  Let $t$ be a positive integer. Following work of D. M. Davis, we study the
topology of complex-projective product spaces, i.e. quotients of cartesian
products of odd dimensional spheres by the diagonal $S^1$-action, and of the
$t$-torsion lens product spaces, i.e. the corresponding quotients when the
action is restricted to the $t^{\mathrm{th}}$ roots of unity. For a commutative
complex-oriented cohomology theory $h^*$, we determine the $h^*$-cohomology
ring of these spaces (in terms of the $t$-series for $h^*$, in the case of
$t$-torsion lens product spaces). When $h^*$ is singular cohomology with mod 2
coefficients, we also determine the action of the Steenrod algebra. We show
that these spaces break apart after a suspension as a wedge of desuspensions of
usual stunted complex projective ($t$-torsion lens) spaces. We estimate the
category and topological complexity of complex-projective and lens product
spaces, showing in particular that these invariants are usually much lower than
predicted by the usual dimensional bounds. We extend Davis' analysis of
manifold properties such as immersion dimension, (stable-)span, and
(stable-)parallelizability of real projective product spaces to the
complex-projective and lens product cases.


  Bagging has achieved great success in the field of machine learning by
integrating multiple base classifiers to build a single strong classifier to
reduce model variance. The performance improvement of bagging mainly relies on
the number and diversity of base classifiers. However, traditional deep
learning model training methods are expensive to train individually and
difficult to train multiple models with low similarity in a restricted dataset.
Recently, diffusion models, which have been tremendously successful in the
fields of imaging and vision, have been found to be effective in generating
neural network model weights and biases with diversity. We creatively propose a
Bagging deep learning training algorithm based on Efficient Neural network
Diffusion (BEND). The originality of BEND comes from the first use of a neural
network diffusion model to efficiently build base classifiers for bagging. Our
approach is simple but effective, first using multiple trained model weights
and biases as inputs to train autoencoder and latent diffusion model to realize
a diffusion model from noise to valid neural network parameters. Subsequently,
we generate several base classifiers using the trained diffusion model.
Finally, we integrate these ba se classifiers for various inference tasks using
the Bagging method. Resulting experiments on multiple models and datasets show
that our proposed BEND algorithm can consistently outperform the mean and
median accuracies of both the original trained model and the diffused model. At
the same time, new models diffused using the diffusion model have higher
diversity and lower cost than multiple models trained using traditional
methods. The BEND approach successfully introduces diffusion models into the
new deep learning training domain and provides a new paradigm for future deep
learning training and inference.


  Active depth sensors like structured light, lidar, and time-of-flight systems
sample the depth of the entire scene uniformly at a fixed scan rate. This leads
to limited spatio-temporal resolution where redundant static information is
over-sampled and precious motion information might be under-sampled. In this
paper, we present an efficient bio-inspired event-camera-driven depth
estimation algorithm. In our approach, we dynamically illuminate areas of
interest densely, depending on the scene activity detected by the event camera,
and sparsely illuminate areas in the field of view with no motion. The depth
estimation is achieved by an event-based structured light system consisting of
a laser point projector coupled with a second event-based sensor tuned to
detect the reflection of the laser from the scene. We show the feasibility of
our approach in a simulated autonomous driving scenario and real indoor
sequences using our prototype. We show that, in natural scenes like autonomous
driving and indoor environments, moving edges correspond to less than 10% of
the scene on average. Thus our setup requires the sensor to scan only 10% of
the scene, which could lead to almost 90% less power consumption by the
illumination source. While we present the evaluation and proof-of-concept for
an event-based structured-light system, the ideas presented here are applicable
for a wide range of depth-sensing modalities like LIDAR, time-of-flight, and
standard stereo. Video is available at \url{https://youtu.be/Rvv9IQLYjCQ}.


  We study the complete graph equipped with a topology induced by independent
and identically distributed edge weights. The focus of our analysis is on the
weight W_n and the number of edges H_n of the minimal weight path between two
distinct vertices in the weak disorder regime. We establish novel and simple
first and second moment methods using path counting to derive first order
asymptotics for the considered quantities. Our results are stated in terms of a
sequence of parameters (s_n) that quantifies the extreme-value behaviour of the
edge weights, and that describes different universality classes for first
passage percolation on the complete graph. These classes contain both
n-independent and n-dependent edge weight distributions. The method is most
effective for the universality class containing the edge weights E^{s_n}, where
E is an exponential(1) random variable and s_n log n -> infty, s_n^2 log n ->
0. We discuss two types of examples from this class in detail. In addition, the
class where s_n log n stays finite is studied. This article is a contribution
to the program initiated in \cite{BhaHof12}.


  Answering questions using pre-trained language models (LMs) and knowledge
graphs (KGs) presents challenges in identifying relevant knowledge and
performing joint reasoning.We compared LMs (fine-tuned for the task) with the
previously published QAGNN method for the Question-answering (QA) objective and
further measured the impact of additional factual context on the QAGNN
performance. The QAGNN method employs LMs to encode QA context and estimate KG
node importance, and effectively update the question choice entity
representations using Graph Neural Networks (GNNs). We further experimented
with enhancing the QA context encoding by incorporating relevant knowledge
facts for the question stem. The models are trained on the OpenbookQA dataset,
which contains ~6000 4-way multiple choice questions and is widely used as a
benchmark for QA tasks. Through our experimentation, we found that
incorporating knowledge facts context led to a significant improvement in
performance. In contrast, the addition of knowledge graphs to language models
resulted in only a modest increase. This suggests that the integration of
contextual knowledge facts may be more impactful for enhancing question
answering performance compared to solely adding knowledge graphs.


  Given an integer weighted bipartite graph $\{G=(U\sqcup V, E), w:E\rightarrow
\mathbb{Z}\}$ we consider the problems of finding all the edges that occur in
some minimum weight matching of maximum cardinality and enumerating all the
minimum weight perfect matchings. Moreover, we construct a subgraph $G_{cs}$ of
$G$ which depends on an $\epsilon$-optimal solution of the dual linear program
associated to the assignment problem on $\{G,w\}$ that allows us to reduced
this problems to their unweighed variants on $G_{cs}$. For instance, when $G$
has a perfect matching and we have an $\epsilon$-optimal solution of the dual
linear program associated to the assignment problem on $\{G,w\}$, we solve the
problem of finding all the edges that occur in some minimum weight perfect
matching in linear time on the number of edges. Therefore, starting from
scratch we get an algorithm that solves this problem in time
$O(\sqrt{n}m\log(nW))$, where $n=|U|\geq |V|$, $m=|E|$, and $W={\rm
max}\{|w(e)|\, :\, e\in E\}$.


  Motivated by a wonderful paper by Ngoc Ai Van Nguyen, Anthony Po\"els and
Damien Roy, where a powerful method was introduced, we prove a criterion for a
vector $\pmb{\alpha}\in \mathbb{R}^d$ to be a badly approximable vector.
Moreover we construct certain examples which show that a more general version
of our criterion is not valid.


  Experimental realization of a universal set of quantum logic gates is the
central requirement for implementation of a quantum computer. An all-geometric
approach to quantum computation offered a paradigm for implementation where all
the quantum gates are achieved based on the Berry phases and their non-abelian
extensions, the holonomies, from geometric transformation of quantum states in
the Hilbert space. Apart from its fundamental interest and rich mathematical
structure, the geometric approach has some built-in noise-resilient features.
On the experimental side, geometric phases and holonomies have been observed
using nuclear magnetic resonance with thermal ensembles of liquid molecules,
however, such systems are known to be non-scalable for quantum computing. There
are proposals to implement geometric quantum computation in scalable
experimental platforms such as trapped ions, superconducting qubits, or quantum
dots, and a recent experiment has realized geometric single-bit gates with the
superconducting system. Here, we report the experimental realization of a
universal set of geometric quantum gates with solid-state spins of the diamond
defects. The diamond defects provide a scalable experimental platform with the
potential for room-temperature quantum computing, which has attracted strong
interest in recent years. Based on advance of coherent control in this system,
our experiment shows that all-geometric and potentially robust quantum
computation can be realized with solid-state spin qubits.


  If $n \equiv 0,1~mod~4$, we prove a sum formula $V_{\theta_{0}}
(a_{0},a_{R}^{n}) = n \cdot V_{\theta_{0}} (a_{0},a_{R})$ for the generalized
Vaserstein symbol whenever $R$ is a smooth affine algebra over a perfect field
$k$ with $char(k) \neq 2$ such that $-1 \in {k^{\times}}^{2}$. This enables us
to generalize a result of Fasel-Rao-Swan on transformations of unimodular rows
via elementary matrices over normal affine algebras of dimension $d \geq 4$
over algebraically closed fields of characteristic $\neq 2$. As a consequence,
we prove that any projective module of rank $3$ with trivial Chern classes over
a smooth affine algebra of dimension $4$ over an algebraically closed field $k$
with $char(k) \neq 2,3$ is cancellative.


  In this report, the impact of the Casimir effect in the near-nuclear
environment on electrons, in particular of the K-shell, is investigated. It has
long been known that the experimentally measured binding energies of the inner
electrons, especially for heavy elements, agree only to a very limited extent
with the theoretical solutions of the Schroedinger and Dirac equations. This
report presents a modification for the potential of electrons near the nucleus
by investigating the impact of the Casimir effect on the innermost electrons.
It can be shown that with this approach the calculated binding energies agree
much better with the literature values from spectroscopy, especially for heavy
elements. In addition, the innermost electrons are apparently influenced much
more strongly than previously assumed by the nature of the nuclear surface, in
particular the deviation from the spherical geometry, e.g. the multipole
moments and not least the range of the strong interaction. This effect is not
to be confused with the quantum-mechanical quadrupole energy (Casimir 1936),
which is determined by spin interactions between nucleus and electron, but the
effect discussed in this report is a distance dominated effect of the
s-electrons to the nucleus. It offers the possibility of investigating the
nucleus structure and isotope effects on the binding energies much more
precisely than expected, e.g. by spectroscopy, and thus gaining new insights
into the respective nuclear structure and geometry. At the same time, this
approach shows that the probability of the electrons staying close to the
nucleus is apparently significantly higher than assumed in previous atomic
models. This may also increase the transition probabilities of electron
capture, and thus the model enables a higher accuracy for the calculation of
the theoretical half lifetimes for electron capture decay.


  We equip the category of vector bundles over the vertices of a locally finite
$\tilde{A}_{n-1}$ building $\Delta$ with the structure of a module category
over a category of type $A_{n}$ webs in positive characteristic. This module
category is a $q$-analogue of the $Rep(SL_{n})$ action on vector bundles over
the $sl_n$ weight lattice. We show our module categories are equivariant with
respect to symmetries of the building, and when a group $G$ acts simply
transitively on the vertices of $\Delta$ this recovers the fiber functors
constructed by Jones.


  Ferroelectric field-effect-transistor (FEFET) has emerged as a scalable
solution for 3D NAND and embedded flash (eFlash), with recent progress in
achieving large memory window (MW) using
metal-insulator-ferroelectric-insulator-semiconductor (MIFIS) gate stacks.
Although the physical origin of the large MW in the MIFIS stack has already
been discussed, its retention characteristics have not been explored yet. Here,
we demonstrate MIFIS FEFET with a maximum MW of 9.7 V, and show that MIFIS
FEFET has unstable retention characteristics, especially after erase. We
discover the origin of the unstable retention characteristics and prove our
hypothesis with absolute polarization measurement and different operation
modes, showing that the unstable retention characteristics is a fundamental
issue. Based on the understanding, we discuss a novel charge compensation model
and promising engineering methodologies to achieve stable retention in MIFIS
FEFET.


  We search for stellar and substellar companions of young nearby stars to
investigate stellar multiplicity and formation of stellar and substellar
companions. We detect common proper-motion companions of stars via multi-epoch
imaging. Their companionship is finally confirmed with photometry and
spectroscopy. Here we report the discovery of a new co-moving (13 sigma)
stellar companion ~17.8 arcsec (350 AU in projected separation) north of the
nearby star HD141272 (21 pc). With EMMI/NTT optical spectroscopy we determined
the spectral type of the companion to be M3+-0.5V. The derived spectral type as
well as the near infrared photometry of the companion are both fully consistent
with a 0.26+-0.07 Msol dwarf located at the distance of HD141272 (21 pc).
Furthermore the photometry data rules out the pre-main sequence status, since
the system is consistent with the ZAMS of the Pleiades.


  One of the most common problems of weakly supervised object localization is
that of inaccurate object coverage. In the context of state-of-the-art methods
based on Class Activation Mapping, this is caused either by localization maps
which focus, exclusively, on the most discriminative region of the objects of
interest or by activations occurring in background regions. To address these
two problems, we propose two representation regularization mechanisms: Full
Region Regularizationwhich tries to maximize the coverage of the localization
map inside the object region, and Common Region Regularization which minimizes
the activations occurring in background regions. We evaluate the two
regularizations on the ImageNet, CUB-200-2011 and OpenImages-segmentation
datasets, and show that the proposed regularizations tackle both problems,
outperforming the state-of-the-art by a significant margin.


  It is now well known that a primordial chemistry, involving light elements
produced during the nucleosynthesis period, might develop during the hydrogen
post-recombination era. In particular, molecular ions and primordial molecules
such as H2, HD and LiH will be produced. We summarize this primordial chemistry
after the recombination epoch, and then present a simple gravitational collapse
model of a cloud. The potentiality of fragmentation of this collapsing
protoclouds through the thermal instability is also discussed. We suggest that
this study could also be extended to the CO molecule, because the carbon
reservoir molecule CO has already been observed in high redshifts objects.


  Raster well-log images are digital representations of well-logs data
generated over the years. Raster digital well logs represent bitmaps of the log
image in a rectangular array of black (zeros) and white dots (ones) called
pixels. Experts study the raster logs manually or with software applications
that still require a tremendous amount of manual input. Besides the loss of
thousands of person-hours, this process is erroneous and tedious. To digitize
these raster logs, one must buy a costly digitizer that is not only manual and
time-consuming but also a hidden technical debt since enterprises stand to lose
more money in additional servicing and consulting charges. We propose a deep
neural network architecture called VeerNet to semantically segment the raster
images from the background grid and classify and digitize the well-log curves.
Raster logs have a substantially greater resolution than images traditionally
consumed by image segmentation pipelines. Since the input has a low
signal-to-resolution ratio, we require rapid downsampling to alleviate
unnecessary computation. We thus employ a modified UNet-inspired architecture
that balances retaining key signals and reducing result dimensionality. We use
attention augmented read-process-write architecture. This architecture
efficiently classifies and digitizes the curves with an overall F1 score of 35%
and IoU of 30%. When compared to the actual las values for Gamma-ray and
derived value of Gamma-ray from VeerNet, a high Pearson coefficient score of
0.62 was achieved.


  We developed a website for kids where they can share new as well as remixed
animations and games, e.g., interactive music videos, which they created on
their smartphones or tablets using a visual "LEGO-style" programming
environment called Catroid. Online communities for children like our website
have unique requirements, and keeping the commitment of kids on a high level is
a continuous challenge. For instance, one key motivator for kids is the ability
to entertain their friends. Another success factor is the ability to learn from
and cooperate with other children. In this short position paper we attempt at
identifying the requirements for the success of such an online community, both
from the point of view of the kids as well as of their parents, and at finding
ways to make it attractive for both.


  In this paper, we lay the groundwork on the comparison of phylogenetic
networks based on edge contractions and expansions as edit operations, as
originally proposed by Robinson and Foulds to compare trees. We prove that
these operations connect the space of all phylogenetic networks on the same set
of leaves, even if we forbid contractions that create cycles. This allows to
define an operational distance on this space, as the minimum number of
contractions and expansions required to transform one network into another. We
highlight the difference between this distance and the computation of the
maximum common contraction between two networks. Given its ability to outline a
common structure between them, which can provide valuable biological insights,
we study the algorithmic aspects of the latter. We first prove that computing a
maximum common contraction between two networks is NP-hard, even when the
maximum degree, the size of the common contraction, or the number of leaves is
bounded. We also provide lower bounds to the problem based on the
Exponential-Time Hypothesis. Nonetheless, we do provide a polynomial-time
algorithm for weakly-galled networks, a generalization of galled trees.


  Random tessellations of the space represent a class of prototype models of
heterogeneous media, which are central in several applications in physics,
engineering and life sciences. In this work, we investigate the statistical
properties of $d$-dimensional isotropic Poisson geometries by resorting to
Monte Carlo simulation, with special emphasis on the case $d=3$. We first
analyse the behaviour of the key features of these stochastic geometries as a
function of the dimension $d$ and the linear size $L$ of the domain. Then, we
consider the case of Poisson binary mixtures, where the polyhedra are assigned
two `labels' with complementary probabilities. For this latter class of random
geometries, we numerically characterize the percolation threshold, the strength
of the percolating cluster and the average cluster size.


  This paper presents the development process of a Vietnamese spoken language
corpus for machine reading comprehension (MRC) tasks and provides insights into
the challenges and opportunities associated with using real-world data for
machine reading comprehension tasks. The existing MRC corpora in Vietnamese
mainly focus on formal written documents such as Wikipedia articles, online
newspapers, or textbooks. In contrast, the VlogQA consists of 10,076
question-answer pairs based on 1,230 transcript documents sourced from YouTube
-- an extensive source of user-uploaded content, covering the topics of food
and travel. By capturing the spoken language of native Vietnamese speakers in
natural settings, an obscure corner overlooked in Vietnamese research, the
corpus provides a valuable resource for future research in reading
comprehension tasks for the Vietnamese language. Regarding performance
evaluation, our deep-learning models achieved the highest F1 score of 75.34% on
the test set, indicating significant progress in machine reading comprehension
for Vietnamese spoken language data. In terms of EM, the highest score we
accomplished is 53.97%, which reflects the challenge in processing spoken-based
content and highlights the need for further improvement.


  The recently proposed Detection Transformer (DETR) model successfully applies
Transformer to objects detection and achieves comparable performance with
two-stage object detection frameworks, such as Faster-RCNN. However, DETR
suffers from its slow convergence. Training DETR from scratch needs 500 epochs
to achieve a high accuracy. To accelerate its convergence, we propose a simple
yet effective scheme for improving the DETR framework, namely Spatially
Modulated Co-Attention (SMCA) mechanism. The core idea of SMCA is to conduct
location-aware co-attention in DETR by constraining co-attention responses to
be high near initially estimated bounding box locations. Our proposed SMCA
increases DETR's convergence speed by replacing the original co-attention
mechanism in the decoder while keeping other operations in DETR unchanged.
Furthermore, by integrating multi-head and scale-selection attention designs
into SMCA, our fully-fledged SMCA can achieve better performance compared to
DETR with a dilated convolution-based backbone (45.6 mAP at 108 epochs vs. 43.3
mAP at 500 epochs). We perform extensive ablation studies on COCO dataset to
validate SMCA. Code is released at https://github.com/gaopengcuhk/SMCA-DETR .


  We investigate the feasibility of measuring weak gravitational lensing using
21cm intensity mapping with special emphasis on the performance of the planned
Square Kilometre Array (SKA). We find that the current design for SKA-Mid
should be able to measure the evolution of the lensing power spectrum at z~2-3
using this technique. This will be a probe of the expansion history of the
universe and gravity at a unique range in redshift. The signal-to-noise is
found to be highly dependent on evolution of the neutral hydrogen fraction in
the universe with a higher HI density resulting in stronger signal. With
realistic models for this, SKA Phase 1 should be capable of measuring the
lensing power spectrum and its evolution. The signal-to-noise's dependence on
the area and diameter of the telescope array is quantified. We further
demonstrate the applications of this technique by applying it to two specific
coupled dark energy models that would be difficult to observationally
distinguish without information from this range of redshift. We also
investigate measuring the lensing signal with 21cm emission from the Epoch of
Reionization (EoR) using SKA-Low and find that it is unlikely to constrain
cosmological parameters because of the small survey size, but could provide a
map of the dark matter within a small region of the sky.


  Following the work of Hildebrand we improve the Po'lya- Vinogradov inequality
in a specific range, we also give a general result that shows its dependency on
Burgess bound and at last we improve the range of validity for a special case
of Burgess' character sum estimate.


  Time dependent Schr\"odinger equations with conservative force field U
commonly constitute a major challenge in the numerical approximation,
especially when they are analysed in the semiclassical regime. Extremely high
oscillations originate from the semiclassical parameter, and call for
appropriate methods. We propose to employ a combination of asymptotic
Zassenhaus splitting with time adaptivity. While the former turns the
disadvantage of the semiclassical parameter into an advantage, leading to
highly efficient methods with low error constants, the latter enables to choose
an optimal time step and to speed up the calculations when the oscillations
subside. We support the results with numerical examples.


  This paper addresses the problem of joint channel estimation and channel
decoding in physical-layer network coding (PNC) systems. In PNC, multiple users
transmit to a relay simultaneously. PNC channel decoding is different from
conventional multi-user channel decoding: specifically, the PNC relay aims to
decode a network-coded message rather than the individual messages of the
users. Although prior work has shown that PNC can significantly improve the
throughput of a relay network, the improvement is predicated on the
availability of accurate channel estimates. Channel estimation in PNC, however,
can be particularly challenging because of 1) the overlapped signals of
multiple users; 2) the correlations among data symbols induced by channel
coding; and 3) time-varying channels. We combine the expectation-maximization
(EM) algorithm and belief propagation (BP) algorithm on a unified factor-graph
framework to tackle these challenges. In this framework, channel estimation is
performed by an EM subgraph, and channel decoding is performed by a BP subgraph
that models a virtual encoder matched to the target of PNC channel decoding.
Iterative message passing between these two subgraphs allow the optimal
solutions for both to be approached progressively. We present extensive
simulation results demonstrating the superiority of our PNC receivers over
other PNC receivers.


  We introduce the notion of a ``non-commutative crepant'' resolution of a
singularity and show that it exists in certain cases. We also give some
evidence for an extension of a conjecture by Bondal and Orlov, stating that
different crepant resolutions of a Gorenstein singularity have the same derived
category.


  Although current face alignment algorithms have obtained pretty good
performances at predicting the location of facial landmarks, huge challenges
remain for faces with severe occlusion and large pose variations, etc. On the
contrary, semantic location of facial boundary is more likely to be reserved
and estimated on these scenes. Therefore, we study a two-stage but end-to-end
approach for exploring the relationship between the facial boundary and
landmarks to get boundary-aware landmark predictions, which consists of two
modules: the self-calibrated boundary estimation (SCBE) module and the
boundary-aware landmark transform (BALT) module. In the SCBE module, we modify
the stem layers and employ intermediate supervision to help generate
high-quality facial boundary heatmaps. Boundary-aware features inherited from
the SCBE module are integrated into the BALT module in a multi-scale fusion
framework to better model the transformation from boundary to landmark heatmap.
Experimental results conducted on the challenging benchmark datasets
demonstrate that our approach outperforms state-of-the-art methods in the
literature.


  We establish the moderate deviation principle for the solutions of a class of
stochastic partial differential equations with non-Lipschitz continuous
coefficients. As an application, we derive the moderate deviation principle for
two important population models: super-Brownian motion and Fleming-Viot
process.


  The Event Causality Identification Shared Task of CASE 2022 involved two
subtasks working on the Causal News Corpus. Subtask 1 required participants to
predict if a sentence contains a causal relation or not. This is a supervised
binary classification task. Subtask 2 required participants to identify the
Cause, Effect and Signal spans per causal sentence. This could be seen as a
supervised sequence labeling task. For both subtasks, participants uploaded
their predictions for a held-out test set, and ranking was done based on binary
F1 and macro F1 scores for Subtask 1 and 2, respectively. This paper summarizes
the work of the 17 teams that submitted their results to our competition and 12
system description papers that were received. The best F1 scores achieved for
Subtask 1 and 2 were 86.19% and 54.15%, respectively. All the top-performing
approaches involved pre-trained language models fine-tuned to the targeted
task. We further discuss these approaches and analyze errors across
participants' systems in this paper.


  In this paper we present a new approach to proving some exponential
inequalities involving the sinc function. Power series expansions are used to
generate new polynomial inequalities that are sufficient to prove the given
exponential inequalities.


  The groundstate configuration and the eigenmodes of two parallel
two-dimensional classical atoms are obtained as function of the inter-atomic
distance (d). The classical particles are confined by identical harmonic wells
and repel each other through a Coulomb potential. As function of d we find
several structural transitions which are of first or second order. For first
(second) order transitions the first (second) derivative of the energy with
respect to d is discontinuous, the radial position of the particles changes
discontinuously (continuously) and the frequency of the eigenmodes exhibit a
jump (one mode becomes soft, i.e. its frequency becomes zero).


  Since the strong interlayer interaction of AB-stacked graphene in bulk form
degrades the superior property of single-layer graphene, formation of randomly
stacked graphene is required to apply the high performances of graphene to
macroscopic devices. However, conventional methods to obtain bulk-scale
graphene suffer from a low crystallinity and/or the formation of a
thermodynamically stable AB-stacked structure. This study develops a novel
approach to produce bulk-scale graphene with a high crystallinity and high
fractions of random stacking by utilizing the porous morphology of a graphene
oxide sponge and an ultrahigh temperature treatment of 1500-1800 {\deg}C with
ethanol vapor. Raman spectroscopy indicates that the obtained bulk-scale
graphene sponge possesses a high crystallinity and a high fraction of random
stacking of 80%. The large difference in the random-stacking ratio between the
sponge and the aggregate samples confirms the importance of accessibility of
ethanol-derived species into the internal area. By investigating the effect of
treatment temperature, a higher random-stacking ratio is obtained at 1500
{\deg}C. Moreover, the AB-stacking fraction was reduced to less than 10% by
introducing cellulose nanofiber as a spacer to prevent direct stacking of
graphene. The proposed method is effective for large-scale production of
high-performance bulk-scale graphene.


  We analyze the anomalous magnetic moment of the muon (a_{\mu}) in
supersymmetric scenarios. First we concentrate on scenarios with universal soft
terms. We find that a moderate increase of a_{\mu} can be obtained by lowering
the unification scale M_{GUT} to intermediate values 10^{10-12} GeV. However,
large values of \tan \beta are still favored. Then we study the case of
non-universal soft terms. For the usual value M_{GUT}~10^{16} GeV, we obtain
a_{\mu} in the favored experimental range even for moderate \tan \beta regions
\tan\beta ~ 5$. Finally, we give an explicit example of these scenarios. In
particular, we show that in a D-brane model, where the string scale is
naturally of order 10^{10-12} GeV and the soft terms are non universal, a_{\mu}
is enhanced with low \tan\beta.


  This literature focuses on doing a comparative analysis between Modular Audio
Recognition Framework (MARF) and the General Intentional Programming System
(GIPSY) with the help of different software metrics. At first, we understand
the general principles, architecture and working of MARF and GIPSY by looking
at their frameworks and running them in the Eclipse environment. Then, we study
some of the important metrics including a few state of the art metrics and rank
them in terms of their usefulness and their influence on the different quality
attributes of a software. The quality attributes are viewed and computed with
the help of the Logiscope and McCabe IQ tools. These tools perform a
comprehensive analysis on the case studies and generate a quality report at the
factor level, criteria level and metrics level. In next step, we identify the
worst code at each of these levels, extract the worst code and provide
recommendations to improve the quality. We implement and test some of the
metrics which are ranked as the most useful metrics with a set of test cases in
JDeodorant. Finally, we perform an analysis on both MARF and GIPSY by doing a
fuzzy code scan using MARFCAT to find the list of weak and vulnerable classes.


  The T=0 dynamical properties of the one-dimensional (1D) $s=1/2$
  Heisenberg antiferromagnet in a uniform magnetic field are studied via Bethe
ansatz for cyclic chains of $N$ sites. The ground state at magnetization
$0<M_z<N/2$, which can be interpreted as a state with $2M_z$ spinons or as a
state of $M_z$ magnons, is reconfigured here as the vacuum for a different
species of quasiparticles, the {\em psinons} and {\em antipsinons}. We
investigate three kinds of quantum fluctuations, namely the spin fluctuations
parallel and perpendicular to the direction of the applied magnetic field and
the dimer fluctuations. The dynamically dominant excitation spectra are found
to be sets of collective excitations composed of two quasiparticles excited
from the psinon vacuum in different configurations. The Bethe ansatz provides a
framework for (i) the characterization of the new quasiparticles in relation to
the more familiar spinons and magnons, (ii) the calculation of spectral
boundaries and densities of states for each continuum, (iii) the calculation of
transition rates between the ground state and the dynamically dominant
collective excitations, (iv) the prediction of lineshapes for dynamic structure
factors relevant for experiments performed on a variety of quasi-1D
antiferromagnetic compounds, including KCuF$_3$,
  Cu(C$_4$H$_4$N$_2)(NO_3)_2$, and CuGeO$_3$.


  We consider the problem of sparse signal reconstruction from noisy one-bit
compressed measurements when the receiver has access to side-information (SI).
We assume that compressed measurements are corrupted by additive white Gaussian
noise before quantization and sign-flip error after quantization. A generalized
approximate message passing-based method for signal reconstruction from noisy
one-bit compressed measurements is proposed, which is then extended for the
case where the receiver has access to a signal that aids signal reconstruction,
i.e., side-information. Two different scenarios of side-information are
considered-a) side-information consisting of support information only, and b)
side information consisting of support and amplitude information. SI is either
a noisy version of the signal or a noisy estimate of the support of the signal.
We develop reconstruction algorithms from one-bit measurements using noisy SI
available at the receiver. Laplacian distribution and Bernoulli distribution
are used to model the two types of noises which, when applied to the signal and
the support, yields the SI for the above two cases, respectively. The
Expectation-Maximization algorithm is used to estimate the noise parameters
using noisy one-bit compressed measurements and the SI. We show that one-bit
compressed measurement-based signal reconstruction is quite sensitive to noise,
and the reconstruction performance can be significantly improved by exploiting
available side-information at the receiver.


  In a measurement-induced continuous-time quantum walk, we address the problem
of detecting a particle in a subspace, instead of a fixed position. In this
configuration, we develop an approach of bright and dark states based on the
unit and vanishing detection probability respectively for a particle-detection
in the subspace. Specifically, by employing the rank-nullity theorem, we
determine several properties of dark and bright states in terms of energy
spectrum of the Hamiltonian used for a quantum walk and the projectors applied
to detect the subspace. We provide certain conditions on the position and the
rank of the subspace to be detected, resulting in the unit total detection
probability, which has broad implications for quantum computing. Further, we
illustrate the forms of dark as well as bright states and the dependence of
detection probability on the number of dark states by considering a cyclic
graph with nearest-neighbor and next nearest-neighbor hopping. Moreover, we
observe that the divergence in the average number of measurements for detecting
a particle successfully in a subspace can be reduced by performing high rank
projectors.


  Quantum computers can outperform classical computers in certain tasks.
However, there are still many challenges to the current quantum computers such
as decoherence and fault tolerance, and other drawbacks such as portability and
accessibility. In this study, we circumvent these issues by realizing an analog
quantum emulation device (AQED) where each qubit state is represented by a
unique analog signal. It is possible to do this because previously it was shown
that Hermitian operations on a Hilbert space are not unique to quantum systems
and can also be applied to a basis of complex signals that form a Hilbert
space. Orthogonality of the complex signal basis can be maintained by
separating the signals into the frequency domain or the spatial domain. We
study both these approaches and present a comparison. We finally realize the
entire device on a UMC 180nm processing node and demonstrate the computational
advantage of an AQED by emulating Grover's search algorithm (GSA) and Quantum
Fourier Transform (QFT). We also present the equivalent quantum volume achieved
by this device.


  We consider systems of weakly coupled Schr\"odinger equations with
nonconstant potentials and we investigate the existence of nontrivial
nonnegative solutions which concentrate around local minima of the potentials.
We obtain sufficient and necessary conditions for a sequence of least energy
solutions to concentrate.


  We consider a network of banks that optimally choose a strategy of asset
liquidations and borrowing in order to cover short term obligations. The
borrowing is done in the form of collateralized repurchase agreements, the
haircut level of which depends on the total liquidations of all the banks.
Similarly the fire-sale price of the asset obtained by each of the banks
depends on the amount of assets liquidated by the bank itself and by other
banks. By nature of this setup, banks' behavior is considered as a Nash
equilibrium. This paper provides two forms for market clearing to occur:
through a common closing price and through an application of the limit order
book. The main results of this work are providing the existence of maximal and
minimal clearing solutions (i.e., liquidations, borrowing, fire sale prices,
and haircut levels) as well as sufficient conditions for uniqueness of the
clearing solutions.


  The pro-$p$-Iwahori Hecke algebra has an involution $\iota$ defined in terms
of Iwahori-Matsumoto basis. Then for a module $\pi$ of pro-$p$-Iwahori Hecke,
$\pi^\iota = \pi\circ \iota$ is also a module. We calculate $\pi^\iota$ for
simple modules $\pi$. We also calculate the dual of $\pi$.


  Recent rsearches have shown that the lengths of the chemical bonds, whether
completely or partially covalent or ionic, are sums of the radii of the
adjacent atoms and/or ions. On investigating the bond length data for the
molecular components of nucleic acids, all were found (for the first time) to
be effectively the sums of the covalent radii of the adjacent atoms. This work
shows that the bond lengths in caffeine and related molecules are likewise sums
of the covalent radii of C, N, O and H. This has enabled arriving at the atomic
structures of these molecules, also for the first time.


  In this paper we show that using frame-like gauge invariant formulation for
the massive bosonic and fermionic fields in three dimensions the free
Lagrangians for these fields can be rewritten in the explicitly gauge invariant
form in terms of the appropriately chosen set of gauge invariant objects. This
in turn opens the possibility to apply the Fradkin-Vasiliev formalism to the
investigation of possible interactions of such fields.


  In this paper, we exploit a Fully Convolutional Network (FCN) to analyze the
audio data of spontaneous speech for dementia detection. A fully convolutional
network accommodates speech samples with varying lengths, thus enabling us to
analyze the speech sample without manual segmentation. Specifically, we first
obtain the Mel Frequency Cepstral Coefficient (MFCC) feature map from each
participant's audio data and convert the speech classification task on audio
data to an image classification task on MFCC feature maps. Then, to solve the
data insufficiency problem, we apply transfer learning by adopting a
pre-trained backbone Convolutional Neural Network (CNN) model from the
MobileNet architecture and the ImageNet dataset. We further build a
convolutional layer to produce a heatmap using Otsu's method for visualization,
enabling us to understand the impact of the time-series audio segments on the
classification results. We demonstrate that our classification model achieves
66.7% over the testing dataset, 62.5% of the baseline model provided in the
ADReSS challenge. Through the visualization technique, we can evaluate the
impact of audio segments, such as filled pauses from the participants and
repeated questions from the investigator, on the classification results.


  We identify two unreasonable, though standard, assumptions made by database
query optimizers that can adversely affect the quality of the chosen evaluation
plans. One assumption is that it is enough to optimize for the expected
case---that is, the case where various parameters (like available memory) take
on their expected value. The other assumption is that the parameters are
constant throughout the execution of the query. We present an algorithm based
on the ``System R''-style query optimization algorithm that does not rely on
these assumptions. The algorithm we present chooses the plan of the least
expected cost instead of the plan of least cost given some fixed value of the
parameters. In execution environments that exhibit a high degree of
variability, our techniques should result in better performance.


  Many-electron dynamics induced by a symmetric monocycle electric-field pulse
of large amplitude is theoretically investigated in one- and two-dimensional
half-filled extended Hubbard models on regular lattices (i.e., without
dimerization) using the exact diagonalization method for small systems and the
Hartree-Fock approximation for large systems. The formation of a
negative-temperature state and the change from repulsive interactions to
effective attractive interactions are shown to be realized for a wide region of
the field amplitude and the excitation energy. For a nonnegligible intersite
repulsive interaction, the numerical results are consistent with the fact that
the phase separation between charge-rich and charge-poor regions is caused by
the corresponding effective attraction.


  Despite its success in the weak gravity regime, General Relativity (GR) has
yet to be verified in the regime of strong gravity. In this paper, we present
the results of detailed ray tracing simulations aiming at clarifying if the
combined information from X-ray spectroscopy, timing, and polarization
observations of stellar mass and supermassive black holes can be used to test
GR's no-hair theorem. The latter states that stationary astrophysical black
holes are described by the Kerr-family of metrics with the black hole mass and
spin being the only free parameters. We use four "non-Kerr metrics", some
phenomenological in nature and others motivated by alternative theories of
gravity, and study the observational signatures of deviations from the Kerr
metric. Particular attention is given to the case when all the metrics are set
to give the same Innermost Stable Circular Orbit (ISCO) in quasi-Boyer
Lindquist coordinates. We give a detailed discussion of similarities and
differences of the observational signatures predicted for BHs in the Kerr
metric and the non-Kerr metrics. We emphasize that even though some regions of
the parameter space are nearly degenerate even when combining the information
from all observational channels, X-ray observations of very rapidly spinning
black holes can be used to exclude large regions of the parameter space of the
alternative metrics. Although it proves difficult to distinguish between the
Kerr and non-Kerr metrics for some portions of the parameter space, the
observations of very rapidly spinning black holes like Cyg X-1 can be used to
rule out large regions for several black hole metrics.


  We study the effect of Majorana transition magnetic moments on the flavor
evolution of neutrinos and antineutrinos inside the core of Type-II supernova
explosions. We find non-trivial collective oscillation effects relating
neutrinos and antineutrinos of different flavors, even if one restricts the
discussion to Majorana transition electromagnetic moment values that are not
much larger than those expected from standard model interactions and nonzero
neutrino Majorana masses. This appears to be, to the best of our knowledge, the
only potentially observable phenomenon sensitive to such small values of
Majorana transition magnetic moments. We briefly comment on the effect of Dirac
transition magnetic moments and on the consequences of our results for future
observations of the flux of neutrinos of different flavors from a nearby
supernova explosion.


  In mixture model-based clustering applications, it is common to fit several
models from a family and report clustering results from only the `best' one. In
such circumstances, selection of this best model is achieved using a model
selection criterion, most often the Bayesian information criterion. Rather than
throw away all but the best model, we average multiple models that are in some
sense close to the best one, thereby producing a weighted average of clustering
results. Two (weighted) averaging approaches are considered: averaging the
component membership probabilities and averaging models. In both cases, Occam's
window is used to determine closeness to the best model and weights are
computed within a Bayesian model averaging paradigm. In some cases, we need to
merge components before averaging; we introduce a method for merging mixture
components based on the adjusted Rand index. The effectiveness of our
model-based clustering averaging approaches is illustrated using a family of
Gaussian mixture models on real and simulated data.


  Flow and bridge matching are a novel class of processes which encompass
diffusion models. One of the main aspect of their increased flexibility is that
these models can interpolate between arbitrary data distributions i.e. they
generalize beyond generative modeling and can be applied to learning stochastic
(and deterministic) processes of arbitrary transfer tasks between two given
distributions. In this paper, we highlight that while flow and bridge matching
processes preserve the information of the marginal distributions, they do
\emph{not} necessarily preserve the coupling information unless additional,
stronger optimality conditions are met. This can be problematic if one aims at
preserving the original empirical pairing. We show that a simple modification
of the matching process recovers this coupling by augmenting the velocity field
(or drift) with the information of the initial sample point. Doing so, we lose
the Markovian property of the process but preserve the coupling information
between distributions. We illustrate the efficiency of our augmentation in
learning mixture of image translation tasks.


  A wide range of models have been proposed for Graph Generative Models,
necessitating effective methods to evaluate their quality. So far, most
techniques use either traditional metrics based on subgraph counting, or the
representations of randomly initialized Graph Neural Networks (GNNs). We
propose using representations from contrastively trained GNNs, rather than
random GNNs, and show this gives more reliable evaluation metrics. Neither
traditional approaches nor GNN-based approaches dominate the other, however: we
give examples of graphs that each approach is unable to distinguish. We
demonstrate that Graph Substructure Networks (GSNs), which in a way combine
both approaches, are better at distinguishing the distances between graph
datasets.


  This paper reviews and advocates against the use of permute-and-predict (PaP)
methods for interpreting black box functions. Methods such as the variable
importance measures proposed for random forests, partial dependence plots, and
individual conditional expectation plots remain popular because they are both
model-agnostic and depend only on the pre-trained model output, making them
computationally efficient and widely available in software. However, numerous
studies have found that these tools can produce diagnostics that are highly
misleading, particularly when there is strong dependence among features. The
purpose of our work here is to (i) review this growing body of literature, (ii)
provide further demonstrations of these drawbacks along with a detailed
explanation as to why they occur, and (iii) advocate for alternative measures
that involve additional modeling. In particular, we describe how breaking
dependencies between features in hold-out data places undue emphasis on sparse
regions of the feature space by forcing the original model to extrapolate to
regions where there is little to no data. We explore these effects across
various model setups and find support for previous claims in the literature
that PaP metrics can vastly over-emphasize correlated features in both variable
importance measures and partial dependence plots. As an alternative, we discuss
and recommend more direct approaches that involve measuring the change in model
performance after muting the effects of the features under investigation.


  We give a unique classical solution to initial value problem for a system of
partial differential equations for the densities of components of one
dimensional incompressible fluid mixture driven by evaporation. Motivated by
the known fact that the solution appears as an infinite particle limit of
stochastic ranking processes, which is a simple stochastic model of time
evolutions of e.g., Amazon Sales Ranks, we collected data from the web and
performed statistical fits to our formula. The results suggest that the fluid
equations and solutions may have an application in the analysis of online
rankings.


  This paper reviews some of the key enabling technologies for advanced and
future laser interferometer gravitational wave detectors, which must combine
test masses with the lowest possible optical and acoustic losses, with high
stability lasers and various techniques for suppressing noise. Sect. 1 of this
paper presents a review of the acoustic properties of test masses. Sect. 2
reviews the technology of the amorphous dielectric coatings which are currently
universally used for the mirrors in advanced laser interferometers, but for
which lower acoustic loss would be very advantageous. In sect. 3 a new
generation of crystalline optical coatings that offer a substantial reduction
in thermal noise is reviewed. The optical properties of test masses are
reviewed in sect. 4, with special focus on the properties of silicon, an
important candidate material for future detectors. Sect. 5 of this paper
presents the very low noise, high stability laser technology that underpins all
advanced and next generation laser interferometers.


  Galaxy cluster surveys offer great promise for measuring cosmological
parameters, but survey analysis methods have not been widely studied. Using
methods developed decades ago for galaxy clustering studies, it is shown that
nearly exact likelihood functions can be written down for galaxy cluster
surveys. The sparse sampling of the density field by galaxy clusters allows
simplifications that are not possible for galaxy surveys. An application to
counts in cells is explicitly tested using cluster catalogs from numerical
simulations and it is found that the calculated probability distributions are
very accurate at masses above several times 10^{14}h^{-1} solar masses at z=0
and lower masses at higher redshift.


  We characterize vesicle adhesion onto homogeneous substrates by means of a
perturbative expansion around the infinite adhesion limit, where curvature
elasticity effects are absent. At first order in curvature elasticity, we
determine analytically various global physical quantities associated with
adhering vesicles: height, adhesion radius, etc. Our results are valid for
adhesion energies above a certain threshold, that we determine numerically. We
discuss the haptotactic force acting on a vesicle in the limit of weak adhesion
gradients. We also propose novel methods for measuring adhesion energies and we
suggest a possible way of determining the size of suboptical vesicles using
controlled adhesion gradients.


  We study the thermoelectric effect between a conducting lead and a Majorana
edge state. In the tunneling limit, we first use the Landuaer-Buttiker
formalism to derive the Mott formula relating the thermopower and the
differential conductance between a conducting lead and a superconductor. When
the tunneling takes place between a conducting lead and a Majorana edge state,
we show that a non-vanishing thermopower can exist. Combining measurements of
the differential conductance and the voltage difference induced by the
temperature difference between the conducting lead and the edge state, the Mott
formula provides a unique way to infer the temperature of the Majorana edge
state.


  In this paper we study the regularity properties of the Gaussian Bessel
potentials and Gaussian Bessel fractional derivatives on variable Gaussian
Besov-Lipschitz spaces $B_{p(\cdot),q(\cdot)}^{\alpha}(\gamma_{d}),$ that were
defined in a previous paper \cite{Pinrodurb}, under certain conditions on
$p(\cdot)$ and $q(\cdot)$.


  In 2005, Nualart and Peccati showed that, surprisingly, the convergence in
distribution of a normalized sequence of multiple Wiener-It\^o integrals
towards a standard Gaussian law is equivalent to convergence of just the fourth
moment to 3. Recently, this result has been extended to a sequence of multiple
Wigner integrals, in the context of free Brownian motion. The goal of the
present paper is to offer an elementary, unifying proof of these two results.
The only advanced, needed tool is the product formula for multiple integrals.
Apart from this formula, the rest of the proof only relies on soft
combinatorial arguments.


  We present new high-statistics results for nucleon form factors at pion
masses of approximately 290, 350, 500, and 600 MeV using a mixed action of
domain wall valence quarks on an improved staggered sea. We perform chiral fits
to both vector and axial form factors and compare our results to experiment.


  We extend the technique of using the Trapezoidal Rule for efficient
evaluation of the Special Functions of Mathematical Physics given by integral
representations. This technique was recently used for Bessel functions, and
here we treat Incomplete Gamma functions and the general Confluent
Hypergeometric Function.


  The impact of leading collective electronic fluctuations on a free energy of
a prototype 1D model for molecular systems is considered within the recently
developed Fluctuating Local Field (FLF) approach. The FLF method is a
non-perturbative extension of a mean-field theory, where a self-consistent
effective constant field is replaced by a fluctuating one. Integrating the
fluctuating field out numerically exactly allows to account for collective
electronic fluctuations mediated by this field without any assumptions on their
magnitude, degree of nonlinearity, etc. Using a half-filled Hubbard ring as a
benchmark system, we find that the FLF method noticeably improves a mean-field
estimation for the free energy, in particular below the mean-field Ne\'el
temperature. We further demonstrate that the mean-field result can be even more
improved introducing a multi-mode FLF scheme that additionally takes into
account sub-leading fluctuations. Possible applications for the thermodynamics
of real molecules are also discussed.


  Let ${\mathfrak H}_A$, ${\mathfrak H}_B$, and ${\mathfrak H}$ be Hilbert
spaces. Let $A$ be a linear relation from ${\mathfrak H}$ to ${\mathfrak H}_A$
and let $B$ be a linear relation from ${\mathfrak H}$ to ${\mathfrak H}_B$. If
there exists an operator $Z \in \mathbf{B}({\mathfrak H}_B,{\mathfrak H}_A)$
such that $ZB \subset A$, then $B$ is said to dominate $A$. This notion plays a
major role in the theory of Lebesgue type decompositions of linear relations
and operators. There is a strong connection to the majorization and
factorization in the well-known lemma of Douglas, when put in the context of
linear relations. In this note some aspects of the lemma of Douglas are
discussed in the context of linear relations and the connections with the
notion of domination will be treated.


  In top quark production, the polarization of top quarks, decided by the
chiral structure of couplings, is likely to be modified in the presence of any
new physics contribution to the production. Hence the same is a good
discriminator for those new physics models wherein the couplings have a chiral
structure different than that in the Standard Model (SM). In this note we
construct probes of the polarization of a top quark decaying hadronically,
using easily accessible kinematic variables such as the energy fraction or
angular correlations of the decay products. Tagging the boosted top quark using
the usual jet sub structure technique we study robustness of these observables
for a benchmark process, $W^{\prime} \to tb$. We demonstrate that the energy
fraction of b-jet in the laboratory frame and a new angular variable,
constructed by us in the top rest frame, are both very powerful tools to
discriminate between the left and right polarized top quarks. Based on the
polarization sensitive angular variables, we construct asymmetries which
reflect the polarization. We study the efficiency of these variables for two
new physics processes where which give rise to boosted top quarks: (i) decay of
the top squark in the context of supersymmetry searches, and (ii) decays of the
Kaluza-Klein(KK) graviton and KK gluon, in Randall Sundrum(RS) model.
Remarkably, it is found that the asymmetry can vary over a wide range about
+20\% to -20\%. The dependence of asymmetry on top quark couplings of the new
particles present in these models beyond the SM (BSM) is also investigated in
detail.


  We present three-dimensional, self-consistent radiative transfer solutions
with a new Monte Carlo radiative equilibrium code. The code, RADISHE
($\bf{RAD}$iative transfer $\bf{I}$n $\bf{S}$moothed particle
$\bf{H}$ydrodynamics and $\bf{E}$ulerian codes), can be applied to calculate
the emergent spectral energy distributions (SEDs) and broadband images from
optical to millimeter wavelengths of arbitrary density geometries with
distributed sources of radiation. One of the primary uses of this code has been
to interface with hydrodynamical codes to calculate emergent SEDs along a
simulation time sequence. The primary methodological focus of this paper is on
the radiative equilibrium temperature calculation. We find that an iterative
calculation of the temperature, which takes as the Monte Carlo estimator for
the mean free intensity the sum of photon flight paths, is significantly faster
than relaxation temperature calculation methods, particularly when large
numbers of grid cells are required, i.e., in modeling three-dimensional
geometries such as the dust envelopes of turbulent massive protostellar cores
or infrared bright galaxies. We present simulated color-color plots for
infrared bright galaxies at a range of redshifts, and unfold these plots as
color vs the fractional AGN luminosity, to demonstrate that $\it{Herschel}$
will be able to effectively discriminate between submillimeter galaxies where
the energy source is dominated by AGN and those where star formation dominates.
[abridged]


  This study presents Fermi-arcs mediated transport in a Weyl semimetal thin
slab, interfacing two $s$-wave superconductors. We present detailed study with
both time-reversal and inversion symmetry broken Weyl semimetals under
grounding, orbital magnetic fields, and Zeeman fields. An orbital magnetic
field induces energy level oscillations, while a Zeeman field give rise to the
periodic anomalous oscillations in the Josephson current. These anomalous
oscillations correlate with the separation of Weyl nodes in momentum space,
junction length, and system symmetries. Additionally, we present an explanation
by scattering theory modeling the Fermi-arcs as a network model.


  In this paper, we construct an infinite family of five-weight codes from
trace codes over the ring $R=\mathbb{F}_2+u\mathbb{F}_2$, where $u^2=0.$ The
trace codes have the algebraic structure of abelian codes. Their Lee weight is
computed by using character sums. Combined with Pless power moments and
Newton's Identities, the weight distribution of the Gray image of trace codes
was present. Their support structure is determined. An application to secret
sharing schemes is given.


  We study the effect of time evolution on galaxy bias. We argue that at any
order in perturbations, the galaxy density contrast can be expressed in terms
of a finite set of locally measurable operators made of spatial and temporal
derivatives of the Newtonian potential. This is checked in an explicit third
order calculation. There is a systematic way to derive a basis for these
operators. This basis spans a larger space than the expansion in gravitational
and velocity potentials usually employed, although new operators only appear at
fourth order. The basis is argued to be closed under renormalization. Most of
the arguments also apply to the structure of the counter-terms in the effective
theory of large-scale structure.


  We study properties of a Green function G_A with singularities along a
complex subspace A of a complex manifold X. It is defined as the largest
negative plurisubharmonic function u satisfying locally u\leq \log|\psi|+C,
where \psi=(\psi_1, ...,\psi_m), \psi_1, ...,\psi_m are local generators for
the ideal sheaf I_A of A, and C is a constant depending on the function u and
the generators. A motivation for this study is to estimate global bounded
functions from the sheaf I_A and thus proving a ``Schwarz Lemma'' for I_A.


  Let $SL_{2}(F_{q})$ be the special linear group over a finite field $F_{q}$,
$V$ be the 2-dimensional natural representation of $SL_{2}(F_{q})$ and
$V^{\ast}$ be the dual representation. We denote by $F_{q}[V\oplus
V^{\ast}]^{SL_{2}(F_{q})}$ the corresponding invariant ring of a vector and a
covector for $SL_{2}(F_{q})$. In this paper, we construct a free module basis
over some homogeneous system of parameters of $F_{q}[V\oplus
V^{\ast}]^{SL_{2}(F_{q})}$. We calculate the Hilbert series of $F_{q}[V\oplus
V^{\ast}]^{SL_{2}(F_{q})}$, and prove that it is a Gorenstein algebra. As an
application, we confirm a special case of the recent conjecture of Bonnafe and
Kemper in 2011.


  Let $\mathrm{G} = \mathrm{Gl}_{n}(K)$, and $\mathrm{H} = \mathrm{G}^{\sigma}$
for $\sigma$ an involution of the form $g\rightarrow aga^{-1}$, It is known
that for $K =\mathbb{Q}_q$ any irreducible representation of $\mathrm{G}$ with
an $\mathrm{H}$ invariant functional, is self dual, we prove an analogous
result for $\mathbb{F}_{q}$.


  We consider a point-to-point system with simultaneous wireless information
and power transfer (SWIPT) over a block fading channel. Each transmission block
consists of a training phase and a data transmission phase. Pilot symbols are
transmitted during the training phase for channel estimation at the receiver.
To enable SWIPT, the receiver adopts a power-splitting design, such that a
portion of the received signal is used for channel estimation or data
detection, while the remaining is used for energy harvesting. We optimally
design the power-splitting ratios for both training and data phases to achieve
the best ergodic capacity performance while maintaining a required energy
harvesting rate. Our result shows how a power-splitting receiver can make the
best use of the received pilot and data signals to obtain the optimal SWIPT
performance.


  Let U be an open subset of a unirational variety (or more generally of a
separably rationally connected variety). We prove that there is rational curve
C in U such that the fundamental group of C surjects onto the fundamental group
of U. The proof improves the earlier proof (math.AG/0003138) and works in any
characteristic. Much improved version, thanks to comments of
Colliot-Th\'el\`ene.


  Music Structure Analysis (MSA) consists of representing a song in sections
(such as ``chorus'', ``verse'', ``solo'' etc), and can be seen as the retrieval
of a simplified organization of the song. This work presents a new algorithm,
called Convolutive Block-Matching (CBM) algorithm, devoted to MSA. In
particular, the CBM algorithm is a dynamic programming algorithm, applying on
autosimilarity matrices, a standard tool in MSA. In this work, autosimilarity
matrices are computed from the feature representation of an audio signal, and
time is sampled on the barscale. We study three different similarity functions
for the computation of autosimilarity matrices. We report that the proposed
algorithm achieves a level of performance competitive to that of supervised
State-of-the-Art methods on 3 among 4 metrics, while being unsupervised.


  We present the timing analysis of 10 archived \XMM observations with an
exposure of $>40$ ks of Markarian 421. Mrk 421 is the brightest
high-frequency-peaked BL Lac object (HBL) emitting in X-rays produced by
electrons accelerated in the innermost regions of a relativistic jet pointing
toward us. For each observation, we construct averaged X-ray spectra in 0.5--10
keV band, as well as 100 s binned light curves (LCs) in various subbands.
During these observations, the source exhibited various intensity states
differing by close to an order of magnitude in flux, with the fractional
variability amplitude increasing with energy through the X-ray band. Bayesian
power spectral density analysis reveals that the X-ray variability can be
characterized by a colored noise, with an index ranging from $\sim-1.9$ to
$-3.0$. Moreover, both the standard cross-correlation function and
cross-spectral methods indicate that the amount of time lags increases with the
energy difference between two compared LCs. A time-dependent two-zone jet model
is developed to extract physical information from the X-ray emission of Mrk
421. In the model, we assume that the jet emission mostly comprises a
quasi-stationary component and a highly variable one. Our results show that the
two-zone model can simultaneously provide a satisfactory description for both
the X-ray spectra and time lags observed in different epochs, with the model
parameters constrained in a fully acceptable interval. We suggest that shocks
within the jets may be the primary energy dissipation process responsible for
triggering the rapid variability, although magnetic reconnection cannot be
excluded.


  We prove a generalization for some $\mathbb C$ domains of Gehring - Palka
theorem on Moebius transformations regarding the distance ratio metric. Namely,
we show that this theorem is valid for arbitrary holomorphic mappings $f : H
\to H$ or $f : D \to D$ with the same Lipschitz constant $2$.


  Recent inertial confinement fusion experiments have shown primary fusion
spectral moments which are incompatible with a Maxwellian velocity distribution
description. These results show that an ion kinetic description of the reacting
ions is necessary. We develop a theoretical classification of non-Maxwellian
ion velocity distributions using the spectral moments. At the mesoscopic level,
a monoenergetic decomposition of the velocity distribution reveals there are
constraints on the space of spectral moments accessible by isotropic
distributions. General expressions for the directionally dependent spectral
moments of anisotropic distributions are derived. At the macroscopic level, a
distribution of fluid element velocities modifies the spectral moments in a
constrained manner. Experimental observations can be compared to these
constraints to identify the character and isotropy of the underlying reactant
ion velocity distribution and determine if the plasma is hydrodynamic or
kinetic.


  Collapsing shells form horizons, and when the curvature is small classical
general relativity is believed to describe this process arbitrarily well. On
the other hand, quantum information theory based (fuzzball/firewall) arguments
suggest the existence of some structure at the black hole horizon. This
structure can only form if classical general relativity stops being the correct
description of the collapsing shell before it reaches the horizon size. We
present strong evidence that classical general relativity can indeed break down
prematurely, by explicitly computing the quantum tunneling amplitude of a
collapsing shell of branes into smooth horizonless microstate geometries. We
show that the amplitude for tunneling into microstate geometries with a large
number of topologically non-trivial cycles is parametrically larger than
exp(-S), which indicates that the shell can tunnel into a horizonless
configuration long before the horizon has any chance to form. We also use this
technology to investigate the tunneling of M2 branes into LLM bubbling
geometries.


  We present results for matched distributions of a range of dijet event shapes
at hadron colliders, combining next-to-leading logarithmic (NLL) accuracy in
the resummation exponent, next-to-next-to leading logarithmic (NNLL) accuracy
in its expansion and next-to-leading order (NLO) accuracy in a pure alpha_s
expansion. This is the first time that such a matching has been carried out for
hadronic final-state observables at hadron colliders. We compare our results to
Monte Carlo predictions, with and without matching to multi-parton tree-level
fixed-order calculations. These studies suggest that hadron-collider event
shapes have significant scope for constraining both perturbative and
non-perturbative aspects of hadron-collider QCD. The differences between
various calculational methods also highlight the limits of relying on
simultaneous variations of renormalisation and factorisation scale in making
reliable estimates of uncertainties in QCD predictions. We also discuss the
sensitivity of event shapes to the topology of multi-jet events, which are
expected to appear in many New Physics scenarios.


  The Wigner distribution is a milestone of Time-frequency Analysis. In order
to cope with its drawbacks while preserving the desirable features that made it
so popular, several kind of modifications have been proposed. This
contributions fits into this perspective. We introduce a family of phase-space
representations of Wigner type associated with invertible matrices and explore
their general properties. As main result, we provide a characterization for the
Cohen's class. This feature suggests to interpret this family of
representations as linear perturbations of the Wigner distribution. We show
which of its properties survive under linear perturbations and which ones are
truly distinctive of its central role.


  Neural language models are a powerful tool to embed words into semantic
vector spaces. However, learning such models generally relies on the
availability of abundant and diverse training examples. In highly specialised
domains this requirement may not be met due to difficulties in obtaining a
large corpus, or the limited range of expression in average use. Such domains
may encode prior knowledge about entities in a knowledge base or ontology. We
propose a generative model which integrates evidence from diverse data sources,
enabling the sharing of semantic information. We achieve this by generalising
the concept of co-occurrence from distributional semantics to include other
relationships between entities or words, which we model as affine
transformations on the embedding space. We demonstrate the effectiveness of
this approach by outperforming recent models on a link prediction task and
demonstrating its ability to profit from partially or fully unobserved data
training labels. We further demonstrate the usefulness of learning from
different data sources with overlapping vocabularies.


  The main purpose of this paper is to obtain analytical solutions for
radiative transfer equations related to the vertical structure of accretion
discs with finite optical depth. In the non-gray atmosphere, we employ the
optical-depth dependent Eddington factor to define the relationship between the
mean intensity and radiation stress tensor. Analytical solutions are achieved
for two cases: (i) radiative equilibrium, and (ii) a disc with uniform internal
heating and both cases are assumed to be in local thermodynamical equilibrium
(LTE), too. These solutions enable us to study the probable role of scattering
and disc optical depth on the emergent intensity and other radiative
quantities. Our results show that for the first case, the surface value of mean
intensity with constant Eddington factor is three times larger than that with a
variable factor. Moreover, scattering has no role in the vertical radiative
structure of discs with the assumptions of the first case. On the other hand,
for the second case, we encounter reductions in all radiative quantities as the
photon destruction probability decreases (which is equivalent to increasing
scattering). Furthermore, for both cases with total optical depth less than
unity, the outward intensity towards the polar direction becomes less than that
from the edges of disc which is contrary to limb-darkening. In the end, we
apply our results to find the spectrum from accretion systems, based on two
dynamical models. Consequently, we can see how the total optical depth varies
with frequency and causes remarkable changes in the emergent spectra.


  Latent Dirichlet Allocation (LDA) is a popular tool for analyzing discrete
count data such as text and images. Applications require LDA to handle both
large datasets and a large number of topics. Though distributed CPU systems
have been used, GPU-based systems have emerged as a promising alternative
because of the high computational power and memory bandwidth of GPUs. However,
existing GPU-based LDA systems cannot support a large number of topics because
they use algorithms on dense data structures whose time and space complexity is
linear to the number of topics. In this paper, we propose SaberLDA, a GPU-based
LDA system that implements a sparsity-aware algorithm to achieve sublinear time
complexity and scales well to learn a large number of topics. To address the
challenges introduced by sparsity, we propose a novel data layout, a new
warp-based sampling kernel, and an efficient sparse count matrix updating
algorithm that improves locality, makes efficient utilization of GPU warps, and
reduces memory consumption. Experiments show that SaberLDA can learn from
billions-token-scale data with up to 10,000 topics, which is almost two orders
of magnitude larger than that of the previous GPU-based systems. With a single
GPU card, SaberLDA is able to learn 10,000 topics from a dataset of billions of
tokens in a few hours, which is only achievable with clusters with tens of
machines before.


  In the case of a system with an unbounded hamiltonian the entropic index q of
non-extensive thermodynamics has an upperbound q_c>1 beyond which the formalism
becomes meaningless. The expression 1/(q_c-1) is the dimension of the state
space (i.e. the manifold of density matrices) in the context of non-commutative
geometry. For q=q_c an ultraviolet cutoff E< E_ub is needed to guarantee the
existence of the equilibrium density matrix. Duality between q>1 and
q<1-statistics via a q --> 1/q transformation is established. It leads to an
overall picture in which the meaning of both q>1-statistics and q<1-statistics
is clarified.


  We show the necessity of two-nucleon axial currents and associated pion
emission/ absorption operators for the partial conservation of the axial
current (PCAC) nuclear matrix elements with arbitrary nuclear dynamics
described by a nonrelativistic Schroedinger equation. As examples we construct
such nonrelativistic axial two-body currents in the linear- and heterotic (g_A
= 1.26) sigma models, with an optional isoscalar vector (omega) meson exchange.
The nuclear matrix elements obey PCAC only if the nuclear wave functions used
in the calculation are solutions to the Schroedinger equation with the static
one-meson-exchange potential constructed in the respective (sigma) model. The
same holds true for the nucler pion production amplitude, since it is
proportional to the divergence of the axial current matrix element, by virtue
of PCAC. Thus we found a new consistency condition between the pion
creation/absorption operator and the nuclear Hamiltonian. We present examples
drawn from our models and discuss implications for one-pion-two-nucleon
processes.


  I deduce an inequality between the helicity and the transversity distribution
of a quark in a nucleon, at small energy scales. Then I establish, thanks to
the positivity constraint, a rigorous bound on longitudinally polarized valence
quark densities, which finds nontrivial applications to d-quarks. This, in
turn, implies a bound for the distributions of the longitudinally polarized
sea, which is probably not SU(3)-symmetric. Some model predictions and
parametrizations of quark distributions are examined in the light of these
results.


  The study of the propagation of electrons with a varying spinor orientability
is performed using the coordinate transformation method. Topological Insulators
are characterized by an odd number of changes of the orientability in the
Brillouin zone. For defects the change in orientability takes place for closed
orbits in real space. Both cases are characterized by nontrivial spin
connections. Using this method , we derive the form of the spin connections for
topological defects in three dimensional Topological Insulators. On the surface
of a Topological Insulator, the presence an edge dislocation gives rise to a
spin connection controlled by torsion. We find that electrons propagate along
two dimensional regions and confined circular contours. We compute for the edge
dislocations the tunneling density of states. The edge dislocations violates
parity symmetry resulting in a current measured by the in-plane component of
the spin on the surface.


  We investigate the secular dynamics of three-body circumbinary systems under
the effect of tides. We use the octupolar non-restricted approximation for the
orbital interactions, general relativity corrections, the quadrupolar
approximation for the spins, and the viscous linear model for tides. We derive
the averaged equations of motion in a simplified vectorial formalism, which is
suitable to model the long-term evolution of a wide variety of circumbinary
systems in very eccentric and inclined orbits. In particular, this vectorial
approach can be used to derive constraints for tidal migration, capture in
Cassini states, and stellar spin-orbit misalignment. We show that circumbinary
planets with initial arbitrary orbital inclination can become coplanar through
a secular resonance between the precession of the orbit and the precession of
the spin of one of the stars. We also show that circumbinary systems for which
the pericenter of the inner orbit is initially in libration present chaotic
motion for the spins and for the eccentricity of the outer orbit. Because our
model is valid for the non-restricted problem, it can also be applied to any
three-body hierarchical system such as star-planet-satellite systems and triple
stellar systems.


  In order to manipulate a deformable object, such as rope or cloth, in
unstructured environments, robots need a way to estimate its current shape.
However, tracking the shape of a deformable object can be challenging because
of the object's high flexibility, (self-)occlusion, and interaction with
obstacles. Building a high-fidelity physics simulation to aid in tracking is
difficult for novel environments. Instead we focus on tracking the object based
on RGBD images and geometric motion estimates and obstacles. Our key
contributions over previous work in this vein are: 1) A better way to handle
severe occlusion by using a motion model to regularize the tracking estimate;
and 2) The formulation of \textit{convex} geometric constraints, which allow us
to prevent self-intersection and penetration into known obstacles via a
post-processing step. These contributions allow us to outperform previous
methods by a large margin in terms of accuracy in scenarios with severe
occlusion and obstacles.


  Computer simulations of a model glass-forming system are presented, which are
particularly sensitive to the correlation between the dynamics and the
topography of the potential energy landscape. This analysis clearly reveals
that in the supercooled regime the dynamics is strongly influenced by the
presence of deep valleys in the energy landscape, corresponding to long-lived
metastable amorphous states. We explicitly relate non-exponential relaxation
effects and dynamic heterogeneities to these metastable states and thus to the
specific topography of the energy landscape.


  Epitaxial thin films of Fe doped SrTiO3 have been studied by the use of
resonant photoemission. This technique allowed to identify contributions of the
Fe and Ti originating electronic states to the valence band. Two valence states
of iron Fe2+ and Fe3+, detected on the base of XAS spectra, appeared to form
quite different contributions to the valence band of SrTiO3. The electronic
states within the in-gap region can be attributed to Fe2+, Fe3+ and Ti3+ ions.
Fe2+ originating states which can be connected to the presence of oxygen
vacancies form a broad band reaching binding energies of about 0.5 eV below the
conduction band while Fe3+ states form in the gap a sharp feature localized
just above the top of the valence band. It has been shown that Fe doping
induced Ti originating states in the energy gap which can be related to
hybridization of Ti and Fe 3d orbitals.


  Gaia DR3 provides >181, 000 radial velocity solutions for single-lined
spectroscopic binaries (SB1s) which can be used to search for non-interacting
compact object+star binary candidates by selecting systems with large mass
functions. We selected 234 such systems and identified 114 systems with good RV
solutions in DR3. We used light curves from ASAS-SN and TESS to identify and
remove 31 eclipsing binaries to produce a catalog of 80 compact object+star
candidates, including 38 ellipsoidal variables. The positions of these
candidates on Gaia and 2MASS CMDs suggest that many of these systems are
binaries with luminous companions. We compared the periods and eccentricities
of detached eclipsing binaries in Gaia DR3 and ASAS-SN, and found that ~11% and
~60% of the binaries had different periods and eccentricities. We also compared
RV solutions for 311 binaries in both Gaia DR3 and the Ninth Catalog of
Spectroscopic Binary Orbits (SB9), and found similar results. We do not
identify any strong candidates for non-interacting compact object+star
binaries.


  The generic structure of 1-, 2- and 3-point functions of fields residing in
indecomposable representations of arbitrary rank are given. These in turn
determine the structure of the operator product expansion in logarithmic
conformal field theory. The crucial role of zero modes is discussed in some
detail.


  Periodic arrays of antidots, i.e. nanoscale perforations, in graphene enable
tight confinement of carriers and efficient transport barriers. Such barriers
evade the Klein tunneling mechanism by being of the mass rather than
electrostatic type. While all graphene antidot lattices (GALs) may support
directional barriers, we show, however, that a full transport gap exists only
for certain orientations of the GAL. Moreover, we assess the applicability of
gapped graphene and the Dirac continuum approach as simplified models of
various antidot structures showing that, in particular, the former is an
excellent approximation for transport in GALs supporting a bulk band gap.
Finally, the transport properties of a GAL based resonant tunneling diode is
analyzed indicating that such advanced graphene based devices may, indeed, be
realized using GAL structures.


  Civil applications for unmanned aerial vehicles (UAVs) have increased rapidly
over the last few years. In the realm of civil applications, many aircraft
carry cameras that are physically fixed to the airframe. While this yields a
simple and robust remote sensing platform, the imagery quality diminishes with
increasing attitude errors. A rudder augmented trajectory correction method for
small unmanned aerial vehicles is discussed in this paper. The goal of this
type of controller is to minimize the lateral image errors of body fixed
non-gimbaled cameras. We present a comparison to current aileron only
trajectory correction autopilots. Simulation and flight test results are
presented that show significant reduction in the roll angle present during
trajectory correction resulting in a large effect on total flight line image
deviations.


  Supply chain security has become a growing concern in security risk analysis
of the Internet of Things (IoT) systems. Their highly connected structures have
significantly enlarged the attack surface, making it difficult to track the
source of the risk posed by malicious or compromised suppliers. This chapter
presents a system-scientific framework to study the accountability in IoT
supply chains and provides a holistic risk analysis technologically and
socio-economically. We develop stylized models and quantitative approaches to
evaluate the accountability of the suppliers. Two case studies are used to
illustrate accountability measures for scenarios with single and multiple
agents. Finally, we present the contract design and cyber insurance as economic
solutions to mitigate supply chain risks. They are incentive-compatible
mechanisms that encourage truth-telling of the supplier and facilitate reliable
accountability investigation for the buyer.


  The stationary distribution of the diffusion limit of the 2-island, 2-allele
Wright-Fisher with small but otherwise arbitrary mutation and migration rates
is investigated. Following a method developed by Burden and Tang (2016, 2017)
for approximating the forward Kolmogorov equation, the stationary distribution
is obtained to leading order as a set of line densities on the edges of the
sample space, corresponding to states for which one island is bi-allelic and
the other island is non-segregating, and a set of point masses at the corners
of the sample space, corresponding to states for which both islands are
simultaneously non-segregating. Analytic results for the corner probabilities
and line densities are verified independently using the backward generator and
for the corner probabilities using the coalescent.


  This paper is a summary of the recently accomplished research work on
flexible gripping systems. The goal is to develop a gripper which can be used
for a great amount of geometrically variant workpieces. The economic aspect is
of particular importance during the whole development. The high flexibility of
the gripper is obtained by three parallel used principles. These are human and
computer based analysis of the gripping object as well as mechanical adaptation
of the gripper to the object with the help of servo motors. The focus is on the
gripping of free-form surfaces with suction cup.


  We propose a mechanism for high critical temperature (T_c) in the coexistent
phase of superconducting- (SC) and antiferromagnetic (AF) CuO_2 planes in
multilayered cuprates. The Josephson coupling between the SC planes separated
by an AF insulator (Mott insulator) is calculated perturbatively up to the
fourth order in terms of the hopping integral between adjacent CuO_2 planes. It
is shown that the AF exchange splitting in the AF plane suppresses the
so-called pi-Josephson coupling, and the long-ranged 0-Josephson coupling leads
to coexistence with a rather high value of T_c.


  Traditional Lyapunov based transient stability assessment approaches focus on
identifying the stability region (SR) of the equilibrium point under study.
When trying to estimate this region using Lyapunov functions, the shape of the
final estimate is often limited by the degree of the function chosen, a
limitation that results in conservativeness in the estimate of the SR. More
conservative the estimate is in a particular region of state space, smaller is
the estimate of the critical clearing time for disturbances that drive the
system towards that region. In order to reduce this conservativeness, we
propose a methodology that uses the disturbance trajectory data to skew the
shape of the final Lyapunov based SR estimate. We exploit the advances made in
the theory of sum of squares decomposition to algorithmically estimate this
region. The effectiveness of this technique is demonstrated on a power systems
classical model.


  We introduce a relative version of the $2$-Segal simplicial spaces defined by
Dyckerhoff and Kapranov and G\'{a}lvez-Carrillo, Kock and Tonks. Examples of
relative $2$-Segal spaces include the categorified unoriented cyclic nerve,
real pseudoholomorphic polygons in almost complex manifolds and the
$\mathcal{R}_{\bullet}$-construction from Grothendieck-Witt theory. We show
that a relative $2$-Segal space defines a categorical representation of the
Hall algebra associated to the base $2$-Segal space. In this way, after
decategorification we recover a number of known constructions of Hall algebra
representations. We also describe some higher categorical interpretations of
relative $2$-Segal spaces.


  In today's data-driven world, the sensitivity of information has been a
significant concern. With this data and additional information on the person's
background, one can easily infer an individual's private data. Many
differentially private iterative algorithms have been proposed in interactive
settings to protect an individual's privacy from these inference attacks. The
existing approaches adapt the method to compute differentially private(DP)
centroids by iterative Llyod's algorithm and perturbing the centroid with
various DP mechanisms. These DP mechanisms do not guarantee convergence of
differentially private iterative algorithms and degrade the quality of the
cluster. Thus, in this work, we further extend the previous work on
'Differentially Private k-Means Clustering With Convergence Guarantee' by
taking it as our baseline. The novelty of our approach is to sub-cluster the
clusters and then select the centroid which has a higher probability of moving
in the direction of the future centroid. At every Lloyd's step, the centroids
are injected with the noise using the exponential DP mechanism. The results of
the experiments indicate that our approach outperforms the current
state-of-the-art method, i.e., the baseline algorithm, in terms of clustering
quality while maintaining the same differential privacy requirements. The
clustering quality significantly improved by 4.13 and 2.83 times than baseline
for the Wine and Breast_Cancer dataset, respectively.


  The current-carrying ability of a type-II superconductor is generally
represented by its critical current density. This can be determined by
measuring a flux relaxation process starting with a testing current density
that is greater than or equal to the critical value. Here we show that a flux
relaxation process starting with an intermediate current density can be
converted into a process starting with the critical current density by
introducing a virtual time interval. Therefore, one may calculate the critical
current density from the flux relaxation process starting with a current
density below the critical value. The exact solutions of the time dependence of
current density in the flux relaxation process were also discussed.


  Compared to the standard success (coverage) probability, the meta
distribution of the signal-to-interference ratio (SIR) provides much more
fine-grained information about the network performance. We consider general
heterogeneous cellular networks (HCNs) with base station tiers modeled by
arbitrary stationary and ergodic non-Poisson point processes. The exact
analysis of non-Poisson network models is notoriously difficult, even in terms
of the standard success probability, let alone the meta distribution. Hence we
propose a simple approach to approximate the SIR meta distribution for
non-Poisson networks based on the ASAPPP ("approximate SIR analysis based on
the Poisson point process") method. We prove that the asymptotic horizontal gap
$G_0$ between its standard success probability and that for the Poisson point
process exactly characterizes the gap between the $b$th moment of the
conditional success probability, as the SIR threshold goes to $0$. The gap
$G_0$ allows two simple approximations of the meta distribution for general
HCNs: 1) the per-tier approximation by applying the shift $G_0$ to each tier
and 2) the effective gain approximation by directly shifting the meta
distribution for the homogeneous independent Poisson network. Given the
generality of the model considered and the fine-grained nature of the meta
distribution, these approximations work surprisingly well.


  We investigate the pressure dependence of the optical properties of CeTe$_3$,
which exhibits an incommensurate charge-density-wave (CDW) state already at 300
K. Our data are collected in the mid-infrared spectral range at room
temperature and at pressures between 0 and 9 GPa. The energy for the single
particle excitation across the CDW gap decreases upon increasing the applied
pressure, similarly to the chemical pressure by rare-earth substitution. The
broadening of the bands upon lattice compression removes the perfect nesting
condition of the Fermi surface and therefore diminishes the impact of the CDW
transition on the electronic properties of $R$Te$_3$.


  Formation of diverse patterns in spatially extended reaction-diffusion
systems is an important aspect of study which is pertinent to many chemical and
biological processes. Of special interest is the peculiar phenomenon of chimera
state having spatial coexistence of coherent and incoherent dynamics in a
system of identically interacting individuals. In the present article, we
report the emergence of various collective dynamical patterns while considering
a system of prey-predator dynamics in presence of a two-dimensional diffusive
environment. Particularly, we explore the observance of four distinct
categories of spatial arrangements among the species, namely spiral wave,
spiral chimera, completely synchronized oscillations, and oscillation death
states in a broad region of the diffusion-driven parameter space. Emergence of
amplitude mediated spiral chimera states displaying drifted amplitudes and
phases in the incoherent subpopulation is detected for parameter values beyond
both Turing and Hopf bifurcations. Transition scenarios among all these
distinguishable patterns are numerically demonstrated for a wide range of the
diffusion coefficients which reveal that the chimera states arise during the
transition from oscillatory to steady state dynamics. Furthermore, we
characterize the occurrence of each of the recognizable patterns by estimating
the strength of incoherent subpopulations in the two-dimensional space.


  Inclusive K+ production in proton-proton collisions has been measured at a
beam energy of 2.16 GeV using the COSY-ANKE magnetic spectrometer. The
resulting spectrum, as well as those corresponding to K+ p and K+ pi+
correlated pairs, can all be well described using consistent values of the
total cross sections for the pp->K+ p Lambda, pp->K+ p Sigma0, and pp->K+ n
Sigma+ reactions. While the resulting values for Lambda and Sigma0 production
are in good agreement with world data, our value for the total Sigma+
production cross section, sigma(pp->K+ n Sigma+) = 2.5+/-0.6(stat)+/-0.4(syst)
mkb at an excess energy of epsilon=129 MeV, could only be reconciled with other
recently published data if there were a highly unusual near--threshold
behaviour.


  Two sets of modes of a massive free scalar field are quantized in a pair of
Poincar\'e patches of Lorentzian anti-de Sitter (AdS) space, AdS$_{d+1}$ ($d
\geq 2$). It is shown that in Poincar\'e coordinates $(r,t,\vec{x})$, the two
boundaries at $r=\pm \infty$ are connected. When the scalar mass $m$ satisfies
a condition $0 < \nu=\sqrt{(d^2/4)+(m\ell)^2} <1$, there exist two sets of mode
solutions to Klein-Gordon equation, with distinct fall-off behaviors at the
boundary. By using the fact that the boundaries at $r=\pm \infty$ are
connected, a conserved Klein-Gordon norm can be defined for these two sets of
scalar modes, and these modes are canonically quantized. Energy is also
conserved. A prescription within the approximation of semi-classical gravity is
presented for computing two- and three-point functions of the operators in the
boundary CFT, which correspond to the two fall-off behaviours of scalar field
solutions.