  In this work we suggest and consider an original, simple mathematical model
of a "quasi-rapid" extinction population dynamics. It describes a decrease and
final extinction of the population of one prey species by a "quasi-rapid"
interaction with one predator species with increasing population. This
"quasi-rapid" interaction means ecologically that prey species behaves
practically quite passively (since there is no time for any reaction, i.e.
defense), like an appropriate environment, in respect to "quasi-rapid" activity
of the predator species that can have different "quasi-rapid" hunting
abilities. Mathematically, our model is based on a non-Lotka-Volterraian system
of two differential equations of the first order, first of which is linear
while second, depending of a parameter that characterizes hunting ability is
nonlinear. We compare suggested "quasi-rapid" extinction population dynamics
and the global model of the overkill of the prehistoric megafauna (mammoths).
We demonstrate that our "quasi-rapid" extinction population dynamics is able to
restitute successfully correlations between empirical (archeological) data and
overkill theory in North America as well as Australia. For this reason, we
conclude that global overkill theory, completely mathematically modelable by
"quasi-rapid" extinction population dynamics can consistently explain the
Pleistocene extinctions of the megafauna.


  A spectacular manifestation of solar activity is the appearance of transient
brightenings in the far wings of the H$\alpha$ line, known as Ellerman bombs
(EBs). Recent observations obtained by the Interface Region Imaging
Spectrograph (IRIS) have revealed another type of plasma "bombs" (UV bursts)
with high temperatures of perhaps up to $8\times 10^4$ K within the cooler
lower solar atmosphere. Realistic numerical modeling showing such events is
needed to explain their nature. Here, we report on 3D radiative
magneto-hydrodynamic simulations of magnetic flux emergence in the solar
atmosphere. We find that ubiquitous reconnection between emerging bipolar
magnetic fields can trigger EBs in the photosphere, UV bursts in the mid/low
chromosphere and small (nano-/micro-) flares ($10^6$ K) in the upper
chromosphere. These results provide new insights on the emergence and build up
of the coronal magnetic field and the dynamics and heating of the solar surface
and lower atmosphere.


  Omnidirectional micro aerial vehicles are a growing field of research, with
demonstrated advantages for aerial interaction and uninhibited observation.
While systems with complete pose omnidirectionality and high hover efficiency
have been developed independently, a robust system that combines the two has
not been demonstrated to date. This paper presents the design and optimal
control of a novel omnidirectional vehicle that can exert a wrench in any
orientation while maintaining efficient flight configurations. The system
design is motivated by the result of a morphology design optimization. A six
degrees of freedom optimal controller is derived, with an actuator allocation
approach that implements task prioritization, and is robust to singularities.
Flight experiments demonstrate and verify the system's capabilities.


  This paper describes a blended learning implementation and experience
supported with intelligent learning environments included in a learning
management system (LMS) called @KU-UZEM. The blended learning model is realized
as a combination of face to face education and e-learning. The intelligent
learning environments consist of two applications named CTutor, ITest. In
addition to standard e-learning tools, students can use CTutor to resolve C
programming exercises. CTutor is a problem-solving environment, which diagnoses
students' knowledge level but also gives feedbacks and tips to help them to
understand the course subject, overcome their misconceptions and reinforce
learnt concepts. ITest provides an assessment environment in which students can
take quizzes that were prepared according to their learning levels. The
realized model was used for two terms in the "C Programming" course given at
Afyon Kocatepe University. A survey was conducted at the end of the course to
find out to what extent the students were accepting the blended learning model
supported with @KU-UZEM and to discover students' attitude towards intelligent
learning environments. Additionally, an experiment formed with an experimental
group who took an active part in the realized model and a control group who
only took the face to face education was performed during the first term of the
course. According to the results, students were satisfied with intelligent
learning environments and the realized learning model. Furthermore, the use of
intelligent learning environments improved the students' knowledge about C
programming.


  We survey the underlying theory behind the large-scale and linear scaling DFT
code, Conquest, which shows excellent parallel scaling and can be applied to
thousands of atoms with exact solutions, and millions of atoms with linear
scaling. We give details of the representation of the density matrix and the
approach to finding the electronic ground state, and discuss the implementation
of molecular dynamics with linear scaling. We give an overview of the
performance of the code, focussing in particular on the parallel scaling, and
provide examples of recent developments and applications.


  All molecular clouds are observed to be turbulent, but the origin, means of
sustenance, and evolution of the turbulence remain debated. One possibility is
that stellar feedback injects enough energy into the cloud to drive observed
motions on parsec scales. Recent numerical studies of molecular clouds have
found that feedback from stars, such as protostellar outflows and winds,
injects energy and impacts turbulence. We expand upon these studies by
analyzing magnetohydrodynamic simulations of molecular clouds, including
stellar winds, with a range of stellar mass-loss rates and magnetic field
strengths. We generate synthetic $^{12}$CO(1-0) maps assuming that the
simulations are at the distance of the nearby Perseus molecular cloud. By
comparing the outputs from different initial conditions and evolutionary times,
we identify differences in the synthetic observations and characterize these
using common astrostatistics. We quantify the different statistical responses
using a variety of metrics proposed in the literature. We find that multiple
astrostatistics, including principal component analysis, the spectral
correlation function and the velocity coordinate spectrum, are sensitive to
changes in stellar mass-loss rates and/or time evolution. A few statistics,
including the Cramer statistic and velocity coordinate spectrum, are sensitive
to the magnetic field strength. These findings demonstrate that stellar
feedback influences molecular cloud turbulence and can be identified and
quantified observationally using such statistics.


  In this short note we review results on equilibrium shapes of minimizers to
the sessile drop problem. More precisely, we study the Winterbottom problem and
prove that the Winterbottom shape is indeed optimal. The arguments presented
here are based on relaxation and the (anisotropic) isoperimetric inequality.


  3D-MHD numerical simulations of bipolar, hypersonic, weakly magnetized jets
and synthetic synchrotron observations are presented to study the structure and
evolution of magnetic fields in FR II radio sources. The magnetic field setup
in the jet is initially random. The power of the jets as well as the
observational viewing angle are investigated. We find that synthetic
polarization maps agree with observations and show that magnetic fields inside
the sources are shaped by the jets' backflow. Polarimetry statistics correlates
with time, the viewing angle and the jet-to-ambient density contrast. The
magnetic structure inside thin elongated sources is more uniform than for ones
with fatter cocoons. Jets increase the magnetic energy in cocoons, in
proportion to the jet velocity. Both, filaments in synthetic emission maps and
3D magnetic power spectra suggest that turbulence develops in evolved sources.


  One of the keys to interpreting the character and evolution of interstellar
matter in the galaxy is understanding the distribution of the low density hot
(10^5 K -10^6 K) phase of the interstellar medium (ISM). This phase is much
more difficult to observe than the cooler high density components of the ISM
because of its low density and lack of easily observable tracers. Because gas
of this temperature emits mainly in the far ultraviolet (912 angstrom - 1800
angstroms) and extreme ultraviolet (80 angstrom - 912 angstrom), and (for gas
hotter than 10^6 K) X-rays, observations in these bands can provide important
constraints to the distribution of this gas. Because of interstellar opacity at
EUV wavelengths, only FUV and X-ray observations can provide clues to the
properties of hot gas from distant regions. We present results from a search
for FUV emission from the diffuse ISM conducted with an orbital FUV
spectrometer, DUVE, which was launched in July, 1992. The DUVE spectrometer,
which covers the band from 950 angstrom to 1080 angstrom with 3.2 angstrom
resolution, observed a region of low neutral hydrogen column density near the
south galactic pole for a total effective integration time of 1583 seconds. The
only emission line detected was a geocoronal hydrogen line at 1025 angstrom. We
are able to place upper limits to several expected emission features that
provide constraints on interstellar plasma parameters. We are also able to
place limits on the continuum emission throughout the bandpass. We compare
these limits and other diffuse observations with several models of the
structure of the interstellar medium and discuss the ramifications.


  The most general method for encoding quantum information is not to encode the
information into a subspace of a Hilbert space, but to encode information into
a subsystem of a Hilbert space. Recently this notion has led to a more general
notion of quantum error correction known as operator quantum error correction.
In standard quantum error correcting codes, one requires the ability to apply a
procedure which exactly reverses on the error correcting subspace any
correctable error. In contrast, for operator error correcting subsystems, the
correction procedure need not undo the error which has occurred, but instead
one must perform correction only modulo the subsystem structure. This does not
lead to codes which differ from subspace codes, but does lead to recovery
routines which explicitly make use of the subsystem structure. Here we present
two examples of such operator error correcting subsystems. These examples are
motivated by simple spatially local Hamiltonians on square and cubic lattices.
In three dimensions we provide evidence, in the form a simple mean field
theory, that our Hamiltonian gives rise to a system which is self-correcting.
Such a system will be a natural high-temperature quantum memory, robust to
noise without external intervening quantum error correction procedures.


  I present an up to date set of limits on the fundamental Planck scale M_D.
The best limit for each number of extra dimensions n is shown in bold font. For
n = 2, M_D > 5.6 TeV; n = 3, M_D > 4.4 TeV; n = 4, M_D > 3.9 TeV; n = 5, M_D >
3.6 TeV; n = 6, M_D > 3.3 TeV; and for 6 < n < 9, M_D > 0.8 TeV.


  We review the resonance gas formalism of hadron thermodynamics and recall
that an exponential increase of the resonance spectrum leads to a limiting
temperature of hadronic matter. We then show that the number p(n) of ordered
partitions of an integer n grows exponentially with n and satisfies the integer
counterpart of the statistical bootstrap equation. Considering the set of all
partitions as a Gibbs ensemble provides a partition thermodynamics which is
also governed by a limiting temperature, determined by the combinatorial
structure of the problem. Further associating intrinsic quantum numbers to
integers results in a phase diagram equivalent to that found in QCD for
hadronic matter as function of temperature and baryochemical potential.


  We investigate the scattering and absorption properties of anisotropic
metal-dielectric core-shell nanoparticles. It is revealed that the radially
anisotropic dielectric layer can accelerate the evanescent decay of the
localized resonant surface modes, leading to Q-factor and absorption rate
enhancement. Moreover, the absorption cross section can be maximized to reach
the single resonance absorption limit. We further show that such artificial
anisotropic cladding materials can be realized by isotropic layered structures,
which may inspire many applications based on scattering and absorption of
plasmonic nanoparticles.


  We discuss some of the issues to be accounted under conducting spin physics
program at RHIC.


  In this paper we prove that for non-negative measurable functions $f$,
\begin{align*} I_\alpha f \in BMO(\mathbb{R}^n) \text{ if and only if }
I_\alpha f \in BMO^\beta(\mathbb{R}^n) \text{ for } \beta \in (n-\alpha,n].
\end{align*} Here $I_\alpha$ denotes the Riesz potential of order $\alpha$ and
$BMO^\beta$ represents the space of functions of bounded $\beta$-dimensional
mean oscillation.


  In this paper, we use a branch of polyhedral geometry, Ehrhart theory, to
expand our combinatorial understanding of congruences for partition functions.
Ehrhart theory allows us to give a new decomposition of partitions, which in
turn allows us to define statistics called {\it supercranks} that
combinatorially witness every instance of divisibility of $p(n,3)$ by any prime
$m \equiv -1 \pmod 6$, where $p(n,3)$ is the number of partitions of $n$ into
three parts. A rearrangement of lattice points allows us to demonstrate with
explicit bijections how to divide these sets of partitions into $m$
equinumerous classes. The behavior for primes $m' \equiv 1 \pmod 6$ is also
discussed.


  The proposed Deep Underground Neutrino Experiment (DUNE) utilizes a wide-band
on-axis tunable muon-(anti)neutrino beam with a baseline of 1300 km to search
for CP violation with high precision. Given the long baseline, DUNE is also
sensitive to effects due to non-standard neutrino interactions (NSI) which can
interfere with the standard 3-flavor oscillation paradigm. In this Letter, we
exploit the tunability of the DUNE neutrino beam over a wide-range of energies
and utilize a new theoretical metric to devise an experimental strategy for
separating oscillation effects due to NSI from the standard 3-flavor
oscillation scenario. Using our metric, we obtain an optimal combination of
beam tunes and distribution of run times in neutrino and anti-neutrino modes
that would enable DUNE to isolate new physics scenarios from the standard. To
the best of our knowledge, our strategy is entirely new and has not been
reported elsewhere.


  We present a new semi-supervised domain adaptation framework that combines a
novel auto-encoder-based domain adaptation model with a simultaneous learning
scheme providing stable improvements over state-of-the-art domain adaptation
models. Our framework holds strong distribution matching property by training
both source and target auto-encoders using a novel simultaneous learning scheme
on a single graph with an optimally modified MMD loss objective function.
Additionally, we design a semi-supervised classification approach by
transferring the aligned domain invariant feature spaces from source domain to
the target domain. We evaluate on three datasets and show proof that our
framework can effectively solve both fragile convergence (adversarial) and weak
distribution matching problems between source and target feature space
(discrepancy) with a high `speed' of adaptation requiring a very low number of
iterations.


  We prove two supercongruences for specific truncated hypergeometric series.
These include an uniparametric extension of a supercongruence that was recently
established by Long and Ramakrishna. Our proofs involve special instances of
various hypergeometric identities including Whipple's transformation and the
Karlsson--Minton summation.


  The new arguments based on Majorana fermions indicating that non-completely
positive maps can describe open quantum evolution are presented.


  We consider the problem of \emph{fully decentralized} multi-agent
reinforcement learning (MARL), where the agents are located at the nodes of a
time-varying communication network. Specifically, we assume that the reward
functions of the agents might correspond to different tasks, and are only known
to the corresponding agent. Moreover, each agent makes individual decisions
based on both the information observed locally and the messages received from
its neighbors over the network. Within this setting, the collective goal of the
agents is to maximize the globally averaged return over the network through
exchanging information with their neighbors. To this end, we propose two
decentralized actor-critic algorithms with function approximation, which are
applicable to large-scale MARL problems where both the number of states and the
number of agents are massively large. Under the decentralized structure, the
actor step is performed individually by each agent with no need to infer the
policies of others. For the critic step, we propose a consensus update via
communication over the network. Our algorithms are fully incremental and can be
implemented in an online fashion. Convergence analyses of the algorithms are
provided when the value functions are approximated within the class of linear
functions. Extensive simulation results with both linear and nonlinear function
approximations are presented to validate the proposed algorithms. Our work
appears to be the first study of fully decentralized MARL algorithms for
networked agents with function approximation, with provable convergence
guarantees.


  We consider correlations, $p_{n,x}$, arising from measuring a maximally
entangled state using $n$ measurements with two outcomes each, constructed from
$n$ projections that add up to $xI$. We show that the correlations $p_{n,x}$
robustly self-test the underlying states and measurements. To achieve this, we
lift the group-theoretic Gowers-Hatami based approach for proving robust
self-tests to a more natural algebraic framework. A key step is to obtain an
analogue of the Gowers-Hatami theorem allowing to perturb an "approximate"
representation of the relevant algebra to an exact one.
  For $n=4$, the correlations $p_{n,x}$ self-test the maximally entangled state
of every odd dimension as well as 2-outcome projective measurements of
arbitrarily high rank. The only other family of constant-sized self-tests for
strategies of unbounded dimension is due to Fu (QIP 2020) who presents such
self-tests for an infinite family of maximally entangled states with even local
dimension. Therefore, we are the first to exhibit a constant-sized self-test
for measurements of unbounded dimension as well as all maximally entangled
states with odd local dimension.


  We present a theoretical framework allowing to properly address the nature of
surface-like eigenmodes in a hypersonic surface phononic crystal, a composite
structure made of periodic metal stripes of nanometer size and periodicity of 1
micron, deposited over a semi-infinite silicon substrate. In surface-based
phononic crystals there is no distinction between the eigenmodes of the
periodically nanostructured overlayer and the surface acoustic modes of the
semi-infinite substrate, the solution of the elastic equation being a
pseudo-surface acoustic wave partially localized on the nanostructures and
radiating energy into the bulk. This problem is particularly severe in the
hypersonic frequency range, where semi-infinite substrate's surface acoustic
modes strongly couple to the periodic overlayer, thus preventing any
perturbative approach. We solve the problem introducing a surface-likeness
coefficient as a tool allowing to find pseudo-surface acoustic waves and to
calculate their line shapes. Having accessed the pseudo-surface modes of the
composite structure, the same theoretical frame allows reporting on the gap
opening in the now well-defined pseudo-SAW frequency spectrum. We show how the
filling fraction, mass loading and geometric factors affect both the frequency
gap, and how the mechanical energy is scattered out of the surface waveguiding
modes.


  Astrophysics and cosmology are rich with data. The advent of wide-area
digital cameras on large aperture telescopes has led to ever more ambitious
surveys of the sky. Data volumes of entire surveys a decade ago can now be
acquired in a single night and real-time analysis is often desired. Thus,
modern astronomy requires big data know-how, in particular it demands highly
efficient machine learning and image analysis algorithms. But scalability is
not the only challenge: Astronomy applications touch several current machine
learning research questions, such as learning from biased data and dealing with
label and measurement noise. We argue that this makes astronomy a great domain
for computer science research, as it pushes the boundaries of data analysis. In
the following, we will present this exciting application area for data
scientists. We will focus on exemplary results, discuss main challenges, and
highlight some recent methodological advancements in machine learning and image
analysis triggered by astronomical applications.


  Second harmonic generation (SHG) is a coherent nonlinear phenomenon that
plays an important role in laser color conversion. Lithium niobate (LN), which
features both a large band gap and outstanding second-order nonlinearities,
acts as an important optical material for nonlinear frequency conversion
covering a wide spectral range from ultraviolet to mid-infrared. Here we
experimentally demonstrate LN metasurfaces with controllable SHG properties.
Distinct enhancements for the SHG efficiency are observed at Mie-resonances.
And by changing the geometric parameters thus the resonances of the
metasurfaces, we manage to selectively boost the SHG efficiency at different
wavelengths. Our results would pave a way for developing with high flexibility
the novel compact nonlinear light sources for applications, such as biosensing
and optical communications.


  We present a solution to the long outstanding meter barrier problem in planet
formation theory. As solids spiral inward due to aerodynamic drag, they will
enter disk regions that are characterized by high temperatures, densities, and
pressures. High partial pressures of rock vapor can suppress solid evaporation,
and promote collisions between partially molten solids, allowing rapid growth.
This process should be ubiquitous in planet-forming disks, which may be
evidenced by the abundant class of Systems with Tightly-packed Inner Planets
(STIPs) discovered by the NASA Kepler mission.


  We apply the specialization technique based on the decomposition of the
diagonal to find an explicit example over $\mathbb{Q}$ of a quadric and cubic
hypersurface in $\mathbb{P}^6$ such that their intersection is a smooth stably
irrational fourfold. Using the same degeneration, Nicaise and Ottem have
already proven that the the very general complete intersection of this type is
stably irrational using the motivic volume.


  Computational mathematics plays an increasingly important role in
computational fluid dynamics (CFD). The aeronautics and aerospace re- search
community is working on next generation of CFD capacity that is accurate,
automatic, and fast. A key component of the next generation of CFD is a greatly
enhanced capacity for mesh generation and adaptivity of the mesh according to
solution and geometry. In this paper, we propose a new method that generates
triangular meshes on domains of curved boundary. The method deforms a Cartesian
mesh that covers the domain to generate a mesh with prescribed boundary nodes.
The deformation fields are generated by a system of divergence and curl
equations which are solved effectively by the least square finite element
method.


  This paper presents HoughNet, a one-stage, anchor-free, voting-based,
bottom-up object detection method. Inspired by the Generalized Hough Transform,
HoughNet determines the presence of an object at a certain location by the sum
of the votes cast on that location. Votes are collected from both near and
long-distance locations based on a log-polar vote field. Thanks to this voting
mechanism, HoughNet is able to integrate both near and long-range,
class-conditional evidence for visual recognition, thereby generalizing and
enhancing current object detection methodology, which typically relies on only
local evidence. On the COCO dataset, HoughNet's best model achieves 46.4 $AP$
(and 65.1 $AP_{50}$), performing on par with the state-of-the-art in bottom-up
object detection and outperforming most major one-stage and two-stage methods.
We further validate the effectiveness of our proposal in another task, namely,
"labels to photo" image generation by integrating the voting module of HoughNet
to two different GAN models and showing that the accuracy is significantly
improved in both cases. Code is available at
https://github.com/nerminsamet/houghnet.


  We present a novel data-driven regularizer for weakly-supervised learning of
3D human pose estimation that eliminates the drift problem that affects
existing approaches. We do this by moving the stereo reconstruction problem
into the loss of the network itself. This avoids the need to reconstruct 3D
data prior to training and unlike previous semi-supervised approaches, avoids
the need for a warm-up period of supervised training. The conceptual and
implementational simplicity of our approach is fundamental to its appeal. Not
only is it straightforward to augment many weakly-supervised approaches with
our additional re-projection based loss, but it is obvious how it shapes
reconstructions and prevents drift. As such we believe it will be a valuable
tool for any researcher working in weakly-supervised 3D reconstruction.
Evaluating on Panoptic, the largest multi-camera and markerless dataset
available, we obtain an accuracy that is essentially indistinguishable from a
strongly-supervised approach making full use of 3D groundtruth in training.


  Reflection electron energy loss spectroscopy (EELS) in specular and off
specular geometry has been employed to study the early stage of the copper
phthalocyanine (CuPc) growth on Al (100) substrate. EEL spectroscopy has been a
useful tool in order to study electronic structure of molecular films also in
the submonolayer regime. The electronic structure of the first deposited layer
of CuPc is strongly influenced by charge transfer from the Al substrate to the
lowest unoccupied molecular orbital (LUMO). The strong molecule-substrate
interaction gives rise to a coverage dependent frequency shift of the Al
surface plasmon. Successive layers have essentially the electronic structure of
the molecular solid. Momentum resolved EELS measurements reveal that, in the
case of the thicker film investigated (22 \AA), the plane of the molecule is
almost perpendicular to the surface of the substrate.


  We prove that for any choice of parameters $k,t,\lambda$ the class of all
finite ordered designs with parameters $k,t,\lambda$ is a Ramsey class.


  We consider the volume expansion of the Blaschke metric, which is a
projectively invariant metric on a strictly convex domain in a locally flat
projective manifold. When the boundary is even dimensional, we express the
logarithmic coefficient L as the integral of affine invariants over the
boundary. We also formulate an intrinsic geometry of the boundary as a
conformal Codazzi structure and show that L gives a global conformal invariant
of the boundary.


  Displays based on organic light-emitting diode (OLED) technology are
appearing on many mobile devices. Unlike liquid crystal displays (LCD), OLED
displays consume dramatically different power for showing different colors. In
particular, OLED displays are inefficient for showing bright colors. This has
made them undesirable for mobile devices because much of the web content is of
bright colors.
  To tackle this problem, we present the motivational studies, design, and
realization of Chameleon, a color adaptive web browser that renders web pages
with power-optimized color schemes under user-supplied constraints. Driven by
the findings from our motivational studies, Chameleon provides end users with
important options, offloads tasks that are not absolutely needed in real-time,
and accomplishes real-time tasks by carefully enhancing the codebase of a
browser engine. According to measure-ments with OLED smartphones, Chameleon is
able to re-duce average system power consumption for web browsing by 41% and
reduce display power consumption by 64% without introducing any noticeable
delay.


  By means of oxide molecular beam epitaxy with shutter-growth mode, we have
fabricated a series of electron-doped (Sr1-xLax)2IrO4(001)(x = 0, 0.05, 0.1 and
0.15) single crystalline thin films and then investigated the doping dependence
of electronic structure utilizing in-situ angle-resolved photoemission
spectroscopy. We find that with increasing doping proportion, the Fermi levels
of samples progressively shift upward. Prominently, an extra electron pocket
crossing the Fermi level around the M point has been evidently observed in 15 %
nominal doping sample. Moreover, bulk-sensitive transport measurements confirm
that doping effectively suppresses the insulating state with respect to the
as-grown Sr2IrO4, though doped samples still remain insulating at low
temperatures due to the localization effect possibly stemming from disorders
including oxygen deficiencies. Our work provides another feasible doping method
to tune electronic structure of Sr2IrO4.


  We propose a general approach to modeling semi-supervised learning (SSL)
algorithms. Specifically, we present a declarative language for modeling both
traditional supervised classification tasks and many SSL heuristics, including
both well-known heuristics such as co-training and novel domain-specific
heuristics. In addition to representing individual SSL heuristics, we show that
multiple heuristics can be automatically combined using Bayesian optimization
methods. We experiment with two classes of tasks, link-based text
classification and relation extraction. We show modest improvements on
well-studied link-based classification benchmarks, and state-of-the-art results
on relation-extraction tasks for two realistic domains.


  Graph states are a family of stabilizer states which can be tailored towards
various applications in photonic quantum computing and quantum communication.
In this paper, we present a modular design based on quantum dot emitters
coupled to a waveguide and optical fiber delay lines to deterministically
generate N-dimensional cluster states and other useful graph states such as
tree states and repeater states. Unlike previous proposals, our design requires
no two-qubit gates on quantum dots and at most one optical switch, thereby,
minimizing challenges usually posed by these requirements. Furthermore, we
discuss the error model for our design and demonstrate a fault-tolerant quantum
memory with an error threshold of 0.53% in the case of a 3d graph state on a
Raussendorf-Harrington-Goyal (RHG) lattice. We also provide a fundamental upper
bound on the correctable loss in the fault-tolerant RHG state based on the
percolation theory, which is 1.24 dB or 0.24 dB depending on whether the state
is directly generated or obtained from a simple cubic cluster state,
respectively.


  The ship-detection task in satellite imagery presents significant obstacles
to even the most state of the art segmentation models due to lack of labelled
dataset or approaches which are not able to generalize to unseen images. The
most common methods for semantic segmentation involve complex two-stage
networks or networks which make use of a multi-scale scene parsing module. In
this paper, we propose a modified version of the popular U-Net architecture
called Squeeze and Excitation U-Net and train it with a loss that helps in
directly optimizing the intersection over union (IoU) score. Our method gives
comparable performance to other methods while having the additional benefit of
being computationally efficient.


  Toroidal magnetic fields subject to the Tayler instability can transport
angular momentum. We show that the Maxwell and Reynolds stress of the
nonaxisymmetric field pattern depend linearly on the shear in the cylindrical
gap geometry. Resulting angular momentum transport also scales linear with
shear. It is directed outwards for astrophysical relevant flows and directed
inwards for superrotating flows with dOmega/dR>0. We define an eddy viscosity
based on the linear relation between shear and angular momentum transport and
show that its maximum for given Prandtl and Hartmann number depends linear on
the magnetic Reynolds number Rm. For Rm=1000 the eddy viscosity is of the size
of 30 in units of the microscopic value.


  The topology of entanglement in multipartite states with translational
invariance is discussed in this article. Two global features are foundby which
one can distinguish distinct states. These are the cyclic unit and the
quantised geometric phase. Furthermore the topology is indicated by the
fractional spin. Finally a scheme is presented for preparation of these types
of states in spin chain systems, in which the degeneracy of the energy levels
characterises the robustness of the states with translational invariance.


  It is often of interest to make inference on an unknown function that is a
local parameter of the data-generating mechanism, such as a density or
regression function. Such estimands can typically only be estimated at a
slower-than-parametric rate in nonparametric and semiparametric models, and
performing calibrated inference can be challenging. In many cases, these
estimands can be expressed as the minimizer of a population risk functional.
Here, we propose a general framework that leverages such representation and
provides a nonparametric extension of the score test for inference on an
infinite-dimensional risk minimizer. We demonstrate that our framework is
applicable in a wide variety of problems. As both analytic and computational
examples, we describe how to use our general approach for inference on a mean
regression function under (i) nonparametric and (ii) partially additive models,
and evaluate the operating characteristics of the resulting procedures via
simulations.


  Our goal is to classify all generically transitive actions of commutative
unipotent groups on flag varieties up to conjugation. We establish relationship
between this problem and classification of multiplications with certain
properties on Lie algebra representations. Then we classify multiplications
with the desired properties and solve the initial classification problem.


  FIR and submm observations have established the fundamental role of
dust-obscured star formation in the assembly of stellar mass over the past 12
billion years. At z between 2 and 4, the bulk of star formation is enshrouded
in dust, and dusty star forming galaxies (DSFGs) contain about half of the
total stellar mass density. Star formation develops in dense molecular clouds,
and is regulated by a complex interplay between all the ISM components that
contribute to the energy budget of a galaxy: gas, dust, cosmic rays,
interstellar electromagnetic fields, gravitational field, dark matter.
Molecular gas is the actual link between star forming gas and its complex
environment, providing by far the richest amount of information about the star
formation process. However, molecular lines interpretation requires complex
modeling of astrochemical networks, which regulate the molecular formation and
establishes molecular abundances in a cloud, and a modeling of the physical
conditions of the gas in which molecular energy levels become populated. This
paper critically reviews the main astrochemical parameters needed to get
predictions about molecular signals in DSFGs. We review the current knowledge
and the open questions about the interstellar medium of DSFGs, outlying the key
role of molecular gas as a tracer and shaper of the star formation process.


  Due to the limited generation and finite inertia, microgrid suffers from the
large frequency and voltage deviation which can lead to system collapse. Thus,
reliable load shedding to keep frequency stable is required. Wireless network,
benefiting from the high flexibility and low deployment cost, is considered as
a promising technology for fine-grained management. In this paper, for
balancing the supply-demand and reducing the load-shedding amount, a
distributed load shedding solution via wireless network is proposed. Firstly,
active power coordination of different priority loads is formulated as an
optimisation problem. To solve it, a distributed load shedding algorithm based
on subgradient method (DLSS) is developed for gradually shedding loads. Using
this method, power compensation can be utilised and has more time to lower the
power deficit so as to reduce the load-shedding amount. Secondly, to increase
the response rate and enhance the reliability of our method, a multicast
metropolis schedule based on TDMA (MMST) is developed. In this protocol, time
slots are dedicatedly allocated and a checking and retransmission mechanism is
utilised. Finally, the proposed solution is evaluated by NS3-Matlab
co-simulator. The numerical results demonstrate the feasibility and
effectiveness of our solution.


  The new phenomenon of semiquantum chaos is analyzed in a classically regular
double-well oscillator model. Here it arises from a doubling of the number of
effectively classical degrees of freedom, which are nonlinearly coupled in a
Gaussian variational approximation (TDHF) to full quantum mechanics. The
resulting first-order nondissipative autonomous flow system shows energy
dependent transitions between regular behavior and semiquantum chaos, which we
monitor by Poincar\'e sections and a suitable frequency correlation function
related to the density matrix. We discuss the general importance of this new
form of deterministic chaos and point out the necessity to study open
(dissipative) quantum systems, in order to observe it experimentally.


  We present a method to map the total intrinsic reddening of a foreground
extinguishing medium via the analysis of spectral energy distributions (SEDs)
of background galaxies. In this pilot study, we implement this technique in two
distinct regions of the Small Magellanic Cloud (SMC) - the bar and the southern
outskirts - using a combination of optical and near-infrared
$ugrizYJK_{\mathrm{s}}$ broadband imaging. We adopt the LePhare
$\chi^{2}$-minimisation SED-fitting routine and various samples of galaxies
and/or quasi-stellar objects to investigate the intrinsic reddening. We find
that only when we construct reddening maps using objects classified as galaxies
with low levels of intrinsic reddening (i.e. ellipticals/lenticulars and
early-type spirals), the resultant maps are consistent with previous literature
determinations i.e. the intrinsic reddening of the SMC bar is higher than that
in the outer environs. We employ two sets of galaxy templates - one theoretical
and one empirical - to test for template dependencies in the resulting
reddening maps and find that the theoretical templates imply systematically
higher reddening values by up to 0.20 mag in $E(B-V)$. A comparison with
previous reddening maps, based on the stellar components of the SMC, typically
shows reasonable agreement. There is, however, significant variation amongst
the literature reddening maps as to the level of intrinsic reddening associated
with the bar. Thus, it is difficult to unambiguously state that instances of
significant discrepancies are the result of appreciable levels of dust not
accounted for in some literature reddening maps or whether they reflect issues
with our adopted methodology.


  We present F-theories that reduce to 10D Type II Green-Schwarz superstrings.
They vary in manifest U-duality according to division between spacetime and
"internal" coordinates. They are defined by selfdual current superalgebras in
higher worldvolume dimensions with manifest $\mathrm G\times \mathrm G'$
symmetry where the spacetime symmetry $\mathrm G=\mathrm E_{n(n)}$ ranges over
the (split form of the) exceptional groups with ranks $n=\mathrm D+1 \leq 7$
and the internal symmetry $\mathrm {G'= GL(10-D)}$.


  Validating the accelerating expansion of the universe is an important issue
for understanding the evolution of the universe. By constraining the cosmic
acceleration parameter $X_H$, we can discriminate between the $\Lambda
\mathrm{CDM}$ (cosmological constant plus cold dark matter) model and LTB (the
Lema\^itre-Tolman-Bondi) model. In this paper, we explore the possibility of
constraining the cosmic acceleration parameter with the inspiral gravitational
waveform of neutron star binaries (NSBs) in the frequency range of 0.1Hz-10Hz,
which can be detected by the second-generation space-based gravitational wave
detector DECIGO. We use a convolutional neural network (CNN), a long short-term
memory (LSTM) network combined with a gated recurrent unit (GRU), and Fisher
information matrix to derive constraints on the cosmic acceleration parameter
$X_H$. Based on the simulated gravitational wave data with a time duration of 1
month, we conclude that CNN can limit the relative error to 14.09%, while LSTM
network combined with GRU can limit the relative error to 13.53%. Additionally,
using Fisher information matrix for gravitational wave data with a 5-year
observation can limit the relative error to 32.94%. Compared with the Fisher
information matrix method, deep learning techniques will significantly improve
the constraints on the cosmic acceleration parameters at different redshifts.
Therefore, DECIGO is expected to provide direct measurements of the
acceleration of the universe, by observing the chirp signals of coalescing
binary neutron stars.


  Missingness is a common occurrence in educational assessment and
psychological measurement. It could not be casually ignored as it may threaten
the validity of the test if not handled properly. Considering the difference
between omitted and not-reached items, we developed an IRT-based model to
handle these missingness. In the proposed method, not-reached responses are
captured by the cumulative missingness. Moreover, the nonignorability is
attributed to the correlation between ability and person missing trait. We
proved that its item parameters estimate under maximum marginal likelihood
(MML) estimation is consistent. We further proposed a Bayesian estimation
procedure using MCMC methods to estimate all the parameters. The simulation
results indicate that the model parameters under the proposed method are better
recovered than that under listwise deletion, and the nonignorable model fits
the simulated nonignorable nonresponses better than ignorable model in terms of
Bayesian model selection. Furthermore, the Program for International Student
Assessment (PISA) data set was analyzed to further illustrate the usage of the
proposed method.


  We provide new methods to straightforwardly obtain compact and analytic
expressions for epsilon-expansions of functions appearing in both field and
string theory amplitudes. An algebraic method is presented to explicitly solve
for recurrence relations connecting different epsilon-orders of a power series
solution in epsilon of a differential equation. This strategy generalizes the
usual iteration by Picard's method. Our tools are demonstrated for generalized
hypergeometric functions. Furthermore, we match the epsilon-expansion of
specific generalized hypergeometric functions with the underlying Drinfeld
associator with proper Lie algebra and monodromy representations. We also apply
our tools for computing epsilon-expansions for solutions to generic first-order
Fuchsian equations (Schlesinger system). Finally, we set up our methods to
systematically get compact and explicit alpha'-expansions of tree-level
superstring amplitudes to any order in alpha'.


  The global need for clean water requires sustainable technology for purifying
contaminated water. Highly efficient solar-driven photodegradation is a
sustainable strategy for wastewater treatment. In this work, we demonstrate
that the photodegradation efficiency of micropollutants in water can be
improved by ~2-24 times by leveraging polymeric microlenses (MLs). These
microlenses (MLs) are fabricated from the in-situ polymerization of surface
nanodroplets. We found that photodegradation efficiency ({\eta}) in water
correlates approximately linearly with the sum of the intensity from all focal
points of MLs, although no difference in the photodegradation pathway is
detected from the chemical analysis of the byproducts. With the same overall
power over a given surface area, {\eta} is doubled by using ordered arrays,
compared to heterogeneous MLs on an unpatterned substrate. Higher {\eta} from
ML arrays may be attributed to a coupled effect from the focal points on the
same plane that creates high local concentrations of active species to further
speed up the rate of photodegradation. As a proof-of-concept for ML-enhanced
water treatment, MLs were formed on the inner wall of glass bottles that were
used as containers for water to be treated. Three representative
micropollutants (norfloxacin, sulfadiazine, and sulfamethoxazole) in the
bottles functionalized by MLs were photodegraded by 30% to 170% faster than in
normal bottles. Our findings suggest that the ML-enhanced photodegradation may
lead to a highly efficient solar water purification approach without a large
solar collector size. Such an approach may be particularly suitable for
portable transparent bottles in remote regions.


  SynDiffix is a new open-source tool for structured data synthesis. It has
anonymization features that allow it to generate multiple synthetic tables
while maintaining strong anonymity. Compared to the more common single-table
approach, multi-table leads to more accurate data, since only the features of
interest for a given analysis need be synthesized. This paper compares
SynDiffix with 15 other commercial and academic synthetic data techniques using
the SDNIST analysis framework, modified by us to accommodate multi-table
synthetic data. The results show that SynDiffix is many times more accurate
than other approaches for low-dimension tables, but somewhat worse than the
best single-table techniques for high-dimension tables.


  In the present paper we develop a Bayesian analysis of radar target detection
that uses the parameters of conventional radar analysis to provide a valid
prediction of target presence or absence when received signals cross or fail to
cross chosen threshold values. A Positive Predictive Value parameter is added
to the normal Receiver Operating Characteristic to provide information that
allows the radar operator to make an informed decision in the choice of
threshold.


  We develop Bayesian statistical methods for discovering and assigning
probabilities to non-random (e.g., physical) stellar companions. These
companions are either presently bound or were previously bound. The
probabilities depend on similarities in corrected proper motion parallel and
perpendicular to the brighter component's motion, parallax, and the local
phase-space density of field stars. Control experiments are conducted to
understand the behavior of false positives. The technique is applied to the
Hipparcos Catalogue within 100 pc. This is the first all-sky survey to locate
escaped companions still drifting along with each other. In the <100 pc
distance range, ~220 high probability companions with separations between 0.01
- 1 pc are found. The first evidence for a population (~300) of companions
separated by 1 - 8 pc is found. We find these previously unnoticed naked-eye
companions (both with V<6th mag): Capella & 50 Per, delta Vel & HIP 43797,
Alioth (epsilon UMa), Megrez (delta UMa) & Alcor (80 UMA), gamma & tau Cen, phi
Eri & eta Hor, 62 & 63 Cnc, gamma & tau Per, zeta & delta Hya, beta01, beta02 &
beta03 Tuc, N Vel & HIP 47479, HIP 98174 & HIP 97646, 44 & 58 Oph, s Eri & HIP
14913, and pi & rho Cep. High probability fainter companions (V > 6th mag) of
primaries with V < 4th mag are found for: Fomalhaut (alpha PsA), gamma UMa,
alpha Lib, Alvahet (iota Cephi), delta Ara, beta Ser, iota Peg, beta Pic, kappa
Phe and gamma Tuc.


  We investigate the joint dynamics of spot and implied volatility from an
empirical perspective. We focus on the equity market with the SPX Index our
underlying of choice. Using only observable quantities, we extract the
instantaneous variance curves implied by the market and study their daily
variations jointly with spot returns. We analyze the characteristics of their
individual and joint densities, quantify the non-linear relationship between
spot and volatility, and discuss the modeling implications on the implied
leverage and the volatility clustering effects. We show that non-linearities
have little impact on the dynamics of at-the-money volatilities, but can have a
significant effect on the pricing and hedging of volatility derivatives.


  While sensitivity analysis improves the transparency and reliability of
mathematical models, its uptake by modelers is still scarce. This is partially
explained by its technical requirements, which may be hard to understand and
implement by the non-specialist. Here we propose a sensitivity analysis
approach based on the concept of discrepancy that is as easy to understand as
the visual inspection of input-output scatterplots. Firstly, we show that some
discrepancy measures are able to rank the most influential parameters of a
model almost as accurately as the variance-based total sensitivity index. We
then introduce an ersatz-discrepancy whose performance as a sensitivity measure
matches that of the best-performing discrepancy algorithms, is simple to
implement, easier to interpret and orders of magnitude faster.


  Based on a new sample of 355 quasars with significant optical polarization
and using complementary statistical methods, we confirm that quasar
polarization vectors are not randomly oriented over the sky with a probability
often in excess of 99.9%. The polarization vectors appear coherently oriented
or aligned over huge (~ 1 Gpc) regions of the sky located at both low (z ~ 0.5)
and high (z ~ 1.5) redshifts and characterized by different preferred
directions of the quasar polarization. In fact, there seems to exist a regular
alternance along the line of sight of regions of randomly and aligned
polarization vectors with a typical comoving length scale of 1.5 Gpc.
Furthermore, the mean polarization angle \bar{\theta} appears to rotate with
redshift at the rate of ~ 30\degr per Gpc. The symmetry of the the \bar{\theta}
-z relation is mirror-like, the mean polarization angle rotating clockwise with
increasing redshift in North Galactic hemisphere and counter-clockwise in the
South Galactic one. These characteristics make the alignment effect difficult
to explain in terms of local mechanisms, namely a contamination by interstellar
polarization in our Galaxy. While interpretations like a global rotation of the
Universe can potentially explain the effect, the properties we observe
qualitatively correspond to the dichroism and birefringence predicted by
photon-pseudoscalar oscillation within a magnetic field. Interestingly, the
alignment effect seems to be prominent along an axis not far from preferred
directions tentatively identified in the CMB maps. Although many questions and
more particularly the interpretation of the effect remain open, alignments of
quasar polarization vectors appear as a promising new way to probe the Universe
and its dark components at extremely large scales.


  The XMM-Newton spectral-fit database (XMMFITCAT) is a catalogue of spectral
fitting results for the source detections within the XMM-Newton Serendipitous
source catalogue with more than 50 net (background-subtracted) counts per
detector in the 0.5-10 keV energy band. Its most recent version, constructed
from the latest version of the XMM-Newton catalogue, the 3XMM Data Release 4
(3XMM-DR4), contains spectral-fitting results for $\gtrsim$ 114,000 detections,
corresponding to $\simeq$ 78,000 unique sources. Three energy bands are defined
and used in the construction of XMMFITCAT: Soft (0.5-2 keV), Hard (2-10 keV),
and Full (0.5-10 keV) bands. Six spectral models, three simple and three more
complex models, were implemented and applied to the spectral data. Simple
models are applied to all sources, whereas complex models are applied to
observations with more than 500 counts (30%). XMMFITCAT includes best-fit
parameters and errors, fluxes, and goodness of fit estimates for all fitted
models. XMMFITCAT has been conceived to provide the astronomical community with
a tool to construct large and representative samples of X-ray sources by
allowing source selection according to spectral properties, as well as
characterise the X-ray properties of samples selected in different wavelengths.
We present in this paper the main details of the construction of this database,
and summarise its main characteristics.


  Magnetic skyrmions can be driven by an applied spin-polarized electron
current which exerts a spin-transfer torque on the localized spins constituting
the skyrmion. However, the longitudinal dynamics is plagued by the skyrmion
Hall effect which causes the skyrmions to acquire a transverse velocity
component. We show how to use spin-orbit interaction to control the skyrmion
Hall angle and how the interplay of spin-transfer and spin-orbit torques can
lead to a complete suppression of the transverse motion. Since the spin-orbit
torques can be controlled all-electronically by a gate voltage, the skyrmion
motion can be steered all-electronically on a broad racetrack at high speed and
conceptually new writing and gating operations can be realized.


  We investigate the electrical conductivity (sigma) of quark matter via the
Kubo formula at finite temperature and zero quark density (T>0, mu=0) in the
presence of an external strong magnetic field. For this purpose, we employ the
dilute instanton-liquid model, taking into account its temperature modification
with the trivial-holonomy caloron distribution. By doing that, the momentum and
temperature dependences for the effective quark mass and model renormalization
scale are carefully evaluated. From the numerical results, it turns out that
sigma\approx(0.02 ~ 0.15)/fm for T=(0 ~ 400) MeV with the relaxation time
tau=(0.3 ~ 0.9) fm. In addition, we also parameterize the electrical
conductivity as sigma/T ~ (0.46,0.77,1.08,1.39)C_EM for tau=(0.3,0.5,0.7,0.9)
fm, respectively. These results are well compatible with other theoretical
estimations, including those from the lattice QCD simulations. It also turns
out that the external magnetic field plays only a minor role for sigma even for
the very strong one B_0 ~ m^2_pi*10 and becomes relatively effective for
T\lesssim200 MeV. Moreover, we compute the soft photon emission rate from the
quark-gluon plasma, using the electrical conductivity calculated.


  We consider the generalized momentum-depending quon algebra in a dynamically
evolving curved spacetime and perform a type of analysis similar to that of
J.W.Goodison and D.J.Toms. We find that, at least in principle, all kinds of
statistics may occur in some regions, i.e. phases in momentum space, depending
on Bogoliubov coefficients determined by a specific dynamical model.


  Foot ulcer is a common complication of diabetes mellitus and, associated with
substantial morbidity and mortality, remains a major risk factor for lower leg
amputations. Extracting accurate morphological features from foot wounds is
crucial for appropriate treatment. Although visual inspection by a medical
professional is the common approach for diagnosis, this is subjective and
error-prone, and computer-aided approaches thus provide an interesting
alternative. Deep learning-based methods, and in particular convolutional
neural networks (CNNs), have shown excellent performance for various tasks in
medical image analysis including medical image segmentation.
  In this paper, we propose an ensemble approach based on two
encoder-decoder-based CNN models, namely LinkNet and U-Net, to perform foot
ulcer segmentation. To deal with a limited number of available training
samples, we use pre-trained weights (EfficientNetB1 for the LinkNet model and
EfficientNetB2 for the U-Net model) and perform further pre-training using the
Medetec dataset while also applying a number of morphological-based and
colour-based augmentation techniques. To boost the segmentation performance, we
incorporate five-fold cross-validation, test time augmentation and result
fusion.
  Applied on the publicly available chronic wound dataset and the MICCAI 2021
Foot Ulcer Segmentation (FUSeg) Challenge, our method achieves state-of-the-art
performance with data-based Dice scores of 92.07% and 88.80%, respectively, and
is the top ranked method in the FUSeg challenge leaderboard. The Dockerised
guidelines, inference codes and saved trained models are publicly available at
https://github.com/masih4/Foot_Ulcer_Segmentation.


  We train an agent to compete in the game of Gardner minichess, a downsized
variation of chess played on a 5x5 board. We motivated and applied a SOTA
actor-critic method Proximal Policy Optimization with Generalized Advantage
Estimation. Our initial task centered around training the agent against a
random agent. Once we obtained reasonable performance, we then adopted a
version of iterative policy improvement adopted by AlphaGo to pit the agent
against increasingly stronger versions of itself, and evaluate the resulting
performance gain. The final agent achieves a near (.97) perfect win rate
against a random agent. We also explore the effects of pretraining the network
using a collection of positions obtained via self-play.


  We consider the problem of learning to simplify medical texts. This is
important because most reliable, up-to-date information in biomedicine is dense
with jargon and thus practically inaccessible to the lay audience. Furthermore,
manual simplification does not scale to the rapidly growing body of biomedical
literature, motivating the need for automated approaches. Unfortunately, there
are no large-scale resources available for this task. In this work we introduce
a new corpus of parallel texts in English comprising technical and lay
summaries of all published evidence pertaining to different clinical topics. We
then propose a new metric based on likelihood scores from a masked language
model pretrained on scientific texts. We show that this automated measure
better differentiates between technical and lay summaries than existing
heuristics. We introduce and evaluate baseline encoder-decoder Transformer
models for simplification and propose a novel augmentation to these in which we
explicitly penalize the decoder for producing "jargon" terms; we find that this
yields improvements over baselines in terms of readability.


  Changes in atomic coordination numbers at high pressures are fundamental to
condensed-matter physics because they initiate the emergence of unexpected
structures and phenomena. Silicon is capable of forming eight-, nine-, and
ten-coordinated structures under compression,in addition to the usual
six-coordinated structures. The missing seven-coordinated silicon remains an
open question, but here our theoretical study provides evidence for its
existence at high pressures. A combination of a crystal-structure prediction
method and first-principles calculations allowed prediction of a stable SiO2He
compound containing unique SiO7 polyhedrons, which is a configuration unknown
in any proposed silica phase. Consequently, seven-coordinated SiO7 is a
possible form of silica at high pressures. Further calculations indicate that
the SiO2He phase remains energetically stable with a solid character over a
wide range of pressures exceeding 607 GPa and temperatures of 0-9000 K,
covering the extreme conditions of the core-mantle boundary in super-Earth
exoplanets, or even the Solar System's ice giant planets. Our results may
provide theoretical guidance for the discovery of other silicides at high
pressures, promote the exploration of materials at planetary core-mantle
boundaries, and enable planetary models to be refined.


  In this paper, we present a formalization of Matiyasevi\v{c}'s theorem, which
states that the power function is Diophantine, forming the last and hardest
piece of the MRDP theorem of the unsolvability of Hilbert's 10th problem. The
formalization is performed within the Lean theorem prover, and necessitated the
development of a small number theory library, including in particular the
solution to Pell's equation and properties of the Pell $x,y$ sequences.


  We describe the $\mathfrak sl_3$ non-elliptic webs in terms of convex sets in
the affine building. Kuperberg defined the non-elliptic web basis in his work
on rank-$2$ spider categories. Fontaine, Kamnitzer, Kuperberg showed that the
$\mathfrak sl_3$ non-elliptic webs are dual to CAT(0) triangulated diskoids in
the affine building. We show that each such triangulated diskoid is the
intersection of the min-convex and max-convex hulls of a generic polygon in the
building. Choosing a generic polygon from each of the components of the Satake
fiber produces the non-elliptic web basis. The convex hulls in the affine
building were first introduced by Faltings and are related to tropical
convexity, as discussed in work by Joswig, Sturmfels, Yu and by Zhang.


  The information age challenges computer technology to process an
exponentially increasing computational load on a limited energy budget - a
requirement that demands an exponential reduction in energy per operation. In
digital logic circuits, the switching energy of present FET devices is
intimately connected with the switching voltage, and can no longer be lowered
sufficiently, limiting the ability of current technology to address the
challenge. Quantum computing offers a leap forward in capability, but a clear
advantage requires algorithms presently developed for only a small set of
applications. Therefore, a new, general purpose, classical technology based on
a different paradigm is needed to meet the ever increasing demand for data
processing.


  Short-form video (SFV) has become a globally popular form of entertainment in
recent years, appearing on major social media platforms. However, current
research indicate that short video addiction can lead to numerous negative
effects on both physical and psychological health, such as decreased attention
span and reduced motivation to learn. Additionally, Short-form Video Addiction
(SFVA) has been linked to other issues such as a lack of psychological support
in real life, family or academic pressure, and social anxiety. Currently, the
detection of SFVA typically occurs only after users experience negative
consequences. Therefore, we aim to construct a short video addiction dataset
based on social network behavior and design an early detection framework for
SFVA. Previous mental health detection research on online social media has
mostly focused on detecting depression and suicidal tendency. In this study, we
propose the first early detection framework for SFVA EarlySD. We first
introduce large language models (LLMs) to address the common issues of sparsity
and missing data in graph datasets. Meanwhile, we categorize social network
behavior data into different modalities and design a heterogeneous social
network structure as the primary basis for detecting SFVA. We conduct a series
of quantitative analysis on short video addicts using our self-constructed
dataset, and perform extensive experiments to validate the effectiveness of our
method EarlySD, using social data and heterogeneous social graphs in the
detection of short video addiction.


  QCD is not an approximately scale invariant theory. Hence a dilaton field is
not expected to provide a good description of the low-energy dynamics
associated with the gluon condensate. Even if such a field is introduced, it
remains almost unchanged in hadronic matter at normal densities. This is
because the large glueball mass together with the size of the phenomenological
gluon condensate ensure that changes to that condensate are very small at such
densities. Any changes in hadronic masses and decay constants in matter
generated by that condensate will be much smaller that those produced directly
by changes in the quark condensate. Hence masses and decay constants are not
expected to display a universal scaling.


  In this paper, we will show that the affine cones over any smooth Fano-Mukai
fourfold of genus $7$ are flexible.


  The limit cycle of the van der Pol oscillator, $\ddot{x}+ \epsilon (x^2-1)
\dot{x} + x =0$, is studied in the plane $(x,\dot{x})$ by applying the homotopy
analysis method. A recursive set of formulas that approximate the amplitude and
form of this limit cycle for the whole range of the parameter $\epsilon$ is
obtained. These formulas generate the amplitude with an error less than 0.1%.
To our knowledge, this is the first time where an analytical approximation of
the amplitude of the van der Pol limit cycle, with validity from the weakly up
to the strongly nonlinear regime, is given.


  The rotation curves of spiral galaxies obey strong scaling relations. These
include the Tully-Fisher and baryonic Tully-Fisher relations, and the mass
discrepancy--acceleration relation. These relations can be used to place
constraints on the mass-to-light ratios of stars. Once the stellar mass is
constrained, the distribution of dark matter follows. The shape of the dark
matter distribution is consistent with the expectations of NFW halos exterior
to 1 kpc, but the amplitude is wrong. This is presumably related to the
long-standing problem of the normalization of the Tully-Fisher relation and may
imply a downturn in the amplitude of the power spectrum at small scales. More
fundamentally, the persistent success of MOND remains a troubling fact.


  We extend the classic convergence rate theory for subgradient methods to
apply to non-Lipschitz functions. For the deterministic projected subgradient
method, we present a global $O(1/\sqrt{T})$ convergence rate for any convex
function which is locally Lipschitz around its minimizers. This approach is
based on Shor's classic subgradient analysis and implies generalizations of the
standard convergence rates for gradient descent on functions with Lipschitz or
H\"older continuous gradients. Further, we show a $O(1/\sqrt{T})$ convergence
rate for the stochastic projected subgradient method on convex functions with
at most quadratic growth, which improves to $O(1/T)$ under either strong
convexity or a weaker quadratic lower bound condition.


  We give the complete list of the 29 irreducible triangulations of the Klein
bottle. We show how the construction of Lawrencenko and Negami, which listed
only 25 such irreducible triangulations, can be modified at two points to
produce the 4 additional irreducible triangulations of the Klein bottle.


  The physical, chemical and kinetic characteristics of 12 northern infrared
dark clouds (IRDCs) are systematic studied using the $\rm ^{13}CO $ (1-0) and
$\rm C^{18}O$ (1-0) lines, observed with the PMO 13.7 m radio telescope, the
1.1 mm Bolocam Galactic Plane Survey (BGPS) data and GLIMPSE Spitzer IRAC $\rm
8 \,\mu m$ data. The molecular lines emission and 1.1 mm continuum emission
almost coincide in morphology for each IRDC and both are associated well with
the IRDCs. 10 IRDCs present the filamentary structure and substructures.
Totally, 41 IRDC cores are identified and a statistic research for them shows
that the northern IRDC cores have a typical excitation temperature $8\sim10$ K,
a integrated intensity ratio of $\rm ^{13}CO$ to $\rm C^{18}O$ $3\sim6$ and the
column density $(1\sim6)\times 10^{22}\, \rm cm^{-2}$. About $57.5\%$ of the
IRDC cores are gravitationally bound, which are more compact, warmer and
denser. In addition, we study the mass distribution functions of the whole IRDC
cores as well as the gravitational bound cores, finding that they almost have
the same power-law indexes. This indicates that the evolution of the IRDC cores
almost have no effect on the mass spectrum of the molecular cores and thus can
be used to study the stellar initial mass function. Moreover, three IRDC cores
G24.00-3, G31.38-1 and G34.43-4 are detected to have large-scaled infall
motions. Two different outflows are further found for IRDC core G34.43-4 and
one of them is in high collimation.


  We examine an extension to the theory of Gaussian wave packet dynamics in a
one-dimensional potential by means of a sequence of time dependent displacement
and squeezing transformations. Exact expressions for the quantum dynamics are
found, and relationships are explored between the squeezed system, Gaussian
wave packet dynamics, the time dependent harmonic oscillator, and wave packet
dynamics in a Gauss-Hermite basis. Expressions are given for the matrix
elements of the potential in some simple cases. Several examples are given,
including the propagation of a non-Gaussian initial state in a Morse potential.


  We studied the dynamics of an initially inverted atom in a semi-infinite
waveguide, in the presence of a single propagating photon. We show that atomic
relaxation is enhanced by a factor of 2, leading to maximal bunching in the
output field. This optimal irreversible stimulated emission is a novel
phenomenon that can be observed with state-of-the-art solid-state atoms and
waveguides. When the atom interacts with two one-dimensional electromagnetic
environments, the preferential emission in the stimulated field can be
exploited to efficiently amplify a classical or a quantum state.


  I outline Bell's vision of the "great enterprise" of science, and his view
that conventional teachings about quantum mechanics constituted a betrayal of
this enterprise. I describe a proposal of his to put the theory on a more
satisfactory footing, and review the subsequent uses that have been made of one
element of this proposal, namely Bell's transition probabilities regarded as
fundamental physical processes.


  Recent progress in few-shot learning promotes a more realistic cross-domain
setting, where the source and target datasets are from different domains. Due
to the domain gap and disjoint label spaces between source and target datasets,
their shared knowledge is extremely limited. This encourages us to explore more
information in the target domain rather than to overly elaborate training
strategies on the source domain as in many existing methods. Hence, we start
from a generic representation pre-trained by a cross-entropy loss and a
conventional distance-based classifier, along with an image retrieval view, to
employ a re-ranking process for calibrating a target distance matrix by
discovering the reciprocal k-nearest neighbours within the task. Assuming the
pre-trained representation is biased towards the source, we construct a
non-linear subspace to minimise task-irrelevant features therewithin while keep
more transferrable discriminative information by a hyperbolic tangent
transformation. The calibrated distance in this target-aware non-linear
subspace is complementary to that in the pre-trained representation. To impose
such distance calibration information onto the pre-trained representation, a
Kullback-Leibler divergence loss is employed to gradually guide the model
towards the calibrated distance-based distribution. Extensive evaluations on
eight target domains show that this target ranking calibration process can
improve conventional distance-based classifiers in few-shot learning.


  We consider the following tokenization repair problem: Given a natural
language text with any combination of missing or spurious spaces, correct
these. Spelling errors can be present, but it's not part of the problem to
correct them. For example, given: "Tispa per isabout token izaionrep air",
compute "Tis paper is about tokenizaion repair". We identify three key
ingredients of high-quality tokenization repair, all missing from previous
work: deep language models with a bidirectional component, training the models
on text with spelling errors, and making use of the space information already
present. Our methods also improve existing spell checkers by fixing not only
more tokenization errors but also more spelling errors: once it is clear which
characters form a word, it is much easier for them to figure out the correct
word. We provide six benchmarks that cover three use cases (OCR errors, text
extraction from PDF, human errors) and the cases of partially correct space
information and all spaces missing. We evaluate our methods against the best
existing methods and a non-trivial baseline. We provide full reproducibility
under https://ad.cs.uni-freiburg.de/publications .


  The ARES linac at DESY aims to generate and characterize ultrashort electron
bunches (fs to sub-fs duration) with high momentum and arrival time stability
for the purpose of applications related to accelerator R&D, e.g. development of
advanced and compact diagnostics and accelerating structures, test of new
accelerator components, medical applications studies, machine learning, etc.
During its commissioning phase, the bunch duration characterization of the
electron bunches generated at ARES has been performed with an RF-phasing
technique relying on momentum spectra measurements, using only common
accelerator elements (RF accelerating structures and magnetic spectrometers).
The sensitivity of the method allowed highlighting different response times for
Mo and Cs2Te cathodes. The measured electron bunch duration in a wide range of
machine parameters shows excellent agreement overall with the simulation
predictions, thus demonstrating a very good understanding of the ARES operation
on the bunch duration aspect. The importance of a precise in-situ experimental
determination of the phase velocity of the first travelling wave accelerating
structure after the electron source, for which we propose a simple new
beam-based method precise down to sub-permille variation respective to the
speed of light in vacuum, is emphasized for this purpose. A minimum bunch
duration of 20 fs rms, resolution-limited by the space charge forces, is
reported. This is, to the best of our knowledge, around 4 times shorter than
what has been previously experimentally demonstrated based on RF-phasing
techniques with a single RF structure. The present study constitutes a strong
basis for future time characterization down to the sub-fs level at ARES, using
dedicated X-band transverse deflecting structures.


  Reliability, security and stability of cloud services without sacrificing too
much resources have become a desired feature in the area of workload management
in clouds. The paper proposes and evaluates a lightweight framework for
scheduling a workload which part could be unreliable. This unreliability could
be caused by various types of failures or attacks. Our framework for robust
workload scheduling efficiently combines classic fault-tolerant and security
tools, such as packet/job scanning, with workload scheduling, and it does not
use any heavy resource-consuming tools, e.g., cryptography or non-linear
optimization. More specifically, the framework uses a novel objective function
to allocate jobs to servers and constantly decides which job to scan based on a
formula associated with the objective function. We show how to set up the
objective function and the corresponding scanning procedure to make the system
provably stable, provided it satisfies a specific stability condition. As a
result, we show that our framework assures cloud stability even if naive
scanning-all and scanning-none strategies are not stable. We extend the
framework to decentralized scheduling and evaluate it under several popular
routing procedures.


  A new class of black hole solutions of the five dimensional minimal gauged
supergravity is presented. They are characterized by the mass, the electric
charge, two equal magnitude angular momenta and the magnitude of the magnetic
potential at infinity. These black holes possess a horizon of spherical
topology; however, both the horizon and the sphere at infinity can be
arbitrarily squashed, with nonextremal solutions interpolating between black
strings and black branes. A particular set of extremal configurations
corresponds to a new one-parameter family of supersymmetric black holes. While
their conserved charges are determined by the squashing of the sphere at
infinity, these supersymmetric solutions possess the same horizon geometry.


  Motivated by recent experiments on AV$_3$Sb$_5$ (A=K,Rb,Cs), the chiral flux
phase has been proposed to explain time-reversal symmetry breaking. To fully
understand the physics behind the chiral flux phase, we construct a low-energy
effective theory based on the van-Hove points around the Fermi surface. The
possible symmetry-breaking states and their classifications of the low-energy
effective theory are completely studied, especially the flux phases on Kagome
lattice. In addition, we discuss the relations between the low-energy symmetry
breaking orders, the chiral flux and charge bond orders. We find all possible
183 flux phases on Kagome lattice within 2*2 unit cell by brute-force approach
and classify them by point group symmetry. Among the 183 phases, we find 3
classes in 1*1 unit cell, 8 classes in 1*2 unit cell and 18 classes in 2*2 unit
cell, respectively. These results provide a full picture of the time-reversal
symmetry breaking in Kagome lattices.


  Recommendation systems form the center piece of a rapidly growing trillion
dollar online advertisement industry. Even with numerous optimizations and
approximations, collaborative filtering (CF) based approaches require real-time
computations involving very large vectors. Curating and storing such related
profile information vectors on web portals seriously breaches the user's
privacy. Modifying such systems to achieve private recommendations further
requires communication of long encrypted vectors, making the whole process
inefficient. We present a more efficient recommendation system alternative, in
which user profiles are maintained entirely on their device, and appropriate
recommendations are fetched from web portals in an efficient privacy preserving
manner. We base this approach on association rules.


  The interaction of a resonant light field with a quantum two-level system is
of key interest both for fundamental quantum optics and quantum technological
applications employing resonant excitation. While emission under resonant
continuous-wave excitation has been well-studied, the more complex emission
spectrum of dynamically dressed states, a quantum two-level system driven by
resonant pulsed excitation, has so far been investigated in detail only
theoretically. Here, we present the first experimental observation of the
complete resonance fluorescence emission spectrum of a single quantum two-level
system, in form of an excitonic transition in a semiconductor quantum dot,
driven by finite Gaussian pulses. We observe multiple emerging sidebands as
predicted by theory with an increase of their number and spectral detuning with
excitation pulse intensity and a dependence of their spectral shape and
intensity on the pulse length. Detuning-dependent measurements provide
additional insights into the emission features. The experimental results are in
excellent agreement with theoretical calculations of the emission spectra,
corroborating our findings.


  The Flory Huggins equation of state for monodisperse polymers can be turned
into a density functional by adding a square gradient term, with a coefficient
fixed by appeal to RPA (random phase approximation). We present instead a model
nonlocal functional in which each polymer is replaced by a deterministic,
penetrable particle of known shape. This reproduces the RPA and square gradient
theories in the small deviation and/or weak gradient limits, and can readily be
extended to polydisperse chains. The utility of the new functional is shown for
the case of a polydisperse polymer solution at coexistence in a poor solvent.


  Today's Internet is awash in memes as they are humorous, satirical, or ironic
which make people laugh. According to a survey, 33% of social media users in
age bracket [13-35] send memes every day, whereas more than 50% send every
week. Some of these memes spread rapidly within a very short time-frame, and
their virality depends on the novelty of their (textual and visual) content. A
few of them convey positive messages, such as funny or motivational quotes;
while others are meant to mock/hurt someone's feelings through sarcastic or
offensive messages. Despite the appealing nature of memes and their rapid
emergence on social media, effective analysis of memes has not been adequately
attempted to the extent it deserves.
  In this paper, we attempt to solve the same set of tasks suggested in the
SemEval'20-Memotion Analysis competition. We propose a multi-hop
attention-based deep neural network framework, called MHA-MEME, whose prime
objective is to leverage the spatial-domain correspondence between the visual
modality (an image) and various textual segments to extract fine-grained
feature representations for classification. We evaluate MHA-MEME on the
'Memotion Analysis' dataset for all three sub-tasks - sentiment classification,
affect classification, and affect class quantification. Our comparative study
shows sota performances of MHA-MEME for all three tasks compared to the top
systems that participated in the competition. Unlike all the baselines which
perform inconsistently across all three tasks, MHA-MEME outperforms baselines
in all the tasks on average. Moreover, we validate the generalization of
MHA-MEME on another set of manually annotated test samples and observe it to be
consistent. Finally, we establish the interpretability of MHA-MEME.


  Aims: We simulate the spectra of massive binaries at different phases of the
orbital cycle, accounting for the gravitational influence of the companion star
on the shape and physical properties of the stellar surface. Methods: We used
the Roche potential modified to account for radiation pressure to compute the
stellar surface of close circular systems and we used the TIDES code for
surface computation of eccentric systems. In both cases, we accounted for
gravity darkening and mutual heating generated by irradiation to compute the
surface temperature. We then interpolated NLTE plane-parallel atmosphere model
spectra in a grid to obtain the local spectrum at each surface point. We
finally summed all contributions, accounting for the Doppler shift,
limb-darkening, and visibility to obtain the total synthetic spectrum. We
computed different orbital phases and sets of physical and orbital parameters.
Results: Our models predict line strength variations through the orbital cycle,
but fail to completely reproduce the Struve-Sahade effect. Including radiation
pressure allows us to reproduce a surface temperature distribution that is
consistent with observations of semi-detached binary systems. Conclusions:
Radiation pressure effects on the stellar surface are weak in (over)contact
binaries and well-detached systems but can become very significant in
semi-detached systems. The classical von Zeipel theorem is sufficient for the
spectral computation. Broad-band light curves derived from the spectral
computation are different from those computed with a model in which the stellar
surfaces are equipotentials of the Roche potential scaled by the instantaneous
orbital separation. In many cases, the fit of two Gaussian/Lorentzian profiles
fails to properly measure the equivalent width of the lines and leads to
apparent variations that could explain some of the effects reported in the
literature.


  We establish a pair of criteria for proving that most knot complements
obtained as Dehn fillings of a given two-component hyperbolic link complement
lack hidden symmetries. To do this, we use certain rational functions on
varieties associated to the link. We apply our criteria to show that among
certain infinite families of knot complements, all but finitely many members
lack hidden symmetries.


  Medium modification of \gamma-tagged jets in high-energy heavy-ion collisions
is investigated within a Linearized Boltzmann Transport model for jet
propagation that includes both elastic parton scattering and induced gluon
emission. Inclusion of recoiled medium partons in the reconstruction of
partonic jets is found to significantly reduce the net jet energy loss.
Experimental data on \gamma-jet asymmetry and survival rate in Pb + Pb
collisions at \sqrt{s}=2.76 TeV can be reproduced. Medium modifications of
reconstructed jet fragmentation function, transverse profile and energy flow
outside the jet-cone are found to be sizable especially for \gamma-tagged jets
with small values of x=p_T^{jet}/p_T^\gamma.


  The next generation of bolometric experiments searching for rave events, in
particular for the neutrino-less double beta decay, needs fast,
high-sensitivity and easy-to-scale cryogenic light detectors. The CALDER
project (2014-2020) developed a new technology for light detection at cryogenic
temperature. In this paper we describe the achievements and the final prototype
of this project, consisting of a $5\times5$ cm$^2$, 650 $\mu$m thick silicon
substrate coupled to a single kinetic inductance detector made of a three-layer
aluminum-titanium-aluminum. The baseline energy resolution is
34$\pm$1(stat)$\pm$2(syst) eV RMS and the response time is 120 $\mu$s. These
features, along with the natural multiplexing capability of kinetic inductance
detectors, meet the requirements of future large-scale experiments.


  In the Nikoli pencil-and-paper game Tatamibari, a puzzle consists of an $m
\times n$ grid of cells, where each cell possibly contains a clue among +, -,
|. The goal is to partition the grid into disjoint rectangles, where every
rectangle contains exactly one clue, rectangles containing + are square,
rectangles containing - are strictly longer horizontally than vertically,
rectangles containing | are strictly longer vertically than horizontally, and
no four rectangles share a corner. We prove this puzzle NP-complete,
establishing a Nikoli gap of 16 years. Along the way, we introduce a gadget
framework for proving hardness of similar puzzles involving area coverage, and
show that it applies to an existing NP-hardness proof for Spiral Galaxies. We
also present a mathematical puzzle font for Tatamibari.


  We study the dynamical out-of-equilibrium behavior of a $J = \pm 1$ Ising
spin glass on quenched $\phi^{3}$ graphs. We show that magnetization and energy
decay with a power law behavior, with exponents that are linear in
$\frac{T}{T_{c}}$. Quenched $\phi^{3}$ graphs turn out to be a very effective
way to study numerically mean field spin glasses.


  The efficiency and uniqueness of the diffusive shock acceleration is studied
on the basis of the novel kinetic solutions. These solutions obtained earlier
(paper I, astro-ph/9707152) selfconsistently describe a strong coupling of
cosmic rays with the gas flow. They show that the dependence of the
acceleration efficiency upon physical parameters is critical in nature. In this
paper we investigate a steady acceleration in the parameter space formed by the
injection rate (\nu ), the upper cut-off momentum p_1 and the Mach number M
while the flow compression R serves as an order parameter. We determine a
manifold of all possible solutions in this parameter space.


  We consider the conventional left-right gauge extension of the standard model
of quarks and leptons without a scalar bidoublet. We study systematically how
one-loop radiative Dirac neutrino masses may be obtained. In addition to two
well-known cases from almost 30 years ago, we find two new scenarios with
verifiable predictions.


  Tracking the displacement between the pre- and post-deformed radio-frequency
(RF) frames is a pivotal step of ultrasound elastography, which depicts tissue
mechanical properties to identify pathologies. Due to ultrasound's poor ability
to capture information pertaining to the lateral direction, the existing
displacement estimation techniques fail to generate an accurate lateral
displacement or strain map. The attempts made in the literature to mitigate
this well-known issue suffer from one of the following limitations: 1) Sampling
size is substantially increased, rendering the method computationally and
memory expensive. 2) The lateral displacement estimation entirely depends on
the axial one, ignoring data fidelity and creating large errors. This paper
proposes exploiting the effective Poisson's ratio (EPR)-based mechanical
correspondence between the axial and lateral strains along with the RF data
fidelity and displacement continuity to improve the lateral displacement and
strain estimation accuracies. We call our techniques MechSOUL
(Mechanically-constrained Second-Order Ultrasound eLastography) and L1-MechSOUL
(L1-norm-based MechSOUL), which optimize L2- and L1-norm-based penalty
functions, respectively. Extensive validation experiments with simulated,
phantom, and in vivo datasets demonstrate that MechSOUL and L1-MechSOUL's
lateral strain and EPR estimation abilities are substantially superior to those
of the recently-published elastography techniques. We have published the MATLAB
codes of MechSOUL and L1-MechSOUL at http://code.sonography.ai.


  Imitation learning, in which learning is performed by demonstration, has been
studied and advanced for sequential decision-making tasks in which a reward
function is not predefined. However, imitation learning methods still require
numerous expert demonstration samples to successfully imitate an expert's
behavior. To improve sample efficiency, we utilize self-supervised
representation learning, which can generate vast training signals from the
given data. In this study, we propose a self-supervised representation-based
adversarial imitation learning method to learn state and action representations
that are robust to diverse distortions and temporally predictive, on non-image
control tasks. In particular, in comparison with existing self-supervised
learning methods for tabular data, we propose a different corruption method for
state and action representations that is robust to diverse distortions. We
theoretically and empirically observe that making an informative feature
manifold with less sample complexity significantly improves the performance of
imitation learning. The proposed method shows a 39% relative improvement over
existing adversarial imitation learning methods on MuJoCo in a setting limited
to 100 expert state-action pairs. Moreover, we conduct comprehensive ablations
and additional experiments using demonstrations with varying optimality to
provide insights into a range of factors.


  Transition state calculation is a critical technique to understand and
predict versatile dynamical phenomena in solids. However, the transition state
results obtained at zero Kelvin are often utilized for prediction or
interpretation of dynamical processes at high temperatures, and the error bars
of such approximation are largely unknown. In this benchmark study, all the
major temperature effects including lattice expansion, lattice vibration,
electron excitation, and band-edge shift are evaluated with first-principles
calculations for defects diffusion in solids. With inclusion of these
temperature effects, the notable discrepancies between theoretical predictions
at zero Kelvin and the experimental diffusivities at high temperatures are
dramatically reduced. Particularly, we find that the lattice expansion and
lattice vibration are dominant factors lowering the defect formation energies
and hopping barriers at high temperatures, but the electron excitation exhibits
minor effects. In sharp contrast to typical assumption, the attempt frequency
with lattice expansion and vibration varies significantly with materials:
several THz for aluminum bulk but surprisingly over 500 THz for 4H-SiC. For
defects in semiconductors, the band-edge shift is also significant at high
temperatures and plays a vital role in the defect diffusion. We expect this
study would help accurately predict the dynamical processes at high
temperatures.


  We present preliminary abundance analyses of a group of high-velocity
metal-poor stars. This report describes our work in progress. We examine the
derived abundances in the context of previous studies of metal-poor stars with
unusual abundances and/or extreme galactocentric orbits. Included in this
sample are BD+80 245, a star previously known to have unusually low
alpha-element abundances, and G4-36, a new low-alpha star.


  Equivariant graph neural networks force fields (EGraFFs) have shown great
promise in modelling complex interactions in atomic systems by exploiting the
graphs' inherent symmetries. Recent works have led to a surge in the
development of novel architectures that incorporate equivariance-based
inductive biases alongside architectural innovations like graph transformers
and message passing to model atomic interactions. However, thorough evaluations
of these deploying EGraFFs for the downstream task of real-world atomistic
simulations, is lacking. To this end, here we perform a systematic benchmarking
of 6 EGraFF algorithms (NequIP, Allegro, BOTNet, MACE, Equiformer, TorchMDNet),
with the aim of understanding their capabilities and limitations for realistic
atomistic simulations. In addition to our thorough evaluation and analysis on
eight existing datasets based on the benchmarking literature, we release two
new benchmark datasets, propose four new metrics, and three challenging tasks.
The new datasets and tasks evaluate the performance of EGraFF to
out-of-distribution data, in terms of different crystal structures,
temperatures, and new molecules. Interestingly, evaluation of the EGraFF models
based on dynamic simulations reveals that having a lower error on energy or
force does not guarantee stable or reliable simulation or faithful replication
of the atomic structures. Moreover, we find that no model clearly outperforms
other models on all datasets and tasks. Importantly, we show that the
performance of all the models on out-of-distribution datasets is unreliable,
pointing to the need for the development of a foundation model for force fields
that can be used in real-world simulations. In summary, this work establishes a
rigorous framework for evaluating machine learning force fields in the context
of atomic simulations and points to open research challenges within this
domain.


  This paper presents a novel on-line path planning method that enables aerial
robots to interact with surfaces. We present a solution to the problem of
finding trajectories that drive a robot towards a surface and move along it.
Triangular meshes are used as a surface map representation that is free of
fixed discretization and allows for very large workspaces. We propose to
leverage planar parametrization methods to obtain a lower-dimensional
topologically equivalent representation of the original surface. Furthermore,
we interpret the original surface and its lower-dimensional representation as
manifold approximations that allow the use of Riemannian Motion Policies
(RMPs), resulting in an efficient, versatile, and elegant motion generation
framework. We compare against several Rapidly-exploring Random Tree (RRT)
planners, a customized CHOMP variant, and the discrete geodesic algorithm.
Using extensive simulations on real-world data we show that the proposed
planner can reliably plan high-quality near-optimal trajectories at minimal
computational cost. The accompanying multimedia attachment demonstrates
feasibility on a real OMAV. The obtained paths show less than 10% deviation
from the theoretical optimum while facilitating reactive re-planning at kHz
refresh rates, enabling flying robots to perform motion planning for
interaction with complex surfaces.


  The Jiangmen Underground Neutrino Observatory (JUNO) will install about
18,000 20-inch Photomultiplier Tubes (PMTs) in the center detector to achieve
3%/sqrt(E(MeV)) energy resolution. From the full detector Monte Carlo (MC)
simulation, besides the liquid scitillator (LS) and Acrylic nodes, PMT glass
has the largest contribution to the natural radioactive background. Various
technologies have been developed in the Chinese industry to control the
environment and to improve the production process. We have monitored the glass
production for more than two months, and the radioactivity in glass was
measured using a low background gamma ray spectrometer equipped with a high
resolution HPGe detector. The 238U, 232Th and 40K of the glass bulb are reduced
by a factor of 2, 9 and 15 respectively, and now they can reach 2.5 Bq/kg for
238U, 0.5 Bq/kg for 232Th and 0.5 Bq/kg for 40K.


  The harmonious chromatic number of a graph $G$ is the minimum number of
colors that can be assigned to the vertices of $G$ in a proper way such that
any two distinct edges have different color pairs. This paper gives various
results on harmonious chromatic number related to homomorphisms, incidence
graphs of finite linear systems, and some circulant graphs.


  Let $X$ be a normal complex space such that the tangent sheaf $T_X$ is
locally free and locally admits a basis consisting of pairwise commuting vector
fields. Then $X$ is smooth.


  This paper is intended to tackle the control problem associated with an
extended phase field system of Cahn-Hilliard type that is related to a tumor
growth model. This system has been investigated in previous contributions from
the viewpoint of well-posedness and asymptotic analyses. Here, we aim to extend
the mathematical studies around this system by introducing a control variable
and handling the corresponding control problem. We try to keep the potential as
general as possible, focusing our investigation towards singular potentials,
such as the logarithmic one. We establish the existence of optimal control, the
Lipschitz continuity of the control-to-state mapping and even its Fr\'echet
differentiability in suitable Banach spaces. Moreover, we derive the
first-order necessary conditions that an optimal control has to satisfy.


  This paper introduces the atomic Write and Read Next ($\text{WRN}_{k}$)
deterministic shared memory object, that for any $k\ge3$, is stronger than
read-write registers, but is unable to implement $2$-processor consensus. In
particular, it refutes the conjecture claiming that every deterministic object
of consensus number $1$ is computationally equivalent to read-write registers.