Abstract:
  Direct evidence of micro-turbulence effect on the onset of neoclassical
tearing mode (NTM) is reported for the first time in this letter. A puzzling
positive correlation between critical width of seed island of NTM and
normalized plasma pressure beta_p is first observed employing a novel method
for clearly separating the processes of seed island and the onset of NTM in the
EAST tokamak. Different from the methods developed before, the width of the
seed island is well controlled by slowly ramping up the current in resonant
magnetic perturbation coils. It is revealed that the positive correlation is
mainly attributed to the enhancement of perpendicular transport by
micro-turbulence, which overcomes the destabilizing effect of beta_p on the
onset of NTM. Reduced magnetohydrodynamics (MHD) modeling well reproduced the
two states of nonlinear bifurcations observed in this experiment by including
the finite transport effect. This result provides a new route for understanding
multi-scale interaction in plasma physics.


Abstract:
  In this paper, we use the Bessel $\delta$-method, along with new variants of
the van der Corput method in two dimensions, to prove non-trivial bounds for
$\mathrm{GL}(2)$ exponential sums beyond the Weyl barrier. More explicitly, for
sums of $\mathrm{GL}(2)$ Fourier coefficients twisted by $e(f(n))$, with length
$N$ and phase $f(n)=N^{\beta} \log n / 2\pi$ or $a n^{\beta}$, non-trivial
bounds are established for $ \beta < 1.63651... $, which is beyond the Weyl
barrier at $\beta = 3/2$.


Abstract:
  We study the recovery of multivariate functions from reproducing kernel
Hilbert spaces in the uniform norm. Our main interest is to obtain
preasymptotic estimates for the corresponding sampling numbers. We obtain
results in terms of the decay of related singular numbers of the compact
embedding into $L_2(D,\varrho_D)$ multiplied with the supremum of the
Christoffel function of the subspace spanned by the first $m$ singular
functions. Here the measure $\varrho_D$ is at our disposal. As an application
we obtain near optimal upper bounds for the sampling numbers for periodic
Sobolev type spaces with general smoothness weight. Those can be bounded in
terms of the corresponding benchmark approximation number in the uniform norm,
which allows for preasymptotic bounds. By applying a recently introduced
sub-sampling technique related to Weaver's conjecture we mostly lose a
$\sqrt{\log n}$ and sometimes even less. Finally we point out a relation to the
corresponding Kolmogorov numbers.


Abstract:
  This work presents a probabilistic channel pruning method to accelerate
Convolutional Neural Networks (CNNs). Previous pruning methods often zero out
unimportant channels in training in a deterministic manner, which reduces CNN's
learning capacity and results in suboptimal performance. To address this
problem, we develop a probability-based pruning algorithm, called batch
whitening channel pruning (BWCP), which can stochastically discard unimportant
channels by modeling the probability of a channel being activated. BWCP has
several merits. (1) It simultaneously trains and prunes CNNs from scratch in a
probabilistic way, exploring larger network space than deterministic methods.
(2) BWCP is empowered by the proposed batch whitening tool, which is able to
empirically and theoretically increase the activation probability of useful
channels while keeping unimportant channels unchanged without adding any extra
parameters and computational cost in inference. (3) Extensive experiments on
CIFAR-10, CIFAR-100, and ImageNet with various network architectures show that
BWCP outperforms its counterparts by achieving better accuracy given limited
computational budgets. For example, ResNet50 pruned by BWCP has only 0.70\%
Top-1 accuracy drop on ImageNet, while reducing 43.1\% FLOPs of the plain
ResNet50.


Abstract:
  A new basis has been found for the theory of self-organization of transport
avalanches and jet zonal flows in L-mode tokamak plasma, the so-called "plasma
staircase." The jet zonal flows are considered as a wave packet of coupled
nonlinear oscillators characterized by a complex time- and wave-number
dependent wave function; in a mean-field approximation this function is argued
to obey a discrete nonlinear Schr\"odinger equation with subquadratic power
nonlinearity. It is shown that the subquadratic power leads directly to a white
L\'evy noise, and to a L\'evy-fractional Fokker-Planck equation for radial
transport of test particles (via wave-particle interactions). In a
self-consistent description the avalanches, which are driven by the white
L\'evy noise, interact with the jet zonal flows, which form a system of
semi-permeable barriers to radial transport. We argue that the plasma staircase
saturates at a state of marginal stability, in whose vicinity the avalanches
undergo an ever-pursuing localization-delocalization transition. At the
transition point, the event-size distribution of the avalanches is found to be
a power-law $w_\tau (\Delta n) \sim \Delta n^{-\tau}$, with the drop-off
exponent $\tau = ({\sqrt{17}} + 1)/{2} \simeq 2.56$. This value is an exact
result of the self-consistent model. The edge behavior bears signatures
enabling to associate it with the dynamics of a self-organized critical (SOC)
state. At the same time the critical exponents, pertaining to this state, are
found to be inconsistent with classic models of avalanche transport based on
sand-piles and their generalizations, suggesting that the coupled avalanche-jet
zonal flow system operates on different organizing principles. The results
obtained have been validated in a numerical simulation of the plasma staircase
using flux-driven gyrokinetic code for L-mode Tore-Supra plasma.


Abstract:
  This paper deals with a general system of equations and conditions arising
from a mathematical model of prostate cancer growth with chemotherapy and
antiangiogenic therapy that has been recently introduced and analyzed (see [P.
Colli et al., Mathematical analysis and simulation study of a phase-field model
of prostate cancer growth with chemotherapy and antiangiogenic therapy effects,
Math. Models Methods Appl. Sci. 30 (2020), 1253-1295], preprint in
arXiv:1907.11618 [math.AP]). The related system includes two evolutionary
operator equations involving fractional powers of selfadjoint, nonnegative,
unbounded linear operators having compact resolvents. Both equations contain
nonlinearities and in particular the equation describing the dynamics of the
tumor phase variable has the structure of a Allen-Cahn equation with
double-well potential and additional nonlinearity depending also on the other
variable, which represents the nutrient concentration. The equation for the
nutrient concentration is nonlinear as well, with a term coupling both
variables. For this system we design an existence, uniqueness and continuous
dependence theory by setting up a careful analysis which allows the
consideration of nonsmooth potentials and the treatment of continuous
nonlinearities with general growth properties.


Abstract:
  We study evolution equations of drift-diffusion type when various parameters
are random. Motivated by applications in pedestrian dynamics, we focus on the
case when the total mass is, due to boundary or reaction terms, not conserved.
After providing existence and stability for the deterministic problem, we
consider uncertainty in the data. Instead of a sensitivity analysis we propose
to measure functionals of the solution, so-called quantities of interest (QoI),
by involving scalarizing statistics. For these summarizing statistics we
provide probabilistic continuity results.


Abstract:
  We study the gravitational lensing properties of a massive object in a dark
matter halo, concentrating on the critical curves and caustics of the combined
lens. We model the system in the simplest approximation by a point mass
embedded in a spherical Navarro$-$Frenk$-$White density profile. The low number
of parameters of such a model permits a systematic exploration of its parameter
space. We present galleries of critical curves and caustics for different
masses and positions of the point in the halo. We demonstrate the existence of
a critical mass, above which the gravitational influence of the centrally
positioned point is strong enough to eliminate the radial critical curve and
caustic of the halo. In the point-mass parameter space we identify the
boundaries at which critical-curve transitions and corresponding caustic
metamorphoses occur. The number of transitions as a function of position of the
point is surprisingly high, ranging from three for higher masses to as many as
eight for lower masses. On the caustics we identify the occurrence of six
different types of caustic metamorphoses. We illustrate the peculiar properties
of the single radial critical curve and caustic appearing in an additional
unusual non-local metamorphosis for a critical mass positioned at the halo
center. Although we constructed the model primarily to study the lensing
influence of individual galaxies in a galaxy cluster, it can also be used to
study the lensing by dwarf satellite galaxies in the halo of a host galaxy, as
well as (super)massive black holes at a general position in a galactic halo.


Abstract:
  We analyse a central broadcast continuous variable quantum key distribution
protocol in which a beam produced by a thermal source is used to create a
secret key between two parties, Alice and Bob. A beam splitter divides the
initial beam into a pair of output beams, which are sent to Alice and Bob, with
Eve intercepting Bob's beam. We investigate the protocol in detail, calculating
mutual informations through a pair of analytic methods and comparing the
results to the outputs of a Monte Carlo simulation of the protocol. In a
lossless system, we find that a lower bound on the key rate remains positive in
the protocol under a beam splitter attack, provided Bob receives a nonzero
proportion of the beam initially sent to him. This suggests that the thermal
state protocol could be used experimentally to produce secure keys.


Abstract:
  Atomic interference experiments can probe the gravitational redshift via the
internal energy splitting of atoms and thus give direct access to test the
universality of the coupling between matter-energy and gravity at different
spacetime points. By including possible violations of the equivalence principle
in a fully quantized treatment of all degrees of freedom, we characterize how
the sensitivity to gravitational redshift violations arises in atomic clocks
and atom interferometers, as well as their underlying limitations.
Specifically, we show that: (i.) Contributions beyond linear order to trapping
potentials lead to such a sensitivity of trapped atomic clocks. (ii.) While
Bragg-type interferometers, even with a superposition of internal states, with
state-independent, linear interaction potentials are at first insensitive to
gravitational redshift tests, modified configurations, for example by
relaunching the atoms, can mimic such tests tests under certain conditions.
(iii.) Guided atom interferometers are comparable to atomic clocks. (iv.)
Internal transitions lead to state-dependent interaction potentials through
which light-pulse atom interferometers can become sensitive to gravitational
redshift violations.


Abstract:
  Multi-agent reinforcement learning (MARL) has been increasingly used in a
wide range of safety-critical applications, which require guaranteed safety
(e.g., no unsafe states are ever visited) during the learning
process.Unfortunately, current MARL methods do not have safety guarantees.
Therefore, we present two shielding approaches for safe MARL. In centralized
shielding, we synthesize a single shield to monitor all agents' joint actions
and correct any unsafe action if necessary. In factored shielding, we
synthesize multiple shields based on a factorization of the joint state space
observed by all agents; the set of shields monitors agents concurrently and
each shield is only responsible for a subset of agents at each
step.Experimental results show that both approaches can guarantee the safety of
agents during learning without compromising the quality of learned policies;
moreover, factored shielding is more scalable in the number of agents than
centralized shielding.


Abstract:
  We introduce Position Adaptive Convolution (PAConv), a generic convolution
operation for 3D point cloud processing. The key of PAConv is to construct the
convolution kernel by dynamically assembling basic weight matrices stored in
Weight Bank, where the coefficients of these weight matrices are
self-adaptively learned from point positions through ScoreNet. In this way, the
kernel is built in a data-driven manner, endowing PAConv with more flexibility
than 2D convolutions to better handle the irregular and unordered point cloud
data. Besides, the complexity of the learning process is reduced by combining
weight matrices instead of brutally predicting kernels from point positions.
  Furthermore, different from the existing point convolution operators whose
network architectures are often heavily engineered, we integrate our PAConv
into classical MLP-based point cloud pipelines without changing network
configurations. Even built on simple networks, our method still approaches or
even surpasses the state-of-the-art models, and significantly improves baseline
performance on both classification and segmentation tasks, yet with decent
efficiency. Thorough ablation studies and visualizations are provided to
understand PAConv. Code is released on https://github.com/CVMI-Lab/PAConv.


Abstract:
  Robust laser sources are a fundamental building block for contemporary
information technologies. Originating from condensed-matter physics, the
concept of topology has recently entered the realm of optics, offering
fundamentally new design principles for lasers with enhanced robustness. In
analogy to the well-known Majorana fermions in topological superconductors,
Dirac-vortex states have recently been investigated in passive photonic systems
and are now considered as a promising candidate for single-mode large-area
lasers. Here, we experimentally realize the first Dirac-vortex topological
lasers in InAs/InGaAs quantum-dot materials monolithically grown on a silicon
substrate. We observe room-temperature continuous-wave single-mode linearly
polarized vertical laser emission at a telecom wavelength. Most importantly, we
confirm that the wavelength of the Dirac-vortex laser is topologically robust
against variations in the cavity size, and its free spectral range defies the
universal inverse scaling law with the cavity size. These lasers will play an
important role in CMOS-compatible photonic and optoelectronic systems on a
chip.


Abstract:
  We find and classify self-similar solutions of the curve shortening flow in
standard hyperbolic 2-space. Together with earlier work of Halld\'orsson on
curve shortening flow in the plane and Santos dos Reis and Tenenblat in the
2-sphere, this completes the classification of self-similar curve shortening
flows in the constant curvature model spaces in 2-dimensions.


Abstract:
  Quantum walks are the quantum mechanical analogue of classical random walks
and an extremely powerful tool in quantum simulations, quantum search
algorithms, and even for universal quantum computing. In our work, we have
designed and fabricated an 8x8 two-dimensional square superconducting qubit
array composed of 62 functional qubits. We used this device to demonstrate high
fidelity single and two particle quantum walks. Furthermore, with the high
programmability of the quantum processor, we implemented a Mach-Zehnder
interferometer where the quantum walker coherently traverses in two paths
before interfering and exiting. By tuning the disorders on the evolution paths,
we observed interference fringes with single and double walkers. Our work is an
essential milestone in the field, brings future larger scale quantum
applications closer to realization on these noisy intermediate-scale quantum
processors.


Abstract:
  We prove the stability of the ball as global minimizer of an attractive shape
functional under volume constraint, by means of mass transportation arguments.
The stability exponent is $1/2$ and it is sharp. Moreover, we use such
stability result together with the quantitative (possibly fractional)
isoperimetric inequality to prove that the ball is a global minimizer of a
shape functional involving both an attractive and a repulsive term with a
sufficiently large fixed volume and with a suitable (possibly fractional)
perimeter penalization.


Abstract:
  We describe the development, characteristics and availability of a test
collection for the task of Web table retrieval, which uses a large-scale Web
Table Corpora extracted from the Common Crawl. Since a Web table usually has
rich context information such as the page title and surrounding paragraphs, we
not only provide relevance judgments of query-table pairs, but also the
relevance judgments of query-table context pairs with respect to a query, which
are ignored by previous test collections. To facilitate future research with
this benchmark, we provide details about how the dataset is pre-processed and
also baseline results from both traditional and recently proposed table
retrieval methods. Our experimental results show that proper usage of context
labels can benefit previous table retrieval methods.


Abstract:
  Recently, we employed electronic polarization-resolved Raman spectroscopy to
reveal the strongly correlated excitonic insulator (EI) nature of Ta2NiSe5,
Volkov et al. [arXiv:2007.07344], and also showed that for
Ta$_2$Ni(Se$_{1-x}$S$_x$)$_5$ alloys the critical excitonic fluctuations
diminish with sulfur concentration x exposing a cooperating lattice instability
that takes over for large x, Volkov et al. [arXiv:2104.07032]. Here we focus on
the lattice dynamics of this EI family. We identify all Raman-active optical
phonons of fully symmetric and ac-quadrupole-like symmetries and study their
evolution with temperature and sulfur concentration. We demonstrate the change
of selection rules at temperatures below the orthorhombic-to-monoclinic
transition at Tc(x) that is related to the EI phase. We find that Tc(x)
decrease monotonically from 328 K for Ta2NiSe5 to 120 K for Ta2NiS5 and that
the magnitude of lattice distortion also decreases with the sulfur
concentration x. For x < 0.7, the two lowest-frequency B2g phonon modes show
strongly asymmetric lineshapes at high temperatures due to Fano interference
with the broad excitonic continuum present in a semimetallic state. Within the
framework of extended Fano model, we develop a quantitative description of the
interacting exciton-phonon excitation lineshape, enabling us to derive the
intrinsic phonon parameters and determine the exciton-phonon interaction
strength, that affects the transition temperature Tc(x). We also observe
signatures of the acoustic mode scattered assisted by the structural domain
walls formed below Tc. Based on our results, we additionally present a
consistent interpretation of the origin of oscillations observed in
time-resolved pump-probe experiments.


Abstract:
  Optical depth variations in the Galactic neutral interstellar medium (ISM)
with spatial scales from hundreds to thousands of astronomical units have been
observed through HI absorption against pulsars and continuum sources, while
extremely small structures with spatial scales of tens of astronomical units
remain largely unexplored. The nature and formation of such tiny-scale atomic
structures (TSAS) need to be better understood. We report a tentative detection
of TSAS with a signal-to-noise ratio of 3.2 toward PSR B1557$-$50 in the second
epoch of two Parkes sessions just 0.36 yr apart, which are the closest-spaced
spectral observations toward this pulsar. One absorption component showing
marginal variations has been identified. Based on the pulsar's proper motion of
14 mas $\rm yr^{-1}$ and the component's kinematic distance of 3.3 kpc, the
corresponding characteristic spatial scale is 17 au, which is among the
smallest sizes of known TSAS. Assuming a similar line-of-sight (LOS) depth, the
tentative TSAS cloud detected here is overdense and overpressured relative to
the cold neutral medium (CNM), and can radiatively cool fast enough to be in
thermal equilibrium with the ambient environment. We find that turbulence is
not sufficient to confine the overpressured TSAS. We explore the LOS elongation
that would be required for the tentative TSAS to be at the canonical CNM
pressure, and find that it is $\sim5000$ -- much larger than filaments observed
in the ISM. We see some evidence of line width and temperature variations in
the CNM components observed at the two epochs, as predicted by models of
TSAS-like cloud formation colliding warm neutral medium flows.


Abstract:
  For a target task where labeled data is unavailable, domain adaptation can
transfer a learner from a different source domain. Previous deep domain
adaptation methods mainly learn a global domain shift, i.e., align the global
source and target distributions without considering the relationships between
two subdomains within the same category of different domains, leading to
unsatisfying transfer learning performance without capturing the fine-grained
information. Recently, more and more researchers pay attention to Subdomain
Adaptation which focuses on accurately aligning the distributions of the
relevant subdomains. However, most of them are adversarial methods which
contain several loss functions and converge slowly. Based on this, we present
Deep Subdomain Adaptation Network (DSAN) which learns a transfer network by
aligning the relevant subdomain distributions of domain-specific layer
activations across different domains based on a local maximum mean discrepancy
(LMMD). Our DSAN is very simple but effective which does not need adversarial
training and converges fast. The adaptation can be achieved easily with most
feed-forward network models by extending them with LMMD loss, which can be
trained efficiently via back-propagation. Experiments demonstrate that DSAN can
achieve remarkable results on both object recognition tasks and digit
classification tasks. Our code will be available at:
https://github.com/easezyc/deep-transfer-learning


Abstract:
  We give pointwise upper estimate for the gradient of the heat kernel on some
fractal-like cable systems including the Vicsek and the Sierpi\'nski cable
systems. Applications to $L^p$-boundedness of quasi-Riesz transforms are
derived.


Abstract:
  Concerted efforts are underway to establish an infrastructure for a global
quantum internet to realise a spectrum of quantum technologies. This will
enable more precise sensors, secure communications, and faster data processing.
Quantum communications are a front-runner with quantum networks already
implemented in several metropolitan areas. A number of recent proposals have
modelled the use of space segments to overcome range limitations of purely
terrestrial networks. Rapid progress in the design of quantum devices have
enabled their deployment in space for in-orbit demonstrations. We review
developments in this emerging area of space-based quantum technologies and
provide a roadmap of key milestones towards a complete, global quantum
networked landscape. Small satellites hold increasing promise to provide a cost
effective coverage required to realised the quantum internet. We review the
state of art in small satellite missions and collate the most current in-field
demonstrations of quantum cryptography. We summarise important challenges in
space quantum technologies that must be overcome and recent efforts to mitigate
their effects. A perspective on future developments that would improve the
performance of space quantum communications is included. We conclude with a
discussion on fundamental physics experiments that could take advantage of a
global, space-based quantum network.


Abstract:
  People learn motor activities best when they are conscious of their errors
and make a concerted effort to correct them. While haptic interfaces can
facilitate motor training, existing interfaces are often bulky and do not
always ensure post-training skill retention. Here, we describe a programmable
haptic sleeve composed of textile-based electroadhesive clutches for skill
acquisition and retention. We show its functionality in a motor learning study
where users control a drone's movement using elbow joint rotation. Haptic
feedback is used to restrain elbow motion and make users aware of their errors.
This helps users consciously learn to avoid errors from occurring. While all
subjects exhibited similar performance during the baseline phase of motor
learning, those subjects who received haptic feedback from the haptic sleeve
committed 23.5% fewer errors than subjects in the control group during the
evaluation phase. The results show that the sleeve helps users retain and
transfer motor skills better than visual feedback alone. This work shows the
potential for fabric-based haptic interfaces as a training aid for motor tasks
in the fields of rehabilitation and teleoperation.


Abstract:
  Future precision measurements of Higgs boson decays will determine the
branching fraction for the decay into two photons with a precision at the one
percent level. To fully exploit such measurements, equally precise theoretical
predictions need to be available. To this end we compute four-loop QCD
corrections in the large top quark mass expansion to the Higgs boson--photon
form factor, which enter the two-photon decay width at
next-to-next-to-next-to-leading order. Furthermore we obtain corrections to the
two-photon decay width stemming from the emission of additional gluons, which
contribute for the first time at next-to-next-to-leading order. Finally, we
combine our results with other available perturbative corrections and estimate
the residual uncertainty due to missing higher-order contributions.


Abstract:
  We provide a short proof of the theorem that every real multivariate
polynomial has a symmetric determinantal representation, which was first proved
in J. W. Helton, S. A. McCullough, and V. Vinnikov, Noncommutative convexity
arises from linear matrix inequalities, J. Funct. Anal. 240 (2006), 105-191. We
then provide an example using our approach and extend our results from the real
field $\mathbb{R}$ to an arbitrary field $\mathbb{F}$ different from
characteristic $2$. The new approach we take is only based on elementary
results from the theory of determinants, the theory of Schur complements, and
basic properties of polynomials.


Abstract:
  Automated source code summarization is a popular software engineering
research topic wherein machine translation models are employed to "translate"
code snippets into relevant natural language descriptions. Most evaluations of
such models are conducted using automatic reference-based metrics. However,
given the relatively large semantic gap between programming languages and
natural language, we argue that this line of research would benefit from a
qualitative investigation into the various error modes of current
state-of-the-art models. Therefore, in this work, we perform both a
quantitative and qualitative comparison of three recently proposed source code
summarization models. In our quantitative evaluation, we compare the models
based on the smoothed BLEU-4, METEOR, and ROUGE-L machine translation metrics,
and in our qualitative evaluation, we perform a manual open-coding of the most
common errors committed by the models when compared to ground truth captions.
Our investigation reveals new insights into the relationship between
metric-based performance and model prediction errors grounded in an empirically
derived error taxonomy that can be used to drive future research efforts


Abstract:
  CDF-S XT1 is a fast-rising non-thermal X-ray transient detected by
\textit{Chandra} in the Deep-Field South Survey. Although various hypotheses
have been suggested, the origin of this transient remains unclear. Here, we
show that the observations of CDF-S XT1 are well explained as the X-ray
afterglow produced by a relativistic structured jet viewed off-axis. We measure
properties of the jet, showing that they are similar to those of GRB170817A,
albeit at cosmological distances. We measure the observers viewing angle to be
$\theta_{\textrm{obs}} = 10^{\circ}\pm3^{\circ}$ and the core of the
ultra-relativistic jet to be $\theta_{\textrm{core}} =
4.4^{\circ}\pm0.9^{\circ}$, where the uncertainties are the $68\%$ credible
interval. The inferred properties and host galaxy combined with Hubble, radio,
and optical non detections favour the hypothesis that CDF-S XT1 is the off-axis
afterglow of a binary neutron star merger. We find that other previously
suggested hypotheses are unable to explain all properties of CDF-S XT1. At a
redshift of $z=2.23$, this is potentially the most distant observed neutron
star merger to date and the first orphan afterglow of a short gamma-ray burst.
We discuss the implications of a binary neutron star merger at such a high
redshift for the star-formation rate in the early Universe, the nucleosynthesis
of heavy elements, and the prospect of identifying other off-axis afterglows.


Abstract:
  This article compares the distributions of integer-valued random variables
and Poisson random variables. It considers the total variation and the
Wasserstein distance and provides, in particular, explicit bounds on the
pointwise difference between the cumulative distribution functions. Special
attention is dedicated to estimating the difference when the cumulative
distribution functions are evaluated at 0. This permits to approximate the
minimum (or maximum) of a collection of random variables by a suitable random
variable in the Kolmogorov distance. The main theoretical results are obtained
by combining the Chen-Stein method with size-bias coupling and a generalization
of size-bias coupling for integer-valued random variables developed herein. A
wide variety of applications are then discussed with a focus on stochastic
geometry.


Abstract:
  Logic-based argumentation is a well-established formalism modelling
nonmonotonic reasoning. It has been playing a major role in AI for decades,
now. Informally, a set of formulas is the support for a given claim if it is
consistent, subset-minimal, and implies the claim. In such a case, the pair of
the support and the claim together is called an argument. In this paper, we
study the propositional variants of the following three computational tasks
studied in argumentation: ARG (exists a support for a given claim with respect
to a given set of formulas), ARG-Check (is a given set a support for a given
claim), and ARG-Rel (similarly as ARG plus requiring an additionally given
formula to be contained in the support). ARG-Check is complete for the
complexity class DP, and the other two problems are known to be complete for
the second level of the polynomial hierarchy (Parson et al., J. Log. Comput.,
2003) and, accordingly, are highly intractable. Analyzing the reason for this
intractability, we perform a two-dimensional classification: first, we consider
all possible propositional fragments of the problem within Schaefer's framework
(STOC 1978), and then study different parameterizations for each of the
fragment. We identify a list of reasonable structural parameters (size of the
claim, support, knowledge-base) that are connected to the aforementioned
decision problems. Eventually, we thoroughly draw a fine border of
parameterized intractability for each of the problems showing where the
problems are fixed-parameter tractable and when this exactly stops.
Surprisingly, several cases are of very high intractability (paraNP and
beyond).


Abstract:
  We consider convergence of alternating projections between non-convex sets
and obtain applications to convergence of the Gerchberg-Saxton error reduction
method, of the Gaussian expectation-maximization algorithm, and of Cadzow's
algorithm.


Abstract:
  In this work, we propose the use of a fully managed machine learning service,
which utilizes active learning to directly build models from unstructured data.
With this tool, business users can quickly and easily build machine learning
models and then directly deploy them into a production ready hosted environment
without much involvement from data scientists. Our approach leverages
state-of-the-art text representation like OpenAI's GPT2 and a fast
implementation of the active learning workflow that relies on a simple
construction of incremental learning using linear models, thus providing a
brisk and efficient labeling experience for the users. Experiments on both
publicly available and real-life insurance datasets empirically show why our
choices of simple and fast classification algorithms are ideal for the task at
hand.


Abstract:
  Whereas proof assistants based on Higher-Order Logic benefit from external
solvers' automation, those based on Type Theory resist automation and thus
require more expertise. Indeed, the latter use a more expressive logic which is
further away from first-order logic, the logic of most automatic theorem
provers. In this article, we develop a methodology to transform a subset of Coq
goals into first-order statements that can be automatically discharged by
automatic provers. The general idea is to write modular, pairwise independent
transformations and combine them. Each of these eliminates a specific aspect of
Coq logic towards first-order logic. As a proof of concept, we apply this
methodology to a set of simple but crucial transformations which extend the
local context with proven first-order assertions that make Coq definitions and
algebraic types explicit. They allow users of Coq to solve non-trivial goals
automatically. This methodology paves the way towards the definition and
combination of more complex transformations, making Coq more accessible.


Abstract:
  We consider the derivation of data-dependent simultaneous bandwidths for
double kernel heteroskedasticity and autocorrelation consistent (DK-HAC)
estimators. In addition to the usual smoothing over lagged autocovariances for
classical HAC estimators, the DK-HAC estimator also applies smoothing over the
time direction. We obtain the optimal bandwidths that jointly minimize the
global asymptotic MSE criterion and discuss the trade-off between bias and
variance with respect to smoothing over lagged autocovariances and over time.
Unlike the MSE results of Andrews (1991), we establish how nonstationarity
affects the bias-variance trade-o?. We use the plug-in approach to construct
data-dependent bandwidths for the DK-HAC estimators and compare them with the
DK-HAC estimators from Casini (2021) that use data-dependent bandwidths
obtained from a sequential MSE criterion. The former performs better in terms
of size control, especially with stationary and close to stationary data.
Finally, we consider long-run variance estimation under the assumption that the
series is a function of a nonparametric estimator rather than of a
semiparametric estimator that enjoys the usual T^(1/2) rate of convergence.
Thus, we also establish the validity of consistent long-run variance estimation
in nonparametric parameter estimation settings.


Abstract:
  In living cells, protein-rich condensates can wet the cell membrane and
surfaces of membrane-bound organelles. Interestingly, many phase-separating
proteins also bind to membranes leading to a molecular layer of bound
molecules. Here we investigate how binding to membranes affects surface phase
transitions such as wetting and prewetting. We derive a thermodynamic theory
for a three-dimensional bulk in the presence of a two-dimensional, flat
membrane. Above the saturation concentration, we find that membrane binding
facilitates complete wetting and lowers the wetting angle. Moreover, below the
saturation concentration, binding facilitates the formation of a thick layer at
the membrane and thereby shifts the prewetting phase transition far below the
saturation concentration. The distinction between bound and unbound molecules
near the surface leads to a large variety of prewetted states. Our work
suggests that surface phase transitions combined with molecular binding
represent a versatile mechanism to control the formation of protein-rich
domains at intra-cellular surfaces.


Abstract:
  For the wavelet type orthonormal systems $\phi_n$, we establish a new bound
  \begin{equation}
  \left\|\max_{1\le m\le n}\left|\sum_{j\in G_m}\langle f,\phi_j\rangle
\phi_j\right|\right\|_p\lesssim \sqrt{\log (n+1)}\cdot \|f\|_p,\quad
1<p<\infty,
  \end{equation} where $G_m\subset N$ are arbitrary sets of indexes. Using this
estimate, we prove that $\log n$ is an almost everywhere convergence Weyl
multiplier for any orthonormal system of non-overlapping wavelet polynomials.
It will also be remarked that $\log n$ is the optimal sequence in this context.


Abstract:
  Let $W$ be a compact smooth $4$-manifold that deformation retract to a PL
embedded closed surface. One can arrange the embedding to have at most one
non-locally-flat point, and near the point the topology of the embedding is
encoded in the singularity knot $K$. If $K$ is slice, then $W$ has a smooth
spine, i.e., deformation retracts onto a smoothly embedded surface. Using the
obstructions from the Heegaard Floer homology and the high-dimensional surgery
theory, we show that $W$ has no smooth spines if $K$ is a knot with nonzero Arf
invariant, a nontrivial L-space knot, the connected sum of nontrivial L-space
knots, or an alternating knot of signature $<-4$. We also discuss examples
where the interior of $W$ is negatively curved.


Abstract:
  This paper explores the potential for performing temporal semantic
segmentation in the context of agricultural robotics without temporally
labelled data. We achieve this by proposing to generate virtual temporal
samples from labelled still images. By exploiting the relatively static scene
and assuming that the robot (camera) moves we are able to generate virtually
labelled temporal sequences with no extra annotation effort. Normally, to train
a recurrent neural network (RNN), labelled samples from a video (temporal)
sequence are required which is laborious and has stymied work in this
direction. By generating virtual temporal samples, we demonstrate that it is
possible to train a lightweight RNN to perform semantic segmentation on two
challenging agricultural datasets. Our results show that by training a temporal
semantic segmenter using virtual samples we can increase the performance by an
absolute amount of $4.6$ and $4.9$ on sweet pepper and sugar beet datasets,
respectively. This indicates that our virtual data augmentation technique is
able to accurately classify agricultural images temporally without the use of
complicated synthetic data generation techniques nor with the overhead of
labelling large amounts of temporal sequences.


Abstract:
  Let $A$ be an abelian variety and $G$ a finite group of automorphisms of $A$
fixing the origin such that $A/G$ is smooth. The quotient $A/G$ can be seen as
a fibration over an abelian variety whose fibers are isomorphic to a product of
projective spaces. We classify how the fibers are glued in the case when the
fibers are isomorphic to a projective space and we prove that, in general, the
quotient $A/G$ is a fibered product of such fibrations.


Abstract:
  We report on the formation of a dispersive shock wave in a nonlinear optical
medium. We monitor the evolution of the shock by tuning the incoming beam
power. The experimental observations for the position and intensity of the
solitonic edge of the shock, as well as the location of the nonlinear
oscillations are well described by recent developments of Whitham modulation
theory. Our work constitutes a detailed and accurate benchmark for this
approach. It opens exciting possibilities to engineer specific configurations
of optical shock wave for studying wave-mean flow interaction.


Abstract:
  Pansharpening is a widely used image enhancement technique for remote
sensing. Its principle is to fuse the input high-resolution single-channel
panchromatic (PAN) image and low-resolution multi-spectral image and to obtain
a high-resolution multi-spectral (HRMS) image. The existing deep learning
pansharpening method has two shortcomings. First, features of two input images
need to be concatenated along the channel dimension to reconstruct the HRMS
image, which makes the importance of PAN images not prominent, and also leads
to high computational cost. Second, the implicit information of features is
difficult to extract through the manually designed loss function. To this end,
we propose a generative adversarial network via the fast guided filter (FGF)
for pansharpening. In generator, traditional channel concatenation is replaced
by FGF to better retain the spatial information while reducing the number of
parameters. Meanwhile, the fusion objects can be highlighted by the spatial
attention module. In addition, the latent information of features can be
preserved effectively through adversarial training. Numerous experiments
illustrate that our network generates high-quality HRMS images that can surpass
existing methods, and with fewer parameters.


Abstract:
  Transition metal defects in SiC give rise to localized electronic states that
can be optically addressed in the telecom range in an industrially mature
semiconductor platform. This has led to intense scrutiny of the spin and
optical properties of these defect centers. For spin-1/2 defects, a combination
of the defect symmetry and the strong spin-orbit coupling may restrict the
allowed spin transitions, giving rise to defect spins that are long lived, but
hard to address via microwave spin manipulation. Here, we show via analytical
and numerical results that the presence of a central nuclear spin can lead to a
non-trivial mixing of electronic spin states, while preserving the defect
symmetry. The interplay between a small applied magnetic field and hyperfine
coupling opens up magnetic microwave transitions that are forbidden in the
absence of hyperfine coupling, enabling efficient manipulation of the
electronic spin. We also find that an electric microwave field parallel to the
c-axis can be used to manipulate the electronic spin via modulation of the
relative strength of the dipolar hyperfine term.


Abstract:
  Processes that lead to the critical-current suppression and change of
impedance of a superconductor under the application ofan external voltage is an
active area of research, especially due to various possible technological
applications. In particular,field-effect transistors and radiation detectors
have been developed in the recent years, showing the potential for precision
andsensitivity exceeding their normal-metal counterparts. In order to describe
the phenomenon that leads to the critical-currentsuppression in metallic
superconducting structures, a field-effect hypothesis has been formulated,
stating that an electricfield can penetrate the metallic superconductor and
affect its characteristics. The existence of such an effect would imply
theincompleteness of the underlying theory, and hence indicate an important gap
in the general comprehension of superconductors.In addition to its theoretical
value, a complete understanding of the phenomenon underneath the electric-field
response ofthe superconductor is important in the light of the related
technological applications. In this paper, we study the changeof the
characteristics of a superconductor implementing a coplanar-waveguide resonator
as a tank circuit, by relating ourmeasurements to the reactance and resistance
of the material. Namely, we track the state of the superconductor at
differentvoltages and resulting leakage currents of a nearby gate electrode
which is not galvanically connected to the resonator. Bycomparing the effects
of the leakage current and of a change in the temperature of the system, we
conclude that the observedbehaviour in the superconductor is [...]


Abstract:
  The origin of the interfacial perpendicular magnetic anisotropy (PMA) induced
in the ultrathin Fe layer on the Au(111) surface was examined using
synchrotron-radiation-based M\"{o}ssbauer spectroscopy (MS), X-ray magnetic
circular dichroism (XMCD), and angle-resolved photoemission spectroscopy
(ARPES). To probe the detailed interfacial electronic structure of orbital
hybridization between the Fe 3$d$ and Au 6$p$ bands, we detected the
interfacial proximity effect, which modulates the valence-band electronic
structure of Fe, resulting in PMA. MS and XMCD measurements were used to detect
the interfacial magnetic structure and anisotropy in orbital magnetic moments,
respectively. $In$-$situ$ ARPES also confirms the initial growth of Fe on large
spin-orbit coupled surface Shockley states under Au(111) modulated electronic
states in the vicinity of the Fermi level. This suggests that PMA in the
Fe/Au(111) interface originates from the cooperation effects among the spin,
orbital magnetic moments in Fe, and large spin-orbit coupling in Au. These
findings pave the way to develop interfacial PMA using $p$-$d$ hybridization
with a large spin-orbit interaction.


Abstract:
  For a unital $C^*$-algebra $\mathcal A$ and a subspace $\mathcal B$ of
$\mathcal A$, a characterization for a best approximation to an element of
$\mathcal A$ in $\mathcal B$ is obtained. As an application, a formula for the
distance of an element of $\mathcal A$ from $\mathcal B$ has been obtained,
when a best approximation of that element to $\mathcal B$ exists. Further, a
characterization for Birkhoff-James orthogonality of an element of a Hilbert
$C^*$-module to a subspace is obtained.


Abstract:
  This paper explores the task of Difficulty-Controllable Question Generation
(DCQG), which aims at generating questions with required difficulty levels.
Previous research on this task mainly defines the difficulty of a question as
whether it can be correctly answered by a Question Answering (QA) system,
lacking interpretability and controllability. In our work, we redefine question
difficulty as the number of inference steps required to answer it and argue
that Question Generation (QG) systems should have stronger control over the
logic of generated questions. To this end, we propose a novel framework that
progressively increases question difficulty through step-by-step rewriting
under the guidance of an extracted reasoning chain. A dataset is automatically
constructed to facilitate the research, on which extensive experiments are
conducted to test the performance of our method.


Abstract:
  We present a novel framework, InfinityGAN, for arbitrary-sized image
generation. The task is associated with several key challenges. First, scaling
existing models to an arbitrarily large image size is resource-constrained, in
terms of both computation and availability of large-field-of-view training
data. InfinityGAN trains and infers in a seamless patch-by-patch manner with
low computational resources. Second, large images should be locally and
globally consistent, avoid repetitive patterns, and look realistic. To address
these, InfinityGAN disentangles global appearances, local structures, and
textures. With this formulation, we can generate images with spatial size and
level of details not attainable before. Experimental evaluation validates that
InfinityGAN generates images with superior realism compared to baselines and
features parallelizable inference. Finally, we show several applications
unlocked by our approach, such as spatial style fusion, multi-modal
outpainting, and image inbetweening. All applications can be operated with
arbitrary input and output sizes.


Abstract:
  Survival in remote snow bounded areas is unsafe and risky for mankind. Many
problems like arthritis, frostbite, asthma, starvation can caused and lead to
death. Indian Military provides transportation vehicles which are heavily built
and needs manpower for monitoring. Hence it necessitates facilitating compact
transportation to fulfill all requirements. This research aimed at design and
analysis of mobile unmanned vehicle for transportation & providing medical
help, food and other essential things necessary for surviving in such areas.
This can also be used for military services to save the life of solider with
less risk. It is typical medium weight, high speed vehicle which carries up to
35 kg load and can negotiate through loose snow, rough terrain with use of
caterpillar track. The noteworthy feature of the vehicle is that it constitutes
of spiral blades and V shape snowplow to make its way through snow. Hence it
will repel the snow in outward direction for self-extraction. It also
incorporates skis and hubs for changing the direction and smooth suspension. 3D
model of the vehicle is drafted in CATIA and structural analysis is carried out
in ANSYS. Control system design and mechatronics integration is proposed to
develop the prototype by assembling various components.


Abstract:
  We investigate parity-time reversal (PT) phase transitions in open quantum
systems and discuss a criterion of Liouvillian PT symmetry proposed recently by
J. Huber, P. Kirton, S. Rotter and P. Rabl. Using the third quantization, which
is a general method to solve the Lindblad equation for open quadratic systems,
we show, with a proposed criterion of PT symmetry, that the eigenvalue
structure of the Liouvillian clearly changes at the PT symmetry breaking point
for an open 2-spin model with exactly balanced gain and loss if the total spin
is large. Specially, in a PT unbroken phase, some eigenvalues are pure
imaginary numbers while in a PT broken phase, all the eigenvalues are real.
From this result, it is analytically shown for open quantum system including
quantum jumps that the dynamics in the long time limit changes from an
oscillatory to an overdamped behavior at the proposed PT symmetry breaking
point. Furthermore, we show a direct relation between the criterion of Huber et
al. of Liouvillian PT symmetry and dynamics of the physical quantities for
quadratic bosonic systems. Our results support validity of the proposed
criterion of Liouvillian PT symmetry.


Abstract:
  Photonics is a promising platform for demonstrating quantum computational
supremacy (QCS) by convincingly outperforming the most powerful classical
supercomputers on a well-defined computational task. Despite this promise,
existing photonics proposals and demonstrations face significant hurdles.
Experimentally, current implementations of Gaussian boson sampling lack
programmability or have prohibitive loss rates. Theoretically, there is a
comparative lack of rigorous evidence for the classical hardness of GBS. In
this work, we make significant progress in improving both the theoretical
evidence and experimental prospects. On the theory side, we provide strong
evidence for the hardness of Gaussian boson sampling, placing it on par with
the strongest theoretical proposals for QCS. On the experimental side, we
propose a new QCS architecture, high-dimensional Gaussian boson sampling, which
is programmable and can be implemented with low loss rates using few optical
components. We show that particular classical algorithms for simulating GBS are
vastly outperformed by high-dimensional Gaussian boson sampling experiments at
modest system sizes. This work thus opens the path to demonstrating QCS with
programmable photonic processors.


Abstract:
  It is possible to describe the scattering and the radiative capture with help
of a Breit-Wigner resonance with negative energy.


Abstract:
  The LHCb Collaboration announced the observation of doubly charmed baryon
through $\Xi_{c c}^{++} \rightarrow \Lambda_{c}^{+} K^{-} \pi^{+} \pi^{+}$ in
2017. Since then, a series of studies of doubly heavy baryons have been
presented. $\Xi_{cc}^{++}$ was discovered through nonleptonic four-body decay
mode, and experimental data has indicated that the decay modes of $\Xi_{c
c}^{++}$ are not saturated by two and three-body intermediate states. In this
work, we analyze the four-body weak decays of doubly heavy baryons
$\Xi_{cc}^{++}, \Xi_{cc}^+$, and $\Omega_{cc}^+$. Decay amplitudes for various
channels are parametrized in terms of SU(3) irreducible amplitudes. We point
out that branching fractions for Cabibbo-allowed processes
$\Xi_{cc}^{+}\to\Lambda_c^+\pi^+ \pi^0 K^-$,
$\Omega_{cc}^{+}\to\Lambda_c^+\pi^+ \overline K^0 K^-$ would be helpful to
search for $\Xi_{cc}^+$ and $\Omega_{cc}^+$ in future measurements at
experimental facilities like LHC, Belle II, and CEPC.


Abstract:
  In this entry point into the subject, combining two elementary proofs, we
decrease the gap between the upper and lower bounds by $0.2\%$ in a classical
combinatorial number theory problem. We show that the maximum size of a Sidon
set of $\{ 1, 2, \ldots, n\}$ is at most $\sqrt{n}+ 0.998n^{1/4}$ for
sufficiently large $n$.


Abstract:
  This paper elaborates the integral transformation technique of [K. Pachucki,
W. Cencek, and J. Komasa, J. Chem. Phys. 122, 184101 (2005)] and uses it for
the case of the non-relativistic kinetic and Coulomb potential energy
operators, as well as for the relativistic mass-velocity and Darwin terms. The
techniques are tested for the ground electronic state of the helium atom and
new results are reported for the ground electronic state of the H$_3^+$
molecular ion near its equilibrium structure.


Abstract:
  By modulating the intensity of laser light before the rotating groundglass,
the well-known pseudothermal light source can be modified into superbunching
pseudothermal light source, in which the degree of second-order coherence of
the scattered light is larger than 2. With the modulated intensities following
binary distribution, we experimentally observed the degree of second- and
third-order coherence equaling 20.45 and 227.07, which is much larger than the
value of thermal or pseudothermal light, 2 and 6, respectively. Numerical
simulation predicts that the degree of second-order coherence can be further
improved by tuning the parameters of binary distribution. It is also predicted
that the quality of temporal ghost imaging can be improved with this
superbunching pseudothermal light. This simple and efficient superbunching
pseudothermal light source provides an interesting alternative to study the
second- and higher-order interference of light in these scenarios where thermal
or pseudothermal light source were employed.


Abstract:
  The solar minimum 23/24 is considered to be unusual because it exhibits
features that differ notably from those commonly seen in pervious minima. In
this letter, we analyze the solar polar magnetic field, the potential-field
solution of the solar corona, and the in-situ solar wind measurements to see
whether the recent solar minimum 24/25 is another unusual one. While the
dipolar configuration that are commonly seen during minimum 22/23 and earlier
minima persist for about half a year after the absolute minimum of solar cycle
24, the corona has a morphology more complex than a simple dipole before the
absolute minimum. The fast solar wind streams are less dominant than minimum
23/24. The IMF strength, density and mass flux that are historically low in the
minimum 23/24 are regained during minimum 24/25, but still do not reach the
minimum 22/23 level. From the analysis of this Letter, it seems that the
minimum 24/25 is only partially unusual, and the recovery of the commonly
minimum features may result from the enhancement of the polar field.


Abstract:
  We use the general relativistic radiation magnetohydrodynamics code
\verb=KORAL= to simulate the early stages of accretion disk formation resulting
from the tidal disruption of a solar mass star around a super massive black
hole (BH) of mass $10^6\,M_\odot$. We simulate the disruption of artificially
more bound stars with orbital eccentricity $e\leq0.99$ (compared to the more
realistic case of parabolic orbits with $e=1$) on close orbits with impact
parameter $\beta\geq3$. We use a novel method of injecting the tidal stream
into the domain. For two simulations, we choose $e=0.99$ and inject mass at a
rate that is similar to realistic TDEs. We find that the disk only becomes
mildly circularized with eccentricity $e\approx0.6$ within the $3.5$ days that
we simulate. The rate of circularization is faster for pericenter radii that
come closer to the BH. The emitted radiation is mildly super-Eddington with
$L_{\rm{bol}}\approx3-5\,L_{\rm{Edd}}$ and the photosphere is highly asymmetric
with the photosphere being significantly closer to the inner accretion disk for
viewing angles near pericenter. We find that soft X-ray radiation with
$T_{\rm{rad}} \approx 3-5\times 10^5$ K may be visible for chance viewing
angles. Our simulations predict that TDEs should be radiatively inefficient
with $\eta\approx0.009-0.014$. These are the first simulations which
simultaneously capture the stream, disk formation, and emitted radiation.


Abstract:
  It's well-known that inverse problems are ill-posed and to solve them
meaningfully one has to employ regularization methods. Traditionally, the most
popular regularization approaches are Variational-type approaches, i.e.,
penalized/constrained functional minimization. In recent years, the classical
regularization approaches have been replaced by the so-called plug-and-play
(PnP) algorithms, which copies the proximal gradient minimization processes,
such as ADMM or FISTA, but with any general denoiser. However, unlike the
traditional proximal gradient methods, the theoretical analysis and convergence
results have been insufficient for these PnP-algorithms. Hence, the results
from these algorithms, though empirically outstanding, are not well-defined, in
the sense of, being a minimizer of a Variational problem. In this paper, we
address this question of "well-definedness", but from a different angle. We
explain these algorithms from the viewpoint of a semi-iterative regularization
method. In addition, we expand the family of regularized solutions,
corresponding to the classical semi-iterative methods, to further generalize
the explainability of these algorithms, as well as, enhance the recovery
process. We conclude with several numerical results which validate the
developed theories and reflect the improvements over the traditional
PnP-algorithms, such as ADMM-PnP and FISTA-PnP.


Abstract:
  Let $(\sigma_N^{(i)})_{i \in I}$ be a family of symmetric permutations of the
entries of a Wigner matrix $\mathbf{W}_N$. We characterize the limiting traffic
distribution of the corresponding family of dependent Wigner matrices
$(\mathbf{W}_N^{\sigma_N^{(i)}})_{i \in I}$ in terms of the geometry of the
permutations. We also consider the analogous problem for the limiting joint
distribution of $(\mathbf{W}_N^{\sigma_N^{(i)}})_{i \in I}$. In particular, we
obtain a description in terms of semicircular families with general covariance
structures. As a special case, we derive necessary and sufficient conditions
for traffic independence as well as sufficient conditions for free
independence.


Abstract:
  This paper presents an efficient equivalent circuit approach (ECA), based on
a Floquet modal expansion, for the study of the co- and cross-polarization in
frequency selective surfaces (FSS) formed by periodic arrays of
patches/apertures in either single or stacked configurations. The ECA makes it
possible the derivation of analytical expressions for the generalized
scattering parameters associated with the proposed circuit networks.
Furthermore, the proposed circuit approach is an efficient surrogate model that
can be combined with optimization techniques and artificial intelligence
algorithms for the efficient design of FSS structures, saving efforts in the
computation compared to time-consuming full-wave simulators and tedious
synthesis (simulation-assisted) techniques. Due to the simplicity of the
topology of the involved networks, the ECA can also be advantageously used to
gain physical insight. The proposed approach is applied and validated in
different FSS configurations where the cross-pol component plays a fundamental
role in the design, as in circular polarizers, polarization rotators, and
reflectarray cells.


Abstract:
  Ongoing or upcoming surveys such as Gaia, ZTF, or LSST will observe
light-curves of billons or more astronomical sources. This presents new
challenges for identifying interesting and important types of variability.
Collecting a sufficient number of labelled data for training is difficult,
however, especially in the early stages of a new survey. Here we develop a
single-band light-curve classifier based on deep neural networks, and use
transfer learning to address the training data paucity problem by conveying
knowledge from one dataset to another. First we train a neural network on 16
variability features extracted from the light-curves of OGLE and EROS-2
variables. We then optimize this model using a small set (e.g. 5%) of periodic
variable light-curves from the ASAS dataset in order to transfer knowledge
inferred from OGLE/EROS-2 to a new ASAS classifier. With this we achieve good
classification results on ASAS, thereby showing that knowledge can be
successfully transferred between datasets. We demonstrate similar transfer
learning using Hipparcos and ASAS-SN data. We therefore find that it is not
necessary to train a neural network from scratch for every new survey, but
rather that transfer learning can be used even when only a small set of
labelled data is available in the new survey.


Abstract:
  Context. The birth environments of planetary systems are thought to influence
planet formation and orbital evolution, through external photoevaporation and
stellar flybys. Recent work has claimed observational support for this, in the
form of a correlation between the properties of planetary systems and the local
Galactic phase space density of the host star. In particular, Hot Jupiters are
found overwhelmingly around stars in regions of high phase space density, which
may reflect a formation environment with high stellar density. Aims. We instead
investigate whether the high phase space density may have a galactic kinematic
origin: Hot Jupiter hosts may be biased towards being young and therefore
kinematically cold, because tidal inspiral leads to the destruction of the
planets on Gyr timescales, and the velocity dispersion of stars in the Galaxy
increases on similar timescales. Methods. We use 6D positions and kinematics
from Gaia for the Hot Jupiter hosts and their neighbours, and construct
distributions of the phase space density. We investigate correlations between
the stars' local phase space density and peculiar velocity. Results. We find a
strong anticorrelation between the phase space density and the host star's
peculiar velocity with respect to the Local Standard of Rest. Therefore, most
stars in "high-density" regions are kinematically cold, which may be caused by
the aforementioned bias towards detecting Hot Jupiters around young stars
before the planets' tidal destruction. Conclusions. We do not find evidence in
the data for Hot Jupiter hosts preferentially being in phase space
overdensities compared to other stars of similar kinematics, nor therefore for
their originating in birth environments of high stellar density.


Abstract:
  In this paper, we study the importance of pruning in Deep Networks (DNs) and
the yin & yang relationship between (1) pruning highly overparametrized DNs
that have been trained from random initialization and (2) training small DNs
that have been "cleverly" initialized. As in most cases practitioners can only
resort to random initialization, there is a strong need to develop a grounded
understanding of DN pruning. Current literature remains largely empirical,
lacking a theoretical understanding of how pruning affects DNs' decision
boundary, how to interpret pruning, and how to design corresponding principled
pruning techniques. To tackle those questions, we propose to employ recent
advances in the theoretical analysis of Continuous Piecewise Affine (CPA) DNs.
From this perspective, we will be able to detect the early-bird (EB) ticket
phenomenon, provide interpretability into current pruning techniques, and
develop a principled pruning strategy. In each step of our study, we conduct
extensive experiments supporting our claims and results; while our main goal is
to enhance the current understanding towards DN pruning instead of developing a
new pruning method, our spline pruning criteria in terms of layerwise and
global pruning is on par with or even outperforms state-of-the-art pruning
methods.


Abstract:
  We develop a numerical method to nonperturbatively study scattering and gluon
emission of a quark from a colored target using a light-front Hamiltonian
approach. The target is described as a classical color field, as in the Color
Glass Condensate effective theory. The Fock space of the scattering system is
restricted to the $\ket{q}+\ket{qg}$ sectors, but the time evolution of this
truncated system is solved exactly. This method allows us to study the
interplay between coherence and multiple scattering in gluon emission. It could
be applied both to studying subeikonal effects in high energy scattering and to
understanding jet quenching in a hot plasma.


Abstract:
  Despite the great progress of person re-identification (ReID) with the
adoption of Convolutional Neural Networks, current ReID models are opaque and
only outputs a scalar distance between two persons. There are few methods
providing users semantically understandable explanations for why two persons
are the same one or not. In this paper, we propose a post-hoc method, named
Attribute-guided Metric Distillation (AMD), to explain existing ReID models.
This is the first method to explore attributes to answer: 1) what and where the
attributes make two persons different, and 2) how much each attribute
contributes to the difference. In AMD, we design a pluggable interpreter
network for target models to generate quantitative contributions of attributes
and visualize accurate attention maps of the most discriminative attributes. To
achieve this goal, we propose a metric distillation loss by which the
interpreter learns to decompose the distance of two persons into components of
attributes with knowledge distilled from the target model. Moreover, we propose
an attribute prior loss to make the interpreter generate attribute-guided
attention maps and to eliminate biases caused by the imbalanced distribution of
attributes. This loss can guide the interpreter to focus on the exclusive and
discriminative attributes rather than the large-area but common attributes of
two persons. Comprehensive experiments show that the interpreter can generate
effective and intuitive explanations for varied models and generalize well
under cross-domain settings. As a by-product, the accuracy of target models can
be further improved with our interpreter.


Abstract:
  Software-defined networking (SDN) was devised to simplify network management
and automate infrastructure sharing in wired networks. These benefits motivated
the application of SDN in wireless sensor networks to leverage solutions for
complex applications. However, some of the core SDN traits turn the networks
prone to denial of service attacks (DoS). There are proposals in the literature
to detect DoS in wireless SDN networks, however, not without shortcomings:
there is little focus on resource constraints, high detection rates have been
reported only for small networks, and the detection is disengaged from the
identification of the type of the attack or the attacker. Our work targets
these shortcomings by introducing a lightweight, online change point detector
to monitor performance metrics that are impacted when the network is under
attack. A key novelty is that the proposed detector is able to operate in
either centralized or distributed mode. The centralized detector has very high
detection rates and can further distinguish the type of the attack (from a list
of known attacks). On the other hand, the distributed detector provides
information that allows to identify the nodes launching the attack. Our
proposal is tested over IEEE 802.15.4 networks. The results show detection
rates exceeding $96\%$ in networks of 36 and 100 nodes and identification of
the type of the attack with a probability exceeding $0.89$ when using the
centralized approach. Additionally, for some types of attack it was possible to
pinpoint the attackers with an identification probability over $0.93$ when
using distributed detectors.


Abstract:
  Prediction tasks with high-dimensional nonorthogonal predictor sets pose a
challenge for least squares based fitting procedures. A large and productive
literature exists, discussing various regularized approaches to improving the
out-of-sample robustness of parameter estimates. This paper proposes a novel
cluster-based regularization - the hierarchical feature regression (HFR) -,
which mobilizes insights from the domains of machine learning and graph theory
to estimate parameters along a supervised hierarchical representation of the
predictor set, shrinking parameters towards group targets. The method is
innovative in its ability to estimate optimal compositions of predictor groups,
as well as the group targets endogenously. The HFR can be viewed as a
supervised factor regression, with the strength of shrinkage governed by a
penalty on the extent of idiosyncratic variation captured in the fitting
process. The method demonstrates good predictive accuracy and versatility,
outperforming a panel of benchmark regularized estimators across a diverse set
of simulated regression tasks, including dense, sparse and grouped data
generating processes. An application to the prediction of economic growth is
used to illustrate the HFR's effectiveness in an empirical setting, with
favorable comparisons to several frequentist and Bayesian alternatives.


Abstract:
  System-of-systems (SoS) approach is often used for simulating disruptions to
business and infrastructure system networks allowing for integration of several
models into one simulation. However, the integration is frequently challenging
as each system is designed individually with different characteristics, such as
time granularity. Understanding the impact of time granularity on propagation
of disruptions between businesses and infrastructure systems and finding the
appropriate granularity for the SoS simulation remain as major challenges. To
tackle these, we explore how time granularity, recovery time, and disruption
size affect the propagation of disruptions between constituent systems of an
SoS simulation. To address this issue, we developed a High Level Architecture
(HLA) simulation of 3 networks and performed a series of simulation
experiments. Our results revealed that time granularity and especially recovery
time have huge impact on propagation of disruptions. Consequently, we developed
a model for selecting an appropriate time granularity for an SoS simulation
based on expected recovery time. Our simulation experiments show that time
granularity should be less than 1.13 of expected recovery time. We identified
some areas for future research centered around extending the experimental
factors space.


Abstract:
  We present Atacama Large Millimeter/submillimeter Array (ALMA) gas and dust
observations at band 7 (339~GHz: 0.89~mm) of the protoplanetary disk around a
very low mass star ZZ~Tau~IRS with a spatial resolution of 0\farcs25. The
$^{12}$CO~$J=3\rightarrow2$ position--velocity diagram suggests a dynamical
mass of ZZ~Tau~IRS of $\sim$0.1--0.3~$M_{\sun}$. The disk has a total flux
density of 273.9 mJy, corresponding to an estimated mass of 24--50~$M_\oplus$
in dust. The dust emission map shows a ring at $r=$ 58~au and an azimuthal
asymmetry at $r=$ \jh{45}~au with a position angle of 135\degr. The properties
of the asymmetry, including radial width, aspect ratio, contrast, and
contribution to the total flux, were found to be similar to the asymmetries
around intermediate mass stars ($\sim$2~$M_{\sun}$) such as MWC~758 and IRS~48.
This implies that the asymmetry in the ZZ~Tau~IRS disk shares a similar origin
with others, despite the star being $\sim$10 times less massive. Our
observations also suggest that the inner and outer parts of the disk may be
misaligned. Overall, the ZZ~Tau~IRS disk shows evidence of giant planet
formation at $\sim$10 au scale at a few Myr. If confirmed, it will challenge
existing core accretion models, in which such planets have been predicted to be
extremely hard to form around very low mass stars.


Abstract:
  In this paper, following Nourdin-Peccati's methodology, we combine the
Malliavin calculus and Stein's method to provide general bounds on the
Wasserstein distance between functionals of a compound Hawkes process and a
given Gaussian density. To achieve this, we rely on the Poisson embedding
representation of an Hawkes process to provide a Malliavin calculus for the
Hawkes processes, and more generally for compound Hawkes processes. As an
application, we close a gap in the literature by providing the first
Berry-Ess\'een bounds associated to Central Limit Theorems for the compound
Hawkes process.


Abstract:
  Phase transitions and critical phenomena, which are dominated by fluctuations
and correlations, are one of the fields replete with physical paradigms and
unexpected discoveries. Especially for two-dimensional magnetism, the
limitation of the Ginzburg criterion leads to enhanced fluctuations breaking
down the mean-field theory near a critical point. Here, by means of magnetic
resonance, we investigate the behavior of critical fluctuations in the
two-dimensional ferromagnetic insulators $\rm CrXTe_3 (X=Si, Ge)$. After
deriving the classical and quantum models of magnetic resonance, we deem the
dramatic anisotropic shift of the measured $g$ factor to originate from
fluctuations with anisotropic interactions. The deduction of the $g$ factor
behind the fluctuations is consistent with the spin-only state (${g\approx}$
2.050(10) for $\rm CrSiTe_3$ and 2.039(10) for $\rm CrGeTe_3$). Furthermore,
the abnormal enhancement of $g$ shift, supplemented by specific heat and
magnetometry measurements, suggests that $\rm CrSiTe_3$ exhibits a more typical
two-dimensional nature than $\rm CrGeTe_3$ and may be closer to the quantum
critical point.


Abstract:
  We present an algorithm for determining whether a bipartite graph $G$ is
2-chordal (formerly doubly chordal bipartite). At its core this algorithm is an
extension of the existing efficient algorithm for determining whether a graph
is chordal bipartite. We then introduce the notion of $k$-chordal bipartite
graphs and show by inductive means that a slight modification of our algorithm
is sufficient to detect this property. We show that there are no nontrivial
$k$-chordal bipartite graphs for $k \geq 4$ and that both the 2-chordal
bipartite and 3-chordal bipartite problem are contained within complexity class
P.


Abstract:
  For a continuous state branching process with two types of individuals which
are subject to selection and density dependent competition, we characterize the
joint evolution of population size, type configurations and genealogies as the
unique strong solution of a system of SDE's. Our construction is achieved in
the lookdown framework and provides a synthesis as well as a generalization of
cases considered separately in two seminal papers by Donnelly and Kurtz (1999),
namely fluctuating population sizes under neutrality, and selection with
constant population size. As a conceptual core in our approach, we introduce
the selective lookdown space which is obtained from its neutral counterpart
through a state-dependent thinning of "potential" selection/competition events
whose rates interact with the evolution of the type densities. The updates of
the genealogical distance matrix at the "active" selection/competition events
are obtained through an appropriate sampling from the selective lookdown space.
The solution of the above mentioned system of SDE's is then mapped into the
joint evolution of population size and symmetrized type configurations and
genealogies, i.e. marked distance matrix distributions. By means of Kurtz's
Markov mapping theorem, we characterize the latter process as the unique
solution of a martingale problem. For the sake of transparency we restrict the
main part of our presentation to a prototypical example with two types, which
contains the essential features. In the final section we outline an extension
to processes with multiple types including mutation.


Abstract:
  We use mixed correlators in thermal CFT as clean probes of the strong gravity
effects in their holographic duals. The dual interpretation of mixing is an
inelastic conversion of one field to another field, induced by gravity: tidal
excitation. We find an enhanced mixing at high temperatures, corresponding to
large AdS black holes, concentrated to small boundary momenta, dual to the deep
bulk, where strong gravitational fields are expected. We also find large
$\mathcal{O}(1/G_{N})$ tidal conversion in the low temperature phase of the
$U(N)$ vector model, strengthening suspicions that the bulk dual of this phase
also houses extremely compact objects.


Abstract:
  Fluctuation theorems are fundamental extensions of the second law of
thermodynamics for small nonequilibrium systems. While work and heat are
equally important forms of energy exchange, fluctuation relations have not been
experimentally assessed for the generic situation of simultaneous mechanical
and thermal changes. Thermal driving is indeed generally slow and more
difficult to realize than mechanical driving. Here, we use feedback cooling
techniques to implement fast and controlled temperature variations of an
underdamped levitated microparticle that are one order of magnitude faster than
the equilibration time. Combining mechanical and thermal control, we verify the
validity of a fluctuation theorem that accounts for both contributions, well
beyond the range of linear response theory. Our results allow the investigation
of general far-from-equilibrium processes in microscopic systems that involve
fast mechanical and thermal changes at the same time.


Abstract:
  We introduce a class of $\gamma$-negatively dependent random samples. We
prove that this class includes, apart from Monte Carlo samples, in particular
Latin hypercube samples and Latin hypercube samples padded by Monte Carlo.
  For a $\gamma$-negatively dependent $N$-point sample in dimension $d$ we
provide probabilistic upper bounds for its star discrepancy with explicitly
stated dependence on $N$, $d$, and $\gamma$. These bounds generalize the
probabilistic bounds for Monte Carlo samples from [Heinrich et al., Acta Arith.
96 (2001), 279--302] and [C.~Aistleitner, J.~Complexity 27 (2011), 531--540],
and they are optimal for Monte Carlo and Latin hypercube samples. In the
special case of Monte Carlo samples the constants that appear in our bounds
improve substantially on the constants presented in the latter paper and in
[C.~Aistleitner, M.~T.~Hofer, Math. Comp.~83 (2014), 1373--1381].


Abstract:
  One key vertical application that will be enabled by 6G is the automation of
the processes with the increased use of robots. As a result, sensing and
localization of the surrounding environment becomes a crucial factor for these
robots to operate. Light detection and ranging (LiDAR) has emerged as an
appropriate method of sensing due to its capability of generating detail-rich
information with high accuracy. However, LiDARs are power hungry devices that
generate a lot of data, and these characteristics limit their use as on-board
sensors in robots. In this paper, we present a novel approach on the
methodology of generating an enhanced 3D map with improved field-of-view using
multiple LiDAR sensors. We utilize an inherent property of LiDAR point clouds;
rings and data from the inertial measurement unit (IMU) embedded in the sensor
for registration of the point clouds. The generated 3D map has an accuracy of
10 cm when compared to the real-world measurements. We also carry out the
practical implementation of the proposed method using two LiDAR sensors.
Furthermore, we develop an application to utilize the generated map where a
robot navigates through the mapped environment with minimal support from the
sensors on-board. The LiDARs are fixed in the infrastructure at elevated
positions. Thus this is applicable to vehicular and factory scenarios. Our
results further validate the idea of using multiple elevated LiDARs as a part
of the infrastructure for various applications.


Abstract:
  Upcoming measurements of the small-scale primary cosmic microwave background
(CMB) temperature and polarization power spectra ($TT$/$TE$/$EE$) are
anticipated to yield transformative constraints on new physics, including the
effective number of relativistic species in the early universe ($N_{\rm eff}$).
However, at multipoles $\ell \gtrsim 3000$, the primary CMB power spectra
receive significant contributions from gravitational lensing. While these modes
still carry primordial information, their theoretical modeling requires
knowledge of the CMB lensing convergence power spectrum, $C_L^{\kappa\kappa}$,
including on small scales where it is affected by nonlinear gravitational
evolution and baryonic feedback processes. Thus, the high-$\ell$ primary CMB is
sensitive to these late-time, nonlinear effects. Here, we show that
inaccuracies in the modeling of $C_L^{\kappa\kappa}$ can yield surprisingly
large biases on cosmological parameters inferred from the primary CMB power
spectra measured by the upcoming Simons Observatory and CMB-S4 experiments. For
CMB-S4, the biases can be as large as $1.6\sigma$ on the Hubble constant $H_0$
in a fit to $\Lambda$CDM and $1.2\sigma$ on $N_{\rm eff}$ in a fit to
$\Lambda$CDM+$N_{\rm eff}$.
  We show that these biases can be mitigated by explicitly discarding all $TT$
data at $\ell>3000$ or by marginalizing over parameters describing baryonic
feedback processes, both at the cost of slightly larger error bars. We also
discuss an alternative, data-driven mitigation strategy based on delensing the
CMB $T$ and $E$-mode maps. Finally, we show that analyses of upcoming data will
require Einstein-Boltzmann codes to be run with much higher numerical precision
settings than is currently standard, so as to avoid similar -- or larger --
parameter biases due to inaccurate theoretical predictions.


Abstract:
  The bus system is a critical component of sustainable urban transportation.
However, due to the significant uncertainties in passenger demand and traffic
conditions, bus operation is unstable in nature and bus bunching has become a
common phenomenon that undermines the reliability and efficiency of bus
services. Despite recent advances in multi-agent reinforcement learning (MARL)
on traffic control, little research has focused on bus fleet control due to the
tricky asynchronous characteristic -- control actions only happen when a bus
arrives at a bus stop and thus agents do not act simultaneously. In this study,
we formulate route-level bus fleet control as an asynchronous multi-agent
reinforcement learning (ASMR) problem and extend the classical actor-critic
architecture to handle the asynchronous issue. Specifically, we design a novel
critic network to effectively approximate the marginal contribution for other
agents, in which graph attention neural network is used to conduct inductive
learning for policy evaluation. The critic structure also helps the ego agent
optimize its policy more efficiently. We evaluate the proposed framework on
real-world bus services and actual passenger demand derived from smart card
data. Our results show that the proposed model outperforms both traditional
headway-based control methods and existing MARL methods.


Abstract:
  Insight into the electroweak (EW) and Higgs sectors can be achieved through
measurements of vector boson scattering (VBS) processes. The scattering of EW
bosons are rare processes that are precisely predicted in the Standard Model
(SM) and are closely related to the Higgs mechanism. Modifications to VBS
processes are also predicted in models of physics beyond the SM (BSM), for
example through changes to the Higgs boson couplings to gauge bosons and the
resonant production of new particles. In this review, experimental results and
theoretical developments of VBS at the Large Hadron Collider, its high
luminosity upgrade, and future colliders are presented.


Abstract:
  Using an artificial neutral network we explore the parameter space of
supergravity grand unified models consistent with the combined Fermilab E989
and Brookhaven E821 data on $(g-2)_\mu$. The analysis indicates that the region
favored by the data is the one generated by gluino-driven radiative breaking of
the electroweak symmetry. This region naturally leads to a split sparticle
spectrum with light sleptons and weakinos but heavy squarks, with the stau and
the chargino as the lightest charged particles. We show that if the entire
deviation from the standard model $(g-2)_{\mu}$ arises from supersymmetry, then
supersymmetry is discoverable at HL-LHC and HE-LHC via production and decay of
sleptons within the optimal integrated luminosity of HL-LHC and with a smaller
integrated luminosity at HE-LHC.


Abstract:
  Researchers have long observed that the "small-world" property, which
combines the concepts of high transitivity or clustering with a low average
path length, is ubiquitous for networks obtained from a variety of disciplines
including social sciences, biology, neuroscience, and ecology. However, we find
three shortcomings of the currently popular definition and detection methods
rendering the concept less powerful. First, the classical definition combines
high transitivity with a low average path length in a rather ad-hoc fashion
which confounds the two separate aspects. We find that in several cases,
networks get flagged as "small world" by the current methodology solely because
of their high transitivity. Second, the detection methods lack a formal
statistical inference, and third, the comparison is typically performed against
simplistic random graph models as the baseline which ignores well-known network
characteristics. We propose three innovations to address these issues. First,
we decouple the properties of high transitivity and low average path length as
separate events to test for. Second, we define the property as a statistical
test between a suitable null model and a superimposed alternative model. Third,
the test is performed using parametric bootstrap with several null models to
allow a wide range of background structures in the network. In addition to the
bootstrap tests, we also propose an asymptotic test under the
Erd\"{o}s-Ren\'{y}i null model for which we provide theoretical guarantees on
the asymptotic level and power. Applying the proposed methods on a large number
of network datasets, we uncover new insights about their small-world property.


Abstract:
  The zeroth law is one of the oldest conjecture in turbulence that is still
unproven. Here, we consider weak solutions of one-dimensional compressible
magnetohydrodynamics and demonstrate that the lack of smoothness of the fields
introduces a new dissipative term, named inertial dissipation, into the
expression of energy conservation that is neither viscous nor resistive in
nature. We propose exact solutions assuming that the kinematic viscosity and
the magnetic diffusivity are equal, and we demonstrate that the associated
inertial dissipation is positive and equal on average to the mean viscous
dissipation rate in the limit of small viscosity, proving the conjecture of the
zeroth law of turbulence and the existence of an anomalous dissipation. As an
illustration, we evaluate the shock heating produced by discontinuities
detected by Voyager in the solar wind around 5 AU. We deduce a heating rate of
$\sim 10^{-18}$J m$^{-3}$ s$^{-1}$, which is significantly higher than the
value obtained from the turbulent fluctuations. This suggests that
collisionless shocks can be a dominant source of heating in the outer solar
wind.


Abstract:
  The $\Lambda$CDM model provides a good fit to a large span of cosmological
data but harbors areas of phenomenology. With the improvement of the number and
the accuracy of observations, discrepancies among key cosmological parameters
of the model have emerged. The most statistically significant tension is the
$4-6\sigma$ disagreement between predictions of the Hubble constant $H_0$ by
early time probes with $\Lambda$CDM model, and a number of late time,
model-independent determinations of $H_0$ from local measurements of distances
and redshifts. The high precision and consistency of the data at both ends
present strong challenges to the possible solution space and demand a
hypothesis with enough rigor to explain multiple observations--whether these
invoke new physics, unexpected large-scale structures or multiple, unrelated
errors. We present a thorough review of the problem, including a discussion of
recent Hubble constant estimates and a summary of the proposed theoretical
solutions. Some of the models presented are formally successful, improving the
fit to the data in light of their additional degrees of freedom, restoring
agreement within $1-2\sigma$ between {\it Planck} 2018, using CMB power spectra
data, BAO, Pantheon SN data, and R20, the latest SH0ES Team measurement of the
Hubble constant ($H_0 = 73.2 \pm 1.3{\rm\,km\,s^{-1}\,Mpc^{-1}}$ at 68\%
confidence level). Reduced tension might not simply come from a change in $H_0$
but also from an increase in its uncertainty due to degeneracy with additional
physics, pointing to the need for additional probes. While no specific proposal
makes a strong case for being highly likely or far better than all others,
solutions involving early or dynamical dark energy, neutrino interactions,
interacting cosmologies, primordial magnetic fields, and modified gravity
provide the best options until a better alternative comes along.[Abridged]


Abstract:
  An iterative configuration interaction (iCI)-based multiconfigurational
self-consistent field (SCF) theory, iCISCF, is proposed to handle systems that
require large complete active spaces (CAS). The success of iCISCF stems from
three ingredients: (1) efficient selection of individual configuration state
functions spanning the CAS, meanwhile maintaining full spin symmetry; (2) the
use of Jacobi rotation for the optimization of active orbitals, in conjunction
with a quasi-Newton algorithm for the core/active-virtual and core-active
orbital rotations; (3) a second-order perturbative treatment of the residual
space left over by the selection procedure (i.e., iCISCF(2)). Just like
selected iCI being a very accurate approximation to CASCI, iCISCF(2) is a very
accurate approximation to CASSCF. Several examples that go beyond the
capability of CASSCF are taken as showcases to reveal the performances of
iCISCF and iCISCF(2).


Abstract:
  We consider a homogeneous plasma composed of $N$ particles of the same
electric charge which interact through a Coulomb potential. In the large plasma
parameter limit, classical kinetic theories justify that the empirical density
is the solution of the Balescu-Guernsey-Lenard equation, at leading order. This
is a law of large numbers. The Balescu-Guernsey-Lenard equation is approximated
by the Landau equation for scales much smaller than the Debye length. In order
to describe typical and rare fluctuations, we compute for the first time a
large deviation principle for dynamical paths of the empirical density, within
the Landau approximation. We obtain a large deviation Hamiltonian that
describes fluctuations and rare excursions of the empirical density, in the
large plasma parameter limit. We obtain this large deviation Hamiltonian either
from the Boltzmann large deviation Hamiltonian in the grazing collision limit,
or directly from the dynamics, extending the classical kinetic theory for
plasmas within the Landau approximation. We also derive the large deviation
Hamiltonian for the empirical density of $N$ particles, each of which is
governed by a Markov process, and coupled in a mean field way. We explain that
the plasma large deviation Hamiltonian is not the one of $N$ particles coupled
in a mean-field way.


Abstract:
  Disasters are constant threats to humankind, and beyond losses in lives, they
cause many implicit yet profound societal issues such as wealth disparity and
digital divide. Among those recovery measures in the aftermath of disasters,
restoring and improving communication services is of vital importance. Although
existing works have proposed many architectural and protocol designs, none of
them have taken human factors and social equality into consideration. Recent
sociological studies have shown that people from marginalized groups (e.g.,
minority, low income, and poor education) are more vulnerable to communication
outages. In this work, we take pioneering efforts in integrating human factors
into an empirical optimization model to determine strategies for post-disaster
communication restoration. We cast the design into a mix-integer non-linear
programming problem, which is proven too complex to be solved. Through a suite
of convex relaxations, we then develop heuristic algorithms to efficiently
solve the transformed optimization problem. Based on a collected dataset, we
further evaluate and demonstrate how our design will prioritize communication
services for vulnerable people and promote social equality compared with an
existing modeling benchmark.


Abstract:
  The Maryland model was introduced more than 30 years ago as an integrable
model of localization by aperiodic order. Even though quite popular and rich of
fascinating mathematical properties, this model has so far remained quite
artificial, as compared to other models displaying dynamical localization like
the periodically-kicked quantum rotator or the Aubry-Andre$^{\prime}$ model.
Here we suggest that light propagation in a polygonal optical waveguide lattice
provides a photonic realization of the Maryland model and enables to observe a
main prediction of this model, namely fragility of wave localization in the
commensurate potential limit.


Abstract:
  An analysis involving the light front variables is performed over the
inclusively produced $\pi^{\pm}$, $K^{\pm}$ and $p(\bar{p})$ in the Pb-Pb
collisions simulated using the UrQMD event generator at $\sqrt{s} = 2.76$ TeV
to study the possible formation of a thermalised medium in these collisions. It
is demonstrated that there exist surfaces defined by constant values of the
light front variable in the phase space of these hadrons which can select a
group of thermalised particles. The temperatures are extracted for each species
and are compared with those obtained from a similar calculation involving the
kinematical and acceptance restrictions which are typically bound to the
collider experiments. It is shown that the analysis is feasible to perform with
the data collected at the ALICE experiment at LHC. The analysis may be
considered as a prototypical study to a similar experimental analysis of the
data from the heavy-ion collisions at RHIC and LHC after implementing the
necessary corrections and calibrations relevant to the experimental
apparatuses.


Abstract:
  Cryo-Electron Tomography (cryo-ET) is a new 3D imaging technique with
unprecedented potential for resolving submicron structural detail. Existing
volume visualization methods, however, cannot cope with its very low
signal-to-noise ratio. In order to design more powerful transfer functions, we
propose to leverage soft segmentation as an explicit component of visualization
for noisy volumes. Our technical realization is based on semi-supervised
learning where we combine the advantages of two segmentation algorithms. A
first weak segmentation algorithm provides good results for propagating sparse
user provided labels to other voxels in the same volume. This weak segmentation
algorithm is used to generate dense pseudo labels. A second powerful
deep-learning based segmentation algorithm can learn from these pseudo labels
to generalize the segmentation to other unseen volumes, a task that the weak
segmentation algorithm fails at completely. The proposed volume visualization
uses the deep-learning based segmentation as a component for segmentation-aware
transfer function design. Appropriate ramp parameters can be suggested
automatically through histogram analysis. Finally, our visualization uses
gradient-free ambient occlusion shading to further suppress visual presence of
noise, and to give structural detail desired prominence. The cryo-ET data
studied throughout our technical experiments is based on the highest-quality
tilted series of intact SARS-CoV-2 virions. Our technique shows the high impact
in target sciences for visual data analysis of very noisy volumes that cannot
be visualized with existing techniques.


Abstract:
  Magnetars have been proposed to be the origin of FRBs soon after its initial
discovery. The detection of the first Galactic FRB 20200428 from SGR 1935+2154
has made this hypothesis more convincing. In October 2020, this source was
supposed to be in an extremely active state again. We then carried out a
1.6-hours follow-up observation of SGR 1935+2154 using the new ultra-wideband
low (UWL) receiver of the Parkes 64\,m radio telescope covering a frequency
range of 704$-$4032 MHz. However, no convincing signal was detected in either
of our single pulse or periodicity searches. We obtained a limit on the flux
density of periodic signal of $\rm 3.6\,\mu Jy$ using the full 3.3GHz bandwidth
data sets, which is the strictest limit for that of SGR 1935+2154. Our full
bandwidth limit on the single pulses fluence is 35mJy ms, which is well below
the brightest single pulses detected by the FAST radio telescope just two
before our observation. Assuming that SGR 1935+2154 is active during our
observation, our results suggest that its radio bursts are either intrinsically
narrowband or show a steep spectrum.


Abstract:
  A unit fraction representation of a rational number $r$ is a finite sum of
reciprocals of positive integers that equals $r$. Of particular interest is the
case when all denominators in the representation are distinct, resulting in an
Egyptian fraction representation of $r$. Common algorithms for computing
Egyptian fraction representations of a given rational number tend to result in
extremely large denominators and cannot be adapted to restrictions on the
allowed denominators. We describe an algorithm for finding all unit fraction
representations of a given rational number using denominators from a given
finite multiset of positive integers. The freely available algorithm,
implemented in Scheme, is particularly well suited to computing dense Egyptian
fraction representations, where the allowed denominators have a prescribed
maximum.