  The 2015 nuclear physics long-range plan endorsed the realization of an
Electron-Ion Collider (EIC) as the next large construction project after the
completion of FRIB. With its high luminosity ( $> 10^{33} cm^{-2}s^{-1}$), wide
kinematic reach in center-of-mass-energy (45GeV to 145GeV) and high lepton and
proton beam polarization, an EIC provides an unprecedented opportunity to reach
new frontiers in our understanding of the spin and dynamic structure of nuclei.
Despite of the success of the HERA collider in investigating the structure of a
single nucleon, the partonic structure of nuclei at moderate-to-small Bjorken's
$x$ still remains elusive. We present the evaluated impact of an EIC in
extracting the nuclear structure-functions from measurements of the reduced
cross section in deep inelastic scattering, including also the case of
measuring heavy quark production events. The potential constraints offered by
the EIC data in extracting the nuclear parton distribution functions is also
discussed.


  On the basis of the proposed algorithm for calculation of the hadron reaction
rates, the space-time structure of the relativistic nucleus-nucleus collisions
is studied. The reaction zones and the reaction frequencies for various types
of reactions are calculated for AGS and SPS energies within the microscopic
transport model. The relation of the reaction zones to the kinetic and chemical
freeze-out processes is discussed. It is shown that the space-time freeze-out
layer is most extended in time in the central region, while, especially for
higher collision energies, the layer becomes very narrow at the sides. The
parametrization of freeze-out hypersurface in the form of specific hyperbola of
constant proper time was confirmed. The specific characteristic time moments of
the fireball evolution are introduced. It is found that the time of the
division of a reaction zone into two separate parts does not depend on the
collision energy. Calculations of the hadronic reaction frequency show that the
evolution of nucleus-nucleus collision can be divided into two hadronic stages.


  We have developed a method to make a spectral-line-based survey of hot cores,
which represent an important stage of high-mass star formation, and applied the
method to the data of the FUGIN (FOREST Unbiased Galactic plane Imaging survey
with the Nobeyama 45-m telescope) survey. First, we select hot core candidates
by searching the FUGIN data for the weak hot core tracer lines (HNCO and
CH$_3$CN) by stacking, and then we conduct follow-up pointed observations on
these candidates in C$^{34}$S, SO, OCS, HC$_3$N, HNCO, CH$_3$CN, and CH$_3$OH
$J=2-1$ and $J=8-7$ lines to confirm and characterize them. We applied this
method to the $l = 10^\circ-20^\circ$ portion of the FUGIN data and identified
22 "Hot Cores" (compact sources with more than two significant detection of the
hot core tracer lines, i.e., SO, OCS, HC$_3$N, HNCO, CH$_3$CN, or CH$_3$OH
$J=8-7$ lines) and 14 "Dense Clumps" (sources with more than two significant
detection of C$^{34}$S, CH$_3$OH $J=2-1$, or the hot core tracer lines). The
identified Hot Cores are found associated with signposts of high-mass star
formation such as ATLASGAL clumps, WISE HII regions, and Class II methanol
masers. For those associated with ATLASGAL clumps, their bolometric luminosity
to clump mass ratios are consistent with the star formation stages centered at
the hot core phase. The catalog of FUGIN Hot Cores provides a useful starting
point for further statistical studies and detailed observations of high-mass
star forming regions.


  The current trend in the evolution of wind turbines is to increase their
rotor size in order to capture more power. This leads to taller, slender and
more flexible towers, which thus experience higher dynamical loads due to the
turbine rotation and environmental factors. It is hence compelling to deploy
advanced control methods that can dynamically counteract such loads, especially
at tower positions that are more prone to develop cracks or corrosion damages.
Still, to the best of the authors' knowledge, little to no attention has been
paid in the literature to load mitigation at multiple tower locations.
Furthermore, there is a need for control schemes that can balance load
reduction with optimization of power production. In this paper, we develop an
Economic Model Predictive Control (eMPC) framework to address such needs.
First, we develop a linear modal model to account for the tower flexural
dynamics. Then we incorporate it into an eMPC framework, where the dynamics of
the turbine rotation are expressed in energy terms. This allows us to obtain a
convex formulation, that is computationally attractive. Our control law is
designed to avoid the 'turn-pike' behavior and guarantee recursive feasibility.
We demonstrate the performance of the proposed controller on a 5MW reference WT
model: the results illustrate that the proposed controller is able to reduce
the tower loads at multiple locations, without significant effects to the
generated power.


  We analyze the statics for pure $p$-spin spherical spin glass models with
$p\geq3$, at low enough temperature. With $F_{N,\beta}$ denoting the free
energy, we compute the second order (logarithmic) term of $NF_{N,\beta}$ and
prove that, for an appropriate centering $c_{N,\beta}$,
$NF_{N,\beta}-c_{N,\beta}$ is a tight sequence. We establish the absence of
temperature chaos and analyze the transition rate to disorder chaos of the
Gibbs measure and ground state. Those results follow from the following
geometric picture we prove for the Gibbs measure, of interest by itself:
asymptotically, the measure splits into infinitesimal spherical `bands'
centered at deep minima, playing the role of so-called `pure states'. For the
pure models, the latter makes precise the so-called picture of `many valleys
separated by high mountains' and significant parts of the TAP analysis from the
physics literature.


  The synthetic control method is an empirical methodology forcausal inference
using observational data. By observing thespread of COVID-19 throughout the
world, we analyze the dataon the number of deaths and cases in different
regions usingthe power of prediction, counterfactual analysis, and
syntheticinterventions of the synthetic control and its extensions. Weobserve
that the number of deaths and cases in different re-gions would have been much
smaller had the lockdowns beenimposed earlier and had the re-openings been done
later, es-pecially among indoor bars and restaurants. We also analyzethe
speculated impact of herd immunity on the spread giventhe population of each
region and show that lockdown policieshave a very strong impact on the spread
regardless of the levelof prior infections.
  Our most up-to-date code, model, and data can be foundon github:
https://github.com/niloofarbayat/COVID19-synthetic-control-analysis


  The different behaviour of first order interferences and second order
correlations are investigated for the case of two coherently excited atoms. For
intensity measurements this problem is equivalent to Young's double slit
experiment and was investigated in an experiment by Eichmann et al. [Phys. Rev.
Lett. 70, 2359 (1993)] and later analyzed in detail by Itano et al. [Phys. Rev.
A 57, 4176 (1998)]. Our results show that in cases where the intensity
interferences disappear the intensity-intensity correlations can display an
interference pattern with a visibility of up to 100%. The contrast depends on
the polarization selected for the detection and is independent of the strength
of the driving field. The nonclassical nature of the calculated
intensity-intensity correlations is also discussed.


  We generalize an efficient decomposition method for diagonal operators by
Welch et al. to qudit systems. The phase-context aware method focusses on
cascaded entanglers whose decomposition into multi-controlled INC-gates can be
optimized by the choice of a proper signed base-$d$ representation for the
natural numbers.
  While the gate count of the best known decomposition method for general
diagonal operators on qubit systems scales with $\mathcal{O}(2^n)$, the
circuits synthesized by the Welch algorithm for diagonal operators with $k$
distinct phases are upper-bounded by $\mathcal{O}(n^2k)$, which is generalized
to $\mathcal{O}(dn^2k)$ for the qudit case in this paper.


  We describe a topological protection mechanism for highly-twisted
two-dimensional Skyrmions in systems with Dyloshinskii-Moriya (DM) coupling,
where non-zero DM energy density (dubbed "twisting energy density") acts as a
kind of band gap in real space, yielding an N invariant for highly twisted
Skyrmions. We prove our result through the application of contact topology by
extending our system along a fictitious third dimension, and further establish
the structural stability of highly-twisted Skyrmions under arbitrary
chirality-preserving distortions. Our results apply for all two-dimensional
systems hosting Skyrmion excitations including spin-orbit coupled systems
exhibiting quantum Hall ferromagnetism.


  Conventional machine learning applications in the mobile/IoT setting transmit
data to a cloud-server for predictions. Due to cost considerations (power,
latency, monetary), it is desirable to minimise device-to-server transmissions.
The budget learning (BL) problem poses the learner's goal as minimising use of
the cloud while suffering no discernible loss in accuracy, under the constraint
that the methods employed be edge-implementable.
  We propose a new formulation for the BL problem via the concept of
bracketings. Concretely, we propose to sandwich the cloud's prediction, $g,$
via functions $h^-, h^+$ from a `simple' class so that $h^- \le g \le h^+$
nearly always. On an instance $x$, if $h^+(x)=h^-(x)$, we leverage local
processing, and bypass the cloud. We explore theoretical aspects of this
formulation, providing PAC-style learnability definitions; associating the
notion of budget learnability to approximability via brackets; and giving
VC-theoretic analyses of their properties. We empirically validate our theory
on real-world datasets, demonstrating improved performance over prior gating
based methods.


  This paper discusses some aspects of the estimates of Higgs boson coupling
sensitivities for LHC and ILC presented at the Snowmass 2013 meeting. I
estimate the measurement accuracies underlying the CMS presentation to
Snowmass. I present new fits for the ILC capabilities. I present some joints
fits to prospective LHC and ILC data that demonstrates the synergy of the
High-Luminosity LHC and ILC programs. Contributed to the proceedings of the
Snowmass 2013 Community Summer Study.


  In this paper we present some properties for projective hypersurfaces, smooth
and singular, to be criteria for identification. To make the decision with
these criteria, we have included procedures written in Singular language.


  Using data on rotation curves of 134 spiral galaxies we have calculated the
possible value of the tau neutrino mass. We have shown that this mass could
explain the nature of the dark matter in spiral galaxies.


  In industrial defect segmentation tasks, while pixel accuracy and
Intersection over Union (IoU) are commonly employed metrics to assess
segmentation performance, the output consistency (also referred to equivalence)
of the model is often overlooked. Even a small shift in the input image can
yield significant fluctuations in the segmentation results. Existing
methodologies primarily focus on data augmentation or anti-aliasing to enhance
the network's robustness against translational transformations, but their shift
equivalence performs poorly on the test set or is susceptible to nonlinear
activation functions. Additionally, the variations in boundaries resulting from
the translation of input images are consistently disregarded, thus imposing
further limitations on the shift equivalence. In response to this particular
challenge, a novel pair of down/upsampling layers called component attention
polyphase sampling (CAPS) is proposed as a replacement for the conventional
sampling layers in CNNs. To mitigate the effect of image boundary variations on
the equivalence, an adaptive windowing module is designed in CAPS to adaptively
filter out the border pixels of the image. Furthermore, a component attention
module is proposed to fuse all downsampled features to improve the segmentation
performance. The experimental results on the micro surface defect (MSD) dataset
and four real-world industrial defect datasets demonstrate that the proposed
method exhibits higher equivalence and segmentation performance compared to
other state-of-the-art methods.Our code will be available at
https://github.com/xiaozhen228/CAPS.


  Deformable image registration is fundamental to many medical imaging
applications. Registration is an inherently ambiguous task often admitting many
viable solutions. While neural network-based registration techniques enable
fast and accurate registration, the majority of existing approaches are not
able to estimate uncertainty. Here, we present PULPo, a method for
probabilistic deformable registration capable of uncertainty quantification.
PULPo probabilistically models the distribution of deformation fields on
different hierarchical levels combining them using Laplacian pyramids. This
allows our method to model global as well as local aspects of the deformation
field. We evaluate our method on two widely used neuroimaging datasets and find
that it achieves high registration performance as well as substantially better
calibrated uncertainty quantification compared to the current state-of-the-art.


  We examine a condition on a simply connected 2-complex X ensuring that groups
acting properly on X are coherent. This extends earlier work on 2-complexes
with negative sectional curvature which covers the case that G acts freely. Our
extension of these results involves a generalization of the notion of sectional
curvature, an extension of the combinatorial Gauss-Bonnet theorem to complexes
of groups, and surprisingly requires the use of L^2-Betti numbers. We also
prove local quasiconvexity of G under the additional assumption that X is
CAT(0) space.


  Providing adaptive scaffolds to help learners develop self-regulated learning
(SRL) processes has been an important goal for intelligent learning
environments. Adaptive scaffolding is especially important in open-ended
learning environments (OELE), where novice learners often face difficulties in
completing their learning tasks. This paper presents a systematic framework for
adaptive scaffolding in Betty's Brain, a learning-by-teaching OELE for middle
school science, where students construct a causal model to teach a virtual
agent, generically named Betty. We evaluate the adaptive scaffolding framework
and discuss its implications on the development of more effective scaffolds for
SRL in OELEs. We detect key cognitive/metacognitive inflection points, i.e.,
instances where students' behaviors and performance change as they work on
their learning tasks. At such inflection points, Mr. Davis (a mentor agent) or
Betty (the teachable agent) provide conversational feedback, focused on
strategies to help students become productive learners. We conduct a classroom
study with 98 middle schoolers to analyze the impact of adaptive scaffolds on
students' learning behaviors and performance. Adaptive scaffolding produced
mixed results, with some scaffolds (viz., strategic hints that supported
debugging and assessment of causal models) being generally more useful to
students than others (viz., encouragement prompts). We also note differences in
learning behaviors of High and Low performers after receiving scaffolds.
Overall, our findings suggest how adaptive scaffolding in OELEs like Betty's
Brain can be further improved to narrow the gap between High and Low
performers.


  The first catalog of chromospherically active and ellipsoidal variable stars
in the OGLE database is presented. The periods, the observed magnitudes and
colors, their values free of interstellar extinction, and Fourier coefficients
of light modulation are given for 549 stars. New color-magnitude diagrams based
on new color determinations are presented. The chromospherically active stars
are all among the bulge red giants, while the ellipsoidal binary stars are near
the disk main sequence turn-off point. A discovery of a small group of very red
($2<(V-I)_0<6$) and bright, chromospherically active bulge giants
($I_0\approx13$ mag) is also reported.


  Generalized synchronization of chaos is a type of cooperative behavior in
directionally-coupled oscillators that is characterized by existence of stable
and persistent functional dependence of response trajectories from the chaotic
trajectory of driving oscillator. In many practical cases this function is
non-differentiable and has a very complex shape. The generalized synchrony in
such cases seems to be undetectable, and only the cases, in which a
differentiable synchronization function exists, are considered to make sense in
practice. We show that this viewpoint is not always correct and the
non-differentiable generalized synchrony can be revealed in many practical
cases. Conditions for detection of generalized synchrony are derived
analytically, and illustrated numerically with a simple example of
non-differentiable generalized synchronization.


  The Extended Theory of Finite Fermi Systems is based on the conventional
Landau-Migdal theory and includes the coupling to the low-lying phonons in a
consistent way. The phonons give rise to a fragmentation of the single-particle
strength and to a compression of the single-particle spectrum. Both effects are
crucial for a quantitative understanding of nuclear structure properties. We
demonstrate the effects on the electric dipole states in $^{208}$Pb (which
possesses 50% more neutrons then protons) where we calculated the low-lying
non-collective spectrum as well as the high-lying collective resonances. Below
8 MeV, where one expects the so called isovector pygmy resonances, we also find
a strong admixture of isoscalar strength that comes from the coupling to the
high-lying isoscalar electric dipole resonance, which we obtain at about 22
MeV. The transition density of this resonance is very similar to the breathing
mode, which we also calculated. We shall show that the extended theory is the
correct approach for self-consistent calculations, where one starts with
effective Lagrangians and effective Hamiltonians, respectively, if one wishes
to describe simultaneously collective and non-collective properties of the
nuclear spectrum. In all cases for which experimental data exist the agreement
with the present theory results is good.


  There are several mathematical models yielding 1/f noise. For example, 1/f
spectrum can be obtained from stochastic sequence of pulses having power-law
distribution of pulse durations or from nonlinear stochastic differential
equations. We show that a couple of seemingly different models exhibiting 1/f
spectrum are due to the similar scaling properties of the signals. In addition,
we demonstrate a connection between signals with the power-law behavior of the
power spectral density generated by the nonlinear stochastic differential
equations and modeled by a sequence of random different pulses. An
approximation of solutions of the nonlinear stochastic differential equations
by the sequence of pulses correctly reproduces the power-law parts of the
probability density function and of the power spectral density. This connection
provides further insights into the origin of 1/f noise.


  With the recent rise of toxicity in online conversations on social media
platforms, using modern machine learning algorithms for toxic comment detection
has become a central focus of many online applications. Researchers and
companies have developed a variety of models to identify toxicity in online
conversations, reviews, or comments with mixed successes. However, many
existing approaches have learned to incorrectly associate non-toxic comments
that have certain trigger-words (e.g. gay, lesbian, black, muslim) as a
potential source of toxicity. In this paper, we evaluate several
state-of-the-art models with the specific focus of reducing model bias towards
these commonly-attacked identity groups. We propose a multi-task learning model
with an attention layer that jointly learns to predict the toxicity of a
comment as well as the identities present in the comments in order to reduce
this bias. We then compare our model to an array of shallow and deep-learning
models using metrics designed especially to test for unintended model bias
within these identity groups.


  A technique used to accelerate an adaptive optics simulation platform using
reconfigurable logic is described. The performance of parts of this simulation
have been improved by up to 600 times (reducing computation times by this
factor) by implementing algorithms within hardware and enables adaptive optics
simulations to be carried out in a reasonable timescale. This demonstrates that
it is possible to use reconfigurable logic to accelerate computational codes by
very large factors when compared with conventional software approaches, and
this has relevance for many computationally intensive applications. The use of
reconfigurable logic for high performance computing is currently in its infancy
and has never before been applied to this field.


  In the framework of the Relativistic Mean Field Approach a pseudospin
dependence of the residual forces in nuclei is considered. It is shown that
this dependence is relatively weak. As a consequence, a pseudospin dependence
of the particle--core coupling is weak as well. This leads to a small splitting
of the pseudospin doublets produced by a vector coupling of an odd particle
pseudospin and a pseudo--orbital momentum of the core. Some possibilities for
experimental investigations of the manifestations of the pseudospin symmetry in
the spectra of odd nuclei with $Z\ge$ 100 are indicated.


  We consider a number of questions regarding the Luttinger-Ward functional and
the many-body perturbation series expansion of the proper self-energy
$\Sigma(\mathbf{k};z)$ specific to uniform ground states (ensemble of states)
of interacting fermion systems in terms of skeleton self-energy diagrams and
the interacting Green function $G(\mathbf{k};z)$. Utilising a link between the
latter series expansion and the classical moment problem (of the Hamburger
type), along with the associated continued-fraction expansion, we reaffirm our
earlier observation (2007) that for lattice models of fermions interacting
through short-range two-body potentials (i.e. for Hubbard-like models) this
series is uniformly convergent for almost all wave vectors $\mathbf{k}$ and
complex energies $z$. The limit of this series is unique. We inquire into the
reasons underlying the contrary observation by Kozik et al. (2015) regarding
skeleton-diagrammatic perturbation series expansion of $\Sigma$. In doing so,
we make a number of observations of general interest. Chief amongst these, we
observe that contrary to general belief thermal correlation functions
calculated in the energy domain (i.e. over the set of the relevant Matsubara
frequencies) are generally unreliable, in contrast to those calculated in the
imaginary-time domain. In an appendix we present a symbolic computational
formalism and short programs for singling out topologically distinct proper
self-energy diagrams that are algebraically equivalent up to determinate
multiplicative constants. This complements the work presented in our previous
publication (2019). [Abridged abstract]


  The aim of this paper is to provide an expansion to Abe-Nakaoka's heart
construction of the following two different realizations of the module category
over the endomorphism ring of a rigid object in a triangulated category:
Buan-Marsh's localization and Iyama-Yoshino's subfactor. Our method depends on
a modification of Nakaoka-Palu's HTCP localization, a Gabriel-Zisman
localization of extriangulated categories which is also realized as a subfactor
of the original ones. Besides of the heart construction, our generalized HTCP
localization involves the following phenomena: (1) stable category with respect
to a class of objects; (2) recollement of triangulated categories; (3)
recollement of abelian categories under a mild assumption.


  As service robots become more capable of autonomous behaviors, it becomes
increasingly important to consider how people communicate with a robot what
task it should perform and how to do the task. Accordingly, there has been a
rise in attention to end-user development (EUD) interfaces, which enable
non-roboticist end users to specify tasks for autonomous robots to perform.
However, state-of-the-art EUD interfaces are often constrained through
simplified domains or restrictive end-user interaction. Motivated by prior
qualitative design work that explores how to integrate a care robot in an
assisted living community, we discuss the challenges of EUD in this complex
domain. One set of challenges stems from different user-facing representations,
e.g., certain tasks may lend themselves better to rule-based trigger-action
representations, whereas other tasks may be easier to specify via sequences of
actions. The other stems from considering the needs of multiple stakeholders,
e.g., caregivers and residents of the facility may all create tasks for the
robot, but the robot may not be able to share information about all tasks with
all residents due to privacy concerns. We present scenarios that illustrate
these challenges and also discuss possible solutions.


  The phase diagrams and transitions of nonequilibrium systems with
multiplicative noise are studied theoretically. We show the existence of both
strong and weak-coupling critical behavior, of two distinct active phases, and
of a nonzero range of parameter values over which the susceptibility is
infinite in any dimension. A scaling theory of the strong-coupling transition
is constructed.


  We adapt the classical notion of learning from text to computable structure
theory. Our main result is a model-theoretic characterization of the
learnability from text for classes of structures. We show that a family of
structures is learnable from text if and only if the structures can be
distinguished in terms of their theories restricted to positive infinitary
$\Sigma_2$ sentences.


  Linde's proposal of a Euclidean path integral with the ``wrong'' sign of
Euclidean action is often identified with the tunneling proposal for the wave
function of the universe. However, the two proposals are in fact quite
different. I illustrate the difference and point out that recent criticism by
Hawking and Turok does not apply to the tunneling proposal.


  One-dimensional systems, such as nanowires or electrons moving along strong
magnetic field lines, have peculiar thermalization physics. The binary
collision of point-like particles, typically the dominant process for reaching
thermal equilibrium in higher dimensional systems, cannot thermalize a 1D
system. We study how dilute classical 1D gases thermalize through three-body
collisions. We consider a system of identical classical point particles with
pairwise repulsive inverse power-law potential $V_{ij} \propto 1/|x_i-x_j|^n$
or the pairwise Lennard-Jones potential. Using Monte Carlo methods, we compute
a collision kernel and use it in the Boltzmann equation to evolve a perturbed
thermal state with temperature $T$ toward equilibrium. We explain the shape of
the kernel and its dependence on the system parameters. Additionally, we
implement molecular dynamics simulations of a many-body gas and show agreement
with the Boltzmann evolution in the low density limit. For the inverse
power-law potential, the rate of thermalization is proportional to $\rho^2
T^{\frac{1}{2}-\frac{1}{n}}$ where $\rho$ is the number density. The
corresponding proportionality constant decreases with increasing $n$.


  In this work, we demonstrate that the nonlinear response of certain
soft-matter systems can be tailored at will by appropriately engineering their
optical polarizability. In particular, we deliberately synthesize stable
colloidal suspensions with negative polarizabilities, and observe for the first
time robust propagation and enhanced transmission of self-trapped light over
long distances that would have been otherwise impossible in conventional
suspensions with positive polarizabilities. What greatly facilitates this
behavior is an induced saturable nonlinear optical response introduced by the
thermodynamic properties of these colloidal systems. This in turn leads to a
substantial reduction in scattering via self-activated transparency effects.
Our results may open up new opportunities in developing soft-matter systems
with tunable optical nonlinearities.


  In this work, we discovered a fundamental connection between selection for
protein stability and emergence of preferred structures of proteins. Using
standard exact 3-dimensional lattice model we evolve sequences starting from
random ones and determining exact native structure after each mutation.
Acceptance of mutations is biased to select for stable proteins. We found that
certain structures, wonderfold, are independently discovered numerous times as
native states of stable proteins in many unrelated runs of selection. Diversity
of sequences that fold into wonderfold structures gives rise to superfamilies,
i.e. sets of dissimilar sequences that fold into the same or very similar
structures. Wonderfolds appear to be the most designable structures out of
complete set of compact lattice proteins. Furthermore, proteins having
wondefolds as their native structure tend to be most thermostable among all
evolved proteins. This effect is purely due to the favorable geometric
properties of wonderfolds and, thus, dominates any dependence on sequences. The
present work establishes a model of prebiotic structure selection, which
identifies dominant structural patterns emerging upon optimization of proteins
for survival in hot environment. Convergently discovered prebiotic initial
superfamilies with wonderfold structures could have served as a seed for
subsequent biological evolution involving gene duplications and divergence.


  Research on monocular 3D object detection is being actively studied, and as a
result, performance has been steadily improving. However, 3D object detection
performance is significantly reduced when applied to a camera system different
from the system used to capture the training datasets. For example, a 3D
detector trained on datasets from a passenger car mostly fails to regress
accurate 3D bounding boxes for a camera mounted on a bus. In this paper, we
conduct extensive experiments to analyze the factors that cause performance
degradation. We find that changing the camera pose, especially camera
orientation, relative to the road plane caused performance degradation. In
addition, we propose a generalized 3D object detection method that can be
universally applied to various camera systems. We newly design a compensation
module that corrects the estimated 3D bounding box location and heading
direction. The proposed module can be applied to most of the recent 3D object
detection networks. It increases AP3D score (KITTI moderate, IoU $> 70\%$)
about 6-to-10-times above the baselines without additional training. Both
quantitative and qualitative results show the effectiveness of the proposed
method.


  We introduce MindSpore Quantum, a pioneering hybrid quantum-classical
framework with a primary focus on the design and implementation of noisy
intermediate-scale quantum (NISQ) algorithms. Leveraging the robust support of
MindSpore, an advanced open-source deep learning training/inference framework,
MindSpore Quantum exhibits exceptional efficiency in the design and training of
variational quantum algorithms on both CPU and GPU platforms, delivering
remarkable performance. Furthermore, this framework places a strong emphasis on
enhancing the operational efficiency of quantum algorithms when executed on
real quantum hardware. This encompasses the development of algorithms for
quantum circuit compilation and qubit mapping, crucial components for achieving
optimal performance on quantum processors. In addition to the core framework,
we introduce QuPack, a meticulously crafted quantum computing acceleration
engine. QuPack significantly accelerates the simulation speed of MindSpore
Quantum, particularly in variational quantum eigensolver (VQE), quantum
approximate optimization algorithm (QAOA), and tensor network simulations,
providing astonishing speed. This combination of cutting-edge technologies
empowers researchers and practitioners to explore the frontiers of quantum
computing with unprecedented efficiency and performance.


  We use ALMA observations of four sub-millimetre galaxies (SMGs) at $z\sim2-3$
to investigate the spatially resolved properties of the inter-stellar medium
(ISM) at scales of 1--5 kpc (0.1--0.6$''$). The velocity fields of our sources,
traced by the $^{12}$CO($J$=3-2) emission, are consistent with disk rotation to
first order, implying average dynamical masses of
$\sim$3$\times10^{11}$M$_{\odot}$ within two half-light radii. Through a
Bayesian approach we investigate the uncertainties inherent to dynamically
constraining total gas masses. We explore the covariance between the stellar
mass-to-light ratio and CO-to-H$_{2}$ conversion factor, $\alpha_{\rm CO}$,
finding values of $\alpha_{\rm CO}=1.1^{+0.8}_{-0.7}$ for dark matter fractions
of 15 \%. We show that the resolved spatial distribution of the gas and dust
continuum can be uncorrelated to the stellar emission, challenging energy
balance assumptions in global SED fitting. Through a stacking analysis of the
resolved radial profiles of the CO(3-2), stellar and dust continuum emission in
SMG samples, we find that the cool molecular gas emission in these sources
(radii $\sim$5--14 kpc) is clearly more extended than the rest-frame $\sim$250
$\mu$m dust continuum by a factor $>2$. We propose that assuming a constant
dust-to-gas ratio, this apparent difference in sizes can be explained by
temperature and optical-depth gradients alone. Our results suggest that caution
must be exercised when extrapolating morphological properties of dust continuum
observations to conclusions about the molecular gas phase of the ISM.


  The concept of intelligent software is flawed. The behaviour of software is
determined by the hardware that "interprets" it. This undermines claims
regarding the behaviour of theorised, software superintelligence. Here we
characterise this problem as "computational dualism", where instead of mental
and physical substance, we have software and hardware. We argue that to make
objective claims regarding performance we must avoid computational dualism. We
propose a pancomputational alternative wherein every aspect of the environment
is a relation between irreducible states. We formalise systems as behaviour
(inputs and outputs), and cognition as embodied, embedded, extended and
enactive. The result is cognition formalised as a part of the environment,
rather than as a disembodied policy interacting with the environment through an
interpreter. This allows us to make objective claims regarding intelligence,
which we argue is the ability to "generalise", identify causes and adapt. We
then establish objective upper bounds for intelligent behaviour. This suggests
AGI will be safer, but more limited, than theorised.


  We investigate the evolution of Boolean networks subject to a selective
pressure which favors robustness against noise, as a model of evolved genetic
regulatory systems. By mapping the evolutionary process into a statistical
ensemble and minimizing its associated free energy, we find the structural
properties which emerge as the selective pressure is increased and identify a
phase transition from a random topology to a "segregated core" structure, where
a smaller and more densely connected subset of the nodes is responsible for
most of the regulation in the network. This segregated structure is very
similar qualitatively to what is found in gene regulatory networks, where only
a much smaller subset of genes --- those responsible for transcription factors
--- is responsible for global regulation. We obtain the full phase diagram of
the evolutionary process as a function of selective pressure and the average
number of inputs per node. We compare the theoretical predictions with Monte
Carlo simulations of evolved networks and with empirical data for Saccharomyces
cerevisiae and Escherichia coli.


  In the digital era, QR codes serve as a linchpin connecting virtual and
physical realms. Their pervasive integration across various applications
highlights the demand for aesthetically pleasing codes without compromised
scannability. However, prevailing methods grapple with the intrinsic challenge
of balancing customization and scannability. Notably, stable-diffusion models
have ushered in an epoch of high-quality, customizable content generation. This
paper introduces Text2QR, a pioneering approach leveraging these advancements
to address a fundamental challenge: concurrently achieving user-defined
aesthetics and scanning robustness. To ensure stable generation of aesthetic QR
codes, we introduce the QR Aesthetic Blueprint (QAB) module, generating a
blueprint image exerting control over the entire generation process.
Subsequently, the Scannability Enhancing Latent Refinement (SELR) process
refines the output iteratively in the latent space, enhancing scanning
robustness. This approach harnesses the potent generation capabilities of
stable-diffusion models, navigating the trade-off between image aesthetics and
QR code scannability. Our experiments demonstrate the seamless fusion of visual
appeal with the practical utility of aesthetic QR codes, markedly outperforming
prior methods. Codes are available at \url{https://github.com/mulns/Text2QR}


  Context: Asteroseismic measurements reveal that an unknown efficient angular
momentum (AM) transport mechanism is needed for subgiant and red giant stars. A
revised prescription for AM transport by the magnetic Tayler instability has
been recently proposed as a possible candidate for such a missing mechanism.
  Results: The revised prescription for the transport by the Tayler instability
leads to low core rotation rates after the main sequence that are in better
global agreement with asteroseismic measurements than those predicted by models
with purely hydrodynamic processes or with the original Tayler-Spruit dynamo. A
detailed comparison with asteroseismic data shows that the rotational
properties of at most two of the six subgiants can be correctly reproduced by
models accounting for this revised magnetic transport process. This result is
obtained independently of the value adopted for the calibration parameter in
this prescription. We also find that this transport by the Tayler instability
faces difficulties in simultaneously reproducing asteroseismic measurements
available for subgiant and red giant stars. The low values of the calibration
parameter needed to correctly reproduce the rotational properties of two of the
six subgiants lead to core rotation rates during the red giant phase that are
too high. Inversely, the higher values of this parameter needed to reproduce
the core rotation rates of red giants lead to a very low degree of radial
differential rotation before the red giant phase, which is in contradiction
with the internal rotation of subgiant stars.
  Conclusions: In its present form, the revised prescription for the transport
by the Tayler instability does not provide a complete solution to the missing
AM transport revealed by asteroseismology of evolved stars.


  Preference-based reinforcement learning (PbRL) provides a natural way to
align RL agents' behavior with human desired outcomes, but is often restrained
by costly human feedback. To improve feedback efficiency, most existing PbRL
methods focus on selecting queries to maximally improve the overall quality of
the reward model, but counter-intuitively, we find that this may not
necessarily lead to improved performance. To unravel this mystery, we identify
a long-neglected issue in the query selection schemes of existing PbRL studies:
Query-Policy Misalignment. We show that the seemingly informative queries
selected to improve the overall quality of reward model actually may not align
with RL agents' interests, thus offering little help on policy learning and
eventually resulting in poor feedback efficiency. We show that this issue can
be effectively addressed via near on-policy query and a specially designed
hybrid experience replay, which together enforce the bidirectional query-policy
alignment. Simple yet elegant, our method can be easily incorporated into
existing approaches by changing only a few lines of code. We showcase in
comprehensive experiments that our method achieves substantial gains in both
human feedback and RL sample efficiency, demonstrating the importance of
addressing query-policy misalignment in PbRL tasks.


  By combining related objects, unsupervised machine learning techniques aim to
reveal the underlying patterns in a data set. Non-negative Matrix Factorization
(NMF) is a data mining technique that splits data matrices by imposing
restrictions on the elements' non-negativity into two matrices: one
representing the data partitions and the other to represent the cluster
prototypes of the data set. This method has attracted a lot of attention and is
used in a wide range of applications, including text mining, clustering,
language modeling, music transcription, and neuroscience (gene separation). The
interpretation of the generated matrices is made simpler by the absence of
negative values. In this article, we propose a study on multi-modal clustering
algorithms and present a novel method called multi-modal multi-view
non-negative matrix factorization, in which we analyze the collaboration of
several local NMF models. The experimental results show the value of the
proposed approach, which was evaluated using a variety of data sets, and the
obtained results are very promising compared to state of art methods.


  We report nuclear quadrupole resonance (NQR) studies on the chain Cu sites of
PrBa$_{2}$Cu$_{4}$O$_{8}$, a quasi-one-dimensional conductor with a nearly
quarter-filled band. The nuclear spin-lattice relaxation rate $1/T_{1}$ shows a
pronounced peak near 100 K caused by fluctuations of electric field gradient
(EFG). Similar peak was observed for the spin-echo decay rate $1/T_{2}$,
however, at a different temperature near 50 K. These results and broadening of
the NQR spectrum at low temperatures indicate that slow charge fluctuations of
either electronic or ionic origin freeze gradually at low temperatures.


  Recent experimental evidence shows that the pi-orbitals along the stacking of
base pairs can facilitate the long-range charge transfer in DNA. Proton motion
in the base pair hydrogen bonds has also been found to affect the transfer
rate. To explain this behavior we propose a model considering interactions of
doped charges with hydrogen bonds and vibrations in DNA. The charge trapped by
either protons or vibrations can cause structural distortions leading to
polaron formation. By further considering polaron diffusion in DNA we find that
the charge transfer rate derived from the diffusion coefficient is in agreement
with the experimental results.


  We prove a Tb Theorem that characterizes all Calderon-Zygmund operators that
extend compactly on L^p(R^n), 1<p<\infty . The result, whose proof does not
require the property of accretivity, can be used to prove compactness of the
Double Layer Potential operator on a wide class of domains. The study also
provides conditions for boundedness of singular integral operators by means of
non-accretive testing functions.


  Deep learning (DL)-based channel state information (CSI) feedback has
received significant research attention in recent years. However, previous
research has overlooked the potential privacy disclosure problem caused by the
transmission of CSI datasets during the training process. In this work, we
introduce a federated edge learning (FEEL)-based training framework for
DL-based CSI feedback. This approach differs from the conventional centralized
learning (CL)-based framework in which the CSI datasets are collected at the
base station (BS) before training. Instead, each user equipment (UE) trains a
local autoencoder network and exchanges model parameters with the BS. This
approach provides better protection for data privacy compared to CL. To further
reduce communication overhead in FEEL, we quantize uplink and downlink model
transmission into different bits based on their influence on feedback
performance. Additionally, since the heterogeneity of CSI datasets in different
UEs can degrade the performance of the FEEL-based framework, we introduce a
personalization strategy to improve feedback performance. This strategy allows
for local fine-tuning to adapt the global model to the channel characteristics
of each UE. Simulation results indicate that the proposed personalized
FEEL-based training framework can significantly improve the performance of
DL-based CSI feedback while reducing communication overhead.


  Spiral galaxies that are deficient in neutral Hydrogen are observed on the
outskirts of the Virgo cluster. If their orbits have crossed the inner parts of
the cluster, their interstellar gas may have been lost through ram pressure
stripping by the hot X-ray emitting gas of the cluster. We estimate the maximum
radius out to which galaxies can bounce out of a virialized system using
analytical arguments and cosmological N-body simulations. In particular, we
derive an expression for the turnaround radius in a flat cosmology with a
cosmological constant that is simpler than previously derived expressions. We
find that the maximum radius reached by infalling galaxies as they bounce out
of their cluster is roughly between 1 and 2.5 virial radii. Comparing to the
virial radius of the Virgo cluster, which we estimate from X-ray observations,
these HI-deficient galaxies appear to lie significantly further away from the
cluster center. Therefore, if their distances to the cluster core are correct,
the HI-deficient spiral galaxies found outside of the Virgo cluster cannot have
lost their gas by ram pressure from the hot intracluster gas.


  The subgap conductivity of a normal-superconductor (NS) tunnel junction is
thought to be due to tunneling of two electrons. There is a strong interference
between these two electrons, originating from the spatial phase coherence in
the normal metal at a mesoscopic length scale and the intrinsic coherence of
the superconductor.
  We evaluated the interference effect on the transport through an NS junction.
  We propose the layouts to observe drastic Aharonov-Bohm and Josephson
effects.


  Manipulating quantum thermal transport relies on uncovering the principle
working cycles of quantum devices. Here, we apply the cycle flux ranking of
network analysis to nonequilibrium thermal devices described by graphs of
quantum state transitions. To excavate the principal mechanism out of complex
transport behaviors, we decompose the quantum-transition network into cycles,
calculate the cycle flux by algebraic graph theory, and pick out the dominant
cycles with top-ranked fluxes, i.e., the cycle trajectories with highest
probabilities. We demonstrate the cycle flux ranking in typical quantum device
models, such as a thermal-drag spin-Seebeck pump, and a quantum thermal
transistor as thermal switch or heat amplifier. The dominant cycle trajectories
indeed elucidate the principal working mechanisms of those quantum devices. The
cycle flux analysis provides an alternative perspective that naturally
describes the working cycle corresponding to the main functionality of quantum
thermal devices, which would further guide the device optimization with desired
performance


  We have investigated, using effective mass approach (EMA), magnetic
properties of a one-dimensional electron gas in graphene armchair ribbons when
the electrons of occupy only the lowest conduction subband. We find that
magnetic properties of the one-dimensional electron gas may depend sensitively
on the width of the ribbon. For ribbon widths $L_x=3Ma_0$, a critical point
separates ferromagnetic and paramagnetic states while for $L_x=(3M+1)a_0$
paramagnetic state is stable ($M$ is an integer and $a_{0}$ is the length of
the unit cell). These width-dependent properties are a consequence of
eigenstates that have a subtle width-dependent mixture of $\mathbf{K}$ and
$\mathbf{K'}$ states, and can be understood by examining the wavefunction
overlap that appears in the expression for the many-body exchange self-energy.
Ferromagnetic and paramagnetic states may be used for spintronic purposes.


  We compute the temporal evolution of the pressure anisotropy and bulk
pressure of a massive gas using second-order viscous hydrodynamics and
anisotropic hydrodynamics. We then compare our results with an exact solution
of the Boltzmann equation for a massive gas in the relaxation time
approximation. We demonstrate that, within second-order viscous hydrodynamics,
the inclusion of the full set of kinetic coefficients, particularly the
shear-bulk couplings, is necessary to properly describe the time evolution of
the bulk pressure. We also compare the results of second-order hydrodynamics
with those obtained using the anisotropic hydrodynamics approach. We find that
anisotropic hydrodynamics and second-order viscous hydrodynamics including the
shear-bulk couplings are both able to reproduce the exact evolution with
comparable accuracy.


  Global 21cm experiments aim to measure the sky averaged HI absorption signal
from cosmic dawn and the epoch of reionisation. However, antenna chromaticity
coupling to bright foregrounds can introduce distortions into the observational
data of such experiments. We demonstrate a method for guiding the antenna
design of a global experiment through data analysis simulations. This is done
by performing simulated observations for a range of inserted 21cm signals, then
attempting to identify the signals with a data analysis pipeline. We
demonstrate this method on five antennae that were considered as potential
designs for the Radio Experiment for the Analysis of Cosmic Hydrogen (REACH); a
conical log spiral antenna, an inverted conical sinuous antenna and polygonal-,
square- and elliptical-bladed dipoles. We find that the log spiral performs
significantly better than the other antennae tested, able to correctly and
confidently identify every inserted 21cm signal. In second place is the
polygonal dipole antenna, which was only unable to detect signals with both
very low amplitudes of 0.05K and low centre frequency of 80MHz. The conical
sinuous antenna was found to perform least accurately, only able to detect the
highest amplitude 21cm signals, and even then with biases. We also demonstrate
that, due to the non-trivial nature of chromatic distortion and the processes
of correcting for it, these are not the results that could have been expected
superficially from the extent of chromatic variation in each antenna.


  New experiments were proposed recently to investigate the regime of cold
atomic and molecular ion-atom collision processes in a special hybrid
neutral-atom--ion trap under high vacuum conditions. The collisional cooling of
laser pre-cooled Ca$^+$ ions by ultracold Na atoms is being studied. Modeling
this process requires knowledge of the radiative lifetime of the excited
singlet A$^1\Sigma^+$ state of the (NaCa)$^+$ molecular system. We calculate
the rate coefficient for radiative charge transfer using a semiclassical
approach. The dipole radial matrix elements between the ground and the excited
states, and the potential curves were calculated using Complete Active Space
Self-Consistent field and M\"oller-Plesset second order perturbation theory
(CASSCF/MP2) with an extended Gaussian basis, 6-311+G(3df). The semiclassical
charge transfer rate coefficient was averaged over a thermal Maxwellian
distribution. In addition we also present elastic collision cross sections and
the spin-exchange cross section. The rate coefficient for charge transfer was
found to be $2.3\times 10^{-16}$ cm$^3$/sec, while those for the elastic and
spin-exchange cross sections were found to be several orders of magnitude
higher ($1.1\times 10^{-8}$ cm$^3$/sec and $2.3\times 10^{-9}$ cm$^3$/sec,
respectively). This confirms our assumption that the milli-Kelvin regime of
collisional cooling of calcium ions by sodium atoms is favorable with the
respect to low loss of calcium ions due to the charge transfer.


  In a paper by Asaka, Ishida and Tanaka \cite{hiding}, they proposed a novel
possibility (which will be referred to as the AIT ansatz) that, in spite of the
Majorana nature of neutrinos, the neutrinoless double beta ($0\nu \beta\beta$)
decay can be hidden. In the original AIT model, the AIT ansatz is realized in
the minimal seesaw model with two right-handed neutrinos which have a
hierarchical mass structure: the lighter and heavier right-handed neutrinos are
respectively much lighter and heavier than the typical Fermi-momentum scale of
nuclei. However, the original AIT model does not accommodate a successful
leptogenesis. For this problem, in this paper we study a split seesaw model
with one lighter right-handed neutrino but two heavier right-handed neutrinos
which can realize the AIT ansatz and accommodate a successful leptogenesis
simultaneously. We first give the condition on the neutrino Yukawa couplings
for realizing the AIT ansatz, discuss its realization by employing an Abelian
flavor symmetry, and study its implications for the mixing of the lighter
right-handed neutrino with three left-handed neutrinos. We then successively
study the implications for leptogenesis of the interesting scenarios where
$M^{}_{\rm D}$ is a triangular matrix (which has maximally-restricted texture
zeros, in line with the simplicity principle) or respects the $\mu$-$\tau$
reflection symmetry (which is well motivated by the experimental results), on
top of the AIT ansatz.


  The electronic structure (electron charges and spins) of a perfect crystal
under external magnetic field is analyzed using the spin-polarized
Thomas-Fermi-Dirac-von Weizsacker model. An extension of the classical
Cauchy-Born rule for crystal lattices is established for the electronic
structure under sharp stability conditions on charge density wave and spin
density wave. A Landau-Lifschitz type micromagnetic energy functional is
derived.


  We present observations of a giant Lyman-alpha blob in the SSA22
proto-cluster at z=3.1, SSA22-LAB1, taken with the Atacama Large
Millimeter/submillimeter Array (ALMA). Dust continuum, along with [C II]158um,
and CO(4-3) line emission have been detected in LAB1, showing complex
morphology and kinematics across a ~100 kpc central region. Seven galaxies at
z=3.0987-3.1016 in the surroundings are identified in [C II] and dust continuum
emission, with two of them potential companions or tidal structures associated
with the most massive galaxies. Spatially resolved [C II] and infrared
luminosity ratios for the widely distributed media (L[C II]/LIR~0.01-0.001)
suggest that the observed extended interstellar media are likely to have
originated from star-formation activity and the contribution from shocked gas
is probably not dominant. LAB1 is found to harbour a total molecular gas mass
Mmol=(8.7+/-2.0)e+10 Msun, concentrated in the core region of the
Ly-alpha-emitting area. While (primarily obscured) star-formation activity in
the LAB1 core is one of the most plausible power sources for the Ly-alpha
emission, multiple major-mergers found in the core may also play a role in
making LAB1 exceptionally bright and extended in Ly-alpha as a result of
cooling radiation induced by gravitational interactions.


  The Higgs bosons and the top quark decay into rich and diverse final states,
containing both light and heavy quarks, gluons, photons as well as W and Z
bosons. This article reviews the challenges involved in reconstructing Higgs
and top events at the FCC-ee and identifies the areas where novel developments
are needed. The precise identification and reconstruction of these final states
at the FCC-ee rely on the capability of the detector to provide excellent
flavour tagging, jet energy and angular resolution, and global kinematic event
reconstruction. Excellent flavour tagging performance requires low-material
vertex and tracking detectors, and advanced machine learning techniques as
successfully employed in LHC experiments. In addition, the Z pole run will
provide abundant samples of heavy flavour partons that can be used for
calibration of the tagging algorithms. For the reconstruction of jets, leptons,
and missing energy, particle-flow algorithms are crucial to explore the full
potential of the highly granular tracking and calorimeter systems, and give
access to excellent energy-momentum resolution and precise identification of
heavy bosons in their hadronic decays. This enables, among many other key
elements, the reconstruction of Higgsstrahlung processes with leptonically and
hadronically decaying Z bosons, and an almost background-free identification of
top quark pair events. Exploiting the full available kinematic constraints
together with exclusive jet clustering algorithms will allow for the
optimisation of global event reconstruction with kinematic fitting techniques.


  The nature of the progenitor system[s] of Type Ia Supernovae is still
unclear. In this contribution I review the projects that have been undertaken
to answer this question and the results they have led to. The conclusion is
that, as of today, we have reasonable guesses but none of them has yet been
proven by direct observations.


  In this paper, we study a class of bilevel optimization problems, also known
as simple bilevel optimization, where we minimize a smooth objective function
over the optimal solution set of another convex constrained optimization
problem. Several iterative methods have been developed for tackling this class
of problems. Alas, their convergence guarantees are either asymptotic for the
upper-level objective, or the convergence rates are slow and sub-optimal. To
address this issue, in this paper, we introduce a novel bilevel optimization
method that locally approximates the solution set of the lower-level problem
via a cutting plane, and then runs a conditional gradient update to decrease
the upper-level objective. When the upper-level objective is convex, we show
that our method requires ${\mathcal{O}}(\max\{1/\epsilon_f,1/\epsilon_g\})$
iterations to find a solution that is $\epsilon_f$-optimal for the upper-level
objective and $\epsilon_g$-optimal for the lower-level objective. Moreover,
when the upper-level objective is non-convex, our method requires
${\mathcal{O}}(\max\{1/\epsilon_f^2,1/(\epsilon_f\epsilon_g)\})$ iterations to
find an $(\epsilon_f,\epsilon_g)$-optimal solution. We also prove stronger
convergence guarantees under the H\"olderian error bound assumption on the
lower-level problem. To the best of our knowledge, our method achieves the
best-known iteration complexity for the considered class of bilevel problems.


  In this paper we give a trace formula for Hecke operators acting on the
cohomology of a Fuchsian group of finite covolume, with coefficients in a
module $V$. The proof is based on constructing an operator whose trace on $V$
equals the Lefschetz number of the Hecke correspondence on cohomology,
generalizing the operator introduced together with Don Zagier for the modular
group.


  A mixed circulant graph is called integral if all eigenvalues of its
Hermitian adjacency matrix are integers. The main purpose of this paper is to
investigate the existence of perfect state transfer (PST for short) and
multiple state transfer (MST for short) on integral mixed circulant graphs.
Concretely, we provide sufficient and necessary conditions for the existence of
PST and MST between specified pairs of vertices on integral mixed circulant
graphs, respectively.


  We study the extended Hubbard model with both on-site and nearest neighbor
Coulomb repulsion ($U$ and $V$, respectively) in the Dynamical Mean Field
theory. At quarter filling, the model shows a transition to a charge ordered
phase with different sublattice occupancies $n_A \nen_B$. The effective mass
increases drastically at the critical $V$ and a pseudo-gap opens in the
single-particle spectral function for higher values of $V$. The $V_c(T)$-curve
has a negative slope for small temperatures, i.e. the charge ordering
transition can be driven by increasing the temperature. This is due to the
higher spin-entropy of the charge ordered phase.


  Needle-like twins are observed experimentally within the transition layer at
the martensite-twinned martensite interface. We utilize a phase-field approach
to investigate this microstructure. Our goal is to simulate the morphology of
the transition layer and to perform a detailed analysis to characterize its
interfacial and elastic micro-strain energy. To illustrate the micromechanical
framework developed for that purpose, sample computations are carried out for a
CuAlNi shape memory alloy undergoing the cubic-to-orthorhombic martensitic
transformation. A particular focus of the study is on size-dependent morphology
through examining the impact of twin spacing. Additionally, our results reveal
that certain twin volume fractions lead to the emergence of twin branching, as
a way to minimize the total free energy stored in the microstructure.


  The study of effective connectivity (EC) is essential in understanding how
the brain integrates and responds to various sensory inputs. Model-driven
estimation of EC is a powerful approach that requires estimating global and
local parameters of a generative model of neural activity. Insights gathered
through this process can be used in various applications, such as studying
neurodevelopmental disorders. However, accurately determining EC through
generative models remains a significant challenge due to the complexity of
brain dynamics and the inherent noise in neural recordings, e.g., in
electroencephalography (EEG). Current model-driven methods to study EC are
computationally complex and cannot scale to all brain regions as required by
whole-brain analyses. To facilitate EC assessment, an inference algorithm must
exhibit reliable prediction of parameters in the presence of noise. Further,
the relationship between the model parameters and the neural recordings must be
learnable. To progress toward these objectives, we benchmarked the performance
of a Bi-LSTM model for parameter inference from the Jansen-Rit neural mass
model (JR-NMM) simulated EEG under various noise conditions. Additionally, our
study explores how the JR-NMM reacts to changes in key biological parameters
(i.e., sensitivity analysis) like synaptic gains and time constants, a crucial
step in understanding the connection between neural mechanisms and observed
brain activity. Our results indicate that we can predict the local JR-NMM
parameters from EEG, supporting the feasibility of our deep-learning-based
inference approach. In future work, we plan to extend this framework to
estimate local and global parameters from real EEG in clinically relevant
applications.


  Evolution in finite populations is often modelled using the classical Moran
process. Over the last ten years this methodology has been extended to
structured populations using evolutionary graph theory. An important question
in any such population, is whether a rare mutant has a higher or lower chance
of fixating (the fixation probability) than the Moran probability, i.e. that
from the original Moran model, which represents an unstructured population. As
evolutionary graph theory has developed, different ways of considering the
interactions between individuals through a graph and an associated matrix of
weights have been considered, as have a number of important dynamics. In this
paper we revisit the original paper on evolutionary graph theory in light of
these extensions to consider these developments in an integrated way. In
particular we find general criteria for when an evolutionary graph with general
weights satisfies the Moran probability for the set of six common evolutionary
dynamics.


  Multiple kernel learning (MKL) method is generally believed to perform better
than single kernel method. However, some empirical studies show that this is
not always true: the combination of multiple kernels may even yield an even
worse performance than using a single kernel. There are two possible reasons
for the failure: (i) most existing MKL methods assume that the optimal kernel
is a linear combination of base kernels, which may not hold true; and (ii) some
kernel weights are inappropriately assigned due to noises and carelessly
designed algorithms. In this paper, we propose a novel MKL framework by
following two intuitive assumptions: (i) each kernel is a perturbation of the
consensus kernel; and (ii) the kernel that is close to the consensus kernel
should be assigned a large weight. Impressively, the proposed method can
automatically assign an appropriate weight to each kernel without introducing
additional parameters, as existing methods do. The proposed framework is
integrated into a unified framework for graph-based clustering and
semi-supervised classification. We have conducted experiments on multiple
benchmark datasets and our empirical results verify the superiority of the
proposed framework.


  Variational quantum eigensolver (VQE) emerged as a first practical algorithm
for near-term quantum computers. Its success largely relies on the chosen
variational ansatz, corresponding to a quantum circuit that prepares an
approximate ground state of a Hamiltonian. Typically, it either aims to achieve
high representation accuracy (at the expense of circuit depth), or uses a
shallow circuit sacrificing the convergence to the exact ground state energy.
Here, we propose the approach which can combine both low depth and improved
precision, capitalizing on a genetically-improved ansatz for hardware-efficient
VQE. Our solution, the multiobjective genetic variational quantum eigensolver
(MoG-VQE), relies on multiobjective Pareto optimization, where topology of the
variational ansatz is optimized using the non-dominated sorting genetic
algorithm (NSGA-II). For each circuit topology, we optimize angles of
single-qubit rotations using covariance matrix adaptation evolution strategy
(CMA-ES) -- a derivative-free approach known to perform well for noisy
black-box optimization. Our protocol allows preparing circuits that
simultaneously offer high performance in terms of obtained energy precision and
the number of two-qubit gates, thus trying to reach Pareto-optimal solutions.
Tested for various molecules (H$_2$, H$_4$, H$_6$, BeH$_2$, LiH), we observe
nearly ten-fold reduction in the two-qubit gate counts as compared to the
standard hardware-efficient ansatz. For 12-qubit LiH Hamiltonian this allows
reaching chemical precision already at 12 CNOTs. Consequently, the algorithm
shall lead to significant growth of the ground state fidelity for near-term
devices.


  Confronting the challenges of data scarcity and advanced motion synthesis in
human-scene interaction modeling, we introduce the TRUMANS dataset alongside a
novel HSI motion synthesis method. TRUMANS stands as the most comprehensive
motion-captured HSI dataset currently available, encompassing over 15 hours of
human interactions across 100 indoor scenes. It intricately captures whole-body
human motions and part-level object dynamics, focusing on the realism of
contact. This dataset is further scaled up by transforming physical
environments into exact virtual models and applying extensive augmentations to
appearance and motion for both humans and objects while maintaining interaction
fidelity. Utilizing TRUMANS, we devise a diffusion-based autoregressive model
that efficiently generates HSI sequences of any length, taking into account
both scene context and intended actions. In experiments, our approach shows
remarkable zero-shot generalizability on a range of 3D scene datasets (e.g.,
PROX, Replica, ScanNet, ScanNet++), producing motions that closely mimic
original motion-captured sequences, as confirmed by quantitative experiments
and human studies.


  The success of deep learning models deployed in the real world depends
critically on their ability to generalize well across diverse data domains.
Here, we address a fundamental challenge with selective classification during
automated diagnosis with domain-shifted medical images. In this scenario,
models must learn to avoid making predictions when label confidence is low,
especially when tested with samples far removed from the training set
(covariate shift). Such uncertain cases are typically referred to the clinician
for further analysis and evaluation. Yet, we show that even state-of-the-art
domain generalization approaches fail severely during referral when tested on
medical images acquired from a different demographic or using a different
technology. We examine two benchmark diagnostic medical imaging datasets
exhibiting strong covariate shifts: i) diabetic retinopathy prediction with
retinal fundus images and ii) multilabel disease prediction with chest X-ray
images. We show that predictive uncertainty estimates do not generalize well
under covariate shifts leading to non-monotonic referral curves, and severe
drops in performance (up to 50%) at high referral rates (>70%). We evaluate
novel combinations of robust generalization and post hoc referral approaches,
that rescue these failures and achieve significant performance improvements,
typically >10%, over baseline methods. Our study identifies a critical
challenge with referral in domain-shifted medical images and finds key
applications in reliable, automated disease diagnosis.


  A particular strength of ultracold quantum gases are the versatile detection
methods available. Since they are based on atom-light interactions, the whole
quantum optics toolbox can be used to tailor the detection process to the
specific scientific question to be explored in the experiment. Common methods
include time-of-flight measurements to access the momentum distribution of the
gas, the use of cavities to monitor global properties of the quantum gas with
minimal disturbance and phase-contrast or high-intensity absorption imaging to
obtain local real space information in high-density settings. Even the ultimate
limit of detecting each and every atom locally has been realized in
two-dimensions using so-called quantum gas microscopes. In fact, these
microscopes not only revolutionized the detection, but also the control of
lattice gases. Here we provide a short overview of this technique, highlighting
new observables as well as key experiments that have been enabled by quantum
gas microscopy.


  Let $\mu$ be a measure from Szeg\H{o} class on the unit circle $\mathbb T$
and let $\{f_n\}$ be the family of Schur functions generated by $\mu$. In this
paper, we prove a version of the classical Szeg\H{o}'s formula which controls
the oscillation of $f_n$ on $\mathbb T$ for all $n \ge 0$. Then, we focus on an
analog of Lusin's conjecture for polynomials $\{\varphi_n\}$ orthogonal with
respect to measure $\mu$ and prove that pointwise convergence of
$\{|\varphi_n|\}$ almost everywhere on $\mathbb T$ is equivalent to a certain
condition on zeroes of $\varphi_n$.


  It is shown that entangling two-qubit phase gates for quantum computation
with atoms inside a resonant optical cavity can be generated via common laser
addressing, essentially, within one step. The obtained dynamical or geometrical
phases are produced by an evolution that is robust against dissipation in form
of spontaneous emission from the atoms and the cavity and demonstrates
resilience against fluctuations of control parameters. This is achieved by
using the setup introduced by Pachos and Walther [Phys. Rev. Lett. 89, 187903
(2002)] and employing entangling Raman- or STIRAP-like transitions that
restrict the time evolution of the system onto stable ground states.


  We extend the quandle cocycle invariant to oriented singular knots and links
using algebraic structures called \emph{oriented singquandles} and assigning
weight functions at both regular and singular crossings. This invariant
coincides with the classical cocycle invariant for classical knots but provides
extra information about singular knots and links. The new invariant
distinguishes the singular granny knot from the singular square knot.


  The characteristic sequence of hypergraphs $<P_n : n<\omega>$ associated to a
formula $\phi(x;y)$, introduced in [arXiv:0908.4111], is defined by
$P_n(y_1,... y_n) = (\exists x) \bigwedge_{i\leq n} \phi(x;y_i)$. This paper
continues the study of characteristic sequences, showing that graph-theoretic
techniques, notably Szemer\'edi's celebrated regularity lemma, can be naturally
applied to the study of model-theoretic complexity via the characteristic
sequence. Specifically, we relate classification-theoretic properties of $\phi$
and of the $P_n$ (considered as formulas) to density between components in
Szemer\'edi-regular decompositions of graphs in the characteristic sequence. In
addition, we use Szemer\'edi regularity to calibrate model-theoretic notions of
independence by describing the depth of independence of a constellation of sets
and showing that certain failures of depth imply Shelah's strong order property
$SOP_3$; this sheds light on the interplay of independence and order in
unstable theories.


  We report for the first time the stellar atmospheric parameters for a a set
of post-AGB stars classified by Suarez et al. (2006). The stellar spectra were
taken from optical region, with low-resolution and have different spectral
ranges. We select a sample of 70 objects with A-K spectral types and
luminosities I and Ie. The large majority of these objects have been scarcely
studied and are located toward the galactic south pole region. We employ a set
of empirical relationships that use pseudo-equivalent widths like spectral
feature to estimate effective temperature, surface gravity and metallicity. The
criteria chosen for selection of absorption are similar to employed by MK
classification system.


  In this paper we study the the Gauss image problem, which is a generalization
of the Aleksandrov problem in convex geometry. By considering a geometric flow
involving Gauss curvature and functions of normal vectors and radial vectors,
we obtain the existence of smooth solutions to this problem.


  Monodisperse granular flows often develop regions with hexagonal close
packing of particles. We investigate this effect in a system of inelastic hard
spheres driven from below by a "thermal" plate. Molecular dynamics simulations
show, in a wide range of parameters, a close-packed cluster supported by a
low-density region. Surprisingly, the steady-state density profile, including
the close-packed cluster part, is well described by a variant of Navier-Stokes
granular hydrodynamics (NSGH). We suggest a simple explanation for the success
of NSGH beyond the freezing point.


  We have used MERLIN, at 1.4 and 5 GHz, to search for radio supernovae (RSNe)
and supernova remnants (SNRs) in the unobscured irregular dwarf galaxy NGC1569,
and in particular in the region of its super star clusters (SSCs) A and B.
Throughout NGC1569 we find some 5 RSNe and SNRs but the SSCs and their
immediate surroundings are largely devoid of non-thermal radio sources. Even
though many massive stars in the SSCs are expected to have exploded already,
when compared with M82 and its many SSCs the absence of RSNe and SNRs in and
near A and B may seem plausible on statistical arguments. The absence of RSNe
and SNRs in and near A and B may, however, also be due to a violent and
turbulent outflow of stellar winds and supernova ejected material, which does
not provide a quiescent environment for the development of SNRs within and near
the SSCs.


  Multiscale community detection can be viewed from a dynamical perspective
within the Markov Stability framework, which uses the diffusion of a Markov
process on the graph to uncover intrinsic network substructures across all
scales. Here we reformulate multiscale community detection as a max-sum length
vector partitioning problem with respect to the set of time-dependent node
vectors expressed in terms of eigenvectors of the transition matrix. This
formulation provides a geometric interpretation of Markov Stability in terms of
a time-dependent spectral embedding, where the Markov time acts as an
inhomogeneous geometric resolution factor that zooms the components of the node
vectors at different rates. Our geometric formulation encompasses both
modularity and the multi-resolution Potts model, which are shown to correspond
to vector partitioning in a pseudo-Euclidean space, and is also linked to
spectral partitioning methods, where the number of eigenvectors used
corresponds to the dimensionality of the underlying embedding vector space.
Inspired by the Louvain optimisation for community detection, we then propose
an algorithm based on a graph-theoretical heuristic for the vector partitioning
problem. We apply the algorithm to the spectral optimisation of modularity and
Markov Stability community detection. The spectral embedding based on the
transition matrix eigenvectors leads to improved partitions with higher
information content and higher modularity than the eigen-decomposition of the
modularity matrix. We illustrate the results with random network benchmarks.


  The loop quantum cosmological model from ADM Hamiltonian is studied in this
paper. We consider the spatially flat homogeneous FRW model. It turns out that
the modified Friedmann equation keeps the same form as the APS LQC model.
However, the critical matter density for the bounce point is only a quarter of
the previous APS model, i.e. $\rho_{cL}=\frac{\rho_c}{4}$. This is interesting
because the lower critical bounce density means the quantum gravity effects
will get involved earlier than the previous LQC model. Besides, the lower
critical density also means the detection of quantum gravity effects easier
than the previous model.


  By co-designing a meta-optical front end in conjunction with an
image-processing back end, we demonstrate noise sensitivity and compactness
substantially superior to either an optics-only or a computation-only approach,
illustrated by two examples: subwavelength imaging and reconstruction of the
full polarization coherence matrices of multiple light sources. Our end-to-end
inverse designs couple the solution of the full Maxwell equations---exploiting
all aspects of wave physics arising in subwavelength scatterers---with
inverse-scattering algorithms in a single large-scale optimization involving
$\gtrsim 10^4$ degrees of freedom. The resulting structures scatter light in a
way that is radically different from either a conventional lens or a random
microstructure, and suppress the noise sensitivity of the inverse-scattering
computation by several orders of magnitude. Incorporating the full wave physics
is especially crucial for detecting spectral and polarization information that
is discarded by geometric optics and scalar diffraction theory.


  We review our earlier studies on the order parameter distribution of the
quantum Sherrington-Kirkpatrick (SK) model. Through Monte Carlo technique, we
investigate the behavior of the order parameter distribution at finite
temperatures. The zero temperature study of the spin glass order parameter
distribution is made by the exact diagonalization method. We find in
low-temperature (high-transverse-field) spin glass region, the tail (extended
up to zero value of order parameter) and width of the order parameter
distribution become zero in thermodynamic limit. Such observations clearly
suggest the existence of a low-temperature (high-transverse-field) ergodic
region. We also find in high-temperature (low-transverse-field) spin glass
phase the order parameter distribution has nonzero value for all values of the
order parameter even in infinite system size limit, which essentially indicates
the nonergodic behavior of the system. We study the annealing dynamics by the
paths which pass through both ergodic and nonergodic spin glass regions. We
find the average annealing time becomes system size independent for the paths
which pass through the quantum-fluctuation-dominated ergodic spin glass region.
In contrast to that, the annealing time becomes strongly system size dependent
for annealing down through the classical-fluctuation-dominated nonergodic spin
glass region. We investigate the behavior of the spin autocorrelation in the
spin glass phase. We observe that the decay rate of autocorrelation towards its
equilibrium value is much faster in the ergodic region with respect to the
nonergodic region of the spin glass phase.


  After 25 years of PhD supervision, the author noted typical recurring
problems that make papers look sloppy, difficult to read, and incoherent. The
goal is not to write a paper for the sake of writing a paper, but to convey a
valuable message that is clear and precise. The goal is to write papers that
have an impact and are still understandable a couple of decades later. Our
mission should be to create papers of high quality that people want to read and
that can stand the test of time. We use Dijkstra's adagium "Beauty Is Our
Business" to stress the importance of simplicity, correctness, and cleanness.


  I propose monoradical nanographenes without C3 symmetry as building blocks to
design two-dimensional (2D) carbon crystals. As representative examples I study
the honeycomb and Kagome lattices, showing that by replacing the sites with
olympicene radicals the band dispersion near the Fermi energy corresponds,
respectively, to that of Kekul\'e/anti-Kekul\'e graphene and breathing Kagome
tight-binding models. As a consequence, finite islands of these new crystals
present corner states close to the Fermi energy, just like the parent models.
In the case of Kekul\'e/anti-Kekul\'e graphene, such states are topologically
protected, standing as examples of second-order topological insulators with a
non-zero Z2- or Z6-Berry phase. Differently, those of the breathing Kagome
lattice are of trivial nature, but the ground state has been predicted to be a
spin liquid in the antiferromagnetic Heisenberg model. Hence, 2D systems made
of low-symmetric nanographenes may be convenient platforms to explore exotic
physics in carbon materials.


  Spoken language understanding (SLU) topic has seen a lot of progress these
last three years, with the emergence of end-to-end neural approaches. Spoken
language understanding refers to natural language processing tasks related to
semantic extraction from speech signal, like named entity recognition from
speech or slot filling task in a context of human-machine dialogue.
Classically, SLU tasks were processed through a cascade approach that consists
in applying, firstly, an automatic speech recognition process, followed by a
natural language processing module applied to the automatic transcriptions.
These three last years, end-to-end neural approaches, based on deep neural
networks, have been proposed in order to directly extract the semantics from
speech signal, by using a single neural model. More recent works on
self-supervised training with unlabeled data open new perspectives in term of
performance for automatic speech recognition and natural language processing.
In this paper, we present a brief overview of the recent advances on the French
MEDIA benchmark dataset for SLU, with or without the use of additional data. We
also present our last results that significantly outperform the current
state-of-the-art with a Concept Error Rate (CER) of 11.2%, instead of 13.6% for
the last state-of-the-art system presented this year.


  Engineering novel states of matter with light is at the forefront of
materials research. An intensely studied direction is to realize
broken-symmetry phases that are "hidden" under equilibrium conditions but can
be unleashed by an ultrashort laser pulse. Despite a plethora of experimental
discoveries, the nature of these orders and how they transiently appear remain
unclear. To this end, we investigate a nonequilibrium charge density wave (CDW)
in rare-earth tritellurides, which is suppressed in equilibrium but emerges
after photoexcitation. Using a pump-pump-probe protocol implemented in
ultrafast electron diffraction, we demonstrate that the light-induced CDW
consists solely of order parameter fluctuations, which bear striking
similarities to critical fluctuations in equilibrium despite differences in the
length scale. By calculating the dynamics of CDW fluctuations in a
nonperturbative model, we further show that the strength of the light-induced
order is governed by the amplitude of equilibrium fluctuations. These findings
highlight photoinduced fluctuations as an important ingredient for the
emergence of transient orders out of equilibrium. Our results further suggest
that materials with strong fluctuations in equilibrium are promising platforms
to host "hidden" orders after laser excitation.


  We utilize coherent femtosecond extreme ultraviolet (EUV) pulses derived from
a free electron laser (FEL) to generate transient periodic magnetization
patterns with periods as short as 44 nm. Combining spatially periodic
excitation with resonant probing at the dichroic M-edge of cobalt allows us to
create and probe transient gratings of electronic and magnetic excitations in a
CoGd alloy. In a demagnetized sample, we observe an electronic excitation with
50 fs rise time close to the FEL pulse duration and ~0.5 ps decay time within
the range for the electron-phonon relaxation in metals. When the experiment is
performed on a sample magnetized to saturation in an external field, we observe
a magnetization grating, which appears on a sub-picosecond time scale as the
sample is demagnetized at the maxima of the EUV intensity and then decays on
the time scale of tens of picoseconds via thermal diffusion. The described
approach opens prospects for studying dynamics of ultrafast magnetic phenomena
on nanometer length scales.


  In this work, we construct and derive a new class of exponentially fitted
two-derivative diagonally implicit Runge--Kutta (EFTDDIRK) methods for the
numerical solution of differential equations with oscillatory solutions. First,
a general format of so-called modified two-derivative diagonally implicit
Runge--Kutta methods (TDDIRK) is proposed. Their order conditions up to order
six are derived by introducing a set of bi-coloured rooted trees and deriving
new elementary weights. Next, we build exponential fitting conditions in order
for these modified TDDIRK methods to treat oscillatory solutions, leading to
EFTDDIRK methods. In particular, a family of 2-stage fourth-order, a
fifth-order, and a 3-stage sixth-order EFTDDIRK schemes are derived. These can
be considered as superconvergent methods. The stability and phase-lag analysis
of the new methods are also investigated, leading to optimized fourth-order
schemes, which turn out to be much more accurate and efficient than their
non-optimized versions. Finally, we carry out numerical experiments on some
oscillatory test problems. Our numerical results clearly demonstrate the
accuracy and efficiency of the newly derived methods when compared with
existing implicit Runge--Kutta methods and two-derivative Runge--Kutta methods
of the same order in the literature.


  The paper concerns the magnetic Schr\"odinger operator on $R^n$. We prove
some $L^p$ estimates on the Riesz transforms and we establish some related
maximal inequalities. The conditions that we arrive at, are essentially based
on the control of the magnetic field by the electric potential.


  We consider cosmological models where dark matter is charged under a dark
Abelian gauge field. This new interaction is repulsive and competes with
gravity on large scales and in the dynamics of galaxies and clusters. We focus
on non-linear models of dark electrodynamics where the effects of the new force
are screened within a K-mouflage radius that helps avoiding traditional
constraints on charged dark matter models. We discuss the background cosmology
of these models in a Newtonian approach and show the equivalence with
relativistic Lema\^itre models where an inhomogeneous pressure due to the
electrostatic interaction is present. In particular, we find that dark matter
shells of different radii evolve differently as they exit their K-mouflage
radii at different times, resulting in a breaking of the initial comoving
evolution. In the large time regime, the background cosmology is described by
comoving but inhomogeneous model with a reduced gravitational Newton constant
and a negative curvature originating from the electrostatic pressure. Baryons
do not directly feel the electrostatic interaction, but are influenced by the
inhomogeneous matter distribution induced by the electric force. We find that
shells of smaller radii evolve faster than the outer shells which feel the
repulsive interaction earlier. This mimics the discrepancy between the large
scale Hubble rate and the local one. Similarly, as galaxies and clusters are
not screened by the new interaction, large scale global flows would result from
the existence of the new dark electromagnetic interaction.


  The ratios of the yields of charged antiparticles to particles have been
obtained for pions, kaons, and protons near mid-rapidity for d+Au collisions at
sqrt(s_NN) = 200 GeV as a function of centrality. The reported values represent
the ratio of the yields averaged over the rapidity range of 0.1<y_pi<1.3 and
0<y_(K,p)<0.8, where positive rapidity is in the deuteron direction, and for
transverse momenta 0.1<p_(T)^(pi,K)<1.0 GeV/c and 0.3<p_(T)^(p)<1.0 GeV/c.
Within the uncertainties, a lack of centrality dependence is observed in all
three ratios. The data are compared to results from other systems and model
calculations.


  Large language models (LLMs) often incorporate multiple text chunks in their
inputs to provide the necessary contexts. To speed up the prefill of the long
LLM inputs, one can pre-compute the KV cache of a text and re-use the KV cache
when the context is reused as the prefix of another LLM input. However, the
reused text chunks are not always the input prefix, and when they are not,
their precomputed KV caches cannot be directly used since they ignore the
text's cross-attention with the preceding text in the LLM input. Thus, the
benefits of reusing KV caches remain largely unrealized.
  This paper tackles just one question: when an LLM input contains multiple
text chunks, how to quickly combine their precomputed KV caches in order to
achieve the same generation quality as the expensive full prefill (i.e.,
without reusing KV cache)? We present CacheBlend, a scheme that reuses the
pre-computed KV caches, regardless prefix or not, and selectively recomputes
the KV values of a small subset of tokens to partially update each reused KV
cache. In the meantime,the small extra delay for recomputing some tokens can be
pipelined with the retrieval of KV caches within the same job,allowing
CacheBlend to store KV caches in slower devices with more storage capacity
while retrieving them without increasing the inference delay. By comparing
CacheBlend with the state-of-the-art KV cache reusing schemes on three
open-source LLMs of various sizes and four popular benchmark datasets of
different tasks, we show that CacheBlend reduces time-to-first-token (TTFT) by
2.2-3.3X and increases the inference throughput by 2.8-5X, compared with full
KV recompute, without compromising generation quality or incurring more storage
cost.


  We study the transport property for armchair-edge graphene nanoribbons
(AGNRs) with an adatom coupling to a semi-infinite quantum wire. Using the
nonequilibrium Green's function approach with tight-binding approximation, we
demonstrate that the tunneling current through the system is sensitively
dependent on both the AGNR width and adatom location. Interestingly, when the
adatom locates onto a carbon atom in the 3$j$th chain from the edge of a
metallic AGNR, the system shows a transmission gap accompanied by a threshold
voltage in $I-V$ curve like a semiconducting AGNR. This effect may be useful in
scanning tunneling microscopy experimental characterization on graphene
samples.


  We report an experimental investigation of the wake of free falling
super-hydrophobic spheres. The mutual interaction between the air layer
(plastron) encapsulating the super-hydrophobic spheres and the flow is
emphasised by studying the hydrodynamic performances. It is found that the air
plastron adapts its shape to the flow-induced stresses which compete with the
surface tension. This competition is characterised by introducing the Weber
number $\mathcal{W}e$, whilst the plastron deformation is estimated via the
aspect ratio $\chi$. While noticeable distortions are locally observed, the
plastron becomes more and more spherical in average (i.e. $\chi \rightarrow 1$)
as far as $\mathcal{W}e$ increases. In comparison to the reference spheres,
high deformation of the air plastron plastron (oblate shape) leads to lift and
drag increase, whereas low deformation (spherical shape) yields lift and drag
mitigation. Accordingly, taking into account the plastron deformation provides
an attractive way to explain the somehow discordant results reported in other
studies at comparable Reynolds numbers. If confirmed by additional studies, our
findings would imply that plastron compliance and its feedback on the flow,
which are currently neglected in most theoretical works and numerical
simulations, must be accounted for to design super-hydrophobic surfaces and/or
predict their performances.


  This paper considers a conjecture-based distributed learning approach that
enables autonomous nodes to independently optimize their transmission
probabilities in random access networks. We model the interaction among
multiple self-interested nodes as a game. It is well-known that the Nash
equilibria in this game result in zero throughput for all the nodes if they
take myopic best-response, thereby leading to a network collapse. This paper
enables nodes to behave as intelligent entities which can proactively gather
information, form internal conjectures on how their competitors would react to
their actions, and update their beliefs according to their local observations.
In this way, nodes are capable to autonomously "learn" the behavior of their
competitors, optimize their own actions, and eventually cultivate reciprocity
in the random access network. To characterize the steady-state outcome, the
conjectural equilibrium is introduced. Inspired by the biological phenomena of
"derivative action" and "gradient dynamics", two distributed conjecture-based
action update mechanisms are proposed to stabilize the random access network.
The sufficient conditions that guarantee the proposed conjecture-based learning
algorithms to converge are derived. Moreover, it is shown that all the
achievable operating points in the throughput region are essentially stable
conjectural equilibria corresponding to different conjectures. We investigate
how the conjectural equilibrium can be selected in heterogeneous networks and
how the proposed methods can be extended to ad-hoc networks. Simulations verify
that the system performance significantly outperforms existing protocols, such
as IEEE 802.11 DCF protocol and the PMAC protocol, in terms of throughput,
fairness, convergence, and stability.


  Despite the enormous relevance of zoonotic infections to world- wide public
health, and despite much effort in modeling individual zoonoses, a fundamental
understanding of the disease dynamics and the nature of outbreaks arising in
such systems is still lacking. We introduce a simple stochastic model of
susceptible-infected- recovered dynamics in a coupled animal-human
metapopulation, and solve analytically for several important properties of the
cou- pled outbreaks. At early timescales, we solve for the probability and time
of spillover, and the disease prevalence in the animal population at spillover
as a function of model parameters. At long times, we characterize the
distribution of outbreak sizes and the critical threshold for a large human
outbreak, both of which show a strong dependence on the basic reproduction
number in the animal population. The coupling of animal and human infection
dynamics has several crucial implications, most importantly al- lowing for the
possibility of large human outbreaks even when human-to-human transmission is
subcritical.


  Falls among the elderly are a major health concern, frequently resulting in
serious injuries and a reduced quality of life. In this paper, we propose
"BlockTheFall," a wearable device-based fall detection framework which detects
falls in real time by using sensor data from wearable devices. To accurately
identify patterns and detect falls, the collected sensor data is analyzed using
machine learning algorithms. To ensure data integrity and security, the
framework stores and verifies fall event data using blockchain technology. The
proposed framework aims to provide an efficient and dependable solution for
fall detection with improved emergency response, and elderly individuals'
overall well-being. Further experiments and evaluations are being carried out
to validate the effectiveness and feasibility of the proposed framework, which
has shown promising results in distinguishing genuine falls from simulated
falls. By providing timely and accurate fall detection and response, this
framework has the potential to substantially boost the quality of elderly care.


  In this paper, we use Cartan estimate for meromorphic functions to prove
Anderson localization for a class of long-range operators with singular
potenials.


  In the setting of a proper, cocompact action by a locally compact, unimodular
group $G$ on a Riemannian manifold, we construct equivariant spectral flow of
paths of Dirac-type operators. This takes values in the $K$-theory of the group
$C^*$-algebra of $G$. In the case where $G$ is the fundamental group of a
compact manifold, the summation map maps equivariant spectral flow on the
universal cover to classical spectral flow on the base manifold. We obtain
"index equals spectral flow" results. In the setting of a smooth path of
$G$-invariant Riemannian metrics on a $G$-spin manifold, we show that the
equivariant spectral flow of the corresponding path of spin Dirac operators
relates delocalised $\eta$-invariants and $\rho$-invariants for different
positive scalar curvature metrics to each other.