  We have investigated two-electron bound state formation in a square
two-dimensional t-J-U model with hopping anisotropies for zero electron
density; these anisotropies are introduced to mimic the hopping energies
similar to those expected in stripe-like arrangements of holes and spins found
in various transition metal oxides. In this report we provide analytical
solutions to this problem, and thus demonstrate that bound-state formation
occurs at a critical exchange coupling, J_c, that decreases to zero in the
limit of extreme hopping anisotropy t_y/t_x -> 0. This result should be
contrasted with J_c/t = 2 for either a one-dimensional chain, or a
two-dimensional plane with isotropic hopping. Most importantly, this behaviour
is found to be qualitatively similar to that of two electrons on the two-leg
ladder problem in the limit of t_interchain/t_intrachain -> 0. Using the latter
result as guidance, we have evaluated the pair correlation function, thus
determining that the bound state corresponds to one electron moving along one
chain, with the second electron moving along the opposite chain, similar to two
electrons confined to move along parallel, neighbouring, metallic stripes. We
emphasize that the above results are not restricted to the zero density limit -
we have completed an exact diagonalization study of two holes in a 12 X 2
two-leg ladder described by the t-J model and have found that the
above-mentioned lowering of the binding energy with hopping anisotropy persists
near half filling.


  In the Bulk Randall-Sundrum model, the Kaluza-Klein excitations of the gauge
bosons are the primary signatures. In particular, the search for the
Kaluza-Klein (KK) excitation of the gluon at hadron colliders is of great
importance in testing this model. At the leading order in QCD, the production
of this KK-gluon proceeds only via q qbar-initial states. We study the
production of KK-gluons from gluon initial states at next-to-leading order in
QCD. We find that, even after including the sub-dominant KK-gluon loops at this
order, the next-to-leading order (NLO) cross-section is tiny compared to the
leading order cross-section and unlikely to impact the searches for this
resonance at hardon colliders.


  A vector field coming from spontaneous Lorentz violation mechanism, namely
Bumblebee model is analysed in a topological framework in a $(1+2)D$ Minkowski
space-time. Taking a $(1+2)D$ nonlinear Bumblebee vector matter field dynamics
where we include topological like Chern-Simons type terms, a vector version of
a soliton state, or vortex was found. The Nielsen-Olesen procedure was used in
order to derive a Lorentz-violation vector parameter which characterizes, via
Spontaneous Symmetry Breaking mechanism, the non-trivial vacuum. We verify the
stability of the model as much as the magnetic vortex, and noticed that the
soliton modes with polarized direction generated can be associated with local
anisotropy of vacuum energy. The vortex equations of motion and the asymptotic
behaviour is presented. We have obtained that the effect of the Lorentz
symmetry violation expressed by the a time-like Bumblebee vector field vacuum
could be shown as kind of pulse at a fixed point $r_0$ in a limitless universe,
or as a barrier at $r_0$ which can represent a boundary in the universe, if the
Bumblebee vector field vacuum has space-like characteristic. We also analyse
the spectrum via propagators where we note that the topological mass
contributes as well to the dynamical mass poles. We obtain that the
Chern-Simons type terms, in fact, indicates the "speed" of the field to
saturate the asymptotic limit and that the vortex core can not be dimension
zero.


  We study extensions of the classical SIR model of epidemic spread. First, we
consider a single population modified SIR epidemics model in which the contact
rate is allowed to be an arbitrary function of the fraction of susceptible and
infected individuals. This allows one to model either the reaction of
individuals to the information about the spread of the disease or the result of
government restriction measures, imposed to limit social interactions and
contain contagion. We study the effect of both smooth dependancies of the
contact rate for which we prove the existence of a threshold phenomenon that
generalizes the well-known dichotomy associated to the reproduction rate
parameter in the classical SIR model, and discontinuous feedback terms, which
can be studied using tools from sliding mode control. Finally, we consider
network SIR models involving different subpopulations that interact on a
contact graph and present some preliminary simulations of modified versions of
the classic SIR network.


  Supersymmetric and parasupersymmetric quantum mechanics are now recognized as
two further parts of quantum mechanics containing a lot of new informations
enlightening (solvable) physical applications. Both contents are here analysed
in connection with generalized quantum deformations. In fact, the
parasupersymmetric context is visited when the order of paraquantization p is
limited to the first nontrivial value p = 2.


  Deep convolutional neural networks (CNN) are widely used in modern artificial
intelligence (AI) and smart vision systems but also limited by computation
latency, throughput, and energy efficiency on a resource-limited scenario, such
as mobile devices, internet of things (IoT), unmanned aerial vehicles (UAV),
and so on. A hardware streaming architecture is proposed to accelerate
convolution and pooling computations for state-of-the-art deep CNNs. It is
optimized for energy efficiency by maximizing local data reuse to reduce
off-chip DRAM data access. In addition, image and feature decomposition
techniques are introduced to optimize memory access pattern for an arbitrary
size of image and number of features within limited on-chip SRAM capacity. A
prototype accelerator was implemented in TSMC 65 nm CMOS technology with 2.3 mm
x 0.8 mm core area, which achieves 144 GOPS peak throughput and 0.8 TOPS/W peak
energy efficiency.


  Motivated by recent experiments in ultracold atomic gases that explore the
nonequilibrium dynamics of interacting quantum many-body systems, we
investigate the opposite limit of Landau's Fermi liquid paradigm: We study a
Hubbard model with a sudden interaction quench, that is the interaction is
switched on at time t=0. Using the flow equation method, we are able to study
the real time dynamics for weak interaction U in a systematic expansion and
find three clearly separated time regimes: i) An initial buildup of
correlations where the quasiparticles are formed. ii) An intermediate
quasi-steady regime resembling a zero temperature Fermi liquid with a
nonequilibrium quasiparticle distribution function. iii) The long time limit
described by a quantum Boltzmann equation leading to thermalization with a
temperature T proportional to U.


  We describe a light-weight, weather and lighting invariant, Semantic Bird's
Eye View (S-BEV) signature for vision-based vehicle re-localization. A
topological map of S-BEV signatures is created during the first traversal of
the route, which are used for coarse localization in subsequent route
traversal. A fine-grained localizer is then trained to output the global 3-DoF
pose of the vehicle using its S-BEV and its coarse localization. We conduct
experiments on vKITTI2 virtual dataset and show the potential of the S-BEV to
be robust to weather and lighting. We also demonstrate results with 2 vehicles
on a 22 km long highway route in the Ford AV dataset.


  Graph Neural Networks (GNNs) are powerful deep learning models to generate
node embeddings on graphs. When applying deep GNNs on large graphs, it is still
challenging to perform training in an efficient and scalable way. We propose a
novel parallel training framework. Through sampling small subgraphs as
minibatches, we reduce training workload by orders of magnitude compared with
state-of-the-art minibatch methods. We then parallelize the key computation
steps on tightly-coupled shared memory systems. For graph sampling, we exploit
parallelism within and across sampler instances, and propose an efficient data
structure supporting concurrent accesses from samplers. The parallel sampler
theoretically achieves near-linear speedup with respect to number of processing
units. For feature propagation within subgraphs, we improve cache utilization
and reduce DRAM traffic by data partitioning. Our partitioning is a
2-approximation strategy for minimizing the communication cost compared to the
optimal. We further develop a runtime scheduler to reorder the training
operations and adjust the minibatch subgraphs to improve parallel performance.
Finally, we generalize the above parallelization strategies to support multiple
types of GNN models and graph samplers. The proposed training outperforms the
state-of-the-art in scalability, efficiency and accuracy simultaneously. On a
40-core Xeon platform, we achieve 60x speedup (with AVX) in the sampling step
and 20x speedup in the feature propagation step, compared to the serial
implementation. Our algorithm enables fast training of deeper GNNs, as
demonstrated by orders of magnitude speedup compared to the Tensorflow
implementation. We open-source our code at
https://github.com/GraphSAINT/GraphSAINT.


  We study the distribution and physical properties of molecular gas in the
disc around the T Tauri star IM Lup on scales close to 200 AU. We investigate
how well the gas and dust distributions compare and work towards a unified disc
model that can explain both gas and dust emission. 12CO, 13CO, and C18O J=2-1
line emission, as well as the dust continuum at 1.3 mm, is observed at 1.8"
resolution towards IM Lup using the Submillimeter Array. A detailed disc model
based on the dust emission is tested against these observations with the aid of
a molecular excitation and radiative transfer code. Apparent discrepancies
between the gas and dust distribution are investigated by adopting simple
modifications to the existing model. The disc is seen at an inclination of
54+/-3 degrees and is in Keplerian rotation around a 0.8-1.6 Msun star. The
outer disc radius traced by molecular gas emission is 900 AU, while the dust
continuum emission and scattered light images limit the amount of dust present
beyond 400 AU and are consistent with the existing model that assumes a 400 AU
radius. Our observations require a drastic density decrease close to 400 AU
with the vertical gas column density at 900 AU in the range of 5.d20 - 1.d22
cm-2. We derive a gas-to-dust mass ratio of 100 or higher in disc regions
beyond 400 AU. Within 400 AU from the star our observations are consistent with
a gas-to-dust ratio of 100 but other values are not ruled out.


  We calculate the deflection angle of light from a distant source by a galaxy
cluster in Weyl's conformal gravity. The general method of calculation is first
applied to calculate the deflection angle in Schwarzschild-de Sitter (Kottler)
spacetime. The deflection angle calculated in Kottler spacetime includes the
contribution of the cosmological constant, which quantitatively agrees with one
work and disagrees with many works in the literature. We then calculate the
deflection angle in Mannheim-Kazanas spacetime in two conformally related
coordinate systems and find that the result includes contributions from both
the cosmological constant and the Mannheim-Kazanas parameter. There are
conflicting results on the deflection angle for light in Weyl gravity in the
literature. We point out a possible reason for the discrepancy between our work
and the others.


  Fairness is steadily becoming a crucial requirement of Machine Learning (ML)
systems. A particularly important notion is subgroup fairness, i.e., fairness
in subgroups of individuals that are defined by more than one attributes.
Identifying bias in subgroups can become both computationally challenging, as
well as problematic with respect to comprehensibility and intuitiveness of the
finding to end users. In this work we focus on the latter aspects; we propose
an explainability method tailored to identifying potential bias in subgroups
and visualizing the findings in a user friendly manner to end users. In
particular, we extend the ALE plots explainability method, proposing FALE
(Fairness aware Accumulated Local Effects) plots, a method for measuring the
change in fairness for an affected population corresponding to different values
of a feature (attribute). We envision FALE to function as an efficient, user
friendly, comprehensible and reliable first-stage tool for identifying
subgroups with potential bias issues.


  The properties of the highest velocity ejecta of normal Type Ia supernovae
(SNe Ia) are studied via models of very early optical spectra of 6 SNe. At
epochs earlier than 1 week before maximum, SNe with a rapidly evolving Si II
6355 line velocity (HVG) have a larger photospheric velocity than SNe with a
slowly evolving Si II 6355 line velocity (LVG). Since the two groups have
comparable luminosities, the temperature at the photosphere is higher in LVG
SNe. This explains the different overall spectral appearance of HVG and LVG
SNe. However, the variation of the Ca II and Si II absorptions at the highest
velocities (v >~ 20,000 km/s) suggests that additional factors, such as
asphericity or different abundances in the progenitor white dwarf, affect the
outermost layers. The C II 6578 line is marginally detected in 3 LVG SNe,
suggesting that LVG undergo less intense burning. The carbon mass fraction is
small, only less than 0.01 near the photosphere, so that he mass of unburned C
is only <~ 0.01 Msun. Radioactive 56Ni and stable Fe are detected in both LVG
and HVG SNe. Different Fe-group abundances in the outer layers may be one of
the reasons for spectral diversity among SNe Ia at the earliest times. The
diversity among SNe Ia at the earliest phases could also indicate an intrinsic
dispersion in the width-luminosity relation of the light curve.


  We propose the supervised hierarchical Dirichlet process (sHDP), a
nonparametric generative model for the joint distribution of a group of
observations and a response variable directly associated with that whole group.
We compare the sHDP with another leading method for regression on grouped data,
the supervised latent Dirichlet allocation (sLDA) model. We evaluate our method
on two real-world classification problems and two real-world regression
problems. Bayesian nonparametric regression models based on the Dirichlet
process, such as the Dirichlet process-generalised linear models (DP-GLM) have
previously been explored; these models allow flexibility in modelling nonlinear
relationships. However, until now, Hierarchical Dirichlet Process (HDP)
mixtures have not seen significant use in supervised problems with grouped data
since a straightforward application of the HDP on the grouped data results in
learnt clusters that are not predictive of the responses. The sHDP solves this
problem by allowing for clusters to be learnt jointly from the group structure
and from the label assigned to each group.


  Around 10% of bright low-frequency radio sources observed with the Murchison
Widefield Array (MWA) show strong Interplanetary Scintillation (IPS) on
timescales of a few seconds, implying that almost all their low-frequency radio
emission comes from a compact component less than 0.5 arcsec in angular size.
Most of these objects are compact steep-spectrum (CSS) or MHz-peaked spectrum
(MPS) radio sources. We have used mid-infrared data from the Wide-field
Infrared Survey Explorer (WISE) catalogue to search for the host galaxies of 65
strongly-scintillating MWA sources and compare their properties with those of
the overall population of bright low-frequency radio sources. We identified
WISE mid-infrared counterparts for 91% of the bright sources in a single 900
square degree MWA field, and found that the hosts of the strongly-scintillating
sources were typically at least 1 mag fainter in the WISE W1 (3.4 micron) band
than the hosts of weakly-scintillating MWA sources of similar radio flux
density. This difference arises mainly because the strongly-scintillating
sources are more distant. We estimate that strongly-scintillating MWA sources
have a median redshift of z ~ 1.5, and that at least 30% of them are likely to
lie at z > 2. The recently-developed wide-field IPS technique therefore has the
potential to provide a powerful new tool for identifying high-redshift radio
galaxies without the need for radio spectral-index selection.


  In this paper, we introduce the problem of finding an orientation of a given
undirected graph that maximizes the burning number of the resulting directed
graph. We show that the problem is polynomial-time solvable on
K\H{o}nig-Egerv\'{a}ry graphs (and thus on bipartite graphs) and that an almost
optimal solution can be computed in polynomial time for perfect graphs. On the
other hand, we show that the problem is NP-hard in general and W[1]-hard
parameterized by the target burning number. The hardness results are
complemented by several fixed-parameter tractable results parameterized by
structural parameters. Our main result in this direction shows that the problem
is fixed-parameter tractable parameterized by cluster vertex deletion number
plus clique number (and thus also by vertex cover number).


  We examine resolved spectroscopic data obtained with the Keck II telescope
for 44 spheroidal galaxies in the fields of two rich clusters, Cl0024+16
(z=0.40) and MS0451-03 (z=0.54), and contrast this with similar data for 23
galaxies within the redshift interval 0.3<z<0.65 in the GOODS northern field.
For each galaxy we examine the case for systemic rotation, derive central
stellar velocity dispersions sigma and photometric ellipticities, epsilon.
Using morphological classifications obtained via Hubble Space Telescope imaging
as the basis, we explore the utility of our kinematic quantities in
distinguishing between pressure-supported ellipticals and
rotationally-supported lenticulars (S0s). We demonstrate the reliability of
using the v/(1-epsilon) vs sigma and v/sigma vs epsilon distributions as
discriminators, finding that the two criteria correctly identify 63%+-3% and
80%+-2% of S0s at z~0.5, respectively, along with 76%+8-3% and 79%+-2% of
ellipticals. We test these diagnostics using equivalent local data in the Coma
cluster, and find that the diagnostics are similarly accurate at z=0. Our
measured accuracies are comparable to the accuracy of visual classification of
morphologies, but avoid the band-shifting and surface brightness effects that
hinder visual classification at high redshifts. As an example application of
our kinematic discriminators, we then examine the morphology-density relation
for elliptical and S0 galaxies separately at z~0.5. We confirm, from kinematic
data alone, the recent growth of rotationally-supported spheroidals. We discuss
the feasibility of extending the method to a more comprehensive study of
cluster and field galaxies to z~1, in order to verify in detail the recent
density-dependent growth of S0 galaxies.


  We provide theoretical analysis of the statistical and computational
properties of penalized $M$-estimators that can be formulated as the solution
to a possibly nonconvex optimization problem. Many important estimators fall in
this category, including least squares regression with nonconvex
regularization, generalized linear models with nonconvex regularization and
sparse elliptical random design regression. For these problems, it is
intractable to calculate the global solution due to the nonconvex formulation.
In this paper, we propose an approximate regularization path-following method
for solving a variety of learning problems with nonconvex objective functions.
Under a unified analytic framework, we simultaneously provide explicit
statistical and computational rates of convergence for any local solution
attained by the algorithm. Computationally, our algorithm attains a global
geometric rate of convergence for calculating the full regularization path,
which is optimal among all first-order algorithms. Unlike most existing methods
that only attain geometric rates of convergence for one single regularization
parameter, our algorithm calculates the full regularization path with the same
iteration complexity. In particular, we provide a refined iteration complexity
bound to sharply characterize the performance of each stage along the
regularization path. Statistically, we provide sharp sample complexity analysis
for all the approximate local solutions along the regularization path. In
particular, our analysis improves upon existing results by providing a more
refined sample complexity bound as well as an exact support recovery result for
the final estimator. These results show that the final estimator attains an
oracle statistical property due to the usage of nonconvex penalty.


  We discuss novel effects in the phenomenology of a light Higgs boson within
the context of composite models. We show that large modifications may arise in
the decay of a composite Nambu-Goldstone boson Higgs to a photon and a Z boson,
h -> Z gamma. These can be generated by the exchange of massive composite
states of a strong sector that breaks a left-right symmetry, which we show to
be the sole symmetry structure responsible for governing the size of these new
effects in the absence of Goldstone-breaking interactions. In this paper we
consider corrections to the decay h -> Z gamma obtained either by integrating
out vectors at tree level, or by integrating out vector-like fermions at loop
level. In each case, the pertinent operators that are generated are
parametrically enhanced relative to other interactions that arise at loop level
in the Standard Model such as h -> gg and h -> gamma gamma. Thus we emphasize
that the effects of interest here provide a unique possibility to probe the
dynamics underlying electroweak symmetry breaking, and do not depend on any
contrivance stemming from carefully chosen spectra. The effects we discuss
naturally lead to concerns of compatibility with precision electroweak
measurements, and we show with relevant computations that these corrections can
be kept well under control in our general parameter space.


  By using superfield techniques, the effective potential of the
$\mathcal{N}=1$ Wess-Zumino model in 2+1 dimensions is computed off-shell up to
two loops. It is shown that supersymmetry is not dynamically broken, and that
dynamical generation of mass does not occur perturbatively. We also investigate
the renormalization of the effective potential and determine the
renormalization group gamma and beta functions, showing that this model is
infrared free. Comparison with some other results in the literature is
provided.


  We propose a novel end-to-end pipeline for online long-range vectorized
high-definition (HD) map construction using on-board camera sensors. The
vectorized representation of HD maps, employing polylines and polygons to
represent map elements, is widely used by downstream tasks. However, previous
schemes designed with reference to dynamic object detection overlook the
structural constraints within linear map elements, resulting in performance
degradation in long-range scenarios. In this paper, we exploit the properties
of map elements to improve the performance of map construction. We extract more
accurate bird's eye view (BEV) features guided by their linear structure, and
then propose a hierarchical sparse map representation to further leverage the
scalability of vectorized map elements and design a progressive decoding
mechanism and a supervision strategy based on this representation. Our
approach, ScalableMap, demonstrates superior performance on the nuScenes
dataset, especially in long-range scenarios, surpassing previous
state-of-the-art model by 6.5 mAP while achieving 18.3 FPS. Code is available
at https://github.com/jingy1yu/ScalableMap.


  We simulate planet migration caused by interactions between planets and a
planetesimal disk. We use an N-body integrator optimized for near-Keplerian
motion that runs in parallel on a video graphics card, and that computes all
pair-wise gravitational interactions. We find that the fraction of
planetesimals found in mean motion resonances is reduced and planetary
migration rates are on average about 50% slower when gravitational interactions
between the planetesimals are computed than when planetesimal self-gravity is
neglected. This is likely due to gravitational stirring of the planetesimal
disk that is not present when self-gravity is neglected that reduces their
capture efficiency because of the increased particle eccentricity dispersion.
We find that migration is more stochastic when the disk is self-gravitating or
comprised of more massive bodies. Previous studies have found that if the
planetesimal disk density is below a critical level, migration is "damped" and
the migration rate decays exponentially, otherwise it is "forced" and the
planet's migration rate could accelerate exponentially. Migration rates
measured from our undamped simulations suggest that the migration rate
saturates at a level proportional to disk density and subsequently is
approximately power law in form with time.


  The fireball model of gamma-ray bursts predicted that when the energetic
blast wave encountered the surrounding medium, there will be afterglow
emission, and the subsequent afterglow observations appeared to confirm this
prediction. In this simplest fireball model, the electrons have been
accelerated to a power law energy distribution in a relativistic blast wave,
then they give afterglow emission through synchrotron radiation. Up to now
synchrotron radiation is believed to be the main mechanism of GRB emission,
however, here we will show that under some circumstances, the inverse Compton
scattering (ICS) may play an important role, and can change the light curves of
GRB afterglows. Here we investigate the effects of ICS in the relativistic case
(the surrounding medium density $\rho \propto r^{-2}$) and in the
non-relativistic case (for both $\rho =constant$ and $\rho \propto r^{-2}$), we
find that in the relativistic case the effect of ICS is usually important,
while in the non-relativistic case, this effect is usually unimportant, unless
the surrounding medium density is very high. We show that if ICS is important,
then it can flatten and steepen the light curves of GRB afterglows, and this
may provide the explanation for some afterglow observations.


  The skyrmion core, percolating the volume of the magnet, forms a skyrmion
string -- the topological Dirac-string-like object. Here we analyze the
nonlinear dynamics of skyrmion string in a low-energy regime by means of the
collective variables approach which we generalized for the case of strings.
Using the perturbative method of multiple scales (both in space and time), we
show that the weakly nonlinear dynamics of the translational mode propagating
along the string is captured by the nonlinear Schroedinger equation of the
focusing type. As a result, the basic "planar-wave" solution, which has a form
of a helix-shaped wave, experiences modulational instability. The latter leads
to the formation of cnoidal waves. Both types of cnoidal waves, dn- and
cn-waves, as well as the separatrix soliton solution, are confirmed by the
micromagnetic simulations. Beyond the class of the traveling-wave solutions, we
found Ma-breather propagating along the string. Finally, we proposed a
generalized approach, which enables one to describe nonlinear dynamics of the
modes of different symmetries, e.g. radially symmetrical or elliptical.


  We show that a microscopic theory of superfluidity, based on the properties
of the many-body spectrum of a superfluid, can explain naturally the
temperature dependence of critical velocities and the long time decay of
supercurrents.


  Multi-task learning (MTL) is an efficient way to improve the performance of
related tasks by sharing knowledge. However, most existing MTL networks run on
a single end and are not suitable for collaborative intelligence (CI)
scenarios. In this work, we propose an MTL network with a deep joint
source-channel coding (JSCC) framework, which allows operating under CI
scenarios. We first propose a feature fusion based MTL network (FFMNet) for
joint object detection and semantic segmentation. Compared with other MTL
networks, FFMNet gets higher performance with fewer parameters. Then FFMNet is
split into two parts, which run on a mobile device and an edge server
respectively. The feature generated by the mobile device is transmitted through
the wireless channel to the edge server. To reduce the transmission overhead of
the intermediate feature, a deep JSCC network is designed. By combining two
networks together, the whole model achieves 512x compression for the
intermediate feature and a performance loss within 2% on both tasks. At last,
by training with noise, the FFMNet with JSCC is robust to various channel
conditions and outperforms the separate source and channel coding scheme.


  We investigate whether pre-trained bidirectional transformers with sentiment
and emotion information improve stance detection in long discussions of
contemporary issues. As a part of this work, we create a novel stance detection
dataset covering 419 different controversial issues and their related pros and
cons collected by procon.org in nonpartisan format. Experimental results show
that a shallow recurrent neural network with sentiment or emotion information
can reach competitive results compared to fine-tuned BERT with 20x fewer
parameters. We also use a simple approach that explains which input phrases
contribute to stance detection.


  Let $b\ge 2$ be an integer. We show that the set of real numbers that are
Poisson generic in base $b$ is $\boldsymbol{\Pi}^0_3$-complete in the Borel
hierarchy of subsets of the real line. Furthermore, the set of real numbers
that are Borel normal in base $b$ and not Poisson generic in base $b$ is
complete for the class given by the differences between $\boldsymbol{\Pi}^0_3$
sets. We also show that the effective versions of these results hold in the
effective Borel hierarchy.


  It is believed that there are currently millions of vehicles on the roads
worldwide. The over speed of vehicles,theft of vehicles, disobeying traffic
rules in public, an unauthorized person entering the restricted area are keep
on increasing. In order restrict against these criminal activities, we need an
automatic public security system. Each vehicle has their own Vehicle
Identification Number (VIN) as their primary identifier. The VIN is actually a
License Number which states a legal license to participate in the public
traffic. The proposed paper is to identify the vehicle with the help of
vehicles License Plate (LP).LPRS is one the most important part of the
Intelligent Transportation System (ITS) to locate the LP. In this paper certain
existing algorithm drawbacks are overcome by the proposed morphological
operations for LPRS. Morphological operation is chosen due to its higher
efficiency, noise filter capacity, accuracy, exact localization of LP and
speed.


  We revisit the moving k nearest neighbor (MkNN) query, which computes one's k
nearest neighbor set and maintains it while at move. Existing MkNN algorithms
are mostly safe region based, which lack efficiency due to either computing
small safe regions with a high recomputation frequency or computing larger safe
regions but with a high cost for each computation. In this demonstration, we
showcase a system named INSQ that adopts a novel algorithm called the
Influential Neighbor Set (INS) algorithm to process the MkNN query in both
two-dimensional Euclidean space and road networks. This algorithm uses a small
set of safe guarding objects instead of safe regions. As long as the the
current k nearest neighbors are closer to the query object than the safe
guarding objects are, the current k nearest neighbors stay valid and no
recomputation is required. Meanwhile, the region defined by the safe guarding
objects is the largest possible safe region. This means that the recomputation
frequency is also minimized and hence, the INS algorithm achieves high overall
query processing efficiency.


  For dynamic visible light communication (VLC) systems, received optical power
fluctuates largely due to the fast movement of VLC terminals. Such fluctuations
will cause possible signal clipping and quantization noise at the
analog-to-digital converters (ADCs) and thus aggravate signal-to-noise ratio
(SNR) and reliability of the communication link. To mitigate this effect, the
automatic gain control (AGC) technique is often introduced in the receiver
front-end to adaptively adjust the electrical signal strength. In this paper,
we provided a VLC front-end analysis considering AGC model. Based on the model,
some design principles of AGC are theoretically derivated: the effects of AGC
index $m$, AGC maximum gain $g_{max}$ and AGC equilibrium range $DR$. Next, an
analog AGC amplifier was carefully implemented and the effectiveness of our AGC
modeling was proved through BER experiments. Finally, realtime 25Mb/s
on-off-keying (OOK) experiments with AGC function were demonstrated in a
dynamic link with different speeds. Experimental results show that the AGC can
stabilize the BER performance and improve the system performance in the speed
of 1m/s.


  Let $(M,J,\omega)$ be a quantizable compact K\"ahler manifold, with
quantizing Hermitian line bundle $(A,h)$, and associated Hardy space $H(X)$,
where $X$ is the unit circle bundle. Given a collection of $r$ Poisson
commuting quantizable Hamiltonian functions $f_j$ on $M$, there is an induced
Abelian unitary action on $H(X)$, generated by certain T\"oplitz operators
naturally induced by the $f_j$'s. As a multi-dimensional analogue of the usual
Weyl law and trace formula, we consider the problem of describing the
asymptotic clustering of the joint eigenvalues of these T\"oplitz operators
along a given ray, and locally on $M$ the asymptotic concentration of the
corresponding joint eigenfunctions. This problem naturally leads to a \lq
directional local trace formula\rq, involving scaling asymptotics in the
neighborhood of certain special loci in $M$. Under natural transversality
assumption, we obtain asymptotic expansions related to the local geometry of
the Hamiltonian action and flow.


  This is the first of a sequence of two papers. Here, a simple algebraic
characterization of the Fano manifolds in the class of homogeneous toric
bundles over a flag manifold G^C/P is provided in terms of symplectic data. The
result of this paper is used in the second paper, where it is proved that an
homogeneous toric bundle over a flag manifold admits a Kaehler-Ricci solitonic
metric if and only if it is Fano.


This survey deals with the Cremona group via its subgroups.


  The development of large quantum computers will have dire consequences for
cryptography. Most of the symmetric and asymmetric cryptographic algorithms are
vulnerable to quantum algorithms. Grover's search algorithm gives a square root
time boost for the searching of the key in symmetric schemes like AES and 3DES.
The security of asymmetric algorithms like RSA, Diffie Hellman, and ECC is
based on the mathematical hardness of prime factorization and discrete
logarithm. The best classical algorithms available take exponential time.
Shor's factoring algorithm can solve the problems in polynomial time. Major
breakthroughs in quantum computing will render all the present-day widely used
asymmetric cryptosystems insecure. This paper analyzes the vulnerability of the
classical cryptosystems in the context of quantum computers discusses various
post-quantum cryptosystem families, discusses the status of the NIST
post-quantum cryptography standardization process, and finally provides a
couple of future research directions in this field.


  Let $G$ and $N$ be finite groups of order $2n$ where $n$ is odd. We say the
pair $(G,N)$ is Hopf-Galois realizable if $G$ is a regular subgroup of
$\h(N)=N\rtimes\au(N)$. In this article we give necessary conditions on $G$
(similarly $N$) when $N$ (similarly $G$) is a group of the form
$\mathbb{Z}_n\rtimes\mathbb{Z}_2$. Further we show that this condition is also
sufficient if radical of $n$ is a Burnside number. This classifies all the skew
braces which has the additive group (or the multiplicative group) to be
isomorphic to $\mathbb{Z}_n\rtimes\mathbb{Z}_2$, in this case.


  This paper proposes novel pilot optimization and channel estimation algorithm
for the downlink multiuser massive multiple input multiple output (MIMO) system
with $K$ decentralized single antenna mobile stations (MSs), and time division
duplex (TDD) channel estimation which is performed by utilizing $N$ pilot
symbols. The proposed algorithm is explained as follows. First, we formulate
the channel estimation problem as a weighted sum mean square error (WSMSE)
minimization problem containing pilot symbols and introduced variables. Second,
for fixed pilot symbols, the introduced variables are optimized using minimum
mean square error (MMSE) and generalized Rayleigh quotient methods. Finally,
for $N=1$ and $N=K$ settings, the pilot symbols of all MSs are optimized using
semi definite programming (SDP) convex optimization approach, and for the other
settings of $N$ and $K$, the pilot symbols of all MSs are optimized by applying
simple iterative algorithm. When $N=K$, it is shown that the latter iterative
algorithm gives the optimal pilot symbols achieved by the SDP method.
Simulation results confirm that the proposed algorithm achieves less WSMSE
compared to that of the conventional semi-orthogonal pilot symbol and MMSE
channel estimation algorithm which creates pilot contamination.


  Person re-identification is a challenging task mainly due to factors such as
background clutter, pose, illumination and camera point of view variations.
These elements hinder the process of extracting robust and discriminative
representations, hence preventing different identities from being successfully
distinguished. To improve the representation learning, usually, local features
from human body parts are extracted. However, the common practice for such a
process has been based on bounding box part detection. In this paper, we
propose to adopt human semantic parsing which, due to its pixel-level accuracy
and capability of modeling arbitrary contours, is naturally a better
alternative. Our proposed SPReID integrates human semantic parsing in person
re-identification and not only considerably outperforms its counter baseline,
but achieves state-of-the-art performance. We also show that by employing a
\textit{simple} yet effective training strategy, standard popular deep
convolutional architectures such as Inception-V3 and ResNet-152, with no
modification, while operating solely on full image, can dramatically outperform
current state-of-the-art. Our proposed methods improve state-of-the-art person
re-identification on: Market-1501 by ~17% in mAP and ~6% in rank-1, CUHK03 by
~4% in rank-1 and DukeMTMC-reID by ~24% in mAP and ~10% in rank-1.


  We study the performance of uplink transmission in a large-scale (massive)
MIMO system, where all the transmitters have single antennas and the base
station has a large number of antennas. Specifically, we first derive the rates
that are possible through minimum mean-squared error (MMSE) channel estimation
and three linear receivers: maximum ratio combining (MRC), zero-forcing (ZF),
and MMSE. Based on the derived rates, we quantify the amount of energy savings
that are possible through increased number of base-station antennas or
increased coherence interval. We also analyze achievable total degrees of
freedom (DoF) of such a system without assuming channel state information at
the receiver, which is shown to be the same as that of a point-to-point MIMO
channel. Linear receiver is sufficient to achieve total DoF when the number of
users is less than the number of antennas. When the number of users is equal to
or larger than the number of antennas, nonlinear processing is necessary to
achieve the full degrees of freedom. Finally, the training period and optimal
training energy allocation under the average and peak power constraints are
optimized jointly to maximize the achievable sum rate when either MRC or ZF
receiver is adopted at the receiver.


  We derive new, explicit representations for the solution to the scalar wave
equation in the exterior of a sphere, subject to either Dirichlet or Robin
boundary conditions. Our formula leads to a stable and high-order numerical
scheme that permits the evaluation of the solution at an arbitrary target,
without the use of a spatial grid and without numerical dispersion error. In
the process, we correct some errors in the analytic literature concerning the
asymptotic behavior of the logarithmic derivative of the spherical modified
Hankel function. We illustrate the performance of the method with several
numerical examples.


  It is shown that certain extremal and next-to-extremal n-point correlators in
four-dimensional N=2 superconformal field theories are free. These results hold
for any gauge group.


  The so-called Dirac oscillator was proposed as a modification of the free
Dirac equation which reproduces many of the properties of the simple harmonic
oscillator but accompanied by a strong spin-orbit coupling term. It has yet to
be extended successfully to the arbitrary spin S case primarily because of the
unwieldiness of general spin Lorentz invariant wave equations. It is shown here
using the formalism of totally symmetric multispinors that the Dirac oscillator
can, however, be made to accommodate spin by incorporating it into the
framework of Galilean relativity. This is done explicitly for spin zero and
spin one as special cases of the arbitrary spin result. For the general case it
is shown that the coefficient of the spin-orbit term has a 1/S behavior by
techniques which are virtually identical to those employed in the derivation of
the g-factor carried out over four decades ago.


  The aim of this paper is to describe the topological equivariant $K$-ring, in
terms of generators and relations, of a Springer variety
$\mathcal{F}_{\lambda}$ of type $A$ associated to a nilpotent operator having
Jordan canonical form whose block sizes form a weakly decreasing sequence
$\lambda=(\lambda_1,\ldots, \lambda_l)$. This parallels the description of the
equivariant cohomology ring of $\mathcal{F}_{\lambda}$ due to Abe and Horiguchi
and generalizes the description of ordinary topological $K$-ring of
$\mathcal{F}_{\lambda}$ due to Sankaran and Uma \cite{su}.


  A cake has to be divided fairly among $n$ agents. When all agents have equal
entitlements, it is known that such a division can be implemented with $n-1$
cuts. When agents may have different entitlements, the paper shows that at
least $2 n -2$ cuts may be necessary, and $O(n \log(n))$ cuts are always
sufficient.


  Given a random sample extracted from a Multivariate Bernoulli Variable (MBV),
we consider the problem of estimating the structure of the undirected graph for
which the distribution is pairwise Markov and the parameters' vector of its
exponential form. We propose a simple method that provides a closed form
estimator of the parameters' vector and through its support also provides an
estimate of the undirected graph associated to the MBV distribution. The
estimator is proved to be consistent but it is feasible only in low-dimensional
regimes. Synthetic examples illustrates its performance compared with another
method that represents the state of the art in literature. Finally, the
proposed procedure is used for the analysis of a real data set in the pediatric
allergology area showing its practical efficiency.


  As a new machine learning approach, extreme learning machine (ELM) has
received wide attentions due to its good performances. However, when directly
applied to the hyperspectral image (HSI) classification, the recognition rate
is too low. This is because ELM does not use the spatial information which is
very important for HSI classification. In view of this, this paper proposes a
new framework for spectral-spatial classification of HSI by combining ELM with
loopy belief propagation (LBP). The original ELM is linear, and the nonlinear
ELMs (or Kernel ELMs) are the improvement of linear ELM (LELM). However, based
on lots of experiments and analysis, we found out that the LELM is a better
choice than nonlinear ELM for spectral-spatial classification of HSI.
Furthermore, we exploit the marginal probability distribution that uses the
whole information in the HSI and learn such distribution using the LBP. The
proposed method not only maintain the fast speed of ELM, but also greatly
improves the accuracy of classification. The experimental results in the
well-known HSI data sets, Indian Pines and Pavia University, demonstrate the
good performances of the proposed method.


  Influence of the fourth generation, if ever exists, on the experimentally
measurable quantities such as invariant dilepton mass distribution, lepton
forward-backward asymmetry, and the ratio (Gamma_L/Gamma_T) of the decay widths
when K* meson is longitudinally and transversally polarized, is studied. Using
the experimental results on the branching ratios for the (B -> X_s) gamma and
semileptonic (B -> X_c l nu) decays, the two possible solutions of the 4x4
Cabibbo-Kobayashi-Maskawa factor (V_{t's} V_{t^'b}) are obtained as a function
of the t'-quark mass. It is observed that the results for the above-mentioned
physical quantities are essentially different from the standard model
predictions only for one solution of the CKM factor. In this case the
above-mentioned physical quantities can serve as efficient tools in search of
the fourth generation. The other solution yields almost identical results with
the SM.


  By Wertheim-method the exact solution of the Percus-Yevick integral equation
for a system of particles with the repulsive step potential interacting
(collapsing hard spheres) is obtained. On the basis of this solution the state
equation for the repulsive step potential is built and determined, that the
Percus-Yevick equation does not show phase transition for collapsing hard
spheres.


  Let $(G_t)_{t \geq 0}$ be the random graph process ($G_0$ is edgeless and
$G_t$ is obtained by adding a uniformly distributed new edge to $G_{t-1}$), and
let $\tau_k$ denote the minimum time $t$ such that the $k$-core of $G_t$ (its
unique maximal subgraph with minimum degree at least $k$) is nonempty. For any
fixed $k\geq 3$ the $k$-core is known to emerge via a discontinuous phase
transition, where at time $t=\tau_k$ its size jumps from 0 to linear in the
number of vertices with high probability. It is believed that for any $k\geq 3$
the core is Hamiltonian upon creation w.h.p., and Bollob\'as, Cooper, Fenner
and Frieze further conjectured that it in fact admits $\lfloor(k-1)/2\rfloor$
edge-disjoint Hamilton cycles. However, even the asymptotic threshold for
Hamiltonicity of the $k$-core in $G(n,p)$ was unknown for any $k$. We show here
that for any fixed $k\ge 15$ the $k$-core of $G_t$ is w.h.p. Hamiltonian for
all $t \geq \tau_k$, i.e., immediately as the $k$-core appears and indefinitely
afterwards. Moreover, we prove that for large enough fixed $k$ the $k$-core
contains $\lfloor (k-3)/2\rfloor$ edge-disjoint Hamilton cycles w.h.p. for all
$t\geq \tau_k$.


  We introduce Orbifold Reduction, a new method for generating $2d$ $(0,2)$
gauge theories associated to D1-branes probing singular toric Calabi-Yau
4-folds starting from $4d$ $\mathcal{N}=1$ gauge theories on D3-branes probing
toric Calabi-Yau 3-folds. The new procedure generalizes dimensional reduction
and orbifolding. In terms of T-dual configurations, it generates brane brick
models starting from brane tilings. Orbifold reduction provides an agile
approach for generating $2d$ $(0,2)$ theories with a brane realization. We
present three practical applications of the new algorithm: the connection
between $4d$ Seiberg duality and $2d$ triality, a combinatorial method for
generating theories related by triality and a $2d$ $(0,2)$ generalization of
the Klebanov-Witten mass deformation.


  The ubiquity of mobile phones makes mobile GUI understanding an important
task. Most previous works in this domain require human-created metadata of
screens (e.g. View Hierarchy) during inference, which unfortunately is often
not available or reliable enough for GUI understanding. Inspired by the
impressive success of Transformers in NLP tasks, targeting for purely
vision-based GUI understanding, we extend the concepts of Words/Sentence to
Pixel-Words/Screen-Sentence, and propose a mobile GUI understanding
architecture: Pixel-Words to Screen-Sentence (PW2SS). In analogy to the
individual Words, we define the Pixel-Words as atomic visual components (text
and graphic components), which are visually consistent and semantically clear
across screenshots of a large variety of design styles. The Pixel-Words
extracted from a screenshot are aggregated into Screen-Sentence with a Screen
Transformer proposed to model their relations. Since the Pixel-Words are
defined as atomic visual components, the ambiguity between their visual
appearance and semantics is dramatically reduced. We are able to make use of
metadata available in training data to auto-generate high-quality annotations
for Pixel-Words. A dataset, RICO-PW, of screenshots with Pixel-Words
annotations is built based on the public RICO dataset, which will be released
to help to address the lack of high-quality training data in this area. We
train a detector to extract Pixel-Words from screenshots on this dataset and
achieve metadata-free GUI understanding during inference. We conduct
experiments and show that Pixel-Words can be well extracted on RICO-PW and well
generalized to a new dataset, P2S-UI, collected by ourselves. The effectiveness
of PW2SS is further verified in the GUI understanding tasks including relation
prediction, clickability prediction, screen retrieval, and app type
classification.


  This specification defines a simple query protocol for retrieving records
from a catalog of astronomical sources. The query describes sky position and an
angular distance, defining a cone on the sky. The response returns a list of
astronomical sources from the catalog whose positions lie within the cone,
formatted as a VOTable. This version of the specification is essentially a
transcription of the original Cone Search specification in order to move it
into the IVOA standardization process.


  We review the construction of six dimensional N=1 fixed points in a brane
picture involving D6 branes stretching between NS 5 branes.


  In order to perform a more complete census of active galactic nuclei (AGN) in
the local Universe, we investigate the use of the HeII~$\lambda 4685$ emission
line diagnostic diagram by Shirazi & Brinchmann (2012) in addition to the
standard methods based on other optical emission lines. The He II based
diagnostics is more sensitive to AGN ionization in the presence of strong star
formation than conventional line diagnostics. We survey a magnitude-limited
sample of 63,915 galaxies from the Sloan Digital Sky Survey Data Release 7 at
$0.02<z<0.05$ and use both the conventional BPT emission line diagnostic
diagrams, as well as the HeII diagram to identify AGN. In this sample, 1,075
galaxies are selected as AGN using the BPT diagram, while an additional 234
galaxies are identified as AGN using the HeII diagnostic diagram, representing
a 22% increase of AGN in the parent galaxy sample. We explore the host galaxy
properties of these new HeII selected AGN candidates and find that they are
most common in star-forming galaxies on the blue cloud and on the main sequence
where ionization from star-formation is most likely to mask AGN emission in the
BPT lines. We note in particular a high HeII AGN fraction in galaxies above the
high-mass end of the main sequence where quenching is expected to occur. We use
archival Chandra observations to confirm the AGN nature of candidates selected
through HeII based diagnostic. Finally, we discuss how this technique can help
inform galaxy/black hole co-evolution scenarios.


  We present a general construction of two types of differential forms, based
on any $(n{-}3)$-dimensional subspace in the kinematic space of $n$ massless
particles. The first type is the so-called projective, scattering forms in
kinematic space, while the second is defined in the moduli space of
$n$-punctured Riemann spheres which we call worldsheet forms. We show that the
pushforward of worldsheet forms, by summing over solutions of scattering
equations, gives the corresponding scattering forms, which generalizes the
results of [1711.09102]. The pullback of scattering forms to subspaces can have
natural interpretations as amplitudes in terms of Bern-Carrasco-Johansson
double-copy construction or Cachazo-He-Yuan formula. As an application of our
formalism, we construct in this way a large class of $d\log$ scattering forms
and worldsheet forms, which are in one-to-one correspondence with non-planar
MHV leading singularities in ${\cal N}=4$ super-Yang-Mills. For every leading
singularity function, we present a new determinant formula in moduli space, as
well as a (combinatoric) polytope and associated scattering form in kinematic
space. These include the so-called Cayley cases, where in each case the
scattering form is the canonical forms of a convex polytope in the subspace,
and scattering equations admit elegant rewritings as a map from the moduli
space to the subspace.


  A stable cyclicity of correlation coefficients Kcorr for some solar activity
indices versus F10,7 was found after monthly averages values analysis. These
indices are: Wolf numbers, 10,7 cm radio flux F10,7, 0,1-0,8 nm background, the
total solar irradiance, Mg II UV-index (280 nm core to wing ratio) and counts
of flares. The correlation coefficients of the linear regression of these solar
activity indices versus F10,7 were analyzed for every year in solar cycles 21 -
23. We found out that the values of yearly determined correlation coefficients
Kcorr for solar activity indices versus F10,7 show the cyclic variations with
stable period closed to half length of 11-year cycle (5,5 years approximately)


  We present a high efficiency source of picosecond pulses derived from a dual
cavity optical frequency comb generator. This approach overcomes the
limitations of single cavity comb generators that are restricted to
efficiencies of a few percent. We achieve picosecond pulses with GHz repetition
rates offering over a hundred times higher output efficiency than a single
cavity design and demonstrate tuning of pulse width by varying the modulation
depth of the intra-cavity electro-optic modulator. These results provide a
wavelength-agnostic design with a compact footprint for the development of
portable picosecond pulsed laser systems for timing, metrology, and LIDAR
applications.


  In patients with atrial fibrillation, local activation time (LAT) maps are
routinely used for characterising patient pathophysiology. The gradient of LAT
maps can be used to calculate conduction velocity (CV), which directly relates
to material conductivity and may provide an important measure of atrial
substrate properties. Including uncertainty in CV calculations would help with
interpreting the reliability of these measurements. Here, we build upon a
recent insight into reduced-rank Gaussian processes (GP) to perform
probabilistic interpolation of uncertain LAT directly on human atrial
manifolds. Our Gaussian Process Manifold Interpolation (GPMI) method accounts
for the topology of the atria, and allows for calculation of statistics for
predicted CV. We demonstrate our method on two clinical cases, and perform
validation against a simulated ground truth. CV uncertainty depends on data
density, wave propagation direction, and CV magnitude. GPMI is suitable for
probabilistic interpolation of other uncertain quantities on non-Euclidean
manifolds.


  The influence of the phase fluctuation of the pump laser on the
phase-correlation between the signal and idler modes of the output fields from
as non-degenerate optical parametric oscillator operating above oscillation
threshold was experimentally investigated. The noise spectra of the
intensity-difference and the phase-sum of the entangled optical beams were
measured with a pair of unbalanced fiber Match-Zehnder interferometers
specifically designed. The experimental results proved the previously
theoretical prediction and are in reasonable agreement with the calculation
based on semiclassical theory involving the phase fluctuation of pump laser.


  Training deep neural networks requires significant computational resources
and large datasets that are often confidential or expensive to collect. As a
result, owners tend to protect their models by allowing access only via an API.
Many works demonstrated the possibility of stealing such protected models by
repeatedly querying the API. However, to date, research has predominantly
focused on stealing classification models, for which a very large number of
queries has been found necessary. In this paper, we study the possibility of
stealing image-to-image models. Surprisingly, we find that many such models can
be stolen with as little as a single, small-sized, query image using simple
distillation. We study this phenomenon on a wide variety of model
architectures, datasets, and tasks, including denoising, deblurring, deraining,
super-resolution, and biological image-to-image translation. Remarkably, we
find that the vulnerability to stealing attacks is shared by CNNs and by models
with attention mechanisms, and that stealing is commonly possible even without
knowing the architecture of the target model.


  Federated Learning (FL) enables large-scale distributed training of machine
learning models, while still allowing individual nodes to maintain data
locally.
  However, executing FL at scale comes with inherent practical challenges:
  1) heterogeneity of the local node data distributions,
  2) heterogeneity of node computational speeds (asynchrony),
  but also 3) constraints in the amount of communication between the clients
and the server.
  In this work, we present the first variant of the classic federated averaging
(FedAvg) algorithm
  which, at the same time, supports data heterogeneity, partial client
asynchrony, and communication compression.
  Our algorithm comes with a rigorous analysis showing that, in spite of these
system relaxations,
  it can provide similar convergence to FedAvg in interesting parameter
regimes.
  Experimental results in the rigorous LEAF benchmark on setups of up to $300$
nodes show that our algorithm ensures fast convergence for standard federated
tasks, improving upon prior quantized and asynchronous approaches.


  We provide necessary and sufficient conditions for joint ergodicity results
for systems of commuting measure preserving transformations for an iterated
Hardy field function of polynomial growth. Our method builds on and improves
recent techniques due to Frantzikinakis and Tsinas, who dealt with multiple
ergodic averages along Hardy field functions; it also enhances an approach
introduced by the authors and Ferr\'e Moragues to study polynomial iterates.
The more general expression, in which the iterate is a linear combination of a
Hardy field function of polynomial growth and a tempered function, is studied
as well.


  Recently the experimental evidence of a charge-$6e$ condensed phase in the
kagome superconductor $\mathrm{AV_3Sb_5}$ (J. Ge, et. al., Phys. Rev. X 14,
021025 (2024)) has sparked significant interest. At a phenomenological level,
the order parameter of the $3Q$ pair-density-wave state can form either a
triangular lattice or a kagome lattice, in which the vortices and antivortices
locate at the center of the plaquettes. When the phase of a Cooper pair is
denoted as a XY spin, the effective model for the phase fluctuation is
characterized by the frustrated XY spin model on a triangular lattice with the
nearest neighbour antiferromagnetic coupling or a kagome lattice with both
nearest neighbour and next-nearest neighbor antiferromagnetic couplings. While
both models produce $\frac{1}{3}$ fractional vortex excitations, which arise as
the kinks on the domain walls in the low-energy excitations, the triangular
lattice model has a much larger domain wall energy than the vortex interaction
energy, so the frustrated kagome lattice model becomes unique. By developing a
state-of-the-art numerical tensor network method, we rigorously solve this
effective model at finite temperatures and confirm the presence of a vestigial
phase with $\frac{1}{3}$ vortex-antivortex paired phase in the absence of phase
coherence of Cooper pairs, which is dual to the charge-$6e$ condensed phase.
Our theory provides a potential explanation for the vestigial charge-$6e$
magneto-resistant oscillations observed in the recent experimental work.


  We discuss the optimal design problem in regression models with long-range
dependence error structure. Asymptotic optimal designs are derived and it is
demonstrated that these designs depend only indirectly on the correlation
function. Several examples are investigated to illustrate the theory. Finally,
the optimal designs are compared with asymptotic optimal designs which were
derived by Bickel and Herzberg [Ann. Statist. 7 (1979) 77--95] for regression
models with short-range dependent error.


  We introduce an unsupervised approach for correcting highly imperfect speech
transcriptions based on a decision-level fusion of stemming and two-way phoneme
pruning. Transcripts are acquired from videos by extracting audio using Ffmpeg
framework and further converting audio to text transcript using Google API. In
the benchmark LRW dataset, there are 500 word categories, and 50 videos per
class in mp4 format. All videos consist of 29 frames (each 1.16 s long) and the
word appears in the middle of the video. In our approach we tried to improve
the baseline accuracy from 9.34% by using stemming, phoneme extraction,
filtering and pruning. After applying the stemming algorithm to the text
transcript and evaluating the results, we achieved 23.34% accuracy in word
recognition. To convert words to phonemes we used the Carnegie Mellon
University (CMU) pronouncing dictionary that provides a phonetic mapping of
English words to their pronunciations. A two-way phoneme pruning is proposed
that comprises of the two non-sequential steps: 1) filtering and pruning the
phonemes containing vowels and plosives 2) filtering and pruning the phonemes
containing vowels and fricatives. After obtaining results of stemming and
two-way phoneme pruning, we applied decision-level fusion and that led to an
improvement of word recognition rate upto 32.96%.


  In this talk I address two high impact physics programs that require the use
of polarized and unpolarized positron beams in addition to using electron beams
of the same energy. First, I address what will be gained from using positron
beams in addition to electron beams in the extraction of the Compton Form
Factors (CFFs) and generalized parton distributions (GPDs) from Deeply Virtual
Compton Scattering (DVCS) on a proton target. As a second high impact science
program I discuss an experimental scenario using unpolarized positrons to
measure elastic scattering on protons in an effort to determine definitively
the 2-photon exchange contributions in order to resolve a longstanding
discrepancy in the determination of the proton's electric and magnetic form
factors.


  We study the spatial distribution of satellite galaxies by assuming that they
follow the dark matter distribution. This assumption is supported by
semi-analytical studies based on high-resolution numerical simulations. We find
that for a Milky-Way type halo, if only a dozen satellite galaxies are
observed, then they can lie on a ``great'' disk with an rms height of about 40
kpc. The normal to the plane is roughly isotropic on the sky. These results are
consistent with the observed properties of the satellite galaxies in the Milky
Way. If, however, the satellite galaxies follow the distribution of
substructure selected by present mass, then great disks similar to the one in
the Milky Way are rare and difficult to reproduce, in agreement with the
conclusion reached by Kroupa et al. (2004).


  Entanglement properties of purified quantum states are of key interest for
two reasons. First, in quantum information theory, minimally entangled purified
states define the Entanglement of Purification as a fundamental measure for the
complexity of the corresponding physical mixed state. Second, dynamical
entanglement growth in purified states represents the main bottleneck for
calculating dynamical physical properties on classical computers in the
framework of tensor network states. Here, we demonstrate how geometric methods
including parallel transport may be harnessed to reduce such dynamical
entanglement growth, and to obtain a general prescription for maintaining
(locally) optimal entanglement entropy when time-evolving a purified state.
Adapting and extending by higher order skew corrections the notion of Uhlmann
geometric phases, we reveal the relation between dynamical entanglement growth
and the geometry of the Hilbert-Schmidt bundle as the mathematical foundation
of purified states. With benchmarks on a non-integrable spin chain model, we
compare the computational performance of matrix product state algorithms based
on our present geometric disentangling method to previous approaches for taming
entanglement growth in purified states. Our findings provide numerical evidence
that geometric disentanglers are a powerful approach, superior in various
aspects to known methods for disentangling purified states in a range of
physically relevant computational scenarios. To exclude the effect of
algorithmic imperfections, we also provide a numerically exact analysis for
systems of moderate size.


  Solutions of five dimensional minimal de Sitter supergravity admitting
Killing spinors are considered. It is shown that the "timelike'' solutions are
determined in terms of a four dimensional hyper-Kahler torsion (HKT) manifold.
If the HKT manifold is conformally hyper-Kahler the most general solution can
be obtained from a sub-class of supersymmetric solutions of minimal N=2
ungauged supergravity, by means of a simple transformation. Examples include a
multi-BMPV de Sitter solution, describing multiple rotating black holes
co-moving with the expansion of the universe. If the HKT manifold is not
conformally hyper-Kahler, examples admitting a tri-holomorphic Killing vector
field are constructed in terms of certain solutions of three dimensional
Einstein-Weyl geometry.


  It is common for researchers to holdout data from a study pool to be used for
external validation as well as for future research, and the same desire is true
to those using machine learning modeling research. For this discussion, the
purpose of the holdout sample it is preserve data for research studies that
will be analysis-naive and randomly selected from the full dataset.
Analysis-naive are records that are not used for testing or training machine
learning (ML) models and records that do not participate in any aspect of the
current machine learning study. The methodology suggested for creating holdouts
is a modification of k-fold cross validation, which takes into account
randomization and efficiently allows a three-way split (holdout, test and
training) as part of the method without forcing. The paper also provides a
working example using set of automated functions in Python and some scenarios
for applicability in healthcare.


  A thermodynamic model for a mixture of alkanethiol-coated nanoparticles (NPs)
and low molecular weight (non-polymeric) solvent is developed, and calculations
of liquid-liquid phase equilibrium for different values of NP core radius,
alkanethiol chain length, solvent molar volume and alkanethiol-solvent
interaction parameter, are presented. The model takes into account the swelling
of the organic coronas and the dispersion of particles with swollen coronas in
the solvent. The energetic interaction between alkyl chains and solvent is
considered, both within the corona and between the outer alkyl segments and
free solvent. Swelling involves mixing of alkanethiol chains and solvent in the
corona and stretching of the organic chains. Dispersion considers an entropic
contribution based on Carnahan Starling equation of state and an enthalpic term
calculated considering the surface contacts between alkyl segments placed in
the external boundary of the corona and the molecules of free solvent. Two
different kinds of phase equilibrium are found. One of them, observed at high
values of the interaction parameter, is the typical liquid-liquid equilibrium
for compact NPs in a poor solvent where a complete phase separation is observed
when cooling (increasing the interaction parameter). The second liquid-liquid
equilibrium is observed at low values of the interaction parameter, where
swelling of coronas is favored. In this region two different phases co-exist,
one more concentrated in NPs that exhibit relatively compact coronas and the
other one more diluted in NPs with extended coronas. In diluted solutions of
NPs the deswelling of the fully extended coronas takes place abruptly in a very
small temperature range, leading to a solution of compact NPs. This critical
transition might find practical applications similar to those found for the
abrupt shrinkage of hydrogels at a critical temperature.


  We have completed an optical spectroscopic survey of an unbiased,
extinction-limited sample of candidate young stars covering 1.3 square degrees
of the Rho Ophiuchi star forming region. While infrared, X-ray, and optical
surveys of the cloud have identified many young stellar objects (YSOs), these
surveys are biased towards particular stages of stellar evolution and are not
optimal for studies of the disk frequency and initial mass function.We have
obtained over 300 optical spectra to help identify 135 association members
based on the presence of H-alpha in emission, lithium absorption, X-ray
emission, a mid-infrared excess, a common proper motion, reflection nebulosity,
and/or extinction considerations. Spectral types along with R and I band
photometry were used to derive effective temperatures and bolometric
luminosities for association members to compare with theoretical tracks and
isochrones for pre-main-sequence stars. An average age of 3.1 Myr is derived
for this population which is intermediate between that of objects embedded in
the cloud core of Rho Ophiuchi and low mass stars in the Upper Scorpius
subgroup. Consistent with this age we find a circumstellar disk frequency of
27% plus or minus 5%. We also constructed an initial mass function for an
extinction-limited sample of 123 YSOs (A_v less than or equal to 8 mag), which
is consistent with the field star initial mass function for YSOs with masses >
0.2 M_sun. There may be a deficit of brown dwarfs but this result relies on
completeness corrections and requires confirmation.


  We present a measurement of galaxy-galaxy lensing around a magnitude-limited
($i_{AB} < 22.5$) sample of galaxies from the Dark Energy Survey Science
Verification (DES-SV) data. We split these lenses into three
photometric-redshift bins from 0.2 to 0.8, and determine the product of the
galaxy bias $b$ and cross-correlation coefficient between the galaxy and dark
matter overdensity fields $r$ in each bin, using scales above 4 Mpc/$h$
comoving, where we find the linear bias model to be valid given our current
uncertainties. We compare our galaxy bias results from galaxy-galaxy lensing
with those obtained from galaxy clustering (Crocce et al. 2016) and CMB lensing
(Giannantonio et al. 2016) for the same sample of galaxies, and find our
measurements to be in good agreement with those in Crocce et al. (2016), while,
in the lowest redshift bin ($z\sim0.3$), they show some tension with the
findings in Giannantonio et al. (2016). We measure $b\cdot r$ to be $0.87\pm
0.11$, $1.12 \pm 0.16$ and $1.24\pm 0.23$, respectively for the three redshift
bins of width $\Delta z = 0.2$ in the range $0.2<z <0.8$, defined with the
photometric-redshift algorithm BPZ. Using a different code to split the lens
sample, TPZ, leads to changes in the measured biases at the 10-20\% level, but
it does not alter the main conclusion of this work: when comparing with Crocce
et al. (2016) we do not find strong evidence for a cross-correlation parameter
significantly below one in this galaxy sample, except possibly at the lowest
redshift bin ($z\sim 0.3$), where we find $r = 0.71 \pm 0.11$ when using TPZ,
and $0.83 \pm 0.12$ with BPZ.


  For a strongly inacessible cardinal $\kappa$, we investigate the
relationships between the following ideals:
  - the ideal of meager sets in the ${<}\kappa$-box product topology
  - the ideal of "null" sets in the sense of [Sh:1004] (arXiv:1202.5799)
  - the ideal of nowhere stationary subsets of a (naturally defined) stationary
set $S_{\rm pr}^\kappa \subseteq \kappa$.
  In particular, we analyse the provable inequalities between the cardinal
characteristics for these ideals, and we give consistency results showing that
certain inequalities are unprovable.
  While some results from the classical case ($\kappa=\omega$) can be easily
generalized to our setting, some key results (such as a Fubini property for the
ideal of null sets) do not hold; this leads to the surprising inequality
cov(null)$\le$non(null). Also, concepts that did not exist in the classical
case (in particular, the notion of stationary sets) will turn out to be
relevant.
  We construct several models to distinguish the various cardinal
characteristics; the main tools are iterations with $\mathord<\kappa$-support
(and a strong "Knaster" version of $\kappa^+$-cc) and one iteration with
${\le}\kappa$-support (and a version of $\kappa$-properness).


  We propose a new framework called Noise2Blur (N2B) for training robust image
denoising models without pre-collected paired noisy/clean images. The training
of the model requires only some (or even one) noisy images, some random
unpaired clean images, and noise-free but blurred labels obtained by predefined
filtering of the noisy images. The N2B model consists of two parts: a denoising
network and a noise extraction network. First, the noise extraction network
learns to output a noise map using the noise information from the denoising
network under the guidence of the blurred labels. Then, the noise map is added
to a clean image to generate a new "noisy/clean" image pair. Using the new
image pair, the denoising network learns to generate clean and high-quality
images from noisy observations. These two networks are trained simultaneously
and mutually aid each other to learn the mappings of noise to clean/blur.
Experiments on several denoising tasks show that the denoising performance of
N2B is close to that of other denoising CNNs trained with pre-collected paired
data.


  New laboratory data of CH$_2$CHCN (vinyl cyanide) in its ground and
vibrationally excited states at the microwave to THz domain allow searching for
these excited state transitions in the Orion-KL line survey.
Frequency-modulated spectrometers combined into a single broadband 50-1900 GHz
spectrum provided measurements of CH$_2$CHCN covering a spectral range of
18-1893 GHz, whose assignments was confirmed by Stark modulation spectra in the
18-40 GHz region and by ab-initio anharmonic force field calculations. For
analyzing the emission lines of CH$_2$CHCN species detected in Orion-KL we used
the excitation and radiative transfer code (MADEX) at LTE conditions. The
rotational transitions of the ground state of this molecule emerge from four
cloud components of hot core nature which trace the physical and chemical
conditions of high mass star forming regions in the Orion-KL Nebula. The total
column density of CH$_2$CHCN in the ground state is (3.0$\pm$0.9)x10$^{15}$
cm$^{-2}$. We report on the first interstellar detection of transitions in the
v10=1/(v11=1,v15=1) dyad in space, and in the v11=2 and v11=3 states in
Orion-KL. The lowest energy vibrationally excited states of vinyl cyanide such
as v11=1 (at 328.5 K), v15=1 (at 478.6 K), v11=2 (at 657.8 K), the
v10=1/(v11=1,v15=1) dyad (at 806.4/809.9 K), and v11=3 (at 987.9 K) are
populated under warm and dense conditions, so they probe the hottest parts of
the Orion-KL source. Column density and rotational and vibrational temperatures
for CH$_2$CHCN in their ground and excited states, as well as for the
isotopologues, have been constrained by means of a sample of more than 1000
lines in this survey. Moreover, we present the detection of methyl isocyanide
(CH$_3$NC) for the first time in Orion-KL and a tentative detection of vinyl
isocyanide (CH$_2$CHNC) and give column density ratios between the cyanide and
isocyanide isomers.


  Nowadays, manufacturing sectors harness the power of machine learning and
data science algorithms to make predictions for the optimization of mechanical
and microstructure properties of fabricated mechanical components. The
application of these algorithms reduces the experimental cost beside leads to
reduce the time of experiments. The present research work is based on the
prediction of penetration depth using Supervised Machine Learning algorithms
such as Support Vector Machines (SVM), Random Forest Algorithm, and Robust
Regression algorithm. A Friction Stir Spot Welding (FSSW) was used to join two
elements of AA1230 aluminum alloys. The dataset consists of three input
parameters: Rotational Speed (rpm), Dwelling Time (seconds), and Axial Load
(KN), on which the machine learning models were trained and tested. It observed
that the Robust Regression machine learning algorithm outperformed the rest of
the algorithms by resulting in the coefficient of determination of 0.96. The
research work also highlights the application of image processing techniques to
find the geometrical features of the weld formation.


  The discovery of the multiple $H$-$T$ phase diagram of CeRh$_2$As$_2$ offers
a new route to designing topological superconductors. Although weak-coupling
theories explain the experimental phase diagram qualitatively, a quantitative
discrepancy between them has discouraged conclusive interpretation. In this
Letter, we thoroughly study the effect of Coulomb interaction and the phase
diagrams of locally noncentrosymmetric superconductors. We reveal even-odd
parity transition and the enhancement of the parity transition field in
strongly correlated superconductors, and an issue of CeRh$_2$As$_2$ is
resolved.


  In the recent years graphitization of ion-beam induced amorphous layers
became the basic tool for device fabrication in diamond. The etchable graphitic
layers can be removed to form free-standing membranes into which the desired
structures can be sculpted using FIB milling. The optical properties of the
devices fabricated using this method are assumed on the model of sharp
diamond-air interface. The real quality of this interface could depend on
degree of graphitization of the amorphous damage layers after annealing. In the
present work the graphitization process was studied using conventional and
analytical TEM. It was found that annealing at 550 {\deg}C results in a partial
graphitization of the implanted volume with formation of the nano-crystalline
graphitic phase sandwiched between layers of tetrahedral amorphous carbon.
Annealing at 1400 {\deg}C resulted in complete graphitization of the amorphous
layers. The average size of graphite nano-crystals did not exceed 5 nm with
predominant orientation of c-planes normal to the sample surface.


  The generalized time-dependent generator coordinate method (TD-GCM) is
extended to include pairing correlations. The correlated GCM nuclear wave
function is expressed in terms of time-dependent generator states and weight
functions. The particle-hole channel of the effective interaction is determined
by a Hamiltonian derived from an energy density functional, while pairing is
treated dynamically in the standard BCS approximation with time-dependent
pairing tensor and single-particle occupation probabilities. With the inclusion
of pairing correlations, various time-dependent phenomena in open-shell nuclei
can be described more realistically. The model is applied to the description of
saddle-to-scission dynamics of induced fission. The generalized TDGCM charge
yields and total kinetic energy distribution for the fission of 240Pu, are
compared to those obtained using the standard time-dependent density functional
theory (TD-DFT) approach, and with available data.


  In solving today's social issues, it is necessary to determine solutions that
are acceptable to all stakeholders and collaborate to apply them. The
conventional technology of "permissive meeting analysis" derives a
consensusable choice that falls within everyone's permissible range through
mathematical analyses; however, it tends to be biased toward the majority in a
group, making it difficult to reach a consensus when a conflict arises. To
support consensus building (defined here as an acceptable compromise that not
everyone rejects), we developed a composite consensus-building process. The
developed process addresses this issue by combining permissible meeting
analysis with a new "compromise choice-exploration" technology, which presents
a consensusable choice that emphasizes fairness and equality among everyone
when permissible meeting analysis fails to do so. When both permissible meeting
analysis and compromise choice exploration do not arrive at a consensus, a
facility is provided to create a sublated choice among those provided by them.
The trial experimental results confirmed that permissive meeting analysis and
compromise choice exploration are sufficiently useful for deriving
consensusable choices. Furthermore, we found that compromise choice exploration
is characterized by its ability to derive choices that control the balance
between compromise and fairness. Our proposed composite consensus-building
approach could be applied in a wide range of situations, from local issues in
municipalities and communities to international issues such as environmental
protection and human rights issues. It could also aid in developing digital
democracy and platform cooperativism.


  We present a recursive method for super Yang-Mills color-ordered n-point tree
amplitudes based on the cohomology of pure spinor superspace in ten space-time
dimensions. The amplitudes are organized into BRST covariant building blocks
with diagrammatic interpretation. Manifestly cyclic expressions (no longer than
one line each) are explicitly given up to n=10 and higher leg generalizations
are straightforward.


  This paper investigates the joint localization, detection, and tracking of
sound events using a convolutional recurrent neural network (CRNN). We use a
CRNN previously proposed for the localization and detection of stationary
sources, and show that the recurrent layers enable the spatial tracking of
moving sources when trained with dynamic scenes. The tracking performance of
the CRNN is compared with a stand-alone tracking method that combines a
multi-source (DOA) estimator and a particle filter. Their respective
performance is evaluated in various acoustic conditions such as anechoic and
reverberant scenarios, stationary and moving sources at several angular
velocities, and with a varying number of overlapping sources. The results show
that the CRNN manages to track multiple sources more consistently than the
parametric method across acoustic scenarios, but at the cost of higher
localization error.


  A long-lived decaying dark matter as a resolution to Fermi, PAMELA and ATIC
anomalies is investigated in the framework of split supersymmetry (SUSY)
without R-parity, where the neutralino is regarded as the dark matter and the
extreme fine-tuned couplings for the long-lived neutralino are naturally evaded
in the usual approach.


  Motivated by the recent developments on cluster Mott insulating materials
such as the cluster magnet LiZn$_2$Mo$_3$O$_8$, we consider the strong
plaquette charge ordered regime of the extended Hubbard model on a breathing
Kagome lattice and reveal the properties of the cluster Mottness. The plaquette
charge order arises from the inter-site charge interaction and the collective
motion of three localized electrons on the hexagon plaquettes. This model leads
naturally to a reduction of the local moments by 2/3 as observed in
LiZn$_2$Mo$_3$O$_8$. Furthermore, at low temperatures each hexagon plaquette
contains an extra orbital-like degree of freedom in addition to the remaining
spin 1/2. We explore the consequence of this emergent orbital degree of
freedom. We point out the interaction between the local moments is naturally
described by a Kugel-Khomskii spin-orbital model. We develop a parton approach
and suggest a spin liquid ground state with spinon Fermi surfaces for this
model. We further predict an emergent orbital order when the system is under a
strong magnetic field. Various experimental consequences for
LiZn$_2$Mo$_3$O$_8$ are discussed, including an argument that the charge
ordering much be short ranged if the charge per Mo is slightly off
stoichiometry.


  Background: Transitioning from an old medical coding system to a new one can
be challenging, especially when the two coding systems are significantly
different. The US experienced such a transition in 2015. Objective: This
research aims to introduce entropic measures to help users prepare for the
migration to a new medical coding system by identifying and focusing
preparation initiatives on clinical concepts with more likelihood of transition
challenges. Methods: Two entropic measures of coding complexity are introduced.
The first measure is a function of the variation in the alphabets of new codes.
The second measure is based on the possible number of valid representations of
an old code. Results: A demonstration of how to implement the proposed
techniques is carried out using the 2015 mappings between ICD-9-CM and
ICD-10-CM/PCS. The significance of the resulting entropic measures is discussed
in the context of clinical concepts that were likely to pose challenges
regarding documentation, coding errors, and longitudinal data comparisons.
Conclusion: The proposed entropic techniques are suitable to assess the
complexity between any two medical coding systems where mappings or crosswalks
exist. The more the entropy, the more likelihood of transition challenges.
Users can utilize the suggested techniques as a guide to prioritize training
efforts to improve documentation and increase the chances of accurate coding,
code validity, and longitudinal data comparisons.


  Control co-design (CCD) explores physical and control design spaces
simultaneously to optimize a system's performance. A commonly used CCD
framework aims to achieve open-loop optimal control (OLOC) trajectory while
optimizing the physical design variables subject to constraints on control and
design parameters. In this study, in contrast with the conventional CCD methods
based on OLOC schemes, we present a CCD formulation that explicitly considers a
feedback controller. In the formulation, we consider two control laws based on
proportional linear and quadratic state feedback, where the control gain is
optimized. The simulation results show that the OLOC trajectory could be
approximated by a feedback controller. While the total energy generated from
the CCD with a feedback controller is slightly lower than that of the CCD with
OLOC, it results in a much simpler control structure and more robust
performance in the presence of uncertainties and disturbances, making it
suitable for real-time control. The study in this paper investigates the
performance of optimal hydrokinetic turbine design with a feedback controller
in the presence of uncertainties and disturbances to demonstrate the benefits
and highlight challenges associated with incorporating the feedback controller
explicitly in the CCD stage.


  Recommender systems (RS) work effective at alleviating information overload
and matching user interests in various web-scale applications. Most RS retrieve
the user's favorite candidates and then rank them by the rating scores in the
greedy manner. In the permutation prospective, however, current RS come to
reveal the following two limitations: 1) They neglect addressing the
permutation-variant influence within the recommended results; 2) Permutation
consideration extends the latent solution space exponentially, and current RS
lack the ability to evaluate the permutations. Both drive RS away from the
permutation-optimal recommended results and better user experience. To
approximate the permutation-optimal recommended results effectively and
efficiently, we propose a novel permutation-wise framework PRS in the
re-ranking stage of RS, which consists of Permutation-Matching (PMatch) and
Permutation-Ranking (PRank) stages successively. Specifically, the PMatch stage
is designed to obtain the candidate list set, where we propose the FPSA
algorithm to generate multiple candidate lists via the permutation-wise and
goal-oriented beam search algorithm. Afterwards, for the candidate list set,
the PRank stage provides a unified permutation-wise ranking criterion named LR
metric, which is calculated by the rating scores of elaborately designed
permutation-wise model DPWN. Finally, the list with the highest LR score is
recommended to the user. Empirical results show that PRS consistently and
significantly outperforms state-of-the-art methods. Moreover, PRS has achieved
a performance improvement of 11.0% on PV metric and 8.7% on IPV metric after
the successful deployment in one popular recommendation scenario of Taobao
application.


  Namakanui is an instrument containing three inserts in an ALMA type Dewar.
The three inserts are Alaihi, Uu and Aweoweo operating around 86, 230 and
345GHz. The receiver is being commissioned on the JCMT. It will be used for
both Single dish and VLBI observations. We will present commissioning results
and the system.


  We show that Wigner's infinite spin particle classically is described by a
reparametrization invariant higher order geometrical Lagrangian. The model
exhibit unconventional features like tachyonic behaviour and momenta
proportional to light-like accelerations. A simple higher order superversion
for half-odd integer particles is also derived. Interaction with external
vector fields and curved spacetimes are analyzed with negative results except
for (anti)de Sitter spacetimes. We quantize the free theories covariantly and
show that the resulting wave functions are fields containing arbitrary large
spins. Closely related infinite spin particle models are also analyzed.


  Realistic numerical 3D models of the outer solar atmosphere show two
different kind of spicule-like phenomena, as also observed on the solar limb.
The numerical models are calculated using the 2 types of spicules "observed" in
3D realistic models Oslo Staggered Code (OSC) to solve the full MHD equations
with non-grey and NLTE radiative transfer and thermal conduction along the
magnetic field lines. The two types of spicules arise as a natural result of
the dynamical evolution in the models. We discuss the different properties of
these two types of spicules, their differences from observed spicules and what
needs to be improved in the models.


  Gravitational-wave detectors on earth have detected gravitational waves from
merging compact objects in the local Universe. In future we will detect
gravitational waves from higher-redshift sources, which trace the high-redshift
structure formation history. That is, by observing high-redshift
gravitational-wave events we will be able to probe structure formation history.
This will provide additional insight into the early Universe when primordial
fluctuations are generated and also into the nature of dark matter.


  In this paper, for small uniformities, we determine the order of magnitude of
the multicolor Ramsey numbers for Berge cycles of length $4$, $5$, $6$, $7$,
$10$, or $11$. Our result follows from a more general setup which can be
applied to other hypergraph Ramsey problems. Using this, we additionally
determine the order of magnitude of the multicolor Ramsey number for
Berge-$K_{a,b}$ for certain $a$, $b$, and uniformities.


  We propose a new mathematical tool for the study of transport properties of
models for lattice vibrations in crystalline solids. By replication of
dynamical degrees of freedom, we aim at a new dynamical system where the
"local" dynamics can be isolated and solved independently from the "global"
evolution. The replication procedure is very generic but not unique as it
depends on how the original dynamics are split between the local and global
dynamics. As an explicit example, we apply the scheme to study thermalization
of the pinned harmonic chain with velocity flips. We improve on the previous
results about this system by showing that after a relatively short time period
the average kinetic temperature profile satisfies the dynamic Fourier's law in
a local microscopic sense without assuming that the initial data is close to a
local equilibrium state. The bounds derived here prove that the above
thermalization period is at most of the order L^(2/3), where L denotes the
number of particles in the chain. In particular, even before the diffusive time
scale Fourier's law becomes a valid approximation of the evolution of the
kinetic temperature profile. As a second application of the dynamic replica
method, we also briefly discuss replacing the velocity flips by an anharmonic
onsite potential.


  Background and objectives. Domain shift is a generalisation problem of
machine learning models that occurs when the data distribution of the training
set is different to the data distribution encountered by the model when it is
deployed. This is common in the context of biomedical image segmentation due to
the variance of experimental conditions, equipment, and capturing settings. In
this work, we address this challenge by studying both neural style transfer
algorithms and unpaired image-to-image translation methods in the context of
the segmentation of tumour spheroids.
  Methods. We have illustrated the domain shift problem in the context of
spheroid segmentation with 4 deep learning segmentation models that achieved an
IoU over 97% when tested with images following the training distribution, but
whose performance decreased up to an 84\% when applied to images captured under
different conditions. In order to deal with this problem, we have explored 3
style transfer algorithms (NST, deep image analogy, and STROTSS), and 6
unpaired image-to-image translations algorithms (CycleGAN, DualGAN, ForkGAN,
GANILLA, CUT, and FastCUT). These algorithms have been integrated into a
high-level API that facilitates their application to other contexts where the
domain-shift problem occurs.
  Results. We have considerably improved the performance of the 4 segmentation
models when applied to images captured under different conditions by using both
style transfer and image-to-image translation algorithms. In particular, there
are 2 style transfer algorithms (NST and deep image analogy) and 1 unpaired
image-to-image translations algorithm (CycleGAN) that improve the IoU of the
models in a range from 0.24 to 76.07. Therefore, reaching a similar performance
to the one obtained with the models are applied to images following the
training distribution.


  We consider the problem of bounded model checking (BMC) for linear temporal
logic (LTL). We present several efficient encodings that have size linear in
the bound. Furthermore, we show how the encodings can be extended to LTL with
past operators (PLTL). The generalised encoding is still of linear size, but
cannot detect minimal length counterexamples. By using the virtual unrolling
technique minimal length counterexamples can be captured, however, the size of
the encoding is quadratic in the specification. We also extend virtual
unrolling to Buchi automata, enabling them to accept minimal length
counterexamples.
  Our BMC encodings can be made incremental in order to benefit from
incremental SAT technology. With fairly small modifications the incremental
encoding can be further enhanced with a termination check, allowing us to prove
properties with BMC. Experiments clearly show that our new encodings improve
performance of BMC considerably, particularly in the case of the incremental
encoding, and that they are very competitive for finding bugs. An analysis of
the liveness-to-safety transformation reveals many similarities to the BMC
encodings in this paper. Using the liveness-to-safety translation with
BDD-based invariant checking results in an efficient method to find shortest
counterexamples that complements the BMC-based approach.


  We construct and analyze the domain wall solution in $D=11$ supergravity
connecting the $N=1$, AdS$_4\times S_{\rm squashed}^7$ vacuum to the $N=8$,
AdS$_4\times S_{\rm round}^7$ vacuum. This domain wall describes the
holographic renormalization group flow from an ${\rm Sp}(2)\times {\rm Sp}(1)$
symmetric UV fixed point to the ${\rm SO}(8)$ symmetric IR fixed point. It
breaks all supersymmetries which are (partially) restored at its endpoints. We
show how recent techniques from exceptional field theory allow us to compute
the quadratic couplings of all Kaluza-Klein fluctuations around the domain wall
background, encoding all two-point correlators along the holographic RG flow.


  We obtain an improved upper bound for the nuclear dimension of extensions of
$\mathcal{O}_\infty$-stable $\rm{C}^*$-algebras. In particular, we prove that
the nuclear dimension of a full extension of an $\mathcal{O}_\infty$-stable
$\rm{C}^*$-algebra by a stable AF algebra is one.