  We show how Jacobian elliptic functions (JEF) can be used to solve ordinary
differential equations (ODE) describing nonlinear dynamics of microtubules
(MT). We demonstrate that only one of JEFs can be used while the remaining two
do not represent the solutions of the crucial differential equation. We show
that a kink-type soliton moves along MT. Beside this solution, we discuss a few
more that may or may not have physical meaning. Finally, we show what kinds of
ODE can be solved using JEFs.


  We study almost complex structures on parallelizable manifolds via the rank
of their Nijenhuis tensor. First, we show how the computations of such rank can
be reduced to finding smooth functions on the underlying manifold solving a
system of first order PDEs. On specific manifolds, we find an explicit
solution. Then we compute the Nijenhuis tensor on curves of almost complex
structures, showing that there is no constraint (except for lower
semi-continuity) to the possible jumps of its rank. Finally, we focus on
$6$-nilmanifolds and the associated Lie algebras. We classify which
$6$-dimensional, nilpotent, real Lie algebras admit almost complex structures
whose Nijenhuis tensor has a given rank, deducing the corresponding
classification for left-invariant structures on $6$-nilmanifolds. We also find
a topological upper-bound for the rank of the Nijenhuis tensor for
left-invariant almost complex structures on solvmanifolds of any dimension,
obtained as a quotient of a completely solvable Lie group. Our results are
complemented by a large number of examples.


  We report the results of a blind search for flavor-changing neutral current,
lepton-flavor violating, and lepton-number violating decays of D+, D(s)+, and
D0 mesons (and their antiparticles) into modes containing muons and electrons.
Using data from Fermilab charm hadroproduction experiment E791, we examine the
pi l l and K l l decay modes of D+ and D(s)+ and the l+ l- decay modes of D0.
No evidence for any of these decays is found. Therefore, we present
branching-fraction upper limits at 90% confidence level for the 24 decay modes
examined. Eight of these modes have no previously reported limits, and fourteen
are reported with significant improvements over previously published results.


  The Right to be Forgotten is part of the recently enacted General Data
Protection Regulation (GDPR) law that affects any data holder that has data on
European Union residents. It gives EU residents the ability to request deletion
of their personal data, including training records used to train machine
learning models. Unfortunately, Deep Neural Network models are vulnerable to
information leaking attacks such as model inversion attacks which extract class
information from a trained model and membership inference attacks which
determine the presence of an example in a model's training data. If a malicious
party can mount an attack and learn private information that was meant to be
removed, then it implies that the model owner has not properly protected their
user's rights and their models may not be compliant with the GDPR law. In this
paper, we present two efficient methods that address this question of how a
model owner or data holder may delete personal data from models in such a way
that they may not be vulnerable to model inversion and membership inference
attacks while maintaining model efficacy. We start by presenting a real-world
threat model that shows that simply removing training data is insufficient to
protect users. We follow that up with two data removal methods, namely
Unlearning and Amnesiac Unlearning, that enable model owners to protect
themselves against such attacks while being compliant with regulations. We
provide extensive empirical analysis that show that these methods are indeed
efficient, safe to apply, effectively remove learned information about
sensitive data from trained models while maintaining model efficacy.


  X-ray variability of the microquasar XTE J1550-564 is studied with time
domain techniques for the data from the RXTE/PCA observation in September 8,
1998. The 2--60 keV averaged shot is obtained from superposing shots with one
millisecond time bin through aligning their peaks. The spectral behavior during
the averaged shot exhibits prominent differences from that observed in Cyg X-1.
The hardness ratio of (13--60 keV)/(2--13 keV) or (16--60 keV)/(13--16 keV)
during a shot is lower or higher than that of steady emission respectively. The
correlation coefficient between intensity and hardness ratio of (13--60
keV)/(2--13 keV) or (16--60 keV)/(13--16 keV) is negative or positive
respectively. These results may indicate that physical processes in the low
state of XTE J1550-564 are different from those in Cyg X-1.


  Nonlinear model order reduction has opened the door to parameter optimization
and uncertainty quantification in complex physics problems governed by
nonlinear equations. In particular, the computational cost of solving these
equations can be reduced by means of local reduced-order bases. This article
examines the benefits of a physics-informed cluster analysis for the
construction of cluster-specific reduced-order bases. We illustrate that the
choice of the dissimilarity measure for clustering is fundamental and highly
affects the performances of the local reduced-order bases. It is shown that
clustering with an angle-based dissimilarity on simulation data efficiently
decreases the intra-cluster Kolmogorov $N$-width. Additionally, an a priori
efficiency criterion is introduced to assess the relevance of a ROM-net, a
methodology for the reduction of nonlinear physics problems introduced in our
previous work in [T. Daniel, F. Casenave, N. Akkari, D. Ryckelynck, Model order
reduction assisted by deep neural networks (ROM-net), Advanced Modeling and
Simulation in Engineering Sciences 7 (16), 2020]. This criterion also provides
engineers with a very practical method for ROM-nets' hyperparameters
calibration under constrained computational costs for the training phase. On
five different physics problems, our physics-informed clustering strategy
significantly outperforms classic strategies for the construction of local
reduced-order bases in terms of projection errors.


  We derive closed expressions for the universal weak localization peak of the
average conductance peak heights in Coulomb blockade quantum dots in the
crossover from orthogonal to unitary symmetry. The scale for the crossover is
independent of the number of channels in each lead, in contrast with the case
of open dots. The functional form of the weak localization peak is independent
of temperature. We also derive analytically the variance of the conductance
peak heights as a function of the crossover parameter.


  In this paper, I give a new construction of a K\"{a}hler-Einstein metrics on
a smooth projective variety with ample canonical bundle. This result can be
generalized to the construction of a singular K\"{a}hler-Einstein metric on a
smooth projective variety of general type which gives an AZD of the canonical
bundle. Also the variation of Bergman kernels and K\"{a}hler-Einstein volume
form have been considered.


  Semantic segmentation from very fine resolution (VFR) urban scene images
plays a significant role in several application scenarios including autonomous
driving, land cover classification, and urban planning, etc. However, the
tremendous details contained in the VFR image, especially the considerable
variations in scale and appearance of objects, severely limit the potential of
the existing deep learning approaches. Addressing such issues represents a
promising research field in the remote sensing community, which paves the way
for scene-level landscape pattern analysis and decision making. In this paper,
we propose a Bilateral Awareness Network which contains a dependency path and a
texture path to fully capture the long-range relationships and fine-grained
details in VFR images. Specifically, the dependency path is conducted based on
the ResT, a novel Transformer backbone with memory-efficient multi-head
self-attention, while the texture path is built on the stacked convolution
operation. Besides, using the linear attention mechanism, a feature aggregation
module is designed to effectively fuse the dependency features and texture
features. Extensive experiments conducted on the three large-scale urban scene
image segmentation datasets, i.e., ISPRS Vaihingen dataset, ISPRS Potsdam
dataset, and UAVid dataset, demonstrate the effectiveness of our BANet.
Specifically, a 64.6% mIoU is achieved on the UAVid dataset. Code is available
at https://github.com/WangLibo1995/GeoSeg.


  For the two-dimensional electron system in silicon MOSFET's, the scaled
magnetoconductivity has been shown to exhibit critical behavior at finite
density $n_0$. Analysis of these magnetotransport experiments yields a product
$g^*m^*$ that diverges at this density (here $g^*$ is the interaction-enhanced
Land\'e $g$-factor and $m^*$ is the effective mass). This claim has been
disputed based on direct determinations of the $g^*m^*$ obtained from
Shubnikov-de Haas measurements. We briefly review these experiments, and
possible sources of the discrepancies.


  In this paper, we propose a recommendation framework named Cayley-Dickson
Recommender. We introduce Cayley-Dickson construction which uses a recursive
process to define hypercomplex algebras and their mathematical operations. We
also design a graph convolution operator to learn representations in the
hypercomplex space. To the best of our knowledge, it is the first time that
Cayley-Dickson construction and graph convolution techniques have been used in
hypercomplex recommendation. Compared with the state-of-the-art recommendation
methods, our method achieves superior performance on real-world datasets.


  Altmetrics are tools for measuring the impact of research beyond scientific
communities. In general, they measure online mentions of scholarly outputs,
such as on online social networks, blogs, and news sites. Some stakeholders in
higher education have championed altmetrics as a new way to understand research
impact and as an alternative or supplement to bibliometrics. Contrastingly,
others have criticized altmetrics for being ill conceived and limited in their
use. This chapter explores the values and limits of altmetrics, including their
role in evaluating, promoting, and disseminating research.


  In a recent work [\textit{J. Mol. Liq}, 2020, \textbf{320}, 114420], using
molecular dynamics simulations and a core-softened potential approach, we have
shown that adding a simple solute as methanol can "kill" the density anomalous
behavior as the LLCP is suppressed by the spontaneous crystallization in a
hexagonal closed packing (HCP) crystal near the LLPT. Now, we extend this work
in order to realize how longer-chain alcohols will affect the complex behavior
of water-alcohol mixtures in the supercooled regime. Besides core-softened (CS)
methanol, ethanol and 1-propanol were added to a system of identical particles
that interact through the continuous shouldered well (CSW) potential. We
observed that the density anomaly gradually decreases its extension in phase
diagrams until disappearing with the growth of the non-polar chain and the
alcohol concentration, differently from the liquid-liquid phase transition (and
the LLCP), which remained present in all analyzed mixtures, in according to
\textit{Nature}, 2001, \textbf{409}, 692. For our model, the longer non-polar
chains and higher concentrations gradually impact the competition between the
scales in the CS potential, leading to a gradual disappearing of the anomalies
until the TMD total disappearance is observed when the first coordination shell
structure is also affected: the short-range ordering is favored, leading to
less competition between short- and long-range ordering and, consequently, to
the extinction of anomalies. Also, the non-polar chain size and concentration
have an effect on the solid phases, favoring the hexagonal closed packed (HCP)
solid and the amorphous solid phase over the body-centered cubic (BCC) crystal.


  We study the use of linear regression for multiclass classification in the
over-parametrized regime where some of the training data is mislabeled. In such
scenarios it is necessary to add an explicit regularization term, $\lambda
f(w)$, for some convex function $f(\cdot)$, to avoid overfitting the mislabeled
data. In our analysis, we assume that the data is sampled from a Gaussian
Mixture Model with equal class sizes, and that a proportion $c$ of the training
labels is corrupted for each class. Under these assumptions, we prove that the
best classification performance is achieved when $f(\cdot) = \|\cdot\|^2_2$ and
$\lambda \to \infty$. We then proceed to analyze the classification errors for
$f(\cdot) = \|\cdot\|_1$ and $f(\cdot) = \|\cdot\|_\infty$ in the large
$\lambda$ regime and notice that it is often possible to find sparse and
one-bit solutions, respectively, that perform almost as well as the one
corresponding to $f(\cdot) = \|\cdot\|_2^2$.


  Chemical process optimization and control are affected by 1) plant-model
mismatch, 2) process disturbances, and 3) constraints for safe operation.
Reinforcement learning by policy optimization would be a natural way to solve
this due to its ability to address stochasticity, plant-model mismatch, and
directly account for the effect of future uncertainty and its feedback in a
proper closed-loop manner; all without the need of an inner optimization loop.
One of the main reasons why reinforcement learning has not been considered for
industrial processes (or almost any engineering application) is that it lacks a
framework to deal with safety critical constraints. Present algorithms for
policy optimization use difficult-to-tune penalty parameters, fail to reliably
satisfy state constraints or present guarantees only in expectation. We propose
a chance constrained policy optimization (CCPO) algorithm which guarantees the
satisfaction of joint chance constraints with a high probability - which is
crucial for safety critical tasks. This is achieved by the introduction of
constraint tightening (backoffs), which are computed simultaneously with the
feedback policy. Backoffs are adjusted with Bayesian optimization using the
empirical cumulative distribution function of the probabilistic constraints,
and are therefore self-tuned. This results in a general methodology that can be
imbued into present policy optimization algorithms to enable them to satisfy
joint chance constraints with high probability. We present case studies that
analyze the performance of the proposed approach.


  The NiO4 plaquettes in La2NiO4.17, a cousin of the hole-doped
high-temperature superconductor La{2-x}SrxCuO4, have been studied by 61Ni-NMR
in 14 T in a single crystal enriched in 61Ni. Doped and undoped plaquettes are
discriminated by the shift of the NMR resonance, leading to a small line
splitting, which hardly depends on temperature or susceptibility. The smallness
of the effect is additional evidence for the location of the holes as deduced
by Schussler-Langenheine et al., Phys. Rev. Lett. 95, 156402 (2005). The
increase in linewidth with decreasing temperature shows a local field
redistribution, consistent with the formation of charge density waves or
stripes. For comparison, we studied in particular the grandmother of all planar
antiferromagnets K2NiF in the paramagnetic state using natural abundant 61Ni.
The hyperfine fields in both 2-dimensional compounds appear to be remarkably
small, which is well explained by super(transferred) hyperfine interaction. In
K2NiF4 the temperature dependence of the susceptibility and Knight shift cannot
be brought onto a simple scaling curve. This unique feature is ascribed to a
different sensitivity for correlations of these two parameters.


  Solar proton events (SPEs) cause large-scale ionization in the middle
atmosphere leading to ozone loss and changes in the energy budget of the middle
atmosphere. The accurate implementation of SPEs and other particle ionization
sources in climate models is necessary to understand the role of energetic
particle precipitation (EPP) in climate variability. We use riometer
observations from 16 riometer stations and the Whole Atmosphere Community
Climate Model with added D region ion chemistry (WACCM-D) to study the spatial
and temporal extent of cosmic noise absorption (CNA) during 62 solar proton
events from 2000 to 2005. We also present a correction method for the
non-linear response of observed CNA during intense absorption events. We find
that WACCM-D can reproduce the observed CNA well with some need for future
improvement and testing of the used EPP forcing. The average absolute
difference between the model and the observations is found to be less than 0.5
dB poleward of about $66^\circ$ geomagnetic latitude, and increasing with
decreasing latitude to about 1 dB equatorward of about $66^\circ$ geomagnetic
latitude. The differences are largest during twilight conditions where the
modeled changes in CNA are more abrupt compared to observations. An
overestimation of about $1^\circ$ to $3^\circ$ geomagnetic latitude in the
extent of the CNA is observed due to the fixed proton cutoff latitude in the
model. An unexplained underestimation of CNA by the model during sunlit
conditions is observed at stations within the polar cap during 18 of the
studied events.


  The temperature dependence of the relaxation dynamics in the bilayer thin
film heterostructure composed of multiferroic BiFeO$_{3}$ (BFO) and
superconducting YBa$_{2}$Cu$_{3}$O$_{7}$ (YBCO) grown on a (001) SrTiO$_{3}$
substrate is studied by a time-resolved pump-probe technique, and compared with
that of pure YBCO thin film grown under the same growth conditions. The
superconductivity of YBCO is found to be retained in the heterostructure. We
observe a speeding up of the YBCO recombination dynamics in the superconducting
state of the heterostructure, and attribute it to the presence of weak
ferromagnetism at the BFO/YBCO interface as observed in magnetization data. An
extension of the Rothwarf-Taylor model is used to fit the ultrafast dynamics of
BFO/YBCO, that models an increased quasiparticle occupation of the
ferromagnetic interfacial layer in the superconducting state of YBCO.


  We introduce the notion of almost finite dimensionality of algebras and study
its connection with the classical finiteness conditions.


  Freely Long-Thinking Transformer (FraiLT) is an improved transformer model
designed to enhance processing capabilities without scaling up size. It
utilizes a recursive approach, iterating over a subset of layers multiple
times, and introduces iteration encodings to maintain awareness across these
cycles. Iteration encoding allows FraiLT to achieve the interpretive depth of
larger models in a compact form. When evaluated on a synthetic story dataset,
FraiLT outperformed larger models, showcasing its ability to deliver
high-quality performance while reducing memory demands. This model represents a
step forward towards more efficient and accessible language models.


  The 3-Kronecker quiver has two vertices, namely a sink and a source, and 3
arrows. A regular representation of a representation-infinite quiver such as
the 3-Kronecker quiver is said to be elementary provided it is non-zero and not
a proper extension of two regular representations. Of course, any regular
representation has a filtration whose factors are elementary, thus the
elementary representations may be considered as the building blocks for
obtaining all the regular representations. We are going to determine the
elementary $3$-Kronecker modules. It turns out that all the elementary modules
are combinatorially defined.


  Context. Transit detection algorithms are mathematical tools used for
detecting planets in the photometric data of transit surveys. In this work we
study their application to space-based surveys. Aims: Space missions are
exploring the parameter space of the transit surveys where classical algorithms
do not perform optimally, either because of the challenging signal-to-noise
ratio of the signal or its non-periodic characteristics. We have developed an
algorithm addressing these challenges for the mission CoRoT. Here we extend the
application to the data from the space mission Kepler. We aim at understanding
the performances of algorithms in different data sets. Methods: We built a
simple analytical model of the transit signal and developed a strategy for the
search that improves the detection performance for transiting planets. We
analyzed Kepler data with a set of stellar activity filtering and transit
detection tools from the CoRoT community that are designed for the search of
transiting planets. Results: We present a new algorithm and its performances
compared to one of the most widely used techniques in the literature using
CoRoT data. Additionally, we analyzed Kepler data corresponding to quarter Q1
and compare our results with the most recent list of planetary candidates from
the Kepler survey. We found candidates that went unnoticed by the Kepler team
when analyzing longer data sets. We study the impact of instrumental features
on the production of false alarms and false positives. These results show that
the analysis of space mission data advocates the use of complementary
detrending and transit detection tools also for future space-based transit
surveys such as PLATO.


  We propose a new problem called audio-visual segmentation (AVS), in which the
goal is to output a pixel-level map of the object(s) that produce sound at the
time of the image frame. To facilitate this research, we construct the first
audio-visual segmentation benchmark, i.e., AVSBench, providing pixel-wise
annotations for sounding objects in audible videos. It contains three subsets:
AVSBench-object (Single-source subset, Multi-sources subset) and
AVSBench-semantic (Semantic-labels subset). Accordingly, three settings are
studied: 1) semi-supervised audio-visual segmentation with a single sound
source; 2) fully-supervised audio-visual segmentation with multiple sound
sources, and 3) fully-supervised audio-visual semantic segmentation. The first
two settings need to generate binary masks of sounding objects indicating
pixels corresponding to the audio, while the third setting further requires
generating semantic maps indicating the object category. To deal with these
problems, we propose a new baseline method that uses a temporal pixel-wise
audio-visual interaction module to inject audio semantics as guidance for the
visual segmentation process. We also design a regularization loss to encourage
audio-visual mapping during training. Quantitative and qualitative experiments
on AVSBench compare our approach to several existing methods for related tasks,
demonstrating that the proposed method is promising for building a bridge
between the audio and pixel-wise visual semantics. Code is available at
https://github.com/OpenNLPLab/AVSBench. Online benchmark is available at
http://www.avlbench.opennlplab.cn.


  We use model theory of metric structures to prove the pointwise convergence,
with a uniform metastability rate, of averages of a polynomial sequence
$\{T_n\}$ (in Leibman's sense) of unitary transformations of a Hilbert space.
As a special case, this applies to unitary sequences $\{U^{p(n)}\}$ where $p$
is a polynomial $\mathbb{Z}\to\mathbb{Z}$ and $U$ a fixed unitary operator;
however, our convergence results hold for arbitrary Leibman sequences. As a
case study, we show that the non-nilpotent "lamplighter group"
$\mathbb{Z}\wr\mathbb{Z}$ is realized as the range of a suitable quadratic
Leibman sequence. We also indicate how these convergence results generalize to
arbitrary Folner averages of unitary polynomial actions of any abelian group
$\mathbb{G}$ in place of $\mathbb{Z}$.


  We consider a Yang--Mills theory in loop space whose gauge group is a
Kac--Moody group with the central extension. From this theory, we derive a
local field theory constructed of Yang--Mills fields and abelian antisymmetric
and symmetric tensor fields of the second rank. The Chapline--Manton coupling,
that is, coupling of Yang--Mills fields and a second-rank antisymmetric tensor
field via the Chern--Simons 3-form is obtained in a systematic manner.


  In this paper we show that all indecomposable nondegenerate set-theoretical
solutions to the Quantum Yang-Baxter equation on a set of prime order are
affine, which allows us to give a complete and very simple classification of
such solutions. This result is a natural application of the general theory of
set-theoretical solutions to the quantum Yang-Baxter equation. It is also a
generalization of the corresponding statement for involutive set-theoretical
solutions proved in an earlier paper of P.E. and A.S. with T.Schedler.
  In order to prove our main result, we use the classification theory developed
by the third author (based on the ideas of Lu, Yan, and Zhu) to reduce the
problem to a group-theoretical statement: a finite group with trivial center
generated by a conjugacy class of prime order is a subgroup of the affine
group. Unfortunalely, we did not find an elementary proof of this statement,
and our proof relies on the classification of outer automorphisms of finite
simple groups.


  In this paper, the performance of a binary phase shift keyed random
time-hopping impulse radio system with pulse-based polarity randomization is
analyzed. Transmission over frequency-selective channels is considered and the
effects of inter-frame interference and multiple access interference on the
performance of a generic Rake receiver are investigated for both synchronous
and asynchronous systems. Closed form (approximate) expressions for the
probability of error that are valid for various Rake combining schemes are
derived. The asynchronous system is modelled as a chip-synchronous system with
uniformly distributed timing jitter for the transmitted pulses of interfering
users. This model allows the analytical technique developed for the synchronous
case to be extended to the asynchronous case. An approximate closed-form
expression for the probability of bit error, expressed in terms of the
autocorrelation function of the transmitted pulse, is derived for the
asynchronous case. Then, transmission over an additive white Gaussian noise
channel is studied as a special case, and the effects of multiple-access
interference is investigated for both synchronous and asynchronous systems. The
analysis shows that the chip-synchronous assumption can result in
over-estimating the error probability, and the degree of over-estimation mainly
depends on the autocorrelation function of the ultra-wideband pulse and the
signal-to-interference-plus-noise-ratio of the system. Simulations studies
support the approximate analysis.


  The quantum state transmission (QST) through the medium of high-dimensional
many-particle system is studied with a symmetry analysis. We discover that, if
the spectrum matches the symmetry of a fermion or boson system in a certain
fashion, a perfect quantum state transfer can be implemented without any
operation on the medium. Based on this observation the well-established results
for the QST via quantum spin chains can be generalized to the high-dimensional
many-particle systems with pre-engineered nearest neighbor (NN) hopping
constants. By investigating a simple but realistic near half-filled
tight-binding fermion system with uniform NN hopping integral, we show that an
arbitrary many-particle state near the fermi surface can be perfectly
transferred to its translational counterpart.


  The local (central) limit theorem precisely describes the behavior of
iterated convolution powers of a probability distribution on the
$d$-dimensional integer lattice, $\mathbb{Z}^d$. Under certain mild assumptions
on the distribution, the theorem says that the convolution powers are
well-approximated by a single scaled Gaussian density which we call an
attractor. When such distributions are allowed to take on complex values, their
convolution powers exhibit new and disparate behaviors not seen in the
probabilistic setting. Following works of I. J. Schoenberg, T. N. E. Greville,
P. Diaconis, and L. Saloff-Coste, the author and L. Saloff-Coste provided a
complete description of local limit theorems for the class of finitely
supported complex-valued functions on $\mathbb{Z}$. For convolution powers of
complex-valued functions on $\mathbb{Z}^d$, much less is known. In a previous
work by the author and L. Saloff-Coste, local limit theorems were established
for complex-valued functions whose Fourier transform is maximized in absolute
value at so-called points of positive homogeneous type and, in that case, the
resultant attractors are generalized heat kernels corresponding to a class of
higher order partial differential operators. By considering the possibility
that the Fourier transform can be maximized in absolute value at points of
imaginary homogeneous type, this article extends previous work of the author
and L. Saloff-Coste to broaden the class of complex-valued functions for which
it is possible to obtain local limit theorems. These local limit theorems
contain attractors given by certain oscillatory integrals and their convergence
is established using a generalized polar-coordinate integration formula, due to
H. Bui and the author, and the Van der Corput lemma. The article also extends
recent results on sup-norm type estimates of H. Bui and the author.


  Converting source or unit test code to English has been shown to improve the
maintainability, understandability, and analysis of software and tests. Code
summarizers identify important statements in the source/tests and convert them
to easily understood English sentences using static analysis and NLP
techniques. However, current test summarization approaches handle only a subset
of the variation and customization allowed in the JUnit assert API (a critical
component of test cases) which may affect the accuracy of conversions. In this
paper, we present our work towards improving JUnit test summarization with a
detailed process for converting a total of 45 unique JUnit assertions to
English, including 37 previously-unhandled variations of the assertThat method.
This process has also been implemented and released as the AssertConvert tool.
Initial evaluations have shown that this tool generates English conversions
that accurately represent a wide variety of assertion statements which could be
used for code summarization or other NLP analyses.


  A radio network (RN) is a distributed system consisting of $n$ radio
stations. We design and analyze two distributed leader election protocols in RN
where the number $n$ of radio stations is unknown. The first algorithm runs
under the assumption of {\it limited collision detection}, while the second
assumes that {\it no collision detection} is available. By ``limited collision
detection'', we mean that if exactly one station sends (broadcasts) a message,
then all stations (including the transmitter) that are listening at this moment
receive the sent message. By contrast, the second no-collision-detection
algorithm assumes that a station cannot simultaneously send and listen signals.
Moreover, both protocols allow the stations to keep asleep as long as possible,
thus minimizing their awake time slots (such algorithms are called {\it
energy-efficient}). Both randomized protocols in RN areshown to elect a leader
in $O(\log{(n)})$ expected time, with no station being awake for more than
$O(\log{\log{(n)}})$ time slots. Therefore, a new class of efficient algorithms
is set up that match the $\Omega(\log{(n)})$ time lower-bound established by
Kushilevitz and Mansour.


  We classify subsystem symmetry-protected topological (SSPT) phases in $3+1$D
protected by planar subsystem symmetries, which are dual to abelian fracton
topological orders. We distinguish between weak SSPTs, which can be constructed
by stacking $2+1$D SPTs, and strong SSPTs, which cannot. We identify signatures
of strong phases, and show by explicit construction that such phases exist. A
classification of strong phases is presented for an arbitrary finite abelian
group. Finally, we show that fracton orders realizable via $p$-string
condensation are dual to weak SSPTs, while strong SSPTs do not admit such a
realization.


  The Josephson junction is a building block of quantum circuits. Its behavior,
well understood when treated as an isolated entity, is strongly affected by
coupling to an electromagnetic environment. In 1983, Schmid predicted that a
Josephson junction shunted by a resistance exceeding the resistance quantum
$\mathbf{\textit{R}}_\mathrm{Q} = h/4e^2 \approx 6.45$ k$\mathbf{\Omega}$ for
Cooper pairs would become insulating since the phase fluctuations would destroy
the coherent Josephson coupling. However, recent microwave measurements have
questioned this interpretation. Here, we insert a small Josephson junction in a
Johnson-Nyquist-type setup where it is driven by weak current noise arising
from thermal fluctuations. Our heat probe minimally perturbs the junction's
equilibrium, shedding light on features not visible in charge transport. We
find that the Josephson critical current completely vanishes in DC charge
transport measurement, and the junction demonstrates Coulomb blockade in
agreement with the theory. Surprisingly, thermal transport measurements show
that the Josephson junction acts as an inductor at high frequencies,
unambiguously demonstrating that a supercurrent survives despite the Coulomb
blockade observed in DC measurements. The discrepancy between these two
measurements highlights the difference between the low and the high frequency
response of a junction and calls for further theoretical and experimental
inputs on the dynamics of Josephson junctions \textcolor{black}{operating at
high frequencies in highly resistive environments.


  The paper is concerned with a singular limit for the bistable traveling wave
problem in a very large class of two-species fully nonlinear parabolic systems
with competitive reaction terms. Assuming existence of traveling waves and
enough compactness, we derive and characterize the limiting problem. The
assumptions and results are discussed in detail. The free boundary problem
obtained at the limit is specified for important applications.


  We study a scalar zero mode originated from extradimensional components of a
gauge field in a six-dimensional theory compactified on a magnetized torus. We
confirm it is a Nambu-Goldstone boson of the translational symmetry on the
torus which is breaking spontaneously due to magnetic flux. We also show
explicitly it is massless up to the two-loop level. Moreover, we discuss full
order contributions by considering the effective potential.


  In this paper, we propose a Junctionless (JL)/Accumulation Mode (AM)
transistor with an access transistor (JL in series with JL/AM transistor) based
capacitorless Dynamic Random Access Memory (1TDRAM) cell. The JL transistor
overcomes the problem of ultrasharp p-n junction associated with conventional
Metal-Oxide-Semiconductor (MOS) in nanoscale regime. The access transistor (AT)
is utilized to reduces the leakage, and thus, improves the Retention Time (RT)
and Sense Margin (SM) of the proposed capacitorless DRAM cell. Thus, the
proposed DRAM cell achieved a maximum SM of ~4.6 {\mu}A/{\mu}m with RT of ~6.5
s for a gate length (Lg) of 100nm. Further, this topology shows better gate
length scalability with a fixed gate length of AT and achieves RT of ~100 ms
and ~10 ms for a scaled gate length of 10 nm at 27 {\deg}C and 85 {\deg}C,
respectively.


  We use a method of linearization to study the emergence of the future
cosmological singularity characterized by finite value of the cosmological
radius. We uncover such singularities that keep Hubble parameter finite while
making all higher derivatives of the scale factor (starting out from the $\ddot
a$) diverge as the cosmological singularity is approached. Since such
singularities has been obtained before in the brane world model we name them
the "brane-like" singularities. These singularities can occur during the
expanding phase in usual Friedmann universe filled with both a self-acting,
minimally coupled scalar field and a homogeneous tachyon field. We discover a
new type of finite-time, future singularity which is different from type I-IV
cosmological singularities in that it has the scale factor, pressure and
density finite and nonzero. The generalization of $w$-singularity is obtained
as well.


  We present an exact diagonalization study of the self-energy of the
two-dimensional Hubbard model. To increase the range of available cluster sizes
we use a corrected t-J model to compute approximate Greens functions for the
Hubbard model. This allows to obtain spectra for clusters with 18 and 20 sites.
The self-energy has several `bands' of poles with strong dispersion and
extended incoherent continua with k-dependent intensity. We fit the self-energy
by a minimal model and use this to extrapolate the cluster results to the
infinite lattice. The resulting Fermi surface shows a transition from hole
pockets in the underdoped regime to a large Fermi surface in the overdoped
regime. We demonstrate that hole pockets can be completely consistent with the
Luttinger theorem. Introduction of next-nearest neighbor hopping changes the
self-energy stronlgy and the spectral function with nonvanishing
next-nearest-neighbor hopping in the underdoped region is in good agreement
with angle resolved photoelectron spectroscopy.


  Synthetic data from generative models emerges as the privacy-preserving
data-sharing solution. Such a synthetic data set shall resemble the original
data without revealing identifiable private information. The backbone
technology of tabular synthesizers is rooted in image generative models,
ranging from Generative Adversarial Networks (GANs) to recent diffusion models.
Recent prior work sheds light on the utility-privacy tradeoff on tabular data,
revealing and quantifying privacy risks on synthetic data. We first conduct an
exhaustive empirical analysis, highlighting the utility-privacy tradeoff of
five state-of-the-art tabular synthesizers, against eight privacy attacks, with
a special focus on membership inference attacks. Motivated by the observation
of high data quality but also high privacy risk in tabular diffusion, we
propose DP-TLDM, Differentially Private Tabular Latent Diffusion Model, which
is composed of an autoencoder network to encode the tabular data and a latent
diffusion model to synthesize the latent tables. Following the emerging f-DP
framework, we apply DP-SGD to train the auto-encoder in combination with batch
clipping and use the separation value as the privacy metric to better capture
the privacy gain from DP algorithms. Our empirical evaluation demonstrates that
DP-TLDM is capable of achieving a meaningful theoretical privacy guarantee
while also significantly enhancing the utility of synthetic data. Specifically,
compared to other DP-protected tabular generative models, DP-TLDM improves the
synthetic quality by an average of 35% in data resemblance, 15% in the utility
for downstream tasks, and 50% in data discriminability, all while preserving a
comparable level of privacy risk.


  Given an implicit $n\times n$ matrix $A$ with oracle access $x^TA x$ for any
$x\in \mathbb{R}^n$, we study the query complexity of randomized algorithms for
estimating the trace of the matrix. This problem has many applications in
quantum physics, machine learning, and pattern matching. Two metrics are
commonly used for evaluating the estimators: i) variance; ii) a high
probability multiplicative-approximation guarantee. Almost all the known
estimators are of the form $\frac{1}{k}\sum_{i=1}^k x_i^T A x_i$ for $x_i\in
\mathbb{R}^n$ being i.i.d. for some special distribution.
  Our main results are summarized as follows. We give an exact characterization
of the minimum variance unbiased estimator in the broad class of linear
nonadaptive estimators (which subsumes all the existing known estimators). We
also consider the query complexity lower bounds for any (possibly nonlinear and
adaptive) estimators: (1) We show that any estimator requires
$\Omega(1/\epsilon)$ queries to have a guarantee of variance at most
$\epsilon$. (2) We show that any estimator requires
$\Omega(\frac{1}{\epsilon^2}\log \frac{1}{\delta})$ queries to achieve a
$(1\pm\epsilon)$-multiplicative approximation guarantee with probability at
least $1 - \delta$. Both above lower bounds are asymptotically tight.
  As a corollary, we also resolve a conjecture in the seminal work of Avron and
Toledo (Journal of the ACM 2011) regarding the sample complexity of the
Gaussian Estimator.


  Our previous works have shown the statistical mechanics of self-gravitating
system. In this paper, we will show its thermodynamics and compare our results
with observations and simulations. We propose that our statistical mechanics
can be based on ergodicity breaking and Boltzmann entropy, and its assumptions
do not contradict with the reality. With the principle of statistical
mechanics, we will show our definition of temperature and then the capacity of
self-gravitating systems. We find that the gravothermal catastrophe may be a
special case of our theory. Our results also provide new explanations for the
density profiles of observations and numerical simulations, especially we think
that the non-universal density distribution in the simulations of
dissipationless collapse is not caused by the different initial collapse
factor. We will also discuss about the core-cusp problem.


  It is becoming increasingly clear that the supercharacter theory of the
finite group of unipotent upper-triangular matrices has a rich combinatorial
structure built on set-partitions that is analogous to the partition
combinatorics of the classical representation theory of the symmetric group.
This paper begins by exploring a connection to the ring of symmetric functions
in non-commuting variables that mirrors the symmetric group's relationship with
the ring of symmetric functions. It then also investigates some of the
representation theoretic structure constants arising from the restriction,
tensor products and superinduction of supercharacters in this context.


  Radioactive beams of $^{14}$O and $^{15}$O were used to populate the resonant
states 1/2$^+$, 5/2$^+$ and $0^-,1^-,2^-$ in the unbound $^{15}$F and $^{16}$F
nuclei respectively by means of proton elastic scattering reactions in inverse
kinematics. Based on their large proton spectroscopic factor values, the
resonant states in $^{16}$F can be viewed as a core of $^{14}$O plus a proton
in the 2s$_{1/2}$ or 1d$_{5/2}$ shell and a neutron in 1p$_{1/2}$. Experimental
energies were used to derive the strength of the 2s$_{1/2}$-1p$_{1/2}$ and
1d$_{5/2}$-1p$_{1/2}$ proton-neutron interactions. It is found that the former
changes by 40% compared with the mirror nucleus $^{16}$N, and the second by
10%. This apparent symmetry breaking of the nuclear force between mirror nuclei
finds explanation in the role of the large coupling to the continuum for the
states built on an $\ell=0$ proton configuration.


  Multilingual Neural Machine Translation (MNMT) enables one system to
translate sentences from multiple source languages to multiple target
languages, greatly reducing deployment costs compared with conventional
bilingual systems. The MNMT training benefit, however, is often limited to
many-to-one directions. The model suffers from poor performance in one-to-many
and many-to-many with zero-shot setup. To address this issue, this paper
discusses how to practically build MNMT systems that serve arbitrary X-Y
translation directions while leveraging multilinguality with a two-stage
training strategy of pretraining and finetuning. Experimenting with the WMT'21
multilingual translation task, we demonstrate that our systems outperform the
conventional baselines of direct bilingual models and pivot translation models
for most directions, averagely giving +6.0 and +4.1 BLEU, without the need for
architecture change or extra data collection. Moreover, we also examine our
proposed approach in an extremely large-scale data setting to accommodate
practical deployment scenarios.


  We report on Li-6 calculations performed with the IT-NCSM and compare them to
full NCSM calculations. We employ the Entem and Machleidt chiral two-body N3LO
interaction (regulated at 500 MeV/c), which has been modified to a phase-shift
equivalent potential by the similarity renormalization group (SRG) procedure.
We investigate the dependence of the procedure on the technique employed to
extrapolate to the complete Nmax space, the harmonic oscillator energy, and
investigate the dependence on the momentum-decoupling scale (\lambda) used in
the SRG. We also investigate the use of one or several reference states from
which the truncated basis is constructed. We find that the uncertainties
generated from various extrapolating functions used to extrapolate to the
complete Nmax space increase as Nmax increases. The extrapolation uncertainties
range from a few keV for the smallest Nmax spaces to about 50 keV for the
largest Nmax spaces. We note that the difference between extrapolated IT-NCSM
and NCSM ground-state energies, however, can be as large as a 100-250 keV
depending on the chosen harmonic oscillator energy. We also present the
extrapolation of IT-NCSM results to Nmax infinity and compare these to
similarly extrapolated full NCSM results.


  It is argued that the existence of constant dilaton field solutions is a
generic feature of string-inspired dilaton gravity. Such solutins arise in the
extreme limit of black hole metrics. It is shown that in a strong coupling
region quantum effects give rise to two horizons in thermal equilibrium that
has no classical counterpart.


  We study the testable implications of models of dynamically inconsistent
choices when planned choices are unobservable, and thus only "on path" data is
available. First, we discuss the approach in Blow, Browning and Crawford
(2021), who characterize first-order rationalizability of the model of
quasi-hyperbolic discounting. We show that the first-order approach does not
guarantee rationalizability by means of the quasi-hyperbolic model. This
motivates consideration of an abstract model of intertemporal choice, under
which we provide a characterization of different behavioral models -- including
the naive and sophisticated paradigms of dynamically inconsistent choice.


  The Belle and Babar experiments have measured the branching fractions for B+
-> tau+ nu and Ds+ -> mu+ nu decays. From these measurements one can extract
the B+ and Ds+ decay constants, which can be compared to lattice QCD
calculations. For the Ds+ decay constant, there is currently a 2.1 sigma
difference between the calculated value and the measured value.


  Microgrid serves as a promising solution to integrate and manage distributed
renewable energy resources. In this paper, we establish a stochastic
multi-objective sizing optimization (SMOSO) model for microgrid planning, which
fully captures the battery degradation characteristics and the total carbon
emissions. The microgrid operator aims to simultaneously maximize the economic
benefits and minimize carbon emissions, and the degradation of the battery
energy storage system (BESS) is modeled as a nonlinear function of power
throughput. A self-adaptive multi-objective genetic algorithm (SAMOGA) is
proposed to solve the SMOSO model, and this algorithm is enhanced by
pre-grouped hierarchical selection and self-adaptive probabilities of crossover
and mutation. Several case studies are conducted to determine the microgrid
size by analyzing Pareto frontiers, and the simulation results validate that
the proposed method has superior performance over other algorithms on the
solution quality of optimum and diversity.


  One of the first steps in the utterance interpretation pipeline of many
task-oriented conversational AI systems is to identify user intents and the
corresponding slots. Since data collection for machine learning models for this
task is time-consuming, it is desirable to make use of existing data in a
high-resource language to train models in low-resource languages. However,
development of such models has largely been hindered by the lack of
multilingual training data. In this paper, we present a new data set of 57k
annotated utterances in English (43k), Spanish (8.6k) and Thai (5k) across the
domains weather, alarm, and reminder. We use this data set to evaluate three
different cross-lingual transfer methods: (1) translating the training data,
(2) using cross-lingual pre-trained embeddings, and (3) a novel method of using
a multilingual machine translation encoder as contextual word representations.
We find that given several hundred training examples in the the target
language, the latter two methods outperform translating the training data.
Further, in very low-resource settings, multilingual contextual word
representations give better results than using cross-lingual static embeddings.
We also compare the cross-lingual methods to using monolingual resources in the
form of contextual ELMo representations and find that given just small amounts
of target language data, this method outperforms all cross-lingual methods,
which highlights the need for more sophisticated cross-lingual methods.


  We provide a technical report on a computer simulation of general
effectiveness of a hierarchical organization depending on two main aspects:
effects of promotion to managerial levels and efforts to self-promote of
individual employees, reducing their actual productivity. The combination of
judgment by appearance in the promotion to higher levels of hierarchy and the
Peter Principle (which states that people are promoted to their level of
incompetence) results in fast declines in effectiveness of the organization.
The model uses a few synthetic parameters aimed at reproduction of realistic
conditions in typical multilayer organizations.


  The reachability problem for Vector Addition Systems (VASs) is a central
problem of net theory. The general problem is known to be decidable by
algorithms exclusively based on the classical
Kosaraju-Lambert-Mayr-Sacerdote-Tenney decomposition. This decomposition is
used in this paper to prove that the Parikh images of languages recognized by
VASs are semi-pseudo-linear; a class that extends the semi-linear sets, a.k.a.
the sets definable in Presburger arithmetic. We provide an application of this
result; we prove that a final configuration is not reachable from an initial
one if and only if there exists a semi-linear inductive invariant that contains
the initial configuration but not the final one. Since we can decide if a
Presburger formula denotes an inductive invariant, we deduce that there exist
checkable certificates of non-reachability. In particular, there exists a
simple algorithm for deciding the general VAS reachability problem based on two
semi-algorithms. A first one that tries to prove the reachability by
enumerating finite sequences of actions and a second one that tries to prove
the non-reachability by enumerating Presburger formulas.


  The RENO experiment has observed the disappearance of reactor electron
antineutrinos, consistent with neutrino oscillations, with a significance of
4.9 standard deviations. Antineutrinos from six 2.8 GW$_{th}$ reactors at the
Yonggwang Nuclear Power Plant in Korea, are detected by two identical detectors
located at 294 m and 1383 m, respectively, from the reactor array center. In
the 229 day data-taking period between 11 August 2011 and 26 March 2012, the
far (near) detector observed 17102 (154088) electron antineutrino candidate
events with a background fraction of 5.5% (2.7%). The ratio of observed to
expected numbers of antineutrinos in the far detector is $0.920 \pm 0.009({\rm
stat.}) \pm 0.014({\rm syst.})$. From this deficit, we determine $\sin^2 2
\theta_{13} = 0.113 \pm 0.013({\rm stat.}) \pm 0.019({\rm syst.})$ based on a
rate-only analysis.


  Semantic communication is an emerging paradigm that focuses on understanding
and delivering semantics, or meaning of messages. Most existing semantic
communication solutions define semantic meaning as the meaning of object labels
recognized from a source signal, while ignoring intrinsic information that
cannot be directly observed. Moreover, existing solutions often assume the
recognizable semantic meanings are limited by a pre-defined label database. In
this paper, we propose a novel reasoning-based semantic communication
architecture in which the semantic meaning is represented by a graph-based
knowledge structure in terms of object-entity, relationships, and reasoning
rules. An embedding-based semantic interpretation framework is proposed to
convert the high-dimensional graph-based representation of semantic meaning
into a low-dimensional representation, which is efficient for channel
transmission. We develop a novel inference function-based approach that can
automatically infer hidden information such as missing entities and relations
that cannot be directly observed from the message. Finally, we introduce a
life-long model updating approach in which the receiver can learn from
previously received messages and automatically update the reasoning rules of
users when new unknown semantic entities and relations have been discovered.
Extensive experiments are conducted based on a real-world knowledge database
and numerical results show that our proposed solution achieves 76%
interpretation accuracy of semantic meaning at the receiver, notably when some
entities are missing in the transmitted message.


  Motivated by the need of observers that are both robust to disturbances and
guarantee fast convergence to zero of the estimation error, we propose an
observer for linear time-invariant systems with noisy output that consists of
the combination of N coupled observers over a connectivity graph. At each node
of the graph, the output of these interconnected observers is defined as the
average of the estimates obtained using local information. The convergence rate
and the robustness to measurement noise of the proposed observer's output are
characterized in terms of $\mathcal{KL}$ bounds. Several optimization problems
are formulated to design the proposed observer so as to satisfy a given rate of
convergence specification while minimizing the $H_\infty$ gain from noise to
estimates or the size of the connectivity graph. It is shown that that the
interconnected observers relax the well-known tradeoff between rate of
convergence and noise amplification, which is a property attributed to the
proposed innovation term that, over the graph, couples the estimates between
the individual observers. Sufficient conditions involving information of the
plant only, assuring that the estimate obtained at each node of the graph
outperforms the one obtained with a single, standard Luenberger observer are
given. The results are illustrated in several examples throughout the paper.


  Using the semi-classical Boltzmann theory, we calculate the conductivity as
function of the carrier density. As usually, we include the scattering from
charged impurities, but conclude that the estimated impurity density is too low
in order to explain the experimentally observed mobilities. We thus propose an
additional scattering mechanism involving midgap states which leads to a
similar k-dependence of the relaxation time as charged impurities. The new
scattering mechanism can account for the experimental findings such as the
sublinear behavior of the conductivity versus gate voltage and the increase of
the minimal conductivity for clean samples. We also discuss temperature
dependent scattering due to acoustic phonons.


  We study the vertical dispersion and distribution of negatively buoyant rigid
microplastics within a realistic circulation model of the Mediterranean sea. We
first propose an equation describing their idealized dynamics. In that
framework, we evaluate the importance of some relevant physical effects:
inertia, Coriolis force, small-scale turbulence and variable seawater density,
and bound the relative error of simplifying the dynamics to a constant sinking
velocity added to a large-scale velocity field. We then calculate the amount
and vertical distribution of microplastic particles on the water column of the
open ocean if their release from the sea surface is continuous at rates
compatible with observations in the Mediterranean. The vertical distribution is
found to be almost uniform with depth for the majority of our parameter range.
Transient distributions from flash releases reveal a non-Gaussian character of
the dispersion and various diffusion laws, both normal and anomalous. The
origin of these behaviors is explored in terms of horizontal and vertical flow
organization.


  We construct examples of 2-step Carnot groups related to quaternions and
study their fine structure and geometric properties. This involves the
Hamiltonian formalism, which is used to obtain explicit equations for geodesics
and the computation of the number of geodesics joining two different points on
these groups. We able to find the explicit lengths of geodesics. We present the
fundamental solutions of the Heat and sub-Laplace equations for these
anisotropic groups and obtain some estimates for them, which may be useful.


  In this research, a novel contact resistance model for the flat panel display
(FPD) packaging based on the within layer parallel and between layers series
resistance concepts was proposed. The FJ2530 anisotropic conductive films (ACF)
by Sony Inc. containing the currently smallest 3micron conductive particles was
used to conduct the experiments to verify the accuracy of the proposed model.
Calculated resistance of the chip-on-glass (COG) packaging by the proposed
model is 0.163\Omega. It is found that the gold bump with 0.162\Omega
resistance play the major role of the overall resistance. Although the
predicted resistance by the proposed model is only one third of the
experimentally measured value, it has been three-fold improvement compared to
the existing models.


  A large class of machine-learning problems in natural language require the
characterization of linguistic context. Two characteristic properties of such
problems are that their feature space is of very high dimensionality, and their
target concepts refer to only a small subset of the features in the space.
Under such conditions, multiplicative weight-update algorithms such as Winnow
have been shown to have exceptionally good theoretical properties. We present
an algorithm combining variants of Winnow and weighted-majority voting, and
apply it to a problem in the aforementioned class: context-sensitive spelling
correction. This is the task of fixing spelling errors that happen to result in
valid words, such as substituting "to" for "too", "casual" for "causal", etc.
We evaluate our algorithm, WinSpell, by comparing it against BaySpell, a
statistics-based method representing the state of the art for this task. We
find: (1) When run with a full (unpruned) set of features, WinSpell achieves
accuracies significantly higher than BaySpell was able to achieve in either the
pruned or unpruned condition; (2) When compared with other systems in the
literature, WinSpell exhibits the highest performance; (3) The primary reason
that WinSpell outperforms BaySpell is that WinSpell learns a better linear
separator; (4) When run on a test set drawn from a different corpus than the
training set was drawn from, WinSpell is better able than BaySpell to adapt,
using a strategy we will present that combines supervised learning on the
training set with unsupervised learning on the (noisy) test set.


  In this paper, we investigate the existence and the global stability of
periodic solution for dynamical systems with periodic interconnections, inputs
and self-inhibitions. The model is very general, the conditions are quite weak
and the results obtained are universal.


  Fracture functions are parton distributions of an initial hadron in the
presence of an almost collinear particle observed in the final state. They are
important ingredients in QCD factorization for processes where a particle is
produced diffractively. There are different fracture functions for a process in
different kinematic regions. We take the production of a lepton pair combined
with a diffractively produced particle in hadron collisions to discuss this.
Those fracture functions can be factorized further if there are large energy
scales involved. They can be factorized with twist-2 parton distribution
functions and fragmentation functions. We perform one-loop calculations to
illustrate the factorization in the case with the diffractively produced
particle as a real photon. Evolution equations of different fracture functions
are derived from our explicit calculations. They agree with expectations. These
equations can be used for re-summations of large log terms in perturbative
expansions.


  The band structure of an Si inverse diamond structure whose lattice point
shape was vacant regular octahedrons, was calculated using plane wave expansion
method. A complete photonic band gap was theoretically confirmed at around 0.4
THz. It is said that three-dimensional photonic crystals have no polarization
anisotropy in photonic band gap (stop gap, stop band) of high symmetry points
in normal incidence. However, it was experimentally confirmed that the
polarization orientation of a reflected wave was different from that of an
incident wave, [I$(X,Y)$], where $(X,Y)$ is the coordinate system fixed in the
photonic crystal. It was studied on a plane (001) at around X point's photonic
band gap (0.36 $-$ 0.44 THz) for incident wave direction [001] by rotating a
sample in the plane (001), relatively. The polarization orientation of the
reflected wave was parallel to that of the incident wave when that of the
incident wave was I(1, 1) or I(1, $-$1). In contrast, the former was
perpendicular to the latter when that of the incident wave was I(1, 0) or I(0,
$-$1) at around 0.38 THz. As far as the photonic crystal in this work is
concerned, method of resolution and synthesis of the incident polarization
vector is not able to apply to the analyses of rotation of the measured
reflected spectra in appearance.


  Deep learning image classification is vulnerable to adversarial attack, even
if the attacker changes just a small patch of the image. We propose a defense
against patch attacks based on partially occluding the image around each
candidate patch location, so that a few occlusions each completely hide the
patch. We demonstrate on CIFAR-10, Fashion MNIST, and MNIST that our defense
provides certified security against patch attacks of a certain size.


  We report the first measurement of target single spin asymmetries in the
semi-inclusive $^3{He}(e,e'\pi^\pm)X$ reaction on a transversely polarized
target. The experiment, conducted at Jefferson Lab using a 5.9 GeV electron
beam, covers a range of 0.14 $< x <$ 0.34 with 1.3 $<Q^2<$ 2.7 GeV$^2$. The
Collins and Sivers moments were extracted from the azimuthal angular dependence
of the measured asymmetries. The extracted $\pi^\pm$ Collins moments for $^3$He
are consistent with zero, except for the $\pi^+$ moment at $x=0.34$, which
deviates from zero by 2.3$\sigma$. While the $\pi^-$ Sivers moments are
consistent with zero, the $\pi^+$ Sivers moments favor negative values. The
neutron results were extracted using the nucleon effective polarization and the
measured cross section ratio of proton to $^3$He, and are largely consistent
with the predictions of phenomenological fits and quark model calculations.


  We propose a new method to account for quantum-gravitational effects in
cosmological and black hole spacetimes. At the core of our construction is the
"decoupling mechanism": when a physical infrared scale overcomes the effect of
the regulator implementing the Wilsonian integration of fluctuating modes, the
renormalization group flow of the scale-dependent effective action freezes out,
so that at the decoupling scale the latter approximates the standard quantum
effective action. Identifying the decoupling scale allows to access terms in
the effective action that were not part of the original truncation and thus to
study leading-order quantum corrections to field equations and their solutions.
Starting from the Einstein-Hilbert truncation, we exploit for the first time
the decoupling mechanism in quantum gravity to investigate the dynamics of
quantum-corrected black holes from formation to evaporation. Our findings are
in qualitative agreement with previous results in the context of
renormalization group improved black holes, but additionally feature novel
properties reminiscent of higher-derivative operators with specific non-local
form factors.


  BaIrO3 is a novel insulator with coexistent weak ferromagnetism, charge and
spin density wave. Dilute RE doping for Ba induces a metallic state, whereas
application of modest pressure readily restores an insulating state
characterized by a three-order-of-magnitude increase of resistivity. Since
pressure generally increases orbital overlap and broadens energy bands, a
pressure-induced insulating state is not commonplace. The profoundly dissimilar
responses of the ground state to light doping and low hydrostatic pressures
signal an unusual, delicate interplay between structural and electronic degrees
of freedom in BaIrO3.


  We introduce the notion of translational Riemannian manifolds and define a
Gauss map for orientable immersed hypersurfaces lying in these ambients, an
associated translational curvature and prove a Gauss-Bonnet theorem. We also
use this Gauss map to prove that if $M^{n}$ is a compact, connected and
oriented immersed hypersurface of the unit sphere $\mathbb{S}^{n+1}$ ($n\geq2$)
contained in a geodesic ball of radius $R$ and whose principal curvatures are
strictly bigger than $\tan\left( R/2 \right)$, then $M$ is diffeomorphic to
$\mathbb{S}^{n}$. Additionally, we show that for any
$\varepsilon\in(0,\sqrt{2}-1)$ there exists a compact, connected and oriented
immersed hypersurface $M_{\varepsilon}$ of $\mathbb{S}^{n+1}$ whose principal
curvatures are strictly bigger than $\varepsilon \tan \left( R/2 \right)$ but
$M_{\varepsilon}$ is not homeomorphic to a sphere. Finally, using this previous
result, we reobtain a theorem of Qiaoling Wang and Changyu Xia (see [4]) which
asserts that if a compact and oriented hypersurface of $\mathbb{S}^{n+1}$ is
contained in an open hemisphere and has nowhere zero Gauss-Kronecker curvature,
then it is diffeomorphic to $\mathbb{S}^n$.


  Given a nonnegative integrable function $J$ on $\mathbb{R}^n$, we relate the
asymptotic properties of the nonlocal energy functional
  \begin{equation*}
  \int_{\Omega} \int_{\Omega^c} J \bigg(\frac{x-y}{t}\bigg) \ dx dy
  \end{equation*}
  as $t \to 0^+$ with the boundary properties of a given domain $\Omega \subset
\mathbb{R}^n$. Then, we use these asymptotic properties to study the
fluctuations of many determinantal point processes, and show that their
variances measure the Minkowski dimension of $\partial \Omega$.


  We determine the minimal number of variables $\Gamma^*(d, K)$ which
guarantees a nontrivial solution for every additive form of degree $d=4$ over
the four ramified quadratic extensions $\mathbb{Q}_2(\sqrt{2}),
\mathbb{Q}_2(\sqrt{10}), \mathbb{Q}_2(\sqrt{-2}), \mathbb{Q}_2(\sqrt{-10}) $ of
$\mathbb{Q}_2$. In all four fields, we prove that $\Gamma^*(4,K) = 11$. This is
the first example of such a computation for a proper extension of
$\mathbb{Q}_p$ where the degree is a power of $p$ greater than $p$.


  We study the "complexity equals volume" (CV) and "complexity equals action"
(CA) conjectures by examining moments of of time symmetry for $\rm AdS_3$
wormholes having $n$ asymptotic regions and arbitrary (orientable) internal
topology. For either prescription, the complexity relative to $n$ copies of the
$M=0$ BTZ black hole takes the form $\Delta C = \alpha c \chi $, where $c$ is
the central charge and $\chi$ is the Euler character of the bulk time-symmetric
surface. The coefficients $\alpha_V = -4\pi/3$, $\alpha_A = 1/6 $ defined by CV
and CA are independent of both temperature and any moduli controlling the
geometry inside the black hole. Comparing with the known structure of dual CFT
states in the hot wormhole limit, the temperature and moduli independence of
$\alpha_V$, $\alpha_A$ implies that any CFT gate set defining either complexity
cannot be local. In particular, the complexity of an efficient quantum circuit
building local thermofield-double-like entanglement of thermal-sized patches
does not depend on the separation of the patches so entangled. We also comment
on implications of the (positive) sign found for $\alpha_A$, which requires the
associated complexity to decrease when handles are added to our wormhole.


  We present results from a parsec-scale jet kinematics study of 409 bright
radio-loud AGNs based on 15 GHz VLBA data obtained between 1994 August 31 and
2016 December 26 as part of the 2cm VLBA survey and MOJAVE programs. We tracked
1744 individual bright features in 382 jets over at least five epochs. A
majority (59%) of the best-sampled jet features showed evidence of accelerated
motion at the >3sigma level. Although most features within a jet typically have
speeds within ~40% of a characteristic median value, we identified 55 features
in 42 jets that had unusually slow pattern speeds, nearly all of which lie
within 4 pc (100 pc de-projected) of the core feature. Our results combined
with other speeds from the literature indicate a strong correlation between
apparent jet speed and synchrotron peak frequency, with the highest jet speeds
being found only in low-peaked AGNs. Using Monte Carlo simulations, we find
best fit parent population parameters for a complete sample of 174 quasars
above 1.5 Jy at 15 GHz. Acceptable fits are found with a jet population that
has a simple unbeamed power law luminosity function incorporating pure
luminosity evolution, and a power law Lorentz factor distribution ranging from
1.25 to 50 with slope -1.4 +- 0.2. The parent jets of the brightest radio
quasars have a space density of 261 +- 19 Gpc$^{-3}$ and unbeamed 15 GHz
luminosities above ~$10^{24.5}$ W/Hz, consistent with FR II class radio
galaxies.


  Generative AI and LLMs in particular are heavily used nowadays for various
document processing tasks such as question answering and summarization.
However, different LLMs come with different capabilities for different tasks as
well as with different costs, tokenization, and latency. In fact, enterprises
are already incurring huge costs of operating or using LLMs for their
respective use cases.
  In this work, we propose optimizing the usage costs of LLMs by estimating
their output quality (without actually invoking the LLMs), and then solving an
optimization routine for the LLM selection to either keep costs under a budget,
or minimize the costs, in a quality and latency aware manner. We propose a
model to predict the output quality of LLMs on document processing tasks like
summarization, followed by an LP rounding algorithm to optimize the selection
of LLMs. We study optimization problems trading off the quality and costs, both
theoretically and empirically. We further propose a sentence simplification
model for reducing the number of tokens in a controlled manner. Additionally,
we propose several deterministic heuristics for reducing tokens in a quality
aware manner, and study the related optimization problem of applying the
heuristics optimizing the quality and cost trade-off. We perform extensive
empirical validation of our methods on not only enterprise datasets but also on
open-source datasets, annotated by us, and show that we perform much better
compared to closest baselines. Our methods reduce costs by 40%- 90% while
improving quality by 4%-7%. We will release the annotated open source datasets
to the community for further research and exploration.


  In this paper, we study non-linear differential equations associated with
Legendre polynomials and their applications. From our study of non- linear
differential equations, we derive some new and explicit identities for Legendre
polynomials.


  In this note, we prove a power-saving remainder term for the function
counting $S_3$-sextic number fields. We also give a prediction on the second
main term.
  We also present numerical data on counting functions for $S_3$-sextic number
fields. Our data indicates that our prediction is likely to be correct, and it
also suggests the existence of additional lower order terms which we have not
yet been able to explain.


  An interesting analog circuit for simulating a signal with fluctuations
having a probability density function with a power tail has recently been
proposed and constructed. The exponent of the power law can be fixed by tuning
an appropriate circuit element. The proposal is to use the circuit as a
simulator-generator of financial market fluctuations and as a tool for risk
estimations and forecasts. We present a discussion of the stability conditions
for multiplicative noise and an exhaustive analysis of the power law
fluctuation generator in connection with the electronic components and their
parameters. From our studies one can conclude that the proposal is not adequate
to provide a confident experimental scheme to follow the fluctuations of the
financial market due to both electronic implementation and to difficulties in
parameter tuning.


  The purpose of the given message - interpretation of the processes providing
a photoluminescence and laser generation in spectral area 2000 nm at use as the
active laser environment cristall or glass material, activated by an
atomicholmium. Offered in article interpretation is based on use of the law of
the conservation of energy, which attraction allows to reveal in an atomic
holmium the transitions responsible for a photoluminecsence in specified area
of a spectrum (2000 nm).


  We study the zero temperature quenching dynamics of various extensions of the
transverse Ising model (TIM) when the transverse field is linearly quenched
from $-\infty$ to $+\infty$ (or zero) at a finite and uniform rate. The rate of
quenching is dictated by a characteristic scale given by $\tau$. The density of
kinks produced in these extended models while crossing the quantum critical
points during the quenching process is calculated using a many body
generalization of the Landau-Zener transition theory. The density of kinks in
the final state is found to decay as $\tau^{-1/2}$. In the first model
considered here, the transverse Ising Hamiltonian includes an additional
ferromagnetic three spin interaction term of strength $J_3$. For $J_3<0.5$, the
kink density is found to increase monotonically with $J_3$ whereas it decreases
with $J_3$ for $J_3>0.5$. The point $J_3=0.5$ and the transverse field
$h=-0.5$is multicritical where the density shows a slower decay given by
$\tau^{-1/6}$. We also study the effect of ferromagnetic or antiferromagnetic
next nearest neighbor (NNN) interactions on the dynamics of TIM under the same
quenching scheme. In a mean field approximation, the transverse Ising
Hamiltonians with NNN interactions are identical to the three spin Hamiltonian.
The NNN interactions non-trivially modifies the dynamical behavior, for example
an antiferromagnetic NNN interactions results to a larger number of kinks in
the final state in comparison to the case when the NNN interaction is
ferromagnetic.


  This work introduces a co-capture system for multi-animal visual data
acquisition using conventional cameras and event cameras. Event cameras offer
multiple advantages over frame-based cameras, such as a high temporal
resolution and temporal redundancy suppression, which enable us to efficiently
capture the fast and erratic movements of fish. We furthermore present an
event-based multi-animal tracking algorithm, which proves the feasibility of
the approach and sets the baseline for further exploration of combining the
advantages of event cameras and conventional cameras for multi-animal tracking.


  In the framework of the baryogenesis via leptogenesis mechanism we study the
link between the amount of baryon asymmetry and neutrino mass matrices. In
particular, neglecting phases, we find that if the Dirac neutrino mass matrix
is related to the up quark mass matrix, the baryon asymmetry is about three
orders smaller than the required value. If the Dirac neutrino mass matrix is
related to the down quark or the charged lepton mass matrix, the baryon
asymmetry is about two orders smaller than the required value. In order to get
a sufficient amount of baryon asymmetry we need a more moderate hierarchy in
the Dirac neutrino mass matrix.


  We show that in field systems with U(1)-symmetry, first-order transitions are
nucleated by vortex lines, not bubbles, thus calling for a reinvestigation of
the Kibble mechanism for the phase transition of the early universe.


  We give simple characterizations of the category PAsm(A) of partitioned
assemblies, and of the realizability topos RT(A) over a partial combinatory
algebra A. This answers the question for an 'extensional characterization' of
realizability toposes.


  Image segmentation in the medical domain is a challenging field owing to poor
resolution and limited contrast. The predominantly used conventional
segmentation techniques and the thresholding methods suffer from limitations
because of heavy dependence on user interactions. Uncertainties prevalent in an
image cannot be captured by these techniques. The performance further
deteriorates when the images are corrupted by noise, outliers and other
artifacts. The objective of this paper is to develop an effective robust fuzzy
C- means clustering for segmenting vertebral body from magnetic resonance image
owing to its unsupervised form of learning. The motivation for this work is
detection of spine geometry and proper localisation and labelling will enhance
the diagnostic output of a physician. The method is compared with Otsu
thresholding and K-means clustering to illustrate the robustness.The reference
standard for validation was the annotated images from the radiologist, and the
Dice coefficient and Hausdorff distance measures were used to evaluate the
segmentation.


  Remittances have become one of the driving forces of development for
countries all over the world, especially in lower-middle-income nations. This
paper empirically investigates the association between remittance flows and
financial development in 4 lower-middle-income countries of Latin America. By
using a panel data set from 1996 to 2019, the study revealed that remittances
and financial development are positively associated in these countries. The
study also discovered that foreign direct investment and inflation were
positively correlated with financial development while trade openness had a
negative association with financial development. Therefore, policymakers of
these countries should implement and formulate such policies so that migrant
workers would have the incentives to send money through formal channels, which
will augment the effect of remittances on the recipient country.


  We present a novel adversarial detection and correction method for machine
learning classifiers.The detector consists of an autoencoder trained with a
custom loss function based on the Kullback-Leibler divergence between the
classifier predictions on the original and reconstructed instances.The method
is unsupervised, easy to train and does not require any knowledge about the
underlying attack. The detector almost completely neutralises powerful attacks
like Carlini-Wagner or SLIDE on MNIST and Fashion-MNIST, and remains very
effective on CIFAR-10 when the attack is granted full access to the
classification model but not the defence. We show that our method is still able
to detect the adversarial examples in the case of a white-box attack where the
attacker has full knowledge of both the model and the defence and investigate
the robustness of the attack. The method is very flexible and can also be used
to detect common data corruptions and perturbations which negatively impact the
model performance. We illustrate this capability on the CIFAR-10-C dataset.


  The main limitation that constrains the fast and comprehensive application of
Wireless Local Area Network (WLAN) based indoor localization systems with
Received Signal Strength (RSS) positioning algorithms is the building of the
fingerprinting radio map, which is time-consuming especially when the indoor
environment is large and/or with high frequent changes. Different approaches
have been proposed to reduce workload, including fingerprinting deployment and
update efforts, but the performance degrades greatly when the workload is
reduced below a certain level. In this paper, we propose an indoor localization
scenario that applies metric learning and manifold alignment to realize direct
mapping localization (DML) using a low resolution radio map with single sample
of RSS that reduces the fingerprinting workload by up to 87\%. Compared to
previous work. The proposed two localization approaches, DML and $k$ nearest
neighbors based on reconstructed radio map (reKNN), were shown to achieve less
than 4.3\ m and 3.7\ m mean localization error respectively in a typical office
environment with an area of approximately 170\ m$^2$, while the unsupervised
localization with perturbation algorithm was shown to achieve 4.7\ m mean
localization error with 8 times more workload than the proposed methods. As for
the room level localization application, both DML and reKNN can meet the
requirement with at most 9\ m of localization error which is enough to tell
apart different rooms with over 99\% accuracy.


  The LHCb experiment has been taking data at the Large Hadron Collider (LHC)
at CERN since the end of 2009. One of its key detector components is the
Ring-Imaging Cherenkov (RICH) system. This provides charged particle
identification over a wide momentum range, from 2-100 GeV/c. The operation and
control software, and online monitoring of the RICH system are described. The
particle identification performance is presented, as measured using data from
the LHC. Excellent separation of hadronic particle types (pion, kaon and
proton) is achieved.


  High-quality and sizable single crystals are crucial for studying the
intrinsic properties of unconventional superconductors, which are lacking in
the 1111 phase of the Fe-based superconductors. Here we report the successful
growth of CaFeAsF single crystals with the sizes of 1-2 mm using the self-flux
method. Owning to the availability of the high-quality single crystals, the
structure and transport properties were investigated with a high reliability.
The structure was refined by using the single-crystal x-ray diffraction data,
which confirms the reports earlier on the basis of powder data. A clear anomaly
associated with the structural transition was observed at 121 K from the
resistivity, magnetoresistance, and magnetic susceptibility measurements.
Another kink-feature at 110 K, most likely an indication of the
antiferromagnetic transition, was also detected in the resistivity data. Our
results supply a basis to propel the physical investigations on the 1111 phase
of the Fe-based superconductors.


  We start with a particular cosmological model derived from type IIB
supergravity theory with fluxes, where usually the dilaton is interpreted as a
Quintessence field. Instead of that, in this letter we interpret the dilaton as
the dark matter of the universe. With this alternative interpretation we find
that in this supergravity model gives a similar evolution and structure
formation of the universe compared with the $\Lambda$CDM model in the linear
regime of fluctuations of the structure formation. Some free parameters of the
theory are fixed using the present cosmological observations. In the non-linear
regimen there are some differences between the type IIB supergravity theory
with the traditional CDM paradigm. The supergravity theory predicts the
formation of galaxies earlier than the CDM and there is no density cusp in the
center of galaxies. These differences can distinguish both models and can give
a distinctive feature to the phenomenology of the cosmology coming from
superstring theory with fluxes.


  We prove two principal kinematic formulas for germs of closed definable sets
in $\mathbb{R}^n$, that generalize the Cauchy-Crofton formula for the density
due to Comte and the infinitesimal linear kinematic formula due to the author.
In this setting, we do not integrate on the space of euclidian motions, but on
the manifold $SO(n) \times S^{n-1}$.


  A Higgs portal in the form of the operator $|H|^2$ provides a minimal and
theoretically motivated link between the Standard Model (SM) and new physics.
While Higgs portals can be constrained well by exotic Higgs decays if the
beyond-the-SM states are light, testing scenarios where these particles are
kinematically inaccessible is known to be challenging. We explore the
sensitivity of future hadron collider measurements of $ZZ \to 4 \ell$
production in constraining Higgs portal interactions. It is shown that by using
a matrix-element based kinematic discriminant the reach of the high-luminosity
option of the Large Hadron Collider (LHC) can be significantly enhanced
compared to studies that are based on measurements of the four-lepton invariant
mass spectrum alone. We also analyse the potential of the high-energy upgrade
of the LHC and a Future Circular Collider in constraining new physics that
couples to $|H|^2$. The obtained constraints are compared to the limits one
expects to find from other single-Higgs probes. In addition, we provide an
independent analysis of the relevant Higgs portal effects in double-Higgs
production. We find that the constraints obtained from our $ZZ \to 4 \ell$
analysis turn out to be both competitive with and complementary to the
projected limits obtained using other search techniques.


  There are many interesting parallels between systems of interacting
non-Abelian anyons and quantum magnetism, occuring in ordinary SU(2) quantum
magnets. Here we consider theories of so-called su(2)_k anyons, well-known
deformations of SU(2), in which only the first k+1 angular momenta of SU(2)
occur. In this manuscript, we discuss in particular anyonic generalizations of
ordinary SU(2) spin chains with an emphasis on anyonic spin S=1 chains. We find
that the overall phase diagrams for these anyonic spin-1 chains closely mirror
the phase diagram of the ordinary bilinear-biquadratic spin-1 chain including
anyonic generalizations of the Haldane phase, the AKLT construction, and
supersymmetric quantum critical points. A novel feature of the anyonic spin-1
chains is an additional topological symmetry that protects the gapless phases.
Distinctions further arise in the form of an even/odd effect in the deformation
parameter k when considering su(2)_k anyonic theories with k>4, as well as for
the special case of the su(2)_4 theory for which the spin-1 representation
plays a special role. We also address anyonic generalizations of spin-1/2
chains with a focus on the topological protection provided for their gapless
ground states. Finally, we put our results into context of earlier
generalizations of SU(2) quantum spin chains, in particular so-called (fused)
Temperley-Lieb chains.


  We propose using an asymmetric resonant microcavity for the efficient
generation of an optical path that is much longer than the diameter of the
cavity. The path is formed along a star polygonal periodic orbit within the
cavity, which is stable and confined by total internal reflection. We
fabricated a semiconductor device based on this idea with an average diameter
of 0.3 mm, and achieved a path length of 2.79 mm experimentally.


  What is the most statistically efficient way to do off-policy evaluation and
optimization with batch data from bandit feedback? For log data generated by
contextual bandit algorithms, we consider offline estimators for the expected
reward from a counterfactual policy. Our estimators are shown to have lowest
variance in a wide class of estimators, achieving variance reduction relative
to standard estimators. We then apply our estimators to improve advertisement
design by a major advertisement company. Consistent with the theoretical
result, our estimators allow us to improve on the existing bandit algorithm
with more statistical confidence compared to a state-of-the-art benchmark.


  The analysis of cosmic rays fluxes as a function of energy reveals a {\it
knee} slightly below $10^{16}$ eV and an {\it ankle} close to $10^{19}$ eV.
Their physical origins remain up to now quite enigmatic; in particular, no
elementary process is known which occurs at energies close to $10^{16}$ eV. We
propose a phenomenological approach along the lines of nonextensive statistical
mechanics, a formalism which contains Boltzmann-Gibbs statistical mechanics as
a particular case. The knee then appears as a crossover between two
fractal-like thermal regimes, the crossover being caused by process occurring
at energies ten million times lower than that of the knee, in the region of the
quark hadron transition ($\simeq 10^{9}$ eV). This opens the door to an
unexpected standpoint for further clarifying the phenomenon.


  Molecular docking, which aims to find the most stable interacting
configuration of a set of molecules, is of critical importance to drug
discovery. Although a considerable number of classical algorithms have been
developed to carry out molecular docking, most focus on the limiting case of
docking two molecules. Since the number of possible configurations of N
molecules is exponential in N, those exceptions which permit docking of more
than two molecules scale poorly, requiring exponential resources to find
high-quality solutions. Here, we introduce a one-hot encoded quadratic
unconstrained binary optimization formulation (QUBO) of the multibody molecular
docking problem, which is suitable for solution by quantum annealer. Our
approach involves a classical pre-computation of pairwise interactions, which
scales only quadratically in the number of bodies while permitting well-vetted
scoring functions like the Rosetta REF2015 energy function to be used. In a
second step, we use the quantum annealer to sample low-energy docked
configurations efficiently, considering all possible docked configurations
simultaneously through quantum superposition. We show that we are able to
minimize the time needed to find diverse low-energy docked configurations by
tuning the strength of the penalty used to enforce the one-hot encoding,
demonstrating a 3-4 fold improvement in solution quality and diversity over
performance achieved with conventional penalty strengths. By mapping the
configurational search to a form compatible with current- and future-generation
quantum annealers, this work provides an alternative means of solving multibody
docking problems that may prove to have performance advantages for large
problems, potentially circumventing the exponential scaling of classical
approaches and permitting a much more efficient solution to a problem central
to drug discovery and validation pipelines.


  Recently superconductivity was discovered in the Kagome metal AV3Sb5 (A = K,
Rb, and Cs), which has an ideal Kagome lattice of vanadium. These V-based
superconductors also host charge density wave (CDW) and topological nontrivial
band structure. Here we report the ultralow-temperature thermal conductivity
and high pressure resistance measurements on CsV3Sb5 with Tc = 2.5 K, the
highest among AV3Sb5. A finite residual linear term of thermal conductivity at
zero magnetic field and its rapid increase in fields suggest nodal
superconductivity. By applying pressure, the Tc of CsV3Sb5 increases first,
then decreases to lower than 0.3 K at 11.4 GPa, showing a clear first
superconducting dome peaked around 0.8 GPa. Above 11.4 GPa, superconductivity
re-emerges, suggesting a second superconducting dome. Both nodal
superconductivity and superconducting domes point to unconventional
superconductivity in this V-based superconductor. While our finding of nodal
superconductivity puts a strong constrain on the pairing state of the first
dome, which should be related to the CDW instability, the superconductivity of
the second dome may present another exotic pairing state in this ideal Kagome
lattice of vanadium.


  Using density functional theory and non-equilibrium Greens function
technique, we performed theoretical investigations on the structural and
transport properties of zigzag silicene nanoribbons with Stone-Wales defect.
The calculated formation energy is significantly lower than that of graphene
and silicene, which implies the high stability of such defect in SiNRs.
Negative differential resistance can be observed within certain bias voltage
range in both perfect and SW defected SiNRs. In order to elucidate the
mechanism the NDR behavior,the transmission spectra and molecular projected
self-consistent Hamiltonian states are discussed in details.


  In this paper we present a controlled study on the linearized IRM framework
(IRMv1) introduced in Arjovsky et al. (2020). We show that IRMv1 (and its
variants) framework can be potentially unstable under small changes to the
optimal regressor. This can, notably, lead to worse generalisation to new
environments, even compared with ERM which converges simply to the global
minimum for all training environments mixed up all together. We also highlight
the isseus of scaling in the the IRMv1 setup. These observations highlight the
importance of rigorous evaluation and importance of unit-testing for measuring
progress towards IRM.


  The understanding and avoidance of space charge induced resonances is of
utmost importance for long storage times in synchrotrons, as they can lead to
halo generation and subsequent beam loss. This report discusses the absence of
structure resonances for heavy-ion operation in SIS100 in the tune quadrant
foreseen for the fast extraction mode, $18.5 < Q_{x,y} < 19$ . Simulations of
beam losses for the duration of the SIS100 accumulation plateau at nominal
transverse space charge conditions with a maximum tune shift of $\Delta
Q^{SC}_y = -0.3$ supplement the discussion.


  In the classical Approximate Majority problem with two opinions there are
agents with Opinion 1 and with Opinion 2. The goal is to reach consensus and to
agree on the majority opinion if the bias is sufficiently large. It is well
known that the problem can be solved efficiently using the Undecided State
Dynamics (USD) where an agent interacting with an agent of the opposite opinion
becomes undecided. In this paper, we consider a variant of the USD with a
preferred Opinion 1. That is, agents with Opinion 1 behave stubbornly -- they
preserve their opinion with probability $p$ whenever they interact with an
agent having Opinion 2. Our main result shows a phase transition around the
stubbornness parameter $p \approx 1-x_1/x_2$. If $x_1 = \Theta(n)$ and $p \geq
1-x_1/x_2 + o(1)$, then all agents agree on Opinion 1 after $O(n\cdot \log n)$
interactions. On the other hand, for $p \leq 1-x_1/x_2 - o(1)$, all agents
agree on Opinion 2, again after $O(n\cdot \log n)$ interactions. Finally, if $p
\approx 1-x_1/x_2$, then all agents do agree on one opinion after $O(n\cdot
\log^2 n)$ interactions, but either of the two opinions can survive. All our
results hold with high probability.


  We report on extensive V, R, and i band photometry of the black-hole
candidate Nova Sco 1994 (GRO J1655-40) obtained during March 1996, when the
source was close to its quiescent pre-outburst optical brightness (V=17.3).
  From our observations and data taken from the literature we derive a refined
ephemeris for inferior conjunction of the secondary star: HJD 2449838.4198(52)
+ 2.62168(14) x N. We have modeled the V, R, and i band light curves in terms
of a Roche lobe filling secondary and an accretion disk around the compact
star, the latter described as a flat cylinder with a radial temperature
distribution. From the shape of the light curve we constrain the binary
inclination and mass of the secondary star to lie in the ranges 63.7-70.7
degrees and 1.60-3.10 Solar masses, respectively. This limits the mass of the
black-hole to the range 6.29-7.60 Solar Masses. The mass range we obtained for
the secondary star is supported by the results of stellar evolution
calculations.


  In this paper, the statistical properties of the product of independent and
non-identically distributed mixture Gamma (MG) random variables (RVs) are
provided first. Specifically, simple exact closed-form expressions for the
probability density function (PDF), cumulative distribution function (CDF), and
moment generating function (MGF) are derived in terms of univariate Meijer's
$G$-function. The statistical characterisations of the distribution of the
ratio of products of MG variates are then derived. These statistics are used to
analyse the outage probability (OP), the average error probability for
different modulation schemes, the effective rate (ER) of communications systems
and the average area under the receiver operating characteristics (AUC) curve
of energy detection over cascaded fading channels. Additionally, the lower
bound of secure outage probability (SOP$^L$) and probability of non-zero
secrecy capacity (PNSC) of the physical layer and the OP of the multihop
communications systems with decode-and-forward (DF) relaying protocol and
co-channel interference (CCI) are studied by utilising the statistics of the
ratio of the products. The derived performance metrics are applied for the
Beaulieu-Xie and $\alpha-\lambda-\eta-\mu$ shadowed fading channels that have
not been yet investigated in the literature. Accordingly, the equivalent
parameters of a MG distribution for the aforementioned channels are given. A
comparison between the numerical results and the Monte Carlo simulations is
presented to verify the validation of our analysis.