  This study present a comparison of lidar observations of midlatitude cirrus
clouds over the SIRTA observatory between 2002 and 2006 with multiple
tropopauses (MT) retrieved from radiosounding temperature profiles. The
temporal variability of MT properties (frequency, thickness) are discussed.
Results show a marked annual cycle, with MT frequency reaching its lowest point
in May (~18% occurrence of MT) and slowly rising to more than 40% in DJF. The
average thickness of the MT also follows an annual cycle, going from less than
1 km in spring to 1.5 km in late autumn. Comparison with lidar observations
show that cirrus clouds show a preference for being located close below the 1st
tropopause. When the cloud top is above the 1st tropopause (7% of
observations), in 20% of cases the cloud base is above it as well, resulting in
a cirrus cloud "sandwiched" between the two tropopauses. Compared to the
general distribution of cirrus, cross-tropopause cirrus show a higher frequency
of large optical depths, while inter-tropopause cirrus show almost exclusively
low optical depths (Tau < 0.03 in 90% of cases) typical of subvisible clouds.
Results suggest the occurrence of inter-tropopause cirrus clouds is correlated
with the frequency of multiple tropopauses.


  We present results from \textit{Chandra} X-ray observations and 325 MHz Giant
Metrewave Radio Telescope (GMRT) observations of the massive and X-ray luminous
cluster of galaxies Abell S1063. We report the detection of large-scale \lq\lq
excess brightness\rq\rq\ in the residual \textit{Chandra} X-ray surface
brightness map, which extends at least 2.7 Mpc towards the north-east from the
center of the cluster. We also present a high fidelity X-ray flux and
temperature map using \textit{Chandra} archival data of 122 ksec, which shows
the disturbed morphology in the cluster. The residual flux map shows the first
observational confirmation of the merging axis proposed by earlier simulation
by \citet{Gomez2012AJ....144...79G}. The average temperature within $R_{500}$
is $11.7 \pm 0.56$ keV, which makes AS1063 one of the hottest clusters in the
nearby Universe. The integrated radio flux density at 325 MHz is found to be
$62.0\pm6.3$ mJy. The integrated spectrum of the radio halo follows a power-law
with a spectral index $\alpha=-1.43\pm 0.13$. The radio halo is found to be
significantly under-luminous, which favored for both the hadronic as well as
the turbulent re-acceleration mechanism for its origin.


  Motivated by an increase of renewable energy sources we propose a distributed
optimal Load Frequency Control scheme achieving frequency regulation and
economic dispatch. Based on an energy function of the power network we derive
an incremental passivity property for a well known nonlinear structure
preserving network model, differentiating between generator and load buses.
Exploiting this property we design distributed controllers that adjust the
power generation. Notably, we explicitly include the turbine-governor dynamics
where first-order and the widely used second-order dynamics are analyzed in a
unifying way. Due to the non-passive nature of the second-order
turbine-governor dynamics, incorporating them is challenging and we develop a
suitable dissipation inequality for the interconnected generator and
turbine-governor. This allows us to include the generator side more
realistically in the stability analysis of optimal Load Frequency Control than
was previously possible.


  To foster the study of networks on an abstract level, we further study the
formalism of structured cospans. We define a topos of structured cospans and
establish its theory of rewriting. For the rewrite relation, we propose a
double categorical semantics to encode the compositionality of the structure
cospans. For an application, we generalize the inductive viewpoint of graph
rewriting to rewriting in a wider class of topoi.


  In this paper the quantum source coding theorem is obtained for a completely
ergodic source. This results extends Shannon's classical theorem as well as
Schumacher's quantum noiseless coding theorem for memoryless sources. The
control of the memory effects requires earlier results of Hiai and Petz on high
probability subspaces.


  The current PDF4LHC recommendation to estimate uncertainties due to parton
distribution functions (PDFs) in theoretical predictions for LHC processes
involves the combination of separate predictions computed using PDF sets from
different groups, each of which comprises a relatively large number of either
Hessian eigenvectors or Monte Carlo (MC) replicas. While many fixed-order and
parton shower programs allow the evaluation of PDF uncertainties for a single
PDF set at no additional CPU cost, this feature is not universal, and moreover
the a posteriori combination of the predictions using at least three different
PDF sets is still required. In this work, we present a strategy for the
statistical combination of individual PDF sets, based on the MC representation
of Hessian sets, followed by a compression algorithm for the reduction of the
number of MC replicas. We illustrate our strategy with the combination and
compression of the recent NNPDF3.0, CT14 and MMHT14 NNLO PDF sets. The
resulting Compressed Monte Carlo PDF (CMC-PDF) sets are validated at the level
of parton luminosities and LHC inclusive cross-sections and differential
distributions. We determine that around 100 replicas provide an adequate
representation of the probability distribution for the original combined PDF
set, suitable for general applications to LHC phenomenology.


  The main result of this paper that a martingale evolution can be chosen for
Libor such that all the Libor interest rates have a common market measure; the
drift is fixed such that each Libor has the martingale property. Libor is
described using a field theory model, and a common measure is seen to be emerge
naturally for such models. To elaborate how the martingale for the Libor
belongs to the general class of numeraire for the forward interest rates, two
other numeraire's are considered, namely the money market measure that makes
the evolution of the zero coupon bonds a martingale, and the forward measure
for which the forward bond price is a martingale. The price of an interest rate
cap is computed for all three numeraires, and is shown to be numeraire
invariant. Put-call parity is discussed in some detail and shown to emerge due
to some non-trivial properties of the numeraires. Some properties of swaps, and
their relation to caps and floors, are briefly discussed.


  Angiogenesis is a multiscale process by which a primary blood vessel issues
secondary vessel sprouts that reach regions lacking oxygen. Angiogenesis can be
a natural process of organ growth and development or a pathological induced by
a cancerous tumor. A mean field approximation for a stochastic model of
angiogenesis consists of partial differential equation (PDE) for the density of
active tip vessels. Addition of Gaussian and jump noise terms to this equation
produces a stochastic PDE that defines an infinite dimensional L\'evy process
and is the basis of a statistical theory of angiogenesis. The associated
functional equation has been solved and the invariant measure obtained. The
results are compared to a direct numerical simulation of the stochastic model
of angiogenesis and invariant measure multiplied by an exponentially decaying
factor. The results of this theory are compared to direct numerical simulations
of the underlying angiogenesis model. The invariant measure and the moments are
functions of the Korteweg-de Vries soliton which approximates the deterministic
density of active vessel tips.


  Recent efforts on combining deep models with probabilistic graphical models
are promising in providing flexible models that are also easy to interpret. We
propose a variational message-passing algorithm for variational inference in
such models. We make three contributions. First, we propose structured
inference networks that incorporate the structure of the graphical model in the
inference network of variational auto-encoders (VAE). Second, we establish
conditions under which such inference networks enable fast amortized inference
similar to VAE. Finally, we derive a variational message passing algorithm to
perform efficient natural-gradient inference while retaining the efficiency of
the amortized inference. By simultaneously enabling structured, amortized, and
natural-gradient inference for deep structured models, our method simplifies
and generalizes existing methods.


  We present analytical calculations, Finite Element Analysis modeling, and
physical measurements of the interstrip capacitances for different potential
strip geometries and dimensions of the readout boards for the GE2/1 triple-Gas
Electron Multiplier detector in the CMS muon system upgrade. The main goal of
the study is to find configurations that minimize the interstrip capacitances
and consequently maximize the signal-to-noise ratio for the detector. We find
agreement at the 1.5--4.8% level between the two methods of calculations and on
the average at the 17% level between calculations and measurements. A
configuration with halved strip lengths and doubled strip widths results in a
measured 27--29% reduction over the original configuration while leaving the
total number of strips unchanged. We have now adopted this design modification
for all eight module types of the GE2/1 detector and will produce the final
detector with this new strip design.


  Differentially Private Stochastic Gradient Descent (DP-SGD) forms a
fundamental building block in many applications for learning over sensitive
data. Two standard approaches, privacy amplification by subsampling, and
privacy amplification by shuffling, permit adding lower noise in DP-SGD than
via na\"{\i}ve schemes. A key assumption in both these approaches is that the
elements in the data set can be uniformly sampled, or be uniformly permuted --
constraints that may become prohibitive when the data is processed in a
decentralized or distributed fashion. In this paper, we focus on conducting
iterative methods like DP-SGD in the setting of federated learning (FL) wherein
the data is distributed among many devices (clients). Our main contribution is
the \emph{random check-in} distributed protocol, which crucially relies only on
randomized participation decisions made locally and independently by each
client. It has privacy/accuracy trade-offs similar to privacy amplification by
subsampling/shuffling. However, our method does not require server-initiated
communication, or even knowledge of the population size. To our knowledge, this
is the first privacy amplification tailored for a distributed learning
framework, and it may have broader applicability beyond FL. Along the way, we
extend privacy amplification by shuffling to incorporate $(\epsilon,\delta)$-DP
local randomizers, and exponentially improve its guarantees. In practical
regimes, this improvement allows for similar privacy and utility using data
from an order of magnitude fewer users.


  The intensity of the Cosmic UV background (UVB), coming from all sources of
ionising photons such as star-forming galaxies and quasars, determines the
thermal evolution and ionization state of the intergalactic medium (IGM) and
is, therefore, a critical ingredient for models of cosmic structure formation.
Most of the previous estimates are based on the comparison between observed and
simulated Lyman-$\alpha$ forest. We present the results of an independent
method to constrain the product of the UVB photoionisation rate and the
covering fraction of Lyman limit systems (LLSs) by searching for the
fluorescent Lyman-$\alpha$ emission produced by self-shielded clouds. Because
the expected surface brightness is well below current sensitivity limits for
direct imaging, we developed a new method based on three-dimensional stacking
of the IGM around Lyman-$\alpha$ emitting galaxies (LAEs) between 2.9<z<6.6
using deep MUSE observations. Combining our results with covering fractions of
LLSs obtained from mock cubes extracted from the EAGLE simulation, we obtain
new and independent constraints on the UVB at z>3 that are consistent with
previous measurements, with a preference for relatively low UVB intensities at
z=3, and which suggest a non-monotonic decrease of $\Gamma$HI with increasing
redshift between 3<z<5. This could suggest a possible tension between some UVB
models and current observations which however require deeper and wider
observations in Lyman-$\alpha$ emission and absorption to be confirmed.
Assuming instead a value of UVB from current models, our results constrain the
covering fraction of LLSs at 3<z<4.5 to be less than 25% within 150kpc from
LAEs.


  Let $C$ be a nonsingular projective curve over an algebraically closed field
of characteristic $p>0$ and $I\subset C$ be a finite set. If
$\mathcal{U}_{C,\,\omega}$ denotes the moduli space of semistable parabolic
bundles of rank $r$ and degree $d$ on $C$ with parabolic structures determined
by $\omega=(k,\{\vec n(x),\vec a(x)\}_{x\in I})$, we prove that
$\mathcal{U}_{C,\,\omega}$ is \textit{$F$-split} for generic $C$ and generic
choice of $I$ when $p>3r$.


  Automated news generation has become a major interest for new agencies in the
past. Oftentimes headlines for such automatically generated news articles are
unimaginative as they have been generated with ready-made templates. We present
a computationally creative approach for headline generation that can generate
humorous versions of existing headlines. We evaluate our system with human
judges and compare the results to human authored humorous titles. The headlines
produced by the system are considered funny 36\% of the time by human
evaluators.


  Two Kahler manifolds are called relatives if they admit a common Kahler
submanifold with the same induced metrics. In this paper, we show that a
Hartogs domain over an irreducible bounded symmetric domain equipped with the
Bergman metric is not a relative to the complex Euclidean space. This
generalizes the results in [5, 4] and the novelty here is that the Bergman
kernel of the Hartogs domain is not necessarily Nash algebraic.


  We explore the phase diagram of interacting spin-$1/2$ systems in the
presence of anisotropic interactions, spontaneous decay and driving. We find a
rich phase diagram featuring a limit cycle phase in which the magnetization
oscillates in time. We analyze the spatio-temporal fluctuations of this limit
cycle phase at the Gaussian level, and show that spatial fluctuations lead to
quasi-long-range limit cycle ordering for dimension $d = 2$. This result can be
interpreted in terms of a spatio-temporal Goldstone mode corresponding to phase
fluctuations of the limit cycle. We also demonstrate that the limit-cycle phase
exhibits an asymmetric power spectrum measurable in fluorescence experiments.


  Let $(M^m,g)$ be an $m$-dimensional closed Riemannian manifold with
non-negative sectional curvatures, $m\ge 3$. We define a conformal invariant
and prove that, if the conformal invariant is bounded from above by a constant
depending only on $m$, then there are no closed $n$-dimensional stable minimal
submanifolds in $M$ for all $\xi(m)\le n\le m-2$, where $\xi(m)=1$ when $3\le
m\le 5$ and $\xi(m)=2$ when $m\ge 6$. In particular, a conformal $m$-sphere
with non-negative sectional curvatures does not admit any closed
$n$-dimensional stable minimal submanifold for all $\xi(m)\le n\le m-2$.


  We consider a network of agents that are cooperatively solving a global
optimization problem, where the objective function is the sum of privately
known local objective functions of the agents and the decision variables are
coupled via linear constraints. Recent literature focused on special cases of
this formulation and studied their distributed solution through either
subgradient based methods with O(1/sqrt(k)) rate of convergence (where k is the
iteration number) or Alternating Direction Method of Multipliers (ADMM) based
methods, which require a synchronous implementation and a globally known order
on the agents. In this paper, we present a novel asynchronous ADMM based
distributed method for the general formulation and show that it converges at
the rate O(1/k).


  Lock-in feedback circuits are routinely used in physics laboratories all
around the world to extract small signals out of a noisy environment. In a
recent paper (M. Kaptein, R. van Emden, and D. Iannuzzi, paper under review),
we have shown that one can adapt the algorithm exploited in those circuits to
gain insight in behavioral economics. In this paper, we extend this concept to
a very subjective socio-philosophical concept: the concept of beauty. We run an
experiment on 7414 volunteers, asking them to express their opinion on the
physical features of an avatar. Each participant was prompted with an image
whose features were adjusted sequentially via a lock-in feedback algorithm
driven by the opinion expressed by the previous participants. Our results show
that the method allows one to identify the most attractive features of the
avatar.


  BVI photometry of the Magellanic Clouds collected during the OGLE-II
microlensing experiment makes it possible to study in detail photometric
properties of the "major" stellar distance indicators in the Magellanic Clouds.
In addition to Cepheids, RR Lyr and red clump stars, which photometry was
presented in the earlier OGLE papers, we present the so far most accurate
determination of brightness of the tip of the red giant branch in the LMC and
SMC. We analyze the ratios of brightness of all four distance indicators in the
LMC and SMC. Additionally, we include in our analysis, when possible,
photometric data of the distance indicators in the metal poor Carina dwarf
galaxy for which photometry was also collected during the OGLE-II experiment.
The analysis is largely differential, free from zero point and extinction
uncertainties. The main conclusion is that the distance scales resulting from
all four stellar distance indicators are fully consistent with each other.
Therefore the distance scale problem is not a problem resulting from different
distances yielded by distance indicators, but rather a problem of the proper
zero point of this common distance scale. All four stellar distance indicators
have to be treated as an ensemble, and any determination of the zero point for
one of them must predict reasonable luminosities of the others. This puts
strong constraints on many proposed calibrations of distance indicators. For
example, very bright calibrations of Cepheids or RR Lyr can practically be
ruled out. (Abridged).


  We describe a "neutrinogenesis" mechanism whereby, in the presence of
right-handed neutrinos with sufficiently small pure Dirac masses,
(B+L)-violating sphaleron processes create the baryon asymmetry of the
Universe, even when B=L=0 initially. It is shown that the resulting neutrino
mass constraints are easily fulfilled by the neutrino masses suggested by
current experiments. We present a simple toy model which uses this mechanism to
produce the observed baryon asymmetry of the Universe. (PostScript Errors
corrected in latest Version).


  Text-to-image (TTI) generative models can be used to generate photorealistic
images from a given text-string input. These models offer great potential to
mitigate challenges to the uptake of machine learning in the earth sciences.
However, the rapid increase in their use has raised questions about fairness
and biases, with most research to-date focusing on social and cultural areas
rather than domain-specific considerations. We conducted a case study for the
earth sciences, focusing on the field of fluvial geomorphology, where we
evaluated subject-area specific biases in the training data and downstream
model performance of Stable Diffusion (v1.5). In addition to perpetuating
Western biases, we found that the training data over-represented scenic
locations, such as famous rivers and waterfalls, and showed serious under- and
over-representation of many morphological and environmental terms. Despite
biased training data, we found that with careful prompting, the Stable
Diffusion model was able to generate photorealistic synthetic river images
reproducing many important environmental and morphological characteristics.
Furthermore, conditional control techniques, such as the use of condition maps
with ControlNet were effective for providing additional constraints on output
images. Despite great potential for the use of TTI models in the earth sciences
field, we advocate for caution in sensitive applications, and advocate for
domain-specific reviews of training data and image generation biases to
mitigate perpetuation of existing biases.


  (abridged) We study the consequence of star formation (SF) in an self-gravity
dominated accretion disk in quasars. The warm skins of the SF disk are governed
by the radiation from the inner part of the accretion disk to form Compton
atmosphere (CAS). The CAS are undergoing four phases to form broad line
regions. Phase I is the duration of pure accumulation supplied by the SF disk.
During phase II clouds begin to form due to line cooling and sink to the SF
disk. Phase III is a period of preventing clouds from sinking to the SF disk
through dynamic interaction between clouds and the CAS. Finally, phase IV is an
inevitable collapse of the entire CAS through line cooling. This CAS evolution
drives the episodic appearance of BLRs. Geometry and dynamics of BLRs can be
self-consistently derived from the thermal instability of the CAS during phases
II and III by linear analysis. The metallicity gradient of SF disk gives rise
to different properties of clouds from outer to inner part of BLRs. We find
that clouds have column density N_H < 10^22cm^{-2} in the metal-rich regions
whereas they have N_H > 10^22 cm^{-2} in the metal-poor regions. The metal-rich
clouds compose the high ionization line (HIL) regions whereas the metal-poor
clouds are in low ionization line (LIL) regions. Metal-rich clouds in HIL
regions will be blown away by radiation pressure, forming the observed
outflows. The LIL regions are episodic due to the mass cycle of clouds with the
CAS in response to continuous injection by the SF disk, giving rise to
different types of AGNs. Based on SDSS quasar spectra, we identify a spectral
sequence in light of emission line equivalent width from Phase I to IV. A key
phase in the episodic appearance of the BLRs is bright type II AGNs with no or
only weak BLRs. We discuss observational implications and tests of the
theoretical predictions of this model.


  Two solutions of stringy gravity in three and four dimensions which admit
interpretation as a black hole and a black string, respectively, are discussed.
It is demonstrated that they are exact WZWN nonlinear sigma models to all
orders in the inverse string tension, and hence represent exact conformal field
theories on the world-sheet. Furthermore, since the dilaton for these two
solutions is constant, they also solve the equations of motion of standard GR
with a minimally coupled three form field strength. (Based on a talk presented
at the Conference on Quantum Aspects of Black Holes, U. of California, Santa
Barbara CA June 21-27 '93, and a poster presented at 5th Canadian Conference on
General Relativity and Relativistic Astrophysics, Waterloo, Ont., May 13-15,
1993.)


  We report on the properties of 89 low mass star clusters located in the
vicinity of luminous young clusters (blue globulars) in the disk of M31. 82 of
the clusters are newly detected. We have determined their integrated magnitudes
and colors, based on a series of Hubble Space Telescope Wide Field/Planetary
Camera 2 exposures in blue and red (HST filters F450W and F814W). The
integrated apparent magnitudes range from F450W = 17.5 to 22.5, and the colors
indicate a wide range of ages. Stellar color-magnitude diagrams for all
clusters were obtained and those with bright enough stars were fit to
theoretical isochrones to provide age estimates. The ages range from 12 Myr to
>500 Myr. Reddenings, which average E(F450 - F814) = 0.59 with a dispersion of
0.21 magnitudes, were derived from the main sequence fitting for those
clusters. Comparison of these ages and integrated colors with single population
theoretical models with solar abundances suggests a color offset of 0.085
magnitudes at the ages tested. Estimated ages for the remaining clusters are
based on their measured colors. The age-frequency diagram shows a steep decline
of number with age, with a large decrease in number per age interval between
the youngest and the oldest clusters detected.


  We investigate the effect of friction on domain formation in disoriented
chiral condensate. Including a friction term, we solve the equation of motion
of the linear sigma model fields, in the Hartree approximation. With
boost-invariance and cylinderical symmetry, irrespective of friction, on
average, we donot find any indication of domain like formation with quenched
initial condition. However, with or without friction, some events can be found
with large instabilities, indicating possible DCC domain formation in those
events. With friction time scale during which instabilities grows increases.
Correspondingly, with friction, it is possible to obtain large sized domains in
some particular events.


  Nanoporous supercapacitors play an important role in modern energy storage
systems, and their modeling is essential to predict and optimize the charging
behaviour. Two classes of models have been developed that consist of finite and
infinitely long pores. Here, we show that although both types of models predict
qualitatively consistent results, there are important differences emerging due
to the finite pore length. In particular, we find that the ion density inside a
finite pore is not constant but increases linearly from the pore entrance to
the pore end, where the ions form a strongly layered structure. This hinders a
direct quantitative comparison between the two models. In addition, we show
that although the ion density between the electrodes changes appreciably with
the applied potential, this change has a minor effect on charging. Our
simulations also reveal a complex charging behaviour, which is
adsorption-driven at high voltages, but it is dominated either by co-ion
desorption or by adsorption of both types of ions at low voltages, depending on
the ion concentration.


  We propose in this article an adaptation of the basic techniques of the
deterministic network calculus theory to the road traffic flow theory. Network
calculus is a theory based on min-plus algebra. It uses algebraic techniques to
compute performance bounds in communication networks, such as maximum
end-to-end delays and backlogs. The objective of this article is to investigate
the application of such techniques for determining performance bounds on road
networks, such as maximum bounds on travel times. The main difficulty to apply
the network calculus theory on road networks is the modeling of interaction of
cars inside one road, or more precisely the congestion phase. We propose a
traffic model for a single-lane road without passing, which is compatible with
the network calculus theory. The model permits to derive a maximum bound of the
travel time of cars through the road. Then, basing on that model, we explain
how to extend the approach to model intersections and large-scale networks.


  It is well known that reinforcement learning can be cast as inference in an
appropriate probabilistic model. However, this commonly involves introducing a
distribution over agent trajectories with probabilities proportional to
exponentiated rewards. In this work, we formulate reinforcement learning as
Bayesian inference without resorting to rewards, and show that rewards are
derived from agent's preferences, rather than the other way around. We argue
that agent preferences should be specified stochastically rather than
deterministically. Reinforcement learning via inference with stochastic
preferences naturally describes agent behaviors, does not require introducing
rewards and exponential weighing of trajectories, and allows to reason about
agents using the solid foundation of Bayesian statistics. Stochastic
conditioning, a probabilistic programming paradigm for conditioning models on
distributions rather than values, is the formalism behind agents with
probabilistic preferences. We demonstrate realization of our approach on case
studies using both a two-agent coordinate game and a single agent acting in a
noisy environment, showing that despite superficial differences, both cases can
be modeled and reasoned about based on the same principles.


  A brief review is given of recent developments related to the Higgs signal
and its implications for supersymmetry in the supergravity grand unification
framework. The Higgs data indicates that the allowed parameter space largely
lies on focal curves and focal surfaces of the Hyperbolic Branch of radiative
breaking of the electroweak symmetry where TeV size scalars naturally arise.
The high mass of the Higgs leads to a more precise prediction for the allowed
range of the spin independent neutralino -proton cross section which is
encouraging for the detection of dark matter in future experiments with greater
sensitivity. Also discussed is the status of grand unification and a natural
solution to breaking the GUT group at one scale and resolving the
doublet-triplet problem. It is shown that the cosmic coincidence can be
compatible within a supersymmetric framework in a muticomponent dark matter
picture with one component charged under $B-L$ while the other component is the
conventional supersymmetric dark matter candidate, the neutralino.


  Location-based services (LBSs) have become widely popular. Despite their
utility, these services raise concerns for privacy since they require sharing
location information with untrusted third parties. In this work, we study
privacy-utility trade-off in location sharing mechanisms. Existing approaches
are mainly focused on privacy of sharing a single location or myopic location
trace privacy; neither of them taking into account the temporal correlations
between the past and current locations. Although these methods preserve the
privacy for the current time, they may leak significant amount of information
at the trace level as the adversary can exploit temporal correlations in a
trace. We propose an information theoretically optimal privacy preserving
location release mechanism that takes temporal correlations into account. We
measure the privacy leakage by the mutual information between the user's true
and released location traces. To tackle the history-dependent mutual
information minimization, we reformulate the problem as a Markov decision
process (MDP), and solve it using asynchronous actor-critic deep reinforcement
learning (RL).


  Recognition and binding of specific sites on DNA by proteins is central for
many cellular functions such as transcription, replication, and recombination.
In the process of recognition, a protein rapidly searches for its specific site
on a long DNA molecule and then strongly binds this site. Here we aim to find a
mechanism that can provide both a fast search (1-10 sec) and high stability of
the specific protein-DNA complex ($K_d=10^{-15}-10^{-8}$ M).
  Earlier studies have suggested that rapid search involves the sliding of a
protein along the DNA. Here we consider sliding as a one-dimensional (1D)
diffusion in a sequence-dependent rough energy landscape. We demonstrate that,
in spite of the landscape's roughness, rapid search can be achieved if 1D
sliding is accompanied by 3D diffusion. We estimate the range of the specific
and non-specific DNA-binding energy required for rapid search and suggest
experiments that can test our mechanism. We show that optimal search requires a
protein to spend half of time sliding along the DNA and half diffusing in 3D.
We also establish that, paradoxically, realistic energy functions cannot
provide both rapid search and strong binding of a rigid protein. To reconcile
these two fundamental requirements we propose a search-and-fold mechanism that
involves the coupling of protein binding and partial protein folding.
  Proposed mechanism has several important biological implications for search
in the presence of other proteins and nucleosomes, simultaneous search by
several proteins etc. Proposed mechanism also provides a new framework for
interpretation of experimental and structural data on protein-DNA interactions.


  We investigate the differential calculus defined by Ashtekar and Lewandowski
on projective limits of manifolds by means of cylindrical smooth functions and
compare it with the C^infty calculus proposed by Froehlicher and Kriegl in more
general context. For products of connected manifolds, a Boman theorem is
proved, showing the equivalence of the two calculi in this particular case.
Several examples of projective limits of manifolds are discussed, arising in
String Theory and in loop quantization of Gauge Theories.


  Deep regression is an important problem with numerous applications. These
range from computer vision tasks such as age estimation from photographs, to
medical tasks such as ejection fraction estimation from echocardiograms for
disease tracking. Semi-supervised approaches for deep regression are notably
under-explored compared to classification and segmentation tasks, however.
Unlike classification tasks, which rely on thresholding functions for
generating class pseudo-labels, regression tasks use real number target
predictions directly as pseudo-labels, making them more sensitive to prediction
quality. In this work, we propose a novel approach to semi-supervised
regression, namely Uncertainty-Consistent Variational Model Ensembling (UCVME),
which improves training by generating high-quality pseudo-labels and
uncertainty estimates for heteroscedastic regression. Given that aleatoric
uncertainty is only dependent on input data by definition and should be equal
for the same inputs, we present a novel uncertainty consistency loss for
co-trained models. Our consistency loss significantly improves uncertainty
estimates and allows higher quality pseudo-labels to be assigned greater
importance under heteroscedastic regression. Furthermore, we introduce a novel
variational model ensembling approach to reduce prediction noise and generate
more robust pseudo-labels. We analytically show our method generates higher
quality targets for unlabeled data and further improves training. Experiments
show that our method outperforms state-of-the-art alternatives on different
tasks and can be competitive with supervised methods that use full labels. Our
code is available at https://github.com/xmed-lab/UCVME.


  For the problem of nonparametric detection of signal in Gaussian white noise
we point out strong asymptotically minimax tests. The sets of alternatives are
a ball in Besov space $B^r_{2\infty}$ with "small" balls in $L_2$ removed.


  We derive a general form of first-order flow equations for extremal and
non-extremal, static, spherically symmetric black holes in theories with
massless scalars and vectors coupled to gravity. By rewriting the action as a
sum of squares a la Bogomol'nyi, we identify the function governing the
first-order gradient flow, the `generalised superpotential', which reduces to
the `fake superpotential' for non-supersymmetric extremal black holes and to
the central charge for supersymmetric black holes. For theories whose scalar
manifold is a symmetric space after a timelike dimensional reduction, we
present the condition for the existence of a generalised superpotential. We
provide examples to illustrate the formalism in four and five spacetime
dimensions.


  We tell the story of the discovery of an interesting bound on finite sums and
its application to comparison of Ising models.


  Organelles of optimum size are crucial for proper functioning of a living
cell. The cell employs various mechanisms for actively sensing and controlling
the size of its organelles. Recently Bauer et al have opened a new research
frontier in the field of subcellular size control by shedding light on the
noise and fluctuations of organelles of controlled size. Taking eukaryotic
flagellum as a model organelle, which is quite popular for such studies because
of its linear geometry and dynamic nature, Bauer et al have analysed the nature
of fluctuations of its length. Here we summarize the key questions and the
fundamental importance of the recent developments. Although our attention is
focussed here mainly on the experimental and theoretical works on eukaryotic
flagellum, the ideas are general and applicable to wide varieties of cell
organelle.


  We study P-groupoids that arise from certain decompositions of complete
graphs. We show that left distributive P-groupoids are distributive,
quasigroups. We characterize P-groupoids when the corresponding decomposition
is a Hamiltonian decomposition for complete graphs of odd, prime order. We also
study a specific example of a P-quasigroup constructed from cyclic groups of
odd order. We show such P-quasigroups have characteristic left and right
multiplication groups, as well as the right multiplication group is isomorphic
to the dihedral group.


  This is the eighth in a series of papers reporting on a large reverberation
mapping campaign to measure black hole (BH) mass in high accretion rate active
galactic nuclei (AGNs). We employ the recently developed dynamical modeling
approach for broad-line regions (BLRs) based on the method of Pancoast et al.
to analyze the reverberation mapping dataset of Mrk 142 observed in the first
monitoring season. In this approach, continuum variations are reconstructed
using a damped random walk process, and BLR structure is delineated using a
flexible disk-like geometry, in which BLR clouds move around the central BH
with Keplerian orbits or inflow/outflow motion. The approach also includes the
possibilities of anisotropic emission of BLR clouds, non-linear response of the
line emission to the continuum, and different long-term trends in the continuum
and emission-line variations. We implement the approach in a Bayesian framework
that is apt for parallel computation and use a Markov Chain Monte Carlo
technique to recover the parameters and uncertainties for the modeling,
including mass of the central BH. We apply three BLR models with different
prescriptions of BLR clouds distributions and find that the best model for
fitting the data of Mrk 142 is a two-zone BLR model, consistent with the
theoretical BLR model surrounding slim accretion disks. The best model yields a
BH mass of $\log (M_\bullet/M_\odot)=6.23_{-0.45}^{+0.26}$, resulting in a
virial factor of $\log f=-0.36_{-0.54}^{+0.33}$ for the full width at half
maximum of the H$\beta$ line measured from the mean spectrum. The virial
factors for the other measures of the H$\beta$ line width are also presented.


  We suggest to use the action-angle variables for the study of properties of
(quasi)particles in quantum rings. For this purpose we present the action-angle
variables for three two-dimensional singular oscillator systems. The first one
is the usual (Euclidean) singular oscillator, which plays the role of the
confinement potential for the quantum ring. We also propose two singular
spherical oscillator models for the role of the confinement system for the
spherical ring. The first one is based on the standard Higgs oscillator
potential. We show that, in spite of the presence of a hidden symmetry, it is
not convenient for the study of the system's behaviour in a magnetic field. The
second model is based on the so-called CP(1) oscillator potential and respects
the inclusion of a constant magnetic field.


  Machine learning models are being increasingly deployed to take, or assist in
taking, complicated and high-impact decisions, from quasi-autonomous vehicles
to clinical decision support systems. This poses challenges, particularly when
models have hard-to-detect failure modes and are able to take actions without
oversight. In order to handle this challenge, we propose a method for a
collaborative system that remains safe by having a human ultimately making
decisions, while giving the model the best opportunity to convince and debate
them with interpretable explanations. However, the most helpful explanation
varies among individuals and may be inconsistent across stated preferences. To
this end we develop an algorithm, Ardent, to efficiently learn a ranking
through interaction and best assist humans complete a task. By utilising a
collaborative approach, we can ensure safety and improve performance while
addressing transparency and accountability concerns. Ardent enables efficient
and effective decision-making by adapting to individual preferences for
explanations, which we validate through extensive simulations alongside a user
study involving a challenging image classification task, demonstrating
consistent improvement over competing systems.


  Even though the existence of intermediate-mass black holes (IMBHs, black
holes with masses ranging between $10^{2-4}\,M_{\odot}$) has not yet been
corroborated observationally, these objects are of high interest for
astrophysics. Our understanding of the formation and evolution of supermassive
black holes (SMBHs), as well as galaxy evolution modeling and cosmography would
dramatically change if an IMBH were to be observed. From a point of view of
traditional photon-based astronomy, {which relies on the monitoring of
innermost stellar kinematics}, the {\em direct} detection of an IMBH seems to
be rather far in the future. However, the prospect of the detection and
characterization of an IMBH has good chances in lower-frequency
gravitational-wave (GW) astrophysics using ground-based detectors such as LIGO,
Virgo and the future Einstein Telescope (ET). We present an analysis of the
signal of a system of a binary of IMBHs (BBH from now onwards) based on a
waveform model obtained with numerical relativity simulations coupled with
post-Newtonian calculations at the highest available order. IMBH binaries with
total masses between $200-20000\,M_\odot$ would produce significant
signal-to-noise ratios (SNRs) in advanced LIGO and Virgo and the ET. We have
computed the expected event rate of IMBH binary coalescences for different
configurations of the binary, finding interesting values that depend on the
spin of the IMBHs. The prospects for IMBH detection and characterization with
ground-based GW observatories would not only provide us with a robust test of
general relativity, but would also corroborate the existence of these systems.
Such detections should allow astrophysicists to probe the stellar environments
of IMBHs and their formation processes.


  In this paper, we consider the downlink in a K-tier heterogeneous network in
the presence of Nakagami-m fading and noise. For such a system, we derive
closed-form approximations of coverage probability and average rate achievable.
A piece-wise linear approximation is employed in obtaining the simplified
expressions. The proposed results are verified numerically through simulations.
A comparison with existing work shows that the proposed work is a good
approximation.


  This paper is a further development of the approach to weak cosmic censorship
proposed by the authors in Ref. 5. We state and prove a modified version of
that work's main result under significantly relaxed assumptions on the
asymptotic structure of space--time. The result, which imposes strong
constraints on the occurrence of naked singularities of the strong curvature
type, is in particular applicable to physically realistic cosmological models.


  Let $G$ be a simple graph that is properly edge coloured with $m$ colours and
let $\M=\{M_1,\ldots, M_m\}$ be the set of $m$ matchings induced by the colours
in $G$. Suppose that $m\le n-n^{c}$, where $c>9/10$, and every matching in $\M$
has size $n$. Then $G$ contains a full rainbow matching, i.e.\ a matching that
contains exactly one edge from $M_i$ for each $1\le i\le m$. This answers an
open problem of Pokrovskiy and gives an affirmative answer to a generalisation
of a special case of a conjecture of Aharoni and Berger.
  Related results are also found for multigraphs with edges of bounded
multiplicity, and for hypergraphs.
  Finally, we provide counterexamples to several conjectures on full rainbow
matchings made by Aharoni and Berger.


  We study models in which supersymmetry breaking appears at an intermediate
scale, M_in, below the GUT scale. That is, that the soft supersymmetry-breaking
parameters of the MSSM are universal at M_in. We demand that the lightest
neutralino be the LSP, and that the relic neutralino density not conflict with
measurements by WMAP and others, and study the morphology of this constraint as
the universality scale is reduced from the GUT scale. At moderate values of
M_in, we find that the allowed regions of the (m_1/2,m_0) plane are squeezed by
the requirements of electroweak symmetry breaking and that the lightest
neutralino be the LSP, whereas the constraint on the relic density is less
severe. At very low M_in, the electroweak vacuum conditions become the dominant
constraint, and a secondary source of astrophysical cold dark matter would be
necessary to explain the measured relic density for nearly all values of the
soft SUSY-breaking parameters and tan(beta).


  We consider the problem of minimizing a sum of clipped convex functions;
applications include clipped empirical risk minimization and clipped control.
While the problem of minimizing the sum of clipped convex functions is NP-hard,
we present some heuristics for approximately solving instances of these
problems. These heuristics can be used to find good, if not global, solutions
and appear to work well in practice. We also describe an alternative
formulation, based on the perspective transformation, which makes the problem
amenable to mixed-integer convex programming and yields computationally
tractable lower bounds. We illustrate one of our heuristic methods by applying
it to various examples and use the perspective transformation to certify that
the solutions are relatively close to the global optimum. This paper is
accompanied by an open-source implementation.


  Geo-tagged tweets can potentially help with sensing the interaction of people
with their surrounding environment. Based on this hypothesis, this paper makes
use of geotagged tweets in order to ascertain various land uses with a broader
goal to help with urban/city planning. The proposed method utilises supervised
learning to reveal spatial land use within cities with the help of Twitter
activity signatures. Specifically, the technique involves using tweets from
three cities of Australia namely Brisbane, Melbourne and Sydney. Analytical
results are checked against the zoning data provided by respective city
councils and a good match is observed between the predicted land use and
existing land zoning by the city councils. We show that geo-tagged tweets
contain features that can be useful for land use identification.


  One of the primary goals when studying galaxy formation is to understand how
the luminous component of the Universe, galaxies, relates to the growth of
structure which is dominated by the gravitational collapse of dark matter
haloes. The stellar-to-halo mass relation probes how galaxies occupy dark
matter haloes and what that entails for their star formation history. We
deliver the first self-consistent empirical model that can place constraints on
the stellar-to-halo mass relation down to log stellar mass
$\log_{10}(m^*/\mathrm{M}_{\odot}) \leq 5.0$ by fitting our model directly to
Local Group dwarf data. This is accomplished by penalising galaxy growth in
late-forming, low-mass haloes by mimicking the effects of reionization. This
process serves to regulate the number density of galaxies by altering the
scatter in halo peak mass $M^{\mathrm{peak}}_{h}$ at fixed stellar mass,
creating a tighter scatter than would otherwise exist without a high-$z$
quenching mechanism. Our results indicate that the previously established
double-power law stellar-to-halo mass relation can be extended to include
galaxies with
$\log_{10}(M^{\mathrm{peak}}_{\mathrm{h}}/\mathrm{M}_{\odot})\gtrsim 10.0$.
Furthermore, we show that haloes with
$\log_{10}(M^{\mathrm{peak}}_{\mathrm{h}}/\mathrm{M}_{\odot})\lesssim 9.3$ by
$z=4$ are unlikely to host a galaxy with $\log_{10}(m^*/\mathrm{M}_{\odot}) >
5.0$.


  We study adaptive importance sampling (AIS) as an online learning problem and
argue for the importance of the trade-off between exploration and exploitation
in this adaptation. Borrowing ideas from the bandits literature, we propose
Daisee, a partition-based AIS algorithm. We further introduce a notion of
regret for AIS and show that Daisee has $\mathcal{O}(\sqrt{T}(\log
T)^{\frac{3}{4}})$ cumulative pseudo-regret, where $T$ is the number of
iterations. We then extend Daisee to adaptively learn a hierarchical
partitioning of the sample space for more efficient sampling and confirm the
performance of both algorithms empirically.


  We present Python Quasi-classical atom-molecule scattering (PyQCAMS), a new
Python package for atom-molecule scattering within the quasi-classical
trajectory approach. The input consists of mass, collision energy, impact
parameter, and pair-wise interactions to choose between Buckingham, generalized
Lennard-Jones, and Morse potentials. As the output, the code provides the
vibrational quenching, dissociation, and reactive cross sections along with the
rovibrational energy distribution of the reaction products. Furthermore, we
treat H$_2$ + Ca $\rightarrow$ CaH + H reactions as a prototypical example to
illustrate the properties and performance of the software. Finally, we study
the parallelization performance of the code by looking into the time per
trajectory as a function of the number of CPUs used.


  We consider systems of multiple Brownian particles in one dimension that
repel mutually via a logarithmic potential on the real line, more specifically
the Dyson model. These systems are characterized by a parameter that controls
the strength of the interaction, $k>0$. In spite of being a one-dimensional
system, this system is interesting due to the properties that arise from the
long-range interaction between particles. It is a well-known fact that when $k$
is small enough, particle collisions occur almost surely, while when $k$ is
large, collisions never occur. However, aside from this fact there was no
characterization of the collision times until now. In this paper, we derive the
fractal (Hausdorff) dimension of the set of collision times by generalizing
techniques introduced by Liu and Xiao to study the return times to the origin
of self-similar stochastic processes. In our case, we consider the return times
to configurations where at least one collision occurs, which is a condition
that defines unbounded sets, as opposed to a single point, namely, the origin.
We find that the fractal dimension characterizes the collision behavior of
these systems, and establishes a clear delimitation between the colliding and
the non-colliding regions in a way similar to that of an order parameter.


  Idiopathic pulmonary fibrosis (IPF) is a restrictive interstitial lung
disease that causes lung function decline by lung tissue scarring. Although
lung function decline is assessed by the forced vital capacity (FVC),
determining the accurate progression of IPF remains a challenge. To address
this challenge, we proposed Fibro-CoSANet, a novel end-to-end multi-modal
learning-based approach, to predict the FVC decline. Fibro-CoSANet utilized CT
images and demographic information in convolutional neural network frameworks
with a stacked attention layer. Extensive experiments on the OSIC Pulmonary
Fibrosis Progression Dataset demonstrated the superiority of our proposed
Fibro-CoSANet by achieving the new state-of-the-art modified Laplace
Log-Likelihood score of -6.68. This network may benefit research areas
concerned with designing networks to improve the prognostic accuracy of IPF.
The source-code for Fibro-CoSANet is available at:
\url{https://github.com/zabir-nabil/Fibro-CoSANet}.


  We investigate the free energy and the penetration depth of a
quasi-two-dimensional d-wave superconductor in the presence of a weak magnetic
field by taking account of thermal, nonlocal and nonlinear effects. In an
approximation in which the superfluid velocity $v_s$ is assumed to be slowly
varying, the free energy is calculated and compared with available results in
several limiting cases. It is shown that either nonlocal or nonlinear effects
may cut off the linear-$T$ dependence of both the free energy and the
penetration depth in all the experimental geometries. At extremely low $T$, the
nonlocal effects will also generically modify the linear $H$ dependence of the
penetration depth ("nonlinear Meissner effect") in most experimental
geometries, but for supercurrents oriented along the nodal directions, the
effect may be recovered. We compare our predictions with existing experiments
on the cuprate superconductors.


  The generalized Korteweg-de Vries equations are a class of Hamiltonian
systems in infinite dimension derived from the KdV equation where the quadratic
term is replaced by a higher order power term. These equations have two
conservation laws in the energy space H^1(L^2 norm and energy). We consider in
this paper the {\it critical} generalized KdV equation, which corresponds to
the smallest power of the nonlinearity such that the two conservation laws do
not imply a bound in H^1 uniform in time for all H^1 solutions (and thus global
existence).
  From [15], there do exist for this equation solutions u(t) such that
|u(t)|_{H^1} \to +\infty as T\uparrow T, where T\le +\infty (we call them
blow-up solutions). The question is to describe, in a qualitative way, how blow
up occurs.
  For solutions with L^2 mass close to the minimal mass allowing blow up and
with decay in L^2 at the right, we prove after rescaling and translation which
leave invariant the L^2 norm that the solution converges to a {\it universal}
profile locally in space at the blow-up time T. From the nature of this
profile, we improve the standard lower bound on the blow-up rate for finite
time blow-up solutions.


  `Categorification' is the process of replacing equations by isomorphisms. We
describe some of the ways a thoroughgoing emphasis on categorification can
simplify and unify mathematics. We begin with elementary arithmetic, where the
category of finite sets serves as a categorified version of the set of natural
numbers, with disjoint union and Cartesian product playing the role of addition
and multiplication. We sketch how categorifying the integers leads naturally to
the infinite loop space Omega^infinity S^infinity, and how categorifying the
positive rationals leads naturally to a notion of the `homotopy cardinality' of
a tame space. Then we show how categorifying formal power series leads to
Joyal's `especes des structures', or `structure types'. We also describe a
useful generalization of structure types called `stuff types'. There is an
inner product of stuff types that makes the category of stuff types into a
categorified version of the Hilbert space of the quantized harmonic oscillator.
We conclude by sketching how this idea gives a nice explanation of the
combinatorics of Feynman diagrams.


  Sparse tensor algebra is a challenging class of workloads to accelerate due
to low arithmetic intensity and varying sparsity patterns. Prior sparse tensor
algebra accelerators have explored tiling sparse data to increase exploitable
data reuse and improve throughput, but typically allocate tile size in a given
buffer for the worst-case data occupancy. This severely limits the utilization
of available memory resources and reduces data reuse. Other accelerators employ
complex tiling during preprocessing or at runtime to determine the exact tile
size based on its occupancy. This paper proposes a speculative tensor tiling
approach, called overbooking, to improve buffer utilization by taking advantage
of the distribution of nonzero elements in sparse tensors to construct larger
tiles with greater data reuse. To ensure correctness, we propose a low-overhead
hardware mechanism, Tailors, that can tolerate data overflow by design while
ensuring reasonable data reuse. We demonstrate that Tailors can be easily
integrated into the memory hierarchy of an existing sparse tensor algebra
accelerator. To ensure high buffer utilization with minimal tiling overhead, we
introduce a statistical approach, Swiftiles, to pick a tile size so that tiles
usually fit within the buffer's capacity, but can potentially overflow, i.e.,
it overbooks the buffers. Across a suite of 22 sparse tensor algebra workloads,
we show that our proposed overbooking strategy introduces an average speedup of
$52.7\times$ and $2.3\times$ and an average energy reduction of $22.5\times$
and $2.5\times$ over ExTensor without and with optimized tiling, respectively.


  A graph is $(\mathcal{I}, \mathcal{F})$-colorable if its vertex set can be
partitioned into two subsets, one of which is an independent set, and the other
induces a forest. In this paper, we prove that every planar graph without
$5^{-}$-cycles at distance less than $3$ is $(\mathcal{I},
\mathcal{F})$-colorable.


  This paper presents a mathematical model of blood volume kinetics and renal
function in response to burn injury and resuscitation, which is applicable to
the development and non-clinical testing of burn resuscitation protocols and
algorithms. Prior mathematical models of burn injury and resuscitation are not
ideally suited to such applications due to their limited credibility in
predicting blood volume and urinary output observed in wide-ranging burn
patients as well as in incorporating contemporary knowledge of burn
pathophysiology. Our mathematical model consists of an established
multi-compartmental model of blood volume kinetics, a hybrid
mechanistic-phenomenological model of renal function, and novel
lumped-parameter models of burn-induced perturbations in volume kinetics and
renal function equipped with contemporary knowledge on burn-related physiology
and pathophysiology. Using the dataset collected from 16 sheep, we showed that
our mathematical model can be characterized with physiologically plausible
parameter values to accurately predict blood volume kinetic and renal function
responses to burn injury and resuscitation on an individual basis against a
wide range of pathophysiological variability. Pending validation in humans, our
mathematical model may serve as an effective basis for an in-depth
understanding of complex burn-induced volume kinetic and renal function
responses as well as development and non-clinical testing of burn resuscitation
protocols and algorithms. Keywords: Mathematical Model, Burn Injury, Burn
Resuscitation, Non-Clinical Testing, Burn Resuscitation Protocols, and
Algorithms


  Adverse drug reactions / events (ADR/ADE) have a major impact on patient
health and health care costs. Detecting ADR's as early as possible and sharing
them with regulators, pharma companies, and healthcare providers can prevent
morbidity and save many lives. While most ADR's are not reported via formal
channels, they are often documented in a variety of unstructured conversations
such as social media posts by patients, customer support call transcripts, or
CRM notes of meetings between healthcare providers and pharma sales reps. In
this paper, we propose a natural language processing (NLP) solution that
detects ADR's in such unstructured free-text conversations, which improves on
previous work in three ways. First, a new Named Entity Recognition (NER) model
obtains new state-of-the-art accuracy for ADR and Drug entity extraction on the
ADE, CADEC, and SMM4H benchmark datasets (91.75%, 78.76%, and 83.41% F1 scores
respectively). Second, two new Relation Extraction (RE) models are introduced -
one based on BioBERT while the other utilizing crafted features over a Fully
Connected Neural Network (FCNN) - are shown to perform on par with existing
state-of-the-art models, and outperform them when trained with a supplementary
clinician-annotated RE dataset. Third, a new text classification model, for
deciding if a conversation includes an ADR, obtains new state-of-the-art
accuracy on the CADEC dataset (86.69% F1 score). The complete solution is
implemented as a unified NLP pipeline in a production-grade library built on
top of Apache Spark, making it natively scalable and able to process millions
of batch or streaming records on commodity clusters.


  A breathing mode in a Hamiltonian system is a function on the phase space
whose evolution is exactly periodic for all solutions of the equations of
motion. Such breathing modes are familiar from nonlinear dynamics in harmonic
traps or anti-de Sitter spacetimes, with applications to the physics of cold
atomic gases, general relativity and high-energy physics. We discuss the
implications of breathing modes in weakly nonlinear regimes, assuming that both
the Hamiltonian and the breathing mode are linear functions of a coupling
parameter, taken to be small. For a linear system, breathing modes dictate
resonant relations between the normal frequencies. These resonant relations
imply that arbitrarily small nonlinearities may produce large effects over long
times. The leading effects of the nonlinearities in this regime are captured by
the corresponding effective resonant system. The breathing mode of the original
system translates into an exactly conserved quantity of this effective resonant
system under simple assumptions that we explicitly specify. If the nonlinearity
in the Hamiltonian is quartic in the canonical variables, as is common in many
physically motivated cases, further consequences result from the presence of
the breathing modes, and some nontrivial explicit solutions of the effective
resonant system can be constructed. This structure explains in a uniform
fashion a series of results in the recent literature where this type of
dynamics is realized in specific Hamiltonian systems, and predicts other
situations of interest where it should emerge.


  In wireless sensor networks (WSNs), the base station (BS) is a critical
sensor node whose failure causes severe data losses. Deploying multiple fixed
BSs improves the robustness, yet requires all BSs to be installed with large
batteries and large energy-harvesting devices due to the high energy
consumption of BSs. In this paper, we propose a scheme to coordinate the
multiple deployed BSs such that the energy supplies required by individual BSs
can be substantially reduced. In this scheme, only one BS is selected to be
active at a time and the other BSs act as regular sensor nodes. We first
present the basic architecture of our system, including how we keep the network
running with only one active BS and how we manage the handover of the role of
the active BS. Then, we propose an algorithm for adaptively selecting the
active BS under the spatial and temporal variations of energy resources. This
algorithm is simple to implement but is also asymptotically optimal under mild
conditions. Finally, by running simulations and real experiments on an outdoor
testbed, we verify that the proposed scheme is energy-efficient, has low
communication overhead and reacts rapidly to network changes.


  Multilingual language models such as mBERT have seen impressive cross-lingual
transfer to a variety of languages, but many languages remain excluded from
these models. In this paper, we analyse the effect of pre-training with
monolingual data for a low-resource language that is not included in mBERT --
Maltese -- with a range of pre-training set ups. We conduct evaluations with
the newly pre-trained models on three morphosyntactic tasks -- dependency
parsing, part-of-speech tagging, and named-entity recognition -- and one
semantic classification task -- sentiment analysis. We also present a newly
created corpus for Maltese, and determine the effect that the pre-training data
size and domain have on the downstream performance. Our results show that using
a mixture of pre-training domains is often superior to using Wikipedia text
only. We also find that a fraction of this corpus is enough to make significant
leaps in performance over Wikipedia-trained models. We pre-train and compare
two models on the new corpus: a monolingual BERT model trained from scratch
(BERTu), and a further pre-trained multilingual BERT (mBERTu). The models
achieve state-of-the-art performance on these tasks, despite the new corpus
being considerably smaller than typically used corpora for high-resourced
languages. On average, BERTu outperforms or performs competitively with mBERTu,
and the largest gains are observed for higher-level tasks.


  We have observed three clusters at z~0.7, of richness comparable to the low
redshift sample of Butcher & Oemler (BO), and determined their fraction of blue
galaxies. When adopting the standard error definition, two clusters have a low
blue fraction for their redshifts, whereas the fraction of the third one is
compatible with the expected value. A detailed analysis of previous BO-like
studies that adopted different definitions of the blue fraction shows that the
modified definitions are affected by contaminating signals: colour segregation
in clusters affects blue fractions derived in fixed metric apertures,
differential evolution of early and late type spirals potentially affects blue
fractions derived with a non standard choice of the colour cut, the younger age
of the Universe at high redshift affects blue fractions computed with a colour
cut taken relatively to a fixed non evolving colour. Adopting these definitions
we find largely varying blue fractions. This thorough analysis of the drawbacks
of the different possible definitions of the blue fraction should allow future
studies to perform measures in the same scale. Finally, if one adopts a more
refined error analysis to deal with BO and our data, a constant blue fraction
with redshift cannot be excluded, showing that the BO effect is still far from
being detected beyond doubt.


  We have analyzed all available archival XMM-Newton observations of X Comae, a
bright X-ray quasar behind the Coma cluster, to study the properties of the
warm-hot intergalactic medium in the vicinity of the nearest massive galaxy
cluster. The RGS observations confirm the possible presence of a Ne IX K-alpha
absorption line at the redshift of Coma, although with a limited statistical
significance. This analysis is therefore in line with the earlier analysis by
Takei et al. (2007) based on a sub-set of these data. Its large column density
and optical depth, however, point to implausible conditions for the absorbing
medium, thereby casting serious doubts to its reality. Chandra has never
observed X Comae and therefore cannot provide additional information on this
source. We combine upper limits to the presence of other X-ray absorption lines
(notably from O VII and O VIII) at the redshift of Coma with positive
measurements of the soft excess emission from Coma measured by ROSAT (Bonamente
et al. 2003). The combination of emission from warm-hot gas at kT~1/4 keV and
upper limits from absorption lines provide useful constraints on the density
and the sightline length of the putative WHIM towards Coma. We conclude that
the putative warm-hot medium towards Coma is consistent with expected
properties, with a baryon overdensity >=10 and a sightline extent of order of
tens of Mpc.


  Seiberg-Witten geometry of mass deformed $\mathcal N=2$ superconformal ADE
quiver gauge theories in four dimensions is determined. We solve the limit
shape equations derived from the gauge theory and identify the space $\mathfrak
M$ of vacua of the theory with the moduli space of the genus zero holomorphic
(quasi)maps to the moduli space ${\rm Bun}_{\mathbf G} (\mathcal E)$ of
holomorphic $G^{\mathbb C}$-bundles on a (possibly degenerate) elliptic curve
$\mathcal E$ defined in terms of the microscopic gauge couplings, for the
corresponding simple ADE Lie group $G$. The integrable systems $\mathfrak P$
underlying the special geometry of $\mathfrak M$ are identified. The moduli
spaces of framed $G$-instantons on ${\mathbb R}^{2} \times {\mathbb T}^{2}$, of
$G$-monopoles with singularities on ${\mathbb R}^{2} \times {\mathbb S}^{1}$,
the Hitchin systems on curves with punctures, as well as various spin chains
play an important r\^ole in our story. We also comment on the
higher-dimensional theories.


  We complete the computation of the gravitational waveform amplitude for
non-spinning compact binaries to the third and a half post-Newtonian (PN) order
in the quasi-circular case. This is done by performing a spin-weighted
spherical harmonics decomposition of the amplitude. This computation is
achieved using the post-Newtonian-Multipolar-post-Minkowskian formalism. The
amplitude modes are written in a PN expanded way as well as a factorized form
suitable for EOB template building. Combining this work with previous ones, we
provide in the supplemental materials the expressions of the modes including
all non-spinning and spinning effects to the 3.5PN order for non-precessing
quasi-circular binaries.


  In this paper, we introduce the task of visual grounding for remote sensing
data (RSVG). RSVG aims to localize the referred objects in remote sensing (RS)
images with the guidance of natural language. To retrieve rich information from
RS imagery using natural language, many research tasks, like RS image visual
question answering, RS image captioning, and RS image-text retrieval have been
investigated a lot. However, the object-level visual grounding on RS images is
still under-explored. Thus, in this work, we propose to construct the dataset
and explore deep learning models for the RSVG task. Specifically, our
contributions can be summarized as follows. 1) We build the new large-scale
benchmark dataset of RSVG, termed RSVGD, to fully advance the research of RSVG.
This new dataset includes image/expression/box triplets for training and
evaluating visual grounding models. 2) We benchmark extensive state-of-the-art
(SOTA) natural image visual grounding methods on the constructed RSVGD dataset,
and some insightful analyses are provided based on the results. 3) A novel
transformer-based Multi-Level Cross-Modal feature learning (MLCM) module is
proposed. Remotely-sensed images are usually with large scale variations and
cluttered backgrounds. To deal with the scale-variation problem, the MLCM
module takes advantage of multi-scale visual features and multi-granularity
textual embeddings to learn more discriminative representations. To cope with
the cluttered background problem, MLCM adaptively filters irrelevant noise and
enhances salient features. In this way, our proposed model can incorporate more
effective multi-level and multi-modal features to boost performance.
Furthermore, this work also provides useful insights for developing better RSVG
models. The dataset and code will be publicly available at
https://github.com/ZhanYang-nwpu/RSVG-pytorch.


  We show that certain 3D multi-gap topological insulators can host quantized
shift photoconductivities due to bulk invariants that are defined under reality
conditions imposed by additional symmetries. We recast the quantization in
terms of the integrated torsion tensor and the non-Abelian Berry connection
constituting Chern-Simons forms. Physically, we recognize that the topological
quantization emerges purely from virtual transitions contributing to the
optical response. Our findings provide another quantized electromagnetic DC
response due to the non-trivial band topology, beyond the quantum anomalous
Hall effect of Chern insulators and quantized circular photogalvanic effect
found in Weyl semimetals.


  Continuous-time Bayesian networks is a natural structured representation
language for multicomponent stochastic processes that evolve continuously over
time. Despite the compact representation, inference in such models is
intractable even in relatively simple structured networks. Here we introduce a
mean field variational approximation in which we use a product of inhomogeneous
Markov processes to approximate a distribution over trajectories. This
variational approach leads to a globally consistent distribution, which can be
efficiently queried. Additionally, it provides a lower bound on the probability
of observations, thus making it attractive for learning tasks. We provide the
theoretical foundations for the approximation, an efficient implementation that
exploits the wide range of highly optimized ordinary differential equations
(ODE) solvers, experimentally explore characterizations of processes for which
this approximation is suitable, and show applications to a large-scale
realworld inference problem.


  We address the problem of phase shift operator acting as time evolution
operator in Pegg-Barnett formalism. It is argued that standard shift operator
is inconsistent with the behaviour of the state vector under cyclic evolution.
We consider a generally deformed oscillator algebra at q-root of unity, as it
yields the same Pegg-Barnett operator and show that shift operator meets our
requirement.


  Dam failures occur worldwide, often from factors including aging structures,
extreme hydrologic loading, and design oversights related to the changing
climate. Understanding and mitigating risk to downstream inhabited areas
require developing and improving low-cost high-fidelity tools, such as
numerical models, which allow emergency managers to predict the consequences of
dam failures better. Two-dimensional (2D) depth-averaged hydraulic models can
provide valuable insights into the importance of breach parameters or
downstream flow characteristics, but historical studies considering historic
failures using real topographies are less common in literature. This study
compares Geoclaw, a 2D hydraulic model with adaptive mesh refinement
capabilities, to an industry-standard software HEC-RAS (Hydrologic Engineering
Center - River Analysis System) using the 1976 Teton Dam failure as a case
study. The suitability of Geoclaw for dam failure modeling is determined based
on its capability to resolve inundation extent and flood wave arrival times.
This study performs sensitivity analyses of the HEC-RAS model to compare an
instantaneous dam breach assumption with a time-dependent breach formation for
quantifying the model uncertainty. We find the 2D Geoclaw dam-break model
results compare reasonably with historical gauge and field observational data
and HEC-RAS results. The model demonstrates stability and relatively low
computational costs. Our findings highlight opportunities for future work, with
the Geoclaw software performance supporting continued studies to evaluate
performance. Outcomes of this study will assist dam owners, floodplain
managers, and emergency managers by providing an additional tool for estimating
the impacts of dam failures to protect lives and infrastructure downstream.


  The reactivity of interstellar carbon atoms (C) on the water-dominated ices
is one of the possible ways to form interstellar complex organic molecules
(iCOMs). In this work, we report a quantum chemical study of the coupling
reaction of C ($^3$P) with an icy water molecule, alongside possible subsequent
reactions with the most abundant closed shell frozen species (NH$_3$, CO,
CO$_2$ and H$_2$), atoms (H, N and O), and molecular radicals (OH, NH$_2$ and
CH$_3$). We found that C spontaneously reacts with the water molecule,
resulting in the formation of $^3$C-OH$_2$, a highly reactive species due to
its triplet electronic state. While reactions with the closed-shell species do
not show any reactivity, reactions with N and O form CN and CO, respectively,
the latter ending up into methanol upon subsequent hydrogenation. The reactions
with OH, CH$_3$ and NH$_2$ form methanediol, ethanol and methanimine,
respectively, upon subsequent hydrogenation. We also propose an explanation for
methane formation, observed in experiments through H additions to C in the
presence of ices. The astrochemical implications of this work are: i) atomic C
on water ice is locked into $^3$C-OH$_2$, making difficult the reactivity of
bare C atoms on the icy surfaces, contrary to what is assumed in astrochemical
current models; and ii) the extraordinary reactivity of $^3$C-OH$_2$ provides
new routes towards the formation of iCOMs in a non-energetic way, in particular
ethanol, mother of other iCOMs once in the gas-phase.


  We propose a torsional response raised by lattice dislocation in Weyl
semimetals akin to chiral magnetic effect; i.e. a fictitious magnetic field
arising from screw or edge dislocation induces charge current. We demonstrate
that, in sharp contrast to the usual chiral magnetic effect which vanishes in
real solid state materials, the torsional chiral magnetic effect exists even
for realistic lattice models, which implies the experimental detection of the
effect via SQUID or nonlocal resistivity measurements in Weyl semimetal
materials.


  Let X_g=C^{(2)}_g be the second symmetric product of a very general curve of
genus g. We reduce the problem of describing the ample cone on X_g to a problem
involving the Seshadri constant of a point on X_{g-1}. Using this we recover a
result of Ciliberto-Kouvidakis that reduces finding the ample cone of X_g to
the Nagata conjecture when g\ge 9. We also give new bounds on the the ample
cone of X_g when g=5.


  Recent work has demonstrated both benefits and limitations from using
supervised approaches (without temporal-difference learning) for offline
reinforcement learning. While off-policy reinforcement learning provides a
promising approach for improving performance beyond supervised approaches, we
observe that training is often inefficient and unstable due to temporal
difference bootstrapping. In this paper we propose a best-of-both approach by
first learning the behavior policy and critic with supervised learning, before
improving with off-policy reinforcement learning. Specifically, we demonstrate
improved efficiency by pre-training with a supervised Monte-Carlo value-error,
making use of commonly neglected downstream information from the provided
offline trajectories. We find that we are able to more than halve the training
time of the considered offline algorithms on standard benchmarks, and
surprisingly also achieve greater stability. We further build on the importance
of having consistent policy and value functions to propose novel hybrid
algorithms, TD3+BC+CQL and EDAC+BC, that regularize both the actor and the
critic towards the behavior policy. This helps to more reliably improve on the
behavior policy when learning from limited human demonstrations. Code is
available at https://github.com/AdamJelley/EfficientOfflineRL


  The Luttinger liquid in which the concentration of electrons varies randomly
with coordinate is considered. We study the fluctuations of the tunnel
conductance, caused by the randomness in the concentration. If the
concentration changes slowly on the scale of the Fermi wavelength, its prime
role reduces to the scattering of the plasmon waves, propagating along the
system. As a result of such a scattering, plasmons get localized. We show that
the localization length, $l_{\omega}$, of a plasmon with frequency $\omega$ is
inverse proportional to the square of the interaction strength and changes with
frequency as $l_{\omega}\propto \omega^{-2}$. If the relative variation of the
concentration is small, the randomness--induced correction to the tunnel
conductance, $\delta G(V)$, where $V$ is the applied bias, can be expressed
through the spectral characteristics of the localized plasmons. The magnitude
of the correction, $(\overline{ \delta G^2})^{1/2}/ G$, increases with $V$ as
$\sqrt V$. The typical period of the fluctuations in $\delta G(V)$ is of the
order of $V$. At a fixed $V$, the correlator of $\delta G$ at different points
of the liquid falls off with distance as a power law and oscillates with the
period which is one half of the wavelength of a plasmon with frequency $\omega
= eV/\hbar$.


  In this paper, we describe a tool for debugging the output and attention
weights of neural machine translation (NMT) systems and for improved
estimations of confidence about the output based on the attention. The purpose
of the tool is to help researchers and developers find weak and faulty example
translations that their NMT systems produce without the need for reference
translations. Our tool also includes an option to directly compare translation
outputs from two different NMT engines or experiments. In addition, we present
a demo website of our tool with examples of good and bad translations:
http://attention.lielakeda.lv


  A mini-PET style detector system is being developed for a plant imaging
application with a compact array of silicon photomultipliers (SiPM) replacing
position sensitive photomultipliers (PSPMT). In addition to compactness, the
use of SiPMs will allow imaging setups involving high strength MRI-type
magnetic fields. The latter will allow for better position resolution of the
initial positron annihilations in the plant tissue. In the present work,
prototype arrays are tested for the uniformity of their response as it is known
that PSPMTs require significant gain compensation on the individual channels to
achieve an improved uniformity in response. The initial tests indicate a high
likelihood that the SiPM arrays can be used without any gain compensation.


  The phase separation between a striped polaron liquid at the particular
density and a high density polaron liquid is described by a modified Van der
Waals scheme. The striped polaron liquid represents the pseudo gap matter or
Wigner-like polaron phase at 1/8 doping in cuprate superconductors. The model
includes the tendency of pseudo- Jahn-Teller polarons to form anisotropic
directional bonds at a preferential volume with the formation of different
liquid phases. The model gives the coexistence of a first low density polaron
striped liquid and a second high density liquid that appears in cuprate
superconductors for doping larger than 1/8. We discuss how the strength of
anisotropic bonds controls the variation the phase separation scenarios for
complex systems in the presence of a quantum critical point where the phase
separation vanishes.


  4-dimensional homogeneous isotropic cosmological models obtained from
solutions of vacuum 5-dimensional Einstein equations are considered. It is
assumed, that the G(55)-component of the 5-d metric simulates matter in the
comoving frame of reference. Observable 4-d metric is defined up to conformal
transformations of the metric of 4-d section \tilde{g}, with a conformal factor
as a function of the component G(55). It is demonstrated, that the form of this
function determines the matter equation of state. Possible equations of state
are analyzed separately for flat, open and close models.


  Using radiation magnetohydrodynamic simulations of the solar atmospheric
layers from the upper convection zone to the lower corona, we investigate the
self-consistent excitation of slow magneto-acoustic body waves (slow modes) in
a magnetic flux concentration. We find that the convective downdrafts in the
close surroundings of a two-dimensional flux slab "pump" the plasma inside it
in the downward direction. This action produces a downflow inside the flux
slab, which encompasses ever higher layers, causing an upwardly propagating
rarefaction wave. The slow mode, excited by the adiabatic compression of the
downflow near the optical surface, travels along the magnetic field in the
upward direction at the tube speed. It develops into a shock wave at
chromospheric heights, where it dissipates, lifts the transition region, and
produces an offspring in the form of a compressive wave that propagates further
into the corona. In the wake of downflows and propagating shock waves, the
atmosphere inside the flux slab in the chromosphere and higher tends to
oscillate with a period of $\nu\approx 4$~mHz. We conclude that this process of
"magnetic pumping" is a most plausible mechanism for the direct generation of
longitudinal chromospheric and coronal compressive waves within magnetic flux
concentrations, and it may provide an important heat source in the
chromosphere. It may also be responsible for certain types of dynamic fibrils.


  Non-equilibrium states of quantum systems in contact with thermal baths help
telling environments with different temperatures or different statistics apart.
We extend these studies to a more generic problem that consists in
discriminating between two baths with disparate constituents at unequal
temperatures. Notably there exist temperature regimes in which the presence of
coherence in the initial state preparation is beneficial for the discrimination
capability. We also find that non-equilibrium states are not universally
optimal, and detail the conditions in which it becomes convenient to wait for
complete thermalisation of the probe. These concepts are illustrated in a
linear optical simulation.


  This paper presents an ab initio methodology to account for electron-phonon
interactions in 2D materials, focusing on transition metal dichalcogenides
(TMDCs). It combines density functional theory and maximally localized Wannier
functions to acquire material data and relies on the linearized Boltzmann
transport equation (LBTE) and the non-equilibrium Green's functions (NEGF)
method to determine the transport properties of materials and devices,
respectively. It is shown that for MoS$_2$, both LBTE and NEGF return very
close mobility values, without the need to adjust any parameter. The excellent
agreement between both approaches results from the inclusion of non-diagonal
entries in the electron-phonon scattering self-energies. The NEGF solver is
then used to shed light on the "current vs. voltage" characteristics of a
monolayer MoS$_2$ transistor, highlighting how the interactions with phonons
impact both the current magnitude and its distribution. The mobility of other
TMDCs is considered as well, demonstrating the capabilities of the proposed
technique to assess the potential of 2D channel materials in next-generation
logic applications.


  We introduce the GAMMA (Galactic Attributes of Mass, Metallicity, and Age)
dataset, a comprehensive collection of galaxy data tailored for Machine
Learning applications. This dataset offers detailed 2D maps and 3D cubes of 11
727 galaxies, capturing essential attributes: stellar age, metallicity, and
mass. Together with the dataset, we publish our code to extract any other
stellar or gaseous property from the raw simulation suite to extend the dataset
beyond these initial properties, ensuring versatility for various computational
tasks. Ideal for feature extraction, clustering, and regression tasks, GAMMA
offers a unique lens to explore galactic structures using computational methods
and is a bridge between astrophysical simulations and the field of scientific
machine learning (ML). As a first benchmark, we applied Principal Component
Analysis (PCA) to this dataset. We find that PCA effectively captures the key
morphological features of galaxies with a small number of components. We
achieve a dimensionality reduction by a factor of approximately 200 (3650) for
2D images (3D cubes) with a reconstruction accuracy below 5%.


  We study the development of collectivity in the neutron-rich nuclei around
$N=40$, where experimental and theoretical evidences suggest a rapid shape
change from the spherical to the rotational regime, in analogy to what happens
at the {\it island of inversion} surrounding $^{31}$Na. Theoretical
calculations are performed within the interacting shell model framework in a
large valence space, based on a $^{48}$Ca core which encompasses the full $pf$
shell for the protons and the $0f_{5/2}$, $1p_{3/2}$, $1p_{1/2}$, $0g_{9/2}$
and $1d_{5/2}$ orbits for the neutrons. The effective interaction is based on a
G-matrix obtained from a realistic nucleon-nucleon potential whose monopole
part is corrected empirically to produce effective single particle energies
compatible with the experimental data. We find a good agreement between the
theoretical results and the available experimental data. We predict the onset
of deformation at different neutron numbers for the various isotopic chains.
The maximum collectivity occurs in the chromium isotopes, where the large
deformation regime starts already at $N=38$. The shell evolution responsible
for the observed shape changes is discussed in detail, in parallel to the
situation in the $N=20$ region.


  Solar active regions are driven dissipative dynamical systems. The turbulent
convection zone forces new magnetic flux tubes to rise above the photosphere
and shuffles the magnetic fields which are already above the photosphere. The
driven 3D active region responds to the driver with the formation of Thin
Current Sheets in all scales and releases impulsively energy, when special
thresholds are met, on the form of nano-, micro-, flares and large scale
coronal mass ejections. It has been documented that active regions form self
similar structures with area Probability Distribution Functions (PDF's)
following power laws and with fractal dimensions ranging from $1.2-1.7$. The
energy release on the other hand follows a specific energy distribution law
$f(E_T)\sim E_T^{-a}$, where $a \sim 1.6-1.8$ and $E_T$ is the total energy
released. A possible explanation for the statistical properties of the
magnetogrms and the energy release by the active region is that the magnetic
field formation follows rules analogous to \textbf{percolating models}, and the
3D magnetic fields above the photosphere reach a \textbf{Self Organized
Critical (SOC) state}. The implications of these findings on the acceleration
of energetic particles during impulsive phenomena will briefly be outlined.


  In this work, high order splitting methods have been used for calculating the
numerical solutions of the Burgers' equation in one space dimension with
periodic and Dirichlet boundary conditions. However, splitting methods with
real coefficients of order higher than two necessarily have negative
coefficients and can not be used for time-irreversible systems, such as Burgers
equations, due to the time-irreversibility of the Laplacian operator.
Therefore, the splitting methods with complex coefficients and extrapolation
methods with real and positive coefficients have been employed. If we consider
the system as the perturbation of an exactly solvable problem(or can be easily
approximated numerically), it is possible to employ highly efficient methods to
approximate Burgers' equation. The numerical results show that the methods with
complex time steps having one set of coefficients real and positive, say
$a_i\in\mathbb{R}^+$ and $b_i\in\mathbb{C}^+$, and high order extrapolation
methods derived from a lower order splitting method produce very accurate
solutions of the Burgers' equation.


  We apply an approach from cognitive linguistics by mapping Conceptual
Metaphor Theory (CMT) to the visualization domain to address patterns of visual
conceptual metaphors that are often used in science infographics. Metaphors
play an essential part in visual communication and are frequently employed to
explain complex concepts. However, their use is often based on intuition,
rather than following a formal process. At present, we lack tools and language
for understanding and describing metaphor use in visualization to the extent
where taxonomy and grammar could guide the creation of visual components, e.g.,
infographics. Our classification of the visual conceptual mappings within
scientific representations is based on the breakdown of visual components in
existing scientific infographics. We demonstrate the development of this
mapping through a detailed analysis of data collected from four domains
(biomedicine, climate, space, and anthropology) that represent a diverse range
of visual conceptual metaphors used in the visual communication of science.
This work allows us to identify patterns of visual conceptual metaphor use
within the domains, resolve ambiguities about why specific conceptual metaphors
are used, and develop a better overall understanding of visual metaphor use in
scientific infographics. Our analysis shows that ontological and orientational
conceptual metaphors are the most widely applied to translate complex
scientific concepts. To support our findings we developed a visual exploratory
tool based on the collected database that places the individual infographics on
a spatio-temporal scale and illustrates the breakdown of visual conceptual
metaphors.


  Scanning tunneling spectroscopy measurements were made on surfaces of two
different kinds of graphite samples, Kish graphite and highly oriented
pyrolytic graphite (HOPG), at very low temperatures and in high magnetic
fields. We observed a series of peaks in the tunnel spectra, which grow with
increasing field, both at positive and negative bias voltages. These are
associated with Landau quantization of the quasi two-dimensional electrons and
holes in graphite in magnetic fields perpendicular to the basal plane. Almost
field independent Landau levels fixed near the Fermi energy, which are
characteristic of the graphite crystalline structure, were directly observed
for the first time. Calculations of the local density of states at the graphite
surfaces allow us to identify Kish graphite as bulk graphite and HOPG as
graphite with finite thickness effectively.


  We propose a theoretical model for ice growth under a wind-driven supercooled
water film. The thickness and surface velocity of the water layer are variable
by changing the air stream velocity. For a given water supply rate, linear
stability analysis is carried out to study the morphological instability of the
ice-water interface. In this model, water and air boundary layers are
simultaneously disturbed due to the change in ice shape, and the effect of the
interaction between air and water flows on the growth condition of the
ice-water interface disturbance is taken into account. It is shown that as wind
speed increases, the amplification rate of the disturbance is significantly
affected by variable stresses exerted on the water-air interface by the air
flow as well as restoring forces due to gravity and surface tension. We predict
that an ice pattern of a centimeter scale in wavelength appears and the
wavelength decreases as wind speed increases, and that the ice pattern moves in
the direction opposite to the water flow. The effect of the air stress
disturbance on the heat transfer coefficient at the water-air interface is also
investigated for various wind speeds.


  Insufficient modeling of human preferences within the reward model is a major
obstacle for leveraging human feedback to improve translation quality.
Fortunately, quality estimation (QE), which predicts the quality of a given
translation without reference, has achieved impressive alignment with human
evaluations in the last two years. In this work, we investigate the potential
of employing the QE model as the reward model to predict human preferences for
feedback training. We first identify the overoptimization problem during
QE-based feedback training, manifested as an increase in reward while
translation quality declines. We examine the problem and argue that the
vulnerability of the QE model might lead to high rewards for incorrect
translations, resulting in overoptimization and error propagation. To address
the problem, we adopt a simple yet effective method that uses heuristic rules
to detect the incorrect translations and assigns a penalty term to the reward
scores of them. Experimental results show that the proposed QE-based feedback
training achieves consistent and significant improvements across various
settings, further verified through human preference studies. Our subsequent
analysis demonstrates the high data efficiency of the proposed QE-based
feedback training: it outperforms systems using larger parallel corpora by a
small amount of monolingual data. Our code is available at:
https://github.com/zwhe99/FeedbackMT


  This paper proposes an Adaptive Isogeometric Topology Optimization framework
for shell structures based on PHT-splines (PHT-AITO). In this framework, the
design domain, displacement, and density are represented by PHT-splines.
Leveraging the local refinement capability of PHT-splines, mesh elements
defining the density function are adaptively refined to achieve a suitable
resolution at the interface between solid and void regions. This addresses the
issue of excessive degrees of freedom resulting from global refinement. The
refinement of the mesh elements is driven by their density. During the
optimization of the density on a refined mesh, the initial value of the density
is inherited from the optimization results on the previous mesh to accelerate
the iteration process and maintain the stability of the optimized structure.
Numerical experiments on various shell structures have verified the
effectiveness of PHT-AITO. Compared with isogeometric topology optimization
based on tensor-product splines, PHT-AITO can significantly reduce the degrees
of freedom in the optimization problem, thereby improving computational
efficiency.


  We study shear-free spherically symmetric relativistic gravitating fluids
with heat flow and electric charge. The solution to the Einstein-Maxwell system
is governed by the generalised pressure isotropy condition which contains a
contribution from the electric field. This condition is a highly nonlinear
partial differential equation. We analyse this master equation using Lie's
group theoretic approach. The Lie symmetry generators that leave the equation
invariant are found. The first generator is independent of the electromagnetic
field. The second generator depends critically on the form of the charge, which
is determined explicitly in general. We provide exact solutions to the
gravitational potentials using the symmetries admitted by the equation. Our new
exact solutions contain earlier results without charge. We show that other
charged solutions, related to the Lie symmetries, may be generated using the
algorithm of Deng. This leads to new classes of charged Deng models which are
generalisations of conformally flat metrics.