  With the rapid development of quantum computing, automatic verification of
quantum circuits becomes more and more important. While several decision
diagrams (DDs) have been introduced in quantum circuit simulation and
verification, none of them supports symbolic computation. Algorithmic
manipulations of symbolic objects, however, have been identified as crucial, if
not indispensable, for several verification tasks. This paper proposes the
first decision-diagram approach for operating symbolic objects and verifying
quantum circuits with symbolic terms. As a notable example, our symbolic tensor
decision diagrams (symbolic TDD) could verify the functionality of the
160-qubit quantum Fourier transform circuit within three minutes. Moreover, as
demonstrated on Bernstein-Vazirani algorithm, Grover's algorithm, and the
bit-flip error correction code, the symbolic TDD enables efficient verification
of quantum circuits with user-supplied oracles and/or classical controls.


  The number counts of homogeneous samples of radio sources are a tried and
true method of probing the large scale structure of the Universe, as most radio
sources outside the galactic plane are at cosmological distances. As such they
are expected to trace the cosmic radio dipole, an anisotropy analogous to the
dipole seen in the cosmic microwave background (CMB). Results have shown that
although the cosmic radio dipole matches the direction of the CMB dipole, it
has a significantly larger amplitude. This result challenges our assumption of
the Universe being isotropic, which can have large repercussions for the
current cosmological paradigm. Though significant measurements have been made,
sensitivity to the radio dipole is generally hampered by systematic effects
that can cause large biases in the measurement. Here we assess these
systematics with data from the MeerKAT Absorption Line Survey (MALS). We
present the analysis of ten MALS pointings, focusing on systematic effects that
could lead to an inhomogeneous catalogue. We describe the calibration and
creation of full band continuum images and catalogues, producing a combined
catalogue containing 16,313 sources and covering 37.5 square degrees of sky
down to a sensitivity of 10 $\mu$Jy/beam. We measure the completeness, purity,
and flux recovery statistics for these catalogues using simulated data. We
investigate different source populations in the catalogues by looking at flux
densities and spectral indices, and how they might influence source counts.
Using the noise characteristics of the pointings, we find global measures that
can be used to correct for the incompleteness of the catalogue, producing
corrected number counts down to 100 - 200 $\mu$Jy. We show that we can
homogenise the catalogues and properly account for systematic effects. We
determine that we can measure the dipole to $3\sigma$ significance with 100
MALS pointings.


  Image blending aims to combine multiple images seamlessly. It remains
challenging for existing 2D-based methods, especially when input images are
misaligned due to differences in 3D camera poses and object shapes. To tackle
these issues, we propose a 3D-aware blending method using generative Neural
Radiance Fields (NeRF), including two key components: 3D-aware alignment and
3D-aware blending. For 3D-aware alignment, we first estimate the camera pose of
the reference image with respect to generative NeRFs and then perform 3D local
alignment for each part. To further leverage 3D information of the generative
NeRF, we propose 3D-aware blending that directly blends images on the NeRF's
latent representation space, rather than raw pixel space. Collectively, our
method outperforms existing 2D baselines, as validated by extensive
quantitative and qualitative evaluations with FFHQ and AFHQ-Cat.


  In this paper, we present a formalization of an algorithm to construct
admissible discrete vector fields in the Coq theorem prover taking advantage of
the SSReflect library. Discrete vector fields are a tool which has been
welcomed in the homological analysis of digital images since it provides a
procedure to reduce the amount of information but preserving the homological
properties. In particular, thanks to discrete vector fields, we are able to
compute, inside Coq, homological properties of biomedical images which
otherwise are out of the reach of this system.


  We describe some studies related to the frequency of prime values of integer
polynomials.


  The discovery of quasicrystals with crystallographically forbidden rotational
symmetries has changed the notion of the ordering in materials, yet little is
known about the dynamical emergence of such exotic forms of order. Here we
theoretically study a nonequilibrium cavity-QED setup realizing a
zero-temperature quantum phase transition from a homogeneous Bose-Einstein
condensate to a quasicrystalline phase via collective superradiant light
scattering. Across the superradiant phase transition, collective light
scattering creates a dynamical, quasicrystalline optical potential for the
atoms. Remarkably, the quasicrystalline potential is "emergent" as its
eight-fold rotational symmetry is not present in the Hamiltonian of the system,
rather appears solely in the low-energy states. For sufficiently strong
two-body contact interactions between atoms, a quasicrystalline order is
stabilized in the system, while for weakly interacting atoms the condensate is
localized in one or few of the deepest minima of the quasicrystalline
potential.


  The Modeling Instruction program at Arizona State University has developed a
representational tool, called a system schema, to help students make a first
level of abstraction of an actual physical situation [1]. A system schema
consists of identifying and labeling all objects of interest from a given
physical situation, as well as all the different types of interactions between
the objects. Given all the relevant objects and their interactions, students
can explicitly identify which are part of their system and which are not, and
then go on to model the interactions affecting their choice of system as either
(i) mechanisms for energy transfer, or (ii) forces being exerted. In this
paper, I describe the system schema tool, give examples of its use in the
context of forces, and present some evidence on its effectiveness in helping
students understand Newton's Third Law.


  Premise: This report is intended to provide useful background and information
on how we find detect and compile Planetary Nebulae PNe candidates and then
verify them. It is for postgraduate students entering the field and for more
general interest too.


  We show that the adjoint representation of E_{11} contains generators
corresponding to the infinite possible dual descriptions of the bosonic
on-shell degrees of freedom of eleven dimensional supergravity. We also give an
interpretation for the fields corresponding to many of the other generators in
the adjoint representation.


  We study the basic requirements for neutron confinement in the framework of
some 3-D Aharonov-Casher configurations.


  We investigate the structural properties of cluster and group galaxies by
studying the Fornax main cluster and the infalling Fornax A group, exploring
the effects of galaxy preprocessing in this showcase example. Additionally, we
compare the structural complexity of Fornax galaxies to those in the Virgo
cluster and in the field. Our sample consists of 582 galaxies from the Fornax
main cluster and Fornax A group. We quantified the light distributions of each
galaxy based on a combination of aperture photometry, S\'ersic+PSF (point
spread function) and multi-component decompositions, and non-parametric
measures of morphology (Concentration $C$; Asymmetry $A$, Clumpiness $S$; Gini
$G$; second order moment of light $M_{20}$), and structural complexity based on
multi-component decompositions. These quantities were then compared between the
Fornax main cluster and Fornax A group. The structural complexity of Fornax
galaxies were also compared to those in Virgo and in the field. Overall, we
find significant differences in the distributions of quantities derived from
S\'ersic profiles ($g'-r'$, $r'-i'$, $R_e$, and $\bar{\mu}_{e,r'}$), and
non-parametric indices ($A$ and $S$) between the Fornax main cluster and Fornax
A group. Moreover, we find significant cluster-centric trends with $r'-i'$,
$R_e$, and $\bar{\mu}_{e,r'}$, as well as $A$, $S$, $G$, and $M_{20}$ for
galaxies in the Fornax main cluster. We find the structural complexity of
galaxies increases as a function of the absolute $r'$-band magnitude (and
stellar mass), with the largest change occurring between -14 mag $\lesssim
M_{r'}\lesssim$ -19 mag. This same trend was observed for galaxies in the Virgo
cluster and in the field, which suggests that the formation or maintenance of
morphological structures (e.g. bulges, bar) is largely dependent on the stellar
mass of the galaxies, rather than their environment.


  We propose and explore the potential of a method to extract high
signal-to-noise (S/N) integrated spectra related to physical and/or
morphological regions on a 2-dimensional field using Integral Field
Spectroscopy (IFS) observations by employing an optimization procedure based on
either continuum (stellar) or line (nebular) emission features. The
optimization method is applied to a set of IFS VLT-VIMOS observations of
(U)LIRG galaxies, describing the advantages of the optimization by comparing
the results with a fixed-aperture, single spectrum case, and by implementing
some statistical tests. We demonstrate that the S/N of the IFS optimized
integrated spectra is significantly enhanced when compared with the single
aperture unprocessed case. We provide an iterative user-friendly and versatile
IDL algorithm that allows the user to spatially integrate spectra following
more standard procedures. This is made available to the community as part of
the PINGSoft IFS software package.


  We describe important considerations to create top-down fabricated planar
quantum dots in silicon, often not discussed in detail in literature. The
subtle interplay between intrinsic material properties, interfaces and
fabrication processes plays a crucial role in the formation of
electrostatically defined quantum dots. Processes such as oxidation, physical
vapor deposition and atomic-layer deposition must be tailored in order to
prevent unwanted side effects such as defects, disorder and dewetting. In two
directly related manuscripts written in parallel we use techniques described in
this work to create depletion-mode quantum dots in intrinsic silicon, and
low-disorder silicon quantum dots defined with palladium gates. While we
discuss three different planar gate structures, the general principles also
apply to 0D and 1D systems, such as self-assembled islands and nanowires.


  In Ref. [1], exact (not only conformally related) analogue models for the
Schwarzschild and Reissner-Nordstr\"om spacetimes were found. The background
non-relativistic fluid flow was sustained by an external body force which is
not affected by the linearised fluctuations. Following a different route, by
modelling the external force as the gradient of an external pressure, it was
shown in~\cite{bilic} that the speed of sound is significantly modified. This
effective speed of sound turns out to be inconsistent with the continuity
equation. In this comment we analyse these two contradictory conclusions. We
first show that the heuristic justification for the introduction of the
external force via a variational principle given in Ref. [2] is conceptually
incorrect. Then, by adding the external force appropriately, we show that the
conclusions in [1] remain valid.


  Blazar emission is dominated by nonthermal radiation processes that are
highly variable across the entire electromagnetic spectrum. Turbulence, which
can be a major source of nonthermal particle acceleration, can widely exist in
the blazar emission region. The Turbulent Extreme Multi-Zone (TEMZ) model has
been widely used to describe turbulent radiation signatures. Recent
particle-in-cell (PIC) simulations have also revealed the stochastic nature of
the turbulent emission region and particle acceleration therein. However,
radiation signatures have not been systematically studied via
first-principle-integrated simulations. In this paper, we perform combined PIC
and polarized radiative transfer simulations to study synchrotron emission from
magnetic turbulence in the blazar emission region. We find that the
multi-wavelength flux and polarization are generally characterized by
stochastic patterns. Specifically, the variability time scale and average
polarization degree (PD) are governed by the correlation length of the
turbulence. Interestingly, magnetic turbulence can result in polarization angle
(PA) swings with arbitrary amplitudes and duration, in either directions, that
are not associated to changes in flux or PD. Surprisingly, these swings, which
are of stochastic nature, can appear either bumpy or smooth, although large
amplitude swings ($>180^{\circ}$) are very rare as expected. Our radiation and
polarization signatures from first-principle-integrated simulations are
consistent with the TEMZ model.


  In this article, we provide perspectives for atomtronics circuits on quantum
technology platforms beyond simple bosonic or fermionic cold atom matter-wave
currents. Specifically, we consider (i) matter-wave schemes with
multi-component quantum fluids; (ii) networks of Rydberg atoms that provide a
radically new concept of atomtronics circuits in which the flow, rather than in
terms of matter, occurs through excitations; (iii) hybrid matter-wave circuits
- cavities systems that can be used to study atomtronic circuits beyond the
standard solutions and provide new schemes for integrated matter-wave networks.
We also sketch how driving these systems can open new pathways for atomtronics.


  In this contribution we apply the QCD dipole picture combined with
$k_T$-factorization to get predictions for deep-inelastic scattering on an
onium. Assuming renormalization-group factorization, we get predictions for the
$F_2, F_G$ and $R= F_L/F_T$ proton structure functions. We obtain a
three-parameter fit of the 1994 H1 data in the low-$x_{bj}$, moderate-$Q^2$
range. $F_G/F_2$ and $R$ are then predicted without any additionnal parameter.
The BFKL dynamics contained in the dipole model is shown to provide a relevant
model in describing the HERA data. The prediction for $F_2$ and $F_G$ are
compatible with next-to-leading order DGLAP analysis. By contrast, $R$ is
expected to be much lower at small $x_{bj}$.


  We prove sharper versions of theorems of Linial-Meshulam and Meshulam-Wallach
which describe the behavior for (Z/2)-cohomology of a random k-dimensional
simplicial complex within a narrow transition window. In particular, we show
that within this window the (k-1)st Betti number is in the limit Poisson
distributed. For k=2 we also prove that in an accompanying growth process, with
high probability, first cohomology vanishes exactly at the moment when the last
isolated (k-1)-simplex gets covered by a k-simplex.


  Reinforcement learning (RL) is a popular technique that allows an agent to
learn by trial and error while interacting with a dynamic environment. The
traditional Reinforcement Learning (RL) approach has been successful in
learning and predicting Euclidean robotic manipulation skills such as
positions, velocities, and forces. However, in robotics, it is common to
encounter non-Euclidean data such as orientation or stiffness, and failing to
account for their geometric nature can negatively impact learning accuracy and
performance. In this paper, to address this challenge, we propose a novel
framework for RL that leverages Riemannian geometry, which we call Geometric
Reinforcement Learning (G-RL), to enable agents to learn robotic manipulation
skills with non-Euclidean data. Specifically, G-RL utilizes the tangent space
in two ways: a tangent space for parameterization and a local tangent space for
mapping to a nonEuclidean manifold. The policy is learned in the
parameterization tangent space, which remains constant throughout the training.
The policy is then transferred to the local tangent space via parallel
transport and projected onto the non-Euclidean manifold. The local tangent
space changes over time to remain within the neighborhood of the current
manifold point, reducing the approximation error. Therefore, by introducing a
geometrically grounded pre- and post-processing step into the traditional RL
pipeline, our G-RL framework enables several model-free algorithms designed for
Euclidean space to learn from non-Euclidean data without modifications.
Experimental results, obtained both in simulation and on a real robot, support
our hypothesis that G-RL is more accurate and converges to a better solution
than approximating non-Euclidean data.


  For tasks like code synthesis from natural language, code retrieval, and code
summarization, data-driven models have shown great promise. However, creating
these models require parallel data between natural language (NL) and code with
fine-grained alignments. Stack Overflow (SO) is a promising source to create
such a data set: the questions are diverse and most of them have corresponding
answers with high-quality code snippets. However, existing heuristic methods
(e.g., pairing the title of a post with the code in the accepted answer) are
limited both in their coverage and the correctness of the NL-code pairs
obtained. In this paper, we propose a novel method to mine high-quality aligned
data from SO using two sets of features: hand-crafted features considering the
structure of the extracted snippets, and correspondence features obtained by
training a probabilistic model to capture the correlation between NL and code
using neural networks. These features are fed into a classifier that determines
the quality of mined NL-code pairs. Experiments using Python and Java as test
beds show that the proposed method greatly expands coverage and accuracy over
existing mining methods, even when using only a small number of labeled
examples. Further, we find that reasonable results are achieved even when
training the classifier on one language and testing on another, showing promise
for scaling NL-code mining to a wide variety of programming languages beyond
those for which we are able to annotate data.


  Age estimation from images can be used in many practical scenes. Most of the
previous works targeted on the estimation from images in which only one face
exists. Also, most of the open datasets for age estimation contain images like
that. However, in some situations, age estimation in the wild and for
multi-person is needed. Usually, such situations were solved by two separate
models; one is a face detector model which crops facial regions and the other
is an age estimation model which estimates from cropped images. In this work,
we propose a method that can detect and estimate the age of multi-person with a
single model which estimates age with focusing on faces and still seeing
surroundings. Also, we propose a training method which enables the model to
estimate multi-person well despite trained with images in which only one face
is photographed. In the experiments, we evaluated our proposed method compared
with the traditional approach using two separate models. As the result, the
accuracy could be enhanced with our proposed method. We also adapted our
proposed model to commonly used single person photographed age estimation
datasets and it is proved that our method is also effective to those images and
outperforms the state of the art accuracy.


  The smaller the size of a light-emitting microcavity, the more important it
becomes to understand the effects of the cavity boundary on the optical mode
profile. Conventional methods of laser physics, such as the paraxial
approximation, become inapplicable in many of the more exotic cavity designs to
be discussed here. Cavities in the shape of microdisks, pillars and rings can
yield low lasing thresholds in a wide variety of gain media: quantum wells,
wires and even dots, as well as quantum cascade superlattices and GaN. An
overview of the experimental and theoretical status is provided, with special
emphasis on the light extraction problem.


  We use a large-scale Lambda-CDM hydrodynamical simulation to assess the
dependence of the cosmic Star Formation Rate (SFR) on overdensity of
luminosity. The `morphology-density relation' is qualitatively captured when
the SFR is plotted as a function of redshift, divided into the quartiles of the
overdensity distribution.


  The Tautological Lamination arises in holomorphic dynamics as a combinatorial
model for the geometry of 1-dimensional slices of the Shift Locus. In each
degree $q$ the tautological lamination defines an iterated sequence of
partitions of $1$ (one for each integer $n$) into numbers of the form $2^m
q^{-n}$. Denote by $N_q(n,m)$ the number of times $2^mq^{-n}$ arises in the
$n$th partition. We prove a recursion formula for $N_q(n,0)$, and a gap
theorem: $N_q(n,n)=1$ and $N_q(n,m)=0$ for $\lfloor n/2 \rfloor < m < n$.


  A possible explanation is offered for the longstanding mystery surrounding
the meaning of the fine structure constant. The reasoning is based on a
discrete self-similar cosmological paradigm that has shown promise in
explaining the general scaling properties of nature's global hierarchy. The
discrete scale invariance of the paradigm implies that "strong gravity" governs
gravitational interactions within atomic scale systems. Given the revised
gravitational coupling constant and Planck mass, one can demonstrate that the
fine structure constant is the ratio of the strengths of the unit
electromagnetic interaction and the unit gravitational interaction within
atomic scale systems. [Abridged]


  We are interested in the study of caustics by reflection of irreducible
algebraic planar curves (in the complex projective plane). We prove the
birationality of the caustic map (for a generic light position). We also give
simple formulas for the degree and the class of caustics by reflection valid
for any irreducible algebraic curve of degree at least 2 and for a generic
light position.


  Surface granulation can be predicted with the mass, metallicity, and
frequency of maximum power of a star. Using the orders-of-magnitude larger
APOGEE-Kepler sample, we recalibrate the relationship fit by Corsaro et al.
(2017) for "flicker", an easier-to-compute diagnostic of this granulation. We
find that the relationship between the stellar parameters and flicker is
significantly different for dwarf and subgiant stars than it is for red giants.
We also confirm a dependence of flicker amplitude on metallicity as seen
originally by Corsaro et al. (2017), although the dependence found here is
somewhat weaker. Using the same APOGEE-Kepler sample, we demonstrate that
spectroscopic measurements alone provide sufficient information to estimate the
flicker amplitude to 7 percent for giants, and 20 percent for dwarfs and
subgiants. We provide a relationship that depends on effective temperature,
surface gravity, and metallicity, and calculate predicted flicker values for
129,000 stars with APOGEE spectra. Finally, we use published relationships
between flicker and radial velocity jitter to estimate minimum jitter values
for these same 129,000 stars, and we identify stars whose total jitter is
likely to be even larger than the granulation-driven jitter by virtue of
large-amplitude photometric variability.


  The interacting lattice gas model is used to simulate fluid flow through an
open percolating porous medium with the fluid entering at the source-end and
leaving from the opposite end. The shape of the steady-state concentration
profile and therefore the gradient field depends on the is found to scale with
the porosity according to porosity p. The root mean square (rms) displacements
of fluid and its constituents (tracers) show a drift power-law behavior, in the
asymptotic regime. The flux current density is found to scale with the porosity
according to an exponent near 1.7.


  In this paper, we have analysed the time series associated with the iterative
scheme of a double similarity transformed Coupled Cluster theory. The coupled
iterative scheme to solve the ground state Schr{\"o}dinger equation is cast as
a multivariate time-discrete map, the solutions show the universal Feigenbaum
dynamics. Using recurrence analysis, it is shown that the dynamics of the
iterative process is dictated by a small subgroup of cluster operators, mostly
those involving chemically active orbitals, whereas all other cluster operators
with smaller amplitudes are enslaved. Using Synergetics, we will indicate how
the master-slave dynamics can suitably be exploited to develop a novel
coupled-cluster algorithm in a much-reduced dimension.


  One of the central skills that language learners need to practice is speaking
the language. Currently, students in school do not get enough speaking
opportunities and lack conversational practice. Recent advances in speech
technology and natural language processing allow for the creation of novel
tools to practice their speaking skills. In this work, we tackle the first
component of such a pipeline, namely, the automated speech recognition module
(ASR), which faces a number of challenges: first, state-of-the-art ASR models
are often trained on adult read-aloud data by native speakers and do not
transfer well to young language learners' speech. Second, most ASR systems
contain a powerful language model, which smooths out errors made by the
speakers. To give corrective feedback, which is a crucial part of language
learning, the ASR systems in our setting need to preserve the errors made by
the language learners. In this work, we build an ASR system that satisfies
these requirements: it works on spontaneous speech by young language learners
and preserves their errors. For this, we collected a corpus containing around
85 hours of English audio spoken by learners in Switzerland from grades 4 to 6
on different language learning tasks, which we used to train an ASR model. Our
experiments show that our model benefits from direct fine-tuning on children's
voices and has a much higher error preservation rate than other models.


  We present a new derivation of the CORS Baade-Wesselink method in the
Walraven photometric system. We solved the complete Baade-Wesselink equation by
calibrating the surface brightness function with a recent grid of atmosphere
models. The new approach was adopted to estimate the mean radii of a sample of
Galactic Cepheids for which are available precise light curves in the Walraven
bands. Current radii agree, within the errors, quite well with Cepheid radii
based on recent optical and near-infrared interferometric measurements. We also
tested the impact of the projection factor on the Period-Radius relation using
two different values (p=1.36, p=1.27) that bracket the estimates available in
the literature. We found that the agreement of our Period-Radius relation with
similar empirical and theoretical Period-Radius relations in the recent
literature, improves by changing the projection factor from p=1.36 to p=1.27.
Our Period-Radius relation is log(R)=(0.75\pm 0.03)log(P)+(1.10 \pm 0.03), with
a rms=0.03 dex. Thanks to accurate estimates of the effective temperature of
the selected Cepheids, we also derived the Period-Luminosity relation in the V
band and we found Mv=(-2.78 \pm 0.11)log(P)+(-1.42 \pm 0.11) with rms=0.13 mag,
for p=1.27. It agrees quite well with recent results in the literature, while
the relation for p=1.36 deviates by more than 2sigma. We conclude that, even
taking into account the intrinsic dispersion of the obtained Period-Luminosity
relations, that is roughly of the same order of magnitude as the effect of the
projection factor, the results of this paper seem to favour the value p = 1.27.


  The affine Yangian of $\mathfrak{gl}_1$ has recently appeared simultaneously
in the work of Maulik-Okounkov and Schiffmann-Vasserot in connection with the
Alday-Gaiotto-Tachikawa conjecture. While the former presentation is purely
geometric, the latter algebraic presentation is quite involved. In this
article, we provide a simple loop realization of this algebra which can be
viewed as an "additivization" of the quantum toroidal algebra of
$\mathfrak{gl}_1$ in the same way as the Yangian $Y_h(\mathfrak{g})$ is an
"additivization" of the quantum loop algebra $U_q(L\mathfrak{g})$ for a simple
Lie algebra $\mathfrak{g}$. We also explain the similarity between the
representation theories of the affine Yangian and the quantum toroidal algebras
of $\mathfrak{gl}_1$ by generalizing the milestone result of Gautam and
Toledano Laredo to the current settings.


  In the 1950's Wigner and collaborators provided an explanation for the
spectrum of hadronic excitations in terms of Random Matrix Theory. In the
1980's it was understood that some hadronic spectral properties were generic to
systems whose classical limit is chaotic. We use string theory to demonstrate
explicitly how, under very general conditions, recent holographic models of
strong interactions have a spectrum compatible with Wigner's conjecture.


  We explore structural correlations of strongly asymmetric mixtures of binary
charged colloids within the primitive model of electrolytes considering large
charge and size ratios of 10 and higher. Using computer simulations with
explicit microions, we obtain the partial pair correlation functions between
the like-charged colloidal macroions. Interestingly the big-small correlation
peak amplitude is smaller than that of the big-big and small-small macroion
correlation peaks, which is unfamiliar for additive repulsive interactions.
Extracting optimal effective microion-averaged pair interactions between the
macroions, we find that on top of non-additive Yukawa-like repulsions an
additional shifted Gaussian attractive potential between the small macroions is
needed to accurately reproduce their correct pair correlations. For small
Coulomb couplings, the behavior is reproduced in a coarse-grained theory with
microion-averaged effective interactions between the macroions. However, the
accuracy of the theory deteriorates with increasing Coulomb coupling. We
emphasize the relevance of entropic interactions exerted by the microions on
the macroions. Our results are experimentally verifiable in binary mixtures of
micron-sized colloids and like-charge nanoparticles


  Large language models (LLMs) showcase many desirable traits for intelligent
and helpful robots. However, they are also known to hallucinate predictions.
This issue is exacerbated in consumer robotics where LLM hallucinations may
result in robots confidently executing plans that are contrary to user goals,
relying more frequently on human assistance, or preventing the robot from
asking for help at all. In this work, we present LAP, a novel approach for
utilizing off-the-shelf LLM's, alongside scene and object Affordances, in
robotic Planners that minimize harmful hallucinations and know when to ask for
help. Our key finding is that calculating and leveraging a scene affordance
score, a measure of whether a given action is possible in the provided scene,
helps to mitigate hallucinations in LLM predictions and better align the LLM's
confidence measure with the probability of success. We specifically propose and
test three different affordance scores, which can be used independently or in
tandem to improve performance across different use cases. The most successful
of these individual scores involves prompting an LLM to determine if a given
action is possible and safe in the given scene and uses the LLM's response to
compute the score. Through experiments in both simulation and the real world,
on tasks with a variety of ambiguities, we show that LAP significantly
increases success rate and decreases the amount of human intervention required
relative to prior art. For example, in our real-world testing paradigm, LAP
decreases the human help rate of previous methods by over 33% at a success rate
of 70%.


  We develop statistically based methods to detect single nucleotide DNA
mutations in next generation sequencing data. Sequencing generates counts of
the number of times each base was observed at hundreds of thousands to billions
of genome positions in each sample. Using these counts to detect mutations is
challenging because mutations may have very low prevalence and sequencing error
rates vary dramatically by genome position. The discreteness of sequencing data
also creates a difficult multiple testing problem: current false discovery rate
methods are designed for continuous data, and work poorly, if at all, on
discrete data. We show that a simple randomization technique lets us use
continuous false discovery rate methods on discrete data. Our approach is a
useful way to estimate false discovery rates for any collection of discrete
test statistics, and is hence not limited to sequencing data. We then use an
empirical Bayes model to capture different sources of variation in sequencing
error rates. The resulting method outperforms existing detection approaches on
example data sets.


  Prolate rotation (i.e. rotation around the long axis) has been reported for
two Local Group dwarf galaxies: Andromeda II, a dwarf spheroidal satellite of
M31, and Phoenix, a transition type dwarf galaxy. The prolate rotation may be
an exceptional indicator of a past major merger between dwarf galaxies. We
showed that this type of rotation cannot be obtained in the tidal stirring
scenario, in which the satellite is transformed from disky to spheroidal by
tidal forces of the host galaxy. However, we successfully reproduced the
observed Andromeda II kinematics in controlled, self-consistent simulations of
mergers between equal-mass disky dwarf galaxies on a radial or close-to-radial
orbit. In simulations including gas dynamics, star formation and ram pressure
stripping, we are able to reproduce more of the observed properties of
Andromeda II: the unusual rotation, the bimodal star formation history and the
spatial distribution of the two stellar populations, as well as the lack of
gas. We support this scenario by demonstrating the merger origin of prolate
rotation in the cosmological context for sufficiently resolved galaxies in the
Illustris large-scale cosmological hydrodynamical simulation.


  We present measurements of the magnetic susceptibility $\chi$ and the
magnetization M of single crystals of metallic \ypp, where localized Yb moments
lie on the geometrically frustrated Shastry-Sutherland Lattice (SSL). Strong
magnetic frustration is found in this quasi-two dimensional system, which
orders antiferromagnetically (AF) at T$_{N}$=2.02 \emph{K} from a paramagnetic
liquid of Yb-dimers, having a gap $\Delta$=4.6 \emph{K} between the singlet
ground state and the triplet excited states. Magnetic fields suppress the AF
order, which vanishes at a 1.25 \emph{T} quantum critical point. The spin gap
$\Delta$ persists to 1.5 \emph{T}, indicating that the AF degenerates into a
liquid of dimer triplets at T=0. Quantized steps are observed in M(B) within
the AF state, a signature of SSL systems. Our results show that \ypp is unique,
both as a metallic SSL system that is close to an AF quantum critical point,
and as a heavy fermion compound where geometrical frustration plays a decisive
role.


  The first part of this article is devoted to a brief review of the results
about representation theory of the spin group Spin(m) from the point of view of
Clifford analysis. In the second part we are interested in Clifford-valued
functions and wavelets on the sphere. The connection of representations of
Spin(m) and the concept of diffusive wavelets leads naturally to investigations
of a modified diffusion equation on the sphere, that makes use of the Gamma
operator. We will achieve to obtain Clifford-valued diffusion wavelets with
respect to a modified diffusion operator.
  Since we are able to characterize all representations of Spin(m) and even to
obtain all eigenvectors of the (by representation) regarded Casimir operator in
representation spaces, it seems appropriate to look at functions on Spin(m)
directly. Concerning this, our aim shall be to formulate eigenfunctions for the
Laplace-Beltrami operator on Spin(m) and give the series expansion of the heat
kernel on Spin(m) in terms of eigenfunctions.


  Let $U$ be the quantum group with divided powers in $l-$th root of unity and
let $u\subset U$ be the Frobenius kernel. V.Ginzburg and S.Kumar proved that
the cohomology algebra of $u$ with trivial coefficients is isomorphic to the
functions algebra of the nilpotent cone of the corresponding Lie algebra. In
this note we show that there exists tilting module $T$ such that the cohomology
of $u$ with coefficients in $T$ is isomorphic to the functions algebra of the
closure of the subregular nilpotent orbit.


  We present a dynamical mechanism leading to dissipationless conductance,
whose quantized value is controllable, in a (3+1)-dimensional electronic
system. The mechanism is exemplified by a theory of Weyl fermions coupled to a
Higgs field--also known as an axion insulator. We show that the insertion of an
axial gauge flux can induce vortex lines in the Higgs field, similarly to the
development of vortices in a superconductor upon the insertion of magnetic
flux. We further show that the necessary axial gauge flux can be generated
using Rashba spin-orbit coupling or a magnetic field. Vortex lines in the Higgs
field are known to bind chiral fermionic modes, each of which serves as a
one-way channel for electric charge with conductance $e^2/h$. Combining these
elements, we present a physical picture, the "topological coaxial cable,"
illustrating how the value of the quantized conductance could be controlled in
such an axion insulator.


  Embedding high-dimensional data into a 2D canvas is a popular strategy for
their visualization.


  The nature of ultraluminous X-ray sources (ULXs) is presently unknown. A
possible explanation is that they are accreting intermediate mass black holes
(IBHs) that are fed by Roche lobe overflow from a tidally captured stellar
companion. We show that a star can circularize around an IBH without being
destroyed by tidal heating (in contrast to the case of M_bh> 10^6 M_sun massive
black holes in galactic centers, where survival is unlikely). We find that the
capture and circularization rate is of the order of 5 \times 10^-8 yr^-1,
almost independently of the cluster's relaxation time. We follow the luminosity
evolution of the binary system during the main sequence Roche lobe overflow
phase and show it can maintain ULX-like luminosities for >10 Myr. In
particular, we show that the ULX in the young cluster MGG-11 in star-burst
galaxy M82, which possibly harbors an IBH, is well explained by this mechanism,
and we predict that \gtrsim 10% of similar clusters with IBHs have a tidally
captured circularized star. The cluster can evaporate on a time-scale shorter
than the lifetime of the binary. This raises the possibility of a ULX that
outlives its host cluster, or even lights up only after the cluster has
evaporated, in agreement with observations of host-less ULXs.


  We introduce extreme summarization, a new single-document summarization task
which does not favor extractive strategies and calls for an abstractive
modeling approach. The idea is to create a short, one-sentence news summary
answering the question "What is the article about?". We collect a real-world,
large-scale dataset for this task by harvesting online articles from the
British Broadcasting Corporation (BBC). We propose a novel abstractive model
which is conditioned on the article's topics and based entirely on
convolutional neural networks. We demonstrate experimentally that this
architecture captures long-range dependencies in a document and recognizes
pertinent content, outperforming an oracle extractive system and
state-of-the-art abstractive approaches when evaluated automatically and by
humans.


  We describe a made-to-measure algorithm for constructing N-particle models of
stellar systems from observational data (Chi-Squared-M2M), extending earlier
ideas by Syer and Tremaine. The algorithm properly accounts for observational
errors, is flexible, and can be applied to various systems and geometries. We
implement this algorithm in a parallel code NMAGIC and carry out a sequence of
tests to illustrate its power and performance: (i) We reconstruct an isotropic
Hernquist model from density moments and projected kinematics and recover the
correct differential energy distribution and intrinsic kinematics. (ii) We
build a self-consistent oblate three-integral maximum rotator model and compare
how the distribution function is recovered from integral field and slit
kinematic data. (iii) We create a non-rotating and a figure rotating triaxial
stellar particle model, reproduce the projected kinematics of the figure
rotating system by a non-rotating system of the same intrinsic shape, and
illustrate the signature of pattern rotation in this model. From these tests we
comment on the dependence of the results from Chi-Squared-M2M on the initial
model, the geometry, and the amount of available data.


  This Thesis presents some physically motivated criteria for the existence of
particles and infra-particles in a given quantum field theory. It is based on a
refined spectral theory of automorphism groups describing the energy-momentum
transfer of local observables. In particular, a novel decomposition of the
algebra of local observables into spectral subspaces is constructed. Apart from
the counterparts of the pure-point and absolutely continuous subspaces,
familiar from the spectral theory of operators, there appears a new
'point-continuous' subspace. It belongs to the singular-continuous part of the
decomposition, but is finite-dimensional in a large class of models; its
dimension carries information about the infrared structure of a theory. It is
shown that this point-continuous subspace is trivial in all theories complying
with a regularity condition proposed in this work. Moreover, this condition
entails the existence of particles if the theory admits a stress-energy tensor.
The uniqueness of the decomposition of the algebra of observables into the
pure-point and continuous subspace is established by proving an ergodic theorem
for automorphism groups. The proof is based on physically motivated phase space
conditions which have a number of interesting consequences pertaining to the
vacuum structure such as the convergence of physical states to a unique vacuum
under large timelike translations.


  We report astrometric results of VLBI phase-referencing observations of 22
GHz \hho\ masers emission toward the red hypergiant \vxsgr, one of most massive
and luminous red hypergiant stars in our Galaxy, using the Very Long Baseline
Array. A background source, \Jtwoze, projected 4\d4 from the target \vxsgr, was
used as the phase reference. For the low declinations of these sources, such a
large separation normally would seriously degrade the relative astrometry. We
use a two-step method of tropospheric delay calibration, which combines the
VLBI geodetic-block (or GPS) calibration with an image-optimization
calibration, to obtain a trigonometric parallax of $0.64\pm0.04$ mas,
corresponding to a distance of 1.56$^{+0.11}_{-0.10}$ kpc. The measured proper
motion of \vxsgr\ is $0.36\pm0.76$ and $-2.92\pm0.78$ \masy\ in the eastward
and northward directions. The parallax and proper motion confirms that \vxsgr\
belong to the Sgr OB1 association. Rescaling bolometric luminosities in the
literature to our parallax distance, we find the luminosity of \vxsgr~is $(1.95
\pm 0.62) \times 10^5$ \Lsun, where the uncertainty is dominated by differing
photometry measurements.


  In this paper we present some compactness results, showing how they can be
applied in dealing with "zero mass" problems by a variational approach. In
particular we use our results in two different situations: we look for complex
valued solutions of a very classical elliptic equation, and we study an
elliptic problem on an axially symmetric unbounded domain.


  Criminal recidivism models are tools that have gained widespread adoption by
parole boards across the United States to assist with parole decisions. These
models take in large amounts of data about an individual and then predict
whether an individual would commit a crime if released on parole. Although such
models are not the only or primary factor in making the final parole decision,
questions have been raised about their accuracy, fairness, and
interpretability. In this paper, various machine learning-based criminal
recidivism models are created based on a real-world parole decision dataset
from the state of Georgia in the United States. The recidivism models are
comparatively evaluated for their accuracy, fairness, and interpretability. It
is found that there are noted differences and trade-offs between accuracy,
fairness, and being inherently interpretable. Therefore, choosing the best
model depends on the desired balance between accuracy, fairness, and
interpretability, as no model is perfect or consistently the best across
different criteria.


  This paper concerns the distributed training of nonlinear kernel machines on
Map-Reduce. We show that a re-formulation of Nystr\"om approximation based
solution which is solved using gradient based techniques is well suited for
this, especially when it is necessary to work with a large number of basis
points. The main advantages of this approach are: avoidance of computing the
pseudo-inverse of the kernel sub-matrix corresponding to the basis points;
simplicity and efficiency of the distributed part of the computations; and,
friendliness to stage-wise addition of basis points. We implement the method
using an AllReduce tree on Hadoop and demonstrate its value on a few large
benchmark datasets.


  We use and adapt the Borsuk-Ulam Theorem from topology to derive limitations
on list-replicable and globally stable learning algorithms. We further
demonstrate the applicability of our methods in combinatorics and topology.
  We show that, besides trivial cases, both list-replicable and globally stable
learning are impossible in the agnostic PAC setting. This is in contrast with
the realizable case where it is known that any class with a finite Littlestone
dimension can be learned by such algorithms. In the realizable PAC setting, we
sharpen previous impossibility results and broaden their scope. Specifically,
we establish optimal bounds for list replicability and global stability numbers
in finite classes. This provides an exponential improvement over previous works
and implies an exponential separation from the Littlestone dimension. We
further introduce lower bounds for weak learners, i.e., learners that are only
marginally better than random guessing. Lower bounds from previous works apply
only to stronger learners.
  To offer a broader and more comprehensive view of our topological approach,
we prove a local variant of the Borsuk-Ulam theorem in topology and a result in
combinatorics concerning Kneser colorings. In combinatorics, we prove that if
$c$ is a coloring of all non-empty subsets of $[n]$ such that disjoint sets
have different colors, then there is a chain of subsets that receives at least
$1+ \lfloor n/2\rfloor$ colors (this bound is sharp). In topology, we prove
e.g. that for any open antipodal-free cover of the $d$-dimensional sphere,
there is a point $x$ that belongs to at least $t=\lceil\frac{d+3}{2}\rceil$
sets.


  Because of the large neutron excess of $^{22}$Ne, this isotope rapidly
sediments in the interior of the white dwarfs. This process releases an
additional amount of energy, thus delaying the cooling times of the white
dwarf. This influences the ages of different stellar populations derived using
white dwarf cosmochronology. Furthermore, the overabundance of $^{22}$Ne in the
inner regions of the star, modifies the Brunt-V\"ais\"al\"a frequency, thus
altering the pulsational properties of these stars. In this work, we discuss
the impact of $^{22}$Ne sedimentation in white dwarfs resulting from Solar
metallicity progenitors ($Z=0.02$). We performed evolutionary calculations of
white dwarfs of masses $0.528$, $0.576$, $0.657$ and $0.833$ M$_{\sun}$,
derived from full evolutionary computations of their progenitor stars, starting
at the Zero Age Main Sequence all the way through central hydrogen and helium
burning, thermally-pulsing AGB and post-AGB phases. Our computations show that
at low luminosities ($\log(L/L_{\sun})\la -4.25$), $^{22}$Ne sedimentation
delays the cooling of white dwarfs with Solar metallicity progenitors by about
1~Gyr. Additionally, we studied the consequences of $^{22}$Ne sedimentation on
the pulsational properties of ZZ~Ceti white dwarfs. We find that $^{22}$Ne
sedimentation induces differences in the periods of these stars larger than the
present observational uncertainties, particularly in more massive white dwarfs.


  The search for a standardized optimum way to communicate using natural
language dialog has involved a lot of research. However, due to the diversity
of communication domains, we think that this is extremely difficult to achieve
and different dialogue management techniques should be applied for different
situations. Our work presents the basis of a communication mechanism that
supports decision processes, is based on decision trees, and minimizes the
number of steps (turn-takes) in the dialogue. The initial dialog workflow is
automatically generated and the user's interaction with the system can also
change the decision tree and create new dialog paths with optimized cost. The
decision tree represents the chronological ordering of the actions (via the
parent-child relationship) and uses an object frame to represent the
information state (capturing the notion of context). This paper presents our
framework, the formalism for interaction and dialogue, and an evaluation of the
system compared to relevant dialog planning frameworks (i.e. finite state
diagrams, frame-based, information state and planning-based dialogue systems).


  The Infrared Camera (IRC) is one of the two instruments on board the AKARI
satellite. In addition to deep imaging from 1.8-26.5um for the pointed
observation mode of the AKARI, it has a spectroscopic capability in its
spectral range. By replacing the imaging filters by transmission-type
dispersers on the filter wheels, it provides low-resolution (lambda/d_lambda ~
20-120) spectroscopy with slits or in a wide imaging field-of-view
(approximately 10'X10'). The IRC spectroscopic mode is unique in space infrared
missions in that it has the capability to perform sensitive wide-field
spectroscopic surveys in the near- and mid-infrared wavelength ranges. This
paper describes specifications of the IRC spectrograph and its in-orbit
performance.


  Large scale projects increasingly operate in complicated settings whilst
drawing on an array of complex data-points, which require precise analysis for
accurate control and interventions to mitigate possible project failure.
Coupled with a growing tendency to rely on new information systems and
processes in change projects, 90% of megaprojects globally fail to achieve
their planned objectives. Renewed interest in the concept of Artificial
Intelligence (AI) against a backdrop of disruptive technological innovations,
seeks to enhance project managers cognitive capacity through the project
lifecycle and enhance project excellence. However, despite growing interest
there remains limited empirical insights on project managers ability to
leverage AI for cognitive load enhancement in complex settings. As such this
research adopts an exploratory sequential linear mixed methods approach to
address unresolved empirical issues on transient adaptations of AI in complex
projects, and the impact on cognitive load enhancement. Initial thematic
findings from semi-structured interviews with domain experts, suggest that in
order to leverage AI technologies and processes for sustainable cognitive load
enhancement with complex data over time, project managers require improved
knowledge and access to relevant technologies that mediate data processes in
complex projects, but equally reflect application across different project
phases. These initial findings support further hypothesis testing through a
larger quantitative study incorporating structural equation modelling to
examine the relationship between artificial intelligence and project managers
cognitive load with project data in complex contexts.


  Hopping cyclic codes (HCCs) are (non-linear) cyclic codes with the additional
property that the $n$ cyclic shifts of every given codeword are all distinct,
where $n$ is the code length. Constant weight binary hopping cyclic codes are
also known as optical orthogonal codes (OOCs). HCCs and OOCs have various
practical applications and have been studied extensively over the years.
  The main concern of this paper is to present improved Gilbert-Varshamov type
lower bounds for these codes, when the minimum distance is bounded below by a
linear factor of the code length. For HCCs, we improve the previously best
known lower bound of Niu, Xing, and Yuan by a linear factor of the code length.
For OOCs, we improve the previously best known lower bound of Chung, Salehi,
and Wei, and Yang and Fuja by a quadratic factor of the code length. As
by-products, we also provide improved lower bounds for frequency hopping
sequences sets and error-correcting weakly mutually uncorrelated codes. Our
proofs are based on tools from probability theory and graph theory, in
particular the McDiarmid's inequality on the concentration of Lipschitz
functions and the independence number of locally sparse graphs.


  Fink is a broker designed to enable science with large time-domain alert
streams such as the one from the upcoming Vera C. Rubin Observatory Legacy
Survey of Space and Time (LSST). It exhibits traditional astronomy broker
features such as automatised ingestion, annotation, selection and
redistribution of promising alerts for transient science. It is also designed
to go beyond traditional broker features by providing real-time transient
classification which is continuously improved by using state-of-the-art Deep
Learning and Adaptive Learning techniques. These evolving added values will
enable more accurate scientific output from LSST photometric data for diverse
science cases while also leading to a higher incidence of new discoveries which
shall accompany the evolution of the survey. In this paper we introduce Fink,
its science motivation, architecture and current status including first science
verification cases using the Zwicky Transient Facility alert stream.


  Flagella of eukaryotic cells are transient long cylindrical protrusions. The
proteins needed to form and maintain flagella are synthesized in the cell body
and transported to the distal tips. What `rulers' or `timers' a specific type
of cells use to strike a balance between the outward and inward transport of
materials so as to maintain a particular length of its flagella in the steady
state is one of the open questions in cellular self-organization. Even more
curious is how the two flagella of biflagellates, like Chlamydomonas
Reinhardtii, communicate through their base to coordinate their lengths. In
this paper we develop a stochastic model for flagellar length control based on
a time-of-flight (ToF) mechanism. This ToF mechanism decides whether or not
structural proteins are to be loaded onto an intraflagellar transport (IFT)
train just before it begins its motorized journey from the base to the tip of
the flagellum. Because of the ongoing turnover, the structural proteins
released from the flagellar tip are transported back to the cell body also by
IFT trains. We represent the traffic of IFT trains as a totally asymmetric
simple exclusion process (TASEP). The ToF mechanism for each flagellum,
together with the TASEP-based description of the IFT trains, combined with a
scenario of sharing of a common pool of flagellar structural proteins in
biflagellates, can account for all key features of experimentally known
phenomena. These include ciliogenesis, resorption, deflagellation as well as
regeneration after selective amputation of one of the two flagella. We also
show that the experimental observations of Ishikawa and Marshall are consistent
with the ToF mechanism of length control if the effects of the mutual exclusion
of the IFT trains captured by the TASEP are taken into account. Moreover, we
make new predictions on the flagellar length fluctuations and the role of the
common pool.


  Elliptic flow ($v_2$) in ultrarelativistic nucleus-nucleus collisions
fluctuates event to event, both in magnitude and in orientation with respect to
the reaction plane. Even though the reaction plane is not known event to event
in experiment, we show that the statistical properties of $v_2$ fluctuations in
the reaction plane can be precisely extracted from experimental data. Previous
studies have shown how to measure the mean, variance and skewness using the
first three cumulants $v_2\{2\}$, $v_2\{4\}$ and $v_2\{6\}$. We complement
these studies by providing a formula for the kurtosis, which requires an
accurate determination of the next cumulant $v_2\{8\}$. Using existing data, we
show that the kurtosis is positive for most centralities, in contrast with the
kurtosis of triangular flow fluctuations, which is negative. We argue that
these features are robust predictions of fluid-dynamical models.


  This paper develops a novel approach for high dynamic-range compression. It
relies on the widely accepted assumption that the human visual system is not
very sensitive to absolute luminance reaching the retina, but rather responds
to relative luminance ratios. Dynamic-range compression is then formulated as a
regularized optimization in which the image dynamic range is reduced while the
local contrast of the original scene is preserved. Our method is shown to be
capable of drastic dynamic-range compression, while preserving fine details and
avoiding common artifacts such as halos, gradient reversals, or loss of local
contrast.


  For natural numbers $x$ and $b$, the classical Kaprekar function is defined
as $K_{b} (x) = D-A$, where $D$ is the rearrangement of the base-$b$ digits of
$x$ in descending order and $A$ is ascending. The bases $b$ for which $K_b$ has
a $4$-digit non-zero fixed point were classified by Hasse and Prichett, and for
each base this fixed point is known to be unique. In this article, we determine
the maximum number of iterations required to reach this fixed point among all
four-digit base-$b$ inputs, thus answering a question of Yamagami. Moreover, we
also explore---as a function of $b$---the fraction of four-digit inputs for
which iterating $K_b$ converges to this fixed point.


  We present new estimates on the two-dimensional Ginzburg-Landau energy of a
type-II superconductor in an applied magnetic field varying between the second
and third critical fields. In this regime, superconductivity is restricted to a
thin layer along the boundary of the sample. We provide new energy lower
bounds, proving that the Ginzburg-Landau energy is determined to leading order
by the minimization of a simplified 1D functional in the direction
perpendicular to the boundary. Estimates relating the density of the
Ginzburg-Landau order parameter to that of the 1D problem follow. In the
particular case of a disc sample, a refinement of our method leads to a
pointwise estimate on the Ginzburg-Landau order parameter, thereby proving a
strong form of uniformity of the surface superconductivity layer, related to a
conjecture by Xing-Bin Pan.


  We explore an algorithm which systematically finds all discrete eigenvalues
of an analytic eigenvalue problem. The algorithm is more simple and elementary
as could be expected before. It consists of Hejhal's identity, linearisation,
and Turing bounds. Using the algorithm, we compute more than one hundredsixty
thousand consecutive eigenvalues of the Laplacian on the modular surface, and
investigate the asymptotic and statistic properties of the fluctuations in the
Weyl remainder. We summarize the findings in two conjectures. One is on the
maximum size of the Weyl remainder, and the other is on the distribution of a
suitably scaled version of the Weyl remainder.


  We study non-convex elastic energy functionals associated to (spatially)
periodic, frame indifferent energy densities with a single non-degenerate
energy well at SO(n). Under the assumption that the energy density admits a
quadratic Taylor expansion at identity, we prove that the Gamma-limits
associated to homogenization and linearization commute. Moreover, we show that
the homogenized energy density, which is determined by a multi-cell
homogenization formula, has a quadratic Taylor expansion with a quadratic term
that is given by the homogenization of the quadratic term associated to the
linearization of the initial energy density.


  Isolated black holes and neutron stars can be revealed through the
observation of long duration gravitational microlensing events. A few
candidates have been found in surveys of stars in the direction of the Galactic
bulge. Recently, thanks to the addition of astrometric information at
milliarcsecond level, it has been possible to reduce the uncertainties in the
masses and distances for some of these "dark" gravitational lenses and select
the most promising candidates. These isolated compact objects might emit X-rays
powered by accretion from the interstellar medium. Using data of the Chandra,
XMM-Newton, and INTEGRAL satellites, we searched for X-ray emission in the
isolated black hole candidate OGLE-2011-BLG-0462, and in several other putative
collapsed objects found with gravitational microlensing. OGLE-2011-BLG-0462 has
been recently interpreted as a 7.1 M_sun black hole at a distance of 1.6 kpc,
although a different group obtained a mass range (1.6-4.4 M_sun) that cannot
exclude a massive neutron star. We have derived upper limits on the flux from
OGLE-2011-BLG-0462 of 9$\times10^{-15}$ erg cm$^{-2}$ s$^{-1}$ in the 0.5-7 keV
range and $\sim2\times10^{-12}$ erg cm$^{-2}$ s$^{-1}$ in the 17-60 keV range.
The implied X-ray luminosity is consistent with the small radiative efficiency
expected for a black hole and disfavours a neutron star interpretation. Limits
down to a factor about five lower are obtained for the soft X-ray flux of other
candidates, but their interpretation is affected by larger uncertainties in the
masses, distances and spatial velocities.


  In this paper, we proposed a population model depicting the dynamics of a
prey species showing group defence against a generalist predator. The group
defence characteristic is represented by a non-monotonic functional response.
We have established the local stability of the model around the co-existent
equilibrium solution using a local Lyapunov function. Condition for existence
Hopf bifurcation is obtained along with its normal form. Numerical simulations
have been done to confirm the obtained analytical results as well as to
validate the proposed model. Sensitivity analysis of the parameters is
performed using Latin hypercube sampling(LHS)/partial rank correlation
coefficient(PRCC). Blow-up in the population is controlled using the Z-type
dynamic method.


  Coronavirus has been spreading around the world since the end of 2019. The
virus can cause acute respiratory syndrome, which can be lethal, and is easily
transmitted between hosts. Most states have issued state-at-home executive
orders, however, parks and other public open spaces have largely remained open
and are seeing sharp increases in public use. Therefore, in order to ensure
public safety, it is imperative for patrons of public open spaces to practice
safe hygiene and take preventative measures. This work provides a scalable
sensing approach to detect physical activities within public open spaces and
monitor adherence to social distancing guidelines suggested by the US Centers
for Disease Control and Prevention (CDC). A deep learning-based computer vision
sensing framework is designed to investigate the careful and proper utilization
of parks and park facilities with hard surfaces (e.g. benches, fence poles, and
trash cans) using video feeds from a pre-installed surveillance camera network.
The sensing framework consists of a CNN-based object detector, a multi-target
tracker, a mapping module, and a group reasoning module. The experiments are
carried out during the COVID-19 pandemic between March 2020 and May 2020 across
several key locations at the Detroit Riverfront Parks in Detroit, Michigan. The
sensing framework is validated by comparing automatic sensing results with
manually labeled ground-truth results. The proposed approach significantly
improves the efficiency of providing spatial and temporal statistics of users
in public open spaces by creating straightforward data visualizations for
federal and state agencies. The results can also provide on-time triggering
information for an alarming or actuator system which can later be added to
intervene inappropriate behavior during this pandemic.


  Certain power-counting non-renormalizable theories, including the most
general self-interacting scalar fields in four and three dimensions and
fermions in two dimensions, have a simplified renormalization structure. For
example, in four-dimensional scalar theories, 2n derivatives of the fields,
n>1, do not appear before the nth loop. A new kind of expansion can be defined
to treat functions of the fields (but not of their derivatives)
non-perturbatively. I study the conditions under which these theories can be
consistently renormalized with a reduced, eventually finite, set of independent
couplings. I find that in common models the number of couplings sporadically
grows together with the order of the expansion, but the growth is slow and a
reasonably small number of couplings is sufficient to make predictions up to
very high orders. Various examples are solved explicitly at one and two loops.


  We introduce an extension of the fellow traveler property which allows fellow
travelers to be at distance bounded from above by a function $f(n)$ growing
slower than any linear function. We study normal forms satisfying this extended
fellow traveler property and certain geometric constraints that naturally
generalize two fundamental properties of an automatic normal form - the
regularity of its language and the bounded length difference property. We show
examples of such normal forms and prove some non-existence theorems.


  Dynamical behaviors of a dissipative particle in a periodic potential subject
to chaotic noise are reported. We discovered a macroscopic symmetry breaking
effect of chaotic noise on a dissipative particle in a multi-stable systems
emerging, even when the noise has a uniform invariant density with parity
symmetry and white Fourier spectrum. The broken parity symmetry of the
multi-stable potential is not necessary for the dynamics with broken symmetry.
We explain the mechanism of the symmetry breaking and estimate the average
velocity of a particle under chaotic noise in terms of unstable fixed points.


  On Dec 27, 2004, the magnetar SGR 1806-20 underwent an enormous outburst
resulting in the formation of an expanding, moving, and fading radio source. We
report observations of this radio source with the Multi-Element Radio-Linked
Interferometer Network (MERLIN) and the Very Long Baseline Array (VLBA). The
observations confirm the elongation and expansion already reported based on
observations at lower angular resolutions, but suggest that at early epochs the
structure is not consistent with the very simplest models such as a smooth flux
distribution. In particular there appears to be significant structure on small
angular scales, with ~10% of the radio flux arising on angular scales <100
milliarcsec. This structure may correspond to localised sites of particle
acceleration during the early phases of expansion and interaction with the
ambient medium.


  We briefly describe a model for soft diffraction. The model description of
ISR, SppbarS and Tevatron data, and the predictions of the total, elastic,
single and double diffractive cross sections at the LHC can be found in the
original paper. Here we address issues raised at Diffraction 2000, in
particular concerning the nature of the resulting Pomeron trajectory.


  Machine learning (ML) models are used in many safety- and security-critical
applications nowadays. It is therefore important to measure the security of a
system that uses ML as a component. This paper focuses on the field of ML,
particularly the security of autonomous vehicles. For this purpose, a technical
framework will be described, implemented, and evaluated in a case study. Based
on ISO/IEC 27004:2016, risk indicators are utilized to measure and evaluate the
extent of damage and the effort required by an attacker. It is not possible,
however, to determine a single risk value that represents the attacker's
effort. Therefore, four different values must be interpreted individually.


  We report nine new transit epochs of the extrasolar planet, observed in the
Bessell-I band with SOAR at the Cerro Pachon Observatory and with the SMARTS
1-m Telescope at CTIO, between August 2008 and October 2009. The new transits
have been combined with all previously published transit data for this planet
to provide a new Transit Timing Variations (TTVs) analysis of its orbit. We
find no evidence of TTVs RMS variations larger than 1 min over a 3 year time
span. This result discards the presence of planets more massive than about 5
M_earth, 1 M_earth and 2 M_earth around the 1:2, 5:3 and 2:1 orbital
resonances. These new detection limits exceed by ~5-30 times the limits imposed
by current radial velocity observations in the Mean Motion Resonances of this
system. Our search for the variation of other parameters, such as orbital
inclination and transit depth also yields negative results over the total time
span of the transit observations. This result supports formation theories that
predict a paucity of planetary companions to Hot Jupiters.


  Our first principles calculations reveal that oxidative cut of graphene is
realized by forming epoxy and then carbonyl pairs. Direct forming carbonyl pair
to tear graphene up from an edge position is not favorable in energy. This
atomic picture is valuable for developing effective graphene manipulation
means. The proposed epoxy pairs may be related to some long puzzling
experimental observations on graphene oxide.


  We measured the rate of velocity-changing collisions (VCCs) between alkali
atoms and background gas in buffer-gas-free anti-relaxation-coated cells. The
average VCC rate in paraffin-coated rubidium vapor cells prepared in this work
was $1 \times 10^{6}$ s$^{-1}$, which corresponds to $\sim 1$ mm in the mean
free path of rubidium atoms. This short mean free path indicates that the
background gas is not negligible in the sense that alkali atoms do not travel
freely between the cell walls. In addition, we found that a heating process
known as "ripening" increases the VCC rate, and also confirmed that ripening
improves the anti-relaxation performance of the coatings.


  This paper considers the problem of offering a scarce object with a common
unobserved quality to strategic agents in a priority queue. Each agent has a
private signal over the quality of the object and observes the decisions made
by other agents. We first show that, under the widely-used
first-come-first-served sequential offering mechanism, herding behavior
emerges: initial rejections create an information cascade resulting in
inefficient waste. To address this issue, we then introduce a class of batching
mechanisms. Agents in each batch report whether they would be willing to accept
or reject the object based on their private signals and prior information. If
the majority opts to accept, the object is randomly allocated within that
batch. We prove that suitable batching mechanisms are incentive-compatible and
improve efficiency. A key property of the mechanism is the gradual increase of
the batch size after each failed allocation; the size is chosen so that it
elicits as much information as possible without distorting the incentives of
agents to report truthfully. Additionally, from a healthcare policy
perspective, our results can shed light on the large wastage in organ
allocation. In particular, wastage that arises due to herding may be reduced by
applying adaptive simultaneous offering mechanisms.


  DaCapo is a specialized deep learning library tailored to expedite the
training and application of existing machine learning approaches on large,
near-isotropic image data. In this correspondence, we introduce DaCapo's unique
features optimized for this specific domain, highlighting its modular
structure, efficient experiment management tools, and scalable deployment
capabilities. We discuss its potential to improve access to large-scale,
isotropic image segmentation and invite the community to explore and contribute
to this open-source initiative.


  Deep neural networks trained on a wide range of datasets demonstrate
impressive transferability. Deep features appear general in that they are
applicable to many datasets and tasks. Such property is in prevalent use in
real-world applications. A neural network pretrained on large datasets, such as
ImageNet, can significantly boost generalization and accelerate training if
fine-tuned to a smaller target dataset. Despite its pervasiveness, few effort
has been devoted to uncovering the reason of transferability in deep feature
representations. This paper tries to understand transferability from the
perspectives of improved generalization, optimization and the feasibility of
transferability. We demonstrate that 1) Transferred models tend to find flatter
minima, since their weight matrices stay close to the original flat region of
pretrained parameters when transferred to a similar target dataset; 2)
Transferred representations make the loss landscape more favorable with
improved Lipschitzness, which accelerates and stabilizes training
substantially. The improvement largely attributes to the fact that the
principal component of gradient is suppressed in the pretrained parameters,
thus stabilizing the magnitude of gradient in back-propagation. 3) The
feasibility of transferability is related to the similarity of both input and
label. And a surprising discovery is that the feasibility is also impacted by
the training stages in that the transferability first increases during
training, and then declines. We further provide a theoretical analysis to
verify our observations.


  The two-stream instability is probably the most important elementary example
of collective instabilities in plasma physics and beam-plasma systems. For a
warm plasma with two charged particle species based on a 1D warm-fluid model,
the instability diagram of the two-stream instability exhibits an interesting
band structure that has not been explained. We show that the band structure for
this instability is the consequence of the Hamiltonian nature of the warm
two-fluid system. Interestingly, the Hamiltonian nature manifests as a complex
G-Hamiltonian structure in wave-number space, which directly determines the
instability diagram. Specifically, it is shown that the boundaries between the
stable and unstable regions are locations for Krein collisions between
eigenmodes with different Krein signatures. In terms of physics, this
rigorously implies that the system is destabilized when a positive-action mode
resonates with a negative-action mode, and that this is the only mechanism by
which the system can be destabilized. It is anticipated that this physical
mechanism of destabilization is valid for other collective instabilities in
conservative systems in plasma physics, accelerator physics, and fluid dynamics
systems, which admit infinite-dimensional Hamiltonian structures.


  By recent results of Baker--Etnyre--Van Horn-Morris, a rational open book
decomposition defines a compatible contact structure. We show that the Heegaard
Floer contact invariant of such a contact structure can be computed in terms of
the knot Floer homology of its (rationally null-homologous) binding. We then
use this description of contact invariants, together with a formula for the
knot Floer homology of the core of a surgery solid torus, to show that certain
manifolds obtained by surgeries on bindings of open books carry tight contact
structures. Possible applications to lens space surgeries are discussed.


  It is shown in \cite{yazar6} that a dynamical system defined by a
nonvanishing vector field on an orientable three dimensional manifold is
globally bi-Hamiltonian if and only if the first Chern class of the normal
bundle of the given vector field vanishes, and the bi-Hamiltonian structure is
globally compatible if and only if the Bott class of the complex codimension
one foliation defined by the nonvanishing vector field vanishes. In this work,
we constructed a dynamical system on $S^3$, which is globally bi-Hamiltonian,
but the Hamiltonians are not globally compatible.


  Predominantly, harmonic oscillator single-particle wave functions are the
choice as a basis in ab-initio nuclear many-body calculations. These
wave-functions, although very convenient in order to evaluate the matrix
elements of the interaction in the laboratory frame, have a too fast fall-off
at large distances. In the past, in alternative to the harmonic oscillator,
other single-particle wave functions have been proposed. In this work we
propose a new single-particle basis, directly linked to the nucleon-nucleon
interaction. This new basis is orthonormal and complete, has the proper
asymptotic behavior at large distances and does not contain the continuum which
would pose severe convergence problems in nuclear many body calculations. We
consider the newly proposed NNLO-opt nucleon-nucleon interaction, without any
renormalization.
  We show that unlike other basis, this single-particle representation has a
computational cost similar to the harmonic oscillator basis with the same space
truncation and it gives lower energies for ${}^6He$ and ${}^6Li$.


  We use inverse methods of statistical mechanics to explore trade-offs
associated with designing interactions to stabilize self-assembled structures
against changes in density or temperature. Specifically, we find
isotropic,convex-repulsive pair potentials that maximize the density range for
which a two-dimensional square lattice is the stable ground state subject to a
constraint on the chemical potential advantage it exhibits over competing
structures (i.e., 'depth' of the associated minimum on the chemical potential
hypersurface). We formulate the design problem as a non-linear program, which
we solve numerically. This allows us to efficiently find optimized interactions
for a wide range of possible chemical potential constraints. We find that
assemblies designed to exhibit a large chemical potential advantage at a
specified density have a smaller overall range of densities for which they are
stable. This trend can be understood by considering the separation-dependent
features of the pair potential and its gradient required to enhance the
stability of the target structure relative to competitors. Using molecular
dynamics simulations, we further show that potentials designed with larger
chemical potential advantages exhibit higher melting temperatures.


  The Internet inter-domain routing system is vulnerable. On the control plane,
the de facto Border Gateway Protocol (BGP) does not have built-in mechanisms to
authenticate routing announcements, so an adversary can announce virtually
arbitrary paths to hijack network traffic; on the data plane, it is difficult
to ensure that actual forwarding path complies with the control plane
decisions. The community has proposed significant research to secure the
routing system. Yet, existing secure BGP protocols (e.g., BGPsec) are not
incrementally deployable, and existing path authorization protocols are not
compatible with the current Internet routing infrastructure. In this paper, we
propose FC-BGP, the first secure Internet inter-domain routing system that can
simultaneously authenticate BGP announcements and validate data plane
forwarding in an efficient and incrementally-deployable manner. FC-BGP is built
upon a novel primitive, name Forwarding Commitment, to certify an AS's routing
intent on its directly connected hops. We analyze the security benefits of
FC-BGP in the Internet at different deployment rates. Further, we implement a
prototype of FC-BGP and extensively evaluate it over a large-scale overlay
network with 100 virtual machines deployed globally. The results demonstrate
that FC-BGP saves roughly 55% of the overhead required to validate BGP
announcements compared with BGPsec, and meanwhile FC-BGP introduces a small
overhead for building a globally-consistent view on the desirable forwarding
paths.


  In this work, we present a review and an example on some latter results on
the problem of approximating the Euler-Mascheroni constant. We use the method
firstly introduced in [C. Mortici, Product Approximations via Asymptotic
Integration Amer. Math. Monthly 117 (5) (2010) 434-441].


  Quantum scale invariant regularization is a variant of dimensional
regularization where the renormalization scale is treated as a dynamical field.
But, rather than be regarded as a novel regularization method on par with
dimensional regularization, momentum cutoff, Pauli-Villars etc., it should be
understood as a way to define a subset in the infinite space of
nonrenormalizable models of certain type. The subset realizes the demand that
renormalization scale, along with any other dimensionful parameters, should be
interpreted as a dynamical field's homogeneous background. This restriction is
most straightforwardly implemented using dimensional regularization but it can
hypothetically be imposed with any regularization method. Theories that satisfy
it offer a new perspective on the radiative violation of global scale symmetry
associated with RGE functions. As a result of the quantum scale invariant
regularization being implemented, the scale symmetry is preserved at the
quantum level despite the RGE functions being non-zero, as can be inspected at
the level of composite quantum operators that govern dilatation of Green
functions. We analyze these statements in explicit detail using a specific but
easily generalized toy model with scalar fields.


  Orthogonal Matching Pursuit (OMP) has been a powerful method in sparse signal
recovery and approximation. However OMP suffers computational issue when the
signal has large number of non-zeros. This paper advances OMP in two fronts: it
offers a fast algorithm for the orthogonal projection of the input signal at
each iteration, and a new selection criterion for making the greedy choice,
which reduces the number of iterations it takes to recover the signal. The
proposed modifications to OMP directly reduce the computational complexity.
Experiment results show significant improvement over the classical OMP in
computation time. The paper also provided a sufficient condition for exact
recovery under the new greedy choice criterion. For general signals that may
not have sparse representations, the paper provides a bound for the
approximation error. The approximation error is at the same order as OMP but is
obtained within fewer iterations and less time.


  We introduce generalized arrow diagrams and generalized Reidemeister moves
for diagrams of links in Seifert fibered spaces. We give a presentation of the
fundamental group of the link complement. As a corollary we are able to compute
the first homology group of the complement and the twisted Alexander
polynomials of the link.


  In the first days of WZ Sge-type dwarf nova (DN) outbursts, the 2:1 resonance
induces a spiral arm structure in the accretion disk, which is observed as
early superhumps in optical light curves. This paper reports our optical
observations of an eclipsing WZ Sge-type DN PNV J00444033+4113068 during its
2021 superoutburst with the 3.8m Seimei telescope and through VSNET
collaboration. The eclipse analysis gave its orbital period as 0.055425534(1)
d. Our observations confirmed early superhumps with an amplitude of 0.7 mag,
the largest amplitude among known WZ Sge-type DNe. More interestingly, its
early superhumps became the reddest around their secondary minimum, whereas
other WZ Sge-type DNe show the reddest color around the early superhump
maximum. The spectrum around the peak of the outburst showed the double-peaked
emission lines of He II 4686\AA~ and H$\alpha$ with a peak separation of $\ge
700$ km/s, supporting a very high-inclination system. With the early superhump
mapping, the unique profile and color of the early superhump of PNV
J00444033+4113068 are successfully reproduced by the accretion disk with
vertically extended double arm structure. Therefore, the large amplitude and
unique color behavior of the early superhumps in PNV J00444033+4113068 can be
explained by the 2:1 resonance model along with other WZ Sge-type DNe.


  UEFA European Championship 1996 qualification is known to violate
strategy-proofness. It has been proved recently that a team could be better off
by exerting a lower effort: it might be optimal to concede some goals in order
to achieve a better position among runners-up, and hence avoid a hazardous
play-off. We show that it is not only an irrelevant scenario with a marginal
probability since France had an incentive to kick two own goals on its last
match against Israel.


  Spectra with a high signal-to-noise ratio of 79 stars which are just below
the main sequence turnoff of M71 are presented. They yield indices for the
strength of the G band of CH and the ultraviolet CN band at 3885 \AA. These
indices are each to first order bimodal and they are anti-correlated. There are
approximately equal numbers of CN weak/CH strong and CN strong/CH weak main
sequence stars in M71. It is not yet clear whether these star-to-star
variations arise from primordial variations or from mixing within a fraction of
individual stars as they evolve.


  We study the thermodynamic formalism of sufficiently regular interval maps
for Holder continuous potentials. We show that for a hyperbolic potential there
is a unique equilibrium state, and that this measure is exponentially mixing.
Moreover, we show the absence of phase transitions: The pressure function is
real analytic at such a potential.


  Object triangulation, 3-D object tracking, feature correspondence, and camera
calibration are key problems for estimation from camera networks. This paper
addresses these problems within a unified Bayesian framework for joint
multi-object tracking and sensor registration. Given that using standard
filtering approaches for state estimation from cameras is problematic, an
alternative parametrisation is exploited, called disparity space. The disparity
space-based approach for triangulation and object tracking is shown to be more
effective than non-linear versions of the Kalman filter and particle filtering
for non-rectified cameras. The approach for feature correspondence is based on
the Probability Hypothesis Density (PHD) filter, and hence inherits the ability
to update without explicit measurement association, to initiate new targets,
and to discriminate between target and clutter. The PHD filtering approach then
forms the basis of a camera calibration method from static or moving objects.
Results are shown on simulated data.


  We show that the configurational probability distribution of a classical gas
always belongs to the q-exponential family. Hence, the configurational
subsystem is non-extensive in the sense of Tsallis. One of the consequences of
this observation is that the thermodynamics of the configurational subsystem is
uniquely determined up to a scaling function. As an example we consider a
system of non-interacting harmonic oscillators. In this example, the scaling
function can be determined from the requirement that in the limit of large
systems the microcanonical temperature of the configurational subsystem should
coincide with that of the canonical ensemble. The result suggests that Renyi's
entropy function is the relevant one rather than that of Tsallis.


  We analyze the time series data of number of districts or cities in India
that are affected by COVID-19 from March 01, 2020 to April 17, 2020. We study
the data in the framework of time series network data. The networks are defined
by using the geodesic distances of the districts or cities specified by the
latitude and longitude coordinates. We particularly restrict our analysis to
all but districts in the north-eastern part of India. Unlike recent studies on
the projection of the number of people infected with SARS-CoV-2 in the near
future, in this note, the emphasis is on understanding the dynamics of the
spread of the virus across the districts of India. We perform spectral and
structural analysis of the model networks by considering several measures,
notably the spectral radius, the algebraic connectivity, the average clustering
coefficient, the average path length and the structure of the communities.
Furthermore, we study the overall expansion properties given by the number of
districts or cities before and after lockdown. These studies show that lockdown
has a significant impact on the spread of SARS-CoV-2 in districts or cities
over long distances. However, this impact is only observed after approximately
two weeks of lockdown. We speculate that this happened due to the insufficient
number of tests for COVID-19 before the lockdown which could not stop the
movement of people infected with the virus but not detected, over long
distances.


  Knowledge Graph (KG) contains entities and the relations between entities.
Due to its representation ability, KG has been successfully applied to support
many medical/healthcare tasks. However, in the medical domain, knowledge holds
under certain conditions. For example, symptom \emph{runny nose} highly
indicates the existence of disease \emph{whooping cough} when the patient is a
baby rather than the people at other ages. Such conditions for medical
knowledge are crucial for decision-making in various medical applications,
which is missing in existing medical KGs. In this paper, we aim to discovery
medical knowledge conditions from texts to enrich KGs.
  Electronic Medical Records (EMRs) are systematized collection of clinical
data and contain detailed information about patients, thus EMRs can be a good
resource to discover medical knowledge conditions. Unfortunately, the amount of
available EMRs is limited due to reasons such as regularization. Meanwhile, a
large amount of medical question answering (QA) data is available, which can
greatly help the studied task. However, the quality of medical QA data is quite
diverse, which may degrade the quality of the discovered medical knowledge
conditions. In the light of these challenges, we propose a new truth discovery
method, MedTruth, for medical knowledge condition discovery, which incorporates
prior source quality information into the source reliability estimation
procedure, and also utilizes the knowledge triple information for trustworthy
information computation. We conduct series of experiments on real-world medical
datasets to demonstrate that the proposed method can discover meaningful and
accurate conditions for medical knowledge by leveraging both EMR and QA data.
Further, the proposed method is tested on synthetic datasets to validate its
effectiveness under various scenarios.


  Spurred by the recent rapid increase in the development and distribution of
large language models (LLMs) across industry and academia, much recent work has
drawn attention to safety- and security-related threats and vulnerabilities of
LLMs, including in the context of potentially criminal activities.
Specifically, it has been shown that LLMs can be misused for fraud,
impersonation, and the generation of malware; while other authors have
considered the more general problem of AI alignment. It is important that
developers and practitioners alike are aware of security-related problems with
such models. In this paper, we provide an overview of existing - predominantly
scientific - efforts on identifying and mitigating threats and vulnerabilities
arising from LLMs. We present a taxonomy describing the relationship between
threats caused by the generative capabilities of LLMs, prevention measures
intended to address such threats, and vulnerabilities arising from imperfect
prevention measures. With our work, we hope to raise awareness of the
limitations of LLMs in light of such security concerns, among both experienced
developers and novel users of such technologies.


  The problem of Wi-Fi and LTE coexistence has been significantly debated in
the last years, with the emergence of LTE extensions enabling the utilization
of unlicensed spectrum for carrier aggregation. Rather than focusing on the
problem of resource sharing between the two technologies, in this paper, we
study the effects of LTE's structured transmissions on the Wi-Fi random access
protocol. We show how the scheduling of periodic LTE transmissions modifies the
behavior of 802.11's distributed coordination function (DCF), leading to a
degradation of Wi-Fi performance, both in terms of channel utilization
efficiency and in terms of channel access fairness. We also discuss the
applicability and limitations of a persistent DCF model in analyzing Wi-Fi
performance under periodic LTE interference.


  Electromechanical systems currently offer a path to engineering quantum
states of microwave and micromechanical modes that are of both fundamental and
applied interest. Particularly desirable, but not yet observed, are mechanical
states that exhibit entanglement, wherein non-classical correlations exist
between distinct modes; squeezing, wherein the quantum uncertainty of an
observable quantity is reduced below the standard quantum limit; and
Schr\"odinger cats, wherein a single mode is cast in a quantum superposition of
macroscopically distinct classical states. Also, while most investigations of
electromechanical systems have focussed on single- or few-body scenarios, the
many-body regime remains virtually unexplored. In such a regime quantum phase
transitions naturally present themselves as a resource for quantum state
generation, thereby providing a route toward entangling a large number of
electromechanical systems in highly non-classical states. Here we show how to
use existing superconducting circuit technology to implement a (quasi) quantum
phase transition in an array of electromechanical systems such that
entanglement, squeezing, and Schr\"odinger cats become simultaneously
observable across multiple microwave and micromechanical oscillators.


  A graph is {\em $1$-planar} if it can be drawn in the plane such that every
edge crosses at most one other edge. A connected graph $H$ is {\em strongly
light} in a family of graphs $\mathfrak{G}$, if there exists a constant
$\lambda$, such that every graph $G$ in $\mathfrak{G}$ contains a subgraph $K$
isomorphic to $H$ with $\deg_{G}(v) \leq \lambda$ for all $v \in V(K)$. In this
paper, we present some strongly light subgraphs in the family of $1$-planar
graphs with minimum degree~$7$.


  Closed string field theory is constructed by stochastically quantizing a
matrix model for Polyakov loops that describes phases of a large N gauge theory
at finite temperature. Coherent states in this string field theory describes
winding string condensation which has been expected to cause a topology change
from thermal AdS geometry to AdS-Schwarzschild black hole geometry. D-branes in
this closed string field theory is also discussed. Slightly extended version of
a talk given at CosPA 2007, Nov.13-15, Taipei, Taiwan.


  We selected 90 massive star-forming clumps with strong N2H+, HCO+, HCN, and
HNC emission from the Millimetre Astronomy Legacy Team 90 GHz survey. We
obtained Herschel data for all 90 sources and NRAO VLA Sky Survey data for 51
of them. We convolved and regridded all images to the same resolution and pixel
size and derived the temperature, H2 column density, molecules' abundances and
abundance, and ratios of each pixel. Our analysis yields three main
conclusions. First, the abundances of N2H+, HCO+, HCN, and HNC increase when
the column density decreases and the temperature increases, with spatial
variations in their abundances dominated by changes in the H2 column density.
Second, the abundance ratios between N2H+, HCO+, HCN, and HNC also display
systemic variations as a function of the column density due to the chemical
properties of these molecules. Third, the sources associated with the 20 cm
continuum emission can be classified into four types based on the behavior of
the abundances of the four molecules considered here as a function of this
emission. The variations of the first three types could also be attributed to
the variation of the H2 column density.


  For prediction and real-time control tasks, machine-learning (ML)-based
digital twins are frequently employed. However, while these models are
typically accurate, they are custom-designed for individual systems, making
system-to-system (S2S) transferability difficult. This occurs even when
substantial similarities exist in the process dynamics across different
chemical systems. To address this challenge, we developed a novel
time-series-transformer (TST) framework that exploits the powerful transfer
learning capabilities inherent in transformer algorithms. This was demonstrated
using readily available process data obtained from different crystallizers
operating under various operational scenarios. Using this extensive dataset, we
trained a TST model (CrystalGPT) to exhibit remarkable S2S transferability not
only across all pre-established systems, but also to an unencountered system.
CrystalGPT achieved a cumulative error across all systems, which is eight times
superior to that of existing ML models. Additionally, we coupled CrystalGPT
with a predictive controller to reduce the variance in setpoint tracking to
just 1%.


  Clustering is well-known to play a prominent role in the description and
understanding of complex networks, and a large spectrum of tools and ideas have
been introduced to this end. In particular, it has been recognized that the
abundance of small subgraphs is important. Here, we study the arrangement of
triangles in a model for scale-free random graphs and determine the asymptotic
behavior of the clustering coefficient, the average number of triangles, as
well as the number of triangles attached to the vertex of maximum degree. We
prove that triangles are power-law distributed among vertices and characterized
by both vertex and edge coagulation when the degree exponent satisfies
$2<\beta<2.5$; furthermore, a finite density of triangles appears as
$\beta=2+1/3$.