  Stellar photospheres of post-asymptotic giant branch stars bear witness to
the internal chemical enrichment processes, integrated over their entire
stellar evolution. Here we study post-AGB stars in the Large Magellanic Cloud
(LMC). With their known distances, these rare objects are ideal tracers of AGB
nucleosynthesis and dredge-up phenomena.
  We used the UVES spectrograph mounted on the Very Large Telescope (VLT) at
the European Southern Observatory (ESO), to obtain high-resolution spectra with
high signal-to-noise (S/N) of a sample of four post-AGB stars. The objects
display a spectral energy distribution (SED) that indicates the presence of
circumstellar dust.
  All objects are carbon-rich, and strongly enhanced in s-process elements. We
deduced abundances of heavy s-process elements for all stars in the sample, and
even found an indication of the presence of Hg in the spectrum of one object.
The metallicity of all stars except J053253.51-695915.1 is considerably lower
than the average value that is observed for the LMC. The derived luminosities
show that we witness the late evolution of low-mass stars with initial masses
close to 1 solar mass. An exception is J053253.51-695915.1 and we argue that
this object is likely a binary.
  We confirmed the correlation between the efficiency of the third-dredge up
and the neutron exposure that is detected in Galactic post-AGB stars. The
non-existence of a correlation between metallicity and neutron irradiation is
also confirmed and expanded to smaller metallicities. We confirm the status of
21-micron stars as post-Carbon stars. Current theoretical AGB models
overestimate the observed C/O ratios and fail to reproduce the variety of
s-process abundance patterns that is observed in otherwise very similar
objects. Similar results have recently been found for a post-AGB star in the
Small Magellanic Cloud (SMC).


  When vector mesons are considered on the same footing as pions as suggested
by hidden local symmetry, the property of the nuclear tensor forces is strongly
controlled by the behavior of the vector mesons in dense medium. This led to BR
scaling in 1991. When baryons as skyrmions are put on crystal, there can be a
phase transition from skyrmions to half-skyrmions at a density above that of
normal nuclear matter. This topology change can induce fundamental changes to
the parameters in hidden local symmetric Lagrangian, hence BR scaling, and
brings a drastic modification to the structure of nuclear forces, in
particulrar, the tensor forces. This can have far-reaching consequences on the
EoS of compact-star matter and the structure of neutron-rich nuclei.


  We consider the compactification of type IIB superstring theory on K3
$\times$ K3. We obtain the massless spectrum of the resulting two dimensional
theory and show that the model is free of gravitational anomaly. We then
consider an orbifold and an orientifold projection of the above model and find
that their spectrum match identically and are anomaly-free as well. This gives
a dual pair of type IIB theory in two dimensions and can be understood as a
consequence of SL(2, Z) symmetry of the ten dimensional theory. We also point
out the M-theory duals of the type IIB compactifications considered here.


  Social media platforms provide users the freedom of expression and a medium
to exchange information and express diverse opinions. Unfortunately, this has
also resulted in the growth of abusive content with the purpose of
discriminating people and targeting the most vulnerable communities such as
immigrants, LGBT, Muslims, Jews and women. Because abusive language is
subjective in nature, there might be highly polarizing topics or events
involved in the annotation of abusive contents such as hate speech (HS).
Therefore, we need novel approaches to model conflicting perspectives and
opinions coming from people with different personal and demographic
backgrounds. In this paper, we present an in-depth study to model polarized
opinions coming from different communities under the hypothesis that similar
characteristics (ethnicity, social background, culture etc.) can influence the
perspectives of annotators on a certain phenomenon. We believe that by relying
on this information, we can divide the annotators into groups sharing similar
perspectives. We can create separate gold standards, one for each group, to
train state-of-the-art deep learning models. We can employ an ensemble approach
to combine the perspective-aware classifiers from different groups to an
inclusive model. We also propose a novel resource, a multi-perspective English
language dataset annotated according to different sub-categories relevant for
characterising online abuse: hate speech, aggressiveness, offensiveness and
stereotype. By training state-of-the-art deep learning models on this novel
resource, we show how our approach improves the prediction performance of a
state-of-the-art supervised classifier.


  Java projects are often built on top of various third-party libraries. If
multiple versions of a library exist on the classpath, JVM will only load one
version and shadow the others, which we refer to as dependency conflicts. This
would give rise to semantic conflict (SC) issues, if the library APIs
referenced by a project have identical method signatures but inconsistent
semantics across the loaded and shadowed versions of libraries. SC issues are
difficult for developers to diagnose in practice, since understanding them
typically requires domain knowledge. Although adapting the existing test
generation technique for dependency conflict issues, Riddle, to detect SC
issues is feasible, its effectiveness is greatly compromised. This is mainly
because Riddle randomly generates test inputs, while the SC issues typically
require specific arguments in the tests to be exposed. To address that, we
conducted an empirical study of 75 real SC issues to understand the
characteristics of such specific arguments in the test cases that can capture
the SC issues. Inspired by our empirical findings, we propose an automated
testing technique Sensor, which synthesizes test cases using ingredients from
the project under test to trigger inconsistent behaviors of the APIs with the
same signatures in conflicting library versions. Our evaluation results show
that \textsc{Sensor} is effective and useful: it achieved a $Precision$ of
0.803 and a $Recall$ of 0.760 on open-source projects and a $Precision$ of
0.821 on industrial projects; it detected 150 semantic conflict issues in 29
projects, 81.8\% of which had been confirmed as real bugs.


  Goursat showed that in the presence of an intermediate integral, the problem
of solving a second-order Monge-Ampere equation can be reduced to solving a
first-order equation, in the sense that the generic solution of the first-order
equation will also be a solution of the original equation. An attempt by
Hermann to give a rigorous proof of this fact contains an error; we show that
there exists an essentially unique counterexample to Hermann's assertion and
state and prove a correct theorem.


  SN2009ku, discovered by Pan-STARRS-1, is a Type Ia supernova (SNIa), and a
member of the distinct SN2002cx-like class of SNeIa. Its light curves are
similar to the prototypical SN2002cx, but are slightly broader and have a later
rise to maximum in g. SN2009ku is brighter (~0.6 mag) than other SN2002cx-like
objects, peaking at M_V = -18.4 mag - which is still significantly fainter than
typical SNeIa. SN2009ku, which had an ejecta velocity of ~2000 kms^-1 at 18
days after maximum brightness is spectroscopically most similar to SN2008ha,
which also had extremely low-velocity ejecta. However, SN2008ha had an
exceedingly low luminosity, peaking at M_V = -14.2 mag, ~4 mag fainter than
SN2009ku. The contrast of high luminosity and low ejecta velocity for SN2009ku
is contrary to an emerging trend seen for the SN2002cx class. SN2009ku is a
counter-example of a previously held belief that the class was more homogeneous
than typical SNeIa, indicating that the class has a diverse progenitor
population and/or complicated explosion physics. As the first example of a
member of this class of objects from the new generation of transient surveys,
SN2009ku is an indication of the potential for these surveys to find rare and
interesting objects.


  Epoch extraction has become increasingly popular in recent years for speech
analysis research because accurately detecting the location of the Epoch is
crucial for analyzing speech signals. The Epoch, occurring at the instant of
excitation in the vocal tract system, particularly during glottal closure,
plays a significant role in differentiating speakers in multi-speaker
conversations. However, the extraction of the Epoch poses a challenge due to
the time-varying factors in the vocal tract system, which makes deconvolution
for obtaining the original excitation location more complex. In this paper,
various methods for Epoch extraction, including Zero Frequency Filtering (ZFF)
and Zero Frequency Resonator (ZFR), will be discussed, and their pros and cons
evaluated. In addition, the stability, accuracy, and feasibility of each method
will be compared. The evaluation will involve a Matlab-based locking algorithm,
and a proposed hardware implementation using Raspberry pi for speaker
differentiation. The experiment includes six individuals uttering the phrase
"The University of Mississippi," with one person acting as the reference or
"lock" speaker. The number of epochs occurring at similar positions to the
reference speaker will be counted as Delta, with larger Delta values indicating
greater speaker similarity. Experimental results demonstrate that when the
speaker remains the same, the average number of Delta is 7.5, while for
different speakers, the average number of Delta decreases to 3, 2, 2, and 1,
respectively, representing a decrease of approximately 73% in the number of
epochs at similar positions compared to the reference speaker.


  The phenomenon of negative-U behavior, where a defect traps a second charge
carrier more strongly than the first, has been established in many host
crystals. Here we report the case of four-carrier transitions for both vacancy
defects in Sb2Se3. A global structure searching strategy is employed to explore
the defect energy landscape from first-principles, revealing
previously-unrealized configurations which facilitate a major charge
redistribution. Thermodynamic analysis of the accessible charge states reveals
a four-electron negative-U transition (delta q = 4) for both V_Se and V_Sb and,
by consequence, amphoteric behavior for all intrinsic defects in Sb2Se3, with
impact on its usage in solar cells. To the best of our knowledge, four-electron
negative-U behavior has not been previously explored in this or other
compounds. The unusual behavior is facilitated by valence alternation, a
reconfiguration of the local bonding environments, characteristic of both Se
and Sb.


  After shortly analyzing data relevant to fission hindrance of odd-A nuclei
and high-$K$ isomers in superheavy (SH) region we point out the inconsistency
of current fission theory and propose an approach based on the instanton
formalism. A few results of this method, simplified by replacing
selfconsistency by elements of the macro-micro model, are given to illustrate
its features.


  We report the synthesis and characterization of bulk form diluted magnetic
semiconductors Ba(Zn1-2xMnxCox)2As2 (0 <= x <= 0.15) with a crystal structure
identical to that of 122-type Fe-based superconductors. Mn and Co co-doping
into the parent compound BaZn2As2 results in a ferromagnetic ordering below TC
~ 80 K. Hall effect measurements indicate that the carrier are n-type with the
density of ~10^17/cm3. The common crystal structure and excellent lattice
matching between the p-type ferromagnetic (Ba1-yKy)(Zn1-xMnx)2As2, the n-type
ferromagnetic Ba(Zn1-2xMnxCox)2As2, the antiferrmagnetic BaMn2As2 and the
superconducting Ba(Fe1-xCox)2As2 systems make it possible to make various
junctions between these systems through the As layer.


  The core of the Web is a hyperlink navigation system collaboratively set up
by webmasters to help users find desired websites. But does this system really
work as expected? We show that the answer seems to be negative: there is a
substantial mismatch between hyperlinks and the pathways that users actually
take. A closer look at empirical surfing activities reveals the reason of the
mismatch: webmasters try to build a global virtual world without geographical
or cultural boundaries, but users in fact prefer to navigate within more
fragmented, language-based groups of websites. We call this type of behavior
"preferential navigation" and find that it is driven by "local" search engines.


  We study a testable dark matter (DM) model outside of the standard WIMP
paradigm in which the observed ratio $\Omega_{\rm dark} \simeq \Omega_{\rm
visible}$ for visible and dark matter densities finds its natural explanation
as a result of their common QCD origin when both types of matter (DM and
visible) are formed at the QCD phase transition and both are proportional to
$\Lambda_{\rm QCD}$. Instead of conventional "baryogenesis" mechanism we
advocate a paradigm when the "baryogenesis" is actually a charge separation
process which always occur in the presence of the $\cal{CP}$ odd axion field
$a(x)$. In this scenario the global baryon number of the Universe remains zero,
while the unobserved anti-baryon charge is hidden in form of heavy nuggets,
similar to Witten's strangelets and compromise the DM of the Universe. We argue
that the nuggets will be inevitably produced during the QCD phase transition as
a result of Kibble-Zurek mechanism on formation of the topological defects
during a phase transition. Relevant topological defects in our scenario are the
closed bubbles made of the $N_{\rm DW}=1$ axion domain walls. These bubbles, in
general, accrete the baryon (or anti baryon) charge, which eventually result in
formation of the nuggets and anti-nuggets carrying a huge baryon (anti-baryon)
charge. The main consequence of the model, $\Omega_{\rm dark} \approx
\Omega_{\rm visible}$ is insensitive to the axion mass which may assume any
value within the observationally allowed window $10^{-6} {\rm eV} \lesssim m_a
\lesssim 10^{-3}{\rm eV}$. We also estimate the baryon to entropy ratio
$\eta\equiv {n_B}/{n_{\gamma}}\sim 10^{-10}$ within this scenario. Finally, we
comment on implications of these results to the axion search experiments,
including microwave cavity and the Orpheus experiments.


  We present RetailOpt, a novel opt-in, easy-to-deploy system for tracking
customer movements offline in indoor retail environments. The system uses
readily accessible information from customer smartphones and retail apps,
including motion data, store maps, and purchase records. This eliminates the
need for additional hardware installations/maintenance and ensures customers
full data control. Specifically, RetailOpt first uses inertial navigation to
recover relative trajectories from smartphone motion data. The store map and
purchase records are cross-referenced to identify a list of visited shelves,
providing anchors to localize the relative trajectories in a store through
continuous and discrete optimization. We demonstrate the effectiveness of our
system in five diverse environments. The system, if successful, would produce
accurate customer movement data, essential for a broad range of retail
applications including customer behavior analysis and in-store navigation.


  The metal-based noble gas compounds exhibit interesting behavior of
electronic valence states under pressure. For example, Xe upon compression can
gain electrons from the alkali metal, or lose electrons unexpectedly to Fe and
Ni, toward formation of stable metal compounds. In addition, the Na2He is not
even stabilized by the local chemical bonds but via the long-range Coulomb
interactions. Herein, by using the first-principles calculations and the
unbiased structure searching techniques, we uncover that the transition metal Y
is able to react with Xe above 60 GPa within various Y-Xe stochiometries,
namely the YXe, YXe2, YXe3 and Y3Xe structures. Surprisingly, it is found that
all the resulting compounds are intermetallic and Xe atoms are positively
charged. We also argue that the pressure-induced changes of the energy orbital
filling are responsible for the electron transfer from Xe to Y. Meanwhile, the
Peierls-like mechanism is found to stabilize the energetically most favorable
YXe-Pbam phase. Furthermore, the predicted YXe-Pbam, YXe-Pnnm, and YXe3-I4/mcm
phases are discovered to be phonon-mediated superconductors under pressure,
with the critical superconducting temperatures in the range of approximately
3-4K, 7-10K, and 5-6K, respectively. In summary, our work promotes further
understanding of the crystal structures and electronic properties of the
metal-based noble gas compounds.


  It is proved that every doubly stochastic quantum channel that is properly
averaged with the completely depolarizing channel can be written as a convex
combination of unitary channels. As a consequence, we find that the collection
of channels expressible as convex combinations of unitary channels has non-zero
Borel measure within the space of doubly stochastic channels.


  Understanding exotic forms of magnetism in quantum mechanical systems is a
central goal of modern condensed matter physics, with implications from high
temperature superconductors to spintronic devices. Simulating magnetic
materials in the vicinity of a quantum phase transition is computationally
intractable on classical computers due to the extreme complexity arising from
quantum entanglement between the constituent magnetic spins. Here we employ a
degenerate Bose gas confined in an optical lattice to simulate a chain of
interacting quantum Ising spins as they undergo a phase transition. Strong spin
interactions are achieved through a site-occupation to pseudo-spin mapping. As
we vary an applied field, quantum fluctuations drive a phase transition from a
paramagnetic phase into an antiferromagnetic phase. In the paramagnetic phase
the interaction between the spins is overwhelmed by the applied field which
aligns the spins. In the antiferromagnetic phase the interaction dominates and
produces staggered magnetic ordering. Magnetic domain formation is observed
through both in-situ site-resolved imaging and noise correlation measurements.
By demonstrating a route to quantum magnetism in an optical lattice, this work
should facilitate further investigations of magnetic models using ultracold
atoms, improving our understanding of real magnetic materials.


  These introductory notes on Whitney towers in 4-manifolds, as developed in
collaboration with Jim Conant and Peter Teichner, are an expansion of three
expository lectures given at the Winter Braids X conference February 2020 in
Pisa, Italy. Topics presented include local manipulations of surfaces in
4-space, fundamental definitions related to Whitney towers and their associated
trees, geometric Jacobi identities, the classification of order n twisted
Whitney towers in the 4-ball and higher-order Arf invariants, and low-order
Whitney towers on 2-spheres in 4-manifolds and related invariants.


  While laboratory instruction is a cornerstone of physics education, the
impact of student behaviours in labs on retention, persistence in the field,
and the formation of students' physics identity remains an open question. In
this study, we performed in-lab observations of student actions over two
semesters in two pedagogically different sections of the same introductory
physics course. We used a cluster analysis to identify different categories of
student behaviour and analyzed how they correlate with lab structure and
gender. We find that, in lab structures which fostered collaborative group work
and promoted decision making, there was a task division along gender lines with
respect to laptop and equipment usage (and found no such divide among students
in guided verification labs).


  High sensitivity of neural networks against malicious perturbations on inputs
causes security concerns. To take a steady step towards robust classifiers, we
aim to create neural network models provably defended from perturbations. Prior
certification work requires strong assumptions on network structures and
massive computational costs, and thus the range of their applications was
limited. From the relationship between the Lipschitz constants and prediction
margins, we present a computationally efficient calculation technique to
lower-bound the size of adversarial perturbations that can deceive networks,
and that is widely applicable to various complicated networks. Moreover, we
propose an efficient training procedure that robustifies networks and
significantly improves the provably guarded areas around data points. In
experimental evaluations, our method showed its ability to provide a
non-trivial guarantee and enhance robustness for even large networks.


  Knots and links in 3-manifolds are studied by applying intersection
invariants to singular concordances. The resulting link invariants generalize
the Arf invariant, the mod 2 Sato-Levine invariants, and Milnor's triple
linking numbers. Besides fitting into a general theory of Whitney towers, these
invariants provide obstructions to the existence of a singular concordance
which can be homotoped to an embedding after stabilization by connected sums
with $S^2\times S^2$. Results include classifications of stably slice links in
orientable 3-manifolds, stable knot concordance in products of an orientable
surface with the circle, and stable link concordance for many links of
null-homotopic knots in orientable 3-manifolds.


  Traditional epidemic detection algorithms make decisions using only local
information. We propose a novel approach that explicitly models spatial
information fusion from several metapopulations. Our method also takes into
account cost-benefit considerations regarding the announcement of epidemic. We
utilize a compartmental stochastic model within a Bayesian detection framework
which leads to a dynamic optimization problem. The resulting adaptive,
non-parametric detection strategy optimally balances detection delay vis-a-vis
probability of false alarms. Taking advantage of the underlying state-space
structure, we represent the stopping rule in terms of a detection map which
visualizes the relationship between the multivariate system state and policy
making. It also allows us to obtain an efficient simulation-based solution
algorithm that is based on the Sequential Regression Monte Carlo (SRMC)
approach of Gramacy and Ludkovski (SIFIN, 2015). We illustrate our results on
synthetic examples and also quantify the advantages of our adaptive detection
relative to conventional threshold-based strategies.


  Capacities that provide both qualitative and quantitative obstructions to the
existence of a Lagrangian cobordism between two $(n-1)$-dimensional
submanifolds in parallel hyperplanes of $\mathbb{R}^{2n}$ are defined using the
theory of generating families. Qualitatively, these capacities show that, for
example, in $\mathbb R^4$ there is no Lagrangian cobordism between two
$\infty$-shaped curves with a negative crossing when the lower end is
"smaller". Quantitatively, when the boundary of a Lagrangian ball lies in a
hyperplane of $\mathbb{R}^{2n}$, the capacity of the boundary gives a
restriction on the size of a rectangular cylinder into which the Lagrangian
ball can be squeezed.


  We update the treatment of chemical evolution in the Munich semi-analytic
model, L-GALAXIES. Our new implementation includes delayed enrichment from
stellar winds, supernovae type II (SNe-II) and supernovae type Ia (SNe-Ia), as
well as metallicity-dependent yields and a reformulation of the associated
supernova feedback. Two different sets of SN-II yields and three different
SN-Ia delay-time distributions (DTDs) are considered, and eleven heavy elements
(including O, Mg and Fe) are self-consistently tracked. We compare the results
of this new implementation with data on a) local, star-forming galaxies, b)
Milky Way disc G dwarfs, and c) local, elliptical galaxies. We find that the
z=0 gas-phase mass-metallicity relation is very well reproduced for all forms
of DTD considered, as is the [Fe/H] distribution in the Milky Way disc. The
[O/Fe] distribution in the Milky Way disc is best reproduced when using a DTD
with less than or equal to 50 per cent of SNe-Ia exploding within ~400 Myrs.
Positive slopes in the mass-[alpha/Fe] relations of local ellipticals are also
obtained when using a DTD with such a minor `prompt' component. Alternatively,
metal-rich winds that drive light alpha elements directly out into the
circumgalactic medium also produce positive slopes for all forms of DTD and
SN-II yields considered. Overall, we find that the best model for matching the
wide range of observational data considered here should include a power-law
SN-Ia DTD, SN-II yields that take account of prior mass loss through stellar
winds, and some direct ejection of light alpha elements out of galaxies.


  Superalgebras including generators having spins up to two and realisable as
tangent vector fields on Lorentz covariant generalised superspaces are
considered. The latter have a representation content reminiscent of
configuration spaces of (super)gravity theories. The most general canonical
supercommutation relations for the corresponding phase space coordinates
allowed by Lorentz covariance are discussed. By including generators
transforming according to every Lorentz representation having spin up to two,
we obtain, from the super Jacobi identities, the complete set of quadratic
equations for the Lorentz covariant structure constants. These defining
equations for spin two Heisenberg superalgebras are highly overdetermined.
Nevertheless, non-trivial solutions can indeed be found. By making some
simplifying assumptions, we explicitly construct several classes of these
superalgebras.


  We solve an inverse spectral problem for a star graph of Krein strings, where
the known spectral data comprises the spectrum associated with the whole graph,
the spectra associated with the individual edges as well as so-called coupling
matrices. In particular, we show that these spectral quantities uniquely
determine the weight within the class of Borel measures on the graph, which
give rise to trace class resolvents. Furthermore, we obtain a concise
characterization of all possible spectral data for this class of weights.


  Given a bounded constructible complex of sheaves $\mathcal{F}$ on a complex
Abelian variety, we prove an equality relating the cohomology jump loci of
$\mathcal{F}$ and its singular support. As an application, we identify two
subsets of the set of holomorphic 1-forms with zeros on a complex smooth
projective irregular variety $X$; one from Green-Lazarsfeld's cohomology jump
loci and one from the Kashiwara's estimates for singular supports. This result
is related to Kotschick's conjecture about the equivalence between the
existence of nowhere vanishing global holomorphic 1-forms and the existence of
a fibre bundle structure over the circle. Our results give a conjecturally
equivalent formulation using singular support, which is equivalent to a
criterion involving cohomology jump loci proposed by Schreieder. As another
application, we reprove a recent result proved by Schreieder and Yang; namely
if $X$ has simple Albanese variety and admits a fibre bundle structure over the
circle, then the Albanese morphism cohomologically behaves like a smooth
morphism with respect to integer coefficients.
  In a related direction, we address the question whether the set of 1-forms
that vanish somewhere is a finite union of linear subspaces of
$H^0(X,\Omega_X^1)$. We show that this is indeed the case for forms admitting
zero locus of codimension 1.


  Convolutional neural networks (ConvNets) are widely used in real life. People
usually use ConvNets which pre-trained on a fixed number of classes. However,
for different application scenarios, we usually do not need all of the classes,
which means ConvNets are redundant when dealing with these tasks. This paper
focuses on the redundancy of ConvNet channels. We proposed a novel idea: using
an interpretable manner to find the most important channels for every single
class (dissect), and dynamically run channels according to classes in need
(reconstruct). For VGG16 pre-trained on CIFAR-10, we only run 11\% parameters
for two-classes sub-tasks on average with negligible accuracy loss. For VGG16
pre-trained on ImageNet, our method averagely gains 14.29\% accuracy promotion
for two-classes sub-tasks. In addition, analysis show that our method captures
some semantic meanings of channels, and uses the context information more
targeted for sub-tasks of ConvNets.


  The existence is proved of two new families of locally Cohen-Macaulay sextic
threefolds in $\mathbb{P}^5$, which are not quadratically normal. These
threefolds arise naturally in the realm of first order congruences of lines as
focal loci and in the study of the completely exceptional Monge-Amp\`ere
equations. One of these families comes from a smooth congruence of multidegree
$(1,3,3)$ which is a smooth Fano fourfold of index two and genus 9.


  To function autonomously in the physical world, humanoid robots need
high-fidelity sensing systems, especially for forces that cannot be easily
modeled. Modeling forces in robot feet is particularly challenging due to
static indeterminacy, thereby requiring direct sensing. Unfortunately,
resolving forces in the feet of some smaller-sized humanoids is limited both by
the quality of sensors and the current algorithms used to interpret the data.
This paper presents light-weight, low-cost and open-source force-sensing shoes
to improve force measurement for popular smaller-sized humanoid robots, and a
method for calibrating the shoes. The shoes measure center of pressure (CoP)
and normal ground reaction force (GRF). The calibration method enables each
individual shoe to reach high measurement precision by applying known forces at
different locations of the shoe and using a regularized least squares
optimization to interpret sensor outputs. A NAO robot is used as our
experimental platform. Experiments are conducted to compare the measurement
performance between the shoes and the robot's factory-installed force-sensing
resistors (FSRs), and to evaluate the calibration method over these two sensing
modules. Experimental results show that the shoes significantly improve CoP and
GRF measurement precision compared to the robot's built-in FSRs. Moreover, the
developed calibration method improves the measurement performance for both our
shoes and the built-in FSRs.


  The first stage of the hybrid quantum repeaters is entanglement generation
based on transmission of pulses in coherent states over a lossy channel.
Protocols to make entanglement with only one type of error are favorable for
rendering subsequent entanglement distillation efficient. Here we provide the
tight upper bound on performances of these protocols that is determined only by
the channel loss. In addition, we show that this bound is achievable by
utilizing a proposed protocol [arXiv:0811.3100] composed of a simple
combination of linear optical elements and photon-number-resolving detectors.


  A class of stochastic processes, called "weak Dirichlet processes", is
introduced and its properties are investigated in detail. This class is much
larger than the class of Dirichlet processes. It is closed under
C^1$-transformations and under absolutely continuous change of measure. If a
weak Dirichlet process has finite energy, as defined by Graversen and Rao, its
Doob-Meyer type decomposition is unique. The developed methods have been
applied to a study of generalized martingale convolutions.


  We derive the probability for a newly formed binary black hole (BBH) to
undergo an eccentric gravitational wave (GW) merger during binary-single
interactions inside a stellar cluster. By integrating over the hardening
interactions such a BBH must undergo before ejection, we find that the
observable rate of BBH mergers with eccentricity $>0.1$ at $10\ \rm{Hz}$
relative to the rate of circular mergers can be as high as $\sim 5\%$ for a
typical globular cluster (GC). This further suggests that BBH mergers forming
through GW captures in binary-single interactions, eccentric or not, are likely
to constitute $\sim 10\%$ of the total BBH merger rate from GCs. Such GW
capture mergers can only be probed with an $N$-body code that includes General
Relativistic corrections, which explains why recent Newtonian cluster studies
not have been able to resolve this population. Finally, we show that the
relative rate of eccentric BBH mergers depends on the compactness of their host
cluster, suggesting that an observed eccentricity distribution can be used to
probe the origin of BBH mergers.


  As first demonstrated by Hanbury Brown and Twiss, it is possible to observe
interference between independent light sources by measuring correlations in
their intensities rather than their amplitudes. In this work, we apply this
concept of intensity interferometry to holography. We combine a signal beam
with a reference and measure their intensity cross-correlations using a
time-tagging single-photon camera. These correlations reveal an interference
pattern from which we reconstruct the signal wavefront in both intensity and
phase. We demonstrate the principle with classical and quantum light, including
a single photon. Since the signal and reference do not need to be phase-stable,
this technique can be used to generate holograms of self-luminous or remote
objects using a local reference, thus opening the door to new holography
applications.


  Dissipative time crystals can appear in spin systems, when the $Z_2$ symmetry
of the Hamiltonian is broken by the environment, and the square of total spin
operator $S^2$ is conserved. In this manuscript, we relax the latter condition
and show that time-translation-symmetry breaking collective oscillations
persist, in the thermodynamic limit, even in the absence of spin symmetry. We
engineer an \textit{ad hoc} Lindbladian using power-law decaying spin operators
and show that time-translation symmetry breaking appears when the decay
exponent obeys $0<\eta\leq 1$. This model shows a surprisingly rich phase
diagram, including the time-crystal phase as well as first-order, second-order,
and continuous transitions of the fixed points. We study the phase diagram and
the magnetization dynamics in the mean-field approximation. We prove that this
approximation is quantitatively accurate, when $0<\eta\leq1$ and the
thermodynamic limit is taken, because the system does not develop sizable
quantum fluctuations, if the Gaussian approximation is considered.


  Cyclic poset are generalizations of cyclically ordered sets. In this paper we
show that any cyclic poset gives rise to a Frobenius category over any discrete
valuation ring R. The continuous cluster categories of arXiv:1209.1879 are
examples of this construction. If we twist the construction using an admissible
automorphism of the cyclic poset, we generate other examples such as the
m-cluster category of type A-infinity (m>2).


  Within baseball analytics, there is substantial interest in comprehensive
statistics intended to capture overall player performance. One such measure is
Wins Above Replacement (WAR), which aggregates the contributions of a player in
each facet of the game: hitting, pitching, baserunning, and fielding. However,
current versions of WAR depend upon proprietary data, ad hoc methodology, and
opaque calculations. We propose a competitive aggregate measure, openWAR, that
is based upon public data and methodology with greater rigor and transparency.
We discuss a principled standard for the nebulous concept of a "replacement"
player. Finally, we use simulation-based techniques to provide interval
estimates for our openWAR measure.


  Insect swarms are common phenomena in nature and therefore have been actively
pursued in computer animation. Realistic insect swarm simulation is difficult
due to two challenges: high-fidelity behaviors and large scales, which make the
simulation practice subject to laborious manual work and excessive
trial-and-error processes. To address both challenges, we present a novel
data-driven framework, FASTSWARM, to model complex behaviors of flying insects
based on real-world data and simulate plausible animations of flying insect
swarms. FASTSWARM has a linear time complexity and achieves real-time
performance for large swarms. The high-fidelity behavior model of FASTSWARM
explicitly takes into consideration the most common behaviors of flying
insects, including the interactions among insects such as repulsion and
attraction, the self-propelled behaviors such as target following and obstacle
avoidance, and other characteristics such as the random movements. To achieve
scalability, an energy minimization problem is formed with different behaviors
modelled as energy terms, where the minimizer is the desired behavior. The
minimizer is computed from the real-world data, which ensures the plausibility
of the simulation results. Extensive simulation results and evaluations show
that FASTSWARM is versatile in simulating various swarm behaviors, high
fidelity measured by various metrics, easily controllable in inducing user
controls and highly scalable.


  Recent studies have shown that line profile distortions are commonly observed
in gravitationally lensed quasar spectra. We investigate the effect of
gravitational microlensing on quasar broad emission line profiles and their
underlying continuum, combining the emission from simple representative BLR
models with generic microlensing magnification maps. Specifically, we
considered Keplerian disk, polar, and equatorial wind BLR models of various
sizes. The effect of microlensing has been quantified with four observables:
$\mu^{BLR}$, the total magnification of the broad emission line; $\mu^{cont}$,
the magnification of the underlying continuum; as well as red/blue, RBI and
wings/core, WCI, indices that characterize the line profile distortions. The
simulations showed that distortions of line profiles, such as those recently
observed in lensed quasars, can indeed be reproduced and attributed to the
differential effect of microlensing on spatially separated regions of the BLR.
While the magnification of the emission line $\mu^{BLR}$ sets an upper limit on
the BLR size and, similarly, the magnification of the continuum $\mu^{cont}$
sets an upper limit on the size of the continuum source, the line profile
distortions mainly depend on the BLR geometry and kinematics. We thus built
(WCI,RBI) diagrams that can serve as diagnostic diagrams to discriminate
between the various BLR models on the basis of quantitative measurements. It
appears that a strong microlensing effect puts important constraints on the
size of the BLR and on its distance to the high-magnification caustic. In that
case, BLR models with different geometries and kinematics are more prone to
produce distinctive line profile distortions for a limited number of caustic
configurations, which facilitates their discrimination.


  The shift in Aharanov-Bohm electron-interference fringe positions has been
previously derived as resulting from phase differences induced by the magnetic
vector potential, without being clear on the physical mechanism behind it. In
this paper, we show that the de Broglie wavelength of the electron wavefunction
is changed locally by its interaction with the vector potential. The vector
potential thus acts as a quantum "phase plate", changing the phase difference
between interfering electron wavefunctions in a non-dispersive, gauge-invariant
manner.


  For any flag simplicial complex $K$, we describe the multigraded Poincare
series, the minimal number of relations and the degrees of these relations in
the Pontryagin algebra of the corresponding moment-angle complex $Z_K$. We
compute the LS-category of $Z_K$ for flag complexes and give a lower bound in
the general case. The key observation is that the Milnor-Moore spectral
sequence collapses at the second sheet for flag $K$. We also show that the
results of Panov and Ray about the Pontryagin algebras of Davis-Januszkiewicz
spaces are valid for arbitrary coefficient rings, and introduce the
$(\mathbb{Z}\times\mathbb{Z}^m)$-grading on the Pontryagin algebras which is
similar to the multigrading on the cohomology of $Z_K$.


  Young nearby runaway stars are suitable to search for their place of origin
and possibly associated objects, for example neutron stars. Tetzlaff,
Neuh\"auser & Hohle (2011) selected young ($\le 50$ Myr) runaway star
candidates from Hipparcos, for which they had estimated the ages from the
location in the Hertzsprung-Russell diagram and evolutionary models. Here, we
redetermine or constrain their young ages more precisely not only by using the
new Gaia DR2 data, but also by measuring lithium, which is a youth indicator.
For 308 stars, we took spectra to search for the strong resonance doublet of
the lithium-7 isotope at 6708 $\unicode{xC5}$. The spectra were taken with the
\'Echelle spectrograph FLECHAS at the University Observatory Jena between
February 2015 and June 2018 and with TRES between April 2011 and June 2017 at
the Fred L. Whipple Observatory. We found 208 stars with significant occurrence
of lithium in their spectra, and five possess a possible age younger or about
50 Myr. Three of these targets are even closer than GJ 182, the nearest known
runaway star at about 24 pc. Theses stars are young runaway stars suitable for
further investigation of their origin from either a dynamical or supernova
ejection.


  Background: The increasing environmental impact of Information Technologies,
particularly in Machine Learning (ML), highlights the need for sustainable
practices in software engineering. The escalating complexity and energy
consumption of ML models need tools for assessing and improving their energy
efficiency. Goal: This paper introduces GAISSALabel, a web-based tool designed
to evaluate and label the energy efficiency of ML models. Method: GAISSALabel
is a technology transfer development from a former research on energy
efficiency classification of ML, consisting of a holistic tool for assessing
both the training and inference phases of ML models, considering various
metrics such as power draw, model size efficiency, CO2e emissions and more.
Results: GAISSALabel offers a labeling system for energy efficiency, akin to
labels on consumer appliances, making it accessible to ML stakeholders of
varying backgrounds. The tool's adaptability allows for customization in the
proposed labeling system, ensuring its relevance in the rapidly evolving ML
field. Conclusions: GAISSALabel represents a significant step forward in
sustainable software engineering, offering a solution for balancing
high-performance ML models with environmental impacts. The tool's effectiveness
and market relevance will be further assessed through planned evaluations using
the Technology Acceptance Model.


  A generalised quasilinear (GQL) approximation (Marston \emph{et al.},
\emph{Phys. Rev. Lett.}, vol. 116, 104502, 2016) is applied to turbulent
channel flow at $Re_\tau \simeq 1700$ ($Re_\tau$ is the friction Reynolds
number), with emphasis on the energy transfer in the streamwise wavenumber
space. The flow is decomposed into low and high streamwise wavenumber groups,
the former of which is solved by considering the full nonlinear equations
whereas the latter is obtained from the linearised equations around the former.
The performance of the GQL approximation is subsequently compared with that of
a QL model (Thomas \emph{et al.}, \emph{Phys. Fluids.}, vol. 26, no. 10,
105112, 2014), in which the low-wavenumber group only contains zero streamwise
wavenumber. It is found that the QL model exhibits a considerably reduced
multi-scale behaviour at the given moderately high Reynolds number. This is
improved significantly by the GQL approximation which incorporates only a few
more streamwise Fourier modes into the low-wavenumber group, and it reasonably
well recovers the distance-from-the-wall scaling in the turbulence statistics
and spectra. Finally, it is proposed that the energy transfer from the low to
the high-wavenumber group in the GQL approximation, referred to as the
`scattering' mechanism, depends on the neutrally stable leading Lyapunov
spectrum of the linearised equations for the high wavenumber group. In
particular, it is shown that if the threshold wavenumber distinguishing the two
groups is sufficiently high, the scattering mechanism can completely be absent
due to the linear nature of the equations for the high-wavenumber group.


  Network load balancers are central components in data centers, that
distributes workloads across multiple servers and thereby contribute to
offering scalable services. However, when load balancers operate in dynamic
environments with limited monitoring of application server loads, they rely on
heuristic algorithms that require manual configurations for fairness and
performance. To alleviate that, this paper proposes a distributed asynchronous
reinforcement learning mechanism to-with no active load balancer state
monitoring and limited network observations-improve the fairness of the
workload distribution achieved by a load balancer. The performance of proposed
mechanism is evaluated and compared with stateof-the-art load balancing
algorithms in a simulator, under configurations with progressively increasing
complexities. Preliminary results show promise in RLbased load balancing
algorithms, and identify additional challenges and future research directions,
including reward function design and model scalability.


  To date, toxicity mitigation in language models has almost entirely been
focused on single-language settings. As language models embrace multilingual
capabilities, it's crucial our safety measures keep pace. Recognizing this
research gap, our approach expands the scope of conventional toxicity
mitigation to address the complexities presented by multiple languages. In the
absence of sufficient annotated datasets across languages, we employ translated
data to evaluate and enhance our mitigation techniques. We also compare
finetuning mitigation approaches against retrieval-augmented techniques under
both static and continual toxicity mitigation scenarios. This allows us to
examine the effects of translation quality and the cross-lingual transfer on
toxicity mitigation. We also explore how model size and data quantity affect
the success of these mitigation efforts. Covering nine languages, our study
represents a broad array of linguistic families and levels of resource
availability, ranging from high to mid-resource languages. Through
comprehensive experiments, we provide insights into the complexities of
multilingual toxicity mitigation, offering valuable insights and paving the way
for future research in this increasingly important field. Code and data are
available at https://github.com/for-ai/goodtriever.


  We propose a purely algebraic approach to construct invariants of transversal
links in the standard contact structure on the 3-sphere generalizing Jones'
approach to invariant of usual links. The only geometry used is the analogue of
Alexander and Markov theorems. More precisely, we construct a trace on a
certain cubic Hecke algebra which is invariant under positive Markov moves only
(we propose to call it a semi-Markov trace). The trace takes its values in the
quotient of a polynomial ring by a certain ideal. An algorithm for computing a
Groebner base of the ideal is given.


  We propose two novel models in the framework of $f(Q)$ gravity to explain our
accelerated universe, namely the exponential $f(Q)_{EXP}$ model and the
hyperbolic tangent $f(Q)_{HT}$ model. The current cosmological electromagnetic
observations including the cosmic microwave background anisotropies (CMB), the
baryon acoustic oscillations(BAO), the type Ia supernovae (SN) and the direct
measurements of H(z), combined with the simulated gravitational-wave data are
used to constrain the $f(Q)$ models. We find that the Hubble tension can be
significantly alleviated to $1.40\sigma$ level in the $f(Q)_{EXP}$ model. The
fitting $\chi^2$ of the $f(Q)_{HT}$ model is $9.75\sigma$ poorer than that of
the $f(Q)_{EXP}$ model, implying the $f(Q)_{HT}$ model would be excluded by
future gravitational-wave observation.


  We investigate a notion of inverse for neutrices inspired by Van den Berg and
Koudjeti's decomposition of a neutrix as the product of a real number and an
idempotent neutrix. We end up with an algebraic structure that can be
characterized axiomatically and generalizes involutive meadows. The latter are
algebraic structures where the inverse for multiplication is a total operation.
As it turns out, the structures satisfying the axioms of flexible involutive
meadows are of interest beyond nonstandard analysis.


  A quantum wire is fabricated on (001)-GaAs at the intersection of two
overgrown cleaves. The wire is contacted at each end to n+ GaAs layers via
two-dimensional (2D) leads. A sidegate controls the density of the wire
revealing conductance quantization. The step height is strongly reduced from
2e^2/h due to the 2D-lead series resistance. We characterize the 2D density and
mobility for both cleave facets with four-point measurements. The density on
the first facet is modulated by the substrate potential, depleting a 2um wide
strip that defines the wire length. Micro-photoluminescence shows an extra peak
consistent with 1D electron states at the corner.


  Let $F:\Sigma^n \times [0,T)\to \R^{n+m}$ be a family of compact immersed
submanifolds moving by their mean curvature vectors. We show the Gauss maps
$\gamma:(\Sigma^n, g_t)\to G(n, m)$ form a harmonic heat flow with respect to
the time-dependent induced metric $g_t$. This provides a more systematic
approach to investigating higher codimension mean curvature flows. A direct
consequence is any convex function on $G(n,m)$ produces a subsolution of the
nonlinear heat equation on $(\Sigma, g_t)$. We also show the condition that the
image of the Gauss map lies in a totally geodesic submanifold of $G(n, m)$ is
preserved by the mean curvature flow. Since the space of Lagrangian subspaces
is totally geodesic in G(n,n), this gives an alternative proof that any
Lagrangian submanifold remains Lagrangian along the mean curvature flow.


  Nowadays, science has been coming into a new paradigm, called data-intensive
science. While current studies of the new phenomenon focused on building up
infrastructure for this new paradigm, yet a few studies concern users of
scientific data, particularly their usage practices in the newly emerging
paradigm, even though the importance of understanding users' work flow and
practices has been summoned. This study endeavors to improve our understanding
of users' data usage behavior through a content analysis of publications in a
frequently cited new paradigm-related project, Sloan Digital Sky Survey (SDSS).
We found that (1) nearly half studies used one data source only. A few studies
exploited three or more data sources; (2) the number of objects that were
analyzed in SDSS publications is in all scales from one digit to millions; (3)
different paper types may affect the data usage patterns; (4) Users are not
only consumers of scientific data. They are producers too; (5) studies that can
use multiple large scale data sources are relative rare. Issues of data
provenance, trust, and usability may prevent researchers from doing this kind
of research.


  Earth's distant past and potentially its future include extremely warm
"hothouse" climate states, but little is known about how the atmosphere behaves
in such states. One distinguishing characteristic of hothouse climates is that
they feature lower-tropospheric radiative heating, rather than cooling, due to
the closing of the water vapor infrared window regions. Previous work has
suggested that this could lead to temperature inversions and significant
changes in cloud cover, but no previous modeling of the hothouse regime has
resolved convective-scale turbulent air motions and cloud cover directly, thus
leaving many questions about hothouse radiative heating unanswered. Here, we
conduct simulations that explicitly resolve convection and find that
lower-tropospheric radiative heating in hothouse climates causes the hydrologic
cycle to shift from a quasi-steady regime to a "relaxation oscillator" regime,
in which precipitation occurs in short and intense outbursts separated by
multi-day dry spells. The transition to the oscillatory regime is accompanied
by strongly enhanced local precipitation fluxes, a significant increase in
cloud cover, and a transiently positive (unstable) climate feedback parameter.
Our results indicate that hothouse climates may feature a novel form of
"temporal" convective self-organization, with implications for both cloud
coverage and erosion processes.


  Analyzing texts from social media encounters many challenges due to their
unique characteristics of shortness, massiveness, and dynamic. Short texts do
not provide enough context information, causing the failure of the traditional
statistical models. Furthermore, many applications often face with massive and
dynamic short texts, causing various computational challenges to the current
batch learning algorithms. This paper presents a novel framework, namely Bag of
Biterms Modeling (BBM), for modeling massive, dynamic, and short text
collections. BBM comprises of two main ingredients: (1) the concept of Bag of
Biterms (BoB) for representing documents, and (2) a simple way to help
statistical models to include BoB. Our framework can be easily deployed for a
large class of probabilistic models, and we demonstrate its usefulness with two
well-known models: Latent Dirichlet Allocation (LDA) and Hierarchical Dirichlet
Process (HDP). By exploiting both terms (words) and biterms (pairs of words),
the major advantages of BBM are: (1) it enhances the length of the documents
and makes the context more coherent by emphasizing the word connotation and
co-occurrence via Bag of Biterms, (2) it inherits inference and learning
algorithms from the primitive to make it straightforward to design online and
streaming algorithms for short texts. Extensive experiments suggest that BBM
outperforms several state-of-the-art models. We also point out that the BoB
representation performs better than the traditional representations (e.g, Bag
of Words, tf-idf) even for normal texts.


  In this work, we examine the extraordinary behavior of polarization and spin
angular momentum (AM) density in the vicinity of longitudinal field zeros in
three-dimensional monochromatic acoustic fields. We demonstrate that, as
governed by the continuity equation, the velocity fields of arbitrary acoustic
sources maintain non-diffractive elliptical polarization structures that
enclose longitudinal field zeros, despite having divergent transverse spatial
profiles of intensity. Furthermore, embedded in these nonparaxial field
contours, for infinite distance, are threads of circular polarization
singularities. We illuminate these inherent properties in acoustic vortex
fields, dipole arrays, and the famous Young's double slit experiment. Our
results reveal novel characteristics of vector sound waves that provide a
platform for future studies and applications of structured acoustic waves and
chiral acoustic phenomena.


  Positronium is a unique laboratory to study fundamental symmetries in the
Standard Model, reflection in space ($\mathcal{P}$), reversal in time
($\mathcal{T}$), charge conjugation ($\mathcal{C}$) and their combinations. The
experimental limits on the $\mathcal{C}$, $\mathcal{CP}$ and $\mathcal{CPT}$
symmetries violation in the decays of positronium are still several orders of
magnitude higher than the expectations. The newly constructed Jagiellonian
Positron Emission Tomograph (J-PET) was optimized for the registration of
photons from the electron-positron annihilations. It enables tests of discrete
symmetries in decays of positronium atoms via the determination of the
expectation values of the discrete-symmetries-odd operators. In this article we
present the capabilities of the J-PET detector in improving the current
precision of discrete symmetries tests and report on the progress of analysis
data from the first data-taking runs.


  Transformer-based models are the state-of-the-art for Natural Language
Understanding (NLU) applications. Models are getting bigger and better on
various tasks. However, Transformer models remain computationally challenging
since they are not efficient at inference-time compared to traditional
approaches. In this paper, we present FastFormers, a set of recipes to achieve
efficient inference-time performance for Transformer-based models on various
NLU tasks. We show how carefully utilizing knowledge distillation, structured
pruning and numerical optimization can lead to drastic improvements on
inference efficiency. We provide effective recipes that can guide practitioners
to choose the best settings for various NLU tasks and pretrained models.
Applying the proposed recipes to the SuperGLUE benchmark, we achieve from 9.8x
up to 233.9x speed-up compared to out-of-the-box models on CPU. On GPU, we also
achieve up to 12.4x speed-up with the presented methods. We show that
FastFormers can drastically reduce cost of serving 100 million requests from
4,223 USD to just 18 USD on an Azure F16s_v2 instance. This translates to a
sustainable runtime by reducing energy consumption 6.9x - 125.8x according to
the metrics used in the SustaiNLP 2020 shared task.


  We examine the $\Lambda$ potential from the chiral effective field theory
($\chi$EFT) via the $\Lambda$ directed flow from heavy-ion collisions. We
implement the $\Lambda$ potential obtained from the $\chi$EFT in a vector
potential version of relativistic quantum molecular dynamics. We find that the
$\Lambda$ potentials obtained from the $\chi$EFT assuming weak momentum
dependence reproduce the $\Lambda$ directed flow measured by the STAR
collaboration in the Beam Energy Scan program. While the $\Lambda$ directed
flow is not very sensitive to the density dependence of the potential, the
directed flow at large rapidities is susceptible to the momentum dependence.
Thus understanding the directed flow of hyperons in a wide range of beam energy
and rapidity is helpful in understanding hyperon potentials in dense matter.


  State-of-the-art intent classification (IC) and slot filling (SF) methods
often rely on data-intensive deep learning models, limiting their practicality
for industry applications. Large language models on the other hand,
particularly instruction-tuned models (Instruct-LLMs), exhibit remarkable
zero-shot performance across various natural language tasks. This study
evaluates Instruct-LLMs on popular benchmark datasets for IC and SF,
emphasizing their capacity to learn from fewer examples. We introduce
ILLUMINER, an approach framing IC and SF as language generation tasks for
Instruct-LLMs, with a more efficient SF-prompting method compared to prior
work. A comprehensive comparison with multiple baselines shows that our
approach, using the FLAN-T5 11B model, outperforms the state-of-the-art joint
IC+SF method and in-context learning with GPT3.5 (175B), particularly in slot
filling by 11.1--32.2 percentage points. Additionally, our in-depth ablation
study demonstrates that parameter-efficient fine-tuning requires less than 6%
of training data to yield comparable performance with traditional full-weight
fine-tuning.


  We determine the dipole in the WISE galaxy catalogue. After reducing star
contamination to <0.1% by rejecting sources with high apparent motion and those
close to the Galactic plane, we eliminate low redshift sources to suppress the
non-kinematic, clustering dipole. We remove sources within {\pm}5{\deg} of the
super-galactic plane, as well as those within 1'' of 2MRS sources at redshift z
< 0.03. We enforce cuts on the source angular extent to preferentially select
distant ones. As we progress along these steps, the dipole converges in
direction to within 5{\deg} of the CMB dipole and its magnitude also
progressively reduces but stabilises at {\sim}0.012, corresponding to a
velocity >1000 km/s if it is solely of kinematic origin. However, previous
studies have shown that only {\sim}70% of the velocity of the Local Group as
inferred from the CMB dipole is due to sources at z < 0.03. We examine the Dark
Sky simulations to quantify the prevalence of such environments and find that
<2.1% of Milky Way-like observers in a {\Lambda}CDM universe should observe the
bulk flow (> 240 km/s extending to z > 0.03) that we do. We construct mock
catalogues in the neighbourhood of such peculiar observers in order to mimic
our final galaxy selection and quantify the residual clustering dipole. After
subtracting this the remaining dipole is 0.0048 {\pm} 0.0022, corresponding to
a velocity of 420 {\pm} 213 km/s which is consistent with the CMB. However the
sources (at z > 0.03) of such a large clustering dipole remain to be
identified.


  The separability assumption (Donoho & Stodden, 2003; Arora et al., 2012)
turns non-negative matrix factorization (NMF) into a tractable problem.
Recently, a new class of provably-correct NMF algorithms have emerged under
this assumption. In this paper, we reformulate the separable NMF problem as
that of finding the extreme rays of the conical hull of a finite set of
vectors. From this geometric perspective, we derive new separable NMF
algorithms that are highly scalable and empirically noise robust, and have
several other favorable properties in relation to existing methods. A parallel
implementation of our algorithm demonstrates high scalability on shared- and
distributed-memory machines.


  We propose randomized subspace gradient methods for high-dimensional
constrained optimization. While there have been similarly purposed studies on
unconstrained optimization problems, there have been few on constrained
optimization problems due to the difficulty of handling constraints. Our
algorithms project gradient vectors onto a subspace that is a random projection
of the subspace spanned by the gradients of active constraints. We determine
the worst-case iteration complexity under linear and nonlinear settings and
theoretically confirm that our algorithms can take a larger step size than
their deterministic version. From the advantages of taking longer step and
randomized subspace gradients, we show that our algorithms are especially
efficient in view of time complexity when gradients cannot be obtained easily.
Numerical experiments show that they tend to find better solutions because of
the randomness of their subspace selection. Furthermore, they performs well in
cases where gradients could not be obtained directly, and instead, gradients
are obtained using directional derivatives.


  We explore type systems and programming abstractions for the safe use of
resources. In particular, we investigate how to use types to modularly specify
and check when programs are allowed to use their resources, e.g., when
programming a robot arm on a production line, it is crucial that painted parts
are given enough time to dry before assembly. We capture such temporal
resources using a time-graded variant of Fitch-style modal type systems,
develop a corresponding modally typed, effectful core calculus, and equip it
with a graded-monadic denotational semantics illustrated by a concrete presheaf
model. Our calculus also includes temporally-aware graded algebraic effects and
effect handlers. The former are given a novel temporal treatment, where
operations' specifications include their execution times, and their
continuations know that an operation's worth of additional time has passed
before they start executing, making it possible to safely access further
temporal resources in them, and where effect handlers have to respect this
temporal discipline.


  We study the relationship between depth and regularity of a homogeneous ideal
I and those of (I,f) and I:f, where f is a linear form or a monomial. Our
results has several interesting consequences on depth and regularity of edge
ideals of hypegraphs and of powers of ideals.


  Science journalism refers to the task of reporting technical findings of a
scientific paper as a less technical news article to the general public
audience. We aim to design an automated system to support this real-world task
(i.e., automatic science journalism) by 1) introducing a newly-constructed and
real-world dataset (SciTechNews), with tuples of a publicly-available
scientific paper, its corresponding news article, and an expert-written short
summary snippet; 2) proposing a novel technical framework that integrates a
paper's discourse structure with its metadata to guide generation; and, 3)
demonstrating with extensive automatic and human experiments that our framework
outperforms other baseline methods (e.g. Alpaca and ChatGPT) in elaborating a
content plan meaningful for the target audience, simplifying the information
selected, and producing a coherent final report in a layman's style.


  DNA shape readout is an important mechanism of target site recognition by
transcription factors, in addition to the sequence readout. Several models of
transcription factor-DNA binding which consider DNA shape have been developed
in recent years. We present a new biophysical model of protein-DNA interaction
by considering the DNA shape features, which is based on a neighbour
dinucleotide dependency model BayesPI2. The parameters of the new model are
restricted to a subspace spanned by the 2-mer DNA shape features, which
allowing a biophysical interpretation of the new parameters as
position-dependent preferences towards certain values of the features. Using
the new model, we explore the variation of DNA shape preferences in several
transcription factors across cancer cell lines and cellular conditions. We find
evidence of DNA shape variations at FOXA1 binding sites in MCF7 cells after
treatment with steroids. The new model is useful for elucidating finer details
of transcription factor-DNA interaction. It may be used to improve the
prediction of cancer mutation effects in the future.


  We introduce a class of Lie algebras called admissible Lie algebras. We show
that a locally finite admissible simple Lie algebra contains a nonzero maximal
toral subalgebra and the corresponding root system is an irreducible locally
finite root system.


  Cosmological evolution of the QSO luminosity functions (LFs) at
NIR/optical/X-ray bands for 1.3 < z < 3.5 is investigated based on the
realistic QSO spectra. The accretion-disk theory predicts that although QSO
luminosities only depend on mass-accretion rate, \Mdot, QSO spectra have a
dependence on black-hole mass, M_{BH}, as well. The smaller M_{BH} is and/or
the larger \Mdot is, the harder becomes the QSO NIR/optical/UV spectrum. We
model disk spectra which can reproduce these features and calculated LFs for
redshift z ~ 3 with the assumption of new-born QSOs being shining at the
Eddington luminosity. The main results are: (i) the observed LFs at optical and
X-rays can be simultaneously reproduced. (ii) LFs at optical and X-ray bands
are not sensitive to M_{BH}, while LFs at NIR bands are; about one order of
magnitude difference is expected in volume number densities at L_{I, J} ~
10^{46} erg s^{-1} between the case that all QSOs would have the same spectral
shape as that of M_{BH} = 10^{9} M_sun and the case with M_{BH} = 10^{11}
M_sun. (iii) The resultant LFs at NIR are dominated by 10^{7} M_sun black-holes
at L_{I, J} ~ 10^{44} erg s^{-1}, and by 10^{11} M_sun black-holes at L_{I, J}
\~ 10^{46} erg s^{-1}. Future infrared observations from space(e.g.NGST) will
probe cosmological evolution of black hole masses. For redshift z < 3, on the
other hand, the observed optical/X-ray LFs can be fitted, if the initial QSO
luminosity L_0 is below the Eddington luminosity. Interestingly, the best
fitting values of l = L_0/L_{Edd} are different in B- and X-ray bands; l_B ~
2.5 l_X. The reason for this discrepancy is briefly discussed.


  Quasi-Monte Carlo methods have become the industry standard in computer
graphics. For that purpose, efficient algorithms for low discrepancy sequences
are discussed. In addition, numerical pitfalls encountered in practice are
revealed. We then take a look at massively parallel quasi-Monte Carlo
integro-approximation for image synthesis by light transport simulation. Beyond
superior uniformity, low discrepancy points may be optimized with respect to
additional criteria, such as noise characteristics at low sampling rates or the
quality of low-dimensional projections.


  Research is a continuous phenomenon. It is recursive in nature. Every
research is based on some earlier research outcome. A general approach in
reviewing the literature for a problem is to categorize earlier work for the
same problem as positive and negative citations. In this paper, we propose a
novel automated technique, which classifies whether an earlier work is cited as
sentiment positive or sentiment negative. Our approach first extracted the
portion of the cited text from citing paper. Using a sentiment lexicon we
classify the citation as positive or negative by picking a window of at most
five (5) sentences around the cited place (corpus). We have used Na\"ive-Bayes
Classifier for sentiment analysis. The algorithm is evaluated on a manually
annotated and class labelled collection of 150 research papers from the domain
of computer science. Our preliminary results show an accuracy of 80%. We assert
that our approach can be generalized to classification of scientific research
papers in different disciplines.


  We have developed a wideband receiver system for simultaneous observations in
CO lines of J = 2-1 and J = 3-2 transitions using the Osaka 1.85-m mm-submm
telescope. As a frequency separation system, we developed multiplexers that
connect three types of diplexers, each consisting of branch-line couplers and
high-pass filters. The radio frequency (RF) signal is eventually distributed
into four frequency bands, each of which is fed to a
superconductor-insulator-superconductor (SIS) mixer. The RF signal from the
horn is divided into two frequency bands by a wideband diplexer with a
fractional bandwidth of 56%, and then each frequency band is further divided
into two bands by each diplexer. The developed multiplexers were designed,
fabricated, and characterized using a vector network analyzer. The measurement
results showed good agreement with the simulation. The receiver noise
temperature was measured by connecting the SIS-mixers, one of which has a
wideband 4-21GHz intermediate frequency (IF) output. The receiver noise
temperatures were measured to be ~70K in the 220GHz band, ~100K in the 230GHz
band, 110-175K in the 330GHz band, and 150-250K in the 345GHz band. This
receiver system has been installed on the 1.85-m telescope at the Nobeyama
Radio Observatory. We succeeded in the simultaneous observations of six CO
isotopologue lines with the transitions of J = 2-1 and J = 3-2 toward the Orion
KL as well as the on-the-fly (OTF) mappings toward the Orion KL and W 51.


  Query expansion is a widely used technique to improve the recall of search
systems. In this paper, we propose an approach to query expansion that
leverages the generative abilities of Large Language Models (LLMs). Unlike
traditional query expansion approaches such as Pseudo-Relevance Feedback (PRF)
that relies on retrieving a good set of pseudo-relevant documents to expand
queries, we rely on the generative and creative abilities of an LLM and
leverage the knowledge inherent in the model. We study a variety of different
prompts, including zero-shot, few-shot and Chain-of-Thought (CoT). We find that
CoT prompts are especially useful for query expansion as these prompts instruct
the model to break queries down step-by-step and can provide a large number of
terms related to the original query. Experimental results on MS-MARCO and BEIR
demonstrate that query expansions generated by LLMs can be more powerful than
traditional query expansion methods.


  The QCD vacuum can be studied using external fields. We report here results
respectively obtained probing the lattice QCD vacuum by means of an abelian
monopole field and of an abelian chromomagnetic field


  We consider an $M/M/1$ queueing system with impatient customers with multiple
and single vacations. It is assumed that customers are impatient whenever the
state of the server. We derive the probability generating functions of the
number of customers in the system and we obtain some performance measures.


  Hamiltonian Monte Carlo samplers have become standard algorithms for MCMC
implementations, as opposed to more basic versions, but they still require some
amount of tuning and calibration. Exploiting the U-turn criterion of the NUTS
algorithm (Hoffman and Gelman, 2014), we propose a version of HMC that relies
on the distribution of the integration time of the associated leapfrog
integrator. Using in addition the primal-dual averaging method for tuning the
step size of the integrator, we achieve an essentially calibration free version
of HMC. When compared with the original NUTS on several benchmarks, this
algorithm exhibits a significantly improved efficiency.


  An explicit convergent iterative solution for the lowest energy state of the
Schroedinger equation with generalized $N$-dimensional Sombrero-shaped
potential is presented. The condition for the convergence of the iteration
procedure and the dependence of the shape of the groundstate wave function on
the parameters are discussed.


  A large-Ud theory is constructed for the metallic state of high-Tc cuprates.
The Emery three-band model, extended with Ox-Oy hopping tpp, and with Ud
tending to infinity, is mapped on slave fermions. The Dyson time-dependent
diagrammatic theory in terms of the Cu-O hopping tpd, which starts from the
nondegenerate unperturbed ground state, is translationally and asymptotically
locally gauge invariant. The small parameter of the theory is nd. The lowest
order of the theory generates the single particle propagators of the hybridized
pdp- and dpd-fermions with the exact three band structure. The leading
many-body effect is band narrowing, obtained without mean-field slave particle
approximations. Damping found in the single particle propagation is related to
incoherent Cu-O2 intracell "mixed valence" fluctuations. The corresponding
continuum falls below the Fermi level. The conventional Luttinger sum rule for
coherent bands is thus broken. Due to local gauge invariance, these results are
insensitive to the replacement of the Cu-O anticommutations by commutations.
This replacement affects the effective local kinematic repulsion Ud\mu\ between
the hybridized pdp hole propagators. The a posteriori antisymmetrization of
Ud\mu\ removes the repulsion between the pdp particles in the triplet
configuration, keeping their singlet repulsion intact. Such a Ud\mu\ is
moderate, and close to optimal doping favors the coherent, antinodal, low
energy SDW correlations. Resonant valence bonds which involve Jpd appear as
incoherent perturbative corrections to these correlations. In function of
doping and/or frequency, the incoherent "mixed valence" fluctuations compete
with magnetic coherence. This dichotomy, between properties local in the direct
and reciprocal space, is the fundamental feature of the Emery model in the
large Ud metallic limit.


  We consider the inertial Kuramoto model of $N$ globally coupled oscillators
characterized by both their phase and angular velocity, in which there is a
time delay in the interaction between the oscillators. Besides the academic
interest, we show that the model can be related to a network of phase-locked
loops widely used in electronic circuits for generating a stable frequency at
multiples of an input frequency. We study the model for a generic choice of the
natural frequency distribution of the oscillators, to elucidate how a
synchronized phase bifurcates from an incoherent phase as the coupling constant
between the oscillators is tuned. We show that in contrast to the case with no
delay, here the system in the stationary state may exhibit either a subcritical
or a supercritical bifurcation between a synchronized and an incoherent phase,
which is dictated by the value of the delay present in the interaction and the
precise value of inertia of the oscillators. Our theoretical analysis,
performed in the limit $N \to \infty$, is based on an unstable manifold
expansion in the vicinity of the bifurcation, which we apply to the kinetic
equation satisfied by the single-oscillator distribution function. We check our
results by performing direct numerical integration of the dynamics for large
$N$, and highlight the subtleties arising from having a finite number of
oscillators.


  In this paper, we focus on devising a versatile framework for dense pixelwise
prediction whose goal is to assign a discrete or continuous label to each pixel
for an image. It is well-known that the reduced feature resolution due to
repeated subsampling operations poses a serious challenge to Fully
Convolutional Network (FCN) based models. In contrast to the commonly-used
strategies, such as dilated convolution and encoder-decoder structure, we
introduce the Flattening Module to produce high-resolution predictions without
either removing any subsampling operations or building a complicated decoder
module. In addition, the Flattening Module is lightweight and can be easily
combined with any existing FCNs, allowing the model builder to trade off among
model size, computational cost and accuracy by simply choosing different
backbone networks. We empirically demonstrate the effectiveness of the proposed
Flattening Module through competitive results in human pose estimation on MPII,
semantic segmentation on PASCAL-Context and object detection on PASCAL VOC. We
hope that the proposed approach can serve as a simple and strong alternative of
current dominant dense pixelwise prediction frameworks.


  The sum-essential graph $ \mathcal{S}_R(M) $ of a left $R$-module $M$ is a
graph whose vertices are all nontrivial submodules of $M$ and two distinct
submodules are adjacent iff their sum is an essential submodule of $M$.
Properties of the graph $\mathcal{S}_R(M)$ and its subgraph $\mathcal{P}_R(M)$
induced by vertices which are not essential as submodules of $M$ are
investigated. The interplay between module properties of $M$ and properties of
those graphs is studied.


  We demonstrate that a recently published quasiclassical M\oller type approach
[Geyer and Rost 2002, J. Phys. B 35 1479] can be used to overcome the problem
of autoionization, which arises in classical trajectory calculations for many
electron targets. In this method the target is stabilized dynamically by a
backward--forward propagation scheme. We illustrate this refocusing and present
total cross sections for single and double ionization of helium by electron
impact.


  In this work we derive Born's rule from the pilot-wave theory of de Broglie
and Bohm. Based on a toy model involving a particle coupled to a environement
made of "qubits" (i.e., Bohmian pointers) we show that entanglement together
with deterministic chaos lead to a fast relaxation from any statistitical
distribution $\rho(x)$ (of finding a particle at point $x$) to the Born
probability law $|\Psi(x)|^2$. Our model is discussed in the context of
Boltzmann's kinetic theory and we demonstrate a kind of H theorem for the
relaxation to the quantum equilibrium regime.


  Ranking risks and countermeasures is one of the foremost goals of
quantitative security analysis. One of the popular frameworks, used also in
industrial practice, for this task are attack-defense trees. Standard
quantitative analyses available for attack-defense trees can distinguish likely
from unlikely vulnerabilities. We provide a tool that allows for easy synthesis
and analysis of those models, also featuring probabilities, costs and time.
Furthermore, it provides a variety of interfaces to existing model checkers and
analysis tools. Unfortunately, currently available tools rely on precise
quantitative inputs (probabilities, timing, or costs of attacks), which are
rarely available. Instead, only statistical, imprecise information is typically
available, leaving us with probably approximately correct (PAC) estimates of
the real quantities. As a part of our tool, we extend the standard analysis
techniques so they can handle the PAC input and yield rigorous bounds on the
imprecision and uncertainty of the final result of the analysis.


  The $^{235}$U(n,f) reaction cross section was measured relative to
neutron-proton elastic scattering for the first time in the energy region from
10 MeV to 440 MeV at the CERN n\_TOF facility, extending the upper limit of the
only previous measurement in the literature by more than 200 MeV. Two
independent detection systems were used simultaneously to extract the fission
cross section. For neutron energies below 200 MeV, the present results agree
within one standard deviation with the data available in literature and are
well reproduced by the IAEA evaluation. Above 200 MeV, the comparison of model
calculations to the present data indicates the need to introduce a transient
time in the description of the neutron-induced fission process in order to
allow the simultaneous description of (n,f) and (p,f) reactions.


  Existing works based on latent factor models have focused on representing the
rating matrix as a product of user and item latent factor matrices, both being
dense. Latent (factor) vectors define the degree to which a trait is possessed
by an item or the affinity of user towards that trait. A dense user matrix is a
reasonable assumption as each user will like/dislike a trait to certain extent.
However, any item will possess only a few of the attributes and never all.
Hence, the item matrix should ideally have a sparse structure rather than a
dense one as formulated in earlier works. Therefore we propose to factor the
ratings matrix into a dense user matrix and a sparse item matrix which leads us
to the Blind Compressed Sensing (BCS) framework. We derive an efficient
algorithm for solving the BCS problem based on Majorization Minimization (MM)
technique. Our proposed approach is able to achieve significantly higher
accuracy and shorter run times as compared to existing approaches.


  This work studies a Reinforcement Learning (RL) problem in which we are given
a set of trajectories collected with K baseline policies. Each of these
policies can be quite suboptimal in isolation, and have strong performance in
complementary parts of the state space. The goal is to learn a policy which
performs as well as the best combination of baselines on the entire state
space. We propose a simple imitation learning based algorithm, show a sample
complexity bound on its accuracy and prove that the the algorithm is minimax
optimal by showing a matching lower bound. Further, we apply the algorithm in
the setting of machine learning guided compiler optimization to learn policies
for inlining programs with the objective of creating a small binary. We
demonstrate that we can learn a policy that outperforms an initial policy
learned via standard RL through a few iterations of our approach.


  We present the emerging panorama of Astroparticle Physics at Eastern
Colombia, and describe several ongoing projects, most of them related to the
Latin American Giant Observatory (LAGO) Project. This research work is carried
out at the Grupo de Investigaciones en Relatividad y Gravitaci\'on of
Universidad Industrial de Santander.


  We study in this paper the problem of adaptive trajectory tracking control
for a class of nonlinear systems with parametric uncertainties. We propose to
use a modular approach, where we first design a robust nonlinear state feedback
which renders the closed loop input-to-state stable (ISS), where the input is
considered to be the estimation error of the uncertain parameters, and the
state is considered to be the closed-loop output tracking error. Next, we
augment this robust ISS controller with a model-free learning algorithm to
estimate the model uncertainties. We implement this method with two different
learning approaches. The first one is a model-free multi-parametric extremum
seeking (MES) method and the second is a Bayesian optimization-based method
called Gaussian Process Upper Confidence Bound (GP-UCB). The combination of the
ISS feedback and the learning algorithms gives a learning-based modular
indirect adaptive controller. We show the efficiency of this approach on a
two-link robot manipulator example.


  In recent years, narrative visualization has gained much attention.
Researchers have proposed different design spaces for various narrative
visualization genres and scenarios to facilitate the creation process. As
users' needs grow and automation technologies advance, increasingly more tools
have been designed and developed. In this study, we summarized six genres of
narrative visualization (annotated charts, infographics, timelines &
storylines, data comics, scrollytelling & slideshow, and data videos) based on
previous research and four types of tools (design spaces, authoring tools,
ML/AI-supported tools and ML/AI-generator tools) based on the intelligence and
automation level of the tools. We surveyed 105 papers and tools to study how
automation can progressively engage in visualization design and narrative
processes to help users easily create narrative visualizations. This research
aims to provide an overview of current research and development in the
automation involvement of narrative visualization tools. We discuss key
research problems in each category and suggest new opportunities to encourage
further research in the related domain.


  Mobile cloud gaming places the simultaneous demands of high capacity and low
latency on the wireless network, demands that Private and Metropolitan-Area
Standalone 5G networks are poised to meet. However, lacking introspection into
the 5G Radio Access Network (RAN), cloud gaming servers are ill-poised to cope
with the vagaries of the wireless last hop to a mobile client, while 5G network
operators run mostly closed networks, limiting their potential for co-design
with the wider internet and user applications. This paper presents Telesa, a
passive, incrementally-deployable, and independently-deployable Standalone 5G
network telemetry system that streams fine-grained RAN capacity, latency, and
retransmission information to application servers to enable better millisecond
scale, application-level decisions on offered load and bit rate adaptation than
end-to-end latency measurements or end-to-end packet losses currently permit.
We design, implement, and evaluate a Telesa telemetry-enhanced game streaming
platform, demonstrating exact congestion-control that can better adapt game
video bitrate while simultaneously controlling end-to-end latency, thus
maximizing game quality of experience. Our experimental evaluation on a
production 5G Standalone network demonstrates a 178-249% Quality of Experience
improvement versus two state-of-the-art cloud gaming applications.


  Let ${\bf X}$ and ${\bf X}$ be two $n$-dimensional elliptical random vectors,
we establish an identity for $E[f({\bf Y})]-E[f({\bf X})]$, where $f: \Bbb{R}^n
\rightarrow \Bbb{R}$ fulfilling some regularity conditions. Using this identity
we provide a unified derivation of sufficient and necessary conditions for
classifying multivariate elliptical random vectors according to several main
integral stochastic orders. As a consequence we obtain new inequalities by
applying it to multivariate elliptical distributions. The results generalize
the corresponding ones for multivariate normal random vectors in the
literature.


  We critically reconsider the argument based on 't Hooft anomaly matching that
aims at proving chiral symmetry breaking in 4d QCD-like theories with $N_c>2$
colors and $N_f$ flavors. The main line of reasoning relies on a property of
the solutions of the anomaly matching and persistent mass equations called
$N_f$-independence. In previous works, the validity of $N_f$-independence was
assumed based on qualitative arguments, but it was never proven rigorously. We
provide a detailed proof and clarify under which (dynamical) conditions it
holds. Our results are valid for a generic spectrum of massless composite
fermions including baryons and exotics.


  We study a natural generalization of the additivity problem in quantum
information theory: given a pair of quantum channels, then what is the set of
convex trace functions that attain their maximum on unentangled inputs, if they
are applied to the corresponding output state?
  We prove several results on the structure of the set of those convex
functions that are "additive" in this more general sense. In particular, we
show that all operator convex functions are additive for the Werner-Holevo
channel in 3x3 dimensions, which contains the well-known additivity results for
this channel as special cases.


  By employing large-scale molecular dynamics simulations of atomistically
resolved oligoelectrolytes in aqueous solutions, we study in detail the first
four layer-by-layer deposition cycles of an oligoelectrolyte multilayer made of
poly(diallyl dimethyl ammonium chloride)/poly(styrene sulfonate sodium salt)
(PDADMAC/PSS). The multilayers are grown on a silica substrate in 0.1M {NaCl}
electrolyte solutions and the swollen structures are then subsequently exposed
to varying added salt concentration. We investigated the microscopic properties
of the films, analyzing in detail the differences between three- and
four-layers systems. Our simulations provide insights on the early stages of
growth of a multilayer, which are particularly challenging for experimental
observations. We found a rather strong entanglement of the oligoelectrolytes,
with a fuzzy layering of the film structure. The main charge compensation
mechanism is for all cases intrinsic, whereas extrinsic compensation is
relatively ehanced for the layer of the last deposition cycle. In addition, we
quantified other fundamental observables of these systems, as the film
thickness, water uptake, and overcharge fractions for each deposition layer.


  Inferring high-fidelity constraints on the spatial curvature parameter,
$\Omega_{\rm K}$, under as few assumptions as possible, is of fundamental
importance in cosmology. We propose a method to non-parametrically infer
$\Omega_{\rm K}$ from late-Universe probes alone. Using Gaussian Processes (GP)
to reconstruct the expansion history, we combine Cosmic Chronometers (CC) and
Type Ia Supernovae (SNe~Ia) data to infer constraints on curvature,
marginalized over the expansion history, calibration of the CC and SNe~Ia data,
and the GP hyper-parameters. The obtained constraints on $\Omega_{\rm K}$ are
free from parametric model assumptions for the expansion history, and are
insensitive to the overall calibration of both the CC and SNe~Ia data (being
sensitive only to relative distances and expansion rates). Applying this method
to \textit{Pantheon} SNe~Ia and the latest compilation of CCs, we find
$\Omega_{\rm K} = -0.03 \pm 0.26$, consistent with spatial flatness at the
$\mathcal{O}(10^{-1})$ level, and independent of any early-Universe probes.
Applying our methodology to future Baryon Acoustic Oscillations and SNe~Ia data
from upcoming Stage IV surveys, we forecast the ability to constrain
$\Omega_{\rm K}$ at the $\mathcal{O}(10^{-2})$ level.


  LLM-based agents have demonstrated impressive zero-shot performance in the
vision-language navigation (VLN) task. However, these zero-shot methods focus
only on solving high-level task planning by selecting nodes in predefined
navigation graphs for movements, overlooking low-level control in realistic
navigation scenarios. To bridge this gap, we propose AO-Planner, a novel
affordances-oriented planning framework for continuous VLN task. Our AO-Planner
integrates various foundation models to achieve affordances-oriented motion
planning and action decision-making, both performed in a zero-shot manner.
Specifically, we employ a visual affordances prompting (VAP) approach, where
visible ground is segmented utilizing SAM to provide navigational affordances,
based on which the LLM selects potential next waypoints and generates low-level
path planning towards selected waypoints. We further introduce a high-level
agent, PathAgent, to identify the most probable pixel-based path and convert it
into 3D coordinates to fulfill low-level motion. Experimental results on the
challenging R2R-CE benchmark demonstrate that AO-Planner achieves
state-of-the-art zero-shot performance (5.5% improvement in SPL). Our method
establishes an effective connection between LLM and 3D world to circumvent the
difficulty of directly predicting world coordinates, presenting novel prospects
for employing foundation models in low-level motion control.


  In this work, we study the full set of eigenstates of a $p_x+ip_y$
topological superconductor coupled to a particle bath which can be described in
terms of an integrable Hamiltonian of the Richardson-Gaudin class. The results
derived in this work also characterise the behaviour of an anisotropic XXZ
central spin model in a external magnetic field since both types of Hamiltonian
are know to share the exact same conserved quantities making them formally
equivalent.
  We show how by ramping the coupling strength (or equivalently the magnetic
field acting in the z-direction on the central spin), each individual
eigenstate undergoes a sequence of gain/loss of excitations when crossing the
specific values known as Read-Green points. These features are shown to be
completely predictable, for every one of the $2^N$ eigenstates, using only two
integers obtainable easily from the zero-coupling configuration which defines
the eigenstate in question.
  These results provide a complete map of the particle-number sectors
(superconductor) or magnetisation sectors (central spin) involved in the large
number of level-crossings which occur in these systems at the Read-Green
points. It further allows us to define quenching protocols which could create
states with remarkably large excitation-number fluctuations.


  This paper proposes a new highly scalable and asymptotically optimal control
synthesis algorithm from linear temporal logic specifications, called
$\text{STyLuS}^{*}$ for large-Scale optimal Temporal Logic Synthesis, that is
designed to solve complex temporal planning problems in large-scale multi-robot
systems. Existing planning approaches with temporal logic specifications rely
on graph search techniques applied to a product automaton constructed among the
robots. In our previous work, we have proposed a more tractable sampling-based
algorithm that builds incrementally trees that approximate the state-space and
transitions of the synchronous product automaton and does not require
sophisticated graph search techniques. Here, we extend our previous work by
introducing bias in the sampling process which is guided by transitions in the
B$\ddot{\text{u}}$chi automaton that belong to the shortest path to the
accepting states. This allows us to synthesize optimal motion plans from
product automata with hundreds of orders of magnitude more states than those
that existing optimal control synthesis methods or off-the-shelf model checkers
can manipulate. We show that $\text{STyLuS}^{*}$ is probabilistically complete
and asymptotically optimal and has exponential convergence rate. This is the
first time that convergence rate results are provided for sampling-based
optimal control synthesis methods. We provide simulation results that show that
$\text{STyLuS}^{*}$ can synthesize optimal motion plans for very large
multi-robot systems which is impossible using state-of-the-art methods.


  The curvature and the reduced curvature are basic differential invariants of
the pair (Hamiltonian system, Lagrange distribution) on the symplectic
manifold. It is shown that the negativity of the reduced curvature implies the
hyperbolicity of any compact invariant set of the Hamiltonian flow restricted
to a prescribed energy level. We consider the Hamiltonian flows of the curve of
least action of natural mechanical systems in sub-Riemannian structures with
symmetries. We give sufficient conditions for the reduced flows (after
reduction of the first integrals induced from the symmetries) to be hyperbolic
and show new examples of Anosov flows.


  Attentive video modeling is essential for action recognition in unconstrained
videos due to their rich yet redundant information over space and time.
However, introducing attention in a deep neural network for action recognition
is challenging for two reasons. First, an effective attention module needs to
learn what (objects and their local motion patterns), where (spatially), and
when (temporally) to focus on. Second, a video attention module must be
efficient because existing action recognition models already suffer from high
computational cost. To address both challenges, a novel What-Where-When (W3)
video attention module is proposed. Departing from existing alternatives, our
W3 module models all three facets of video attention jointly. Crucially, it is
extremely efficient by factorizing the high-dimensional video feature data into
low-dimensional meaningful spaces (1D channel vector for `what' and 2D spatial
tensors for `where'), followed by lightweight temporal attention reasoning.
Extensive experiments show that our attention model brings significant
improvements to existing action recognition models, achieving new
state-of-the-art performance on a number of benchmarks.


  We get an upper bound of the slope of each graded quotient for the
Harder-Narasimhan filtration of the Hodge bundle of a Teichm\"{u}ller curve. As
an application, we show that the sum of Lyapunov exponents of a Teichm\"{u}ller
curve does not exceed ${(g+1)}/{2}$, with equality reached if and only if the
curve lies in the hyperelliptic locus induced from
$\mathcal{Q}(2k_1,...,2k_n,-1^{2g+2})$ or it is a special Teichm\"{u}ller curve
in $\Omega\mathcal{M}_g(1^{2g-2})$. It also gives an unified interpretation for
many known results about the special partial sums of Lyapunov exponents on
Teichm\"uller curves.