  We present the optical photometric variability of 32 planet-hosting M dwarfs
within 25 parsecs over timescales of months to decades. The primary goal of
this project, ATLAS -- A Trail to Life Around Stars, is to follow the trail to
life by revealing nearby M dwarfs with planets that are also "quiet", which may
make them more amiable to habitability. There are 69 reported exoplanets
orbiting the 32 stars discussed here, providing a rich sample of worlds for
which environmental evaluations are needed. We examine the optical flux
environments of these planets over month-long timescales for 23 stars observed
by TESS, and find that 17 vary by less than 1% ($\sim$11 mmag). All 32 stars
are being observed at the CTIO/SMARTS 0.9 m, with a median duration of 19.1
years of optical photometric data in the $VRI$ bands. We find over these
extended timescales that six stars show optical flux variations less than 2%,
25 vary from 2--6% ($\sim$22-67 mmag), and only one, Proxima Centauri, varies
by more than 6%. Overall, LHS 1678 exhibits the lowest optical variability
levels measured over all timescales examined, thereby providing one of the most
stable photometric environments among planets reported around M dwarfs within
25 parsecs. More than 600 of the nearest M dwarfs are being observed at the 0.9
m in the RECONS program that began in 1999, and many more planet hosts will
undoubtedly be revealed, providing more destinations to be added to the ATLAS
sample in the future.


  Zero-shot learning (ZSL) aims to learn models that can recognize unseen image
semantics based on the training of data with seen semantics. Recent studies
either leverage the global image features or mine discriminative local patch
features to associate the extracted visual features to the semantic attributes.
However, due to the lack of the necessary top-down guidance and semantic
alignment for ensuring the model attending to the real attribute-correlation
regions, these methods still encounter a significant semantic gap between the
visual modality and the attribute modality, which makes their prediction on
unseen semantics unreliable. To solve this problem, this paper establishes a
novel transformer encoder-decoder model, called hybrid routing transformer
(HRT). In HRT encoder, we embed an active attention, which is constructed by
both the bottom-up and the top-down dynamic routing pathways to generate the
attribute-aligned visual feature. While in HRT decoder, we use static routing
to calculate the correlation among the attribute-aligned visual features, the
corresponding attribute semantics, and the class attribute vectors to generate
the final class label predictions. This design makes the presented transformer
model a hybrid of 1) top-down and bottom-up attention pathways and 2) dynamic
and static routing pathways. Comprehensive experiments on three widely-used
benchmark datasets, namely CUB, SUN, and AWA2, are conducted. The obtained
experimental results demonstrate the effectiveness of the proposed method.


  We calculate the time evolution of the transient reflection signal in an
MoS$_2$ monolayer on a SiO$_2$/Si substrate using first-principles
out-of-equilibrium real-time methods. Our simulations provide a simple and
intuitive physical picture for the delayed, yet ultrafast, evolution of the
signal whose rise time depends on the excess energy of the pump laser: at laser
energies above the A- and B-exciton, the pump pulse excites electrons and holes
far away from the K valleys in the first Brillouin zone. Electron-phonon and
hole-phonon scattering lead to a gradual relaxation of the carriers towards
small $\textit{Active Excitonic Regions}$ around K, enhancing the dielectric
screening. The accompanying time-dependent band gap renormalization dominates
over Pauli blocking and the excitonic binding energy renormalization. This
explains the delayed buildup of the transient reflection signal of the probe
pulse, in excellent agreement with recent experimental data. Our results show
that the observed delay is not a unique signature of an exciton formation
process but rather caused by coordinated carrier dynamics and its influence on
the screening.


  The Lilly-Madau plot is commonly interpreted as the history of the cosmic
star formation of the Universe by showing the co-moving star formation rate
density (SFRD) over cosmic time. Therefore, the Lilly-Madau plot is sensitive
not only to the star formation history (SFH) but also to the number density of
galaxies. Assessing the Catalogue of Neighbouring Galaxies, we reconstruct the
SFHs of galaxies located in the Local Cosmological Volume (LV) based on
delayed-$\tau$ and power-law SFH models. Galaxies with stellar masses of $M_{*}
\gtrsim 10^{10}\,\rm{M_{\odot}}$ typically evolve according to the
delayed-$\tau$ model by having first increasing followed by exponentially
declining SFRs, while the majority of less massive star-forming galaxies have
an almost constant or increasing SFH. Deducing the cosmic SFRD evolution of the
LV reveals that the SFHs of local galaxies are inconsistent with the
Lilly-Madau plot. The SFRDs of the LV are significantly lower at redshifts of
$z \lesssim 3$ underestimating the Lilly-Madau peak at $z = 1.86$ by a factor
of $2.16\pm0.32$ (delayed-$\tau$ model) and $5.90\pm0.88$ (power-law model).
Assuming the delayed-$\tau$ model for galaxies with $M_{*} \geq
10^{10}\,\rm{M_{\odot}}$ and a power-law model for less massive galaxies, the
SFRD is $2.22\pm0.33$ lower than measured at $z = 1.86$. This inconsistency
between the evolution of the local and global SFRD has cosmological
implications. Since the Lilly-Madau plot also constrains the cosmological
matter field, the near-constancy of SFHs of LV galaxies could imply that the
peak of the Lilly-Madau plot at $z = 1.86$ is the imprint of an $\approx~5$
co-moving Gpc-scale inhomogeneity.


  Bose-Einstein condensation has been achieved in a magnetically trapped sample
of 85Rb atoms. Long-lived condensates of up to 10^4 atoms have been produced by
using a magnetic-field-induced Feshbach resonance to reverse the sign of the
scattering length. This system provides many unique opportunities for the study
of condensate physics. The variation of the scattering length near the
resonance has been used to magnetically tune the condensate self-interaction
energy over a very wide range. This range extended from very strong repulsive
to large attractive self-interactions. When the interactions were switched from
repulsive to attractive, the condensate shrank to below our resolution limit,
and after ~5 ms emitted a burst of high-energy atoms.


  Textural Inversion, a prompt learning method, learns a singular text
embedding for a new "word" to represent image style and appearance, allowing it
to be integrated into natural language sentences to generate novel synthesised
images. However, identifying multiple unknown object-level concepts within one
scene remains a complex challenge. While recent methods have resorted to
cropping or masking individual images to learn multiple concepts, these
techniques often require prior knowledge of new concepts and are
labour-intensive. To address this challenge, we introduce Multi-Concept Prompt
Learning (MCPL), where multiple unknown "words" are simultaneously learned from
a single sentence-image pair, without any imagery annotations. To enhance the
accuracy of word-concept correlation and refine attention mask boundaries, we
propose three regularisation techniques: Attention Masking, Prompts Contrastive
Loss, and Bind Adjective. Extensive quantitative comparisons with both
real-world categories and biomedical images demonstrate that our method can
learn new semantically disentangled concepts. Our approach emphasises learning
solely from textual embeddings, using less than 10% of the storage space
compared to others. The project page, code, and data are available at
https://astrazeneca.github.io/mcpl.github.io.


  We study the effect of the off-diagonal elements of the Wannier Hamiltonian
on the electronic structure of low-symmetry material Li$_2$MnO$_3$ ($C2/m$),
using dynamical mean field theory calculations with continuous-time Quantum
Monte Carlo impurity solver. Presence of significant off-diagonal elements
leads to a pronounced suppression of the energy gap. The off-diagonal elements
are largest when the Wannier projection is used based on the global coordinate,
and they remain substantial even with the projection using the local coordinate
close to the direction of Mn-O bonds. We show that the energy gap is enhanced
by the diagonalization of the Mn $d$ block in the full $p$-$d$ Hamiltonian,
with applying unitary rotation matrix. Additionally, the inclusion of a small
double counting energy is crucial for achieving the experimental gap by
reducing $p$-$d$ hybridization. Furthermore, we establish the efficiency of a
low-energy ($d$-only basis) model for studying the electronic structure of
Li$_2$MnO$3$, as the Wannier basis represents a hybridized state of Mn $d$ and
O $p$ orbitals. These findings suggest an appropriate new approach for
investigating low-symmetry materials using the DFT+DMFT method. To the best of
our knowledge, no systematic study of the effect of off-diagonal terms has been
conducted thus far. We also find that the antiferromagnetic ground state
$\Gamma_{2u}$ is stable with $U \leq 2$ eV within density functional theory+$U$
calculations, which is much smaller than widely used $U$=5 eV.


  In this paper, we introduce a novel method of neural network weight
compression. In our method, we store weight tensors as sparse, quantized matrix
factors, whose product is computed on the fly during inference to generate the
target model's weights. We use projected gradient descent methods to find
quantized and sparse factorization of the weight tensors. We show that this
approach can be seen as a unification of weight SVD, vector quantization, and
sparse PCA. Combined with end-to-end fine-tuning our method exceeds or is on
par with previous state-of-the-art methods in terms of the trade-off between
accuracy and model size. Our method is applicable to both moderate compression
regimes, unlike vector quantization, and extreme compression regimes.


  We demonstrate enhanced loading of strontium atoms into a magneto-optical
trap using a repumping scheme from the metastable state via the doubly-excited
state $5\mathrm{s}5\mathrm{p}\,^3\mathrm{P}_2 \rightarrow
5\mathrm{p}^2\,^3\mathrm{P}_2$ at $481~\mathrm{nm}$. The number of trapped
atoms is increased by an order of magnitude. The frequency and intensity
dependence of the atom number enhancement, with respect to the non-repumping
case, is well reproduced by a simple rate equation model, which also describes
single-laser repumping schemes reported previously. The repumping scheme is
limited by a weak additional loss channel into the long-lived
$5\mathrm{s}5\mathrm{p}\,^3\mathrm{P}_0$ state. For low repumping intensities,
the signature of a halo formed by magnetically trapped atoms in the metastable
state is found.


  Using a sample of 19,464 galaxies drawn from the DEEP2 Galaxy Redshift
Survey, we study the relationship between galaxy color and environment at 0.4 <
z < 1.35. We find that the fraction of galaxies on the red sequence depends
strongly on local environment out to z > 1, being larger in regions of greater
galaxy density. At all epochs probed, we also find a small population of red,
morphologically early-type galaxies residing in regions of low measured
overdensity. The observed correlations between the red fraction and local
overdensity are highly significant, with the trend at z > 1 detected at a
greater than 5-\sigma level. Over the entire redshift regime studied, we find
that the color-density relation evolves continuously, with red galaxies more
strongly favoring overdense regions at low z relative to their red-sequence
counterparts at high redshift. At z ~ 1.3, the red fraction only weakly
correlates with overdensity, implying that any color dependence to the
clustering of ~ L* galaxies at that epoch must be small. Our findings add
weight to existing evidence that the build-up of galaxies on the red sequence
has occurred preferentially in overdense environments (i.e., galaxy groups) at
z < 1.5. The strength of the observed evolutionary trends at 0 < z < 1.35
suggests that the correlations observed locally, such as the morphology-density
and color-density relations, are the result of environment-driven mechanisms
(i.e., "nurture'') and do not appear to have been imprinted (by "nature'') upon
the galaxy population during their epoch of formation.


  Mechanical metamaterials designed around a zero-energy pathway of
deformation, known as a mechanism, have repeatedly challenged the conventional
picture of elasticity. However, the complex spatial deformations these
structures are able to support beyond the uniform mechanism remain largely
uncharted. Here we present a unified theoretical framework, showing that the
presence of any uniform mechanism in a two-dimensional structure fundamentally
changes its elastic response by admitting a family of non-uniform zero-energy
deformations. Our formalism reveals a mathematical duality between these
stress-free strains, which we term "sheared analytic modes" and the supported
spatial profiles of stress. These modes undergo a transition from bulk periodic
response to evanescent surface response as the Poisson's ratio $\nu$ of the
mechanism is tuned through an exceptional point at $\nu=0$. We suggest a first
application of these unusual response properties as a switchable signal
amplifier and filter for use in mechanical circuitry and computation.


  We consider a recently proposed fully discrete Galerkin scheme for the
Benjamin-Ono equation which has been found to be locally convergent in finite
time for initial data in $L^2(\mathbb{R})$. By assuming that the initial data
is sufficiently regular we obtain theoretical convergence rates for the scheme
both in the full line and periodic versions of the associated initial value
problem. These rates are illustrated with some numerical examples.


  It is commonly believed that the magnetic field threading a neutron star
provides the ultimate mechanism (on top of fluid viscosity) for enforcing
long-term corotation between the slowly spun down solid crust and the liquid
core. We show that this argument fails for axisymmetric magnetic fields with
closed field lines in the core, the commonly used `twisted torus' field being
the most prominent example. The failure of such magnetic fields to enforce
global crust-core corotation leads to the development of a persistent spin lag
between the core region occupied by the closed field lines and the rest of the
crust and core. We discuss the repercussions of this spin lag for the evolution
of the magnetic field, suggesting that, in order for a neutron star to settle
to a stable state of crust-core corotation, the bulk of the toroidal field
component should be deposited into the crust soon after the neutron star's
birth.


  This paper proposes a neural network architecture and training scheme to
learn the start and end time of sound events (strong labels) in an audio
recording given just the list of sound events existing in the audio without
time information (weak labels). We achieve this by using a stacked
convolutional and recurrent neural network with two prediction layers in
sequence one for the strong followed by the weak label. The network is trained
using frame-wise log mel-band energy as the input audio feature, and weak
labels provided in the dataset as labels for the weak label prediction layer.
Strong labels are generated by replicating the weak labels as many number of
times as the frames in the input audio feature, and used for strong label layer
during training. We propose to control what the network learns from the weak
and strong labels by different weighting for the loss computed in the two
prediction layers. The proposed method is evaluated on a publicly available
dataset of 155 hours with 17 sound event classes. The method achieves the best
error rate of 0.84 for strong labels and F-score of 43.3% for weak labels on
the unseen test split.


  Insufficient availability of molten salt corrosion-resistant alloys severely
limits the fruition of a variety of promising molten salt technologies that
could otherwise have significant societal impacts. To accelerate alloy
development for molten salt applications and develop fundamental understanding
of corrosion in these environments, here we present an integrated approach
using a set of high-throughput alloy synthesis, corrosion testing, and modeling
coupled with automated characterization and machine learning. By using this
approach, a broad range of Cr-Fe-Mn-Ni alloys were evaluated for their
corrosion resistances in molten salt simultaneously demonstrating that
corrosion-resistant alloy development can be accelerated by thousands of times.
Based on the obtained results, we unveiled a sacrificial mechanism in the
corrosion of Cr-Fe-Mn-Ni alloys in molten salts which can be applied to protect
the less unstable elements in the alloy from being depleted, and provided new
insights on the design of high-temperature molten salt corrosion-resistant
alloys.


  We show that a bounded domain in a Euclidean space is a $W^{1,1}$-extension
domain if and only if it is a strong $BV$-extension domain. In the planar case,
bounded and strong $BV$-extension domains are shown to be exactly those
$BV$-extension domains for which the set $\partial\Omega \setminus \bigcup_{i}
\overline{\Omega}_i$ is purely $1$-unrectifiable, where $\Omega_i$ are the open
connected components of $\mathbb{R}^2\setminus\overline{\Omega}$.


  Generative artificial intelligence (AI) refers to algorithms that create
synthetic but realistic output. Diffusion models currently offer state of the
art performance in generative AI for images. They also form a key component in
more general tools, including text-to-image generators and large language
models. Diffusion models work by adding noise to the available training data
and then learning how to reverse the process. The reverse operation may then be
applied to new random data in order to produce new outputs. We provide a brief
introduction to diffusion models for applied mathematicians and statisticians.
Our key aims are (a) to present illustrative computational examples, (b) to
give a careful derivation of the underlying mathematical formulas involved, and
(c) to draw a connection with partial differential equation (PDE) diffusion
models. We provide code for the computational experiments. We hope that this
topic will be of interest to advanced undergraduate students and postgraduate
students. Portions of the material may also provide useful motivational
examples for those who teach courses in stochastic processes, inference,
machine learning, PDEs or scientific computing.


  We present a brief description of the determination of the two-loop
spin-dependent time-like splitting functions relevant for the NLO evolution of
polarized fragmentation functions. Our calculation based on the analytic
continuation of the corresponding space-like results obtained within the
light-cone gauge method proposed by Curci, Furmanski, and Petronzio. As an
application we present an analysis of polarized Lambda production in e+e- and
ep collisions.


  An integrated correlator of four superconformal stress-tensor primaries of
$\mathcal{N}=4$ supersymmetric $SU(N)$ Yang-Mills theory (SYM), originally
obtained by localisation, is re-expressed as a two-dimensional lattice sum that
is manifestly invariant under $SL(2,\mathbb{Z})$ S-duality. This expression is
shown to satisfy a novel Laplace equation in the complex coupling constant
$\tau$ that relates the $SU(N)$ integrated correlator to those of the $SU(N+1)$
and $SU(N-1)$ theories. The lattice sum is shown to precisely reproduce known
perturbative and non-perturbative properties of $\mathcal{N}=4$ SYM for any
finite $N$, as well as extending previously conjectured properties of the
large-$N$ expansion.


  In this thesis, we develop various techniques for working with sets in
machine learning. Each input or output is not an image or a sequence, but a
set: an unordered collection of multiple objects, each object described by a
feature vector. Their unordered nature makes them suitable for modeling a wide
variety of data, ranging from objects in images to point clouds to graphs. Deep
learning has recently shown great success on other types of structured data, so
we aim to build the necessary structures for sets into deep neural networks.
  The first focus of this thesis is the learning of better set representations
(sets as input). Existing approaches have bottlenecks that prevent them from
properly modeling relations between objects within the set. To address this
issue, we develop a variety of techniques for different scenarios and show that
alleviating the bottleneck leads to consistent improvements across many
experiments.
  The second focus of this thesis is the prediction of sets (sets as output).
Current approaches do not take the unordered nature of sets into account
properly. We determine that this results in a problem that causes discontinuity
issues with many set prediction tasks and prevents them from learning some
extremely simple datasets. To avoid this problem, we develop two models that
properly take the structure of sets into account. Various experiments show that
our set prediction techniques can significantly benefit over existing
approaches.


  We study the evolution of an exciton confined in a quantum dot adiabatically
controlled by a frequency-swept (chirped) laser pulse in the presence of
carrier-phonon coupling. We focus on the dynamics induced by a linearly
polarized beam and analyze the decoherence due to phonon-assisted biexciton
generation. We show that if the biexciton state is shifted down by a few meV,
as is typically the case, the resulting decoherence is strong even at low
temperatures. As a result, efficient state preparation is restricted to a small
parameter area corresponding to low temperatures, positive chirps and moderate
pulse areas.


  We explore a connection of the forced Burgers equation with the
Schr\"{o}dinger (diffusive) interpolating dynamics in the presence of
deterministic external forces. This entails an exploration of the consistency
conditions that allow to interpret dispersion of passive contaminants in the
Burgers flow as a Markovian diffusion process. In general, the usage of a
continuity equation $\partial_t\rho =-\nabla (\vec{v}\rho)$, where
$\vec{v}=\vec{v}(\vec{x},t)$ stands for the Burgers field and $\rho $ is the
density of transported matter, is at variance with the explicit diffusion
scenario. Under these circumstances, we give a complete characterisation of the
diffusive matter transport that is governed by Burgers velocity fields. The
result extends both to the approximate description of the transport driven by
an incompressible fluid and to motions in an infinitely compressible medium.


  We derive the high energy asymptotic behavior of gauge boson production cross
section in a spontaneously broken SU(2) gauge theory in the
next-to-next-to-leading logarithmic approximation. On the basis of this result
we obtain the logarithmically enhanced two-loop electroweak corrections to the
differential cross section of W-pair production at ILC/CLIC up to the second
power of the large logarithm.


  We present three 14400 square degree relative extinction maps of the Galactic
Plane (|b|<20degrees) obtained from 2MASS using accumulative star counts (Wolf
diagrams). This method is independent of the colour of the stars and the
variation of extinction with wavelength. Stars were counted in 3.5'x3.5' boxes,
every 20". 1x1degree surrounding fields were chosen for reference, hence the
maps represent local extinction enhancements and ignore any contribution from
the ISM or very large clouds. Data reduction was performed on a Beowulf-type
cluster (in approximately 120 hours). Such a cluster is ideal for this type of
work as areas of the sky can be independently processed in parallel. We studied
how extinction depends on wavelength in all of the high extinction regions
detected and within selected dark clouds. On average a power law opacity index
(\beta) of 1.0 to 1.8 in the NIR was deduced. The index however differed
significantly from region to region and even within individual dark clouds.
That said, generally it was found to be constant, or to increase, with
wavelength within a particular region.


  An important issue towards a broader acceptance of answer-set programming
(ASP) is the deployment of tools which support the programmer during the coding
phase. In particular, methods for debugging an answer-set program are
recognised as a crucial step in this regard. Initial work on debugging in ASP
mainly focused on propositional programs, yet practical debuggers need to
handle programs with variables as well. In this paper, we discuss a debugging
technique that is directly geared towards non-ground programs. Following
previous work, we address the central debugging question why some
interpretation is not an answer set. The explanations provided by our method
are computed by means of a meta-programming technique, using a uniform encoding
of a debugging request in terms of ASP itself. Our method also permits programs
containing comparison predicates and integer arithmetics, thus covering a
relevant language class commonly supported by all state-of-the-art ASP solvers.


  A compact Polish foliated space is considered. Part of this work studies
coarsely quasi-isometric invariants of leaves in some residual saturated subset
when the foliated space is transitive. In fact, we also use "equi-" versions of
this kind of invariants, which means that the definition is satisfied with the
same constants by some given set of leaves. For instance, the following
properties are proved. Either all dense leaves without holonomy are
equi-coarsely quasi-isometric to each other, or else there exist residually
many dense leaves without holonomy such that each of them is coarsely
quasi-isometric to meagerly many leaves. Assuming that the foliated space is
minimal, the first of the above alternatives holds if and if the leaves without
holonomy satisfy a condition called coarse quasi-symmetry. A similar dichotomy
holds for the growth type of the leaves, as well as an analogous
characterization of the first alternative in the minimal case, involving a
property called growth symmetry. Moreover some classes of growth are shared,
either by residually many leaves, or by meagerly many leaves. If some leaf
without holonomy is amenable, then all dense leaves without holonomy are
equi-amenable, and, in the minimal case, they satisfy a property called
amenable symmetry. Residually many leaves have the same asymptotic dimension.
If the foliated space is minimal, then any pair of nonempty open sets in the
Higson coronas of the leaves with holonomy contain homeomorphic nonempty open
subsets. Another part studies limit sets of leaves at points in their Higson
corona, defined like the usual limit sets at their ends.


  Classical varieties were characterized by Lawvere as the categories with
effective congruences and a varietal generator: an abstractly finite regular
generator which is regularly projective (its hom-functor preserves regular
epimorphisms). We characterize varieties of quantitative algebras of Mardare,
Panangaden and Plotkin analogously as metric-enriched categories. We introduce
the concept of a subcongruence (a metric-enriched analogue of a congruence) and
the corresponding subregular epimorphisms obtained via colimits of
subcongruences. Varieties of quantitative algebras are precisely the
metric-enriched categories with effective subcongruences and a subvarietal
generator: an abstractly finite subregular generator which is subregularly
projective (its hom-functor preserves subregular epimorphisms).


  At fine lattice spacings, lattice simulations are plagued by slow
(topological) modes that give rise to large autocorrelation times. These, in
turn, lead to statistical and systematic errors that are difficult to estimate.
We study the problem and possible algorithmic solutions in 4-dimensional SU(3)
gauge theory, with special focus on instanton updates and Metadynamics.


  Multicomputers have traditionally been viewed as powerful compute engines. It
is from this perspective that they have been applied to various problems in
order to achieve significant performance gains. There are many applications for
which this compute intensive approach is only a partial solution. CAD, virtual
reality, simulation, document management and analysis all require timely access
to large amounts of data. This thesis investigates the use of the object store
paradigm to harness the large distributed memories found on multicomputers. The
design, implementation, and evaluation of a distributed object server on the
Fujitsu AP1000 is described. The performance of the distributed object server
under example applications, mainly physical simulation problems, is used to
evaluate solutions to the problems of client space recovery, object migration,
and coherence maintenance.
  The distributed object server follows the client-server model, allows object
replication, and uses binary semaphores as a concurrency control measure.
Instrumentation of the server under these applications supports several
conclusions: client space recovery should be dynamically controlled by the
application, predictively prefetching object replicas yields benefits in
restricted circumstances, object migration by storage unit (segment) is not
generally suitable where there are many objects per storage unit, and binary
semaphores are an expensive concurrency control measure in this environment.


  It is now common to process volumetric biomedical images using 3D
Convolutional Networks (ConvNets). This can be challenging for the teravoxel
and even petavoxel images that are being acquired today by light or electron
microscopy. Here we introduce chunkflow, a software framework for distributing
ConvNet processing over local and cloud GPUs and CPUs. The image volume is
divided into overlapping chunks, each chunk is processed by a ConvNet, and the
results are blended together to yield the output image. The frontend submits
ConvNet tasks to a cloud queue. The tasks are executed by local and cloud GPUs
and CPUs. Thanks to the fault-tolerant architecture of Chunkflow, cost can be
greatly reduced by utilizing cheap unstable cloud instances. Chunkflow
currently supports PyTorch for GPUs and PZnet for CPUs. To illustrate its
usage, a large 3D brain image from serial section electron microscopy was
processed by a 3D ConvNet with a U-Net style architecture. Chunkflow provides
some chunk operations for general use, and the operations can be composed
flexibly in a command line interface.


  Recent advancements in unmanned aerial vehicles, also known as drones, have
motivated logistics to use drones for multiple operations. Collaboration
between drones and trucks in a last-mile delivery system has numerous benefits
and reduces a number of challenges. In this paper, we introduce
\textit{drone-delivery packing problem} (DDP), where we have a set of
deliveries and respective customers with their prescribed locations, delivery
time intervals, associated cost for deliveries, and a set of drones with
identical battery budgets. The objective of the DDP is to find an assignment
for all deliveries to the drones by using the minimum number of drones subject
to the battery budget and compatibility of the assignment of each drone. We
prove that DDP is NP-Hard and formulate the integer linear programming (ILP)
formulation for it. We proposed two greedy approximation algorithms for DDP.
The first algorithm uses at most $2OPT + (\Delta + 1)$ drones. The second
algorithm uses at most $2OPT + \omega$ drones, where OPT is the optimum
solution for DDP, $\omega$ is the maximum clique size, and $\Delta$ is the
maximum degree of the interval graph $G$ constructed from the delivery time
intervals.


  We have compared the star-formation properties of the W49A and W51 regions by
using far-infrared data from the Herschel infrared Galactic Plane Survey
(Hi-GAL) and 850-um observations from the James Clerk Maxwell Telescope (JCMT)
to obtain luminosities and masses, respectively, of associated compact sources.
The former are infrared luminosities from the catalogue of Elia et al. (2017),
while the latter are from the JCMT Plane survey source catalogue as well as
measurements from new data. The clump-mass distributions of the two regions are
found to be consistent with each other, as are the clump-formation efficiency
and star-formation efficiency analogues. However, the frequency distributions
of the luminosities of the young stellar objects are significantly different.
While the luminosity distribution in W51 is consistent with Galaxy-wide
samples, that of W49A is top-heavy. The differences are not dramatic, and are
concentrated in the central regions of W49A. However, they suggest that
physical conditions there, which are comparable in part to those in
extragalactic starbursts, are significantly affecting the star-formation
properties or evolution of the dense clumps in the region.


  We study the problem of constructing approximations to a weighted automaton.
Weighted finite automata (WFA) are closely related to the theory of rational
series. A rational series is a function from strings to real numbers that can
be computed by a finite WFA. Among others, this includes probability
distributions generated by hidden Markov models and probabilistic automata. The
relationship between rational series and WFA is analogous to the relationship
between regular languages and ordinary automata. Associated with such rational
series are infinite matrices called Hankel matrices which play a fundamental
role in the theory of minimal WFA. Our contributions are: (1) an effective
procedure for computing the singular value decomposition (SVD) of such infinite
Hankel matrices based on their representation in terms of finite WFA; (2) a new
canonical form for finite WFA based on this SVD decomposition; and, (3) an
algorithm to construct approximate minimizations of a given WFA. The goal of
our approximate minimization algorithm is to start from a minimal WFA and
produce a smaller WFA that is close to the given one in a certain sense. The
desired size of the approximating automaton is given as input. We give bounds
describing how well the approximation emulates the behavior of the original
WFA.


  We investigate preprocessing for vertex-subset problems on graphs. While the
notion of kernelization, originating in parameterized complexity theory, is a
formalization of provably effective preprocessing aimed at reducing the total
instance size, our focus is on finding a non-empty vertex set that belongs to
an optimal solution. This decreases the size of the remaining part of the
solution which still has to be found, and therefore shrinks the search space of
fixed-parameter tractable algorithms for parameterizations based on the
solution size. We introduce the notion of a c-essential vertex as one that is
contained in all c-approximate solutions. For several classic combinatorial
problems such as Odd Cycle Transversal and Directed Feedback Vertex Set, we
show that under mild conditions a polynomial-time preprocessing algorithm can
find a subset of an optimal solution that contains all 2-essential vertices, by
exploiting packing/covering duality. This leads to FPT algorithms to solve
these problems where the exponential term in the running time depends only on
the number of non-essential vertices in the solution.


  Object counting typically uses 2D point annotations. The complexity of object
shapes and the subjectivity of annotators may lead to annotation inconsistency,
potentially confusing counting model training. Some sophisticated
noise-resistance counting methods have been proposed to alleviate this issue.
Differently, we aim to directly refine the initial point annotations before
training counting models. For that, we propose the Shifted Autoencoders (SAE),
which enhances annotation consistency. Specifically, SAE applies random shifts
to initial point annotations and employs a UNet to restore them to their
original positions. Similar to MAE reconstruction, the trained SAE captures
general position knowledge and ignores specific manual offset noise. This
allows to restore the initial point annotations to more general and thus
consistent positions. Extensive experiments show that using such refined
consistent annotations to train some advanced (including noise-resistance)
object counting models steadily/significantly boosts their performances.
Remarkably, the proposed SAE helps to set new records on nine datasets. We will
make codes and refined point annotations available.


  Knowledge bases are important resources for a variety of natural language
processing tasks but suffer from incompleteness. We propose a novel embedding
model, \emph{ITransF}, to perform knowledge base completion. Equipped with a
sparse attention mechanism, ITransF discovers hidden concepts of relations and
transfer statistical strength through the sharing of concepts. Moreover, the
learned associations between relations and concepts, which are represented by
sparse attention vectors, can be interpreted easily. We evaluate ITransF on two
benchmark datasets---WN18 and FB15k for knowledge base completion and obtains
improvements on both the mean rank and Hits@10 metrics, over all baselines that
do not use additional information.


  We show that the third-order difference equations proposed by Hirota,
  Kimura and Yahagi are generated by a pair of second-order difference
equations. In some cases, the pair of the second-order equations are equivalent
to the Quispel-Robert-Thomson(QRT) system, but in the other cases, they are
irrelevant to the QRT system. We also discuss an ultradiscretization of the
equations.


  Humans have a remarkable ability to quickly and effectively learn new
concepts in a continuous manner without forgetting old knowledge. Though deep
learning has made tremendous successes on various computer vision tasks, it
faces challenges for achieving such human-level intelligence. In this paper, we
define a new problem called continual few-shot learning, in which tasks arrive
sequentially and each task is associated with a few training samples. We
propose Continual Meta-Learner (CML) to solve this problem. CML integrates
metric-based classification and a memory-based mechanism along with adversarial
learning into a meta-learning framework, which leads to the desirable
properties: 1) it can quickly and effectively learn to handle a new task; 2) it
overcomes catastrophic forgetting; 3) it is model-agnostic. We conduct
extensive experiments on two image datasets, MiniImageNet and CIFAR100.
Experimental results show that CML delivers state-of-the-art performance in
terms of classification accuracy on few-shot learning tasks without
catastrophic forgetting.


  We explicitly show that the one loop IR correction to the two--point function
in de Sitter space scalar QFT does not reduce just to the mass renormalization.
The proper interpretation of the loop corrections is via particle creation
revealing itself through the generation of the quantum averages $<a^+_p a_p>$,
$<a_p a_{-p}>$ and $<a^+_p a^+_{-p}>$, which slowly change in time. We show
that this observation in particular means that loop corrections to correlation
functions in de Sitter space can not be obtained via analytical continuation of
those calculated on the sphere.
  We find harmonics for which the particle number $<a^+_p a_p>$ dominates over
the anomalous expectation values $<a_p a_{-p}>$ and $<a^+_p a^+_{-p}>$. For
these harmonics the Dyson--Schwinger equation reduces in the IR limit to the
kinetic equation. We solve the latter equation, which allows us to sum up all
loop leading IR contributions to the Whiteman function. We perform the
calculation for the principle series real scalar fields both in expanding and
contracting Poincare patches.


  Reaction paths and classical and quantum trajectories are studied within a
generalized Hamilton-Jacobi framework, which allows to put on equal footing
topology and dynamics in chemical reactivity problems. In doing so, we show how
high-dimensional problems could be dealt with by means of Caratheodory plots or
how trajectory-based quantum-classical analyses reveal unexpected
discrepancies. As a working model, we consider the reaction dynamics associated
with a Mueller-Brown potential energy surface, where we focus on the
relationship between reaction paths and trajectories as well as on reaction
probability calculations from classical and quantum trajectories.


  We consider a transmission of a delay-sensitive data stream from a single
source to a single destination. The reliability of this transmission may suffer
from bursty packet losses - the predominant type of failures in today's
Internet. An effective and well studied solution to this problem is to protect
the data by a Forward Error Correction (FEC) code and send the FEC packets over
multiple paths.
  In this paper we show that the performance of such a multipath FEC scheme can
often be further improved. Our key observation is that the propagation times on
the available paths often significantly differ, typically by 10-100ms.
  We propose to exploit these differences by appropriate packet scheduling that
we call `Spread'. We evaluate our solution with a precise, analytical
formulation and trace-driven simulations. Our studies show that Spread
substantially outperforms the state-of-the-art solutions. It typically achieves
two- to five-fold improvement (reduction) in the effective loss rate. Or
conversely, keeping the same level of effective loss rate, Spread significantly
decreases the observed delays and helps fighting the delay jitter.


  A non-local toy-model is proposed for the purpose of modelling the ``wave
function collapse'' of a two-state quantum system. The collapse is driven by a
nonlinear evolution equation with an extreme sensitivity to absolute phase. It
is hypothesized that the phase, or a part of it, is displaying chaotic
behaviour. This chaotic behaviour can then be responsible for the indeterminacy
we are experiencing for a single quantum system. Through this randomness, we no
longer need the statistical ``ensemble'' behaviour to describe a single quantum
system. A brief introduction to the ``measurement problem'' is also given.


  Most of the common applications of Named Entity Recognition (NER) is on
English and other highly available languages. In this work, we present our
findings on Named Entity Recognition for 5 Nigerian Languages (Nigerian
English, Nigerian Pidgin English, Igbo, Yoruba and Hausa). These languages are
considered low-resourced, and very little openly available Natural Language
Processing work has been done in most of them. In this work, individual NER
models were trained and metrics recorded for each of the languages. We also
worked on a combined model that can handle Named Entity Recognition (NER) for
any of the five languages. The combined model works well for Named Entity
Recognition(NER) on each of the languages and with better performance compared
to individual NER models trained specifically on annotated data for the
specific language. The aim of this work is to share our learning on how
information extraction using Named Entity Recognition can be optimized for the
listed Nigerian Languages for inclusion, ease of deployment in production and
reusability of models. Models developed during this project are available on
GitHub https://git.io/JY0kk and an interactive web app
https://nigner.herokuapp.com/.


  Nonequilibrium Green's functions provide a powerful tool for computing the
dynamical response and particle exchange statistics of coupled quantum systems.
We formulate the theory in terms of the density matrix in Liouville space and
introduce superoperator algebra that greatly simplifies the derivation and the
physical interpretation of all quantities. Expressions for various observables
are derived directly in real time in terms of superoperator nonequilibrium
Green's functions (SNGF), rather than the artificial time-loop required in
Schwinger's Hilbert-space formulation. Applications for computing interaction
energies, charge densities, average currents, current induced fluorescence,
electroluminescence and current fluctuation (electron counting) statistics are
discussed.


  We study the stable dynamics of non-polynomial automorphisms of
$\mathbb{C}^2$ of the form $F(z,w)=(e^{-z^m}+ \delta e^{\frac{2 \pi}{m}i}\,
w\,,\,z)$, with $m\ge 2$ a natural number and $\mathbb{R}\ni\delta>2$.
  If $m$ is even, there are $\frac{m}{2}$ cycles of escaping Fatou components,
all of period $2m$. If $m$ is odd there are $\frac{m-1}{2}$ cycles of escaping
Fatou components of period $2m$ and just one cycle of escaping Fatou components
of period $m$.
  These maps have two distinct limit functions on each cycle, both of which
have generic rank 1. Each Fatou component in each cycle has two disjoint and
hyperbolic limit sets on the line at infinity, except for the Fatou components
that belong to the unique cycle of period $m$: the latter in fact have the same
hyperbolic limit set on the line at infinity.


  Do current large language models (LLMs) better solve graph reasoning and
generation tasks with parameter updates? In this paper, we propose
InstructGraph, a framework that empowers LLMs with the abilities of graph
reasoning and generation by instruction tuning and preference alignment.
Specifically, we first propose a structured format verbalizer to unify all
graph data into a universal code-like format, which can simply represent the
graph without any external graph-specific encoders. Furthermore, a graph
instruction tuning stage is introduced to guide LLMs in solving graph reasoning
and generation tasks. Finally, we identify potential hallucination problems in
graph tasks and sample negative instances for preference alignment, the target
of which is to enhance the output's reliability of the model. Extensive
experiments across multiple graph-centric tasks exhibit that InstructGraph can
achieve the best performance and outperform GPT-4 and LLaMA2 by more than 13\%
and 38\%, respectively.


  In this paper we contribute a theoretical framework that sheds a new light on
the problem of microgrid analysis and control. The starting point is an energy
function comprising the kinetic energy associated with the elements that
emulate the rotating machinery and terms taking into account the reactive power
stored in the lines and dissipated on shunt elements. We then shape this energy
function with the addition of an adjustable voltage-dependent term, and
construct incremental storage functions satisfying suitable dissipation
inequalities. Our choice of the voltage-dependent term depends on the voltage
dynamics/controller under investigation. Several microgrids dynamics that have
similarities or coincide with dynamics already considered in the literature are
captured in our incremental energy analysis framework. The twist with respect
to existing results is that our incremental storage functions allow for a large
signal analysis of the coupled microgrid obviating the need for simplifying
linearization techniques and for the restrictive decoupling assumption in which
the frequency dynamics is fully separated from the voltage one. A complete
Lyapunov stability analysis of the various systems is carried out along with a
discussion on their active and reactive power sharing properties.


  We use the am, an, as and the a-sigma geomagnetic indices to the explore a
previously overlooked factor in magnetospheric electrodynamics, namely the
inductive effect of diurnal motions of the Earth's magnetic poles toward and
away from the Sun caused by Earth's rotation. Because the offset of the
(eccentric dipole) geomagnetic pole from the rotational axis is roughly twice
as large in the southern hemisphere compared to the northern, the effects there
are predicted to be roughly twice the amplitude. Hemispheric differences have
previously been discussed in terms of polar ionospheric conductivities, effects
which we allow for by studying the dipole tilt effect on time-of-year
variations of the indices. The electric field induced in a geocentric frame is
shown to also be a significant factor and gives a modulation of the voltage
applied by the solar wind flow in the southern hemisphere of typically a 30%
diurnal modulation for disturbed intervals rising to 76% in quiet times. Motion
towards/away from the Sun reduces/enhances the directly-driven ionospheric
voltages and reduces/enhances the magnetic energy stored in the near-Earth
tail: 10% of the effect being directly-driven and 90% being in tail energy
storage/release. Combined with the effect of solar wind dynamic pressure and
dipole tilt on the pressure balance in the near-Earth tail, the effect provides
an excellent explanation of how the observed Russell-McPherron pattern in the
driving power input into the magnetosphere is converted into the equinoctial
pattern in average geomagnetic activity (after correction is made for dipole
tilt effects on ionospheric conductivity), added to a pronounced UT variation
with minimum at 02-10UT. In addition, we show that the predicted and observed
UT variations in average geomagnetic activity has implications for the
occurrence of the largest events that also show the nett UT variation.


  We study the dynamic programming approach to revenue management in the
context of attended home delivery. We draw on results from dynamic programming
theory for Markov decision problems to show that the underlying Bellman
operator has a unique fixed point. We then provide a closed-form expression for
the resulting fixed point and show that it admits a natural interpretation.
Moreover, we also show that -- under certain technical assumptions -- the value
function, which has a discrete domain and a continuous codomain, admits a
continuous extension, which is a finite-valued, concave function of its state
variables, at every time step. This result opens the road for achieving
scalable implementations of the proposed formulation in future work, as it
allows making informed choices of basis functions in an approximate dynamic
programming context. We illustrate our findings on a simple numerical example
and provide suggestions on how our results can be exploited to obtain closer
approximations of the exact value function.


  The critical field of psychology necessitates a comprehensive benchmark to
enhance the evaluation and development of domain-specific Large Language Models
(LLMs). Existing MMLU-type benchmarks, such as C-EVAL and CMMLU, include
psychology-related subjects, but their limited number of questions and lack of
systematic concept sampling strategies mean they cannot cover the concepts
required in psychology. Consequently, despite their broad subject coverage,
these benchmarks lack the necessary depth in the psychology domain, making them
inadequate as psychology-specific evaluation suite. To address this issue, this
paper presents ConceptPsy, designed to evaluate Chinese complex reasoning and
knowledge abilities in psychology. ConceptPsy includes 12 core subjects and
1383 manually collected concepts. Specifically, we prompt GPT-4 to generate
questions for each concept using carefully designed diverse prompts and hire
professional psychologists to review these questions. To help to understand the
fine-grained performances and enhance the weaknesses, we annotate each question
with a chapter label and provide chapter-wise accuracy. Based on ConceptPsy, we
evaluate a broad range of LLMs. We observe that, although some LLMs achieve
similar accuracies on overall performances, they exhibit significant
performance variations across different psychology concepts, even when they are
models from the same series. We hope our work can facilitate the development of
LLMs in the field of psychology.


  Interfacing artificial functional materials and living neuronal tissues is at
the forefront of bio-nano-technology. Attempts have been so far based onto
microscale processing of metals and inorganic semiconductors as electrodes or
photoactive layers in biased devices. More recently, also nanomaterials
properties have been investigated. In spite of extensive research however, the
communication between biological tissues and artificial sensors is still a
challenge. Constraints consist in the complexity of the fabrication processes
(i.e. metal and semiconductor lithography), the mechanical properties (e.g.
flexibility and mechanical invasiveness) and chemical influence (e.g.
inflammatory reactions). In addition, electrodes have fixed geometries that
limit the location in space of the stimulus and often electrical currents are
detrimental for the overall system. To this respect organic soft matter offers
a chance in terms of biological affinity and mechanical properties. In
particular conjugated polymers have appealing optoelectronic features which
could lead to a new generation of neuronal communication and photo-manipulation
techniques. So far conjugated polymers have being only tested as coatings of
electrodes for neuronal activity recording. Here we report an up-scale of their
use: the successful interfacing of an organic semiconductor to a network of
cultured primary neurons, through optical excitation. This allows to a new
paradigm for the optical stimulation of neurons which could have important
implications for the development of an artificial retina based on organic
photodetectors.


  We introduce the notion of finite stature of a family $\{H_i\}$ of subgroups
of a group $G$. We investigate the separability of subgroups of a group $G$
that splits as a graph of hyperbolic special groups with quasiconvex edge
groups. We prove that when the vertex groups of $G$ have finite stature, then
quasiconvex subgroups of the vertex groups of $G$ are separable in $G$. We
present some partial results in a relatively hyperbolic framework.


  We calculate the coherent dynamical scattering function S_c(q,t;N) of a
flexible chain of length N, diffusing through an ordered background of
topological obstacles. As an instructive generalization, we also calculate the
scattering function S_c(q,t;M,N) for the central piece of length M < N of the
chain. Using the full reptation model, we treat global creep, tube length
fluctuations, and internal relaxation within a consistent and unified approach.
Our theory concentrates on the universal aspects of reptational motion, and our
results in all details show excellent agreement with our simulations of the
Evans-Edwards model, provided we allow for a phenomenological prefactor which
accounts for non-universal effects of the micro-structure of the Monte Carlo
chain, present for short times. Previous approaches to the coherent structure
function can be analyzed as special limits of our theory. First, the effects of
internal relaxation can be isolated by studying the limit $N \to \infty$, M
fixed. The results do not support the model of a `Rouse chain in a tube'. We
trace this back to the non-equilibrium initial conditions of the latter model.
Second, in the limit of long chains $(M = N \to \infty)$ and times large
compared to the internal relaxation time $(t/N^2 \to \infty)$, our theory
reproduces the results of the primitive chain model. This limiting form applies
only to extremely long chains, and for chain lengths accessible in practice,
effects of, e.g., tube length fluctuations are not negligible.


  A new random geometric graph model, the so-called secrecy graph, is
introduced and studied. The graph represents a wireless network and includes
only edges over which secure communication in the presence of eavesdroppers is
possible. The underlying point process models considered are lattices and
Poisson point processes. In the lattice case, analogies to standard bond and
site percolation can be exploited to determine percolation thresholds. In the
Poisson case, the node degrees are determined and percolation is studied using
analytical bounds and simulations. It turns out that a small density of
eavesdroppers already has a drastic impact on the connectivity of the secrecy
graph.


  We study time-dependent discrete-time quantum walks on the one-dimensional
lattice. We compute the limit distribution of a two-period quantum walk defined
by two orthogonal matrices. For the symmetric case, the distribution is
determined by one of two matrices. Moreover, limit theorems for two special
cases are presented.


  This document highlights a number of top-quark mass measurements carried out
by the ATLAS and CMS collaborations based on the combined LHC Run 1 datasets at
centre-of-mass energies of $\sqrt{s}=$7 and 8 TeV. A wide range of analysis
strategies are employed for a number of final-state signatures. Measurements of
both the pole mass as well as the value of the top-quark mass as defined by the
Monte Carlo generator in simulated signal samples are discussed.


  Corrections to $K_{L}^{0}\rightarrow \pi^{0} \gamma \gamma$ decay induced by
scalar meson exchange are studied within chiral perturbation theory. In spite
of bad knowledge of scalar-mesons parameters, the calculated branching ratio
can be changed by a few percent.


  The space ML(F) of measured geodesic laminations on a given closed hyperbolic
surface F has a canonical linear structure arising in fact from different
sources in 2-dimensional hyperbolic (earthquake theory) or complex projective
(grafting) geometry as well as in (2+1) Lorentzian one (globally hyperbolic
spacetimes of constant curvature). We investigate this linear structure, by
showing in particular how heavily it depends on the geometric structure of F,
whle to many other extens ML(F) depends only on the topology of F. This is
already manifest when we describe in geometric terms the sum of two measured
geodesic laminations in the simplest non trivial case of two weighted simple
closed geodesics that meet each other at one point.


  Results of searches for the Higgs boson production in association with a pair
of top quarks using the proton-proton collision data collected by the CMS
detector at LHC are presented. First, results of the searches at centre-of-mass
energy of 13 TeV are briefly discussed. Then, the combination of results at
centre-of-mass energies of 7, 8, and 13 TeV are presented, which provides an
observation of the Higgs boson production in association with a pair of top
quarks with a significance of 5.2 standard deviations.


  For any K(=2k+1)th-order Lovelock gravity with fine-tuned Lovelock couplings,
we demonstrate the existence of a special isolated critical point characterized
by non-standard critical exponents in the phase diagram of hyperbolic vacuum
black holes. In the Gibbs free energy this corresponds to a place wherefrom two
swallowtails emerge, giving rise to two first-order phase transitions between
small and large black holes. We believe that this is a first example of a
critical point with non-standard critical exponents obtained in a geometric
theory of gravity.


  In massive multiple-input multiple-output (MIMO) systems, the knowledge of
the users' channel covariance matrix is crucial for minimum mean square error
(MMSE) channel estimation in the uplink as well as it plays an important role
in several multiuser beamforming schemes in the downlink. Due to the large
number of base station antennas in massive MIMO, accurate covariance estimation
is challenging especially in the case where the number of samples is limited
and thus comparable to the channel vector dimension. As a result, the standard
sample covariance estimator may yield a too large estimation error which in
turn may yield significant system performance degradation with respect to the
ideal channel covariance knowledge case. To address such problem, we propose a
method based on a parametric representation of the channel angular scattering
function. The proposed parametric representation includes a discrete specular
component which is addressed using the well-known MUltiple SIgnal
Classification (MUSIC) method, and a diffuse scattering component, which is
modeled as the superposition of suitable dictionary functions. To obtain the
representation parameters we propose two methods, where the first solves a
non-negative least-squares problem and the second maximizes the likelihood
function using expectation-maximization. Our simulation results show that the
proposed methods outperform the state of the art with respect to various
estimation quality metrics and different sample sizes.


  It is demonstrated that in a two-stage scenario with elementary Poissonian
emitters of particles (colour strings) arbitrarily distributed in their number
and average multiplicities, the forward- backward correlations are completely
determined by the final distribution of the forward particles. The observed
linear form of the correlations then necessarily requires this distribution to
have a negative binomial form. For emitters with a negative binomial
distribution of the produced particles distributed so as to give the final
distribution also of a negative binomial form, the forward-backward
correlations have an essentially non-linear form, which disagrees with the
experimental data.


  This paper mainly focuses on solving the dynamic optimization of the planar
controlled crowd motion models with obstacles which is an application of a
class of optimal control problems governed by a general perturbed nonconvex
sweeping process. This can be considered as a significant extension of the
previous work regarding the controlled crowd motion models, where the obstacles
have not been considered. The necessary optimality conditions for the problem
under consideration are established and illustrated by a nontrivial example of
practical importance.


  In this expository article, we present a number of classic theorems that
serve to identify the closure in the sup-norm of various sets of Blaschke
products, inner functions and their quotients, as well as the closure of the
convex hulls of these sets. The results presented include theorems of
Carath\'eodory, Fisher, Helson-Sarason, Frostman, Adamjan-Arov-Krein,
Douglas-Rudin and Marshall. As an application of some of these ideas, we obtain
a simple proof of the Berger-Stampfli spectral mapping theorem for the
numerical range of an operator.


  Low-precision deep neural network (DNN) training has gained tremendous
attention as reducing precision is one of the most effective knobs for boosting
DNNs' training time/energy efficiency. In this paper, we attempt to explore
low-precision training from a new perspective as inspired by recent findings in
understanding DNN training: we conjecture that DNNs' precision might have a
similar effect as the learning rate during DNN training, and advocate dynamic
precision along the training trajectory for further boosting the time/energy
efficiency of DNN training. Specifically, we propose Cyclic Precision Training
(CPT) to cyclically vary the precision between two boundary values which can be
identified using a simple precision range test within the first few training
epochs. Extensive simulations and ablation studies on five datasets and eleven
models demonstrate that CPT's effectiveness is consistent across various
models/tasks (including classification and language modeling). Furthermore,
through experiments and visualization we show that CPT helps to (1) converge to
a wider minima with a lower generalization error and (2) reduce training
variance which we believe opens up a new design knob for simultaneously
improving the optimization and efficiency of DNN training. Our codes are
available at: https://github.com/RICE-EIC/CPT.


  We present a first-principles methodology, within the context of
linear-response theory, that greatly facilitates the perturbative study of
physical properties of metallic crystals. Our approach builds on ensemble
density-functional theory [Phys. Rev. Lett. 79, 1337 (1997)] to write the
adiabatic second-order energy as an unconstrained variational functional of
both the wave functions and their occupancies. Thereby, it enables the
application of standard tools of density-functional perturbation theory (most
notably, the "$2n+1$" theorem) in metals, opening the way to an efficient and
accurate calculation of their nonlinear and spatially dispersive responses. We
apply our methodology to phonons and strain gradients and demonstrate the
accuracy of our implementation by computing the spatial dispersion coefficients
of zone-center optical phonons and the flexoelectric force-response tensor of
selected metal structures.


  The rise of cloud computing has spurred a trend of transferring data storage
and computational tasks to the cloud. To protect confidential information such
as customer data and business details, it is essential to encrypt this
sensitive data before cloud storage. Implementing encryption can prevent
unauthorized access, data breaches, and the resultant financial loss,
reputation damage, and legal issues. Moreover, to facilitate the execution of
data mining algorithms on the cloud-stored data, the encryption needs to be
compatible with domain computation. The $k$-nearest neighbor ($k$-NN)
computation for a specific query vector is widely used in fields like
location-based services. Sanyashi et al. (ICISS 2023) proposed an encryption
scheme to facilitate privacy-preserving $k$-NN computation on the cloud by
utilizing Asymmetric Scalar-Product-Preserving Encryption (ASPE).
  In this work, we identify a significant vulnerability in the aforementioned
encryption scheme of Sanyashi et al. Specifically, we give an efficient
algorithm and also empirically demonstrate that their encryption scheme is
vulnerable to the ciphertext-only attack (COA).


  The image biomarker standardisation initiative (IBSI) is an independent
international collaboration which works towards standardising the extraction of
image biomarkers from acquired imaging for the purpose of high-throughput
quantitative image analysis (radiomics). Lack of reproducibility and validation
of high-throughput quantitative image analysis studies is considered to be a
major challenge for the field. Part of this challenge lies in the scantiness of
consensus-based guidelines and definitions for the process of translating
acquired imaging into high-throughput image biomarkers. The IBSI therefore
seeks to provide image biomarker nomenclature and definitions, benchmark data
sets, and benchmark values to verify image processing and image biomarker
calculations, as well as reporting guidelines, for high-throughput image
analysis.


  We give efficient distributed algorithms for the minimum vertex cover problem
in bipartite graphs in the CONGEST model. From K\H{o}nig's theorem, it is well
known that in bipartite graphs the size of a minimum vertex cover is equal to
the size of a maximum matching. We first show that together with an existing
$O(n\log n)$-round algorithm for computing a maximum matching, the constructive
proof of K\H{o}nig's theorem directly leads to a deterministic $O(n\log
n)$-round CONGEST algorithm for computing a minimum vertex cover. We then show
that by adapting the construction, we can also convert an \emph{approximate}
maximum matching into an \emph{approximate} minimum vertex cover. Given a
$(1-\delta)$-approximate matching for some $\delta>1$, we show that a
$(1+O(\delta))$-approximate vertex cover can be computed in time
$O(D+\mathrm{poly}(\frac{\log n}{\delta}))$, where $D$ is the diameter of the
graph. When combining with known graph clustering techniques, for any
$\varepsilon\in(0,1]$, this leads to a $\mathrm{poly}(\frac{\log
n}{\varepsilon})$-time deterministic and also to a slightly faster and simpler
randomized $O(\frac{\log n}{\varepsilon^3})$-round CONGEST algorithm for
computing a $(1+\varepsilon)$-approximate vertex cover in bipartite graphs. For
constant $\varepsilon$, the randomized time complexity matches the $\Omega(\log
n)$ lower bound for computing a $(1+\varepsilon)$-approximate vertex cover in
bipartite graphs even in the LOCAL model. Our results are also in contrast to
the situation in general graphs, where it is known that computing an optimal
vertex cover requires $\tilde{\Omega}(n^2)$ rounds in the CONGEST model and
where it is not even known how to compute any $(2-\varepsilon)$-approximation
in time $o(n^2)$.


  Recently, promptable segmentation models, such as the Segment Anything Model
(SAM), have demonstrated robust zero-shot generalization capabilities on static
images. These promptable models exhibit denoising abilities for imprecise
prompt inputs, such as imprecise bounding boxes. In this paper, we explore the
potential of applying SAM to track and segment objects in videos where we
recognize the tracking task as a prompt denoising task. Specifically, we
iteratively propagate the bounding box of each object's mask in the preceding
frame as the prompt for the next frame. Furthermore, to enhance SAM's denoising
capability against position and size variations, we propose a multi-prompt
strategy where we provide multiple jittered and scaled box prompts for each
object and preserve the mask prediction with the highest semantic similarity to
the template mask. We also introduce a point-based refinement stage to handle
occlusions and reduce cumulative errors. Without involving tracking modules,
our approach demonstrates comparable performance in video object/instance
segmentation tasks on three datasets: DAVIS2017, YouTubeVOS2018, and UVO,
serving as a concise baseline and endowing SAM-based downstream applications
with tracking capabilities.


  Black holes are unique among astrophysical sources: they are the simplest
macroscopic objects in the Universe, and they are extraordinary in terms of
their ability to convert energy into electromagnetic and gravitational
radiation. Our capacity to probe their nature is limited by the sensitivity of
our detectors. The LIGO/Virgo interferometers are the gravitational-wave
equivalent of Galileo's telescope. The first few detections represent the
beginning of a long journey of exploration. At the current pace of
technological progress, it is reasonable to expect that the gravitational-wave
detectors available in the 2035-2050s will be formidable tools to explore these
fascinating objects in the cosmos, and space-based detectors with peak
sensitivities in the mHz band represent one class of such tools. These
detectors have a staggering discovery potential, and they will address
fundamental open questions in physics and astronomy. Are astrophysical black
holes adequately described by general relativity? Do we have empirical evidence
for event horizons? Can black holes provide a glimpse into quantum gravity, or
reveal a classical breakdown of Einstein's gravity? How and when did black
holes form, and how do they grow? Are there new long-range interactions or
fields in our universe, potentially related to dark matter and dark energy or a
more fundamental description of gravitation? Precision tests of black hole
spacetimes with mHz-band gravitational-wave detectors will probe general
relativity and fundamental physics in previously inaccessible regimes, and
allow us to address some of these fundamental issues in our current
understanding of nature.


  This paper proposes a millimeter wave-NOMA (mmWave-NOMA) system that takes
into account the end-user signal processing capabilities, an important
practical consideration. The implementation of NOMA in the downlink (DL)
direction requires successive interference cancellation (SIC) to be performed
at the user terminals, which comes at the cost of additional complexity. In
NOMA, the weakest user only has to decode its own signal, while the strongest
user has to decode the signals of all other users in the SIC procedure. Hence,
the additional implementation complexity required of the user to perform SIC
for DL NOMA depends on its position in the SIC decoding order. Beyond
fifth-generation (B5G) communication systems are expected to support a wide
variety of end-user devices, each with their own processing capabilities. We
envision a system where users report their SIC decoding capability to the base
station (BS), i.e., the number of other users signals a user is capable of
decoding in the SIC procedure. We investigate the rate maximization problem in
such a system, by breaking it down into a user clustering and ordering problem
(UCOP), followed by a power allocation problem. We propose a NOMA minimum exact
cover (NOMA-MEC) heuristic algorithm that converts the UCOP into a cluster
minimization problem from a derived set of valid cluster combinations after
factoring in the SIC decoding capability. The complexity of NOMA-MEC is
analyzed for various algorithm and system parameters. For a homogeneous system
of users that all have the same decoding capabilities, we show that this
equates to a simple maximum number of users per cluster constraint and propose
a lower complexity NOMA-best beam (NOMA-BB) algorithm. Simulation results
demonstrate the performance superiority in terms of sum rate compared to
orthogonal multiple access (OMA) and traditional NOMA


  This paper introduces a reformulation of the classical convergence theorem
for spectral sequences of filtered complexes which provides an algorithm to
effectively compute the induced filtration on the total (co)homology, as soon
as the complex is of finite type, its filtration is finite, and the underlying
ring is computable. So-called generalized maps play a decisive role in
simplifying and streamlining all involved algorithms.


  A detailed investigation of hybrid inflation and the subsequent reheating
process is performed within a mu problem solving supersymmetric model based on
a left-right symmetric gauge group. The process of baryogenesis via
leptogenesis is especially studied. For mu and tau neutrino masses consistent
with the small angle MSW resolution of the solar neutrino problem and the
recent results of the SuperKamiokande experiment, we show that maximal mu-tau
neutrino mixing can be achieved. The required value of the relevant coupling
constant is, however, quite small (of the order 10^{-6}).


  We describe a test statistic for unbinned goodness-of-fit of data in one
dimension. The statistic is based on the two-dimensional Random Walk. The
rejection power of this test is explored both for simple and compound
hypotheses and, for the examples explored, it is found to be comparable to that
for the chisquare test. We discuss briefly how it may be possible to extend
this test to multi-dimensional data.


  Geometrical Berry phase is recognized as having profound implications for the
properties of electronic systems. Over the last decade, Berry phase has been
essential to our understanding of new materials, including graphene and
topological insulators. The Berry phase can be accessed via its contribution to
the phase mismatch in quantum oscillation experiments, where electrons
accumulate a phase as they traverse closed cyclotron orbits in momentum space.
The high-temperature cuprate superconductors are a class of materials where the
Berry phase is thus far unknown despite the large body of existing quantum
oscillations data. In this report we present a systematic Berry phase analysis
of Shubnikov - de Haas measurements on the hole-doped cuprates
YBa$_2$Cu$_3$O$_{y}$, YBa$_2$Cu$_4$O$_8$, HgBa$_2$CuO$_{4 + \delta}$, and the
electron-doped cuprate Nd$_{2-x}$Ce$_x$CuO$_4$. For the hole-doped materials, a
trivial Berry phase of 0 mod $2\pi$ is systematically observed whereas the
electron-doped Nd$_{2-x}$Ce$_x$CuO$_4$ exhibits a significant non-zero Berry
phase. These observations set constraints on the nature of the high-field
normal state of the cuprates and points towards contrasting behaviour between
hole-doped and electron-doped materials. We discuss this difference in light of
recent developments related to charge density-wave and broken time-reversal
symmetry states.


  From next generation gas turbines to scavenging waste heat from car exhausts,
finding new materials with ultra-low thermal conductivity ($\kappa$) has the
potential to lead to large gains in device efficiency. Crystal structures with
inherently low $\kappa$ are consequently desirable, but candidate materials are
rare and often difficult to make. Using first principles calculations and
inelastic neutron scattering we have studied the pyrochlore La$_2$Zr$_2$O$_7$
which has been proposed as a next generation thermal barrier. We find that
there is a highly anharmonic, approximately flat, vibrational mode associated
with the kagome planes. Our results suggest that this mode is responsible for
the low thermal conductivity observed in the pyrochlores and that kagome
compounds will be a fruitful place to search for other low $\kappa$ materials.


  Normal--mode splitting is the most evident signature of strong coupling
between two interacting subsystems. It occurs when two subsystems exchange
energy between themselves faster than they dissipate it to the environment.
Here we experimentally show that a weakly coupled optomechanical system at room
temperature can manifest normal--mode splitting when the pump field
fluctuations are anti-squashed by a phase-sensitive feedback loop operating
close to its instability threshold. Under these conditions the optical cavity
exhibits an effectively reduced decay rate, so that the system is effectively
promoted to the strong coupling regime.


  The expression for polarized electric dipole moment of well-deformed
reflection asymmetric nuclei is obtained in the framework of liquid-drop model
in the case of geometrically similar proton and neutron surfaces. The
expression for polarized electric dipole moment consists of the first and
second orders terms. It is shown that the second-order correction terms of the
polarized electric dipole moment are important for well-deformed nuclei.


  We study string theory in the background of a two-dimensional black hole
which is described by an $SL(2, R)/U(1)$ coset conformal field theory. We
determine the spectrum of this conformal field theory using supersymmetric
quantum mechanics and give an explicit form of the vertex operators in terms of
the Jacobi functions. We also discuss the applicability of SUSY quantum
mechanics techniques to non-linear $\sigma$-models.


  Non-Gaussian operations on two mode squeezed vacuum states (TMSV) in
continuous variable measurement device independent quantum key distribution
(CV-MDI-QKD) protocols have been shown to effectively increase the total
transmission distances drastically. In this paper we show that photon
subtraction on a two mode squeezed coherent (PSTMSC) state can further improve
the transmission distances remarkably. To that end we also provide a
generalized covariance matrix corresponding to PSTMSC, which has not been
attempted before. We show that coherence, defined as the amount of displacement
of vacuum state, along with non-Gaussianity can help improve the performance of
prevalent CV-MDI-QKD protocols. Furthermore, since we use realistic parameters,
our technique is experimentally feasible and can be readily implemented


  This catalog comprises an up-to-date (December 1999) list of luminous (>10^36
erg/s), binary supersoft X-ray sources. This electronic version (including the
accompannying Web-pages) supersedes the printed version of Greiner (1996).


  Using Brownian Dynamics (BD) simulations we investigate the non-equilibrium
structure formation of a two-dimensional (2D) binary system of dipolar colloids
propelling in opposite directions. Despite of a pronounced tendency for chain
formation, the system displays a transition towards a laned state reminiscent
of lane formation in systems with isotropic repulsive interactions. However,
the anisotropic dipolar interactions induce novel features: First, the lanes
have themselves a complex internal structure characterized by chains or
clusters. Second, laning occurs only in a window of interaction strengths. We
interprete our findings by a phase separation process and simple force balance
arguments.


  We revisit the calculation of electroweak bremsstrahlung contributions to
dark matter annihilation. Dark matter annihilation to leptons is necessarily
accompanied by electroweak radiative corrections, in which a $W$ or $Z$ boson
is also radiated. Significantly, while many dark matter models feature a
helicity suppressed annihilation rate to fermions, bremsstrahlung process can
remove this helicity suppression such that the branching ratios Br($\ell \nu W
$), Br($\ell^+\ell^-Z$), and Br($\bar\nu \nu Z$) dominate over
Br($\ell^+\ell^-$) and Br($\bar\nu \nu$). We find this is most significant in
the limit where the dark matter mass is nearly degenerate with the mass of the
boson which mediates the annihilation process. Electroweak bremsstrahlung has
important phenomenological consequences both for the magnitude of the total
dark matter annihilation cross section and for the character of the
astrophysical signals for indirect detection. Given that the $W$ and $Z$ gauge
bosons decay dominantly via hadronic channels, it is impossible to produce
final state leptons without accompanying protons, antiprotons, and gamma rays.


  In this paper, we consider the global solutions to a generalized 2D
Boussinesq equation \begin{align*} \left \{\begin{aligned} & \partial_{t}
\omega + u\cdot \nabla \omega + \nu \Lambda^{\alpha} \omega = \theta_{x_{1}} ,
\quad \\ & u = \nabla^{\bot} \psi = (-\partial_{x_{2}} , \partial_{x_{1}}) \psi
, \quad \Delta \psi = \Lambda^{\sigma} (\log (I-\Delta))^{\gamma} \omega ,
\quad \\ & \partial_{t} \theta + u\cdot \nabla \theta + \kappa \Lambda^{\beta}
\theta = 0, \quad \\ & \omega(x,0) = \omega_{0}(x) , \quad \theta(x,0) =
\theta_{0}(x), \end{aligned}\right. \end{align*} with $\sigma \geq 0$, $\gamma
\geq 0$, $\nu >0$, $\kappa>0$, $\alpha < 1$ and $\beta < 1$. When $\sigma = 0$,
$\gamma \geq 0$, $\alpha \in [0.95,1)$ and $\beta \in (1-\alpha,g(\alpha))$,
where $g(\alpha)<1$ is an explicit function as a technical bound, we prove that
the above equation has a global and unique solution in suitable functional
space.


  In this note, we will discuss how to compactly express and prove the
Jarzynski identity for an open quantum system with dissipative dynamics. We
will avoid explicitly measuring the work directly, which is tantamount to
continuously monitoring the system, and instead measure the heat flow from the
environment. We represent the measurement of heat flow with Hermitian map
superoperators that act on the system density matrix. Hermitian maps provide a
convenient and compact representation of sequential measurement and correlation
functions.


  Magnetars are highly magnetized neutron stars; their formation mechanism is
unknown. Hot helium-rich stars with spectra dominated by emission lines are
known as Wolf-Rayet stars. We observe the binary system HD 45166 using
spectropolarimetry, finding that it contains a Wolf-Rayet star with a mass of 2
solar masses and a magnetic field of 43 kilogauss. Stellar evolution
calculations indicate that this component will explode as a type Ib or IIb
supernova, and the strong magnetic field favors a magnetar remnant. We propose
that the magnatized Wolf-Rayet star formed by the merger of two lower mass
helium stars.


  We employ the sl(2) foam cohomology to define a cohomology theory for
oriented framed tangles whose components are labelled by irreducible
representations of U_q(sl(2)). We show that the corresponding colored
invariants of tangles can be assembled into invariants of bigger tangles. For
the case of knots and links, the corresponding theory is a categorification of
the colored Jones polynomial, and provides a tool for efficient computations of
the resulting colored invariant of knots and links. Our theory is defined over
the Gaussian integers Z[i] (and more generally over Z[i][a,h], where a,h are
formal parameters), and enhances the existing categorifications of the colored
Jones polynomial.


  We present an algorithm using Principal Component Analysis (PCA) to subtract
galaxies from imaging data, and also two algorithms to find strong,
galaxy-scale gravitational lenses in the resulting residual image. The combined
method is optimized to find full or partial Einstein rings. Starting from a
pre-selection of potential massive galaxies, we first perform a PCA to build a
set of basis vectors. The galaxy images are reconstructed using the PCA basis
and subtracted from the data. We then filter the residual image with two
different methods. The first uses a curvelet (curved wavelets) filter of the
residual images to enhance any curved/ring feature. The resulting image is
transformed in polar coordinates, centered on the lens galaxy center. In these
coordinates, a ring is turned into a line, allowing us to detect very faint
rings by taking advantage of the integrated signal-to-noise in the ring (a line
in polar coordinates). The second way of analysing the PCA-subtracted images
identifies structures in the residual images and assesses whether they are
lensed images according to their orientation, multiplicity and elongation. We
apply the two methods to a sample of simulated Einstein rings, as they would be
observed with the ESA Euclid satellite in the VIS band. The polar coordinates
transform allows us to reach a completeness of 90% and a purity of 86%, as soon
as the signal-to-noise integrated in the ring is higher than 30, and almost
independent of the size of the Einstein ring. Finally, we show with real data
that our PCA-based galaxy subtraction scheme performs better than traditional
subtraction based on model fitting to the data. Our algorithm can be developed
and improved further using machine learning and dictionary learning methods,
which would extend the capabilities of the method to more complex and diverse
galaxy shapes.


  We present high spatial resolution integral-field spectroscopy of 28
elliptical (E) and lenticular (S0) galaxies from the SAURON representative
survey obtained with the OASIS spectrograph during its operation at the CFHT.
These seeing-limited observations explore the central 8"x10" (typically one
kiloparsec diameter) regions of these galaxies using a spatial sampling four
times higher than SAURON (0.27" vs. 0.94" spatial elements), resulting in
almost a factor of two improvement in the median PSF. These data allow accurate
study of the central regions to complement the large-scale view provided by
SAURON. Here we present the stellar and gas kinematics, stellar absorption-line
strengths and nebular emission-line strengths for this sample. We also
characterise the stellar velocity maps using the "kinemetry" technique, and
derive maps of the luminosity-weighted stellar age, metallicity and abundance
ratio via stellar population models. We give a brief review of the structures
found in our maps, linking also to larger-scale structures measured with
SAURON. We present two previously unreported kinematically-decoupled components
(KDCs) in the centres of NGC3032 and NGC4382. We compare the intrinsic size and
luminosity-weighted stellar age of all the visible KDCs in the full SAURON
sample, and find two types of components: kiloparsec-scale KDCs, which are
older than 8 Gyr, and are found in galaxies with little net rotation; and
compact KDCs, which have intrinsic diameters of less than a few hundred parsec,
show a range of stellar ages from 0.5 - 15 Gyr (with 5/6 younger than 5 Gyr),
are found exclusively in fast-rotating galaxies, and are close to
counter-rotating around the same axis as their host. (abridged)


  A proper vertex coloring of a graph is equitable if the sizes of color
classes differ by at most one. The equitable chromatic number of a graph $G$,
denoted by $\chi_=(G)$, is the minimum $k$ such that $G$ is equitably
$k$-colorable. The equitable chromatic threshold of a graph $G$, denoted by
$\chi_=^*(G)$, is the minimum $t$ such that $G$ is equitably $k$-colorable for
$k\ge t$. We develop a formula and a linear-time algorithm which compute the
equitable chromatic threshold of an arbitrary complete multipartite graph.


  We provide a counterexample to Cover's conjecture that the feedback capacity
$C_\textrm{FB}$ of an additive Gaussian noise channel under power constraint
$P$ be no greater than the nonfeedback capacity $C$ of the same channel under
power constraint $2P$, i.e., $C_\textrm{FB}(P) \le C(2P)$.


  Background and Purpose: Our purpose was to develop a deep learning
angiography (DLA) method to generate 3D cerebral angiograms from a single
contrast-enhanced acquisition.
  Material and Methods: Under an approved IRB protocol 105 3D-DSA exams were
randomly selected from an internal database. All were acquired using a clinical
system (Axiom Artis zee, Siemens Healthineers) in conjunction with a standard
injection protocol. More than 150 million labeled voxels from 35 subjects were
used for training. A deep convolutional neural network was trained to classify
each image voxel into three tissue types (vasculature, bone and soft tissue).
The trained DLA model was then applied for tissue classification in a
validation cohort of 8 subjects and a final testing cohort consisting of the
remaining 62 subjects. The final vasculature tissue class was used to generate
the 3D-DLA images. To quantify the generalization error of the trained model,
accuracy, sensitivity, precision and F1-scores were calculated for vasculature
classification in relevant anatomy. The 3D-DLA and clinical 3D-DSA images were
subject to a qualitative assessment for the presence of inter-sweep motion
artifacts.
  Results: Vasculature classification accuracy and 95% CI in the testing
dataset was 98.7% ([98.3, 99.1] %). No residual signal from osseous structures
was observed for all 3D-DLA testing cases except for small regions in the otic
capsule and nasal cavity compared to 37% (23/62) of the 3D-DSAs.
  Conclusion: DLA accurately recreated the vascular anatomy of the 3D-DSA
reconstructions without mask. DLA reduced mis-registration artifacts induced by
inter-sweep motion. DLA reduces radiation exposure required to obtain
clinically useful 3D-DSA


  We compute rates of tidal disruption of stars by supermassive black holes in
galactic nuclei, using downwardly-revised black hole masses from the M-sigma
relation. In galaxies with steep nuclear density profiles, which dominate the
overall event rate, the disruption frequency varies inversely with assumed
black hole mass. We compute a total rate for non-dwarf galaxies that is about a
factor ten higher than in earlier studies. Disruption rates are predicted to be
highest in nucleated dwarf galaxies, assuming that such galaxies contain black
holes. Monitoring of a rich galaxy cluster for a few years could rule out the
existence of intermediate mass black holes in dwarf galaxies.


  While detection of the "local form" bispectrum of primordial perturbations
would rule out all single-field inflation models, multi-field models would
still be allowed. We show that multi-field models described by the $\delta N$
formalism obey an inequality between $f_{\rm NL}$ and one of the local-form
{\it trispectrum} amplitudes, $\tau_{\rm NL}$, such that $\tau_{\rm
NL}>\frac12(\frac65f_{\rm NL})^2$ with a possible logarithmic scale dependence,
provided that 2-loop terms are small. Detection of a violation of this
inequality would rule out most of multi-field models, challenging inflation as
a mechanism for generating the primoridal perturbations.


  The axion, as a leading dark matter candidate, is the target of many on-going
and proposed experimental searches based on its coupling to photons. Ultralight
axions that couple to photons can also cause polarization rotation of light
which can be probed by cosmic microwave background. In this work, we show that
a large axion field is inevitably developed around black holes due to the
Bose-Einstein condensation of axions, enhancing the induced birefringence
effects. Therefore, we propose measuring the modulation of supermassive black
hole imaging polarization angles as a new probe to the axion-photon coupling of
axion dark matter. The oscillating axion field around black holes induces
polarization rotation on the black hole image, which is detectable and
distinguishable from astrophysical effects on the polarization angle, as it
exhibits distinctive temporal variability and frequency invariability. We
present the range of axion-photon couplings within the axion mass range
$10^{-21}-10^{-16}\text{eV}$ that can be probed by the Event Horizon Telescope.
The axion parameter space probed by black hole polarimetry will expand with the
improvement in sensitivity on the polarization measurement and more black hole
polarimetry targets with determined black hole masses.


  Utilizing the large quantity of hyperons and antihyperons produced by the
decay of 10 billion $J/\psi$ and 2.7 billion $\psi(3686)$ collected at BESIII,
the cross sections of several specific elastic or inelastic
(anti-)hyperon-nucleus/nucleon rections have been measured via the scattering
between the (anti-)hyperons and the nucleus in the dense objects of BESIII
detector. The novel method developed in these works extends the research field
and opens a new era for the experiments at $e^+ e^-$ colliders. The results of
such measurements will definitely benefit a lot the precise probe of the
(anti-)hyperon-nucleus/nucleon interactions and provide constraints for the
studies of the potential of strong interaction, the origin of color
confinement, the unified model for baryon-baryon interactions, and the internal
structure of neutron stars. The desirable prospects of corresponding studies in
the future Super Tau-Charm Factory (STCF) are also discussed in this report.


  We analyze the problem of high-order polynomial approximation from a
many-body physics perspective, and demonstrate the descriptive power of
entanglement entropy in capturing model capacity and task complexity.
Instantiated with a high-order nonlinear dynamics modeling problem,
tensor-network models are investigated and exhibit promising modeling
advantages. This novel perspective establish a connection between quantum
information and functional approximation, which worth further exploration in
future research.


  The nematic-to-isotropic orientational phase transition, or equivalently the
$RP^2$ model, is considered in two dimensions and the question of the nature of
the phase transition is addressed. Using powerful conformal techniques adapted
to the investigation of critical properties of two-dimensional scale-invariant
systems, we report strong evidences for a transition governed by topological
defects analogous to the Berezinskii-Kosterlitz-Thouless transition in
two-dimensional XY model.


  Beyond the Li-Haldane-Poilblanc conjecture, we find the entanglement
Hamiltonian (EH) is actually not closely similar to the original Hamiltonian on
the virtual edge. Unexpectedly, the EH has some relevant long-range interacting
terms which hugely affect the physics. Without loss of generality, we study a
spin-1/2 Heisenberg bilayer to obtain the entanglement information between the
two layers through our newly developed quantum Monte Carlo scheme, which can
simulate large-scale EH. Although the entanglement spectrum carrying the
Goldstone mode seems like a Heisenberg model on a single layer, which is
consistent with Li-Haldane-Poilblanc conjecture, we demonstrate that there
actually exists a finite-temperature phase transition of the EH. The results
violate the Mermin-Wagner theorem, which means there should be relevant
long-range terms in the EH. It reveals that the Li-Haldane-Poilblanc conjecture
ignores necessary corrections for the EH which may lead totally different
physics.


  In the fluid dynamics video, we present a microfluidic device to measure the
propulsive power of nematodes. The device consists of a tapered conduit filled
with aqueous solution. The conduit is subjected to a DC electric field with the
negative pole at the narrow end and to pressure-driven flow directed from the
narrow end. The nematode is inserted at the conduit's wide end. Directed by the
electric field (through electrotaxis), the nematode swims deliberately upstream
toward the negative pole of the DC field. As the conduit narrows, the average
fluid velocity and the drag force on the nematode increase. Eventually, the
nematode arrives at an equilibrium position, at which its propulsive force
balances the viscous drag force induced by the adverse flow. The equilibrium
position of different animals, with similar body lengths, was measured as a
function of the flow rate. The flow field around the nematode was obtained by
direct numerical simulations with the experimentally imaged gait and the
tapered geometry of the conduit as boundary conditions. The flow field
generated by a swimming worm is similar to the one induced by two pairs of
counter rotating rotors. Equilibrium positions under different flow rates were
identified by finding the positions at which the horizontal component of the
total force exerted on the worm body vanishes. The theoretically predicted
equilibrium positions were compared and favorably agreed with the experimental
data. The nematode's propulsive power was calculated by integrating the product
of velocity and total stress over the worm's body surface. The device is useful
to retain the nematodes at a nearly fixed position for prolonged observations
of active animals under a microscope, to keep the nematode exercising, and to
estimate the nematode's power consumption based on the conduit's width at the
equilibrium position.


  We derive and study optimal and nearly-optimal strategies for the detection
of sinusoidal signals hidden in additive (Gaussian and non-Gaussian) noise.
Such strategies are an essential part of algorithms for the detection of the
gravitational Continuous Wave
  (CW) signals produced by pulsars. Optimal strategies are derived for the case
where the signal phase is not known and the product of the signal frequency and
the observation time is non-integral.


  We compute spin-flip cross section for graviton photoproduction on a spin-1/2
target of finite mass. Using this tree-level result, we find one-loop graviton
correction to the spin-flip low-energy forward Compton scattering amplitude by
using Gerasimov-Drell-Hearn sum rule. We show that this result agrees with the
corresponding perturbative computations, implying the validity of the sum rule
at one-loop level, contrary to the previous claims. We discuss possible effects
from the black hole production and string Regge trajectory exchange at very
high energies. These effects seem to soften the UV divergence present at
one-loop graviton level. Finally, we discuss the relation of these observations
with the models that involve extra dimensions.