effectiveness of the proposed
algorithm is assessed by comparing its performance with the well-known deferred
acceptance (DA) matching algorithm, in which user association is delayed until
the algorithm finishes. Numerical results show that the proposed EA matching
algorithm reaches a near-optimal solution when compared with a centralized user
association, and leads to a more power-efficient and much faster user
association process when compared to the distributed DA algorithm.


  The effect of Sr chemical pressure on superconductivity was investigated in
Hg2(Ba1-ySry)2YCu2O8-d. The samples were synthesized at high pressure-high
temperature from y = 0.0 to full substitution, y = 1.0. These Sr-substituted
compounds are superconducting, without Ca doping on the Y site, and show an
increasing Tc with Sr, reaching 42 K for y = 1.0. A detailed neutron powder
diffraction study compares the structural changes induced by this chemical
Sr/Ba substitution and the mechanical pressure effects in the Hg-2212 system.
It shows the strong decrease of the three Ba/Sr-O distances and consequently
the shrinkage of Cu-O1(in-plane) and Cu-O2(apical) bonds. These structural
changes, by affecting the charge transfer which occurs between the charge
reservoir and the superconducting block, are responsible of the Tc enhancement
with Sr content.


  In this paper we address the following question: Can we approximately sample
from a Bayesian posterior distribution if we are only allowed to touch a small
mini-batch of data-items for every sample we generate?. An algorithm based on
the Langevin equation with stochastic gradients (SGLD) was previously proposed
to solve this, but its mixing rate was slow. By leveraging the Bayesian Central
Limit Theorem, we extend the SGLD algorithm so that at high mixing rates it
will sample from a normal approximation of the posterior, while for slow mixing
rates it will mimic the behavior of SGLD with a pre-conditioner matrix. As a
bonus, the proposed algorithm is reminiscent of Fisher scoring (with stochastic
gradients) and as such an efficient optimizer during burn-in.