  Magneto-impedance (MI) of soft ferromagnetic materials is a sensitive
function of the amplitude and frequency of exciting a.c magnetic field. MI (Z)
of amorphous ferromagnetic ribbon of nominal composition Fe73.5Nb3 Cu1 Si13.5
B9 is measured at different excitation currents with frequency ranging from
30kHz to 120KHz The excitation (a.c) and biasing (d.c) magnetic fields Hdc are
parallel to ribbon axis. At zero dc fields, Z exhibits non-linear dependence on
excitation amplitude whereas at higher d.c fields (9Oe) Z is nearly independent
of excitation amplitude. The impedance is maximum at zero bias field and
sharply decreases as Hdc increases and almost zero hysterisis has been observed
as Hdc is scanned. The maximum relative change of impedance
(Z(H)-Z(H=0))/Z(H=0) is found to increase from nearly 45 to 60 percent when the
excitation fields goes up from 14A/m to 140A/m. The large MI is associated with
screening of electromagnetic field by magnetization induced by exciting ac
field.


  We present a sequential Monte Carlo sampler variant of the partial rejection
control algorithm, and show that this variant can be considered as a sequential
Monte Carlo sampler with a modified mutation kernel. We prove that the new
sampler can reduce the variance of the incremental importance weights when
compared with standard sequential Monte Carlo samplers. We provide a study of
theoretical properties of the new algorithm, and make connections with some
existing algorithms. Finally, the sampler is adapted for application under the
challenging "likelihood free," approximate Bayesian computation modelling
framework, where we demonstrate superior performance over existing
likelihood-free samplers.


  Measured and calculated cross sections for spin-exchange between alkali atoms
and noble gases (specifically sodium and helium) are used to constrain
anomalous spin-dependent forces between nuclei at the atomic scale ($\sim
10^{-8}~{\rm cm}$). Combined with existing stringent limits on anomalous
short-range, spin-dependent couplings of the proton, the dimensionless coupling
constant for a heretofore undiscovered axial vector interaction of the neutron
arising from exchange of a boson of mass $\lesssim 100~{\rm eV}$ is constrained
to be $g_A^n/\sqrt{4 \pi \hbar c} < 2 \times 10^{-3}$. Constraints are
established for a velocity- and spin-dependent interaction $\propto
\prn{\mathbf{I} \cdot \mathbf{v}} \prn{\mathbf{K} \cdot \mathbf{v}}$, where
$\mathbf{I}$ and $\mathbf{K}$ are the nuclear spins of He and Na, respectively,
and $\mathbf{v}$ is the relative velocity of the atoms. Constraints on torsion
gravity are also considered.


  We study chirality production in the pseudoscalar inflation model of
magnetogenesis taking into account the Schwinger effect and particle collisions
in plasma in the relaxation time approximation. We consider the Schwinger
production of one Dirac fermion species by an Abelian gauge field in two cases:
(i) the fermion carries only the weak charge with respect to the U(1) group and
(ii) it is also charged with respect to another strongly coupled gauge group.
While the gradient-expansion formalism is employed for the description of the
evolution of gauge field, plasma is described by hydrodynamical approach which
allows us to determine the number, energy density, and chirality of produced
fermions. It is found that while chirality production is very efficient for
both, weakly and strongly interacting fermions, the resulting gauge field is
typically stronger in the case of strongly interacting fermions due to
suppression of the Schwinger conductivity by particle collisions.


  If we wish to compromise some password-protected system as an attacker (i.e.
a member of the red team), we have a large number of popular and
actively-maintained tools to choose from in helping us to realise our goal.
Password hash cracking hardware and software, online guessing tools, exploit
frameworks, and a wealth of tools for helping us to perform reconnaissance on
the target system are widely available. By comparison, if we wish to defend a
password-protected system against such an attack (i.e. as a member of the blue
team), we have comparatively few tools to choose from. In this research
abstract, we present our work to date on Passlab, a password security tool
designed to help system administrators take advantage of formal methods in
order to make sensible and evidence-based security decisions using a clean and
intuitive user interface.


  Motivated by exploring the effect of interactions on Chern insulators, and by
recent experiments realizing topological bands for ultracold atoms in synthetic
gauge fields, we study the honeycomb lattice Haldane-Hubbard model of
spin-$1/2$ fermions. Using an unrestricted mean field approach, we map out the
instability of the topological band insulator towards magnetically ordered
insulators which emerge with increasing Hubbard repulsion. In addition to the
topological N\'eel phase, we recover various chiral noncoplanar magnetic orders
previously identified within a strong coupling approach. We compute the band
structure of these ordered phases, showing that the triple-$Q$ tetrahedral
phase harbors topological Chern bands with large Chern numbers.


  An extended social force model with a dynamic navigation field is proposed to
study bidirectional pedestrian movement. The dynamic navigation field is
introduced to describe the desired direction of pedestrian motion resulting
from the decision-making processes of pedestrians. The macroscopic fundamental
diagrams obtained using the extended model are validated against camera-based
observations. Numerical results show that this extended model can reproduce
collective phenomena in pedestrian traffic, such as dynamic multilane flow and
stable separate-lane flow. Pedestrians' path choice behavior significantly
affects the probability of congestion and the number of self-organized lanes.


  Mobile technologies offer opportunities for higher resolution monitoring of
health conditions. This opportunity seems of particular promise in psychiatry
where diagnoses often rely on retrospective and subjective recall of mood
states. However, getting actionable information from these rather complex time
series is challenging, and at present the implications for clinical care are
largely hypothetical. This research demonstrates that, with well chosen cohorts
(of bipolar disorder, borderline personality disorder, and control) and modern
methods, it is possible to objectively learn to identify distinctive behaviour
over short periods (20 reports) that effectively separate the cohorts.
Participants with bipolar disorder or borderline personality disorder and
healthy volunteers completed daily mood ratings using a bespoke smartphone app
for up to a year. A signature-based machine learning model was used to classify
participants on the basis of the interrelationship between the different mood
items assessed and to predict subsequent mood. The signature methodology was
significantly superior to earlier statistical approaches applied to this data
in distinguishing the participant three groups, clearly placing 75% into their
original groups on the basis of their reports. Subsequent mood ratings were
correctly predicted with greater than 70% accuracy in all groups. Prediction of
mood was most accurate in healthy volunteers (89-98%) compared to bipolar
disorder (82-90%) and borderline personality disorder (70-78%).


  The effect of the nucleon recoil for antikaon-deuteron scattering is
investigated in the framework of effective field theory. In particular, we
concentrate on the calculation of the nucleon recoil effect for the
double-scattering process. It is shown that the leading correction to the
static term that emerges at order xi^{1/2} with xi=M_K/m_N vanishes due to a
complete cancellation of individually large contributions. The resulting recoil
effect in this process is found to be of order of 10-15% as compared to the
static term. We also briefly discuss the application of the method in the
calculations of the multiple-scattering diagrams.


  The structure of nuclei around the neutron-rich nucleus 132Sn is of
particular interest due to the vicinity of the Z = 50 and N = 82 shell closures
and the r-process nucleosynthetic path. Four states in 131Sn with a strong
single-particle-like component have previously been studied via the (d,p)
reaction, with limited excitation energy resolution. The 130Sn(9Be,8Be)131Sn
and 130Sn(13C,12C)131Sn single-neutron transfer reactions were performed in
inverse kinematics at the Holifield Radioactive Ion Beam Facility using
particle-gamma coincidence spectroscopy. The uncertainties in the energies of
the single-particle-like states have been reduced by more than an order of
magnitude using the energies of gamma rays. The previous tentative Jpi values
have been confirmed. Decays from high-spin states in 131Sn have been observed
following transfer on the isomeric component of the 130Sn beam. The improved
energies and confirmed spin-parities of the p-wave states important to the
r-process lead to direct-semidirect cross-sections for neutron capture on the
ground state of 130Sn at 30 keV that are in agreement with previous analyses. A
similar assessment of the impact of neutron-transfer on the isomer would
require significant nuclear structure and reaction theory input. There are few
measurements of transfer reaction on isomers, and this is the first on an
isomer in the 132Sn region.


  The compact stars are promising candidates associated with the generation of
gravitational waves (GWs). In this work, we study a special type of compact
stars known as strange stars in the $f(\mathcal{R})$ gravity Palatini
formalism. Here we consider three promising $f(\mathcal{R})$ gravity models
viz., Starobinsky, Hu-Sawicki and Gogoi-Goswami models in the domain of MIT Bag
model and linear equations of state (EoSs). We compute the stellar structures
numerically and constrained the $f(\mathcal{R})$ model parameters with a set of
probable strange star candidates. The study shows that the consideration of
stiffer MIT Bag model and linear EoSs within a favourable set of
$f(\mathcal{R})$ gravity model parameters may result in strange stars with
sufficient compactness to produce echoes of GWs. Thus, we have computed the GWs
echo frequencies and characteristic echo times for such stars. It is found that
in compliance with the experimentally obtained possible strange star
candidates, the obtained GW echo frequencies for all the models are in the
range of $65 - 85$ kHz.


  Some comments are made on the matrices which serve as the basis of a
quaternionic algebra. We show that these matrices are related with the
quaternionic action of the imaginary units from the left and from the right.


  We study the amount of information that is contained in "random pictures", by
which we mean the sample sets of a Boolean model. To quantify the notion
"amount of information", two closely connected questions are investigated: on
the one hand, we study the probability that a large number of balls is needed
for a full reconstruction of a Boolean model sample set. On the other hand, we
study the quantization error of the Boolean model w.r.t. the Hausdorff distance
as a distortion measure.


  Solving partial differential equations (PDEs) is an important research means
in the fields of physics, biology, and chemistry. As an approximate alternative
to numerical methods, PINN has received extensive attention and played an
important role in many fields. However, PINN uses a fully connected network as
its model, which has limited fitting ability and limited extrapolation ability
in both time and space. In this paper, we propose PhyGNNet for solving partial
differential equations on the basics of a graph neural network which consists
of encoder, processer, and decoder blocks. In particular, we divide the
computing area into regular grids, define partial differential operators on the
grids, then construct pde loss for the network to optimize to build PhyGNNet
model. What's more, we conduct comparative experiments on Burgers equation and
heat equation to validate our approach, the results show that our method has
better fit ability and extrapolation ability both in time and spatial areas
compared with PINN.


  Despite its attractive features, Congruent-melted Lithium Niobate (CLN)
suffers from Photo-Refractive Damage (PRD). This light-induced refractive-index
change hampers the use of CLN when high-power densities are in play, a typical
regime in integrated optics. The resistance to PRD can be largely improved by
doping the lithium-niobate substrates with magnesium oxide. However, the
fabrication of waveguides on MgO-doped substrates is not as effective as for
CLN: either the resistance to PRD is strongly reduced by the waveguide
fabrication process (as it happens in Ti-indiffused waveguides) or the
nonlinear conversion efficiency is lowered (as it occurs in annealed-proton
exchange). Here we fabricate, for the first time, waveguides starting from
MgO-doped substrates using the Soft-Proton Exchange (SPE) technique and we show
that this third way represents a promising alternative. We demonstrate that SPE
allows to produce refractive-index profiles almost identical to those produced
on CLN without reducing the nonlinearity in the substrate. We also prove that
the SPE does not affect substantially the resistance to PRD. Since the
fabrication recipe is identical between CLN and MgO-doped substrates, we
believe that SPE might outperform standard techniques to fabricate robust and
efficient waveguides for high-intensity-beam confinement.


  Teleparallel theory of gravity and its modifications have been studied
extensively in literature. However, gravitational waves has not been studied
enough in the framework of teleparallelism. In the present study, we discuss
gravitational waves in general theories of teleparallel gravity containing the
torsion scalar $T$, the boundary term $B$ and a scalar field $\phi$. The goal
is to classify possible new polarizations generalizing results presented in
Ref.[15]. We show that, if the boundary term is minimally coupled to the
torsion scalar and the scalar field, gravitational waves have the same
polarization modes of General Relativity.


  Millimeter wave (mmWave) radars have attracted significant attention from
both academia and industry due to their capability to operate in extreme
weather conditions. However, they face challenges in terms of sparsity and
noise interference, which hinder their application in the field of micro aerial
vehicle (MAV) autonomous navigation. To this end, this paper proposes a novel
approach to dense and accurate mmWave radar point cloud construction via
cross-modal learning. Specifically, we introduce diffusion models, which
possess state-of-the-art performance in generative modeling, to predict
LiDAR-like point clouds from paired raw radar data. We also incorporate the
most recent diffusion model inference accelerating techniques to ensure that
the proposed method can be implemented on MAVs with limited computing
resources.We validate the proposed method through extensive benchmark
comparisons and real-world experiments, demonstrating its superior performance
and generalization ability. Code and pretrained models will be available at
https://github.com/ZJU-FAST-Lab/Radar-Diffusion.


  The micromagnetic distribution in a dielectric nanoparticle is theoretically
considered. It is shown that the existence of inhomogeneous magnetoelectric
interaction in magnetic dielectrics provides the possibility to stabilize the
vortex and antivortex state. The estimation of the critical voltage necessary
for vortex/antivortex nucleation in bismuth ferrite and iron garnet
nanoparticles yields a value of +/-150 V. This system can be considered as
electrically switchable two state-logic magnetic element.


  We study the spin Hall effect taking into account the impurity scattering
effect as general as possible with the focus on the definition of the spin
current. The conserved bulk spin current (Shi et al. [Phys. Rev. Lett. 96,
076604 (2006)]) satisfying the continuity equation of spin is considered in
addition to the conventional one defined by the symmetric product of the spin
and velocity operators. Conditions for non-zero spin Hall current are
clarified. In particular, it is found that (i) the spin Hall current is
non-zero in the Rashba model with a finite-range impurity potential, and (ii)
the spin Hall current vanishes in the cubic Rashba model with a
$\delta$-function impurity potential.


  Keywords play a crucial role in bridging the gap between human understanding
and machine processing of textual data. They are essential to data enrichment
because they form the basis for detailed annotations that provide a more
insightful and in-depth view of the underlying data. Keyword/domain driven term
extraction is a pivotal task in natural language processing, facilitating
information retrieval, document summarization, and content categorization. This
review focuses on keyword extraction methods, emphasizing the use of three
major Large Language Models(LLMs): Llama2-7B, GPT-3.5, and Falcon-7B. We
employed a custom Python package to interface with these LLMs, simplifying
keyword extraction. Our study, utilizing the Inspec and PubMed datasets,
evaluates the performance of these models. The Jaccard similarity index was
used for assessment, yielding scores of 0.64 (Inspec) and 0.21 (PubMed) for
GPT-3.5, 0.40 and 0.17 for Llama2-7B, and 0.23 and 0.12 for Falcon-7B. This
paper underlines the role of prompt engineering in LLMs for better keyword
extraction and discusses the impact of hallucination in LLMs on result
evaluation. It also sheds light on the challenges in using LLMs for keyword
extraction, including model complexity, resource demands, and optimization
techniques.


  We construct the additional symmetries and derive the Adler-Shiota-van
Moerbeke formula for the two-component BKP hierarchy. We also show that the
Drinfeld-Sokolov hierarchies of type D, which are reduced from the
two-component BKP hierarchy, possess symmetries written as the action of a
series of linear Virasoro operators on the tau function. It results in that the
Drinfeld-Sokolov hierarchies of type D coincide with Dubrovin and Zhang's
hierarchies associated to the Frobenius manifolds for Coxeter groups of type D,
and that every solution of such a hierarchy together with the string equation
is annihilated by certain combinations of the Virasoro operators and the time
derivations of the hierarchy.


  Perspiration evaporation plays an indispensable role in human body heat
dissipation. However, conventional textiles show limited perspiration
management capability in moderate/profuse perspiration scenarios, i.e. low
evaporation ability, ineffective evaporative cooling effect, and resultant
human body dehydration and electrolyte disorder. Here, we propose a novel
concept of integrated cooling (i-Cool) textile of heat conduction and sweat
transportation for personal perspiration management based on unique functional
structure design. By integrating heat conductive pathways and water transport
channels decently, this textile not only shows the capability of liquid water
wicking, but also exhibits superior evaporation rate than traditional textiles.
Furthermore, compared with cotton, about 2.8 $^\circ$C cooling effect causing
less than one third amount of dehydration has also been demonstrated on the
artificial sweating skin platform with feedback control loop simulating human
body perspiration situation. Moreover, the practical application feasibility of
the i-Cool textile design principles has been validated as well. Owing to its
exceptional personal perspiration management performance in liquid water
wicking, fast evaporation, efficient cooling effect and reduced human body
dehydration/electrolyte loss, we expect this i-Cool textile provides promising
design guidelines for next-generation personal perspiration management textile.


  A practical method for realizing intense terahertz (THz) emission from
intrinsic Josephson junctions (IJJs) by utilizing external local-heating is
proposed and demonstrated theoretically. An artificial temperature distribution
induced by local heating strongly excites Josephson plasma waves inside IJJs.
Accordingly, the emission power of the THz wave is enhanced drastically, and it
can reach the order of mW. Our result indicates that the use of local heat
control is a powerful method to realize practical solid-state THz-emitters
based on IJJs.


  Auxiliary string fields are introduced in light-cone gauge string field
theory in order to express contact interactions as contractions of cubic
vertices. The auxiliary field in the purely closed-string bosonic theory may be
given a non-zero expectation value, leading to a phase in which world-sheets
have boundaries.


  Logo detection in real-world scene images is an important problem with
applications in advertisement and marketing. Existing general-purpose object
detection methods require large training data with annotations for every logo
class. These methods do not satisfy the incremental demand of logo classes
necessary for practical deployment since it is practically impossible to have
such annotated data for new unseen logo. In this work, we develop an
easy-to-implement query-based logo detection and localization system by
employing a one-shot learning technique. Given an image of a query logo, our
model searches for it within a given target image and predicts the possible
location of the logo by estimating a binary segmentation mask. The proposed
model consists of a conditional branch and a segmentation branch. The former
gives a conditional latent representation of the given query logo which is
combined with feature maps of the segmentation branch at multiple scales in
order to find the matching position of the query logo in a target image, should
it be present. Feature matching between the latent query representation and
multi-scale feature maps of segmentation branch using simple concatenation
operation followed by 1x1 convolution layer makes our model scale-invariant.
Despite its simplicity, our query-based logo retrieval framework achieved
superior performance in FlickrLogos-32 and TopLogos-10 dataset over different
existing baselines.


  In this study, we introduce \textbf{AttendSeg}, a low-precision, highly
compact deep neural network tailored for on-device semantic segmentation.
AttendSeg possesses a self-attention network architecture comprising of
light-weight attention condensers for improved spatial-channel selective
attention at a very low complexity. The unique macro-architecture and
micro-architecture design properties of AttendSeg strike a strong balance
between representational power and efficiency, achieved via a machine-driven
design exploration strategy tailored specifically for the task at hand.
Experimental results demonstrated that the proposed AttendSeg can achieve
segmentation accuracy comparable to much larger deep neural networks with
greater complexity while possessing a significantly lower architecture and
computational complexity (requiring as much as >27x fewer MACs, >72x fewer
parameters, and >288x lower weight memory requirements), making it well-suited
for TinyML applications on the edge.


  An unexplained $>4\,\sigma$ discrepancy persists between "beam" and "bottle"
measurements of the neutron lifetime. A new model proposed that conversions of
neutrons $n$ into mirror neutrons $n'$, part of a dark mirror sector, can
increase the apparent neutron lifetime by $1\%$ via a small mass splitting
$\Delta{m}$ between $n$ and $n'$ inside the 4.6 T magnetic field of the
National Institute of Standards and Technology Beam Lifetime experiment. A
search for neutron conversions in a 6.6 T magnetic field was performed at the
Spallation Neutron Source which excludes this explanation for the neutron
lifetime discrepancy.


  Two kinds of statistical properties of dynamical-triangulated manifolds (DT
mfds) have been investigated. First, the surfaces appearing on the boundaries
of 3D DT mfds were investigated. The string-susceptibility exponent of the
boundary surfaces ($\tilde{\gamma}_{st}$) of 3D DT mfds with $S^{3}$ topology
near to the critical point was obtained by means of a MINBU (minimum neck baby
universes) analysis; actually, we obtained $\tilde{\gamma}_{st} \approx -0.5$.
Second, 3 and 4D DT mfds were also investigated by determining the
string-susceptibility exponent near to the critical point from measuring the
MINBU distributions. As a result, we found a similar behavior of the MINBU
distributions in 3 and 4D DT mfds, and obtained $\gamma_{st}^{(3)} \approx
\gamma_{st}^{(4)} \approx 0$. The existence of common structures in simplicial
quantum gravity is also discussed.


  This paper proposes a learning method to construct an efficient sensing
(measurement) matrix, having orthogonal rows, for compressed sensing of a class
of signals. The learning scheme identifies the sensing matrix by maximizing the
entropy of measurement vectors. The bounds on the entropy of the measurement
vector necessary for the unique recovery of a signal are also proposed. A
comparison of the performance of the designed sensing matrix and the sensing
matrices constructed using other existing methods is also presented. The
simulation results on the recovery of synthetic, speech, and image signals,
compressively sensed using the sensing matrix identified, shows an improvement
in the accuracy of recovery. The reconstruction quality is better, using less
number of measurements, than those measured using sensing matrices identified
by other methods.


  Reinforcement learning (RL) is a powerful approach for training agents to
perform tasks, but designing an appropriate reward mechanism is critical to its
success. However, in many cases, the complexity of the learning objectives goes
beyond the capabilities of the Markovian assumption, necessitating a more
sophisticated reward mechanism. Reward machines and omega-regular languages are
two formalisms used to express non-Markovian rewards for quantitative and
qualitative objectives, respectively. This paper introduces omega-regular
reward machines, which integrate reward machines with omega-regular languages
to enable an expressive and effective reward mechanism for RL. We present a
model-free RL algorithm to compute epsilon-optimal strategies against
omega-egular reward machines and evaluate the effectiveness of the proposed
algorithm through experiments.


  We experimentally investigate the behaviour of instantaneous localised
releases of heavy particles falling as turbulent clouds in quiescent water,
both with and without background rotation. We present the results of 514
systematic experiments for no rotation and for three rotation rates $\Omega =
5,10,20$rpm, and for the size of particles in the range $5\mu m$ to $1mm$,
exploring four decades of the Rouse number $\mathcal{R} \in [6\times
10^{-4},4]$ which quantifies the inertia of particles. In the canonical
framework of turbulent thermals described by Morton \textit{et al.}, [Proc. R.
Soc. A: Math. Phys. Sci. \textbf{234}, 1 (1956)], we compare particle clouds
with salt-water thermals to highlight specificities due to the particulate
nature of the turbulence forcing. In the absence of rotation, particle clouds
initially behave as salty thermals with a modulation of their entrainment
capacity, which is optimally enhanced for a finite inertia $\mathcal{R}\simeq
0.3$ due to particulate effects. However this regime of turbulence is limited
in time due to the inertial decoupling between turbulent eddies and particles.
For the three values of $\Omega$ explored here, the particulate enhancement of
entrainment is inhibited. Moreover the cloud's expansion is interrupted when
the Coriolis force overcomes its inertia, forcing the cloud to transform into
vortical columnar flows which considerably increase the residence time of
particles.


  Control over the optical properties of atomically thin two-dimensional (2D)
layers, including those of transition metal dichalcogenides (TMDs), is needed
for future optoelectronic applications. Remarkable advances have been achieved
through alloying, chemical and electrical doping, and applied strain. However,
the integration of TMDs with other 2D materials in van der Waals
heterostructures (vdWHs) to tailor novel functionalities remains largely
unexplored. Here, the near-field coupling between TMDs and graphene/graphite is
used to engineer the exciton lineshape and charge state. Fano-like asymmetric
spectral features are produced in WS$_{2}$, MoSe$_{2}$ and WSe$_{2}$ vdWHs
combined with graphene, graphite, or jointly with hexagonal boron nitride
(h-BN) as supporting or encapsulating layers. Furthermore, trion emission is
suppressed in h-BN encapsulated WSe$_{2}$/graphene with a neutral exciton
redshift (44 meV) and binding energy reduction (30 meV). The response of these
systems to electron-beam and light probes is well-described in terms of 2D
optical conductivities of the involved materials. Beyond fundamental insights
into the interaction of TMD excitons with structured environments, this study
opens an unexplored avenue toward shaping the spectral profile of narrow
optical modes for application in nanophotonic devices.


  The level of autonomy is increasing in systems spanning multiple domains, but
these systems still experience failures. One way to mitigate the risk of
failures is to integrate human oversight of the autonomous systems and rely on
the human to take control when the autonomy fails. In this work, we formulate a
method of collaborative decision making through action suggestions that
improves action selection without taking control of the system. Our approach
uses each suggestion efficiently by incorporating the implicit information
shared through suggestions to modify the agent's belief and achieves better
performance with fewer suggestions than naively following the suggested
actions. We assume collaborative agents share the same objective and
communicate through valid actions. By assuming the suggested action is
dependent only on the state, we can incorporate the suggested action as an
independent observation of the environment. The assumption of a collaborative
environment enables us to use the agent's policy to estimate the distribution
over action suggestions. We propose two methods that use suggested actions and
demonstrate the approach through simulated experiments. The proposed
methodology results in increased performance while also being robust to
suboptimal suggestions.


  We present a new library of semi-empirical stellar spectra that is based on
the empirical MILES library. A new, high resolution library of theoretical
stellar spectra is generated that is specifically designed for use in stellar
population studies. We test these models across their full wavelength range
against other model libraries and find reasonable agreement in their
predictions of spectral changes due to atmospheric $\alpha$-element variations,
known as differential corrections. We also test the models against the MILES
and MaStar libraries of empirical stellar spectra and also find reasonable
agreements, as expected from previous work. We then use the abundance pattern
predictions of the new theoretical stellar spectra to differentially correct
MILES spectra to create semi-empirical MILES (sMILES) star spectra with
abundance patterns that differ from those present in the Milky Way. The final
result is 5 families of 801 sMILES stars with [$\alpha$/Fe] abundances ranging
from $-$0.20 to 0.60 dex at MILES resolution (FWHM=$2.5\,${\AA}) and wavelength
coverage ($3540.5-7409.6\,${\AA}). We make the sMILES library publicly
available.


  We study the Post-Minkowskian (PM) and Post-Newtonian (PN) expansions of the
gravitational three-body effective potential. At order 2PM a formal result is
given in terms of a differential operator acting on the maximal generalized cut
of the one-loop triangle integral. We compute the integral in all kinematic
regions and show that the leading terms in the PN expansion are reproduced. We
then perform the PN expansion unambiguously at the level of the integrand.
Finding agreement with the 2PN three-body potential after integration, we
explicitly present new $G^2v^4$-contributions at order 3PN and outline the
generalization to $G^2v^{2n}$. The integrals that represent the essential input
for these results are obtained by applying the recent Yangian bootstrap
directly to their $\epsilon$-expansion around three dimensions. The coordinate
space Yangian generator that we employ to obtain these integrals can be
understood as a special conformal symmetry in a dual momentum space.


  New analytical representations of the Stokes flows due to periodic arrays of
point singularities in a two-dimensional no-slip channel and in the half-plane
near a no-slip wall are derived. The analysis makes use of a conformal mapping
from a concentric annulus (or a disc) to a rectangle and a complex variable
formulation of Stokes flow to derive the solutions. The form of the solutions
is amenable to fast and accurate numerical computation without the need for
Ewald summation or other fast summation techniques.


  I argue that, on a judicious reading of two existing criteria--one syntactic
and the other semantic--dual theories can be taken to be empirically
equivalent. The judicious reading is straightforward, but leads to the
surprising conclusion that very different-looking theories can have equivalent
empirical content. And thus it shows how a widespread scientific practice, of
interpreting duals as empirically equivalent, can be understood by a thus-far
unnoticed feature of existing accounts of empirical equivalence.


  Using the deepest and finest resolution images of the Universe acquired with
the Hubble Space Telescope and a similar image taken 7 years later for the
Great Observatories Origins Deep Survey, we have derived proper motions for the
point sources in the Hubble Deep Field--North. Two faint blue objects,HDF2234
and HDF3072, are found to display significant proper motion, 10.0 $\pm$ 2.5 and
15.5 $\pm$ 3.8 mas yr$^{-1}$. Photometric distances and tangential velocities
for these stars are consistent with disk white dwarfs located at $\sim$ 500 pc.
The faint blue objects analyzed by Ibata et al. (1999) and Mendez & Minniti
(2000) do not show any significant proper motion; they are not halo white
dwarfs and they do not contribute to the Galactic dark matter. These objects
are likely to be distant AGN.


  A general definition of a bimodule connection in noncommutative geometry has
been recently proposed. For a given algebra this definition is compared with
the ordinary definition of a connection on a left module over the associated
enveloping algebra. The corresponding curvatures are also compared.


  We investigate the time evolution of odd entanglement entropy (OEE) and
logarithmic negativity (LN) for the thermofield double (TFD) states in free
scalar quantum field theories using the covariance matrix approach. To have
mixed states, we choose non-complementary subsystems, either adjacent or
disjoint intervals on each side of the TFD. We find that the time evolution
pattern of OEE is a linear growth followed by saturation. On a circular
lattice, for longer times the finite size effect demonstrates itself as
oscillatory behavior. In the limit of vanishing mass, for a subsystem
containing a single degree of freedom on each side of the TFD, we analytically
find the effect of zero-mode on the time evolution of OEE which leads to
logarithmic growth in the intermediate times. Moreover, for adjacent intervals
we find that the LN is zero for times $t < \beta/2$ (half of the inverse
temperature) and after that, it begins to grow linearly. For disjoint intervals
at fixed temperature, the vanishing of LN is observed for times $t<d/2$ (half
of the distance between intervals). We also find a similar delay to see linear
growth of $\Delta S=S_{\text{OEE}}-S_{\text{EE}}$. All these results show that
the dynamics of these measures are consistent with the quasi-particle picture,
of course apart from the logarithmic growth.


  Proofreading/editing in protein synthesis is essential for accurate
translation of information from the genetic code. In this article we present a
theoretical investigation of efficiency of a kinetic proofreading mechanism
that employs hydrolysis of the wrong substrate as the discriminatory step in
enzyme catalytic reactions. We consider aminoacylation of tRNA^{Ile} which is a
crucial step in protein synthesis and for which experimental results are now
available. We present an augmented kinetic scheme and then employ methods of
stochastic simulation algorithm to obtain time dependent concentrations of
different substances involved in the reaction and their rates of formation. We
obtain the rates of product formation and ATP hydrolysis for both correct and
wrong substrates (isoleucine and valine in our case), in single molecular
enzyme as well as ensemble enzyme kinetics. The present theoretical scheme
correctly reproduces (i) the amplitude of the discrimination factor in the
overall rates between isoleucine and valine which is obtained as (1.8 \times
10^2).(4.33 \times 10^2) = 7.8 \times 10^4, (ii) the rates of ATP hydrolysis
for both Ile and Val at different substrate concentrations in the
aminoacylation of tRNA^{Ile}. The present study shows a non-michaelis type
dependence of rate of reaction on tRNA^{Ile} concentration in case of valine.
The editing in steady state is found to be independent of amino acid
concentration. Interestingly, the computed ATP hydrolysis rate for valine at
high substrate concentration is same as the rate of formation of Ile-tRNA^{Ile}
whereas at intermediate substrate concentration the ATP hydrolysis rate is
relatively low.


  We consider the simplest model of $SU(3) \times SU(2) \times U(1)_Y \times
U(1)_R$ gauge symmetry with one extra singlet field whose vacuum expectation
value breaks the horizontal $R$-symmetry $U(1)_R$ and gives rise to Yukawa
textures. The $U(1)_R$ symmetry is able to provide both acceptable fermion mass
hierarchies and a natural solution to the $\mu$ problem only if its mixed
anomalies are cancelled by the Green-Schwarz mechanism. When the canonical
normalization $g_3^2=g_2^2={5\over3}g_1^2$ of the gauge coupling constants is
assumed, the Higgs mass parameter $\mu \sim m_{3/2}$ can arise taking into
acount the uncertainty in the ultraviolet relation $m_e m_\mu m_\tau/m_d m_s
m_b \simeq \lambda^q$ with $q \neq 0$. When $q=0$ is taken only a suppressed
value of $\mu \sim \lambda m_{3/2}$ can be obtained.


  X-ray scattering experiments using Free Electron Lasers (XFELs) are a
powerful tool to determine the molecular structure and function of unknown
samples (such as COVID-19 viral proteins). XFEL experiments are a challenge to
computing in two ways: i) due to the high cost of running XFELs, a fast
turnaround time from data acquisition to data analysis is essential to make
informed decisions on experimental protocols; ii) data collection rates are
growing exponentially, requiring new scalable algorithms. Here we report our
experiences analyzing data from two experiments at the Linac Coherent Light
Source (LCLS) during September 2020. Raw data were analyzed on NERSC's Cori
XC40 system, using the Superfacility paradigm: our workflow automatically moves
raw data between LCLS and NERSC, where it is analyzed using the software
package CCTBX. We achieved real time data analysis with a turnaround time from
data acquisition to full molecular reconstruction in as little as 10 min --
sufficient time for the experiment's operators to make informed decisions. By
hosting the data analysis on Cori, and by automating LCLS-NERSC
interoperability, we achieved a data analysis rate which matches the data
acquisition rate. Completing data analysis with 10 mins is a first for XFEL
experiments and an important milestone if we are to keep up with data
collection trends.


  We report the observation of the existence of a possible universal limit for
valence parton distributions that should exist once partonic degrees of freedom
are relevant for high energy scattering from strongly interacting bound systems
like a nucleon, meson or a few nucleon system at very short distances. Our
observation is based on the notion that the Bjorken x weighted valence parton
distribution function has a peak, $x_p$, that characterizes the average
momentum fraction carried out by the valence quarks in the system. Within the
residual mean-field model of the valence quark distribution we found that $x_p$
has an upper limit: $x_p \leq {1\over 2(n_V-1)}$, where $n_V$ is the number of
valence quarks which can be considered in the cluster embedded in the strongly
interacting environment of the bound system. The existence of such a limit
imposes a new constraint on choosing the starting resolution scale $Q_0$ for
PDFs. Our prediction for the nucleon is that $x_p\mid_{Q\to Q_0}\le{1\over 4}$,
which is in agreement with all the available valence PDFs that employ the
standard approach for selecting starting $Q_0$. We also demonstrate how the
existence of this limit can be used to check the onset of quark-clusters in
short range nucleon correlations in nuclei.


  Neural population equations such as neural mass or field models are widely
used to study brain activity on a large scale. However, the relation of these
models to the properties of single neurons is unclear. Here we derive an
equation for several interacting populations at the mesoscopic scale starting
from a microscopic model of randomly connected generalized integrate-and-fire
neuron models. Each population consists of 50 -- 2000 neurons of the same type
but different populations account for different neuron types. The stochastic
population equations that we find reveal how spike-history effects in
single-neuron dynamics such as refractoriness and adaptation interact with
finite-size fluctuations on the population level. Efficient integration of the
stochastic mesoscopic equations reproduces the statistical behavior of the
population activities obtained from microscopic simulations of a full spiking
neural network model. The theory describes nonlinear emergent dynamics like
finite-size-induced stochastic transitions in multistable networks and
synchronization in balanced networks of excitatory and inhibitory neurons. The
mesoscopic equations are employed to rapidly simulate a model of a local
cortical microcircuit consisting of eight neuron types. Our theory establishes
a general framework for modeling finite-size neural population dynamics based
on single cell and synapse parameters and offers an efficient approach to
analyzing cortical circuits and computations.


  I derive a Wick ordered continuous renormalization group equation for fermion
systems and show that a determinant bound applies directly to this equation.
This removes factorials in the recursive equation for the Green functions, and
thus improves the combinatorial behaviour. The form of the equation is also
ideal for the investigation of many-fermion systems, where the propagator is
singular on a surface. For these systems, I define a criterion for Fermi liquid
behaviour which applies at positive temperatures. As a first step towards
establishing such behaviour in d ge 2, I prove basic regularity properties of
the interacting Fermi surface to all orders in a skeleton expansion. The proof
is a considerable simplification of previous ones.


  Today data mining techniques are exploited in medical science for diagnosing,
overcoming and treating diseases. Neural network is one of the techniques which
are widely used for diagnosis in medical field. In this article efficiency of
nine algorithms, which are basis of neural network learning in diagnosing
cardiovascular diseases, will be assessed. Algorithms are assessed in terms of
accuracy, sensitivity, transparency, AROC and convergence rate by means of 10
fold cross validation. The results suggest that in training phase, Lonberg-M
algorithm has the best efficiency in terms of all metrics, algorithm OSS has
maximum accuracy in testing phase, algorithm SCG has the maximum transparency
and algorithm CGB has the maximum sensitivity.


  We prove that a primitive harmonic map is equivariant if and only if it
admits a holomorphic potential of degree one. We investigate when the
equivariant harmonic map is periodic, and as an application discuss constant
mean curvature cylinders with screw motion symmetries.


  The form factor and the coupling constant in the $\eta_{b}BB^{\ast}$ vertex
are evaluated in the framework of three-point QCD sum rules. The correlation
functions responsible for the vertex is evaluated considering contributions of
both $B$ and $B^{\ast}$ mesons as off-shell states. The form factors obtained
are different for the two cases, whereas the final results of the coupling
constant are compatible.


  We consider the Cauchy problem for the energy critical heat equation $$ u_t =
\Delta u + |u|^{\frac 4{n-2}}u {{\quad\hbox{in } }} \ {\mathbb R}^n \times (0,
T), \quad u(\cdot,0) =u_0 {{\quad\hbox{in } }} {\mathbb R}^n $$ in dimension
$n=5$. More precisely we find that for given points $q_1, q_2,\ldots, q_k$ and
any sufficiently small $T>0$ there is an initial condition $u_0$ such that the
solution $u(x,t)$ of the problem blows-up at exactly those $k$ points with
rates type II, namely with absolute size $ \sim (T-t)^{-\alpha} $ for $\alpha >
\frac 34 $. The blow-up profile around each point is of bubbling type, in the
form of sharply scaled Aubin-Talenti bubbles.


  The Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical
algorithm for quantum simulation that can be run on near-term quantum hardware.
A challenge in VQE -- as well as any other heuristic algorithm for finding
ground states of Hamiltonians -- is to know how close the algorithm's output
solution is to the true ground state, when the true ground state and
ground-state energy are unknown. This is especially important in iterative
algorithms, such as VQE, where one wants to avoid erroneous early termination.
Recent developments in Hamiltonian reconstruction -- the inference of a
Hamiltonian given an eigenstate -- give a metric can be used to assess the
quality of a variational solution to a Hamiltonian-eigensolving problem. This
metric can assess the proximity of the variational solution to the ground state
without any knowledge of the true ground state or ground-state energy. In
numerical simulations and in demonstrations on a cloud-based trapped-ion
quantum computer, we show that for examples of both one-dimensional
transverse-field-Ising (11 qubits) and two-dimensional J1-J2
transverse-field-Ising (6 qubits) spin problems, the Hamiltonian-reconstruction
distance gives a helpful indication of whether VQE has yet found the ground
state or not. Our experiments included cases where the energy plateaus as a
function of the VQE iteration, which could have resulted in erroneous early
stopping of the VQE algorithm, but where the Hamiltonian-reconstruction
distance correctly suggests to continue iterating. We find that the
Hamiltonian-reconstruction distance has a useful correlation with the fidelity
between the VQE solution and the true ground state. Our work suggests that the
Hamiltonian-reconstruction distance may be a useful tool for assessing success
in VQE, including on noisy quantum processors in practice.


  An electron/positron linear collider with a center-of-mass energy between 0.5
and 1 TeV would be an important complement to the physics program of the LHC in
the next decade. The Next Linear Collider (NLC) is being designed by a US
collaboration (FNAL, LBNL, LLNL, and SLAC) which is working closely with the
Japanese collaboration that is designing the Japanese Linear Collider (JLC).
The NLC main linacs are based on normal conducting 11 GHz rf. This paper will
discuss the technical difficulties encountered as well as the many changes that
have been made to the NLC design over the last year. These changes include
improvements to the X-band rf system as well as modifications to the injector
and the beam delivery system. They are based on new conceptual solutions as
well as results from the R&D programs which have exceeded initial
specifications. The net effect has been to reduce the length of the collider
from about 32 km to 25 km and to reduce the number of klystrons and modulators
by a factor of two. Together these lead to significant cost savings.


  Novel concentration inequalities are obtained for the missing mass, i.e. the
total probability mass of the outcomes not observed in the sample. We derive
distribution-free deviation bounds with sublinear exponents in deviation size
for missing mass and improve the results of Berend and Kontorovich (2013) and
Yari Saeed Khanloo and Haffari (2015) for small deviations which is the most
important case in learning theory.


  In the paper I prove that there are two squares of opposition. The
unconventional one is built up for synthetic propositions. There a, i are
contrary, a, o (resp. e, i) are contradictory, e, o are subcontrary, a, e
(resp. i, o) are said to stand in the subalternation.


  Although techniques for separate image and audio steganography are widely
known, relatively little has been described concerning the hiding of
information within video streams ("video steganography"). In this paper we
review the current state of the art in this field, and describe the key issues
we have encountered in developing a practical video steganography system. A
supporting video is also available online at
http://www.youtube.com/watch?v=YhnlHmZolRM


  We report results of the performance evaluation of a new hardware correlator
in Korea, the Daejeon correlator, developed by the Korea Astronomy and Space
Science Institute (KASI) and the National Astronomical Observatory of Japan
(NAOJ). We conducted Very Long Baseline Interferometry (VLBI) observations at
22~GHz with the Korean VLBI Network (KVN) in Korea and the VLBI Exploration of
Radio Astrometry (VERA) in Japan, and correlated the aquired data with the
Daejeon correlator. For evaluating the performance of the new hardware
correlator, we compared the correlation outputs from the Daejeon correlator for
KVN observations with those from a software correlator, the Distributed FX
(DiFX). We investigated the correlated flux densities and brightness
distributions of extragalactic compact radio sources. The comparison of the two
correlator outputs show that they are consistent with each other within $<8\%$,
which is comparable with the amplitude calibration uncertainties of KVN
observations at 22~GHz. We also found that the 8\% difference in flux density
is caused mainly by (a) the difference in the way of fringe phase tracking
between the DiFX software correlator and the Daejeon hardware correlator, and
(b) an unusual pattern (a double-layer pattern) of the amplitude correlation
output from the Daejeon correlator. The visibility amplitude loss by the
double-layer pattern is as small as 3\%. We conclude that the new hardware
correlator produces reasonable correlation outputs for continuum observations,
which are consistent with the outputs from the DiFX software correlator.


  We derive a new residual-type a posteriori estimator for a singularly
perturbed reaction-diffusion problem with obstacle constraints. It generalizes
robust residual estimators for unconstrained singularly perturbed equations.
Upper and lower bounds are derived with respect to an error notion which
measures the error of the solution and of a suitable approximation of the
constraining force in a $\epsilon$-dependent energy norm and its dual norm.
While the robust upper bound is proven for non-discrete obstacle function, the
local lower bounds are derived for discrete obstacle functions. For the proof
of the local lower bounds we construct special bubble functions which cope with
the structure of the approximation of the constraining force and the
$\epsilon$-dependency.


  We present here algorithmic cooling (via polarization-heat-bath)- a powerful
method for obtaining a large number of highly polarized spins in liquid
nuclear-spin systems at finite temperature. Given that spin-half states
represent (quantum) bits, algorithmic cooling cleans dirty bits beyond the
Shannon's bound on data compression, by employing a set of rapidly
thermal-relaxing bits. Such auxiliary bits could be implemented using spins
that rapidly get into thermal equilibrium with the environment, e.g., electron
spins.
  Cooling spins to a very low temperature without cooling the environment could
lead to a breakthrough in nuclear magnetic resonance experiments, and our
``spin-refrigerating'' method suggests that this is possible.
  The scaling of NMR ensemble computers is probably the main obstacle to
building useful quantum computing devices, and our spin-refrigerating method
suggests that this problem can be resolved.


  Query complexity measures the amount of information an algorithm needs about
a problem to compute a solution. On a quantum computer there are different
realizations of a query and we will show that these are not always equivalent.
Our definition of equivalence is based on the ability to simulate (or
approximate) one query type by another. We show that a bit query can always
approximate a phase query with just two queries, while there exist problems for
which the number of phase queries which are necessary to approximate a bit
query must grow exponentially with the precision of the bit query. This result
follows from the query complexity bounds for the evaluation problem, for which
we establish a strong lower bound for the number of phase queries by exploiting
a relation between quantum algorithms and trigonometric polynomials.


  We consider global analogues of model-theoretic tree properties. The main
objects of study are the invariants related to Shelah's tree property
$\kappa_{\text{cdt}}(T)$, $\kappa_{\text{sct}}(T)$, and
$\kappa_{\text{inp}}(T)$ and the relations that obtain between them. From
strong colorings, we construct theories $T$ with $\kappa_{\text{cdt}}(T) >
\kappa_{\text{sct}}(T) + \kappa_{\text{inp}}(T)$. We show that these invariants
have distinct structural consequences, by investigating the decay of saturation
in ultrapowers of models of $T$, where $T$ is some theory with
$\kappa_{\text{cdt}}(T)$, $\kappa_{\text{sct}}(T)$, or $\kappa_{\text{inp}}(T)$
large and bounded. This answers some questions of Shelah.


  In this paper we introduce the generalized oscillator model (GOM) as a family
of exactly solvable models useful to investigate theoretical aspects related to
the statistical description of the aging state. GOMs are defined by a potential
function V(x) and characterized by a zero-temperature relaxation determined by
entropy barriers and partial equilibration. Analytic expressions for the
effective temperature can be derived using a fluctuation theorem valid in the
aging regime without the need to solve the dynamical equations for correlations
and responses. Two classes of models are investigated in detail: the
homogeneous potential model with V(x)=(k/2p)x^{2p} (p being a positive integer)
and the wedge potential model (V(x)=k|x|) where V(x) has a singularity at the
ground state coordinate x=0. For the latter, we present some numerical
simulations that reinforce the validity of the main analytical results. GOMs
offer a conceptual framework to develop a statistical description of the
spontaneous relaxation process that has been recently proposed to be at the
root of the intermittency phenomenon observed in glasses and colloids.


  Software testing is aimed to improve the delivered reliability of the users.
Delivered reliability is the reliability of using the software after it is
delivered to the users. Usually the software consists of many modules. Thus,
the delivered reliability is dependent on the operational profile which
specifies how the users will use these modules as well as the defect number
remaining in each module. Therefore, a good testing policy should take the
operational profile into account and dynamically select tested modules
according to the current state of the software during the testing process. This
paper discusses how to dynamically select tested modules in order to maximize
delivered reliability by formulating the selection problem as a dynamic
programming problem. As the testing process is performed only once, risk must
be considered during the testing process, which is described by the tester's
utility function in this paper. Besides, since usually the tester has no
accurate estimate of the operational profile, by employing robust optimization
technique, we analysis the selection problem in the worst case, given the
uncertainty set of operational profile. By numerical examples, we show the
necessity of maximizing delivered reliability directly and using robust
optimization technique when the tester has no clear idea of the operational
profile. Moreover, it is shown that the risk averse behavior of the tester has
a major influence on the delivered reliability.


  Automated software agents --- or bots --- have long been an important part of
how Wikipedia's volunteer community of editors write, edit, update, monitor,
and moderate content. In this paper, I discuss the complex social and technical
environment in which Wikipedia's bots operate. This paper focuses on the
establishment and role of English Wikipedia's bot policies and the Bot
Approvals Group, a volunteer committee that reviews applications for new bots
and helps resolve conflicts between Wikipedians about automation. In
particular, I examine an early bot controversy over the first bot in Wikipedia
to automatically enforce a social norm about how Wikipedian editors ought to
interact in discussion spaces. As I show, bots enforce many rules in Wikipedia,
but humans produce these bots and negotiate rules around their operation.
Because of the openness of Wikipedia's processes around automation, we can
vividly observe the often-invisible human work involved in such algorithmic
systems --- in stark contrast to most other user-generated content platforms.


  Time-varying parameter (TVP) models are very flexible in capturing gradual
changes in the effect of a predictor on the outcome variable. However, in
particular when the number of predictors is large, there is a known risk of
overfitting and poor predictive performance, since the effect of some
predictors is constant over time. We propose a prior for variance shrinkage in
TVP models, called triple gamma. The triple gamma prior encompasses a number of
priors that have been suggested previously, such as the Bayesian lasso, the
double gamma prior and the Horseshoe prior. We present the desirable properties
of such a prior and its relationship to Bayesian Model Averaging for variance
selection. The features of the triple gamma prior are then illustrated in the
context of time varying parameter vector autoregressive models, both for
simulated datasets and for a series of macroeconomics variables in the Euro
Area.


  This paper investigates the Electric Autonomous Dial-A-Ride Problem
(E-ADARP), which consists in designing a set of minimum-cost routes that
accommodates all customer requests for a fleet of Electric Autonomous Vehicles
(EAVs). Problem-specific features of the E-ADARP include: (i) the employment of
EAVs and a partial recharging policy; (ii) the weighted-sum objective function
that minimizes the total travel time and the total excess user ride time. In
this work, we propose a Deterministic Annealing (DA) algorithm and provide the
first heuristic results for the static E-ADARP. Partial recharging (i) is
handled by an exact route evaluation scheme of linear time complexity. To
tackle (ii), we propose a new method that allows effective computations of
minimum excess user ride time by introducing a fragment-based representation of
paths. These two methods compose an exact and efficient optimization of excess
user ride time for a generated E-ADARP route. To validate the performance of
the DA algorithm, we compare our algorithm results to the best-reported
Branch-and-Cut (B\&C) algorithm results on existing instances. Our algorithm
provides 25 new best solutions and 45 equal solutions on 84 existing instances.
To test the algorithm performance on larger-sized instances, we establish new
instances with up to 8 vehicles and 96 requests, and we provide 19 new
solutions for these instances. Our final investigation extends the
state-of-the-art model and explores the effect of allowing multiple visits to
recharging stations. This relaxation can efficiently improve the solution's
feasibility and quality.


  This paper presents a new causal network learning algorithm (FSNN, Feedback
System Neural Network) based on the construction and analysis of a non-linear
system of Ordinary Differential Equations (ODEs). The constructed system
provides insight into the mechanisms responsible for generating the past and
potential future behavior of dynamic systems. It is also interpretable in terms
of real system variables, providing a wholistic, causally accurate, and
systemic understanding of the real-life interactions governing observed
phenomena. This paper demonstrates the generation of an n-dimensional ordinary
differential equation model that can be parameterized to fit measured data
using standard numerical optimization techniques. The model makes use of feed
forward artificial neural nets to capture nonlinearity, but is a parsimonious
and interpretable representation of the network of causal relationships in
complex systems. The generated model can easily and rapidly be experimented
with and analyzed to determine the origins of behavior using the loops that
matter method (Schoenberg et. al 2019). A demonstration of the utility and
applicability of the method is given, showing that it produces an accurate, and
causally correct model for a three state, non-linear, complex dynamic system of
known origin. Generalization to other dynamic systems with other data sources
is then discussed.


  We report the discovery of a thin stellar stream found in Pan-STARRS1
photometry near the Galactic bulge in the constellation of Ophiuchus. It
appears as a coherent structure in the colour-selected stellar density maps
produced to search for tidal debris around nearby globular clusters. The stream
is exceptionally short and narrow; it is about 2.5{\deg} long and 6' wide in
projection. The colour-magnitude diagram of this object, which harbours a blue
horizontal-branch, is consistent with an old and relatively metal-poor
population ([Fe/H]~-1.3) located 9.5 +/- 0.9 kpc away at (l,b) ~
(5{\deg},+32{\deg}), and 5.0 +/- 1.0 kpc from the Galactic centre. These
properties argue for a globular cluster as progenitor. The finding of such a
prominent, nearby stream suggests that many streams could await discovery in
the more densely populated regions of our Galaxy.


  Principal manifolds are defined as lines or surfaces passing through ``the
middle'' of data distribution. Linear principal manifolds (Principal Components
Analysis) are routinely used for dimension reduction, noise filtering and data
visualization. Recently, methods for constructing non-linear principal
manifolds were proposed, including our elastic maps approach which is based on
a physical analogy with elastic membranes. We have developed a general
geometric framework for constructing ``principal objects'' of various
dimensions and topologies with the simplest quadratic form of the smoothness
penalty which allows very effective parallel implementations. Our approach is
implemented in three programming languages (C++, Java and Delphi) with two
graphical user interfaces (VidaExpert
http://bioinfo.curie.fr/projects/vidaexpert and ViMiDa
http://bioinfo-out.curie.fr/projects/vimida applications). In this paper we
overview the method of elastic maps and present in detail one of its major
applications: the visualization of microarray data in bioinformatics. We show
that the method of elastic maps outperforms linear PCA in terms of data
approximation, representation of between-point distance structure, preservation
of local point neighborhood and representing point classes in low-dimensional
spaces.


  Image-to-image translation has recently achieved remarkable results. But
despite current success, it suffers from inferior performance when translations
between classes require large shape changes. We attribute this to the
high-resolution bottlenecks which are used by current state-of-the-art
image-to-image methods. Therefore, in this work, we propose a novel deep
hierarchical Image-to-Image Translation method, called DeepI2I. We learn a
model by leveraging hierarchical features: (a) structural information contained
in the shallow layers and (b) semantic information extracted from the deep
layers. To enable the training of deep I2I models on small datasets, we propose
a novel transfer learning method, that transfers knowledge from pre-trained
GANs. Specifically, we leverage the discriminator of a pre-trained GANs (i.e.
BigGAN or StyleGAN) to initialize both the encoder and the discriminator and
the pre-trained generator to initialize the generator of our model. Applying
knowledge transfer leads to an alignment problem between the encoder and
generator. We introduce an adaptor network to address this. On many-class
image-to-image translation on three datasets (Animal faces, Birds, and Foods)
we decrease mFID by at least 35% when compared to the state-of-the-art.
Furthermore, we qualitatively and quantitatively demonstrate that transfer
learning significantly improves the performance of I2I systems, especially for
small datasets. Finally, we are the first to perform I2I translations for
domains with over 100 classes.


  The temperature dependences of the Hall coefficient and electrical
resistivity recently measured by Moser et al. [Phys. Rev. Lett. 84, 2674
(2000)] in the quasi-one-dimensional organic conductor (TMTSF)2PF6 are
quantitatively compared with our previous theoretical calculations [Synth. Met.
103, 2202 (1999); Eur. Phys. J. B 11, 385 (1999)]. We find a good agreement,
albeit not with a fully consistent set of parameters for the two quantities,
without appealing to the Luttinger liquid concept.


  Recently it was argued that it might be possible treat the conventional
nuclear structure problem -- nonrelativistic point nucleons interacting through
a static and rather singular potential -- as an effective theory in a
shell-model basis. In the first half of this talk we describe how such a
program can be carried out for the simplest nuclei, the deuteron and 3He,
exploiting a new numerical technique for solving the self-consistent
Bloch-Horowitz equation. Some of the properties of proper effective theories
are thus illustrated and contrasted with the shell model. In the second half of
the talk we use these examples to return to a problem that frustrated the field
three decades ago, the possibility of reducing the effective interactions
problem to perturbation theory. We show, by exploiting the Talmi integral
expansion, that hard-core potentials can be systematically softened by the
introduction of a series of contact operators familiar from effective field
theory. The coefficients of these operators can be run analytically by a
renormalization group method in a scheme-independent way, with the introduction
of suitable counterterms. Once these coefficients are run to the shell model
scale, we show that the renormalized coefficients contain all of the
information needed to evaluate perturbative insertions of the remaining soft
potential. The resulting perturbative expansion is shown to converge in lowest
order for the simplest nucleus, the deuteron.


  We investigate the instability of layer pseudospin paramagnetic (PSP) state
to the formation of pseudospin density wave (PSDW) in two-dimensional (2D)
electron bilayers, analogous to the formation of Overhauser spin density wave
(SDW) in a single-layer 2D electron gas (2DEG) with spin 1/2. Our comprehensive
study on phase diagrams, based on the self-consistent Hartree-Fock (HF) theory,
reveals that the PSDW has a lower energy than both PSP and pseudospin
ferromagnetic (PSF) states near the PSP-PSF phase transition boundary. When the
two layers are populated by the same number of electrons, the PSDW momentum
$Q_c \sim 2k_F$ near the PSP-PSDW boundary, where $k_F= (2\pi n)^{1/2}$ is the
Fermi momentum characterized by the density in one of the two layers, and $Q_c$
decreases as the system transitions to the PSF regime. Extending the HF study
to the case of unequal layer densities, the PSP phase is unstable to PSDW for
small density imbalances, with momentum $Q_c \sim k_{F,t} + k_{F,b}$, where
$k_{F,t}$ and $k_{F,b}$ are Fermi momenta of top and bottom layers,
respectively. In PSDW regime, the ground state stability, defined by the energy
difference between PSDW and the second lowest-energy state, is one order of
magnitude lower than that in PSF regime, and decreases with increasing layer
separation $d$. Furthermore, incorporating RPA static screening with the
Hubbard-type local field correction leads to disappearance of both SDW and PSDW
phases, and pushes the phase boundaries of paramagnetic to ferromagnetic
transitions to larger $r_s$ values. Our study on PSDW in 2D electron bilayers
is equally applicable to 2D hole bilayers. The idea of pursuing PSDW is, in
general, relevant across various 2D bilayer systems, not limited to the
parabolic model that we investigate in this paper, and provides a new
possibility of exploring novel coherent phases.


  Let $\Gamma$ be a countable group. A $\Gamma$-action on a compact abelian
group $X$ by continuous automorphisms of $X$ is called Noetherian if the dual
of $X$ is Noetherian as a ${\mathbb Z}(\Gamma)$-module. We prove that any
Noetherian action of a finitely generated virtually nilpotent group has a dense
set of periodic points.


  The well-studied vector balancing constant $\beta(U, V)$ of a pair of convex
bodies $(U,V)$, is lower bounded by a lattice counterpart, $\alpha(U,V)$. In
[BS97], Banaszczyk and Szarek proved that $\alpha(B_2^n, V)\leq c$ when $V$ has
Gaussian measure at least $\frac{1}{2}$, and conjectured that, for centrally
symmetric $V$, $\beta(B_2^n, V)$ is always bounded by a function of the
Gaussian measure of $V$, independent of $n$. We resolve this conjecture in the
affirmative. Moreover, we show that the analogous result holds for
$\alpha(B_2^n, V)$ even without the central symmetry assumption.


  A recent trend in speech processing is the use of embeddings created through
machine learning models trained on a specific task with large datasets. By
leveraging the knowledge already acquired, these models can be reused in new
tasks where the amount of available data is small. This paper proposes a
pipeline to create a new model, called Mel and Wave Embeddings for Human Voice
Tasks (MeWEHV), capable of generating robust embeddings for speech processing.
MeWEHV combines the embeddings generated by a pre-trained raw audio waveform
encoder model, and deep features extracted from Mel Frequency Cepstral
Coefficients (MFCCs) using Convolutional Neural Networks (CNNs). We evaluate
the performance of MeWEHV on three tasks: speaker, language, and accent
identification. For the first one, we use the VoxCeleb1 dataset and present
YouSpeakers204, a new and publicly available dataset for English speaker
identification that contains 19607 audio clips from 204 persons speaking in six
different accents, allowing other researchers to work with a very balanced
dataset, and to create new models that are robust to multiple accents. For
evaluating the language identification task, we use the VoxForge and Common
Language datasets. Finally, for accent identification, we use the Latin
American Spanish Corpora (LASC) and Common Voice datasets. Our approach allows
a significant increase in the performance of state-of-the-art models on all the
tested datasets, with a low additional computational cost.


  We report on a new kind of correlated photon-pair source based on a waveguide
integrated on a Periodically Poled Lithium Niobate substrate. Using a pump
laser of a few micro-Watts at 657 nm, we generate degenerate photon-pairs at
1314 nm. Detecting about 1500 coincidences per second, we can infer a
conversion rate of 10-6 pairs per pump photon, which is four orders of
magnitude higher than that obtained with previous bulk sources. These results
are very promising for the realization of sources for quantum communication and
quantum metrology experiments requiring a high signal-to-noise ratio or working
with more than one photon-pair at a time.


  With the tremendous expansion of graphs data, node classification shows its
great importance in many real-world applications. Existing graph neural network
based methods mainly focus on classifying unlabeled nodes within fixed classes
with abundant labeling. However, in many practical scenarios, graph evolves
with emergence of new nodes and edges. Novel classes appear incrementally along
with few labeling due to its newly emergence or lack of exploration. In this
paper, we focus on this challenging but practical graph few-shot
class-incremental learning (GFSCIL) problem and propose a novel method called
Geometer. Instead of replacing and retraining the fully connected neural
network classifer, Geometer predicts the label of a node by finding the nearest
class prototype. Prototype is a vector representing a class in the metric
space. With the pop-up of novel classes, Geometer learns and adjusts the
attention-based prototypes by observing the geometric proximity, uniformity and
separability. Teacher-student knowledge distillation and biased sampling are
further introduced to mitigate catastrophic forgetting and unbalanced labeling
problem respectively. Experimental results on four public datasets demonstrate
that Geometer achieves a substantial improvement of 9.46% to 27.60% over
state-of-the-art methods.


  This technical report documents the theoretical, computational, and practical
aspects of the one-dimensional Navier-Stokes finite element flow model. The
document is particularly useful to those who are interested in implementing,
validating and utilizing this relatively-simple and widely-used model.


  Mobile Ad Hoc Networks (MANETs) consist of wireless mobile nodes which
coordinate with each other to form temporary network without its pre-existing
infrastructure. AODV is popular Ad-hoc distance vector routing reactive
protocol which is used to find correct & shortest route to destination. Due to
openness, dynamic, infrastructure-less nature, MANET are vulnerable to various
attacks. One of these possible attacks is a Black Hole Attack in which a mobile
node falsely replies to the source node that it is having a shortest path to
the destination without checking its routing table. Therefore source node send
all of its data to the black hole node and it deprives all the traffic of the
source node. In this paper, We are proposing a technique to detect and prevent
the multiple black hole nodes from MANET so that source to destination
communication can be made easily. We also analysed the performance of the
network in terms of number of packets sent, received, throughput, energy of
network before attack and after detection & prevention of Attack. From these
analysis, we can conclude that performance decreased due to attack can be
improved after detection & prevention black hole attack in MANET.


  We analyse the physical properties of 121 SNR $\geq$ 5 sub-millimetre
galaxies (SMGs) from the STUDIES 450-$\mu$m survey. We model their UV-to-radio
spectral energy distributions using MAGPHYS+photo-$z$ and compare the results
to similar modelling of 850-$\mu$m-selected SMG sample from AS2UDS, to
understand the fundamental physical differences between the two populations at
the observed depths. The redshift distribution of the 450-$\mu$m sample has a
median of $z$ = 1.85 $\pm$ 0.12 and can be described by strong evolution of the
far-infrared luminosity function. The fainter 450-$\mu$m sample has $\sim$14
times higher space density than the brighter 850-$\mu$m sample at $z$
$\lesssim$2, and a comparable space density at $z$ = 2-3, before rapidly
declining, suggesting LIRGs are the main obscured population at $z$ $\sim$ 1-2,
while ULIRGs dominate at higher redshifts. We construct rest-frame $\sim$
180-$\mu$m-selected and dust-mass-matched samples at $z$ = 1-2 and $z$ = 3-4
from the 450-$\mu$m and 850-$\mu$m samples, respectively, to probe the
evolution of a uniform sample of galaxies spanning the cosmic noon era. Using
far-infrared luminosity, dust masses and an optically-thick dust model, we
suggest that higher-redshift sources have higher dust densities due to inferred
dust continuum sizes which are roughly half of those for the lower-redshift
population at a given dust mass, leading to higher dust attenuation. We track
the evolution in the cosmic dust mass density and suggest that the dust content
of galaxies is governed by a combination of both the variation of gas content
and dust destruction timescale.


  For every genuine irreducible admissible smooth representation $\pi$ of the
metaplectic group $\widetilde{\Sp}(2n)$ over a p-adic field, and every smooth
oscillator representation $\omega_\psi$ of $\widetilde{\Sp}(2n)$, we prove that
the tensor product $\pi\otimes \omega_\psi$ is multiplicity free as a smooth
representation of the symplectic group $\Sp(2n)$. Similar results are proved
for general linear groups and unitary groups.


  A model for description of nonresonant formation of the muonic molecules in
collisions of the muonic hydrogen atoms with the hydrogenic molecules has been
developed with taking into account the internal motion of all nuclei. It has
been shown that such a motion leads to a significant smearing of the calculated
energy-dependent formation rates at low collision energies. In particular, this
effect is strong in the $dd\mu$ and $dt\mu$ formation. An appreciable isotopic
effect in the case of nonresonant $dd\mu$ formation in $d\mu$ collisions with
the molecules D$_2$ and HD has been found. All these effects are of importance
for many experimental researches in low-energy muon physics.


  Let $\Omega$ be a bounded domain of $\mathbb{R}^{N}$, $p\in
C^{1}(\overline{\Omega}),$ $q\in C(\overline{\Omega})$ and $l,j\in\mathbb{N}.$
We describe the asymptotic behavior of the minimizers of the Rayleigh quotient
$\frac{\Vert\nabla u\Vert_{lp(x)}}{\Vert u\Vert_{jq(x)}}$, first when
$j\rightarrow\infty$ and after when $l\rightarrow\infty.$


  Homotopy is an important feature of associative and Jordan algebraic
structures: such structures always come in families whose members need not be
isomorphic among other, but still share many important properties. One may
regard homotopy as a special kind of deformation of a given algebraic
structure. In this work, we investigate the global counterpart of this
phenomenon on the geometric level of the associated symmetric spaces -- on this
level, homotopy gives rise to conformal deformations of symmetric spaces. These
results are valid in arbitrary dimension and over general base fields and
-rings.


  One route to numerically propagating quantum systems is time dependent
density functional theory (TDDFT). The application of TDDFT to a particular
system's time evolution is predicated on V-representability which we have
analyzed in a previous publication. In this work, we provide new insights
concerning lattice V-representability using an newly developed explicit solver
for the time-dependent Kohn-Sham potential which contrast with implicit solvers
studied in the past few years. We present and interpret the force-balance
equation central to our numerical method, describe details of its
implementation, and present illustrative numerical results. A new
characterization of V-representability for one-electron systems is also
included. Taken together, the results here open the door to deeper theoretical
and numerical investigations of the foundations of TDDFT.


  Forthcoming large-scale structure (LSS) Stage IV surveys will provide us with
unprecedented data to probe the nature of dark matter and dark energy. However,
analysing these data with conventional Markov Chain Monte Carlo (MCMC) methods
will be challenging, due to the increase in the number of nuisance parameters
and the presence of intractable likelihoods. In light of this, we present the
first application of Marginal Neural Ratio Estimation (MNRE) (a recent approach
in simulation-based inference) to LSS photometric probes: weak lensing, galaxy
clustering and the cross-correlation power spectra. In order to analyse the
hundreds of spectra simultaneously, we find that a pre-compression of data
using principal component analysis, as well as parameter-specific data
summaries lead to highly accurate results. Using expected Stage IV experimental
noise, we are able to recover the posterior distribution for the cosmological
parameters with a speedup factor of $\sim 40-60$ compared to classical MCMC
methods. To illustrate that the performance of MNRE is not impeded when
posteriors are highly non-Gaussian, we test a scenario of two-body decaying
dark matter, finding that Stage IV surveys can improve current bounds on the
model by up to one order of magnitude. This result supports that MNRE is a
powerful framework to constrain the standard cosmological model and its
extensions with next-generation LSS surveys.


  A sketch of an approach towards Lorentzian spectral geometry (based on joint
work with Mario Paschke) is described, together with a general way to define
abstractly the quantized Dirac field on such Lorentzian spectral geometries.
Moyal-Minkowski spacetime serves as an example. The scattering of the quantized
Dirac field by a non-commutative (Moyal-deformed) action of an external scalar
potential is investigated. It is shown that differentiating the S-matrix with
respect to the strength of the scattering potential gives rise to quantum field
operators depending on elements of the non-commutative algebra entering the
spectral geometry description of Moyal-Minkowski spacetime, in the spirit of
"Bogoliubov's formula", in analogy to the situation found in external potential
scattering by a usual scalar potential.


  Human motion prediction aims to forecast future poses given a sequence of
past 3D skeletons. While this problem has recently received increasing
attention, it has mostly been tackled for single humans in isolation. In this
paper, we explore this problem when dealing with humans performing
collaborative tasks, we seek to predict the future motion of two interacted
persons given two sequences of their past skeletons. We propose a novel cross
interaction attention mechanism that exploits historical information of both
persons, and learns to predict cross dependencies between the two pose
sequences. Since no dataset to train such interactive situations is available,
we collected ExPI (Extreme Pose Interaction), a new lab-based person
interaction dataset of professional dancers performing Lindy-hop dancing
actions, which contains 115 sequences with 30K frames annotated with 3D body
poses and shapes. We thoroughly evaluate our cross interaction network on ExPI
and show that both in short- and long-term predictions, it consistently
outperforms state-of-the-art methods for single-person motion prediction.


  We give a new proof of an old identity of Dixon (1865-1936) that uses tools
from topological combinatorics. Dixon's identity is re-established by
constructing an infinite family of non-pure simplicial complexes $\Delta(n)$,
indexed by the positive integers, such that the alternating sum of the numbers
of faces of $\Delta(n)$ of each dimension is the left-hand side of the
identity. We show that $\Delta(n)$ is shellable for all $n$. Then, using the
fact that a shellable simplicial complex is homotopy equivalent to a wedge of
spheres, we compute the Betti numbers of $\Delta(n)$ by counting (via a
generating function) the number of facets of $\Delta(n)$ of each dimension that
attach along their entire boundary in the shelling order. In other words,
Dixon's identity is re-established by using the Euler-Poincar\'{e} relation.


  Sentinel-1 is a synthetic aperture radar (SAR) platform with an operational
mode called extra wide (EW) that offers large regions of ocean areas to be
observed. A major issue with EW images is that the cross-polarized HV and VH
channels have prominent additive noise patterns relative to low backscatter
intensity, which disrupts tasks that require manual or automated
interpretation. The European Space Agency (ESA) provides a method for removing
the additive noise pattern by means of lookup tables, but applying them
directly produces unsatisfactory results because characteristics of the noise
still remain. Furthermore, evidence suggests that the magnitude of the additive
noise dynamically depends on factors that are not considered by the ESA
estimated noise field.
  To address these issues we propose a quadratic objective function to model
the mis-scale of the provided noise field on an image. We consider a linear
denoising model that re-scales the noise field for each subswath, whose
parameters are found from a least-squares solution over the objective function.
This method greatly reduces the presence of additive noise while not requiring
a set of training images, is robust to heterogeneity in images, dynamically
estimates parameters for each image, and finds parameters using a closed-form
solution.
  Two experiments were performed to validate the proposed method. The first
experiment simulated noise removal on a set of RADARSAT-2 images with noise
fields artificially imposed on them. The second experiment conducted noise
removal on a set of Sentinel-1 images taken over the five oceans. Afterwards,
quality of the noise removal was evaluated based on the appearance of
open-water. The two experiments indicate that the proposed method marks an
improvement both visually and through numerical measures.


  We investigate the $\Lambda\Lambda$ and $K^-p$ intensity correlations in
high-energy heavy-ion collisions. First, we examine the dependence of the
$\Lambda\Lambda$ correlation on the $\Lambda\Lambda$ interaction and the
$\Lambda\Lambda$ pair purity probability $\lambda$. For small $\lambda$, the
correlation function needs to be suppressed by the $\Lambda\Lambda$ interaction
in order to explain the recently measured $\Lambda\Lambda$ correlation data. By
comparison, when we adopt the $\lambda$ value evaluated from the experimentally
measured $\Sigma^0/\Lambda$ ratio, the correlation function needs to be
enhanced by the interaction. We demonstrate that these two cases correspond to
the two analyses which gave opposite signs of the $\Lambda\Lambda$ scattering
length. Next, we discuss the $K^-p$ correlation function. By using the local
$\bar{K}N$ potential which reproduces the kaonic hydrogen data by SIDDHARTA, we
obtain the $K^-p$ correlation function. We find that the $K^-p$ correlation can
provide a complementary information with the $K^{-}p$ elastic scattering
amplitude.


  Elastic-scattering phase shifts for four-nucleon systems are studied in an
$ab$-$initio$ type cluster model in order to clarify the role of the tensor
force and to investigate cluster distortions in low energy $d+d$ and $t+p$
scattering. In the present method, the description of the cluster wave function
is extended from a simple (0$s$) harmonic-oscillator shell model to a few-body
model with a realistic interaction, in which the wave function of the
subsystems are determined with the Stochastic Variational Method. In order to
calculate the matrix elements of the four-body system, we have developed a
Triple Global Vector Representation method for the correlated Gaussian basis
functions. To compare effects of the cluster distortion with realistic and
effective interactions, we employ the AV8$^{\prime}$ potential as a realistic
interaction and the Minnesota potential as an effective interaction. Especially
for $^1S_0$, the calculated phase shifts show that the $t+p$ and $h+n$ channels
are strongly coupled to the $d+d$ channel for the case of the realistic
interaction. On the contrary, the coupling of these channels plays a relatively
minor role for the case of the effective interaction. This difference between
both potentials originates from the tensor term in the realistic interaction.
Furthermore, the tensor interaction makes the energy splitting of the negative
parity states of $^4$He consistent with experiments. No such splitting is
however reproduced with the effective interaction.


  The equivariant Heegaard genus of a 3-manifold $M$ with the action of a
finite group $G$ of diffeomorphisms is the smallest genus of an equivariant
Heegaard splitting for $M$. Although a Heegaard splitting of a reducible
manifold is reducible and although if $M$ is reducible, there is an equivariant
essential sphere, we show that equivariant Heegaard genus may be
super-additive, additive, or sub-additive under equivariant connected sum.
Using a thin position theory for 3-dimensional orbifolds, we establish sharp
bounds on the equivariant Heegaard genus of reducible manifolds, similar to
those known for tunnel number.


  We study scaling limits of periodically weighted skew plane partitions with
semilocal interactions and general boundary conditions. The semilocal
interactions correspond to the Macdonald symmetric functions which are
$(q,t)$-deformations of the Schur symmetric functions. We show that the height
functions converge to a deterministic limit shape and that the global
fluctuations are given by the $2$-dimensional Gaussian free field as $q,t\to 1$
and the mesh size goes to $0$. Specializing to the noninteracting case, this
verifies the Kenyon-Okounkov conjecture for the case of the
$r^{\mathrm{volume}}$ measure under general boundary conditions. Our approach
uses difference operators on Macdonald processes.


  We study the evolution of opinions on a directed network with community
structure. Individuals update their opinions synchronously based on a weighted
average of their neighbors' opinions, their own previous opinions, and external
media signals. Our model is akin to the popular Friedkin-Johnsen model, and is
able to incorporate factors such as stubbornness, confirmation bias, selective
exposure, and multiple topics, which are believed to play an important role in
the formation of opinions. Our main result shows that, in the large graph
limit, the opinion process concentrates around its mean-field approximation for
any level of edge density, provided the average degree grows to infinity.
Moreover, we show that the opinion process exhibits propagation of chaos. We
also give results for the trajectories of individual vertices and the
stationary version of the opinion process, and prove that the limits in time
and in the size of the network commute. The mean-field approximation is
explicit and can be used to quantify consensus and polarization.


  We present new limits on the ejection of metal-rich old-population
hypervelocity stars from the Galactic center (GC) as probed by the SEGUE-2
survey. Our limits are a factor of 3-10 more stringent than previously
reported, depending on stellar type. Compared to the known population of B-star
ejectees, there can be no more than 30 times more metal-rich old-population F/G
stars ejected from the GC. Because B stars comprise a tiny fraction of a normal
stellar population, this places significant limits on a combination of the GC
mass function and the ejection mechanism for hypervelocity stars. In the
presence of a normal GC mass function, our results require an ejection
mechanism that is about 5.5 times more efficient at ejecting B-stars compared
to low-mass F/G stars.


  Topological insulators (TI) realize a novel state of quantum matter that are
distinguished by topological invariants of bulk band structure rather than
spontaneously broken symmetries. A number of exotic quantum phenomena have been
predicted to exist in multiply-connected geometries which require an enormous
amount of materials flexibility. We have extended our previous search for TI
materials from binary (Bi2X3 series) to the thermoelectric ternary compounds.
We discover that the distorted LuPtSb is the first ternary compound harboring a
3D topological insulator state. We also show that the half-Heusler LuPtSb-type
series is a natural platform that hosts a range of candidate compounds, alloys
and artificial heterostructures (quantum-wells). We also discovered several
different paradigms of trivial and non-trivial topological ordering in this
class, including a metallic nontrivial topological state in YAuPb. Some of
these materials are grown (results will be reported separately).


  Asteroseismology involves probing the interiors of stars and quantifying
their global properties, such as radius and age, through observationsof normal
modes of oscillation. The technical requirements for conducting
asteroseismology include ultra-high precision measured in photometry in parts
per million, as well as nearly continuous time series over weeks to years, and
cadences rapid enough to sample oscillations with periods as shortas a few
minutes. We report on results from the first 43 days of observations in which
the unique capabilities of Kepler in providing a revolutionary advance in
asteroseismology are already well in evidence. The Kepler asteroseismology
program holds intrinsic importance in supporting the core planetary search
program through greatly enhanced knowledge of host star properties, and extends
well beyond this to rich applications in stellar astrophysics.


  We have used Spitzer and its IRAC camera to search for the transit of the
super-Earth HD 40307b. The transiting nature of the planet could not be firmly
discarded from our first photometric monitoring of a transit window because of
the uncertainty coming from the modeling of the photometric baseline. To obtain
a firm result, two more transit windows were observed and a global Bayesian
analysis of the three IRAC time series and the HARPS radial velocities was
performed. Unfortunately, any transit of the planet during the observed phase
window is firmly discarded, while the probability that the planet transits but
that the eclipse was missed by our observations is nearly negligible (0.26%).


  In recent years, basket trials, which enable the evaluation of an
experimental therapy across multiple tumor types within a single protocol, have
gained prominence in early-phase oncology development. Unlike traditional
trials, where each tumor type is evaluated separately with limited sample size,
basket trials offer the advantage of borrowing information across various tumor
types. However, a key challenge in designing basket trials lies in dynamically
determining the extent of information borrowing across tumor types to enhance
statistical power while maintaining an acceptable type I error rate. In this
paper, we propose a local power prior framework that includes a 3-component
borrowing mechanism with explicit model interpretation. Unlike many existing
Bayesian methods that require Markov Chain Monte Carlo (MCMC) sampling, the
proposed framework offers a closed-form solution, eliminating the
time-consuming nature of MCMC in large-scale simulations for evaluating
operating characteristics. Extensive simulations have been conducted and
demonstrated a good performance of the proposal method comparable to the other
complex methods. The significantly shortened computation time further
underscores the practical utility in the context of basket trials.


  A lot has been said and discussed regarding the rationale and significance of
the Bechdel Score. It became a digital sensation in 2013 when Swedish cinemas
began to showcase the Bechdel test score of a film alongside its rating. The
test has drawn criticism from experts and the film fraternity regarding its use
to rate the female presence in a movie. The pundits believe that the score is
too simplified and the underlying criteria of a film to pass the test must
include 1) at least two women, 2) who have at least one dialogue, 3) about
something other than a man, is egregious. In this research, we have considered
a few more parameters which highlight how we represent females in film, like
the number of female dialogues in a movie, dialogue genre, and part of speech
tags in the dialogue. The parameters were missing in the existing criteria to
calculate the Bechdel score. The research aims to analyze 342 movies scripts to
test a hypothesis if these extra parameters, above with the current Bechdel
criteria, are significant in calculating the female representation score. The
result of the Principal Component Analysis method concludes that the female
dialogue content is a key component and should be considered while measuring
the representation of women in a work of fiction.


  The Zassenhaus formula finds many applications in theoretical physics or
mathematics, from fluid dynamics to differential geometry. The
non-commutativity of the elements of the algebra implies that the exponential
of a sum of operators cannot be expressed as the product of exponentials of
operators. The exponential of the sum can then be decomposed as the product of
the exponentials multiplied by a supplementary term which takes generally the
form of an infinite product of exponentials. Such a procedure is often referred
to as ``disentanglement''. However, for some special commutators, closed forms
can be found. In this work, we propose a closed form for the Zassenhaus formula
when the commutator of operators $\hat{X}$ and $\hat{Y}$ satisfy the relation
$[\hat{X},\hat{Y}]=u\hat{X}+v\hat{Y}+c\mathcal{I}$. Such an expression boils
down to three equivalent versions, a left-sided, a centered and a right-sided
formula:
  \begin{equation*}
e^{\hat{X}+\hat{Y}}=e^{\hat{X}}e^{\hat{Y}}e^{g_{r}(u,v)[\hat{X},\hat{Y}]}=e^{\hat{X}}e^{g_{c}(u,v)[\hat{X},\hat{Y}]}e^{\hat{Y}}=e^{g_{\ell}(u,v)[\hat{X},\hat{Y}]}e^{\hat{X}}e^{\hat{Y}},
\end{equation*} with respective arguments, \begin{eqnarray*}
g_{r}(u,v)&=&g_{c}(v,u)e^{u}=g_{\ell}(v,u)=\frac{u\left(e^{u-v}-e^{u}\right)+v\left(e^{u}-1\right)}{vu(u-v)}
\end{eqnarray*} for $u\ne v$ and \begin{eqnarray*}
g_{r}(u,u)=\frac{u+1-e^u}{u^2}\;\;\;\;\mathrm{with}\;\;\;\; g_r(0,0)=-1/2.
\end{eqnarray*} With additional special case \begin{eqnarray*} g_{r}(0,v)=
-\frac{e^{-v}-1+v}{v^{2}}, \quad & g_{r}(u,0)=\frac{e^{u}(1-u)-1}{u^{2}}.
\end{eqnarray*}