  We introduce and study a special class of almost contact metric manifolds,
which we call anti-quasi-Sasakian (aqS). Among the class of transversely
K\"ahler almost contact metric manifolds $(M,\varphi, \xi,\eta,g)$,
quasi-Sasakian and anti-quasi-Sasakian manifolds are characterized,
respectively, by the $\varphi$-invariance and the $\varphi$-anti-invariance of
the $2$-form $d\eta$. A Boothby-Wang type theorem allows to obtain aqS
structures on principal circle bundles over K\"ahler manifolds endowed with a
closed $(2,0)$-form. We characterize aqS manifolds with constant
$\xi$-sectional curvature equal to $1$: they admit an $Sp(n)\times 1$-reduction
of the frame bundle such that the manifold is transversely hyperk\"ahler,
carrying a second aqS structure and a null Sasakian $\eta$-Einstein structure.
We show that aqS manifolds with constant sectional curvature are necessarily
flat and cok\"ahler. Finally, by using a metric connection with torsion, we
provide a sufficient condition for an aqS manifold to be locally decomposable
as the Riemannian product of a K\"ahler manifold and an aqS manifold with
structure of maximal rank. Under the same hypothesis, $(M,g)$ cannot be locally
symmetric.


  Carbon dioxide clouds, which are speculated by models on solar and
extra-solar planets, have been recently observed near the equator of Mars. The
most comprehensive identification of Martian CO2 ice clouds has been obtained
by the near-IR imaging spectrometer OMEGA. CRISM, a similar instrument with a
higher spatial resolution, cannot detect these clouds with the same method due
to its shorter wavelength range. Here we present a new method to detect CO2
clouds using near-IR data based on the comparison of H2O and CO2 ice spectral
properties. The spatial and seasonal distributions of 54 CRISM observations
containing CO2 clouds are reported, in addition to 17 new OMEGA observations.
CRISM CO2 clouds are characterized by grain size in the 0.5-2\mum range and
optical depths lower than 0.3. The distributions of CO2 clouds inferred from
OMEGA and CRISM are consistent with each other and match at first order the
distribution of high altitude (>60km) clouds derived from previous studies. At
second order, discrepancies are observed. We report the identification of H2O
clouds extending up to 80 km altitude, which could explain part of these
discrepancies: both CO2 and H2O clouds can exist at high, mesospheric
altitudes. CRISM observations of afternoon CO2 clouds display morphologies
resembling terrestrial cirrus, which generalizes a previous result to the whole
equatorial clouds season. Finally, we show that morning OMEGA observations have
been previously misinterpreted as evidence for cumuliform, and hence
potentially convective, CO2 clouds.


  Applied statisticians use sequential regression procedures to produce a
ranking of explanatory variables and, in settings of low correlations between
variables and strong true effect sizes, expect that variables at the very top
of this ranking are truly relevant to the response. In a regime of certain
sparsity levels, however, three examples of sequential procedures--forward
stepwise, the lasso, and least angle regression--are shown to include the first
spurious variable unexpectedly early. We derive a rigorous, sharp prediction of
the rank of the first spurious variable for these three procedures,
demonstrating that the first spurious variable occurs earlier and earlier as
the regression coefficients become denser. This counterintuitive phenomenon
persists for statistically independent Gaussian random designs and an
arbitrarily large magnitude of the true effects. We gain a better understanding
of the phenomenon by identifying the underlying cause and then leverage the
insights to introduce a simple visualization tool termed the double-ranking
diagram to improve on sequential methods. As a byproduct of these findings, we
obtain the first provable result certifying the exact equivalence between the
lasso and least angle regression in the early stages of solution paths beyond
orthogonal designs. This equivalence can seamlessly carry over many important
model selection results concerning the lasso to least angle regression.


  Recent scanning tunnelling microscopy (STM) studies on
Bi$_2$Sr$_2$CaCu$_2$O$_{8+\delta}$ (Bi-2212) revealed the presence of severe
inhomogeneity with length scale $L_0 \approx \xi_0$, the coherence length.
Other studies have been interpreted in terms of mesoscale or nanoscale phase
segregation. Here we analyze heat capacity and NMR data on Bi-2212 and
(Y,Ca)Ba$_2$Cu$_3$O$_{7-\delta}$ and find no evidence for phase segregation or
gross inhomogeneity. For Bi-2212 the coherence scale $L_0$ increases with
doping from 5 to 17$\xi_0$ and the hole density inhomogeneity decreases from
0.028 to 0.005 holes/Cu. We conclude that STM measurements considerably
overstate the inhomogeneity in bulk Bi-2212.


  Massive-star-associated models for production of elements heavier than Fe are
reviewed. The important roles of neutrinos in many of these models are
discussed along with uncertainties in the relevant neutrino physics. Data on
elemental abundances in metal-poor stars are presented and their constraints on
diverse sources for elements heavier than Fe are emphasized.


  We consider a multi-dimensional scalar wave equation with memory
corresponding to the viscoelastic material described by a generalized Zener
model. We deduce that this relaxation system is an example of a non-strictly
hyperbolic system satisfying Majda's block structure condition. Well-posedness
of the associated Cauchy problem is established by showing that the symbol of
the spatial derivatives is uniformly diagonalizable with real eigenvalues. A
long-time stability result is obtained by plane-wave analysis when the memory
term allows for dissipation of energy.


  The Green's functions for the Laplace equation respectively satisfying the
Dirichlet and Neumann boundary conditions on the upper side of an infinite
plane with a circular hole are introduced and constructed. These functions
enables solution of the boundary value problems in domains where the hole is
closed by any surface. This approach enables accounting for arbitrary positive
and negative ground elevations inside the domain of interest, which, generally,
is not possible to achieve using the regular method of images. Such problems
appear in electrostatics, however, the methods developed apply to other domains
where the Laplace or Poisson equations govern. Integral and series
representations of the Green's functions are provided. An efficient
computational technique based on the boundary element method with fast
multipole acceleration is developed. A numerical study of some benchmark
problems is presented.


  Boson sampling is a problem for which quantum devices could prove to go
beyond classical computing using only linear optics and photon preparation and
counting. While theoretically important, there is a lack of practical
applications for this task. This paper presents a way to turn any boson
sampling device for which there are certain fabrication limits into a
physically unclonable function which could be used in identification systems
and other security protocols. For a minimum fabrication error, there are
scenarios for which the photon distribution at the output of two different
devices has a minimum distance and they can be told apart. When compared to
classical PUFs these linear optical tokens have the advantage that, for the
right choice of parameters, there is a computational guarantee that no
classical computer can simulate the output. The paper describes the basic
limitations of boson sampling which allow these results and gives a possible
choice for a challenge-response identification protocol. This gives a direct
application of boson sampling which could be simpler than solving the general
problem.


  In a recent article, we gave a full characterization of matrices that can be
decomposed as a linear combination of two idempotents with prescribed
coefficients. In this one, we use those results to improve on a recent theorem
of V. Rabanovich: we establish that every square matrix is a linear combination
of three idempotents (for an arbitrary coefficient field rather than just a
field of characteristic 0).


  In this paper, a time delay neural network (TDNN) based acoustic model is
proposed to implement a fast-converged acoustic modeling for Korean speech
recognition. The TDNN has an advantage in fast-convergence where the amount of
training data is limited, due to subsampling which excludes duplicated weights.
The TDNN showed an absolute improvement of 2.12% in terms of character error
rate compared to feed forward neural network (FFNN) based modelling for Korean
speech corpora. The proposed model converged 1.67 times faster than a
FFNN-based model did.


  I prove closed-form identities that I discovered for the first and second
derivatives of the $q$-rationals at $q = 1$, in which the $q$-rationals are
defined as they are in arXiv:1812.00170.


  We continue our investigation of the Baxter-Bazhanov-Stroganov or
\tau^{(2)}-model using the method of separation of variables
[nlin/0603028,arXiv:0708.4342]. In this paper we derive for the first time the
factorized formula for form-factors of the Ising model on a finite lattice
conjectured previously by A.Bugrij and O.Lisovyy in
[arXiv:0708.3625,arXiv:0708.3643]. We also find the matrix elements of the spin
operator for the finite quantum Ising chain in a transverse field.


  Bayesian paradigm takes advantage of well fitting complicated survival models
and feasible computing in survival analysis owing to the superiority in
tackling the complex censoring scheme, compared with the frequentist paradigm.
In this chapter, we aim to display the latest tendency in Bayesian computing,
in the sense of automating the posterior sampling, through Bayesian analysis of
survival modeling for multivariate survival outcomes with complicated data
structure. Motivated by relaxing the strong assumption of proportionality and
the restriction of a common baseline population, we propose a generalized
shared frailty model which includes both parametric and nonparametric frailty
random effects so as to incorporate both treatment-wise and temporal variation
for multiple events. We develop a survival-function version of ANOVA dependent
Dirichlet process to model the dependency among the baseline survival
functions. The posterior sampling is implemented by the No-U-Turn sampler in
Stan, a contemporary Bayesian computing tool, automatically. The proposed model
is validated by analysis of the bladder cancer recurrences data. The estimation
is consistent with existing results. Our model and Bayesian inference provide
evidence that the Bayesian paradigm fosters complex modeling and feasible
computing in survival analysis and Stan relaxes the posterior inference.


  It is generally assumed that ionization in slow collisions of light atomic
particles, whose constituents (electrons and nuclei) move with velocities
orders of magnitude smaller than the speed of light, is driven solely by the
Coulomb force. Here we show, however, that the Breit interaction -- a
relativistic correction to the Coulomb interaction between electrons -- can
become the main actor when the colliding system couples resonantly to the
quantum radiation field. Our results demonstrate that this ionization mechanism
can be very efficient in various not too dense physical environments, including
stellar plasmas and atomic beams propagating in gases.


  Odometry estimation is crucial for every autonomous system requiring
navigation in an unknown environment. In modern mobile robots, 3D
LiDAR-inertial systems are often used for this task. By fusing LiDAR scans and
IMU measurements, these systems can reduce the accumulated drift caused by
sequentially registering individual LiDAR scans and provide a robust pose
estimate. Although effective, LiDAR-inertial odometry systems require proper
parameter tuning to be deployed. In this paper, we propose LIO-EKF, a
tightly-coupled LiDAR-inertial odometry system based on point-to-point
registration and the classical extended Kalman filter scheme. We propose an
adaptive data association that considers the relative pose uncertainty, the map
discretization errors, and the LiDAR noise. In this way, we can substantially
reduce the parameters to tune for a given type of environment. The experimental
evaluation suggests that the proposed system performs on par with the
state-of-the-art LiDAR-inertial odometry pipelines but is significantly faster
in computing the odometry. The source code of our implementation is publicly
available (https://github.com/YibinWu/LIO-EKF).


  The magnetoelectric (ME) effects are investigated in a cubic compound
SrCuTe2O6, in which uniform Cu2+ (S=1/2) spin chains with considerable spin
frustration exhibit a concomitant antiferromagnetic transition and dielectric
constant peak at TN=5.5 K. Pyroelectric Jp(T) and magnetoelectric current
JME(H) measurements in the presence of a bias electric field are used to reveal
that SrCuTe2O6 shows clear variations of Jp(T) across TN at constant magnetic
fields. Furthermore, isothermal measurements of JME(H) also develop clear peaks
at finite magnetic fields, of which traces are consistent with the spin-flop
transitions observed in the magnetization studies. As a result, the anomalies
observed in Jp(T) and JME(H) curves well match with the field-temperature phase
diagram constructed from magnetization and dielectric constant measurements,
demonstrating that SrCuTe2O6 is a new magnetoelectric compound with S=1/2 spin
chains.


  MV Sgr is a hot, hydrogen-deficient star which has undergone R CrB fadings.
We have used self-correlation analysis and Fourier analysis of CCD V photometry
in the AAVSO International Database to identify a period of 8.0 days in this
star; the amplitude is about 0.03 mag. The variability is most likely due to
pulsation.


  A service-level objective (SLO) is a target performance metric of service
that cloud vendors aim to ensure. Delivering optimized SLOs can enhance user
satisfaction and improve the competitiveness of cloud vendors. As large
language models (LLMs) are gaining increasing popularity across various fields,
it is of great significance to optimize SLOs for LLM inference services. In
this paper, we observe that adjusting the parameters of LLM inference engines
can improve service performance, and the optimal parameter configurations of
different services are different. Therefore, we propose SCOOT, an automatic
performance tuning system to optimize SLOs for each LLM inference service by
tuning the parameters of the inference engine. We first propose a generalized
formulation of the tuning problem to handle various objectives and constraints
between parameters, and SCOOT exploits the Bayesian optimization (BO) technique
to resolve the problem via exploration and exploitation. Moreover, SCOOT adopts
a random forest to learn hidden constraints during the tuning process to
mitigate invalid exploration. To improve the tuning efficiency, SCOOT utilizes
the parallel suggestion to accelerate the tuning process. Extensive experiments
demonstrate that SCOOT can significantly outperform existing tuning techniques
in SLO optimization while greatly improving the tuning efficiency.


  We study a closed model of the universe filled with viscous fluid and
quintessence matter components in a Brans-Dicke type cosmological model. The
dynamical equations imply that the universe may look like an accelerated flat
Friedmann-Robertson-Walker universe at low redshift. We consider here
dissipative processes which follow a causal thermodynamics. The theory is
applied to viscous fluid inflation, where accepted values for the total entropy
in the observable universe is obtained.


  In this paper, we shall discuss possible theories of defining equivariant
singular Bott-Chern classes and corresponding uniqueness property. By adding a
natural axiomatic characterization to the usual ones of equivariant Bott-Chern
secondary characteristic classes, we will see that the construction of Bismut's
equivariant Bott-Chern singular currents provides a unique way to define a
theory of equivariant singular Bott-Chern classes. This generalizes J. I.
Burgos Gil and R. Li\c{t}canu's discussion to the equivariant case. As a
byproduct of this study, we shall prove a concentration formula which can be
used to prove an arithmetic concentration theorem in Arakelov geometry.


  Selective withdrawal is a desired phenomenon in transferring oil from large
caverns in US Strategic petroleum reserve, because entrainment of oil at the
time during withdrawal poses a risk of contaminating the environment. In order
to predict a critical submergence depth at a critical flow rate, a selective
withdrawal experiment at a high Reynolds Number was conducted. A tube was
positioned through a liquid-liquid interface that draws the lower liquid
upwards. Analysis of the normal stress balance across the interface produced a
Weber number, utilizing dynamic pressure scaling, that predicted the transition
to entrainment. An inviscid flow analysis, using Bernoulli's principle,
assuming an ellipsoidal control volume surface for the iso-velocity profile
produced a linear relationship between the Weber number and the scaled critical
submergence depth. The analytical model was validated using the experimental
data resulting in a robust model for predicting transition from selective
withdrawal to entrainment.


  This is the appendix of the paper [T. Arias-Marco, Constant Jacobi osculating
rank of $U(3)/(U(1) \times U(1) \times U(1))$, Arch. Math. (Brno) 45 (2009),
241--254] where we obtain an interesting relation between the covariant
derivatives of the Jacobi operator valid for all geodesic on the flag manifold
$M^6=U(3)/(U(1) \times U(1) \times U(1))$. As a consequence, an explicit
expression of the Jacobi operator independent of the geodesic can be obtained
on such a manifold. Moreover, we show the way to calculate the Jacobi vector
fields on this manifold by a new formula valid on every g.o. space.


  By fitting synthetic spectral models computed via the TLUSTY code, we examine
how the spectra from thin accretion disks are expected to vary in accreting
black hole systems. We fit color-corrected blackbody models to our synthetic
spectra to estimate the spectral hardening factor f, which parameterizes the
departure from blackbody and is commonly used to help interpret
multitemperature blackbody fitting results. We find we can define a reasonably
robust f value to spectra when the effects of Compton scattering dominate
radiation transfer. We examine the evolution of f with black hole mass and
accretion rate, typically finding a moderate variation (f ~ 1.4-2) for
accretion rates between 1% and 100% of the Eddington rate. Consistent with most
previous work, we find f tends to increase with accretion rate, but we also
infer a weaker correlation of f with black holes mass. We find that f is rarely
much larger than 2 unless the disk becomes photon starved, in contention with
some previous calculations. Significant spectral hardening (f > 2) is only
found when the disk mass surface density is lower than expected for alpha-disk
models unless alpha is near unity or larger.


  We present a determination of the parton distribution functions of the proton
in which NLO and NNLO fixed-order calculations are supplemented by NLLx small-x
resummation. Deep inelastic structure functions are computed consistently at
NLO+NLLx or NNLO+NLLx, while for hadronic processes small-x resummation is
included only in the PDF evolution, with kinematic cuts introduced to ensure
the fitted data lie in a region where the fixed-order calculation of the hard
cross-sections is reliable. In all other respects, the fits use the same
methodology and are based on the same global dataset as the recent NNPDF3.1
analysis. We demonstrate that the inclusion of small-x resummation leads to a
quantitative improvement in the perturbative description of the HERA inclusive
and charm-production reduced cross-sections in the small x region. The impact
of the resummation in our fits is greater at NNLO than at NLO, because
fixed-order calculations have a perturbative instability at small x due to
large logarithms that can be cured by resummation. We explore the
phenomenological implications of PDF sets with small-x resummation for the
longitudinal structure function $F_L$ at HERA, for parton luminosities and LHC
benchmark cross-sections, for ultra-high energy neutrino-nucleus
cross-sections, and for future high-energy lepton-proton colliders such as the
LHeC.


  We study the coupling of the Lambda(1520)= Lambda* resonance to the \bar K*
vector meson and nucleon. This coupling is not directly measured from the
resonance decay, but is expected to be important in hyperon production
reactions, in particular for the exotic Theta+ production. We compute the
coupling in two different schemes, one in the chiral unitary model where the
Lambda* is dominated by the quasibound state of mesons and baryons, and the
other in the quark model where the resonance is a p-wave excitation in the
three valence quarks. Although it is possible to construct both models such
that they reproduce the \bar K N and pi Sigma decays, there is a significant
difference between the Lambda* \bar K* N couplings in the two models. In the
chiral unitary model $|g_{\Lambda^*\bar{K}^* N}| \sim 1.5$, while in the quark
model $|g_{\Lambda^*\bar{K}^* N}| \sim 10$. The difference of the results stems
from the different structure of the Lambda* in both models, and hence, an
experimental determination of this coupling would shed light on the nature of
the resonance.


  We present here a detailed numerical study of the dynamical behaviour of
`soft' uncompressed grains in a granular chain where the grains interact via
the intrinsically nonlinear Hertz force. It is well known that such a chain
supports the formation of solitary waves (SWs). Here, however, the system
response to the material properties of the grains and boundaries is further
explored. In particular, we examine the details of the transition of the system
from a SW phase to an equilibrium-like (or quasi-equilibrium) phase and for
this reason we ignore the effects of dissipation in this study. We find that
the soft walls slow the reflection of SWs at the boundaries of the system,
which in turn slows the journey to quasi-equilibrium. Moreover, the increased
grain-wall compression as the boundaries are softened results in fewer average
grain-grain contacts at any given time in the quasi-equilibrium phase. These
effects lead to increased kinetic energy fluctuations in the short term in
softer systems. We conclude with a toy model which exploits the results of
soft-wall systems. This toy model supports the formation of breather-like
entities and may therefore be useful for localizing energy in desired places in
the granular chain.


  In data-driven modelling of complex dynamic processes, it is often desirable
to combine different classes of models to enhance performance. Examples include
coupled models of different fidelities, or hybrid models based on physical
knowledge and data-driven strategies. A key limitation of the broad adoption of
model combination in applications is intrusiveness: training combined models
typically requires significant modifications to the learning algorithm
implementations, which may often be already well-developed and optimized for
individual model spaces. In this work, we propose an iterative, non-intrusive
methodology to combine two model spaces to learn dynamics from data. We show
that this can be understood, at least in the linear setting, as finding the
optimal solution in the direct sum of the two hypothesis spaces, while
leveraging only the projection operators in each individual space. Hence, the
proposed algorithm can be viewed as iterative projections, for which we can
obtain estimates on its convergence properties. To highlight the extensive
applicability of our framework, we conduct numerical experiments in various
problem settings, with particular emphasis on various hybrid models based on
the Koopman operator approach.


  Dynamical systems methods are used to investigate dynamics of a flat
Friedmann-Robertson-Walker cosmological model with the non-minimally coupled
scalar field and a potential function. Performed analysis distinguishes the
value of non-minimal coupling constant parameter $\xi=\frac{3}{16}$, which is
the conformal coupling in five dimensional theory of gravity. It is shown that
for a monomial potential functions at infinite values of the scalar field there
exist generic de Sitter and Einstein-de Sitter states. The de Sitter state is
unstable with respect to expansion of the Universe for potential functions
which do not change faster than linearly. This leads to a generic cosmological
evolution without the initial singularity.


  The thermodynamic Bethe ansatz method is employed for the study of the
integrable critical $RSOS(q_{1}, q_{2};q)$ model. The high and low temperature
behavior are investigated, and the central charge of the effective conformal
field theory is derived. The obtained central charge is expressed as the sum of
the central charges of two generalized coset models.


  Humans perform remarkably well in many cognitive tasks including pattern
recognition. However, the neuronal mechanisms underlying this process are not
well understood. Nevertheless, artificial neural networks, inspired in brain
circuits, have been designed and used to tackle spatio-temporal pattern
recognition tasks. In this paper we present a multineuronal spike pattern
detection structure able to autonomously implement online learning and
recognition of parallel spike sequences (i.e., sequences of pulses belonging to
different neurons/neural ensembles). The operating principle of this structure
is based on two spiking/synaptic neurocomputational characteristics: spike
latency, that enables neurons to fire spikes with a certain delay and
heterosynaptic plasticity, that allows the own regulation of synaptic weights.
From the perspective of the information representation, the structure allows
mapping a spatio-temporal stimulus into a multidimensional, temporal, feature
space. In this space, the parameter coordinate and the time at which a neuron
fires represent one specific feature. In this sense, each feature can be
considered to span a single temporal axis. We applied our proposed scheme to
experimental data obtained from a motor inhibitory cognitive task. The test
exhibits good classification performance, indicating the adequateness of our
approach. In addition to its effectiveness, its simplicity and low
computational cost suggest a large scale implementation for real time
recognition applications in several areas, such as brain computer interface,
personal biometrics authentication or early detection of diseases.


  Several algorithms have been proposed to sample non-uniformly the replay
buffer of deep Reinforcement Learning (RL) agents to speed-up learning, but
very few theoretical foundations of these sampling schemes have been provided.
Among others, Prioritized Experience Replay appears as a hyperparameter
sensitive heuristic, even though it can provide good performance. In this work,
we cast the replay buffer sampling problem as an importance sampling one for
estimating the gradient. This allows deriving the theoretically optimal
sampling distribution, yielding the best theoretical convergence speed.
Elaborating on the knowledge of the ideal sampling scheme, we exhibit new
theoretical foundations of Prioritized Experience Replay. The optimal sampling
distribution being intractable, we make several approximations providing good
results in practice and introduce, among others, LaBER (Large Batch Experience
Replay), an easy-to-code and efficient method for sampling the replay buffer.
LaBER, which can be combined with Deep Q-Networks, distributional RL agents or
actor-critic methods, yields improved performance over a diverse range of Atari
games and PyBullet environments, compared to the base agent it is implemented
on and to other prioritization schemes.


  We develop a theory of singlet fission in carotenoid dimers. Following
photoexcitation of the 'bright' state (i.e., a singlet electron-hole pair) in a
single carotenoid, the first step in the singlet fission process is ultrafast
intramolecular conversion into the highly-correlated 'dark' (or 2Ag) state.
This state has both entangled singlet triplet-pair and charge-transfer
character. Our theory is predicated on the assumption that it is the singlet
triplet-pair component of the 'dark' state that undergoes bimolecular singlet
fission. We use valence bond theory to develop a minimal two-chain model of the
triplet-pair states. The single and double chain triplet-pair spectrum is
described, as this helps explain the dynamics and the equilibrated populations.
We simulate the dynamics of the initial entangled pair state using the quantum
Liouville equation, including both spin-conserving and spin-nonconserving
dephasing processes. By computing the intrachain and interchain singlet,
triplet and quintet triplet-pair populations, we show that singlet fission
depends critically on the interchain coupling and the driving potential (that
determines endothermic versus exothermic fission). We also show that the
Horodecki pair-entanglement provides a good metric for singlet fission.


  Lattice results, kinematical constraints and QCD dispersion relations are
combined for the first time to derive model-independent bounds for QCD form
factors and corresponding rates. To take into account the error bars on the
lattice results we develop a general formalism which ascribes well-defined
statistical properties to these bounds. We concentrate on $\bar B^0 \to \pi^+
l^- \bar\nu_l$ decays because of the relative simplicity of the analytical
behavior of the relevant polarization functions and because of their immediate
phenomenological relevance. To determine the range of applicability of the
dispersive matching required to obtain the bounds, we have evaluated the
leading perturbative and non-perturbative QCD corrections to the relevant
polarization functions. Despite the lattice results' large error bars and
limited kinematical range, the bounds we obtain appear to favor certain
parametrizations for the form factors over others. The bounds also enable the
determination of $|V_{ub}|$ from the total experimental rate with a theoretical
error ranging from 27% to 37%, depending on the assumptions made. The
techniques developed here are, in fact, quite general and are not limited to
use with lattice results nor to semileptonic $\bar B^0 \to \pi^+ l^- \bar\nu_l$
decays.


  Reducing unexpected urban traffic congestion caused by en-route events (e.g.,
road closures, car crashes, etc.) often requires fast and accurate reactions to
choose the best-fit traffic signals. Traditional traffic light control systems,
such as SCATS and SCOOT, are not efficient as their traffic data provided by
induction loops has a low update frequency (i.e., longer than 1 minute).
Moreover, the traffic light signal plans used by these systems are selected
from a limited set of candidate plans pre-programmed prior to unexpected
events' occurrence. Recent research demonstrates that camera-based traffic
light systems controlled by deep reinforcement learning (DRL) algorithms are
more effective in reducing traffic congestion, in which the cameras can provide
high-frequency high-resolution traffic data. However, these systems are costly
to deploy in big cities due to the excessive potential upgrades required to
road infrastructure. In this paper, we argue that Unmanned Aerial Vehicles
(UAVs) can play a crucial role in dealing with unexpected traffic congestion
because UAVs with onboard cameras can be economically deployed when and where
unexpected congestion occurs. Then, we propose a system called "AVARS" that
explores the potential of using UAVs to reduce unexpected urban traffic
congestion using DRL-based traffic light signal control. This approach is
validated on a widely used open-source traffic simulator with practical UAV
settings, including its traffic monitoring ranges and battery lifetime. Our
simulation results show that AVARS can effectively recover the unexpected
traffic congestion in Dublin, Ireland, back to its original un-congested level
within the typical battery life duration of a UAV.


  In this article, we compare the dynamics of the shift map and its induced
counterpart on the hyperspace of the shift space. We show that many of the
properties of induced shift map can be easily demonstrated by appropriate
sequences of symbols. We compare the dynamics of the shift system $(\Omega,
\sigma)$ with its induced counterpart
$(\mathcal{K}(\Omega),\overline{\sigma})$, where $\mathcal{K}(\Omega)$ is the
hyperspace of all nonempty compact subsets of $\Omega$. Recently, such
comparisons have been studied a lot for general spaces. We continue the same
study in case of shift spaces, and bring out the significance of such a study
in terms of sequences.
  We compare the mixing properties, denseness of periodic points, various forms
of sensitivity and expansivity of the shift map and its induced counterpart. In
particular, we show their equivalence in case of the full shift. We also look
into the special case of subshifts of finite type, and in particular prove that
the properties of weakly mixing, mixing and sensitivity are equivalent in both
systems. And in the case of any general subshift, we show that the concept of
cofinite sensitivity is equivalent in both systems and for transitive
subshifts, cofinite sensitivity is equivalent to syndectic sensitivity. In the
process we prove that all sturmian subshifts are cofinitely sensitive.


  In this work, we consider the energy-supercritical defocusing cubic nonlinear
wave equation in dimension d=5 for radially symmetric initial data. We prove
that an a priori bound in the critical space implies global well-posedness and
scattering. The main tool that we use is a frequency localized version of the
classical Morawetz inequality, inspired by recent developments in the study of
the mass and energy critical nonlinear Schr\"odinger equation.


  The propagation of coherent light through a random scattering medium
generates speckle pattern. Although the speckle is random in nature, the object
information is scrambled into it. The scrambled object information can be
retrieved using off-axis holographic technique where the retrieval is made from
coherence hologram which is constructed from the interferogram formed by object
speckle and a tilted reference speckle. It is shown that the SNR of the object
retrieved from the coherence hologram, calculated from the far-field intensity
correlation of the interferogram, depends on the average number of reference
speckles. The effect of average number of reference speckles on the SNR of the
retrieved object in off-axis holographic technique is investigated
experimentally by changing the reference speckle while keeping the object
speckle unchanged. The experimental observations are presented in this paper.


  In the field of Structural Dynamics, modal analysis is the foundation of
System Identification and vibration-based inspection. However, despite their
widespread use, current state-of-the-art methods for extracting modal
parameters from multi-input multi-output (MIMO) frequency domain data are still
affected by many technical limitations. Mainly, they can be computationally
cumbersome and/or negatively affected by close-in-frequency modes. The Loewner
Framework (LF) was recently proposed to alleviate these problems with the
limitation of working with single-input data only. This work proposes a
computationally improved version of the Lowner Framework, or iLF, to extract
modal parameters more efficiently. Also, the proposed implementation is
extended in order to handle multi-input multi-output data in the frequency
domain. This new implementation is compared to state-of-the-art methods such as
the frequency domain implementations of the Least Square Complex Exponential
method and the Numerical Algorithm for Subspace State Space System
Identification on numerical and experimental datasets. More specifically, a
finite element model of a 3D Euler-Bernoulli beam is used for the baseline
comparison and the noise robustness verification of the proposed MIMO iLF
algorithm. Then, an experimental dataset from MIMO ground vibration tests of a
trainer jet aircraft with over 91 accelerometer channels is chosen for the
algorithm validation on a real-life application. Excellent results are achieved
in terms of accuracy, robustness to noise, and computational performance by the
proposed improved MIMO method, both on the numerical and the experimental
datasets. The MIMO iLF MATLAB implementation is shared in the work
supplementary material.


  In this study, we aim to explore the effect of chirality on the phase
behavior of active helical particles driven by two-temperature scalar activity.
We first calculate the equation of state of soft helical particles of various
intrinsic chiralities using molecular dynamics (MD) simulation. In equilibrium,
the emergence of various liquid crystal (LC) phases such as nematic (N),
cholesteric ($N_{c}^{*}$), smectic (Sm), as well as crystal (K), crucially
depends on the presence of the walls, which induce homeotropic alignment. Next,
we introduce activity through the two-temperature model: keep increasing the
temperature of half of the helical particles (labeled as 'hot' particles) while
maintaining the temperature of the other half at a lower value (labeled as
'cold' particles). Starting from a homogeneous isotropic (I) phase, we find the
emergence of 2-TIPS: two temperature-induced phase separations between the hot
and cold particles. We also observe the cold particles undergo an ordering
transition to various LC phases even in the absence of a wall. This observation
reveals that the hot-cold interface in the active system plays the role of a
wall in the equilibrium system by inducing an alignment direction for the cold
particles. However, in the case of a cholesteric phase, we observe activity
destabilizes the $N_{c}^{*}$ phase by inducing smectic ordering in the cold
zone while isotropic structure in the hot zone. The smectic ordering in the
cold zone eventually transforms to a chiral crystal phase at high enough
activity.


  The first generations of astrophysical objects made a substantial impact on
our Universe with their radiation. X-rays from the first sources, with large
mean free paths, likely quickly heated the intergalactic medium (IGM). The
second generation of 21cm instruments can provide a unique view into this early
epoch. The early stages of reionization likely followed, driven by so-called
"minihalos", i.e. molecularly-cooled halos. These small halos were susceptible
to complex feedback mechanisms, especially from the soft-UV background which
preceded reionization, resulting in complex and possibly extended early stages
of reionization. When atomically-cooled galaxies emerged as the dominant
ionizers, reionization could proceed more rapidly, with these being less
sensitive to radiative feedback than previously thought. Reionization could
have slowed in the final stages when the ionized bubbles grew larger than the
separation of Lyman limit absorption systems (LLSs). The final stages likely
involved the photo-evaporation of LLSs, which by then regulated the rise of the
UV background. I discuss the theoretical underpinnings of this narrative, as
well as how future 21cm observations may help shed light on the outstanding
uncertainties.


  Hybrid games are models which combine discrete, continuous, and adversarial
dynamics. Game logic enables proving (classical) existence of winning
strategies. We introduce constructive differential game logic (CdGL) for hybrid
games, where proofs that a player can win the game correspond to computable
winning strategies. This is the logical foundation for synthesis of correct
control and monitoring code for safety-critical cyber-physical systems. Our
contributions include novel static and dynamic semantics as well as soundness
and consistency.


  The aim of these notes is to provide a brief review of the topic of neutrino
astronomy and in particular of neutrinos from core collapse supernovae. They
are addressed to a curious reader, beginning to work in a multidisciplinary
area that involves experimental neutrino physics, astrophysics, nuclear physics
and particle physics phenomenology. After an introduction to the methods and
goals of neutrinos astronomy, we focus on core collapse supernovae, as (one of)
the most promising astrophysical source of neutrinos. The first part is
organized almost as a tale, the last part is a bit more technical. We discuss
the impact of flavor oscillations on the supernova neutrino signal (=the change
of perspective due to recent achievements) and consider one specific example of
signal in detail. This shows that effects of oscillations are important, but
astrophysical uncertainties should be thought as an essential systematics for a
correct interpretation of future experimental data. Three appendices
corroborate the text with further details and some basics on flavor
oscillations; but no attempt of a complete bibliographical survey is done (in
practice, we selected a few references that we believe are useful for a
`modern' introduction to the subject. We suggest the use of public databases
for papers as SPIRES or NASA/ESO or for experiments as SPIRES or PaNAGIC to get
a more complete information).


  We use deep spectroscopy from the SAMI Galaxy Survey to explore the precision
of the fundamental plane of early-type galaxies (FP) as a distance indicator
for future single-fibre spectroscopy surveys. We study the optimal trade-off
between sample size and signal-to-noise ratio (SNR), and investigate which
additional observables can be used to construct hyperplanes with smaller
intrinsic scatter than the FP. We add increasing levels of random noise
(parametrised as effective exposure time) to the SAMI spectra to study the
effect of increasing measurement uncertainties on the FP-and
hyperplane-inferred distances. We find that, using direct-fit methods, the
values of the FP and hyperplane best-fit coefficients depend on the spectral
SNR, and reach asymptotic values for a mean SNR=40 {\AA}$^{-1}$. As additional
variables for the FP we consider three stellar-population observables:
light-weighted age, stellar mass-to-light ratio and a novel combination of Lick
indices (I$_{\rm age}$). For a SNR=45 {\AA}$^{-1}$ (equivalent to 1-hour
exposure on a 4-m telescope), all three hyperplanes outperform the FP as
distance indicators. Being an empirical spectral index, I$_{\rm age}$ avoids
the model-dependent uncertainties and bias underlying age and mass-to-light
ratio measurements, yet yields a 10 per cent reduction of the median distance
uncertainty compared to the FP. We also find that, as a by-product, the Iage
hyperplane removes most of the reported environment bias of the FP. After
accounting for the different signal-to-noise ratio, these conclusions also
apply to a 50 times larger sample from SDSS-III. However, in this case, only
age removes the environment bias.


  In this paper, we consider the sterile neutrino portal dark matter with $Z_3$
symmetry. This model further extends the canonical type-I seesaw with a fermion
singlet $\chi$ and a scalar singlet $\phi$. Under the $Z_3$ symmetry, the dark
sector transforms as $\chi\to e^{i2\pi/3}\chi, \phi\to e^{i2\pi/3}\phi$, while
the standard model particles and the sterile neutrino $N$ transform trivially.
Besides the interactions as $y_{N} \phi \bar{\chi}N$ and $\lambda_{H\phi}
(H^\dag H) (\phi^\dag \phi)$ allowed in the $Z_2$ symmetry, the $Z_3$ symmetry
also introduces two new terms, i.e., $y_\chi \phi \overline{\chi^{c}} \chi$ and
$\mu\phi^3/2$. These new interactions induce additional semi-annihilation
processes as $\chi\chi\to N\chi$ and $\phi\phi\to h\phi$ for WIMP dark matter.
We then perform a comprehensive analysis of the phenomenology of this $Z_3$
symmetric model. Viable parameter space is explored under the constraints from
dark matter relic density, Higgs invisible decay, indirect and direct detection
for both fermion and scalar dark matter. We find that the semi-annihilation
channels $\chi\chi\to N\chi$ and $\phi\phi\to N\chi$ can lead to quite
different phenomena from the $Z_2$ symmetric model, which provides a viable
pathway to distinguish these two kinds of model.


  While bulk bismuth (Bi) is a semimetal, we have found clear evidence of
superconductivity in a crystalline 72 nm diameter Bi nanowire below 1.3 K. In a
parallel magnetic field (H), the residual resistance of the nanowire below Tc
displays periodic oscillations with H, and the period corresponds to the
superconducting flux quantum. This result provides evidence that the
superconductivity comes from the interface shell between Bi and the surface
oxide. In a perpendicular H, the resistance in the superconducting state shows
Shubnikov-de Haas (SdH) oscillations, a signature of a normal metal. These
results indicate a novel coexistence of Bosonic and Fermionic states in the
surface shell of nanowires.


  Measurements of H-alpha, HI, and CO distributions in 61 normal spiral
galaxies are combined with published far-infrared and CO observations of 36
infrared-selected starburst galaxies, in order to study the form of the global
star formation law, over the full range of gas densities and star formation
rates (SFRs) observed in galaxies. The disk-averaged SFRs and gas densities for
the combined sample are well represented by a Schmidt law with index N =
1.4+-0.15. The Schmidt law provides a surprisingly tight parametrization of the
global star formation law, extending over several orders of magnitude in SFR
and gas density. An alternative formulation of the star formation law, in which
the SFR is presumed to scale with the ratio of the gas density to the average
orbital timescale, also fits the data very well. Both descriptions provide
potentially useful "recipes" for modelling the SFR in numerical simulations of
galaxy formation and evolution.


  We consider an interface above an attractive hard wall in the complete
wetting regime, and submitted to the action of an external increasing, convex
potential, and study its delocalization as the intensity of this potential
vanishes. Our main motivation is the analysis of critical prewetting, which
corresponds to the choice of a linear external potential.
  We also present partial results on critical prewetting in the two dimensional
Ising model, as well as a few (weak) results on pathwise estimates for the pure
wetting problem for effective interface models.


  In typical wireless cellular systems, the handover mechanism involves
reassigning an ongoing session handled by one cell into another. In order to
support increased capacity requirement and to enable newer use cases, the next
generation wireless systems will have a very dense deployment with advanced
beam-forming capability. In such systems, providing a better mobility along
with enhanced throughput performance requires an improved handover strategy. In
this paper, we will detail a novel method for handover optimization in a 5G
cellular network using reinforcement learning (RL). In contrast to the
conventional method, we propose to control the handovers between base-stations
(BSs) using a centralized RL agent. This agent handles the radio measurement
reports from the UEs and choose appropriate handover actions in accordance with
the RL framework to maximize a long-term utility. We show that the handover
mechanism can be posed as a contextual multi-armed bandit problem and solve it
using Q-learning method. We analyze the performance of the methods using
different propagation and deployment environment and compare the results with
the state-of-the-art algorithms. Results indicate a link-beam performance gain
of about 0.3 to 0.7 dB for practical propagation environments.


  Reinforcement Learning (RL) algorithms can suffer from poor sample efficiency
when rewards are delayed and sparse. We introduce a solution that enables
agents to learn temporally extended actions at multiple levels of abstraction
in a sample efficient and automated fashion. Our approach combines universal
value functions and hindsight learning, allowing agents to learn policies
belonging to different time scales in parallel. We show that our method
significantly accelerates learning in a variety of discrete and continuous
tasks.


  Due to its advantages over traditional data centers, there has been a rapid
growth in the usage of cloud infrastructures. These include public clouds
(e.g., Amazon EC2), or private clouds, such as clouds deployed using OpenStack.
A common factor in many of the well known infrastructures, for example
OpenStack and CloudStack, is that networked storage is used for storage of
persistent data. However, traditional Big Data systems, including Hadoop, store
data in commodity local storage for reasons of high performance and low cost.
We present an architecture for supporting Hadoop on Openstack using local
storage. Subsequently, we use benchmarks on Openstack and Amazon to show that
for supporting Hadoop, local storage has better performance and lower cost. We
conclude that cloud systems should support local storage for persistent data
(in addition to networked storage) so as to provide efficient support for
Hadoop and other Big Data systems


  Using high-speed video and magnetic resonance imaging (MRI) we study the
motion of a large sphere in a vertically vibrated bed of smaller grains. As
previously reported we find a non-monotonic density dependence of the rise and
sink time of the large sphere. We find that this density dependence is solely
due to air drag. We investigate in detail how the motion of the intruder sphere
is influenced by size of the background particles, initial vertical position in
the bed, ambient pressure and convection. We explain our results in the
framework of a simple model and find quantitative agreement in key aspects with
numerical simulations to the model equations.


  A challenge of managing and extracting useful knowledge from social media
data sources has attracted much attention from academic and industry. To
address this challenge, semantic analysis of textual data is focused in this
paper. We propose an ontology-based approach to extract semantics of textual
data and define the domain of data. In other words, we semantically analyse the
social data at two levels i.e. the entity level and the domain level. We have
chosen Twitter as a social channel challenge for a purpose of concept proof.
Domain knowledge is captured in ontologies which are then used to enrich the
semantics of tweets provided with specific semantic conceptual representation
of entities that appear in the tweets. Case studies are used to demonstrate
this approach. We experiment and evaluate our proposed approach with a public
dataset collected from Twitter and from the politics domain. The ontology-based
approach leverages entity extraction and concept mappings in terms of quantity
and accuracy of concept identification.


  We solve the classification problem for integrable lattices of the form
$u_{,t}=f(u_{-2},\dots,u_2)$ under the additional assumption of invariance with
respect to the group of linear-fractional transformations. The obtained list
contains 5 equations, including 3 new. Difference Miura type substitutions are
found which relate these equations with known polynomial lattices. We also
present some classification results for the generic lattices.


  This paper presents a mathematic dynamic model of a quadrotor unmanned aerial
vehicle (QUAV) by using the symbolic regression approach and then proposes a
hierarchical control scheme for trajectory tracking. The symbolic regression
approach is capable of constructing analytical quadrotor dynamic equations only
through the collected data, which relieves the burden of first principle
modeling. To facilitate position tracking control of a QUAV, the design of
controller can be decomposed into two parts: a proportional-integral controller
for the position subsystem is first designed to obtain the desired horizontal
position and the backstepping method for the attitude subsystem is developed to
ensure that the Euler angles and the altitude can fast converge to the
reference values. The effectiveness is verified through experiments on a
benchmark multicopter simulator.


  Rhombohedral graphene multilayers provide a clean and highly reproducible
platform to explore the emergence of superconductivity and magnetism in a
strongly interacting electron system. Here, we use electronic compressibility
and local magnetometry to explore the phase diagram of this material class in
unprecedented detail. We focus on rhombohedral trilayer in the quarter metal
regime, where the electronic ground state is characterized by the occupation of
a single spin and valley isospin flavor. Our measurements reveal a subtle
competition between valley imbalanced (VI) orbital ferromagnets and intervalley
coherent (IVC) states in which electron wave functions in the two momentum
space valleys develop a macroscopically coherent relative phase. Contrasting
the in-plane spin susceptibility of the IVC and VI phases reveals the influence
of graphene's intrinsic spin-orbit coupling, which drives the emergence of a
distinct correlated phase with hybrid VI and IVC character. Spin-orbit also
suppresses the in-plane magnetic susceptibility of the VI phase, which allows
us to extract the spin-orbit coupling strength of $\lambda \approx 50\mu$eV for
our hexagonal boron nitride-encapsulated graphene system. We discuss the
implications of finite spin-orbit coupling on the spin-triplet superconductors
observed in both rhombohedral and twisted graphene multilayers.


  Second harmonic generation (SHG) is prohibited in centrosymmetric crystals
such as silicon or germanium due to the presence of inversion symmetry.
However, the structuring of such materials makes it possible to break the
inversion symmetry, thus achieving generation of second-harmonic. Moreover,
various symmetry properties of the resulting structure, such as chirality, also
influence the SHG. In this work we investigate second harmonic generation from
an array of nanohelices made of germanium. The intensity of the second harmonic
displayed a remarkable enhancement of over 100 times compared to a
non-structured Ge thin film, revealing the influence of interaction between
nanohelices. In particular, nonlinear circular dichroism, characterized through
the SHG anisotropy factor g, changed its sign not only with the helix
handedness, but with its density as well. We believe that our discoveries will
open up new paths for the development of nonlinear photonics based on
metamaterials and metasurfaces made of centrosymmetric materials.


  In this paper, we propose FFCI, a framework for fine-grained summarization
evaluation that comprises four elements: faithfulness (degree of factual
consistency with the source), focus (precision of summary content relative to
the reference), coverage (recall of summary content relative to the reference),
and inter-sentential coherence (document fluency between adjacent sentences).
We construct a novel dataset for focus, coverage, and inter-sentential
coherence, and develop automatic methods for evaluating each of the four
dimensions of FFCI based on cross-comparison of evaluation metrics and
model-based evaluation methods, including question answering (QA) approaches,
semantic textual similarity (STS), next-sentence prediction (NSP), and scores
derived from 19 pre-trained language models. We then apply the developed
metrics in evaluating a broad range of summarization models across two
datasets, with some surprising findings.


  B\'ar\'any, Katchalski and Pach proved the following quantitative form of
Helly's theorem. If the intersection of a family of convex sets in
$\mathbb{R}^d$ is of volume one, then the intersection of some subfamily of at
most $2d$ members is of volume at most some constant $v(d)$. They proved the
bound $v(d)\leq d^{2d^2}$, and conjectured $v(d)\leq d^{cd}$. We confirm it.


  Partial differential equations (PDEs) see widespread use in sciences and
engineering to describe simulation of physical processes as scalar and vector
fields interacting and coevolving over time. Due to the computationally
expensive nature of their standard solution methods, neural PDE surrogates have
become an active research topic to accelerate these simulations. However,
current methods do not explicitly take into account the relationship between
different fields and their internal components, which are often correlated.
Viewing the time evolution of such correlated fields through the lens of
multivector fields allows us to overcome these limitations. Multivector fields
consist of scalar, vector, as well as higher-order components, such as
bivectors and trivectors. Their algebraic properties, such as multiplication,
addition and other arithmetic operations can be described by Clifford algebras.
To our knowledge, this paper presents the first usage of such multivector
representations together with Clifford convolutions and Clifford Fourier
transforms in the context of deep learning. The resulting Clifford neural
layers are universally applicable and will find direct use in the areas of
fluid dynamics, weather forecasting, and the modeling of physical systems in
general. We empirically evaluate the benefit of Clifford neural layers by
replacing convolution and Fourier operations in common neural PDE surrogates by
their Clifford counterparts on 2D Navier-Stokes and weather modeling tasks, as
well as 3D Maxwell equations. For similar parameter count, Clifford neural
layers consistently improve generalization capabilities of the tested neural
PDE surrogates. Source code for our PyTorch implementation is available at
https://microsoft.github.io/cliffordlayers/.


  We report the high field (up to 14Tesla) magneto-conductivity analysis of
Bi2Se3 topological insulator grown via the self flux method. The detailed
experimental investigations including crystal growth as well as the electrical,
thermal and spectroscopic characterizations of the resultant Bi2Se3 single
crystal are already reported by some of us. The current letter deals with high
field magneto-conductivity analysis in terms of Hikami Larkin Nagaoka (HLN)
model, which revealed that the electronic conduction is dominated by both
surface states driven weak anti localization (WAL), as well the bulk WL (weak
localization) states. Further, by applying the HLN equation we have extracted
the fitting parameters i.e., phase coherence length and the pre-factor. The HLN
equation exhibited values of [pre factor close to -1.0, indicating both WAL and
WL contributions. On the other hand, the extracted phase coherence length is
seen to decrease from 11.125 nm to 5.576 nm as the temperature is increased
from 5K to 200K respectively. Summarily, the short letter discusses primarily
the temperature dependent magneto-conductivity analysis of pristine Bi2Se3
single crystal by the HLN model.


  J-PET is a detector optimized for registration of photons from the
electron-positron annihilation via plastic scintillators where photons interact
predominantly via Compton scattering. Registration of both primary and
scattered photons enables to determinate the linear polarization of the primary
photon on the event by event basis with a certain probability. Here we present
quantitative results on the feasibility of such polarization measurements of
photons from the decay of positronium with the J-PET and explore the physical
limitations for the resolution of the polarization determination of 511 keV
photons via Compton scattering. For scattering angles of about 82 deg (where
the best contrast for polarization measurement is theoretically predicted) we
find that the single event resolution for the determination of the polarization
is about 40 deg (predominantly due to properties of the Compton effect).
However, for samples larger than ten thousand events the J-PET is capable of
determining relative average polarization of these photons with the precision
of about few degrees. The obtained results open new perspectives for studies of
various physics phenomena such as quantum entanglement and tests of discrete
symmetries in decays of positronium and extend the energy range of polarization
measurements by five orders of magnitude beyond the optical wavelength regime.


  Spectra for stars located redward of the fiducial red giant branches of the
Ursa Minor and Draco dwarf spheroidal galaxies have been obtained with the
Hobby-Eberly telescope and the Marcario Low Resolution Spectrometer. From a
comparison of our radial velocities with those reported in previous
medium-resolution studies, we find an average difference of 10 km/s with a
standard deviation of 11 km/s. On the basis of these radial velocities, we
confirm the membership of five stars in Ursa Minor, and find two others to be
nonmembers. One of the confirmed members is a known carbon star which lies
redward of RGB; three others are previously unidentified carbon stars. The
fifth star is a red giant which was found previously by Shetrone et al. (2001)
to have [Fe/H] =-1.68+/-0.11 dex. In Draco, we find eight nonmembers, confirm
the membership of one known carbon star, and find two new members. One of these
stars is a carbon star, while the other shows no evidence for C2 bands or
strong atomic bands, although the signal-to-noise ratio of the spectrum is low.
Thus, we find no evidence for a population of stars more metal-rich than [Fe/H]
\~ -1.45 dex in either of these galaxies. Indeed, our spectroscopic survey
suggests that every candidate suspected of having a metallicity in excess of
this value based on its position in the color-magnitude diagram is, in
actuality, a carbon star. Based on the census of 13 known carbon stars in these
two galaxies, we estimate of the carbon star specific frequency to be e(dSph) ~
2.4E-5/Lsolarv, 25-100 times higher than that of Galactic globular clusters.


  We provide a computational definition of the notions of vector space and
bilinear functions. We use this result to introduce a minimal language
combining higher-order computation and linear algebra. This language extends
the Lambda-calculus with the possibility to make arbitrary linear combinations
of terms alpha.t + beta.u. We describe how to "execute" this language in terms
of a few rewrite rules, and justify them through the two fundamental
requirements that the language be a language of linear operators, and that it
be higher-order. We mention the perspectives of this work in the field of
quantum computation, whose circuits we show can be easily encoded in the
calculus. Finally, we prove the confluence of the entire calculus.


  We derive the spectral indices and their runnings of local form $f_{NL}$ and
$g_{NL}$ from a DBI isocurvature field and we find that the indices are
suppressed by the sound speed $c_s$. This effect can be interpreted by the
Lorentz boost from the viewpoint in the frame where brane is moving.


  Privacy amplification (PA) is an essential part in a quantum key distribution
(QKD) system, distilling a highly secure key from a partially secure string by
public negotiation between two parties. The optimization objectives of privacy
amplification for QKD are large block size, high throughput and low cost. For
the global optimization of these objectives, a novel privacy amplification
algorithm is proposed in this paper by combining multilinear-modular-hashing
and modular arithmetic hashing. This paper proves the security of this hybrid
hashing PA algorithm within the framework of both information theory and
composition security theory. A scheme based on this algorithm is implemented
and evaluated on a CPU platform. The results on a typical CV-QKD system
indicate that the throughput of this scheme<EMAIL_ADDRESS>input block size)
is twice higher than the best existing scheme (140Mbps@1*10^8 input block
size). Moreover, This scheme is implemented on a mobile CPU platform instead of
a desktop CPU or a server CPU, which means that this algorithm has a better
performance with a much lower cost and power consumption.


  Radio point sources are strongly correlated with clusters of galaxies, so a
significant fraction of the thermal Sunyaev-Zel'dovich (SZ) effect signal could
be affected by point source contamination. Based on empirical estimates of the
radio galaxy population, it is shown that the rms temperature fluctuations of
the thermal SZ effect could be underestimated by as much as 30% at an observing
frequency of 30 GHz at l>1000. The effect is larger at higher multipoles. If
the recent report of excess power at small angular scales is to be explained by
the thermal SZ effect, then radio point sources at an observing frequency of 30
GHz must be a surprisingly weak contaminant of the SZ effect for low-mass
clusters.


  Factorizing a large matrix into small matrices is a popular strategy for
model compression. Singular value decomposition (SVD) plays a vital role in
this compression strategy, approximating a learned matrix with fewer
parameters. However, SVD minimizes the squared error toward reconstructing the
original matrix without gauging the importance of the parameters, potentially
giving a larger reconstruction error for those who affect the task accuracy
more. In other words, the optimization objective of SVD is not aligned with the
trained model's task accuracy. We analyze this previously unexplored problem,
make observations, and address it by introducing Fisher information to weigh
the importance of parameters affecting the model prediction. This idea leads to
our method: Fisher-Weighted SVD (FWSVD). Although the factorized matrices from
our approach do not result in smaller reconstruction errors, we find that our
resulting task accuracy is much closer to the original model's performance. We
perform analysis with the transformer-based language models, showing our
weighted SVD largely alleviates the mismatched optimization objectives and can
maintain model performance with a higher compression rate. Our method can
directly compress a task-specific model while achieving better performance than
other compact model strategies requiring expensive model pre-training.
Moreover, the evaluation of compressing an already compact model shows our
method can further reduce 9% to 30% parameters with an insignificant impact on
task accuracy.


  We consider algorithms for going from a "full" matrix to a condensed "band
bidiagonal" form using orthogonal transformations. We use the framework of
"algorithms by tiles". Within this framework, we study: (i) the tiled
bidiagonalization algorithm BiDiag, which is a tiled version of the standard
scalar bidiagonalization algorithm; and (ii) the R-bidiagonalization algorithm
R-BiDiag, which is a tiled version of the algorithm which consists in first
performing the QR factorization of the initial matrix, then performing the
band-bidiagonalization of the R-factor. For both bidiagonalization algorithms
BiDiag and R-BiDiag, we use four main types of reduction trees, namely FlatTS,
FlatTT, Greedy, and a newly introduced auto-adaptive tree, Auto. We provide a
study of critical path lengths for these tiled algorithms, which shows that (i)
R-BiDiag has a shorter critical path length than BiDiag for tall and skinny
matrices, and (ii) Greedy based schemes are much better than earlier proposed
variants with unbounded resources. We provide experiments on a single multicore
node, and on a few multicore nodes of a parallel distributed shared-memory
system, to show the superiority of the new algorithms on a variety of matrix
sizes, matrix shapes and core counts.


  We perform a fluctuation analysis of the 1.1mm Bolocam Lockman Hole Survey,
which covers 324 square arcmin to a very uniform point source-filtered RMS
noise level of 1.4 mJy/beam. The fluctuation analysis has the significant
advantage of utilizing all of the available data. We constrain the number
counts in the 1-10 mJy range, and derive significantly tighter constraints than
in previous work: the power-law index is 2.7 (+0.18, -0.15), while the
amplitude is equal to 1595 (+85,-238) sources per mJy per square degree, or
N(>1 mJy) = 940 (+50,-140) sources/square degree (95% confidence). Our results
agree extremely well with those derived from the extracted source number counts
by Laurent et al (2005). Our derived normalization is about 2.5 times smaller
than determined by MAMBO at 1.2mm by Greve et al (2004). However, the
uncertainty in the normalization for both data sets is dominated by the
systematic (i.e., absolute flux calibration) rather than statistical errors;
within these uncertainties, our results are in agreement. We estimate that
about 7% of the 1.1mm background has been resolved at 1 mJy.


  The fivebrane worldvolume theory in eleven dimensions is known to contain BPS
threebrane solitons which can also be interpreted as a fivebrane whose
worldvolume is wrapped around a Riemann surface. By considering configurations
of intersecting fivebranes and hence intersecting threebrane solitons, we
determine the Bogomol'nyi equations for more general BPS configurations. We
obtain differential equations, generalising Cauchy-Riemann equations, which
imply that the worldvolume of the fivebrane is wrapped around a calibrated
geometry.


  Professor Chen Ning Yang has made seminal and influential contributions in
many different areas in theoretical physics. This talk focuses on his
contributions in statistical mechanics, a field in which Professor Yang has
held a continual interest for over sixty years. His Master's thesis was on a
theory of binary alloys with multi-site interactions, some 30 years before
others studied the problem. Likewise, his other works opened the door and led
to subsequent developments in many areas of modern day statistical mechanics
and mathematical physics. He made seminal contributions in a wide array of
topics, ranging from the fundamental theory of phase transitions, the Ising
model, Heisenberg spin chains, lattice models, and the Yang-Baxter equation, to
the emergence of Yangian in quantum groups. These topics and their
ramifications will be discussed in this talk.


  In the recent literature the important role of depth in deep learning has
been emphasized. In this paper we argue that sufficient width of a feedforward
network is equally important by answering the simple question under which
conditions the decision regions of a neural network are connected. It turns out
that for a class of activation functions including leaky ReLU, neural networks
having a pyramidal structure, that is no layer has more hidden units than the
input dimension, produce necessarily connected decision regions. This implies
that a sufficiently wide hidden layer is necessary to guarantee that the
network can produce disconnected decision regions. We discuss the implications
of this result for the construction of neural networks, in particular the
relation to the problem of adversarial manipulation of classifiers.


  The results are reported of molecular dynamics simulations of the static
longitudinal dielectric and response functions for molten AgI at 923 K using
two ionic models. The first one is a rigid ion model, while in the second the
induced dipole moments of the anions are added to the effective pair potentials
of the first. It is derived theoretically that the dielectric functions for the
polarizable ion model are determined by spatial correlations of charge and
dipole moment densities. The charge structure factor at long wavelengths is
also studied.


  A symplectic manifold that is obtained from the complex projective plane by k
blowups is encoded by k+1 parameters: the size of the initial complex
projective plane, and the sizes of the blowups. We determine which values of
these parameters yield symplectomorphic manifolds.


  Mobile edge computing (MEC) networks are one of the key technologies for
ultra-reliability and low-latency communications. The computing resource
allocation solution needs to be carefully designed to guarantee the computing
resource efficiency of MEC networks. Based on the potential game theory, a
computing resource allocation solution is proposed to reduce energy consumption
and improve computing resource efficiency in MEC networks. The computing
resource allocation solution includes two parts: the first part is the power
control scheme based on the potential game theory and the second part is the
computing resource allocation scheme based on linear programming. The power
control scheme is to find a set of the transmission powers of base stations
(BSs) that maximizes the potential function of MEC networks. The computing
resource allocation scheme is to maximize the average computing resource
allocation coefficient of the MEC networks based on the results of the power
control scheme. Compared with traditional solutions, simulation results
indicate the computing resource utilization and energy efficiency of the
proposed computing resource allocation solution are significantly improved.


  We give a simple necessary and sufficient condition for uniform K-stability
of $\mathbb{Q}$-Fano varieties.


  From experimental studies it is well-known that colloidal particles suspended
in a near-critical binary solvent exhibit interesting aggregation phenomena,
often associated with colloidal phase transitions, and assumed to be driven by
long-ranged solvent mediated (SM) interactions (critical Casimir forces), set
by the (diverging) correlation length of the solvent. We present the first
simulation and theoretical study of an explicit model of a ternary mixture that
mimics this situation. Both the effective SM pair interactions and the full
ternary phase diagram are determined for Brownian discs suspended in an
explicit two-dimensional supercritical binary liquid mixture. Gas-liquid and
fluid-solid transitions are observed in a region that extends well-away from
criticality of the solvent reservoir. We discuss to what extent an effective
pair-potential description can account for the phase behavior we observe. Our
study provides a fresh perspective on how critical fluctuations of the solvent
might influence colloidal self-assembly.


  The COVID-19 pandemic has caused drastic alternations in human life in all
aspects. The government's laws in this regard affected the lifestyle of all
people. Due to this fact studying the sentiment of individuals is essential to
be aware of the future impacts of the coming pandemics. To contribute to this
aim, we proposed an NLP (Natural Language Processing) model to analyze
open-text answers in a survey in Persian and detect positive and negative
feelings of the people in Iran. In this study, a distilBert transformer model
was applied to take on this task. We deployed three approaches to perform the
comparison, and our best model could gain accuracy: 0.824, Precision: 0.824,
Recall: 0.798, and F1 score: 0.804.


  Although topological surface states are known to be robust against
non-magnetic surface perturbations, their band dispersions and spatial
distributions are still sensitive to the surface defects. Take Bi2Se3 as an
example, we demonstrated that Se vacancies modifies the surface band structures
considerably. When large numbers of Se vacancies exist on the surface,
topological surface states may sink down from the first to second quintuple
layer and get separated from the vacancies. We simulated STM images to
distinguish the surfaces with Se- and Bi-terminations.


  First we prove that any inner automorphism in the stabilizer of a
graded-simple unital associative algebra whose grading group is abelian is the
conjugation by a homogeneous element. Now consider a grading by an abelian
group on an associative algebra such that the algebra is graded-simple and
satisfies the DCC on graded left ideals. We give necessary and sufficient
conditions for the grading to be fine. Then we assume that one of these
necessary conditions to be fine is satisfied, and we compute the automorphism
groups of the grading; the results are expressed in terms of the automorphism
groups of a graded-division algebra. Finally we compute the automorphism groups
of graded-division algebras in the case in which the ground field is the field
of real numbers, and the underlying algebra (disregarding the grading) is
simple and of finite dimension.


  Correctly evaluating defenses against adversarial examples has proven to be
extremely difficult. Despite the significant amount of recent work attempting
to design defenses that withstand adaptive attacks, few have succeeded; most
papers that propose defenses are quickly shown to be incorrect.
  We believe a large contributing factor is the difficulty of performing
security evaluations. In this paper, we discuss the methodological foundations,
review commonly accepted best practices, and suggest new methods for evaluating
defenses to adversarial examples. We hope that both researchers developing
defenses as well as readers and reviewers who wish to understand the
completeness of an evaluation consider our advice in order to avoid common
pitfalls.


  Geometric phases are robust against certain types of local noises, and thus
provide a promising way towards high-fidelity quantum gates. However, comparing
with the dynamical ones, previous implementations of nonadiabatic geometric
quantum gates usually require longer evolution time, due to the needed longer
evolution path. Here, we propose a scheme to realize nonadiabatic geometric
quantum gates with short paths based on simple pulse control techniques,
instead of deliberated pulse control in previous investigations, which can thus
further suppress the influence from the environment induced noises.
Specifically, we illustrate the idea on a superconducting quantum circuit,
which is one of the most promising platforms for realizing practical quantum
computer. As the current scheme shortens the geometric evolution path, we can
obtain ultra-high gate fidelity, especially for the two-qubit gate case, as
verified by our numerical simulation. Therefore, our protocol suggests a
promising way towards high-fidelity and roust quantum computation on a
solid-state quantum system.


  In this paper we present a novel algorithm to solve the robot kinematic
structure identification problem. Given a time series of data, typically
obtained processing a set of visual observations, the proposed approach
identifies the ordered sequence of links associated to the kinematic chain, the
joint type interconnecting each couple of consecutive links, and the input
signal influencing the relative motion. Compared to the state of the art, the
proposed algorithm has reduced computational costs, and is able to identify
also the joints' type sequence.


  In Generalized Category Discovery (GCD), we cluster unlabeled samples of
known and novel classes, leveraging a training dataset of known classes. A
salient challenge arises due to domain shifts between these datasets. To
address this, we present a novel setting: Across Domain Generalized Category
Discovery (AD-GCD) and bring forth CDAD-NET (Class Discoverer Across Domains)
as a remedy. CDAD-NET is architected to synchronize potential known class
samples across both the labeled (source) and unlabeled (target) datasets, while
emphasizing the distinct categorization of the target data. To facilitate this,
we propose an entropy-driven adversarial learning strategy that accounts for
the distance distributions of target samples relative to source-domain class
prototypes. Parallelly, the discriminative nature of the shared space is upheld
through a fusion of three metric learning objectives. In the source domain, our
focus is on refining the proximity between samples and their affiliated class
prototypes, while in the target domain, we integrate a neighborhood-centric
contrastive learning mechanism, enriched with an adept neighborsmining
approach. To further accentuate the nuanced feature interrelation among
semantically aligned images, we champion the concept of conditional image
inpainting, underscoring the premise that semantically analogous images prove
more efficacious to the task than their disjointed counterparts.
Experimentally, CDAD-NET eclipses existing literature with a performance
increment of 8-15% on three AD-GCD benchmarks we present.


  Fast and dynamical control of quantum phases is highly desired in the
application of quantum phenomena in devices. On the experimental side,
ultrafast laser pulses provide an ideal platform to induce femtosecond dynamics
in a variety of materials. Here, we show that a laser pulse driven heavy
fermion system, can be tuned to dynamically evolve into a phase, which is not
present in equilibrium. Using the state-of-the-art time-dependent variational
Monte Carlo method, we perform numerical simulations of realistic laser pulses
applied to the Kondo Lattice model. By tracking spin and charge degrees of
freedom we identify a dynamical phase transition from a charge ordered into a
metallic state, while preserving the spin order of the system. We propose to
use higher harmonic generation to identify the dynamical phase transition in an
ultrafast experiment.


  The fault-tolerant operation of logical qubits is an important requirement
for realizing a universal quantum computer. Spin qubits based on quantum dots
have great potential to be scaled to large numbers because of their
compatibility with standard semiconductor manufacturing. Here, we show that a
quantum error correction code can be implemented using a four-qubit array in
germanium. We demonstrate a resonant SWAP gate and by combining controlled-Z
and controlled-$\text{S}^{-1}$ gates we construct a Toffoli-like three-qubit
gate. We execute a two-qubit phase flip code and find that we can preserve the
state of the data qubit by applying a refocusing pulse to the ancilla qubit. In
addition, we implement a phase flip code on three qubits, making use of a
Toffoli-like gate for the final correction step. Both the quality and quantity
of the qubits will require significant improvement to achieve fault-tolerance.
However, the capability to implement quantum error correction codes enables
co-design development of quantum hardware and software, where codes tailored to
the properties of spin qubits and advances in fabrication and operation can now
come together to scale semiconductor quantum technology toward universal
quantum computers.


  Owing to the complexity of turbulent magnetic fields, modeling the diffusion
of cosmic rays is challenging. Based on the current understanding of
anisotropic magnetohydrodynamic (MHD) turbulence, we use test particles to
examine the cosmic rays' superdiffusion in the direction perpendicular to the
mean magnetic field. By changing Alfven Mach number $M_A$ and sonic Mach number
$M_S$ of compressible MHD simulations, our study covers a wide range of
astrophysical conditions including subsonic warm gas phase and supersonic cold
molecular gas. We show that freely streaming cosmic rays' perpendicular
displacement increases as 3/2 to the power of the time traveled along local
magnetic field lines. This power-law index changes to 3/4 if the parallel
propagation is diffusive. We find that the cosmic rays' parallel mean free path
decreases in a power-law relation of $M_A^{-2}$ in supersonic turbulence. We
investigate the energy fraction of slow, fast, and Alfvenic modes and confirm
the dominance of Alfvenic modes in the perpendicular superdiffusion. In
particular, the energy fraction of fast mode, which is the main agent for
pitch-angle scattering, increases with $M_A$ but is insensitive to $M_S \ge 2$.
Accordingly, our results suggest that the suppressed diffusion in supersonic
molecular clouds arises primarily due to the variations of $M_A$ instead of
$M_S$.


  The polar magnet CaBaCo$_{4}$O$_{7}$ is known to exhibit the largest
magnetic-field-driven electric polarization change ($\Delta P$) associated with
an antiferromagnetic (AFM)-ferrimagnetic (FIM) transition in a narrow
temperature range between 62 and 69 K. In this work, we investigate the effect
of Ni doping on its multiferroic properties, by means of magnetization,
electric polarization, dielectric constant, and magnetostriction measurements
on single crystals of CaBaCo$_{3.9}$Ni$_{0.1}$O$_{7}$ up to 50 T. In the doped
material, two kinds of AFM phases appear below 78 K, accompanying negative
$\Delta P$. Upon the application of a magnetic field along any crystallographic
axis, giant positive $\Delta P$ of up to $11 \sim 12$ mC/m$^{2}$ is observed
along with an AFM-FIM transition in the whole temperature range below 78 K. The
giant magnetoelectric effect inherent in CaBaCo$_{4}$O$_{7}$ can be further
enhanced just by a small amount of chemical substitution, in terms of (i)
increasing the magnitude of $\Delta P$ and (ii) expanding the temperature range
in which giant $\Delta P$ appears.


  In this paper, we introduce a type of weighted multilinear Hardy operators
and obtain their sharp bounds on the product of Lebesgue spaces and central
Morrey spaces. In addition, we obtain sufficient and necessary conditions of
the weight functions so that the commutators of the weighted multilinear Hardy
operators (with symbols in central BMO space) are bounded on the product of
central Morrey spaces. These results are further used to prove sharp estimates
of some inequalities due to Riemann-Liouville and Weyl.


  Problems consisting in finding the ground state of particles interacting with
a given potential constrained to move on a particular geometry are surprisingly
difficult. Explicit solutions have been found for small numbers of particles by
the use of numerical methods in some particular cases such as particles on a
sphere and to a much lesser extent on a torus. In this paper we propose a
general solution to the problem in the opposite limit of a very large number of
particles M by expressing the energy as an expansion in M whose coefficients
can be minimized by a geometrical ansatz. The solution is remarkably universal
with respect to the geometry and the interaction potential. Explicit solutions
for the sphere and the torus are provided. The paper concludes with several
predictions that could be verified by further theoretical or numerical work.


  Terahertz (THz) communications is considered as one of key solutions to
support extremely high data demand in 6G. One main difficulty of the THz
communication is the severe signal attenuation caused by the foliage loss,
oxygen/atmospheric absorption, body and hand losses. To compensate for the
severe path loss, multiple-input-multiple-output (MIMO) antenna array-based
beamforming has been widely used. Since the beams should be aligned with the
signal propagation path to achieve the maximum beamforming gain, acquisition of
accurate channel knowledge, i.e., channel estimation, is of great importance.
An aim of this paper is to propose a new type of deep learning (DL)-based
parametric channel estimation technique. In our work, DL figures out the
mapping function between the received pilot signal and the sparse channel
parameters characterizing the spherical domain channel. By exploiting the long
short-term memory (LSTM), we can efficiently extract the temporally correlated
features of sparse channel parameters and thus make an accurate estimation with
relatively small pilot overhead. From the numerical experiments, we show that
the proposed scheme is effective in estimating the near-field THz MIMO channel
in THz downlink environments.


  In this work, we study the phenomena of quantum entanglement by computing de
Sitter entanglement entropy from von Neumann measure. For this purpose we
consider a bipartite quantum field theoretic setup in presence of axion
originating from ${\bf Type~ II~B}$ string theory. We consider the initial
vacuum to be CPT invariant non-adiabatic $\alpha$ vacua state under ${\bf
SO(1,4)}$ ismometry, which is characterized by a real one-parameter family. To
implement this technique we use a ${\bf S^2}$ which divide the de Sitter into
two exterior and interior sub-regions. First, we derive the wave function of
axion in an open chart for $\alpha$ vacua by applying Bogoliubov transformation
on the solution for Bunch-Davies vacuum state. Further, we quantify the density
matrix by tracing over the contribution from the exterior region. Using this
result we derive entanglement entropy, R$\acute{e}$nyi entropy and explain the
long-range quantum effects in primordial cosmological correlations. We also
provide a comparison between the results obtained from Bunch-Davies vacuum and
the generalized $\alpha$ vacua, which implies that the amount of quantum
entanglement and the long-range effects are larger for non zero value of the
parameter $\alpha$. Most significantly, our derived results for $\alpha$ vacua
provides the necessary condition for generating non zero entanglement entropy
in primordial cosmology.


  We obtain a supersymmetric three family chiral SU(6) grand unification model
with the global family symmetry SU(3)[family] from F-theory. This model has
nice features such as all the fermion masses are reasonably generated and there
results only one pair of Higgs doublets, realizing the doublet-triplet
splitting from the family symmetry SU(3)[family]. The proton hexality is
realized toward the proton stability problem. There is a room to fit the three
gauge couplings using the F-theory flux idea and we obtain the proton lifetime
in the 10^{36-37} yr region.


  We generalize the recent results on radial projections by Orponen, Shmerkin,
Wang using two different methods. In particular, we show that given $X,Y\subset
\mathbb{R}^n$ Borel sets and $X\neq \emptyset$. If $\dim Y \in (k,k+1]$ for
some $k\in \{1,\dots, n-1\}$, then \[ \sup_{x\in X} \dim \pi_x(Y\setminus
\{x\}) \geq \min \{\dim X + \dim Y - k, k\}. \] Our results give a new approach
to solving a conjecture of Lund-Pham-Thu in all dimensions and for all ranges
of $\dim Y$.
  The first of our two methods for proving the above theorem is shorter,
utilizing a result of the first author and Gan. Our second method, though
longer, follows the original methodology of Orponen--Shmerkin--Wang, and
requires a higher dimensional incidence estimate and a dual Furstenberg-set
estimate for lines. These new estimates may be of independent interest.


  The logistics of transporting a package from a storage facility to the
consumer's front door usually employs highly specialized robots often times
splitting sub-tasks up to different systems, e.g., manipulator arms to sort and
wheeled vehicles to deliver. More recent endeavors attempt to have a unified
approach with legged and humanoid robots. These solutions, however, occupy
large amounts of space thus reducing the number of packages that can fit into a
delivery vehicle. As a result, these bulky robotic systems often reduce the
potential for scalability and task parallelization. In this paper, we introduce
LIMMS (Latching Intelligent Modular Mobility System) to address both the
manipulation and delivery portion of a typical last-mile delivery while
maintaining a minimal spatial footprint. LIMMS is a symmetrically designed, 6
degree of freedom (DoF) appendage-like robot with wheels and latching
mechanisms at both ends. By latching onto a surface and anchoring at one end,
LIMMS can function as a traditional 6-DoF manipulator arm. On the other hand,
multiple LIMMS can latch onto a single box and behave like a legged robotic
system where the package is the body. During transit, LIMMS folds up compactly
and takes up much less space compared to traditional robotic systems. A large
group of LIMMS units can fit inside of a single delivery vehicle, opening the
potential for new delivery optimization and hybrid planning methods never done
before. In this paper, the feasibility of LIMMS is studied and presented using
a hardware prototype as well as simulation results for a range of sub-tasks in
a typical last-mile delivery.


  In this paper, selection of an active sensor subset for tracking a discrete
time, finite state Markov chain having an unknown transition probability matrix
(TPM) is considered. A total of N sensors are available for making observations
of the Markov chain, out of which a subset of sensors are activated each time
in order to perform reliable estimation of the process. The trade-off is
between activating more sensors to gather more observations for the remote
estimation, and restricting sensor usage in order to save energy and bandwidth
consumption. The problem is formulated as a constrained minimization problem,
where the objective is the long-run averaged mean-squared error (MSE) in
estimation, and the constraint is on sensor activation rate. A Lagrangian
relaxation of the problem is solved by an artful blending of two tools: Gibbs
sampling for MSE minimization and an on-line version of expectation
maximization (EM) to estimate the unknown TPM. Finally, the Lagrange multiplier
is updated using slower timescale stochastic approximation in order to satisfy
the sensor activation rate constraint. The on-line EM algorithm, though adapted
from literature, can estimate vector-valued parameters even under time-varying
dimension of the sensor observations. Numerical results demonstrate
approximately 1 dB better error performance than uniform sensor sampling and
comparable error performance (within 2 dB bound) against complete sensor
observation. This makes the proposed algorithm amenable to practical
implementation.


  The electric dipole moment (EDM) of elementary particles, arising from
flavor-diagonal $CP$ violation, serves as a powerful probe for new physics
beyond the Standard Model and holds the potential to provide novel insights in
unraveling the puzzle of the matter-dominated Universe. Hyperon EDM is a
largely unexplored territory. In this paper, we present a comprehensive angular
analysis that focuses on entangled hyperon-antihyperon pairs in $J/\psi$ decays
for the indirect extraction of hyperon EDM. The statistical sensitivities are
investigated for BESIII and the proposed Super Tau-Charm Facility (STCF).
Leveraging the statistics from the BESIII experiment, the estimated sensitivity
for $\Lambda$ EDM can reach an impressive level of $10^{-19}$ $e$ cm, achieving
a 3-orders-of-magnitude improvement over the only existing measurement in a
fixed-target experiment at Fermilab with similar statistics. The estimated
sensitivities for the $\Sigma^+$, $\Xi^-$, and $\Xi^0$ hyperons at the same
level of $10^{-19}$ $e$ cm will mark the first-ever achievement and the latter
two will be the first exploration of hyperons with two strange valence quarks.
The EDM measurements for hyperons conducted at the BESIII experiment will be a
significant milestone and serve as a litmus test for new physics such as
supersymmetry and the left-right symmetrical model. Furthermore, at the STCF
experiment, the sensitivity of hyperon EDM measurements can be further enhanced
by 2 orders of magnitude. Additionally, this angular analysis enables the
determination of $CP$ violation in hyperon decays, the effective weak mixing
angle, and beam polarization.


  Observed protoplanetary disks consist of a large amount of micrometer-sized
particles. Dullemond and Dominik (2005) pointed out for the first time the
difficulty in explaining the strong mid-IR excess of classical T-Tauri stars
without any dust-retention mechanisms. Because high relative velocities in
between micrometer-sized and macroscopic particles exist in protoplanetary
disks, we present experimental results on the erosion of macroscopic
agglomerates consisting of micrometer-sized spherical particles via the impact
of micrometer-sized particles. We find that after an initial phase, in which an
impacting particle erodes up to 10 particles of an agglomerate, the impacting
particles compress the agglomerate's surface, which partly passivates the
agglomerates against erosion. Due to this effect the erosion halts within our
error bars for impact velocities up to ~30 m/s. For larger velocities, the
erosion is reduced by an order of magnitude. This outcome is explained and
confirmed by a numerical model. In a next step we build an analytical disk
model and implement the experimentally found erosive effect. The model shows
that erosion is a strong source of micrometer-sized particles in a
protoplanetary disk. Finally we use the stationary solution of this model to
explain the amount of micrometer-sized particles in observational infrared data
of Furlan et al. (2006).


  Recently, many works have experimentally demonstrated near-field radiative
heat transfer (NFRHT) exceeding the far-field blackbody limit between planar
surfaces. Due to the difficulties associated with maintaining the nanosize gaps
required for measuring a near-field enhancement, these demonstrations have been
limited to experiments that cannot be implemented into actual applications.
This poses a significant bottleneck to the advancement of NFRHT research. Here,
we describe devices bridging laboratory-scale measurements and potential NFRHT
engineering applications in energy conversion and thermal management. We report
a maximum NFRHT enhancement of ~ 28.5 over the blackbody limit with devices
made of millimeter-sized doped silicon (Si) surfaces separated by vacuum gap
spacings down to ~ 110 nm. The devices capitalize on micropillars, separating
the high-temperature emitter and low-temperature receiver, manufactured within
micrometer-deep pits. These micropillars, which are ~ 4.5 to 45 times longer
than the nanosize vacuum spacing where radiation transfer takes place, minimize
parasitic heat conduction without sacrificing device structural integrity. The
robustness of our devices enables gap spacing visualization via scanning
electron microscopy (SEM) prior to performing NFRHT measurements. Direct gap
spacing characterization is critical for transitioning NFRHT research from
laboratory-scale experiments to applications.