  A universal programmable quantum processor uses program quantum states to
apply an arbitrary quantum channel to an input state. We generalize the concept
of a finite-dimensional programmable quantum processor to infinite dimension
assuming an energy constraint on the input and output of the target quantum
channels. By proving reductions to and from finite-dimensional processors, we
obtain upper and lower bounds on the program dimension required to
approximately implement energy-limited quantum channels. In particular, we
consider the implementation of Gaussian channels. Due to their practical
relevance, we investigate the resource requirements for gauge-covariant
Gaussian channels. Additionally, we give upper and lower bounds on the program
dimension of a processor implementing all Gaussian unitary channels. These
lower bounds rely on a direct information-theoretic argument, based on the
generalization from finite to infinite dimension of a certain replication lemma
for unitaries.


  We construct the minimal off-shell formulation of N = 1 chiral supergravity
(SUGRA) introducing a complex antisymmetric tensor field $B_{\mu \nu}$ and a
complex axial-vector field $A_{\mu}$ as auxiliary fields. The resulting algebra
of the right- and left-handed supersymmetry (SUSY) transformations closes off
shell and generates chiral gauge transforamtions and vector gauge
transformations in addition to the transformations which appear in the case
without auxiliary fields.


  If astrophysical black hole candidates are the Kerr black holes predicted by
General Relativity, the value of their spin parameter must be subject to the
theoretical bound $|a_*| \le 1$. In this work, we consider the possibility that
these objects are either non-Kerr black holes in an alternative theory of
gravity or exotic compact objects in General Relativity. We study the accretion
process when their accretion disk is geometrically thick with a simple version
of the Polish doughnut model. The picture of the accretion process may be
qualitatively different from the one around a Kerr black hole. The inner edge
of the disk may not have the typical cusp on the equatorial plane any more, but
there may be two cusps, respectively above and below the equatorial plane. We
extend previous work on the evolution of the spin parameter and we estimate the
maximum value of $a_*$ for the super-massive black hole candidates in galactic
nuclei. Since measurements of the mean radiative efficiency of AGNs require
$\eta > 0.15$, we infer the "observational" bound $|a_*| \lesssim 1.3$, which
seems to be quite independent of the exact nature of these objects. Such a
bound is only slightly weaker than $|a_*| \lesssim 1.2$ found in previous work
for thin disks.


  Data centers are becoming increasingly popular for their flexibility and
processing capabilities in the modern computing environment. They are managed
by a single entity (administrator) and allow dynamic resource provisioning,
performance optimization as well as efficient utilization of available
resources. Each data center consists of massive compute, network and storage
resources connected with physical wires. The large scale nature of data centers
requires careful planning of compute, storage, network nodes, interconnection
as well as inter-communication for their effective and efficient operations. In
this paper, we present a comprehensive survey and taxonomy of network
topologies either used in commercial data centers, or proposed by researchers
working in this space. We also compare and evaluate some of those topologies
using mininet as well as gem5 simulator for different traffic patterns, based
on various metrics including throughput, latency and bisection bandwidth.


  In learn-as-you-go (LAGO) adaptive studies, the intervention is a complex
package consisting of multiple components, and is adapted in stages during the
study based on past outcome data. This design formalizes standard practice, and
desires for practice, in public health intervention studies. An effective
intervention package is sought, while minimizing intervention package cost.
When analyzing data from a learn-as-you-go study, the interventions in later
stages depend upon the outcomes in the previous stages, violating standard
statistical theory. We develop methods for estimating the intervention effects
in a LAGO study. We prove consistency and asymptotic normality using a novel
coupling argument, ensuring the validity of the test for the hypothesis of no
overall intervention effect. We develop a confidence set for the optimal
intervention package and confidence bands for the success probabilities under
alternative package compositions. We illustrate our methods in the BetterBirth
Study, which aimed to improve maternal and neonatal outcomes among 157,689
births in Uttar Pradesh, India through a complex, multi-component intervention
package.


  Relational networks within a team play a critical role in the performance of
many real-world multi-robot systems. To successfully accomplish tasks that
require cooperation and coordination, different agents (e.g., robots)
necessitate different priorities based on their positioning within the team.
Yet, many of the existing multi-robot cooperation algorithms regard agents as
interchangeable and lack a mechanism to guide the type of cooperation strategy
the agents should exhibit. To account for the team structure in cooperative
tasks, we propose a novel algorithm that uses a relational network comprising
inter-agent relationships to prioritize certain agents over others. Through
appropriate design of the team's relational network, we can guide the
cooperation strategy, resulting in the emergence of new behaviors that
accomplish the specified task. We conducted six experiments in a multi-robot
setting with a cooperative task. Our results demonstrate that the proposed
method can effectively influence the type of solution that the algorithm
converges to by specifying the relationships between the agents, making it a
promising approach for tasks that require cooperation among agents with a
specified team structure.


  We propose a new and, arguably, a very simple reduction of instance
segmentation to semantic segmentation. This reduction allows to train
feed-forward non-recurrent deep instance segmentation systems in an end-to-end
fashion using architectures that have been proposed for semantic segmentation.
Our approach proceeds by introducing a fixed number of labels (colors) and then
dynamically assigning object instances to those labels during training
(coloring). A standard semantic segmentation objective is then used to train a
network that can color previously unseen images. At test time, individual
object instances can be recovered from the output of the trained convolutional
network using simple connected component analysis. In the experimental
validation, the coloring approach is shown to be capable of solving diverse
instance segmentation tasks arising in autonomous driving (the Cityscapes
benchmark), plant phenotyping (the CVPPP leaf segmentation challenge), and
high-throughput microscopy image analysis.
  The source code is publicly available:
https://github.com/kulikovv/DeepColoring.


  This is an introduction to the topics of the title, from the 2017 Grenoble
Summer school on Arakelov geometry and arithmetic applications. We review
Arithmetic intersection numbers, explain the definition of the height of a
variety and its properties, both in the framework of classical Arakelov
geometry and of Zhang's adelic formalism. We then discuss arithmetic ampleness
and its application to the equidistribution theorem of Szpiro-Ullmo-Zhang-Yuan,
both at complex and nonarchimedean places. We conclude with Ullmo-Zhang's proof
of the Bogomolov conjecture over number fields.


  The rapid cycling synchrotron (RCS) of the China Spallation Neutron Source
(CSNS) is a high intensity proton ring with beam power of 100 kW. In order to
control the residual activation to meet the requirements of hands-on
maintenance, a two-stage collimation system has been designed for the RCS. The
collimation system consists of one primary collimator made of thin metal to
scatter the beam and four secondary collimators as absorbers. Thermal analysis
is an important aspect in evaluating the reliability of the collimation system.
The calculation of the temperature distribution and thermal stress of the
primary collimator with different materials is carried out by using ANSYS code.
In order to control the temperature rise and thermal stress of the primary
collimator to a reasonable level, an air cooling structure is intended to be
used. The mechanical design of the cooling structure is presented, and the
cooling effciency with different chin numbers and wind velocity is also
analyzed. Finally, the fatigue lifetime of the collimator under thermal shocks
is estimated.


  Fourier-based wavefront sensors, such as the Pyramid Wavefront Sensor (PWFS),
are the current preference for high contrast imaging due to their high
sensitivity. However, these wavefront sensors have intrinsic nonlinearities
that constrain the range where conventional linear reconstruction methods can
be used to accurately estimate the incoming wavefront aberrations. We propose
to use Convolutional Neural Networks (CNNs) for the nonlinear reconstruction of
the wavefront sensor measurements. It is demonstrated that a CNN can be used to
accurately reconstruct the nonlinearities in both simulations and a lab
implementation. We show that solely using a CNN for the reconstruction leads to
suboptimal closed loop performance under simulated atmospheric turbulence.
However, it is demonstrated that using a CNN to estimate the nonlinear error
term on top of a linear model results in an improved effective dynamic range of
a simulated adaptive optics system. The larger effective dynamic range results
in a higher Strehl ratio under conditions where the nonlinear error is
relevant. This will allow the current and future generation of large
astronomical telescopes to work in a wider range of atmospheric conditions and
therefore reduce costly downtime of such facilities.


  High-Level Synthesis (HLS) is emerging as a mainstream design methodology,
allowing software designers to enjoy the benefits of a hardware implementation.
Significant work has led to effective compilers that produce high-quality
hardware designs from software specifications. However, in order to fully
benefit from the promise of HLS, a complete ecosystem that provides the ability
to analyze, debug, and optimize designs is essential. This ecosystem has to be
accessible to software designers. This is challenging, since software
developers view their designs very differently than how they are physically
implemented on-chip. Rather than individual sequential lines of code, the
implementation consists of gates operating in parallel across multiple clock
cycles. In this paper, we report on our efforts to create an ecosystem that
allows software designers to debug HLS-generated circuits in a familiar manner.
We have implemented our ideas in a debug framework that will be included in the
next release of the popular LegUp high-level synthesis tool.


  We investigate low-energy excitations of the one-dimensional half-filled
SU(4) Hubbard model with an attractive on-site interaction U < 0 using the
density matrix renormalization group method as well as a perturbation theory.
We find that the ground state is a charge density wave state with a long range
order. The ground state is completely incompressible since all the excitations
are gapful. The charge gap which is the same as the four-particle excitation
gap is a non-monotonic function of U, while the spin gap and others increase
with increasing |U| and have linear asymptotic behaviors.


  In this paper, we consider risk-sensitive Markov Decision Processes (MDPs)
with Borel state and action spaces and unbounded cost under both finite and
infinite planning horizons. Our optimality criterion is based on the recursive
application of static risk measures. This is motivated by recursive utilities
in the economic literature, has been studied before for the entropic risk
measure and is extended here to an axiomatic characterization of suitable risk
measures. We derive a Bellman equation and prove the existence of Markovian
optimal policies. For an infinite planning horizon, the model is shown to be
contractive and the optimal policy to be stationary. Moreover, we establish a
connection to distributionally robust MDPs, which provides a global
interpretation of the recursively defined objective function. Monotone models
are studied in particular.


  In this paper, we consider the local unitary classification of the class of
qudit bipartite mixed states for which no information can be obtained locally.
These states are represented by symmetrical density matrices in which both
tracial states are maximally mixed. Interestingly, this symmetry facilitates
the local unitary classification of two-qubit states. However, the same
formalism fails in the case of systems of higher dimensions. We consider a
broader set of states by introducing a family of qudit bipartite mixed states
with maximally mixed marginals. For this family of states, we determine several
constants which are in variant under local unitary transformations so can be
used for entanglement classification. Finally, we consider the two-qutrit case
and in particular, a two-parameter family of states for which the local unitary
classification is complete. We relate this classification to known entanglement
measures such as purity and negativity.


  Privacy estimation techniques for differentially private (DP) algorithms are
useful for comparing against analytical bounds, or to empirically measure
privacy loss in settings where known analytical bounds are not tight. However,
existing privacy auditing techniques usually make strong assumptions on the
adversary (e.g., knowledge of intermediate model iterates or the training data
distribution), are tailored to specific tasks, model architectures, or DP
algorithm, and/or require retraining the model many times (typically on the
order of thousands). These shortcomings make deploying such techniques at scale
difficult in practice, especially in federated settings where model training
can take days or weeks. In this work, we present a novel "one-shot" approach
that can systematically address these challenges, allowing efficient auditing
or estimation of the privacy loss of a model during the same, single training
run used to fit model parameters, and without requiring any a priori knowledge
about the model architecture, task, or DP training algorithm. We show that our
method provides provably correct estimates for the privacy loss under the
Gaussian mechanism, and we demonstrate its performance on well-established FL
benchmark datasets under several adversarial threat models.


  We discuss consistent truncations of type IIB supergravity on resolved warped
deformed conifolds with fluxes. These actions represent the gravitational duals
to the baryonic branch deformation of the Klebanov-Strassler cascading gauge
theory. As an application, we demonstrate that the baryonic branch is lifted in
cascading gauge theory plasma.


  In this paper we propose and analyze a general arbitrarily high-order
modified trapezoidal rule for a class of weakly singular integrals of the forms
$I = \int_{\mathbb{R}^n}\phi(x)s(x)dx$ in $n$ dimensions, where $\phi\in
C_c^N(\mathbb{R}^n)$ for some sufficiently large $N$ and $s$ is the weakly
singular kernel. The admissible class of weakly singular kernel requires $s$
satisfies dilation and symmetry properties and is large enough to contain
functions of the form $\frac{P(x)}{|x|^r}$ where $r > 0$ and $P(x)$ is any
monomials such that $\text{deg} P < r < \text{deg} P + n$. The modified
trapezoidal rule is the singularity-punctured trapezoidal rule added by
correction terms involving the correction weights for grid points around
singularity. Correction weights are determined by enforcing the quadrature rule
exactly evaluates some monomials and solving corresponding linear systems. A
long-standing difficulty of these type of methods is establishing the
non-singularity of the linear system, despite strong numerical evidences. By
using an algebraic-combinatorial argument, we show the non-singularity always
holds and prove the general order of convergence of the modified quadrature
rule. We present numerical experiments to validate the order of convergence.


  We present improved visual and near-infrared empirical mass-luminosity
relations for very low mass stars (M$<$0.6~\Msol). These relations make use of
all stellar masses in this range known with better than 10% accuracy, most of
which are new determinations with 0.2 to 5% accuracy from our own programme,
presented in a companion paper.
  As predicted by stellar structure models, the metallicity dispersion of the
field populations induces a large scatter around the mean V band relation,
while the infrared relations are much tighter. The agreement of the observed
infrared mass-luminosity relations with the theoretical relations of Baraffe et
al. (1998) and Siess et al. (2000) is impressive, while we find an increasingly
significant discrepancy in the V band for decreasing masses. The theoretical
mass-luminosity relation which is insufficiently steep, and has introduced some
errors in the local stellar mass functions derived from V band luminosity
functions.


  The growth rate of material interfaces is an important proxy for mixing and
reaction rates in fluid dynamics, and can also be used to identify regions of
coherence. Estimating such growth rates can be difficult, since they depend on
detailed properties of the velocity field, such as its derivatives, that are
hard to measure directly. When an experiment gives only sparse trajectory data,
it is natural to encode planar trajectories as mathematical braids, which are
topological objects that contain information on the mixing characteristics of
the flow, in particular through their action on topological loops. We test such
braid methods on an experimental system, the rotor-oscillator flow, which is
well-described by a theoretical model. We conduct a series of laboratory
experiments to collect particle tracking and particle image velocimetry data,
and use the particle tracks to identify regions of coherence within the flow
that match the results obtained from the model velocity field. We then use the
data to estimate growth rates of material interface, using both the braid
approach and numerical simulations. The interface growth rates follow similar
qualitative trends in both the experiment and model, but have significant
quantitative differences, suggesting that the two are not as similar as first
seems. Our results shows that there are challenges in using the braid approach
to analyze data, in particular the need for long trajectories, but that these
are not insurmountable.


  People interacting with voice assistants are often frustrated by voice
assistants' frequent errors and inability to respond to backchannel cues. We
introduce an open-source video dataset of 21 participants' interactions with a
voice assistant, and explore the possibility of using this dataset to enable
automatic error recognition to inform self-repair. The dataset includes clipped
and labeled videos of participants' faces during free-form interactions with
the voice assistant from the smart speaker's perspective. To validate our
dataset, we emulated a machine learning classifier by asking crowdsourced
workers to recognize voice assistant errors from watching soundless video clips
of participants' reactions. We found trends suggesting it is possible to
determine the voice assistant's performance from a participant's facial
reaction alone. This work posits elicited datasets of interactive responses as
a key step towards improving error recognition for repair for voice assistants
in a wide variety of applications.


  We apply the method of spectral sequences to study classical problems in
analysis. We illustrate the method by finding polynomial vector fields that
commute with a given polynomial vector field and finding integrals of
polynomial Hamiltonian systems. For the later we describe the integrals for the
Henon-Heiles Hamiltonian which arises in celestial mechanics. The unifying
feature is that these problems seek elements in the kernel of a linear
operator. The spectral sequence approach emphasizes the obstructions
constructed from cokernel of the operator to finding elements in the kernel.


  The indirect RKKY interaction in a layered metal with nearly nested (almost
squared) Fermi surface is evaluated analytically. The final expressions are
obtained in closed form as a combination of Bessel functions. We show that the
expected ``2k_F'' oscillations occur as the far asymptote of our expressions,
where a value of the effective Fermi momentum k_F depends on the direction in
r-space. We demonstrate the existence of the intermediate asymptote of the
interaction which is of the sign-reversal antiferromagnetic type. This part of
RKKY interaction is the only term surviving in the limit of exact nesting. A
good accordance of our analytical formulas with numerical findings is found
until the interatomic distances.


  Neural network force field (NNFF) is a method for performing regression on
atomic structure-force relationships, bypassing expensive quantum mechanics
calculation which prevents the execution of long ab-initio quality molecular
dynamics simulations. However, most NNFF methods for complex multi-element
atomic systems indirectly predict atomic force vectors by exploiting just
atomic structure rotation-invariant features and the network-feature spatial
derivatives which are computationally expensive. We develop a staggered NNFF
architecture exploiting both rotation-invariant and covariant features
separately to directly predict atomic force vectors without using spatial
derivatives, thereby reducing expensive structural feature calculation by
~180-480x. This acceleration enables us to develop NNFF which directly predicts
atomic forces in complex ternary and quaternary-element extended systems
comprised of long polymer chains, amorphous oxide, and surface chemical
reactions. The staggered rotation-invariant-covariant architecture described
here can also directly predict complex covariant vector outputs from local
physical structures in domains beyond computational material science.


  Image semantic segmentation technology is one of the key technologies for
intelligent systems to understand natural scenes. As one of the important
research directions in the field of visual intelligence, this technology has
broad application scenarios in the fields of mobile robots, drones, smart
driving, and smart security. However, in the actual application of mobile
robots, problems such as inaccurate segmentation semantic label prediction and
loss of edge information of segmented objects and background may occur. This
paper proposes an improved structure of a semantic segmentation network based
on a deep learning network that combines self-attention neural network and
neural network architecture search methods. First, a neural network search
method NAS (Neural Architecture Search) is used to find a semantic segmentation
network with multiple resolution branches. In the search process, combine the
self-attention network structure module to adjust the searched neural network
structure, and then combine the semantic segmentation network searched by
different branches to form a fast semantic segmentation network structure, and
input the picture into the network structure to get the final forecast result.
The experimental results on the Cityscapes dataset show that the accuracy of
the algorithm is 69.8%, and the segmentation speed is 48/s. It achieves a good
balance between real-time and accuracy, can optimize edge segmentation, and has
a better performance in complex scenes. Good robustness is suitable for
practical application.


  We solve the Dirac equation, which describes charge massless chiral
relativistic carriers in a two-dimensional graphene. We have identified and
analysed a novel pseudospin-dependent scattering effect. We compute the
tunneling conductance and generalize the analytical result in the presence of
the tunable atomic potential of a graphene strip. The absence of back
scattering in graphene is shown to be due to Berry's phase which corresponds to
a sign change of the wave function under a spin rotation of a particle. We use
the transfer matrix approach and find that the electric conductance of doped
graphene increases due to atomic potential fluctuations.


  A phase-field crystal model based on the density-field approach incorporating
high-order interparticle direct correlations is developed to study
vapor-liquid-solid coexistence and transitions within a single continuum
description. Conditions for the realization of the phase coexistence and
transition sequence are systematically analyzed and shown to be satisfied by a
broad range of model parameters, demonstrating the high flexibility and
applicability of the model. Both temperature-density and temperature-pressure
phase diagrams are identified, while structural evolution and coexistence among
the three phases are examined through dynamical simulations. The model is also
able to produce some temperature and pressure related material properties,
including effects of thermal expansion and pressure on equilibrium lattice
spacing, and temperature dependence of saturation vapor pressure. This model
can be used as an effective approach for investigating a variety of material
growth and deposition processes based on vapor-solid, liquid-solid, and
vapor-liquid-solid growth.


  We investigate the emergent universe scenario in the presence of interacting
fluids. The non-linear equation of state (EoS) considered in the general theory
of relativity for obtaining emergent universe is effectively a cosmological
model with a composition of three fluids. In this paper we consider two models
to realize viable cosmological scenarios, {\it viz.}, (i) a two-fluid model
with interaction of a pressureless fluid with the fluid having the non-linear
EoS needed for the emergent universe, and (ii) a three-fluid model with
interaction among the three fluids which originate from the EoS of the emergent
universe. It is found that realistic cosmological models in accordance with
observations are not ruled out for both the above cases. We further show that
the generalized second law of thermodynamics is found to hold good in the
emergent universe with interacting fluids.


  Modifying the conventional spin-wave theory in a novel manner based on the
Wick decomposition, we present an elaborate thermodynamics of square-lattice
quantum antiferromagnets. Our scheme is no longer accompanied by the notorious
problem of an artificial transition to the paramagnetic state inherent in
modified spin waves in the Hartree-Fock approximation. In the cases of spin
$\frac{1}{2}$ and spin $1$, various modified-spin-wave findings for the
internal energy, specific heat, static uniform susceptibility, and dynamic
structure factor are not only numerically compared with quantum Monte Carlo
calculations and Lanczos exact diagonalizations but also analytically expanded
into low-temperature series. Modified spin waves interacting via the Wick
decomposition provide reliable thermodynamics over the whole temperature range
of absolute zero to infinity. Adding higher-order spin couplings such as ring
exchange interaction to the naivest Heisenberg Hamiltonian, we precisely
reproduce inelastic-neutron-scattering measurements of the
high-temperature-superconductor-parent antiferromagnet
$\mathrm{La}_2\mathrm{CuO}_4$. Modifying Dyson-Maleev bosons combined with
auxiliary pseudofermions also yields thermodynamics of square-lattice
antiferromagnets free from thermal breakdown, but it is less precise unless
temperature is sufficiently low. Applying all the schemes to layered
antiferromagnets as well, we discuss the advantages and disadvantages of
modified spin-wave and combined boson-pseudofermion representations.


  We study kth order systems of two rational difference equations
$$x_n=\frac{\alpha+\sum^{k}_{i=1}\beta_{i}x_{n-i} +
\sum^{k}_{i=1}\gamma_{i}y_{n-i}}{A+\sum^{k}_{j=1}B_{j}x_{n-j} +
\sum^{k}_{j=1}C_{j}y_{n-j}},\quad n\in\mathbb{N},$$
$$y_n=\frac{p+\sum^{k}_{i=1}\delta_{i}x_{n-i} +
\sum^{k}_{i=1}\epsilon_{i}y_{n-i}}{q+\sum^{k}_{j=1}D_{j}x_{n-j} +
\sum^{k}_{j=1}E_{j}y_{n-j}},\quad n\in\mathbb{N}.$$ In particular we assume
non-negative parameters and non-negative initial conditions. We develop several
approaches which allow us to extend well known boundedness results on the kth
order rational difference equation to the setting of systems in certain cases.


  In this paper we consider the classification of singularities (Du Val) and
real structures (Wall) of weak Del Pezzo surfaces from an algorithmic point of
view.
  It is well known that the singularities of weak Del Pezzo surfaces correspond
to root subsystems. We present an algorithm which computes the classification
of these root subsystems. We represent equivalence classes of root subsystems
by unique labels. These labels allow us to construct examples of weak Del Pezzo
surfaces with the corresponding singularity configuration.
  Equivalence classes of real structures of weak Del Pezzo surfaces are also
represented by root subsystems. We present an algorithm which computes the
classification of real structures. This leads to an alternative proof of the
known classification for Del Pezzo surfaces and extends this classification to
singular weak Del Pezzo surfaces.
  As an application we classify families of real conics on cyclides.


  Townsend's attached-eddy hypothesis (AEH) provides a theoretical description
of turbulence statistics in the logarithmic region in terms of coherent motions
that are self-similar with the wall-normal distance (y). Here, we show the
self-similar behaviour of turbulence motions contained within wall-attached
structures of streamwise velocity fluctuations using the direct numerical
simulation dataset of turbulent boundary layer, channel, and pipe flows
($Re_\tau \approx 1000$) The physical sizes of the identified structures are
geometrically self-similar in terms of height, and the associated turbulence
intensity follows the logarithmic variation in all three flows. Moreover, the
corresponding two-dimensional energy spectra are aligned along a linear
relationship between the streamwise and spanwise wavelengths ($\lambda_x$ and
$\lambda_z$, respectively) in the large-scale range ($12y < \lambda_x <$
3--4$\delta$), which is reminiscent of self-similarity. Consequently,
one-dimensional spectra obtained by integrating the two-dimensional spectra
over the self-similar range show some evidence for self-similar scaling
$\lambda_x \sim \lambda_z$ and the possible existence of $k_x^{-1}$ and
$k_z^{-1}$ scaling regions in a similar subrange. The present results reveal
that the asymptotic behaviours can be obtained by identifying the self-similar
coherent structures in canonical wall turbulence, albeit in low Reynolds number
flows.


  Assisted and autonomous driving are rapidly gaining momentum and will soon
become a reality. Artificial intelligence and machine learning are regarded as
key enablers thanks to the massive amount of data that smart vehicles will
collect from onboard sensors. Federated learning is one of the most promising
techniques for training global machine learning models while preserving data
privacy of vehicles and optimizing communications resource usage. In this
article, we propose vehicular radio environment map federated learning
(VREM-FL), a computation-scheduling co-design for vehicular federated learning
that combines mobility of vehicles with 5G radio environment maps. VREM-FL
jointly optimizes learning performance of the global model and wisely allocates
communication and computation resources. This is achieved by orchestrating
local computations at the vehicles in conjunction with transmission of their
local models in an adaptive and predictive fashion, by exploiting radio channel
maps. The proposed algorithm can be tuned to trade training time for radio
resource usage. Experimental results demonstrate that VREM-FL outperforms
literature benchmarks for both a linear regression model (learning time reduced
by 28%) and a deep neural network for semantic image segmentation (doubling the
number of model updates within the same time window).


  In this paper, we propose a parallel shooting algorithm for solving nonlinear
model predictive control problems using sequential quadratic programming. This
algorithm is built on a two-phase approach where we first test and assess
sequential convergence over many initial trajectories in parallel. However, if
none converge, the algorithm starts varying the Newton step size in parallel
instead. Through this parallel shooting approach, it is expected that the
number of iterations to converge to an optimal solution can be decreased.
Furthermore, the algorithm can be further expanded and accelerated by
implementing it on GPUs. We illustrate the effectiveness of the proposed
Parallel Shooting Sequential Quadratic Programming (PS-SQP) method in some
benchmark examples for nonlinear model predictive control. The developed PS-SQP
parallel solver converges faster on average and especially when significant
nonlinear behaviour is excited in the NMPC horizon.


  We report that a very high accuracy on the MNIST test set can be achieved by
using simple convolutional neural network (CNN) models. We use three different
models with 3x3, 5x5, and 7x7 kernel size in the convolution layers. Each model
consists of a set of convolution layers followed by a single fully connected
layer. Every convolution layer uses batch normalization and ReLU activation,
and pooling is not used. Rotation and translation is used to augment training
data, which is frequently used in most image classification tasks. A majority
voting using the three models independently trained on the training data set
can achieve up to 99.87% accuracy on the test set, which is one of the
state-of-the-art results. A two-layer ensemble, a heterogeneous ensemble of
three homogeneous ensemble networks, can achieve up to 99.91% test accuracy.
The results can be reproduced by using the code at:
https://github.com/ansh941/MnistSimpleCNN


  A new tool providing material contrast control in scanning electron
microscopy (SEM) is demonstrated. The approach is based on deep-UV illumination
during SEM imaging and delivers a novel material based contrast as well as
higher resolution due to the photoelectric effect. Electrons liberated from
illuminated sample surface contribute to the imaging which can be carried out
at a faster acquisition rate, provide material selective contrast, reduce
distortions caused by surface charging, and can substitute metal coating in
SEM. These features provide high fidelity SEM imaging and are expected to
significantly improve the performance of electron beam instruments as well as
to open new opportunities for imaging and characterization of materials at the
nanoscale.


  We determine cosmological and evolutionary parameters from the 3CR K-band
Hubble diagram and K-band number counts, assuming that the galaxies in question
undergo pure luminosity evolution. Separately the two data sets are highly
degenerate with respect to choice of cosmological and evolutionary parameters,
but in combination the degeneracy is resolved. Of models which are either flat
or have $\Omega_\Lambda=0$, the preferred ones are close to the canonical case
$\Omega_\CdM=1$, $\Omega_\Lambda=0$, with luminosity evolution amounting to one
magnitude brighter at z=1.


  To better understand the formation of large, low surface brightness galaxies,
we measure the correlation function between ultra-diffuse galaxy (UDG)
candidates and Milky Way analogs (MWAs). We find that (1) the projected radial
distribution of UDG satellites (projected surface density $\propto
r^{-0.84\pm0.06}$) is consistent with that of normal satellite galaxies, (2)
the number of UDG satellites per MWA ($S_{\rm UDG}$) is $\sim 0.5\pm0.1$ over
projected radii from 20 to 250 kpc and $-17< M_r < -13.5$, (3) $S_{\rm UDG}$ is
consistent with a linear extrapolation of the relationship between the number
of UDGs per halo vs. halo mass obtained over galaxy group and cluster scales,
(4) red UDG satellites dominate the population of UDG satellites ($\sim80$%),
(5) over the range of satellite magnitudes studied, UDG satellites comprise
$\sim$ 10% of the satellite galaxy population of MWAs, (6) a significant
fraction of these ($\sim$13%) have estimated total masses $>$ 10$^{10.9}$
M$_\odot$ or, equivalently, at least half the halo mass of the LMC, and
populate a large fraction ($\sim$ 18%) of the expected subhalos down to these
masses. All of these results suggest a close association between the overall
low mass galaxy population and UDGs, which we interpret as favoring models
where UDG formation principally occurs within the general context of low mass
galaxy formation over models invoking more exotic physical processes
specifically invoked to form UDGs.


  Observation of fast radio bursts (FRBs) are rising very quickly with the
advent of specialised instruments and surveys, and it has recently been shown
that some of them repeat quasi-periodically. In particular, evidence of a
$P=16.35$ day period has been reported for FRB 180916.J0158+65. We seek an
explanation within the frame of our orbiting asteroid model, whereby FRBs are
produced in the plasma wake of asteroids immersed in the wind of a pulsar or a
magnetar. We used the data reported by the CHIME/FRB collaboration in order to
infer the orbital characteristics of asteroid swarms, and performed parametric
studies to explore the possible characteristics of the pulsar, its wind, and of
the asteroids, under the constraint that the latter remain dynamically and
thermally stable. We found a plausible configuration in which a young pulsar is
orbited by a main $\sim 10^{-3}M_\odot$ companion with a period $3P = 49$d,
three times longer than the apparent periodicity $P$. Asteroids responsible for
FRBs are located in three dynamical swarms near the L3, L4 and L5 Lagrange
points, in a 2:3 orbital resonance akin to the Hildas class of asteroids in the
Solar system. In addition, asteroids could be present in the Trojan swarms at
the L4 and L5 Lagrange points. Together these swarms form a carousel that
explains the apparent $P$ periodicity and dispersion. We estimated that the
presence of at least a few thousand asteroids, of size $\sim20$km, is necessary
to produce the observed burst rate. We show how radius-to-frequency mapping in
the wind and small perturbations by turbulence can suffice to explain
downward-drifting sub-pulses, micro-structures, and narrow spectral occupancy.


  We discuss an idea whether spherical blast waves can amplify by a non-local
resonant hydrodynamic mechanism inhomogeneities formed by turbulence or phase
segregation in the interstellar medium. We consider the problem of a
blast-wave-turbulence interaction in the Linear Interaction Approximation.
Mathematically, this is an eigenvalue problem for finding the structure and
amplitude of eigenfunctions describing the response of the shock-wave flow to
forced oscillations by external perturbations in the ambient interstellar
medium. Linear analysis shows that the blast wave can amplify density and
vorticity perturbations for a wide range of length scales with amplification
coefficients of up to 20, with amplification the greater, the larger the
length. There also exist resonant harmonics for which the gain becomes formally
infinite in the linear approximation. Their orbital wavenumbers are within the
range of macro- ($l \sim 1$), meso- ($l \sim 20$) and microscopic ($l > 200$)
scales. Since the resonance width is narrow: typically, $\Delta l <1$,
resonance should select and amplify discrete isolated harmonics. We speculate
as to a possible explanation of an observed regular filamentary structure of
regular-shaped round supernova remnants such as SNR 1572, 1006 or 0509-67.5.
Resonant mesoscales found ($l \approx 18 $) are surprisingly close to the
observed scales ($l \approx 15$) of ripples in the shell's surface of SNR
0509-67.5.


  We present a range-based solution for indoor relative localization by Micro
Air Vehicles (MAVs), achieving sufficient accuracy for leader-follower flight.
Moving forward from previous work, we removed the dependency on a common
heading measurement by the MAVs, making the relative localization accuracy
independent of magnetometer readings. We found that this restricts the relative
maneuvers that guarantee observability, and also that higher accuracy range
measurements are required to rectify the missing heading information, yet both
disadvantages can be tackled. Our implementation uses Ultra Wide Band, for both
range measurements between MAVs and sharing their velocities, accelerations,
yaw rates, and height with each other. We used this on real MAVs and performed
leader-follower flight in an indoor environment. The follower MAVs could follow
the leader MAV in close proximity for the entire durations of the flights. The
followers were autonomous and used only on-board sensors to track and follow
the leader.


  Stochastic gradient descent is one of the most successful approaches for
solving large-scale problems, especially in machine learning and statistics. At
each iteration, it employs an unbiased estimator of the full gradient computed
from one single randomly selected data point. Hence, it scales well with
problem size and is very attractive for truly massive dataset, and holds
significant potentials for solving large-scale inverse problems. In the recent
literature of machine learning, it was empirically observed that when equipped
with early stopping, it has regularizing property. In this work, we rigorously
establish its regularizing property (under \textit{a priori} early stopping
rule), and also prove convergence rates under the canonical sourcewise
condition, for minimizing the quadratic functional for linear inverse problems.
This is achieved by combining tools from classical regularization theory and
stochastic analysis. Further, we analyze the preasymptotic weak and strong
convergence behavior of the algorithm. The theoretical findings shed insights
into the performance of the algorithm, and are complemented with illustrative
numerical experiments.


  As attitude and motion sensing components, inertial sensors are widely used
in various portable devices. But the severe errors of inertial sensors restrain
their function, especially the trajectory recovery and semantic recognition. As
a mainstream signal processing method, wavelet is hailed as the mathematical
microscope of signal due to the plentiful and diverse wavelet basis functions.
However, complicated noise types and application scenarios of inertial sensors
make selecting wavelet basis perplexing. To this end, we propose a wavelet
dynamic selection network (WDSNet), which intelligently selects the appropriate
wavelet basis for variable inertial signals. In addition, existing deep
learning architectures excel at extracting features from input data but neglect
to learn the characteristics of target categories, which is essential to
enhance the category awareness capability, thereby improving the selection of
wavelet basis. Therefore, we propose a category representation mechanism (CRM),
which enables the network to extract and represent category features without
increasing trainable parameters. Furthermore, CRM transforms the common fully
connected network into category representations, which provide closer
supervision to the feature extractor than the far and trivial one-hot
classification labels. We call this process of imposing interpretability on a
network and using it to supervise the feature extractor the feature supervision
mechanism, and its effectiveness is demonstrated experimentally and
theoretically in this paper. The enhanced inertial signal can perform
impracticable tasks with regard to the original signal, such as trajectory
reconstruction. Both quantitative and visual results show that WDSNet
outperforms the existing methods. Remarkably, WDSNet, as a weakly-supervised
method, achieves the state-of-the-art performance of all the compared
fully-supervised methods.


  We investigated the application of haptic feedback control and deep
reinforcement learning (DRL) to robot-assisted dressing. Our method uses DRL to
simultaneously train human and robot control policies as separate neural
networks using physics simulations. In addition, we modeled variations in human
impairments relevant to dressing, including unilateral muscle weakness,
involuntary arm motion, and limited range of motion. Our approach resulted in
control policies that successfully collaborate in a variety of simulated
dressing tasks involving a hospital gown and a T-shirt. In addition, our
approach resulted in policies trained in simulation that enabled a real PR2
robot to dress the arm of a humanoid robot with a hospital gown. We found that
training policies for specific impairments dramatically improved performance;
that controller execution speed could be scaled after training to reduce the
robot's speed without steep reductions in performance; that curriculum learning
could be used to lower applied forces; and that multi-modal sensing, including
a simulated capacitive sensor, improved performance.


  Geometric quantum computation offers a practical strategy toward robust
quantum computation due to its inherently error tolerance. However, the
rigorous geometric conditions lead to complex and/or error-disturbed quantum
controls, especially for logical qubits that involve more physical qubits,
whose error tolerance is effective in principle though, their experimental
demonstration is still demanding. Thus, how to best simplify the needed control
and manifest its full advantage has become the key to widespread applications
of geometric quantum computation. Here we propose a new fast and robust
geometric scheme, with the decoherence-free-subspace encoding, and present its
physical implementation on superconducting quantum circuits, where we only
utilize the experimentally demonstrated parametrically tunable coupling to
achieve high-fidelity geometric control over logical qubits. Numerical
simulation verifies that it can efficiently combine the error tolerance from
both the geometric phase and logical-qubit encoding, displaying our
gate-performance superiority over the conventional dynamical one without
encoding, in terms of both gate fidelity and robustness. Therefore, our scheme
can consolidate both error suppression methods for logical-qubit control, which
sheds light on the future large-scale quantum computation.


  While the vast majority of known physical realizations of the
Tomonaga-Luttinger liquid (TLL) have repulsive interactions defined with the
dimensionless interaction parameter $K_{\rm c}<1$, we here report that
Rb$_2$Mo$_3$As$_3$ is in the opposite TLL regime of attractive interactions.
This is concluded from a TLL-characteristic power-law temperature dependence of
the $^{87}$Rb spin-lattice relaxation rates over broad temperature range
yielding the TLL interaction parameter for charge collective modes $K_{\rm
c}=1.4$. The TLL of the one-dimensional band can be traced almost down to
$T_{\rm c} = 10.4 $~K, where the bulk superconducting state is stabilized by
the presence of a three-dimensional band and characterized by the $^{87}$Rb
temperature independent Knight shift and the absence of Hebel-Slichter
coherence peak in the relaxation rates. The small superconducting gap measured
in high magnetic fields reflects either the importance of the vortex core
relaxation or the uniqueness of the superconducting state stemming from the
attractive interactions defining the precursor TLL.


  We consider a certain extension of the Einstein's elevator thought experiment
by assuming that the elevator is charged and falls into an electromagnetic
field. We argue, on grounds of the Equivalence Principle, that an
observer-dependent metric should exist, for which the geodesics coincide with
the trajectories of the charged body in the electromagnetic field. We give a
solution to this problem by finding such a metric and hence we are able to
recover the Lorentz-force law. Recovery of the leading terms of the radiation
reaction force is also possible with this approach.


  We present the results of our computation of the subregion complexity and
also compare it with the entanglement entropy of a $2+1$--dimensional
holographic superconductor which has a fully backreacted gravity dual with a
stable ground sate. We follow the "complexity equals volume" or the CV
conjecture. We find that there is only a single divergence for a strip
entangling surface and the complexity grows linearly with the large strip
width. During the normal phase the complexity increases with decreasing
temperature, but during the superconducting phase it behaves differently
depending on the order of phase transition. We also show that the universal
term is finite and the phase transition occurs at the same critical temperature
as obtained previously from the free energy computation of the system. In one
case, we observe multivaluedness in the complexity in the form of an "S" curve.


  Recently the paper "Schwinger Mechanism for Gluon Pair Production in the
Presence of Arbitrary Time Dependent Chromo-Electric Field" by G. C. Nayak was
published [Eur. Phys. J. C 59, 715 (2009); arXiv:0708.2430]. Its aim is to
obtain an exact expression for the probability of non-perturbative gluon pair
production per unit time per unit volume and per unit transverse momentum in an
arbitrary time-dependent chromo-electric background field. We believe that the
obtained expression is open to question. We demonstrate its inconsistency on
some well-known examples. We think that this is a consequence of using the
so-called "shift theorem" [arXiv:hep-th/0609192] in deriving the expression for
the probability. We make some critical comments on the theorem and its
applicability to the problem in question.


This paper has been superseded by quant-ph/0101003.


  In this paper, we develop a method to obtain the algebraic classification of
noncommutative Jordan algebras from the classification of Jordan algebras of
the same dimension. We use this method to obtain the algebraic classification
of complex $3$-dimensional noncommutative Jordan algebras. As a byproduct, we
obtain the classification of complex $3$-dimensional Kokoris, standard, generic
Poisson, and generic Poisson--Jordan algebras; and also complex $4$-dimensional
nilpotent Kokoris and standard algebras. In addition, we consider the geometric
classification of varieties of cited algebras, that is the description of its
irreducible components.


  The Crank-Nicolson (short for C-N) scheme for solving {\it backward
stochastic differential equation} (BSDE), driven by Brownian motions, was first
developed by the authors W. Zhao, L. Chen and S. Peng [SIAM J. Sci. Comput., 28
(2006), 1563--1581], and numerical experiments showed that the accuracy of this
C-N scheme was of second order for solving BSDE. This C-N scheme was extended
to solve decoupled {\it forward-backward stochastic differential equations}
(FBSDEs) by W. Zhao, Y. Li and Y. Fu [Sci. China. Math., 57 (2014), 665--686],
and it was numerically shown that the accuracy of the extended C-N scheme was
also of second order.
  To our best knowledge, among all one-step (two-time level) numerical schemes
with second-order accuracy for solving BSDE or FBSDEs, such as the ones in the
above two papers and the one developed by the authors D. Crisan and K.
Manolarakis [Ann. Appl. Probab., 24, 2 (2014), 652--678], the C-N scheme is the
simplest one in applications. The theoretical proofs of second-order error
estimates reported in the literature for these schemes for solving decoupled
FBSDEs did not include the C-N scheme.
  The purpose of this work is to theoretically analyze the error estimate of
the C-N scheme for solving decoupled FBSDEs. Based on the Taylor and
It\^o-Taylor expansions, the Malliavin calculus theory (e.g., the multiple
Malliavin integration-by-parts formula), and our new truncation error
cancelation techniques, we rigorously prove that the strong convergence rate of
the C-N scheme is of second order for solving decoupled FBSDEs, which fills the
gap between the second-order numerical and theoretical analysis of the C-N
scheme.


  Quantum walk is one of the main tools for quantum algorithms. Defined by
analogy to classical random walk, a quantum walk is a time-homogeneous quantum
process on a graph. Both random and quantum walks can be defined either in
continuous or discrete time. But whereas a continuous-time random walk can be
obtained as the limit of a sequence of discrete-time random walks, the two
types of quantum walk appear fundamentally different, owing to the need for
extra degrees of freedom in the discrete-time case.
  In this article, I describe a precise correspondence between continuous- and
discrete-time quantum walks on arbitrary graphs. Using this correspondence, I
show that continuous-time quantum walk can be obtained as an appropriate limit
of discrete-time quantum walks. The correspondence also leads to a new
technique for simulating Hamiltonian dynamics, giving efficient simulations
even in cases where the Hamiltonian is not sparse. The complexity of the
simulation is linear in the total evolution time, an improvement over
simulations based on high-order approximations of the Lie product formula. As
applications, I describe a continuous-time quantum walk algorithm for element
distinctness and show how to optimally simulate continuous-time query
algorithms of a certain form in the conventional quantum query model. Finally,
I discuss limitations of the method for simulating Hamiltonians with negative
matrix elements, and present two problems that motivate attempting to
circumvent these limitations.


  {{In this paper}}, we are concerned with a one-parameter family of peakon
equations with cubic nonlinearity parametrized by a parameter usually denoted
by the letter $b$. This family is called the ``$b$-Novikov'' since it reduces
to the integrable Novikov equation in the case $b=3$. By extending the
corresponding linearized operator defined on functions in $H^1(\mathbb{R})$ to
one defined on weaker functions on $L^2(\mathbb{R})$, we prove spectral and
linear instability on $\LT$ of peakons in the $b$-Novikov equations for any
$b$. We also consider the stability on $\HT$ and show that the peakons are
spectrally or linearly stable only in the case $b=3$.


  We propose self-supervised deep algorithms to detect anomalies in
heterogeneous autonomous systems using frontal camera video and IMU readings.
Given that the video and IMU data are not synchronized, each of them are
analyzed separately. The vision-based system, which utilizes a conditional GAN,
analyzes immediate-past three frames and attempts to predict the next frame.
The frame is classified as either an anomalous case or a normal case based on
the degree of difference estimated using the prediction error and a threshold.
The IMU-based system utilizes two approaches to classify the timestamps; the
first being an LSTM autoencoder which reconstructs three consecutive IMU
vectors and the second being an LSTM forecaster which is utilized to predict
the next vector using the previous three IMU vectors. Based on the
reconstruction error, the prediction error, and a threshold, the timestamp is
classified as either an anomalous case or a normal case. The composition of
algorithms won runners up at the IEEE Signal Processing Cup anomaly detection
challenge 2020. In the competition dataset of camera frames consisting of both
normal and anomalous cases, we achieve a test accuracy of 94% and an F1-score
of 0.95. Furthermore, we achieve an accuracy of 100% on a test set containing
normal IMU data, and an F1-score of 0.98 on the test set of abnormal IMU data.


  Some new connections are given between linear orderings and triangular
operator algebras. A lexicograhic product is defined for triangular operator
algebras and the Jacobson radical of an infinite lexicographic product of upper
triangular matrix algebras is determined.


  Gold is inert and forms very few compounds. One of the most interesting of
those is calaverite AuTe2, which has incommensurate structure and which becomes
superconducting when doped or under pressure. There exist a "sibling" of AuTe2
the mineral sylvanite AuAgTe4, which properties are almost unknown. In
sylvanite Au and Ag ions are ordered in stripes, and Te6 octahedra around
metals are distorted in such a way that Ag becomes linearly coordinated, what
is typical for Ag^{1+}, whereas Au is square coordinated - it is typical for
d^8 configurations, i.e. one can assign to Au the valence 3+. Our theoretical
study shows that at pressure P_C ~ 5 GPa there should occur in it a structural
transition such that above this critical pressure Te6 octahedra around Au and
Ag become regular and practically identical. Simultaneously Te-Te dimers,
existing at P = 0 GPa, disappear, and material from a bad metal becomes a usual
metal with predominantly Te 5p states at the Fermi energy. We expect that,
similar to AuTe2, AuAgTe4 should become superconducting above P_C.


  Existing parking recommendation solutions mainly focus on finding and
suggesting parking spaces based on the unoccupied options only. However, there
are other factors associated with parking spaces that can influence someone's
choice of parking such as fare, parking rule, walking distance to destination,
travel time, likelihood to be unoccupied at a given time. More importantly,
these factors may change over time and conflict with each other which makes the
recommendations produced by current parking recommender systems ineffective. In
this paper, we propose a novel problem called multi-objective parking
recommendation. We present a solution by designing a multi-objective parking
recommendation engine called MoParkeR that considers various conflicting
factors together. Specifically, we utilise a non-dominated sorting technique to
calculate a set of Pareto-optimal solutions, consisting of recommended
trade-off parking spots. We conduct extensive experiments using two real-world
datasets to show the applicability of our multi-objective recommendation
methodology.


  The Citizens Broadband Radio Service (CBRS) recently adopted in the U.S.
enables two tiers of commercial users to share spectrum with a third tier of
incumbent users. This sharing can be further assisted by Environmental Sensing
Capability operators (ESCs), that monitor the spectrum occupancy to determine
when use of the spectrum will not harm incumbents. Two key aspects of this
framework that impact how firms may compete are the differences in information
provided by different ESCs and the different tiers in which a firm may access
the spectrum. We develop a game theoretic model that captures both of these
features and analyze it to gain insight into their impact. Specifically, we
consider a priority access (PA) tier firm has access to the both licensed band
and unlicensed band, and a general authorized access (GAA) tier firm has access
only to the unlicensed band. The PA tier and GAA tier firms compete for users.
Our analysis reveals that the amount of unlicensed and licensed bandwidth in
the CBRS must be chosen judiciously in order to maximize the social welfare. We
also show that a limited amount of unlicensed access of PA tier firm is
beneficial to the user's surplus as well as to the social welfare.


  A current trend in networking and cloud computing is to provide compute
resources at widely dispersed places; this is exemplified by developments such
as Network Function Virtualisation. This paves the way for wide-area service
deployments with improved service quality: e.g, a nearby server can reduce the
user-perceived response times. But always using the nearest server can be a bad
decision if that server is already highly utilised. This paper formalises the
two related problems of allocating resources at different locations and
assigning users to them with the goal of minimising the response times for a
given number of resources to use -- a non-linear capacitated facility location
problem with integrated queuing systems. To efficiently handle the
non-linearity, we introduce five linear problem approximations and adapt the
currently best heuristic for a similar problem to our scenario. All six
approaches are compared in experiments for solution quality and solving time.
Surprisingly, our best optimisation formulation outperforms the heuristic in
both time and quality. Additionally, we evaluate the influence ot resource
distributions in the network on the response time: Cut by half for some
configurations. The presented formulations are applicable to a broader
optimisation domain.


  The family of monolayer two-dimensional (2D) materials hosts a wide range of
interesting phenomena, including superconductivity, charge density waves,
topological states and ferromagnetism, but direct evidence for
antiferromagnetism in the monolayer has been lacking. Nevertheless,
antiferromagnets have attracted enormous interest recently in spintronics due
to the absence of stray fields and their terahertz resonant frequency. Despite
the great advantages of antiferromagnetic spintronics, controlling and
detecting N\'eel vectors have been limited in bulk materials. In this work, we
developed a sensitive second harmonic generation (SHG) microscope and detected
long-range N\'eel antiferromagnetic (AFM) order and N\'eel vector switching
down to the monolayer in MnPSe$_3$. Temperature-dependent SHG measurement in
repetitive thermal cooling surprisingly collapses into two curves, which
correspond to the switching of an Ising type N\'eel vector reversed by the
time-reversal operation, instead of a six-state clock ground state expected
from the threefold rotation symmetry in the structure. We imaged the spatial
distribution of the N\'eel vectors across samples and rotated them by an
arbitrary angle irrespective of the lattice in the sample plane by applying
strain. By studying both a Landau theory and a microscopic model that couples
strain to nearest-neighbor exchange, we conclude that the phase transition of
the XY model in the presence of strain falls into the Ising universality class
instead of the XY one, which could explain the extreme strain tunability.
Finally, we found that the 180{\deg} AFM domain walls are highly mobile down to
the monolayer after thermal cycles, paving the way for future control of the
antiferromagnetic domains by strain or external fields on demand for
ultra-compact 2D AFM terahertz spintronics.


  The iterative decoding threshold of low-density parity-check (LDPC) codes
over the binary erasure channel (BEC) fulfills an upper bound depending only on
the variable and check nodes with minimum distance 2. This bound is a
consequence of the stability condition, and is here referred to as stability
bound. In this paper, a stability bound over the BEC is developed for
doubly-generalized LDPC codes, where the variable and the check nodes can be
generic linear block codes, assuming maximum a posteriori erasure correction at
each node. It is proved that in this generalized context as well the bound
depends only on the variable and check component codes with minimum distance 2.
A condition is also developed, namely the derivative matching condition, under
which the bound is achieved with equality.


  A version of the iterated B\"acklund-Darboux transformation, where Darboux
matrix takes a form of the transfer matrix function from the system theory, is
constructed for the discrete canonical system and Non-Abelian Toda lattice.
Results on the transformations of the Weyl functions, insertion of the
eigenvalues, and construction of the bound states are obtained. A wide class of
the explicit solutions is given. An application to the semi-infinite block
Jacobi matrices is treated.


  The Klein-Gordon equation, the Maxwell equation, and the Dirac equation are
presented as partial difference equations in the eight-dimensional covariant
discrete phase space. These equations are also furnished as
difference-differential equations in the arena of discrete phase space and
continuous time. The scalar field and electromagnetic fields are quantized with
commutation relations. The spin-1/2 field is quantized with anti-commutation
relations. Moreover, the total momentum, energy and charge of these free
relativisitic quantized fields in the discrete phase space and continuous time
are computed exactly. The results agree completely with those computed from the
relativisitic fields defned on the space-time continuum.


  Let X be a finite CW-complex of dimension q. If its fundamental group
$\pi_{1}(X)$ is polycyclic of Hirsch number h>q we show that at least one of
the homotopy groups $\pi_{i}(X)$ is not finitely generated. If h=q or h=q-1 the
same conclusion holds unless X is an Eilenberg-McLane space $K(\pi_{1}(X),1)$.


  We study the representation theory of the nested instantons quiver presented
in [1], which describes a particular class of surface defects in
four-dimensional supersymmetric gauge theories. We show that the moduli space
of its stable representations provides an ADHM-like construction for nested
Hilbert schemes of points on $\mathbb C^2$, for rank one, and for the moduli
space of flags of framed torsion-free sheaves on $\mathbb P^2$, for higher
rank. We introduce a natural torus action on this moduli space and use
equivariant localization to compute some of its (virtual) topological
invariants, including the case of compact toric surfaces. We conjecture that
the generating function of holomorphic Euler characteristics for rank one is
given in terms of polynomials in the equivariant weights, which, for specific
numerical types, coincide with (modified) Macdonald polynomials.


  Let $(N,g)$ be a Riemannian manifold with the distance function $d(x,y)$ and
an open subset $M\subset N$. For $x\in M$ we denote by $D_x$ the distance
difference function $D_x:F\times F\to \mathbb R$, given by
$D_x(z_1,z_2)=d(x,z_1)-d(x,z_2)$, $z_1,z_2\in F=N\setminus M$. We consider the
inverse problem of determining the topological and the differentiable structure
of the manifold $M$ and the metric $g|_M$ on it when we are given the distance
difference data, that is, the set $F$, the metric $g|_F$, and the collection
$\mathcal D(M)=\{D_x;\ x\in M\}$. Moreover, we consider the embedded image
$\mathcal D(M)$ of the manifold $M$, in the vector space $C(F\times F)$, as a
representation of manifold $M$. The inverse problem of determining $(M,g)$ from
$\mathcal D(M)$ arises e.g. in the study of the wave equation on $\mathbb
R\times N$ when we observe in $F$ the waves produced by spontaneous point
sources at unknown points $(t,x)\in \mathbb R\times M$. Then $D_x(z_1,z_2)$ is
the difference of the times when one observes at points $z_1$ and $z_2$ the
wave produced by a point source at $x$ that goes off at an unknown time. The
problem has applications in hybrid inverse problems and in geophysical imaging.


  This report, based on the Dark Sectors workshop at SLAC in April 2016,
summarizes the scientific importance of searches for dark sector dark matter
and forces at masses beneath the weak-scale, the status of this broad
international field, the important milestones motivating future exploration,
and promising experimental opportunities to reach these milestones over the
next 5-10 years.


  The feedback of consumers who pass by an advertisement board is crucial for
the marketing teams of corporate companies .If the emotions of a consumer are
analyzed after exposure to the advertisement, it would help to rate the quality
of the advertisement .The state of the art emotion analyzers can do this task
seamlessly .However, if the consumer moves away from the center of the
advertisement board, it becomes difficult for the camera to capture the person
with sufficient details .Here, the role of an auto-pan and tilt camera is
imminent if a person moves out from the frame limits of the camera .This paper
aims to help solve the above issue by panning and tilting the camera by precise
amount automatically using facial detection and interpolation algorithms .We
propose a method where a camera attached to servo motors can automatically pan
and tilt such that the subject is always in the center of the frame .This would
be done by facial detection and interpolation of its position with respect to
the angle of the camera .The direction of the camera is controlled with the
help of a microcontroller, which takes-in the angle values of where the camera
needs to move in order to maintain the subject's face in the center .We have
designed a system that works on the Arduino platform and can pan and tilt the
camera in real-time.


  Multi-mode fibers provide an increased amount of data transfer rates given a
large number of transmission modes. Unfortunately, the increased number of
modes in a multi-mode fiber hinders the accurate transfer of information due to
interference of these modes which results in a random speckle pattern. The
complexity of the system impedes the analytical expression of the system
thereby the information is lost. However, deep learning algorithms can be used
to recover the information efficiently. In this study, we utilize deep learning
architecture to reconstruct input colored images from the output speckle
patterns at telecommunication wavelength (C-band). Our model successfully
identifies hyper-spectral speckle patterns at twenty-six separate wavelengths
and twenty-six distinct letters. Remarkably, we can reconstruct the complete
input images only by analyzing a small portion of the output speckle pattern.
Thereby, we manage to decrease the computational load without sacrificing the
accuracy of the classification. We believe that this study will show a
transformative impact in many fields: biomedical imaging, communication,
sensing, and photonic computing.


  Nowadays, most online services offer different authentication methods that
users can set up for multi-factor authentication but also as a recovery method.
This configuration must be done thoroughly to prevent an adversary's access
while ensuring the legitimate user does not lose access to their account. This
is particularly important for fundamental everyday services, where either
failure would have severe consequences. Nevertheless, little research has been
done on the authentication of actual users regarding security and the risk of
being locked out of their accounts.
  To foster research in this direction, this paper presents a study on the
account settings of Google and Apple users. Considering the multi-factor
authentication configuration and recovery options, we analyzed the account
security and lock-out risks. Our results provide insights into the usage of
multi-factor authentication in practice, show significant security differences
between Google and Apple accounts, and reveal that many users would miss access
to their accounts when losing a single authentication device.


  The long standing problem of non perturbative renormalization of a gauge
field theoretical Hamiltonian is addressed and explicitly carried out within an
(effective) light-cone Hamiltonian approach to QCD. The procedure is in line
with the conventional ideas: The Hamiltonian is first regulated by suitable
cut-off functions, and subsequently renormalized by suitable counter terms to
make it cut-off independent. The formalism is applied to physical mesons with a
different flavor of quark and anti-quark. The excitation spectrum of the
$\rho$-meson with its excellent agreement between theory and experiment is
discussed as a pedagogical example.


  A basic component in Internet applications is the electronic mail and its
various implications. The paper proposes a mechanism for automatically
classifying emails and create dynamic groups that belong to these messages.
Proposed mechanisms will be based on natural language processing techniques and
will be designed to facilitate human-machine interaction in this direction.


  We investigate the decomposability of nonnegative compact r-potent operators
on a separable Hilbert space L2(X). We provide a constructive algorithm to
prove that basis functions of range spaces of nonnegative r-potent operators
can be chosen to be all nonnegative and mutually orthogonal. We use this
orthogonality to establish that nonnegative compact r-potent operators with
range spaces of dimension strictly greater than r-1 are decomposable.


  Let $\mathcal{H}$ be a set of graphs. The planar Tur\'an number,
$ex_\mathcal{P}(n,\mathcal{H})$, is the maximum number of edges in an
$n$-vertex planar graph which does not contain any member of $\mathcal{H}$ as a
subgraph. When $\mathcal{H}=\{H\}$ has only one element, we usually write
$ex_\mathcal{P}(n,H)$ instead. The topic of extremal planar graphs was
initiated by Dowden (2016). He obtained sharp upper bound for both
$ex_\mathcal{P}(n,C_5)$ and $ex_\mathcal{P}(n,K_4)$. Later on, we obtained
sharper bound for $ex_\mathcal{P}(n,\{K_4,C_7\})$. In this paper, we give upper
bounds of $ex_\mathcal{P}(n,\{K_4,C_5\})\leq {15\over 7}(n-2)$ and
$ex_\mathcal{P}(n,\{K_4,C_6\})\leq {7\over 3}(n-2)$. We also give constructions
which show the bounds are tight for infinitely many graphs.


  Learning representation on graph plays a crucial role in numerous tasks of
pattern recognition. Different from grid-shaped images/videos, on which local
convolution kernels can be lattices, however, graphs are fully coordinate-free
on vertices and edges. In this work, we propose a Gaussian-induced convolution
(GIC) framework to conduct local convolution filtering on irregular graphs.
Specifically, an edge-induced Gaussian mixture model is designed to encode
variations of subgraph region by integrating edge information into weighted
Gaussian models, each of which implicitly characterizes one component of
subgraph variations. In order to coarsen a graph, we derive a vertex-induced
Gaussian mixture model to cluster vertices dynamically according to the
connection of edges, which is approximately equivalent to the weighted graph
cut. We conduct our multi-layer graph convolution network on several public
datasets of graph classification. The extensive experiments demonstrate that
our GIC is effective and can achieve the state-of-the-art results.


  We consider the time evolution of local observables after an interaction
quench in the repulsive Lieb-Liniger model. The system is initialized in the
ground state for vanishing interaction and then time-evolved with the
Lieb-Liniger Hamiltonian for large, finite interacting strength $c$. We employ
the Quench Action approach to express the full time evolution of local
observables in terms of sums over energy eigenstates and then derive the
leading terms of a $1/c$ expansion for several one and two-point functions as a
function of time $t > 0$ after the quantum quench. We observe delicate
cancellations of contributions to the spectral sums that depend on the details
of the choice of representative state in the Quench Action approach and our
final results are independent of this choice. Our results provide a highly
non-trivial confirmation of the typicality assumptions underlying the Quench
Action approach.


  We give a general setting for Cram\'er's large deviations theorem for the
empirical means of a field of random vectors, which contains Cram\'er's theorem
for i.i.d. random vectors and Sanov's theorem for asymptotically decoupled
measures.
  -----
  Nous \'etablissons un cadre g\'en\'eral pour le th\'eor\`eme de Cram\'er sur
les grandes d\'eviations des moyennes empiriques d'un champ de vecteurs
al\'eatoires, cadre qui contient le th\'eor\`eme de Cram\'er pour des vecteurs
al\'eatoires i.i.d. et le th\'eor\`eme de Sanov pour les mesures
asymptotiquement d\'ecoupl\'ees.


  We construct motivic versions of the classical tubular neighborhood and the
punctured tubular neighborhood, and give applications to the construction of
tangential base-points for mixed Tate motives, algebraic gluing of curves with
boundary components, and limit motives.


  Xavier and Yushi run a ``random race'' as follows. A continuous probability
distribution $\mu$ on the real line is chosen. The runners begin at zero. At
time $i$ Xavier draws $\mathbf{X}_i$ from $\mu$ and advances that distance,
while Yushi advances by an independent drawing $\mathbf{Y}_i$. After $n$ such
moves, Xavier wins a valuable prize provided he not only wins the race but
leads after every step; that is, $\sum_{i=1}^k \mathbf{X}_i > \sum_{i=1}^k
\mathbf{Y}_i$ for all $k = 1,2, \dots, n$. What distribution is best for
Xavier, and what then is his probability of getting the prize?


  Current dialogue systems focus more on textual and speech context knowledge
and are usually based on two speakers. Some recent work has investigated static
image-based dialogue. However, several real-world human interactions also
involve dynamic visual context (similar to videos) as well as dialogue
exchanges among multiple speakers. To move closer towards such multimodal
conversational skills and visually-situated applications, we introduce a new
video-context, many-speaker dialogue dataset based on live-broadcast soccer
game videos and chats from Twitch.tv. This challenging testbed allows us to
develop visually-grounded dialogue models that should generate relevant
temporal and spatial event language from the live video, while also being
relevant to the chat history. For strong baselines, we also present several
discriminative and generative models, e.g., based on tridirectional attention
flow (TriDAF). We evaluate these models via retrieval ranking-recall, automatic
phrase-matching metrics, as well as human evaluation studies. We also present
dataset analyses, model ablations, and visualizations to understand the
contribution of different modalities and model components.


  Szegedy's quantum walk is an algorithm for quantizing a general Markov chain.
It has plenty of applications such as many variants of optimizations. In order
to check its properties in an error-free environment, it is important to have a
classical simulator. However, the current simulation algorithms require a great
deal of memory due to the particular formulation of this quantum walk. In this
paper we propose a memory-saving algorithm that scales as $\mathcal{O}(N^2)$
with the size $N$ of the graph. We provide additional procedures for simulating
Szegedy's quantum walk over mixed states and also the Semiclassical Szegedy
walk. With these techniques we have built a classical simulator in Python
called SQUWALS. We show that our simulator scales as $\mathcal{O}(N^2)$ in both
time and memory resources. This package provides some high-level applications
for algorithms based on Szegedy's quantum walk, as for example the quantum
PageRank.


  In this paper, we analyze the outage performance of two multicarrier relay
selection schemes, i.e. bulk and per-subcarrier selections, for two-hop
orthogonal frequency-division multiplexing (OFDM) systems. To provide a
comprehensive analysis, three forwarding protocols: decode-and-forward (DF),
fixed-gain (FG) amplify-and-forward (AF) and variable-gain (VG) AF relay
systems are considered. We obtain closed-form approximations for the outage
probability and closed-form expressions for the asymptotic outage probability
in the high signal-to-noise ratio (SNR) region for all cases. Our analysis is
verified by Monte Carlo simulations, and provides an analytical framework for
multicarrier systems with relay selection.


  Galaxy harassment is an important mechanism for the morphological evolution
of galaxies in clusters. The spiral galaxy NGC 4254 in the Virgo cluster is
believed to be a harassed galaxy. We have analyzed the power spectrum of HI
emission fluctuations from NGC 4254 to investigate whether it carries any
imprint of galaxy harassment. The power spectrum, as determined using the 16
central channels which contain most of the HI emission, is found to be well
fitted by a power law $P(U)=AU^{\alpha}$ with $\alpha\ =-\ 1.7\pm 0.2$ at
length-scales $1.7 \, {\rm k pc}$ to $ 8.4 \, {\rm kpc}$. This is similar to
other normal spiral galaxies which have a slope of $\sim -1.5$ and is
interpreted as arising from two dimensional turbulence at length-scales larger
than the galaxy's scale-height. NGC 4254 is hence yet another example of a
spiral galaxy that exhibits scale-invariant density fluctuations out to
length-scales comparable to the diameter of the HI disk. While a large variety
of possible energy sources like proto-stellar winds, supernovae, shocks, etc.
have been proposed to produce turbulence, it is still to be seen whether these
are effective on length-scales comparable to that of the entire HI disk. On
separately analyzing the HI power spectrum in different parts of NGC 4254, we
find that the outer parts have a different slope ($ \alpha = -2.0\pm0.3$)
compared to the central part of the galaxy ($\alpha = -1.5\pm0.2$). Such a
change in slope is not seen in other, undisturbed galaxies. We suggest that, in
addition to changing the overall morphology, galaxy harassment also effects the
fine scale structure of the ISM, causing the power spectrum to have a steeper
slope in the outer parts.


  In the paper [Angelini M C, Parisi G, and Ricci-Tersenghi F, Ensemble
renormalization group for disordered systems, Phys. Rev. B 87 134201 (2013)] we
introduced a real-space renormalization group called Ensemble Renormalization
Group (ERG) and we applied it to the Edwards-Anderson model, obtaining
estimates for the critical exponents in good agreement with those from Monte
Carlo simulations. Recently the paper [Castellana M, Real-space
renormalization-group methods for hierarchical spin glasses, J. Phys. A: Math.
Theor. 52 445002 (2019)] re-examined the ERG method from a different
perspective, concluding that the previous results were wrong, and claiming that
the ERG method predicts trivially wrong critical exponents. In this comment we
explain why the conclusions reached by Castellana are wrong, as they are based
on a misinterpretation of finite-size effects. We conclude that the ERG method
remains a good RG method to obtain critical exponents in strongly disordered
models (if properly used).


  Let $\Omega\subset\mathbb{C}^n$ be a bounded pseudoconvex domain. We define
the Diederich-Forn{\ae}ss index with respect to a family of functions to be the
supremum over the set of all exponents $0<\eta<1$ such that there exists a
function $\rho_\eta$ in this family such that $-\rho_\eta$ is comparable to the
distance to the boundary of $\Omega$ on $\Omega$ and such that
$-(-\rho_\eta)^\eta$ is plurisubharmonic on $\Omega$. We first prove that
computing the Diederich-Forn{\ae}ss index with respect to the family of upper
semi-continuous functions is the same as computing the Diederich-Forn{\ae}ss
index with respect to the family of Lipschitz functions. When the boundary of
$\Omega$ is $C^k$, $k\geq 2$, we prove that the Diederich-Forn{\ae}ss index
with respect to the family of $C^k$ functions is the same as the
Diederich-Forn{\ae}ss index with respect to the family of $C^2$ functions.


  The ATLAS collaboration recently reported a $3\sigma$ excess in the
leptonic-$Z+jets+E_{T}^{miss}$ channel. We intend to interpret this excess by
squark pair production in the Next-to-Minimal Supersymmetric Standard Model
(NMSSM). The decay chain we employ is $\tilde{q} \to q \tilde{\chi}_2^0 \to q
\tilde{\chi}_1^0 Z$, where $\tilde{\chi}_1^0$ and $\tilde{\chi}_2^0$ denote the
lightest and the next-to-lightest neutralinos with singlino and bino as their
dominant components respectively. Our simulations indicate that after
considering the constraints from the ATLAS searches for $jets + E_{T}^{miss}$
signal the central value of the excess can be obtained for $m_{\tilde{q}}
\lesssim 1.2 {\rm TeV}$, and if the constraint from the CMS on-$Z$ search is
further considered, more than 10 signal events are still attainable for
$m_{\tilde{q}} \lesssim 750 {\rm GeV}$. Compared with the interpretation by
gluino pair production, the squark explanation allows for a significantly wider
range of $m_{\tilde{q}}$ as well as a less compressed SUSY mass spectrum. We
also show that the squark explanation will be readily tested at the initial
stage of the 14 TeV LHC.


  Forecasting the flow of crowds is of great importance to traffic management
and public safety, yet a very challenging task affected by many complex
factors, such as inter-region traffic, events and weather. In this paper, we
propose a deep-learning-based approach, called ST-ResNet, to collectively
forecast the in-flow and out-flow of crowds in each and every region through a
city. We design an end-to-end structure of ST-ResNet based on unique properties
of spatio-temporal data. More specifically, we employ the framework of the
residual neural networks to model the temporal closeness, period, and trend
properties of the crowd traffic, respectively. For each property, we design a
branch of residual convolutional units, each of which models the spatial
properties of the crowd traffic. ST-ResNet learns to dynamically aggregate the
output of the three residual neural networks based on data, assigning different
weights to different branches and regions. The aggregation is further combined
with external factors, such as weather and day of the week, to predict the
final traffic of crowds in each and every region. We evaluate ST-ResNet based
on two types of crowd flows in Beijing and NYC, finding that its performance
exceeds six well-know methods.


  In this letter, we study the pion momentum distribution of a coherent source
and investigate the influences of coherent emission on the pion
transverse-momentum ($p_T$) spectrum and elliptic anisotropy. With a partially
coherent source, constructed by a conventional viscous hydrodynamics model
(chaotic part) and a parameterized expanding coherent source model, we
reproduce the pion $p_T$ spectrum and elliptic anisotropy coefficient
$v_2(p_T)$ in the peripheral Pb-Pb collisions at $\sqrt{s_{NN}}=2.76$ TeV. It
is found that the influences of coherent emission on the pion $p_T$ spectrum
and $v_2(p_T)$ are related to the initial size and shape of the coherent
source, largely due to the interference effect. However, the effect of source
dynamical evolution on coherent emission is relatively small. The results of
the partially coherent source with 33% coherent emission and 67% chaotic
emission are consistent with the experimental measurements of the pion $p_T$
spectrum, $v_2(p_T)$, and especially four-pion Bose-Einstein correlations.


  We explore the non-BPS analog of `AdS bubbles', which are regular spherically
symmetric 1/2 BPS geometries in type IIB supergravity. They have regular
horizons and can be thought of as bubbling generalizations of non-extremal AdS
black hole solutions in five-dimensional gauged supergravity. Due to the
appearance of the Heun equation even at the linearized level, various
approximation and numerical methods are needed in order to extract information
about this system. We study how the vacuum expectation value and mass of a
particular dimension two chiral primary operator depend on the temperature and
chemical potential of the thermal Yang-Mills theory. In addition, the mass of
the bubbling AdS black holes is computed. As is shown numerically, there are
also non-BPS solitonic bubbles which are completely regular and arise from
continuous deformations of BPS AdS bubbles.


  We introduce the notions of Schr\"oder shape and of Schr\"oder tableau, which
provide some kind of analogs of the classical notions of Young shape and Young
tableau. We investigate some properties of the partial order given by
containment of Schr\"oder shapes. Then we propose an algorithm which is the
natural analog of the well known RS correspondence for Young tableaux, and we
characterize those permutations whose insertion tableaux have some special
shapes. The last part of the article relates the notion of Schr\"oder tableau
with those of interval order and of weak containment (and strong avoidance) of
posets. We end our paper with several suggestions for possible further work.


  Asteroids and comets dissipate energy when they rotate about the axis
different from the axis of the maximal moment of inertia. We show that the most
efficient internal relaxation happens at the double frequency of body's
tumbling. Therefore the earlier estimates that ignore double frequency input
underestimate the internal relaxation in asteroids and comets. We show that the
Earth seismological data may poorly represent acoustic properties of asteroids
and comet as internal relaxation increases in the presence of moisture. At the
same time owing to non-linearlity of inelastic relaxation small angle nutations
can persist for very long time spans, but our ability to detect such
precessions is limited by the resolution of the radar-generated images.
Wobbling may provide valuable information on the composition and structure of
asteroids and on their recent history of external impacts.


  Text-to-image diffusion techniques have shown exceptional capability of
producing high-quality images from text descriptions. This indicates that there
exists a strong correlation between the visual and textual domains. In
addition, text-image discriminative models such as CLIP excel in image
labelling from text prompts, thanks to the rich and diverse information
available from open concepts. In this paper, we leverage these technical
advances to solve a challenging problem in computer vision: camouflaged
instance segmentation. Specifically, we propose a method built upon a
state-of-the-art diffusion model, empowered by open-vocabulary to learn
multi-scale textual-visual features for camouflaged object representations.
Such cross-domain representations are desirable in segmenting camouflaged
objects where visual cues are subtle to distinguish the objects from the
background, especially in segmenting novel objects which are not seen in
training. We also develop technically supportive components to effectively fuse
cross-domain features and engage relevant features towards respective
foreground objects. We validate our method and compare it with existing ones on
several benchmark datasets of camouflaged instance segmentation and generic
open-vocabulary instance segmentation. Experimental results confirm the
advances of our method over existing ones. We will publish our code and
pre-trained models to support future research.


  We have studied the O and Ne absorption features in the X-ray spectrum of Cyg
X-2 observed with the Chandra LETG. The O absorption edge is represented by the
sum of three absorption-edge components within the limit of the energy
resolution and the photon counting statistics. Two of them are due to the
atomic O; their energies correspond to two distinct spin states of
photo-ionized O atoms. The remaining edge component is considered to represent
compound forms of oxide dust grains. Since Cyg X-2 is about 1.4 kpc above the
galactic disk, the H column densities can be determined by radio (21 cm and CO
emission line) and H alpha observations with relatively small uncertainties.
Thus the O abundance relative to H can be determined from the absorption edges.
We found that the dust scattering can affect the apparent depth of the edge of
the compound forms. We determined the amplitude of the effect, which we
consider is the largest possible correction factor. The ratio of column
densities of O in atomic to compound forms and the O total abundance were
respectively determined to be in the range 1.7^{+3.0}_{-0.9} to
2.8^{+5.1}_{-1.5} (ratio), and 0.63 +/- 0.12 solar to 0.74 +/- 0.14 solar
(total), taking into account the uncertainties in the dust-scattering
correction and in the ionized H column density. We also determined the Ne
abundance from the absorption edge to be 0.75 +/- 0.20 solar. These abundance
values are smaller than the widely-used solar values but consistent with the
latest estimates of solar abundance.


  We present the mass distribution of interstellar grains measured in situ by
the Galileo and Ulysses spaceprobes as cumulative flux. The derived in situ
mass distribution per logarithmic size interval is compared to the distribution
determined by fitting extinction measurements. Large grains measured in situ
contribute significantly to the overall mass of dust in the local interstellar
cloud. The problem of a dust-to-gas mass ratio that contradicts cosmic
abundances is discussed.


  Recently, deep learning with Convolutional Neural Networks (CNNs) and
Transformers has shown encouraging results in fully supervised medical image
segmentation. However, it is still challenging for them to achieve good
performance with limited annotations for training. In this work, we present a
very simple yet efficient framework for semi-supervised medical image
segmentation by introducing the cross teaching between CNN and Transformer.
Specifically, we simplify the classical deep co-training from consistency
regularization to cross teaching, where the prediction of a network is used as
the pseudo label to supervise the other network directly end-to-end.
Considering the difference in learning paradigm between CNN and Transformer, we
introduce the Cross Teaching between CNN and Transformer rather than just using
CNNs. Experiments on a public benchmark show that our method outperforms eight
existing semi-supervised learning methods just with a simpler framework.
Notably, this work may be the first attempt to combine CNN and transformer for
semi-supervised medical image segmentation and achieve promising results on a
public benchmark. The code will be released at:
https://github.com/HiLab-git/SSL4MIS.


  We consider a model of a black hole consisting of a number of elementary
components. Examples of such models occur in the Ashtekar's approach to
canonical Quantum Gravity and in M-theory. We show that treating the elementary
components as completely distinguishable leads to the area law for the black
hole entropy. Contrary to previous results, we show that no Bose condensation
occurs, the area has big local fluctuations and that in the framework of
canonical Quantum Gravity the area of the black hole horizon is equidistantly
quantized.


  We study a skew product transformation associated to an irrational rotation
of the circle [0,1]/~. This skew product keeps track of the number of times an
orbit of the rotation lands in the two complementary intervals of {0,1/2} in
the circle. We show that under certain conditions on the continued fraction
expansion of the irrational number defining the rotation, the skew product
transformation has certain dense orbits. This is in spite of the presence of
numerous non-dense orbits. We use this to construct laminations on infinite
type surfaces with exotic properties. In particular, we show that for every
infinite type surface with an isolated planar end, there is an infinite clique
of 2-filling rays based at that end. These 2-filling rays are relevant to
Bavard--Walker's loop graphs.


  Correct-by-construction synthesis is a cornerstone of the confluence of
formal methods and control theory towards designing safety-critical systems.
Instead of following the time-tested, albeit laborious
(re)design-verify-validate loop, correct-by-construction methodology advocates
the use of continual refinements of formal requirements -- connected by chains
of formal proofs -- to build a system that assures the correctness by design. A
remarkable progress has been made in scaling the scope of applicability of
correct-by-construction synthesis -- with a focus on cyber-physical systems
that tie discrete-event control with continuous environment -- to enlarge
control systems by combining symbolic approaches with principled state-space
reduction techniques.
  Unfortunately, in the security-critical control systems, the security
properties are verified ex post facto the design process in a way that
undermines the correct-by-construction paradigm. We posit that, to truly
realize the dream of correct-by-construction synthesis for security-critical
systems, security considerations must take center-stage with the safety
considerations. Moreover, catalyzed by the recent progress on the opacity
sub-classes of security properties and the notion of hyperproperties capable of
combining security with safety properties, we believe that the time is ripe for
the research community to holistically target the challenge of
secure-by-construction synthesis. This paper details our vision by highlighting
the recent progress and open challenges that may serve as bricks for providing
a solid foundation for secure-by-construction synthesis of cyber-physical
systems.


  We use optical and fluorescence microscopy, densitometry, cryo-transmission
electron microscopy (cryo-TEM), spectroscopy, and synchrotron X-ray scattering,
to study the phase behavior of the reversible self-assembled chromonic
aggregates of an anionic dye Sunset Yellow (SSY) in aqueous solutions crowded
with an electrically neutral polymer polyethylene glycol (PEG) and doped with
the salt NaCl. PEG causes the isotropic SSY solutions to condense into a
liquid-crystalline region with a high concentration of SSY aggregates,
coexisting with a PEG-rich isotropic (I) region. PEG added to the homogeneous
nematic (N) phase causes separation into the coexisting N and I domains; the
SSY concentration in the N domains is higher than the original concentration of
PEG-free N phase. Finally, addition of PEG to the highly concentrated
homogeneous N phase causes separation into the coexisting columnar hexagonal
(C) phase and I phase. This behavior can be qualitatively explained by the
depletion (excluded volume) effects that act at two different levels: at the
level of aggregate assembly from monomers and short aggregates and at the level
of inter-aggregate packing. We also show a strong effect of a monovalent salt
NaCl on phase diagrams that is different for high and low concentrations of
SSY. Upon the addition of salt, dilute I solutions of SSY show appearance of
the condensed N domains, but the highly concentrated C phase transforms into a
coexisting I and N domains. We suggest that the salt-induced screening of
electric charges at the surface of chromonic aggregates leads to two different
effects: (a) increase of the scission energy and the contour length of
aggregates, and (b) decrease of the persistence length of SSY aggregates.