title:
Mitigating Closed-model Adversarial Examples with Bayesian Neural
  Modeling for Enhanced End-to-End Speech Recognition

abstract:
  In this work, we aim to enhance the system robustness of end-to-end automatic
speech recognition (ASR) against adversarially-noisy speech examples. We focus
on a rigorous and empirical "closed-model adversarial robustness" setting
(e.g., on-device or cloud applications). The adversarial noise is only
generated by closed-model optimization (e.g., evolutionary and zeroth-order
estimation) without accessing gradient information of a targeted ASR model
directly. We propose an advanced Bayesian neural network (BNN) based
adversarial detector, which could model latent distributions against adaptive
adversarial perturbation with divergence measurement. We further simulate
deployment scenarios of RNN Transducer, Conformer, and wav2vec-2.0 based ASR
systems with the proposed adversarial detection system. Leveraging the proposed
BNN based detection system, we improve detection rate by +2.77 to +5.42%
(relative +3.03 to +6.26%) and reduce the word error rate by 5.02 to 7.47% on
LibriSpeech datasets compared to the current model enhancement methods against
the adversarial speech examples.


title: AISHELL-NER: Named Entity Recognition from Chinese Speech

abstract:
  Named Entity Recognition (NER) from speech is among Spoken Language
Understanding (SLU) tasks, aiming to extract semantic information from the
speech signal. NER from speech is usually made through a two-step pipeline that
consists of (1) processing the audio using an Automatic Speech Recognition
(ASR) system and (2) applying an NER tagger to the ASR outputs. Recent works
have shown the capability of the End-to-End (E2E) approach for NER from English
and French speech, which is essentially entity-aware ASR. However, due to the
many homophones and polyphones that exist in Chinese, NER from Chinese speech
is effectively a more challenging task. In this paper, we introduce a new
dataset AISEHLL-NER for NER from Chinese speech. Extensive experiments are
conducted to explore the performance of several state-of-the-art methods. The
results demonstrate that the performance could be improved by combining
entity-aware ASR and pretrained NER tagger, which can be easily applied to the
modern SLU pipeline. The dataset is publicly available at
github.com/Alibaba-NLP/AISHELL-NER.


title: A New Mechanism for Noncollision Singularities

abstract:
  In this paper, we prove the existence of noncollision singularities in a
planar four-body problem in a model different from [J. Xue,Acta
Math.V224(2)253-388, 2020.]. In this model, the acceleration can be arbitrarily
fast and the masses can be comparable. This work provides a general principle
to construct noncollision singularities as well as other related orbits with
complicited dynamics. It not only answers a question in [J. Xue,Acta
Math.V224(2) 253-388, 2020.] but also solves an analogous version of a
conjecture of Anosov.


title: Integrable branes in generalized $\lambda$-deformations

abstract:
  We search for integrable boundary conditions and their geometric
interpretation as $D$-branes, in models constructed as generalized
$\lambda$-deformations of products of group- and coset-spaces. Using the
sigma-model approach, we find that all the conformal brane geometries known in
the literature for a product of WZW models solve the corresponding boundary
conditions, thus persisting as integrable branes along the RG flows of our
sigma-models. They consist of the well known $G$-conjugacy classes, twisted
$G$-conjugacy classes by a permutation automorphism (permutation branes) and
generalized permutation branes. Subsequently, we study the properties of the
aforementioned brane geometries, especially of those embedded in the
backgrounds interpolating between the UV and IR fixed points.


title:
Does the End Justify the Means? On the Moral Justification of
  Fairness-Aware Machine Learning

abstract:
  Despite an abundance of fairness-aware machine learning (fair-ml) algorithms,
the moral justification of how these algorithms enforce fairness metrics is
largely unexplored. The goal of this paper is to elicit the moral implications
of a fair-ml algorithm. To this end, we first consider the moral justification
of the fairness metrics for which the algorithm optimizes. We present an
extension of previous work to arrive at three propositions that can justify the
fairness metrics. Different from previous work, our extension highlights that
the consequences of predicted outcomes are important for judging fairness. We
draw from the extended framework and empirical ethics to identify moral
implications of the fair-ml algorithm. We focus on the two optimization
strategies inherent to the algorithm: group-specific decision thresholds and
randomized decision thresholds. We argue that the justification of the
algorithm can differ depending on one's assumptions about the (social) context
in which the algorithm is applied - even if the associated fairness metric is
the same. Finally, we sketch paths for future work towards a more complete
evaluation of fair-ml algorithms, beyond their direct optimization objectives.


title:
Domain Adaptation for Underwater Image Enhancement via Content and Style
  Separation

abstract:
  Underwater image suffer from color cast, low contrast and hazy effect due to
light absorption, refraction and scattering, which degraded the high-level
application, e.g, object detection and object tracking. Recent learning-based
methods demonstrate astonishing performance on underwater image enhancement,
however, most of these works use synthesis pair data for supervised learning
and ignore the domain gap to real-world data. In this paper, we propose a
domain adaptation framework for underwater image enhancement via content and
style separation, we assume image could be disentangled to content and style
latent, and image could be clustered to the sub-domain of associated style in
latent space, the goal is to build up the mapping between underwater style
latent and clean one. Different from prior works of domain adaptation for
underwater image enhancement, which target to minimize the latent discrepancy
of synthesis and real-world data, we aim to distinguish style latent from
different sub-domains. To solve the problem of lacking pair real-world data, we
leverage synthesis to real image-to-image translation to obtain pseudo real
underwater image pairs for supervised learning, and enhancement can be achieved
by input content and clean style latent into generator. Our model provide a
user interact interface to adjust different enhanced level by latent
manipulation. Experiment on various public real-world underwater benchmarks
demonstrate that the proposed framework is capable to perform domain adaptation
for underwater image enhancement and outperform various state-of-the-art
underwater image enhancement algorithms in quantity and quality. The model and
source code are available at https://github.com/fordevoted/UIESS


title:
Two-dimensional structure functions to characterize convective rolls in
  the marine atmospheric boundary layer from Sentinel-1 SAR images

abstract:
  We study the shape of convective rolls in the Marine Atmospheric Boundary
Layer from Synthetic Aperture Radar images of the ocean. We propose a
multiscale analysis with structure functions which allow an easy generalization
to analyse high-order statistics and so to finely describe the shape of the
rolls. The two main results are : 1) second order structure function
characterizes the size and direction of rolls just like correlation or power
spectrum do, 2) high order statistics can be studied with skewness and Flatness
which characterize the asymmetry and intermittency of rolls respectively. From
the best of our knowledge, this is the first time that the asymmetry and
intermittency of rolls is shown from radar images of the ocean surface.

categories: cs.IT eess.SP math.IT physics.ao-ph physics.data-an physics.flu-dyn


title: When, where, and how to add new neurons to ANNs

abstract:
  Neurogenesis in ANNs is an understudied and difficult problem, even compared
to other forms of structural learning like pruning. By decomposing it into
triggers and initializations, we introduce a framework for studying the various
facets of neurogenesis: when, where, and how to add neurons during the learning
process. We present the Neural Orthogonality (NORTH*) suite of neurogenesis
strategies, combining layer-wise triggers and initializations based on the
orthogonality of activations or weights to dynamically grow performant networks
that converge to an efficient size. We evaluate our contributions against other
recent neurogenesis works with MLPs.


title:
Quantum Membrane Phases in Synthetic Lattices of Cold Molecules or
  Rydberg Atoms

abstract:
  We calculate properties of dipolar interacting ultracold molecules or Rydberg
atoms in a semi-synthetic three-dimensional configuration -- one synthetic
dimension plus a two-dimensional real space optical lattice or periodic
microtrap array -- using the stochastic Green function Quantum Monte Carlo
method. Through a calculation of thermodynamic quantities and appropriate
correlation functions, along with their finite size scalings, we show that
there is a second order transition to a low temperature phase in which
two-dimensional `sheets' form in the synthetic dimension of internal rotational
or electronic states of the molecules or Rydberg atoms, respectively.
Simulations for different values of the interaction $V$, which acts between
atoms or molecules that are adjacent both in real and synthetic space, allow us
to compute a phase diagram. We find a finite-temperature transition at
sufficiently large $V$, as well as a quantum phase transition -- a critical
value $V_c$ below which the transition temperature vanishes.


title: Fokker-Planck Multi-Species Equations in the Adiabatic Asymptotics

abstract:
  The main concern of the present paper is the study of the multi-scale
dynamics of thermonuclear fusion plasmas via a multi-species Fokker-Planck
kinetic model. One of the goals is the generalization of the standard
Fokker-Planck collision operator to a multispecies one, conserving mass, total
momentum and energy, as well as satisfying Boltzmann's H-theorem. Secondly, the
paper investigates in more details the reduced model used for the electron
description in present simulations, and which considers the electrons in a
thermodynamic equilibrium (adiabatic regime), whereas the ions are kept
kinetic. On the one hand, we perform some mathematical asymptotic limits to
obtain in the electron/ion low mass ratio limit the above-mentioned electron
adiabatic regime. On the other hand, we develop a numerical scheme , based on a
Hermite spectral method, and perfom numerical simulations to illustrate and
investigate in more details this asymptotics.


title:
The electromagnetic amplitudes in the $J/\psi$ and $\psi(2S)$ decays
  into spin-1/2 baryon-antibaryon pairs

abstract:
  After investigating the decays of a charmonium $\psi = J/\psi, \psi(2S)$ into
a spin-1/2 baryon-antibaryon $B \bar{B}$ pair, with the determination of the
only parameter that gives the EM amplitude for neutral final states, in this
work we focus our attention on the decays into charged baryons, whose EM
amplitudes can be expressed in terms of a further parameter. By considering the
BESIII data on the $e^+e^- \to p \overline p$ cross section we obtain a full
parametrization for the EM amplitudes and make predictions on the cross section
of the decays $e^+ e^- \to \Sigma^+ \overline \Sigma{}^-$, $e^+ e^- \to
\Sigma^- \overline \Sigma{}^+$ and $e^+ e^- \to \Xi^- \overline \Xi{}^+$ at the
$J/\psi$ and $\psi(2S)$ masses.


title:
Resilience-Motivated Distribution System Restoration Considering
  Electricity-Water-Gas Interdependency

abstract:
  A major outage in the electricity distribution system may affect the
operation of water and natural gas supply systems, leading to an interruption
of multiple services to critical customers. Therefore, enhancing resilience of
critical infrastructures requires joint efforts of multiple sectors. In this
paper, a distribution system service restoration method considering the
electricity-water-gas interdependency is proposed. The objective is to provide
electricity, water, and natural gas supplies to critical customers in the
desired ratio according to their needs after an extreme event. The operational
constraints of electricity, water, and natural gas networks are considered. The
characteristics of electricity-driven coupling components, including water
pumps and gas compressors, are also modeled. Relaxation techniques are applied
to nonconvex constraints posed by physical laws of those networks.
Consequently, the restoration problem is formulated as a mixed-integer
second-order cone program, which can readily be solved by the off-the-shelf
solvers. The proposed method is validated by numerical simulations on
electricity-water-gas integrated systems, developed based on benchmark models
of the subsystems. The results indicate that considering the interdependency
refines the allocation of limited generation resources and demonstrate the
exactness of the proposed convex relaxation.


title: Efficient Classification of Local Problems in Regular Trees

abstract:
  We give practical, efficient algorithms that automatically determine the
distributed round complexity of a given locally checkable graph problem, in two
settings. We present one algorithm for unrooted regular trees and another
algorithm for rooted regular trees. The algorithms take the description of a
locally checkable labeling problem as input, and the running time is polynomial
in the size of the problem description. The algorithms decide if the problem is
solvable in $O(\log n)$ rounds. If not, it is known that the complexity has to
be $\Theta(n^{1/k})$ for some $k = 1, 2, \dotsc$, and in this case the
algorithms also output the right value of the exponent $k$.
  In rooted trees in the $O(\log n)$ case we can then further determine the
exact complexity class by using algorithms from prior work; for unrooted trees
the more fine-grained classification in the $O(\log n)$ region remains an open
question.


title: Information Theory with Kernel Methods

abstract:
  We consider the analysis of probability distributions through their
associated covariance operators from reproducing kernel Hilbert spaces. We show
that the von Neumann entropy and relative entropy of these operators are
intimately related to the usual notions of Shannon entropy and relative
entropy, and share many of their properties. They come together with efficient
estimation algorithms from various oracles on the probability distributions. We
also consider product spaces and show that for tensor product kernels, we can
define notions of mutual information and joint entropies, which can then
characterize independence perfectly, but only partially conditional
independence. We finally show how these new notions of relative entropy lead to
new upper-bounds on log partition functions, that can be used together with
convex optimization within variational inference methods, providing a new
family of probabilistic inference methods.


title: An overview of deep learning in medical imaging

abstract:
  Machine learning (ML) has seen enormous consideration during the most recent
decade. This success started in 2012 when an ML model accomplished a remarkable
triumph in the ImageNet Classification, the world's most famous competition for
computer vision. This model was a kind of convolutional neural system (CNN)
called deep learning (DL). Since then, researchers have started to participate
efficiently in DL's fastest developing area of research. These days, DL systems
are cutting-edge ML systems spanning a broad range of disciplines, from human
language processing to video analysis, and commonly used in the scholarly world
and enterprise sector. Recent advances can bring tremendous improvement to the
medical field. Improved and innovative methods for data processing, image
analysis and can significantly improve the diagnostic technologies and
medicinal services gradually. A quick review of current developments with
relevant problems in the field of DL used for medical imaging has been
provided. The primary purposes of the review are four: (i) provide a brief
prolog to DL by discussing different DL models, (ii) review of the DL usage for
medical image analysis (classification, detection, segmentation, and
registration), (iii) review seven main application fields of DL in medical
imaging, (iv) give an initial stage to those keen on adding to the research
area about DL in clinical imaging by providing links of some useful informative
assets, such as freely available DL codes, public datasets Table 7, and medical
imaging competition sources Table 8 and end our survey by outlining distinct
continuous difficulties, lessons learned and future of DL in the field of
medical science.


title:
Nonlinear dynamical modelling of high frequency electrostatic drift
  waves using fluid theoretical approach in magnetized plasma

abstract:
  A novel third order nonlinear evolution equation governing the dynamics of
high frequency electrostatic drift waves has been derived in the framework of a
plasma fluid model in an inhomogeneous magnetized plasma. The linear dispersion
relation arising out of the fluid equations has been studied for the
conventional low frequency and high frequency electrostatic drift waves. This
equation is then decomposed into two second order equations as the order of the
equation becomes reduced after this kind of decomposition under certain
conditions. The detailed analysis of fixed points as well as bifurcations of
the phase portraits have been performed using the theory of planar dynamical
systems. Then some exact as well as approximate travelling wave solutions of
this reduced nonlinear equation for the high frequency electrostatic drift
waves are derived. As the other second order reduced equation is linear in
nature, its exact oscillatory and exponential solutions are derived. The
intersection of the solutions of these two reduced second order equations
provides the solutions of the original third order nonlinear evolution
equation; it is verified that the solutions of the reduced nonlinear second
order equation are subsets of the oscillatory solution of the reduced linear
second order equation in most cases whereas the intersection is different if
the exponential solution of the reduced linear second order equation is
considered. So the solutions of the reduced second order nonlinear equation can
directly represent the solutions of the original third order nonlinear equation
representing the dynamics of the nonlinear high frequency electrostatic drift
waves if the oscillatory solution of the reduced linear equation is considered.


title: The Political Economy of Privacy Enhancing Technologies

abstract:
  \ac{PETs} have increasingly become vital empowering tools in today's highly
datafied society. However, their development has been primarily concerned with
improving usability and ensuring confidentiality online. Privileging these
considerations might unintendedly lead to fixed ideas about users, but
diversity of thought, action, ability, and circumstance play a fundamental role
in the distortion and acceptance of any \ac{PETs}. In this paper we elaborate
some of the manifestations of the distortions, like inadequate and exclusory
design, and uneven distribution of costs and benefits. Drawing on Amartya Sen's
\emph{capability approach} we propose that a normative evaluation of personal,
social, and political diversities can be used as a foundation to conceptualize
and develop \ac{PETs}. We outline a research agenda based on this proposition
and suggest pertinent empirical and methodological research paths. Our
contribution offers an evaluative space to make inter-personal comparisons to
inform the development of \ac{PETs}.


title: Oracle-Efficient Online Learning for Beyond Worst-Case Adversaries

abstract:
  In this paper, we study oracle-efficient algorithms for beyond worst-case
analysis of online learning. We focus on two settings. First, the smoothed
analysis setting of [RST11, HRS21] where an adversary is constrained to
generating samples from distributions whose density is upper bounded by
$1/\sigma$ times the uniform density. Second, the setting of $K$-hint
transductive learning, where the learner is given access to $K$ hints per time
step that are guaranteed to include the true instance. We give the first known
oracle-efficient algorithms for both settings that depend only on the VC
dimension of the class and parameters $\sigma$ and $K$ that capture the power
of the adversary. {In particular, we achieve oracle-efficient regret bounds of
$ O ( \sqrt{T d\sigma^{-1/2}} ) $} and $ O ( \sqrt{T d K } )$ respectively for
these setting. For the smoothed analysis setting, our results give the first
oracle-efficient algorithm for online learning with smoothed adversaries
[HRS21]. This contrasts the computational separation between online learning
with worst-case adversaries and offline learning established by [HK16]. Our
algorithms also achieve improved bounds for worst-case setting with small
domains. In particular, we give an oracle-efficient algorithm with regret of $O
( \sqrt{T(d \vert{\mathcal{X}})\vert^{1/2} })$, which is a refinement of the
earlier $O ( \sqrt{T\vert{\mathcal{X}}\vert })$ bound by [DS16].


title: Delay-adaptive step-sizes for asynchronous learning

abstract:
  In scalable machine learning systems, model training is often parallelized
over multiple nodes that run without tight synchronization. Most analysis
results for the related asynchronous algorithms use an upper bound on the
information delays in the system to determine learning rates. Not only are such
bounds hard to obtain in advance, but they also result in unnecessarily slow
convergence. In this paper, we show that it is possible to use learning rates
that depend on the actual time-varying delays in the system. We develop general
convergence results for delay-adaptive asynchronous iterations and specialize
these to proximal incremental gradient descent and block-coordinate descent
algorithms. For each of these methods, we demonstrate how delays can be
measured on-line, present delay-adaptive step-size policies, and illustrate
their theoretical and practical advantages over the state-of-the-art.


title:
Strong substrate strain effects in multilayered WS2 revealed by
  high-pressure optical measurements

abstract:
  The optical properties of two-dimensional materials can be effectively tuned
by strain induced from a deformable substrate. In the present work we combine
first-principles calculations based on density functional theory and the
effective Bethe-Salpeter equation with high-pressure optical measurements in
order to thoroughly describe the effect of strain and dielectric environment
onto the electronic band structure and optical properties of a few-layered
transition metal dichalcogenide. Our results show that WS2 remains fully
adhered to the substrate at least up to a -0.6% in-plane compressive strain for
a wide range of substrate materials. We provide a useful model to describe
effect of strain on the optical gap energy. The corresponding
experimentally-determined out-of-plane and in-plane stress gauge factors for
WS2 monolayers are -8 and 24 meV/GPa, respectively. The exceptionally large
in-plane gauge factor confirm transition metal dichalcogenides as very
promising candidates for flexible functionalities. Finally, we discuss the
pressure evolution of an optical transition closely-lying to the A exciton for
bulk WS2 as well as the direct-to-indirect transition of the monolayer upon
compression.


title:
EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for
  Image Classification Evaluation

abstract:
  Background and purpose: Colorectal cancer has become the third most common
cancer worldwide, accounting for approximately 10% of cancer patients. Early
detection of the disease is important for the treatment of colorectal cancer
patients. Histopathological examination is the gold standard for screening
colorectal cancer. However, the current lack of histopathological image
datasets of colorectal cancer, especially enteroscope biopsies, hinders the
accurate evaluation of computer-aided diagnosis techniques. Methods: A new
publicly available Enteroscope Biopsy Histopathological H&E Image Dataset
(EBHI) is published in this paper. To demonstrate the effectiveness of the EBHI
dataset, we have utilized several machine learning, convolutional neural
networks and novel transformer-based classifiers for experimentation and
evaluation, using an image with a magnification of 200x. Results: Experimental
results show that the deep learning method performs well on the EBHI dataset.
Traditional machine learning methods achieve maximum accuracy of 76.02% and
deep learning method achieves a maximum accuracy of 95.37%. Conclusion: To the
best of our knowledge, EBHI is the first publicly available colorectal
histopathology enteroscope biopsy dataset with four magnifications and five
types of images of tumor differentiation stages, totaling 5532 images. We
believe that EBHI could attract researchers to explore new classification
algorithms for the automated diagnosis of colorectal cancer, which could help
physicians and patients in clinical settings.


title: 3D-Aware Indoor Scene Synthesis with Depth Priors

abstract:
  Despite the recent advancement of Generative Adversarial Networks (GANs) in
learning 3D-aware image synthesis from 2D data, existing methods fail to model
indoor scenes due to the large diversity of room layouts and the objects
inside. We argue that indoor scenes do not have a shared intrinsic structure,
and hence only using 2D images cannot adequately guide the model with the 3D
geometry. In this work, we fill in this gap by introducing depth as a 3D prior.
Compared with other 3D data formats, depth better fits the convolution-based
generation mechanism and is more easily accessible in practice. Specifically,
we propose a dual-path generator, where one path is responsible for depth
generation, whose intermediate features are injected into the other path as the
condition for appearance rendering. Such a design eases the 3D-aware synthesis
with explicit geometry information. Meanwhile, we introduce a switchable
discriminator both to differentiate real v.s. fake domains and to predict the
depth from a given input. In this way, the discriminator can take the spatial
arrangement into account and advise the generator to learn an appropriate depth
condition. Extensive experimental results suggest that our approach is capable
of synthesizing indoor scenes with impressively good quality and 3D
consistency, significantly outperforming state-of-the-art alternatives.


title: Indestructible Guessing Models And The Approximation Property

abstract:
  In this short note, we shall prove some observations regarding the connection
between indestructible $\omega_1$-guessing models and the
$\omega_1$-approximation property of forcing notions.


title: Query Answering with Transitive and Linear-Ordered Data

abstract:
  We consider entailment problems involving powerful constraint languages such
as frontier-guarded existential rules in which we impose additional semantic
restrictions on a set of distinguished relations. We consider restricting a
relation to be transitive, restricting a relation to be the transitive closure
of another relation, and restricting a relation to be a linear order. We give
some natural variants of guardedness that allow inference to be decidable in
each case, and isolate the complexity of the corresponding decision problems.
Finally we show that slight changes in these conditions lead to undecidability.


title: Heuristic Adaptability to Input Dynamics for SpMM on GPUs

abstract:
  Sparse Matrix-Matrix Multiplication (SpMM) has served as fundamental
components in various domains. Many previous studies exploit GPUs for SpMM
acceleration because GPUs provide high bandwidth and parallelism. We point out
that a static design does not always improve the performance of SpMM on
different input data (e.g., >85\% performance loss with a single algorithm). In
this paper, we consider the challenge of input dynamics from a novel
auto-tuning perspective, while following issues remain to be solved: (1)
Orthogonal design principles considering sparsity. Orthogonal design principles
for such a sparse problem should be extracted to form different algorithms, and
further used for performance tuning. (2) Nontrivial implementations in the
algorithm space. Combining orthogonal design principles to create new
algorithms needs to tackle with new challenges like thread race handling. (3)
Heuristic adaptability to input dynamics. The heuristic adaptability is
required to dynamically optimize code for input dynamics.
  To tackle these challenges, we first propose a novel three-loop model to
extract orthogonal design principles for SpMM on GPUs. The model not only
covers previous SpMM designs, but also comes up with new designs absent from
previous studies. We propose techniques like conditional reduction to implement
algorithms missing in previous studies. We further propose DA-SpMM, a
Data-Aware heuristic GPU kernel for SpMM. DA-SpMM adaptively optimizes code
considering input dynamics. Extensive experimental results show that, DA-SpMM
achieves 1.26x~1.37x speedup compared with the best NVIDIA cuSPARSE algorithm
on average, and brings up to 5.59x end-to-end speedup to applications like
Graph Neural Networks.


title:
CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-based
  Autonomous Urban Driving

abstract:
  Vision-based autonomous urban driving in dense traffic is quite challenging
due to the complicated urban environment and the dynamics of the driving
behaviors. Widely-applied methods either heavily rely on hand-crafted rules or
learn from limited human experience, which makes them hard to generalize to
rare but critical scenarios. In this paper, we present a novel CAscade Deep
REinforcement learning framework, CADRE, to achieve model-free vision-based
autonomous urban driving. In CADRE, to derive representative latent features
from raw observations, we first offline train a Co-attention Perception Module
(CoPM) that leverages the co-attention mechanism to learn the
inter-relationships between the visual and control information from a
pre-collected driving dataset. Cascaded by the frozen CoPM, we then present an
efficient distributed proximal policy optimization framework to online learn
the driving policy under the guidance of particularly designed reward
functions. We perform a comprehensive empirical study with the CARLA NoCrash
benchmark as well as specific obstacle avoidance scenarios in autonomous urban
driving tasks. The experimental results well justify the effectiveness of CADRE
and its superiority over the state-of-the-art by a wide margin.


title:
Large-scale highly-accurate extended full waveform inversion using
  convergent Born series

abstract:
  Full-waveform inversion (FWI) is a seismic imaging method that provides
quantitative inference about subsurface properties with a wavelength-scale
resolution. Its frequency-domain formulation is computationally efficient when
processing only a few discrete frequencies. However, classical FWI, which is
formulated on the reduced-parameter space, requires starting the inversion with
a sufficiently-accurate initial model and low frequency to prevent being stuck
in local minima due to cycle skipping. FWI with extended search space has been
proposed to mitigate this issue. It contains two main steps: first,
data-assimilated (DA) wavefields are computed by allowing for wave-equation
errors to match the data at receivers closely. Then, subsurface parameters are
estimated from these wavefields by minimizing the wave-equation errors. The DA
wavefields are the least-squares solution of an overdetermined system gathering
the wave and observation equations. The numerical bandwidth of the resulting
normal-equation system is two times that of the wave-equation system, which can
be a limiting factor for 3D large-scale applications. Therefore, computing
highly accurate DA wavefields at a reasonable computational cost is an issue in
extended FWI. This issue is addressed here by rewriting the normal system such
that its solution can be computed by solving the time-harmonic wave equation
several times in sequence. Moreover, the computational burden of
multi-right-hand side (RHS) simulations is mitigated with a sketching method.
Finally, we solve the time-harmonic wave equation with the convergent Born
series method, which conciliates accuracy and computational efficiency.
Application of the new extended FWI algorithm on the salt benchmark shows that
it reconstructs at a reasonable cost subsurface models that are similar to
those obtained with the classical extended FWI.


title: Escape Dynamics in an Anisotropically Driven Brownian Magneto-System

abstract:
  Thermally activated escape of a Brownian particle over a potential barrier is
well understood within Kramers theory. When subjected to an external magnetic
field, the Lorentz force slows down the escape dynamics via a rescaling of the
diffusion coefficient without affecting the exponential dependence on the
barrier height. Here, we study the escape dynamics of a charged Brownian
particle from a two-dimensional truncated harmonic potential under the
influence of Lorentz force due to an external magnetic field. The particle is
driven anisotropically by subjecting it to noises with different strengths
along different spatial directions. We show that the escape time can largely be
tuned by the anisotropic driving. While the escape process becomes anisotropic
due to the two different noises, the spatial symmetry is restored in the limit
of large magnetic fields. This is attributed to the Lorentz force induced
coupling between the spatial degrees of freedom which makes the difference
between two noises irrelevant at high magnetic fields. The theoretical
predictions are verified by Brownian dynamics simulations. In principle, our
predictions can be tested by experiments with a Brownian gyrator in the
presence of a magnetic field.


title: Relaxed assumptions and a simplified algorithm for multiobjective MPC

abstract:
  We consider nonlinear model predictive control (MPC) with multiple competing
cost functions. In each step of the scheme, a multiobjective optimal control
problem with a nonlinear system and terminal conditions is solved. We propose
an algorithm and give performance guarantees for the resulting MPC closed loop
system. Thereby, we significantly simplify the assumptions made in the
literature so far by assuming strict dissipativity and the existence of a
compatible terminal cost for one of the competing objective functions only. We
give conditions which ensure asymptotic stability of the closed loop and, what
is more, obtain performance estimates for all cost criteria. Numerical
simulations on various instances illustrate our findings. The proposed
algorithm requires the selection of an efficient solution in each iteration and
we also examine numerically how different selection rules impact the results.


title:
Intense high-harmonic optical vortices generated from a
  micro-plasma-waveguide irradiated by a circularly polarized laser pulse

abstract:
  A scheme for generating intense high-harmonic optical vortices is proposed.
It relies on spin-orbit interaction of light when a relativistically-strong
circularly polarized laser pulse irradiates a micro-plasma-waveguide. The
intense laser field drives a strong surface wave at the inner boundary of the
waveguide, which leads to high-order harmonic generation as the laser traveling
inside. For a circularly polarized drive laser, the optical chirality is
imprinted to the surface wave, which facilitates conversion of spin angular
momentum of the fundamental light into orbital angular momenta of the
harmonics. A "shaken waveguide" model is developed showing that the
aforementioned phenomena arises due to nonlinear plasma response that modifies
electromagnetic mode at high intensities. We further show the phase velocities
of all the harmonic beams are automatically matched to the driving laser, so
that the harmonic intensities increase with propagation distance. The
efficiency of harmonic production are related to the surface wave breaking
effect, which can be significantly enhanced using a tightly focused laser. Our
simulation suggests an overall conversion efficiency $\sim5\%$ can be achieved.


title:
Angular X-ray Cross-Correlation Analysis Applied to the Scattering Data
  in 3D Reciprocal Space from a Single Crystal

abstract:
  We propose an application of the Angular X-ray Cross-Correlation Analysis
(AXCCA) to the scattered intensity distribution measured in three-dimensional
(3D) reciprocal space from a single crystalline sample. Contrary to the
conventional application of AXCCA, when averaging over many two-dimensional
(2D) diffraction patterns collected from different randomly oriented samples is
required, the proposed approach gives an insight into the structure of a single
specimen. This is particularly useful in studies of defect-reach samples that
are unlikely to have the same structure. Here, we demonstrate an example of a
qualitative structure determination of a colloidal crystal on the simulated as
well as experimentally measured 3D scattered intensity distributions.


title: Dynamics of spherical charged dust shells in de Sitter space

abstract:
  We study the dynamics of charged, spherically symmetric dust shells in the
presence of a positive cosmological constant. We find generalizations of the
well-known solutions in asymptotically flat spacetime, in particular orbits
into "parallel universes", but also new solutions corresponding to a "bounce"
of the shell before an event horizon has formed. We also discuss "bubble"
solutions, in which a charged shell and an oppositely charged singularity are
spontaneously created and annihilated.


title:
A 3D index for measuring economic resilience with application to the
  modern international and global financial crises

abstract:
  The study and measurement of economic resilience is ruled by high level of
complexity related to the diverse structure, functionality, spatiality, and
dynamics describing economic systems. Towards serving the demand of
integration, this paper develops a three-dimensional index, capturing
engineering, ecological, and evolutionary aspects of economic resilience that
are considered separately in the current literature. The proposed index is
computed on GDP data of worldwide countries, for the period 1960-2020,
concerning 14 crises considered as shocks, and was found well defined in a
conceptual context of its components. Its application on real-world data allows
introducing a novel classification of countries in terms of economic
resilience, and reveals geographical patterns and structural determinants of
this attribute. Impressively enough, economic resilience appears positively
related to major productivity coefficients, gravitationally driven, and
depended on agricultural specialization, with high structural heterogeneity in
the low class. Also, the analysis fills the literature gap by shaping the
worldwide map of economic resilience, revealing geographical duality and
centrifugal patterns in its geographical distribution, a relationship between
diachronically good performance in economic resilience and geographical
distance from the shocks origin, and a continent differentiation expressed by
the specialization of America in engineering resilience, Africa and Asia in
ecological and evolutionary resilience, and a relative lag of Europe and
Oceania. Finally, the analysis provides insights into the effect of the 2008 on
the globe and supports a further research hypothesis that political instability
is a main determinant of low economic resilience, addressing avenues of further
research.


title: Veronese varieties and Hilbert functions

abstract:
  In this paper we characterize all possible Hilbert functions of reduced
subavrieties of Veronese varieties. In particular, we get an effective
description in the case of Veronese curves and surfaces. In these cases we also
describe all possible reduced complete intersection subvarieties.


title:
Efficient and Reliable Probabilistic Interactive Learning with
  Structured Outputs

abstract:
  In this position paper, we study interactive learning for structured output
spaces, with a focus on active learning, in which labels are unknown and must
be acquired, and on skeptical learning, in which the labels are noisy and may
need relabeling. These scenarios require expressive models that guarantee
reliable and efficient computation of probabilistic quantities to measure
uncertainty. We identify conditions under which a class of probabilistic models
-- which we denote CRISPs -- meet all of these conditions, thus delivering
tractable computation of the above quantities while preserving expressiveness.
Building on prior work on tractable probabilistic circuits, we illustrate how
CRISPs enable robust and efficient active and skeptical learning in large
structured output spaces.


title: Robust SVM Optimization in Banach spaces

abstract:
  We address the issue of binary classification in Banach spaces in presence of
uncertainty. We show that a number of results from classical support vector
machines theory can be appropriately generalised to their robust counterpart in
Banach spaces. These include the Representer Theorem, strong duality for the
associated Optimization problem as well as their geometric interpretation.
Furthermore, we propose a game theoretic interpretation by expressing a Nash
equilibrium problem formulation for the more general problem of finding the
closest points in two closed convex sets when the underlying space is reflexive
and smooth.


title:
Effect of powder bed fusion process parameters on microstructural and
  mechanical properties of FeCrNi MEA: An atomistic study

abstract:
  In our study, molecular dynamics (MD) simulations of laser powder bed fusion
(LPBF) have been conducted on equimolar FeNiCr medium entropy alloy (MEA)
powders. With the development of newer LPBF technologies capable of printing at
the microscale, an even deeper understanding of the underlying atomistic
effects of the process parameters on the microstructural and mechanical
properties of the manufactured FeNiCr MEA products is required. In accordance
with previous literature, the parameters of the LPBF process have been
systematically varied, including layer resolution from 1 to 6, laser power from
100 {\mu}W to 220 {\mu}W, bed temperature from 300 K to 1200 K, and laser scan
speed from 0.5 {\AA}/ps to 0.0625 {\AA}/ps. Consistent with prior macroscopic
experimental findings, the atomistic results suggest that additive
manufacturing using thinner layers imparts higher ultimate tensile strength
(UTS) than fabricating with thicker layers. The latter, however, requires a
shorter process time but induces keyhole defect formation if the laser-induced
temperature is not sufficiently high enough. Increasing the temperature proves
useful in mitigating this problem. Enhancement of UTS for the multi-rowed
powders has been observed by raising the substrate temperature to 600 K or
laser power to 160 {\mu}W during production. Beyond these critical limits,
however, the UTS of the product diminishes due to the emergence of multiple
vacancies. The results of our present study will help researchers to find a
good balance between the production speed and strength of additive manufactured
products at the nanoscale.


title:
Chandra follow up of the Hectospec Cluster Survey: Comparison of Caustic
  and Hydrostatic Masses and Constraints on the Hydrostatic Bias

abstract:
  Clusters of galaxies are powerful probes with which to study cosmology and
astrophysics. However, for many applications an accurate measurement of a
cluster's mass is essential. A systematic underestimate of hydrostatic masses
from X-ray observations (the so-called hydrostatic bias) may be responsible for
tension between the results of different cosmological measurements. We compare
X-ray hydrostatic masses with masses estimated using the caustic method (based
on galaxy velocities) in order to explore the systematic uncertainties of both
methods and place new constraints on the level of hydrostatic bias. Hydrostatic
and caustic mass profiles were determined independently for a sample of 44
clusters based on Chandra observations of clusters from the Hectospec Cluster
Survey. This is the largest systematic comparison of its kind. Masses were
compared at a standardised radius ($R_{500}$) using a model that includes
possible bias and scatter in both mass estimates. The systematics affecting
both mass determination methods were explored in detail. The hydrostatic masses
were found to be systematically higher than caustic masses on average, and we
found evidence that the caustic method increasingly underestimates the mass
when fewer galaxies are used to measure the caustics. We limit our analysis to
the 14 clusters with the best-sampled caustics where this bias is minimised
($\ge210$ galaxies), and find that the average ratio of hydrostatic to caustic
mass at $R_{500}$ is $M_X/M_C=1.12^{+0.11}_{-0.10}$. We interpret this result
as a constraint on the level of hydrostatic bias, favouring small or zero
levels of hydrostatic bias (less than $20\%$ at the $3\sigma$ level). However,
we find systematic uncertainties associated with both mass estimation methods
remain at the $10-15\%$ level, which would permit significantly larger levels
of hydrostatic bias.


title:
Emergence of large quantum oscillation frequencies in thin flakes of a
  kagome superconductor CsV$_{3}$Sb$_{5}$

abstract:
  Kagome metals AV$_3$Sb$_5$ (A = K, Rb, Cs) are recently discovered platforms
featuring an unusual charge-density-wave (CDW) order and superconductivity. The
electronic band structure of a kagome lattice can host both flat bands as well
as Dirac-like bands, offering the possibility to stabilize various quantum
states. Here, we probe the band structure of CsV$_3$Sb$_5$ via Shubnikov-de
Haas quantum oscillations on both bulk single crystals and thin flakes. Our
data for thin flakes unambiguously reveal the existence of new oscillations
with large frequencies ranging from ~2085 T to ~2717 T. The Lifshitz-Kosevich
analysis further uncovers surprisingly small cyclotron effective masses, of the
order of ~0.1 $m_e$, for these frequencies. Consequently, a large number of
high-velocity carriers exists in the thin flake of CsV$_3$Sb$_5$. Comparing
with our band structure calculations, we argue that an orbital-selective
modification of the band structure is active. Our results provide indispensable
information for understanding the fermiology of CsV$_3$Sb$_5$.


title:
Comparison of standard and stabilization free Virtual Elements on
  anisotropic elliptic problems

abstract:
  In this letter we compare the behaviour of standard Virtual Element Methods
(VEM) and stabilization free Enlarged Enhancement Virtual Element Methods
(E$^2$VEM) with the focus on some elliptic test problems whose solution and
diffusivity tensor are characterized by anisotropies. Results show that the
possibility to avoid an arbitrary stabilizing part, offered by E$^2$VEM
methods, can reduce the magnitude of the error on general polygonal meshes and
help convergence.


title:
A Machine Learning Approach for Automated Filling of Categorical Fields
  in Data Entry Forms

abstract:
  Users frequently interact with software systems through data entry forms.
However, form filling is time-consuming and error-prone. Although several
techniques have been proposed to auto-complete or pre-fill fields in the forms,
they provide limited support to help users fill categorical fields, i.e.,
fields that require users to choose the right value among a large set of
options.
  In this paper, we propose LAFF, a learning-based automated approach for
filling categorical fields in data entry forms. LAFF first builds Bayesian
Network models by learning field dependencies from a set of historical input
instances, representing the values of the fields that have been filled in the
past. To improve its learning ability, LAFF uses local modeling to effectively
mine the local dependencies of fields in a cluster of input instances. During
the form filling phase, LAFF uses such models to predict possible values of a
target field, based on the values in the already-filled fields of the form and
their dependencies; the predicted values (endorsed based on field dependencies
and prediction confidence) are then provided to the end-user as a list of
suggestions.
  We evaluated LAFF by assessing its effectiveness and efficiency in form
filling on two datasets, one of them proprietary from the banking domain.
Experimental results show that LAFF is able to provide accurate suggestions
with a Mean Reciprocal Rank value above 0.73. Furthermore, LAFF is efficient,
requiring at most 317 ms per suggestion.


title:
Pattern recovery and signal denoising by SLOPE when the design matrix is
  orthogonal

abstract:
  Sorted $\ell_1$ Penalized Estimator (SLOPE) is a relatively new convex
regularization method for fitting high-dimensional regression models. SLOPE
allows to reduce the model dimension by nullifying some of the regression
coefficients and by equalizing some of nonzero coefficients. This allows to
identify situations where some of true regression coefficients are equal. In
this article we will introduce the SLOPE pattern, i.e., the set of relations
between the true regression coefficients, which can be identified by SLOPE. We
will also present new results on the strong consistency of SLOPE estimators and
on the strong consistency of pattern recovery by SLOPE when the design matrix
is orthogonal and illustrate advantages of the SLOPE clustering in the context
of high frequency signal denoising.


title:
Using Edge Contractions and Vertex Deletions to Reduce the Independence
  Number and the Clique Number

abstract:
  We consider the following problem: for a given graph G and two integers k and
d, can we apply a fixed graph operation at most k times in order to reduce a
given graph parameter $\pi$ by at least d? We show that this problem is NP-hard
when the parameter is the independence number and the graph operation is vertex
deletion or edge contraction, even for fixed d=1 and when restricted to chordal
graphs. We also give a polynomial time algorithm for bipartite graphs when the
operation is edge contraction, the parameter is the independence number and d
is fixed. Further, we complete the complexity dichotomy on H-free graphs when
the parameter is the clique number and the operation is edge contraction by
showing that this problem is NP-hard in ($C_3+P_1$)-free graphs even for fixed
d=1. Our results answer several open questions stated in [Diner et al.,
Theoretical Computer Science, 746, p. 49-72 (2012)].


title:
$\mathcal{O}$-operators and Nijenhius operators of associative conformal
  algebras

abstract:
  We study $\mathcal{O}$-operators of associative conformal algebras with
respect to conformal bimodules. As natural generalizations of
$\mathcal{O}$-operators and dendriform conformal algebras, we introduce the
notions of twisted Rota-Baxter operators and conformal NS-algebras. We show
that twisted Rota-Baxter operators give rise to conformal NS-algebras, the same
as $\mathcal{O}$-operators induce dendriform conformal algebras. And we
introduce a conformal analog of associative Nijenhius operators and enumerate
main properties. By using derived bracket construction of Kosmann-Schwarzbach
and a method of Uchino, we obtain a graded Lie algebra whose Maurer-Cartan
elements are given by $\mathcal{O}$-operators. This allows us to construct
cohomology of $\mathcal{O}$-operators. This cohomology can be seen as the
Hochschild cohomology of an associative conformal algebra with coefficients in
a suitable conformal bimodule.


title: Local Differential Privacy for Belief Functions

abstract:
  In this paper, we propose two new definitions of local differential privacy
for belief functions. One is based on Shafer's semantics of randomly coded
messages and the other from the perspective of imprecise probabilities. We show
that such basic properties as composition and post-processing also hold for our
new definitions. Moreover, we provide a hypothesis testing framework for these
definitions and study the effect of "don't know" in the trade-off between
privacy and utility in discrete distribution estimation.


title:
Magnetic terahertz resonances above the N\'eel temperature in the
  frustrated kagome antiferromagnet averievite

abstract:
  Time-domain magneto-THz spectroscopy is utilized to study the frustrated
magnet averievite Cu$_{5-x}$Zn$_x$V$_2$O$_{10}$(CsCl). Pronounced THz
resonances are observed in unsubstituted samples ($x=0$) when cooling below the
onset of short-range magnetic correlations. The influence of external magnetic
effects confirms the magnetic origin of these resonances. Increasing Zn
substitution suppresses the resonances, as frustration effects dominate,
reflecting the non-magnetic phases for $x> 0.25$ compounds. The temperature
evolution of the THz spectra is complemented with electron spin resonance
spectroscopy. This comparison allows a direct probe of the different
contributions from magnetic order, frustration, and structural properties in
the phase diagram of averievite. Our results illustrate the effect of magnetic
interactions in THz spectra of frustrated magnets.


title: An Equivalence Between Data Poisoning and Byzantine Gradient Attacks

abstract:
  To study the resilience of distributed learning, the "Byzantine" literature
considers a strong threat model where workers can report arbitrary gradients to
the parameter server. Whereas this model helped obtain several fundamental
results, it has sometimes been considered unrealistic, when the workers are
mostly trustworthy machines. In this paper, we show a surprising equivalence
between this model and data poisoning, a threat considered much more realistic.
More specifically, we prove that every gradient attack can be reduced to data
poisoning, in any personalized federated learning system with PAC guarantees
(which we show are both desirable and realistic). This equivalence makes it
possible to obtain new impossibility results on the resilience to data
poisoning as corollaries of existing impossibility theorems on Byzantine
machine learning. Moreover, using our equivalence, we derive a practical attack
that we show (theoretically and empirically) can be very effective against
classical personalized federated learning models.


title: A note on switching eigenvalues under small perturbations

abstract:
  Sensitivity of eigenvectors and eigenvalues of symmetric matrix estimates to
the removal of a single observation have been well documented in the
literature. However, a complicating factor can exist in that the rank of the
eigenvalues may change due to the removal of an observation, and with that so
too does the perceived importance of the corresponding eigenvector. We refer to
this problem as "switching of eigenvalues". Since there is not enough
information in the new eigenvalues post observation removal to indicate that
this has happened, how do we know that this switching has occurred? In this
paper, we show that approximations to the eigenvalues can be used to help
determine when switching may have occurred. We then discuss possible actions
researchers can take based on this knowledge, for example making better choices
when it comes to deciding how many principal components should be retained and
adjustments to approximate influence diagnostics that perform poorly when
switching has occurred. Our results are easily applied to any eigenvalue
problem involving symmetric matrix estimators. We highlight our approach with
application to a real data example.


title:
Anatomically Parameterized Statistical Shape Model: Explaining
  Morphometry through Statistical Learning

abstract:
  Statistical shape models (SSMs) are a popular tool to conduct morphological
analysis of anatomical structures which is a crucial step in clinical
practices. However, shape representations through SSMs are based on shape
coefficients and lack an explicit one-to-one relationship with anatomical
measures of clinical relevance. While a shape coefficient embeds a combination
of anatomical measures, a formalized approach to find the relationship between
them remains elusive in the literature. This limits the use of SSMs to
subjective evaluations in clinical practices. We propose a novel SSM controlled
by anatomical parameters derived from morphometric analysis. The proposed
anatomically parameterized SSM (ANAT-SSM) is based on learning a linear mapping
between shape coefficients and selected anatomical parameters. This mapping is
learned from a synthetic population generated by the standard SSM. Determining
the pseudo-inverse of the mapping allows us to build the ANAT-SSM. We further
impose orthogonality constraints to the anatomical parameterization to obtain
independent shape variation patterns. The proposed contribution was evaluated
on two skeletal databases of femoral and scapular bone shapes using clinically
relevant anatomical parameters. Anatomical measures of the synthetically
generated shapes exhibited realistic statistics. The learned matrices
corroborated well with the obtained statistical relationship, while the two
SSMs achieved moderate to excellent performance in predicting anatomical
parameters on unseen shapes. This study demonstrates the use of anatomical
representation for creating anatomically parameterized SSM and as a result,
removes the limited clinical interpretability of standard SSMs. The proposed
models could help analyze differences in relevant bone morphometry between
populations, and be integrated in patient-specific pre-surgery planning or
in-surgery assessment.


title: Partial ignorance communication tasks in quantum theory

abstract:
  We introduce a generalization of communication of partial ignorance where
both parties of a prepare-and-measure setup receive inputs from a third party
before a success metric is maximized over the measurements and preparations.
Various methods are used to obtain bounds on the success metrics, including
SDPs, ultraweak monotones for communication matrices and frame theory for
quantum states. Simplest scenarios in the new generalized prepare-and-measure
setting, simply called partial ignorance communication tasks, are analysed
exhaustively for bits and qudits. Finally, the new generalized setting allows
the introduction of operational equivalences to the preparations and
measurements, allowing us to analyse and observe a contextual advantage for
quantum theory in one of the communication tasks.


title:
Localization and delocalization properties in quasi-periodically driven
  one-dimensional disordered system

abstract:
  Localization and delocalization of quantum diffusion in time-continuous
one-dimensional Anderson model perturbed by the quasi-periodic harmonic
oscillations of $M$ colors is investigated systematically, which has been
partly reported by the preliminary letter [PRE {\bf 103}, L040202(2021)]. We
investigate in detail the localization-delocalization characteristics of the
model with respect to three parameters: the disorder strength $W$, the
perturbation strength $\epsilon$ and the number of the colors $M$ which plays
the similar role of spatial dimension. In particular, attentions are focused on
the presence of localization-delocalization transition (LDT) and its critical
properties. For $M\geq 3$ the LDT exists and a normal diffusion is recovered
above a critical strength $\epsilon$, and the characteristics of diffusion
dynamics mimic the diffusion process predicted for the stochastically perturbed
Anderson model even though $M$ is not large. These results are compared with
the results of time-discrete quantum maps, i.e., Anderson map and the standard
map. Further, the features of delocalized dynamics is discussed in comparison
with a limit model which has no static disordered part.


title: Point cloud completion on structured feature map with feedback network

abstract:
  In this paper, we tackle the challenging problem of point cloud completion
from the perspective of feature learning. Our key observation is that to
recover the underlying structures as well as surface details given a partial
input, a fundamental component is a good feature representation that can
capture both global structure and local geometric details. Towards this end, we
first propose FSNet, a feature structuring module that can adaptively aggregate
point-wise features into a 2D structured feature map by learning multiple
latent patterns from local regions. We then integrate FSNet into a
coarse-to-fine pipeline for point cloud completion. Specifically, a 2D
convolutional neural network is adopted to decode feature maps from FSNet into
a coarse and complete point cloud. Next, a point cloud upsampling network is
used to generate dense point cloud from the partial input and the coarse
intermediate output. To efficiently exploit the local structures and enhance
the point distribution uniformity, we propose IFNet, a point upsampling module
with self-correction mechanism that can progressively refine details of the
generated dense point cloud. We conduct both qualitative and quantitative
experiments on ShapeNet, MVP, and KITTI datasets, which demonstrate that our
method outperforms state-of-the-art point cloud completion approaches.


title:
Well-balanced Central Scheme for the System of MHD Equations with
  Gravitational source term

abstract:
  A well-balanced second order finite volume central scheme for the
magnetohydrodynamic (MHD) equations with gravitational source term is developed
in this paper. The scheme is an unstaggered central scheme that evolves the
numerical solution on a single grid and avoids solving Riemann problems at the
cell interfaces using ghost staggered cells. A subtraction technique is used on
the conservative variables with the support of a known steady state in order to
manifest the well-balanced property of the scheme. The divergence-free
constraint of the magnetic field is satisfied after applying the constrained
transport method (CTM) for unstaggered central schemes at the end of each
time-step by correcting the components of the magnetic field. The robustness of
the proposed scheme is verified on a list of numerical test cases from the
literature.


title:
Ultrafast X-Ray hyperspectral imaging of a photo-induced phase
  transition with nanometer space and femtosecond time resolution

abstract:
  Using light to control transient phases in quantum materials is an emerging
route to engineer new properties and functionality, with both thermal and
non-thermal phases observed out-of-equilibrium. Transient phases are expected
to be heterogeneous, either through photo-generated domain growth or through
the generation of topological defects, which impacts the dynamics of the
system. Yet to date this nanoscale heterogeneity has not been directly
observed. Here, we use time- and spectrally-resolved coherent X-ray imaging to
track the prototypical light-induced insulator-to-metal phase transition in
vanadium dioxide (VO2) on the nanoscale with femtosecond time resolution.
Remarkably, the early-time dynamics are independent of the initial spatial
heterogeneity. We observe a sub-140-fs switch to the metallic phase which
launches strain waves that propagate on the few picosecond timescale into the
sample, with a heterogeneous response emerging only after hundreds of
picoseconds. This direct method of imaging nanoscale dynamics reveals an
interpretation of the transition in VO2 in strong contrast to those based on
spatially averaged probes and suggest that photo-excitation generates a
highly-orthorhombically-strained rutile metallic phase. Our results demonstrate
the critical importance of spatially and spectrally resolved measurements for
understanding and interpreting the transient phases of quantum materials.


title: Collisional regime during the discharge of a 2D silo

abstract:
  The present work reports a novel investigation into the collisional dynamics
of particles in the vicinity of the outlet of a 2D silo using molecular
dynamics simulations. Most studies on this granular system focus in the bulk of
the medium. In this region contacts are permanent or long-lived, so continuous
approximations are able to yield results for velocity distributions or mass
flow. Close to the exit, however, the density of the medium decreases and
contacts are instantaneous. Thus, the collisional nature of the dynamics
becomes significant, warranting a dedicated investigation as carried out in
this work. More interesting, the vicinity of the outlet is the region where the
arches that block the flow for small apertures are formed. It is found that the
transition from the clogging regime (at small apertures) to the continuous flow
regime is smooth in collisional variables. Furthermore, the dynamics of
particles as reflected by the distributions of the velocities is as well
unaffected. This result implies that there is no critical outlet size that
separates both regimes, as had been proposed in the literature. Instead, the
results achieved support the alternative picture in which a clog is possible
for any outlet size.


title: Gradients without Backpropagation

abstract:
  Using backpropagation to compute gradients of objective functions for
optimization has remained a mainstay of machine learning. Backpropagation, or
reverse-mode differentiation, is a special case within the general family of
automatic differentiation algorithms that also includes the forward mode. We
present a method to compute gradients based solely on the directional
derivative that one can compute exactly and efficiently via the forward mode.
We call this formulation the forward gradient, an unbiased estimate of the
gradient that can be evaluated in a single forward run of the function,
entirely eliminating the need for backpropagation in gradient descent. We
demonstrate forward gradient descent in a range of problems, showing
substantial savings in computation and enabling training up to twice as fast in
some cases.


title: Hybrid Routhian reduction for simple hybrid forced Lagrangian systems

abstract:
  This paper discusses Routh reduction for simple hybrid forced mechanical
systems. We give general conditions on whether it is possible to perform
symmetry reduction for a simple hybrid Lagrangian system subject to
non-conservative external forces, emphasizing the case of case of cyclic
coordinates. We illustrate the applicability of the symmetry reduction
procedure with an example and numerical simulations.


title: Single UHD Image Dehazing via Interpretable Pyramid Network

abstract:
  Currently, most single image dehazing models cannot run an
ultra-high-resolution (UHD) image with a single GPU shader in real-time. To
address the problem, we introduce the principle of infinite approximation of
Taylor's theorem with the Laplace pyramid pattern to build a model which is
capable of handling 4K hazy images in real-time. The N branch networks of the
pyramid network correspond to the N constraint terms in Taylor's theorem.
Low-order polynomials reconstruct the low-frequency information of the image
(e.g. color, illumination). High-order polynomials regress the high-frequency
information of the image (e.g. texture). In addition, we propose a Tucker
reconstruction-based regularization term that acts on each branch network of
the pyramid model. It further constrains the generation of anomalous signals in
the feature space. Extensive experimental results demonstrate that our approach
can not only run 4K images with haze in real-time on a single GPU (80FPS) but
also has unparalleled interpretability.
  The developed method achieves state-of-the-art (SOTA) performance on two
benchmarks (O/I-HAZE) and our updated 4KID dataset while providing the reliable
groundwork for subsequent optimization schemes.


title: Emerging trends in soybean industry

abstract:
  Soybean is the most globalized, traded and processed crop commodity. USA,
Argentina and Brazil continue to be the top three producers and exporters of
soybean and soymeal. Indian soyindustry has also made a mark in the national
and global arena. While soymeal, soyoil, lecithin and other soy-derivatives
stand to be driven up by commerce, the soyfoods for human health and nutrition
need to be further promoted. The changing habitat of commerce in soyderivatives
necessitates a shift in strategy, technological tools and policy environment to
make Indian soybean industry continue to thrive in the new industrial era.
Terms of trade for soyfarming and soy-industry could be further improved.
Present trends, volatilities, slowdowns, challenges faced and associated
desiderata are accordingly spelt out in the present article.


title:
Improving the probabilistic quantum teleportation efficiency of
  arbitrary superposed coherent state using multipartite even and odd j-spin
  coherent states as resource

abstract:
  Quantum teleportation is one of the most important techniques for quantum
information secure transmission. Using preshared entanglement, quantum
teleportation is designed as a basic key in many quantum information tasks and
features prominently in quantum technologies, especially in quantum
communication. In this work, we provide a new probabilistic teleportation
protocol scheme for arbitrary superposed coherent states by employing the
multipartite even and odd $j$-spin coherent states as the entangled resource
connecting Alice (sender) and Bob (receiver). Here, Alice possesses both even
and odd spin coherent states and makes repeated GHZ states measurements
(GHZSMs) on the pair of spins, consisting of (1) the unknown spin state and (2)
one of the two coherent spin states, taken alternately, until reaching a
quantum teleportation with maximal average fidelity. We provide the
relationship between the entanglement amount of the shared state, quantified by
the concurrence, with the teleportation fidelity and the success probability of
the teleported target state up to the $n^{\rm th}$ repeated attempt. In this
scheme, we show that the perfect quantum teleportation can be done even with a
non-maximally entangled state. Furthermore, this repeated GHZSMs attempt
process significantly increases both the average fidelity of the teleported
state and the probability of a successful run of the probabilistic protocol.
Also on our results, we show that the j-spin number, the target state parameter
and the overlap between coherent states provide important additional control
parameters that can be adjusted to maximize the teleportation efficiency.


title:
The Hausdorff dimension of spectrum of a class of gerneralized
  Thue-Morse Hamiltonians

abstract:
  We study a class of Schr\"odinger operators $H_{m,\lambda}$ with generalized
Thue-Morse potential that generated by the substitution $\tau(a)=a^mb^m$,
$\tau(b)=b^ma^m$ on two symbol alphabet $\Sigma=\{a,b\}$ for integer $m\ge 2$
and coupling $\lambda>0$. We show that $$\dim_H \sigma(H_{m,\lambda})\ge
\frac{\log \Lambda_m}{\log 64m+4},$$ where $\sigma(H_{m,\lambda})$ is the
spectrum of $H_{m,\lambda}$, $\Lambda_2=2$, and for $m>2$, $\Lambda_m=m$, if
$m\equiv0\mod 4$; $\Lambda_m=m-3$, if $m\equiv1\mod 4$; $\Lambda_m=m-2$, if
$m\equiv2\mod 4$; $\Lambda_m=m-1$, if $m\equiv3\mod 4$. This implies that
$\dim_H \sigma(H_{m,\lambda})$ tends to $1$ as $m$ tends to infinity.


title:
The LOFAR view of giant, early-type galaxies: radio emission from active
  nuclei and star formation

abstract:
  We study the properties and the origin of the radio emission in the most
luminous early-type galaxies (ETGs) in the nearby Universe (MK<-25, recession
velocity < 7,500 km/s) as seen by the 150 MHz Low-Frequency ARray (LOFAR)
observations. LOFAR images are available for 188 of these giant ETGs (gETGs)
and 146 (78%) of them are detected above a typical luminosity of ~10E21 W/Hz.
They show a large spread in power, reaching up to ~10E26 W/Hz. We confirm a
positive link between the stellar luminosity of gETGs and their median radio
power, the detection rate, and the fraction of extended sources. About
two-thirds (91) of the detected gETGs are unresolved, with sizes <4 kpc,
confirming the prevalence of compact radio sources in local sources. Forty-six
gETGs show extended emission on scales ranging from 4 to 340 kpc, at least 80%
of which have a FRI class morphology. Based on the morphology and spectral
index of the extended sources, ~30% of them might be remnant or restarted
sources but further studies are needed to confirm this. Optical spectroscopy
(available for 44 gETGs) indicates that for seven of them the nuclear gas is
ionized by young stars suggesting a contribution to their radio emission from
star forming regions. Their radio luminosities correspond to a star formation
rate (SFR) in the range 0.1-8 Msun/yr and a median specific SFR of 0.8x10E-12
yr-1. The gas flowing toward the center of gETGs can accrete onto the
supermassive black hole but also stall at larger radii and form new stars, an
indication that feedback does not completely quench star formation. The most
luminous gETGs (25 galaxies with MK < -25.8) are all detected at 150 MHz
however they are not all currently turned on: at least four of them are remnant
sources and at least one is likely powered by star formation.


title:
Evidence of a dibaryon spectrum in coherent $\pi^0\pi^0 d$
  photoproduction at forward deuteron angles

abstract:
  The coherent reaction, $\gamma d \rightarrow \pi^0\pi^0 d$ was studied with
the BGOOD experiment at ELSA from threshold to a centre-of-mass energy of
2850\,MeV. A full kinematic reconstruction was made, with final state deuterons
identified in the forward spectrometer and $\pi^0$ decays in the central BGO
Rugby Ball. The strength of the differential cross section exceeds what can be
described by models of coherent photoproduction and instead supports the three
isoscalar dibaryon candidates reported by the ELPH collaboration at 2.38, 2.47
and 2.63\,GeV/c$^2$. A low mass enhancement in the $\pi^0\pi^0$ invariant mass
is also observed at the $d^*(2380)$ centre-of-mass energy which is consistent
with the ABC effect. At higher centre-of-mass energies, a narrow peak in the
$\pi^0 d$ invariant mass at 2114\,MeV/c$^2$ with a width of 20\,MeV/c$^2$
supports a sequential two-dibaryon decay mechanism.


title: Deep VQA based on a Novel Hybrid Training Methodology

abstract:
  In recent years, deep learning techniques have been widely applied to video
quality assessment (VQA), showing significant potential to achieve higher
correlation performance with subjective opinions compared to conventional
approaches. However, these methods are often developed based on limited
training materials and evaluated through cross validation, due to the lack of
large scale subjective databases. In this context, this paper proposes a new
hybrid training methodology, which generates large volumes of training data by
using quality indices from an existing perceptual quality metric, VMAF, as
training targets, instead of actual subjective opinion scores. An additional
shallow CNN is also employed for temporal pooling, which was trained based on a
small subjective video database. The resulting Deep Video Quality Metric (based
on Hybrid Training), DVQM-HT, has been fully tested on eight HD subjective
video databases, and consistently exhibits higher correlation with perceptual
quality compared to other deep quality assessment methods, with an average
SROCC value of 0.8263.


title:
Augmented Lagrangian approach to deriving discontinuous Galerkin methods
  for nonlinear elasticity problems

abstract:
  We use the augmented Lagrangian formalism to derive discontinuous Galerkin
formulations for problems in nonlinear elasticity. In elasticity stress is
typically a symmetric function of strain, leading to symmetric tangent
stiffness matrices in Newtons method when conforming finite elements are used
for discretization. By use of the augmented Lagrangian framework, we can also
obtain symmetric tangent stiffness matrices in discontinuous Galerkin methods.
We suggest two different approaches and give examples from plasticity and from
large deformation hyperelasticity.


title:
Properties and challenges of hot-phonon physics in metals: MgB$_2$ and
  other compounds

abstract:
  The ultrafast dynamics of electrons and collective modes in systems out of
equilibrium is crucially governed by the energy transfer from electronic
degrees of freedom, where the energy of the pump source is usually absorbed, to
lattice degrees of freedom. In conventional metals such process leads to an
overall heating of the lattice, usually described by an effective lattice
temperature $T_{\rm ph}$, until final equilibrium with all the degrees of
freedom is reached. In specific materials, however, few lattice modes provide a
preferential channel for the energy transfer, leading to a non-thermal
distribution of vibrations and to the onset of {\em hot phonons}, i.e., lattice
modes with a much higher population than the other modes. Hot phonons are
usually encountered in semiconductors or semimetal compounds, like graphene,
where the preferential channel towards hot modes is dictated by the reduced
electronic phase space. Following a different path, the possibility of
obtaining hot-phonon physics also in metals has been however also recently
prompted in literature, as a result of a strong anisotropy of the
electron-phonon (el-ph) coupling. In the present paper, taking MgB$_2$ as a
representative example, we review the physical conditions that allow a
hot-phonon scenario in metals with anisotropic el-ph coupling, and we discuss
the observable fingerprints of hot phonons. Novel perspectives towards the
prediction and experimental observation of hot phonons in other metallic
compounds are also discussed.

categories: cond-mat.mtrl-sci cond-mat.str-el cond-mat.supr-con quant-ph


title:
Enhancement of photoacoustic spectroscopy with sorption enrichment for
  ppt-level benzene detection

abstract:
  A real time trace gas detector for benzene is demonstrated. The measurement
system takes an advantage of modest enrichment through short adsorption periods
to reach a ppt-level detection limit with a sampling cycle of 90 s, which
includes sample adsorption, desorption and a spectroscopic measurement. Benzene
is collected on Tenax TA sorbent for 30 s and then detected from the enriched
samples with photoacoustic spectroscopy. A high sensitivity is achieved using
cantilever-enhanced photoacoustic spectroscopy and a continuous-wave quantum
cascade laser emitting at the wavelength 14.8 $\mu$m, which corresponds to the
absorption wavelength of the strongest benzene infrared band. We reach a
detection limit of 150 ppt of benzene, over one sampling cycle. Interference
from humidity and other common petrochemicals is evaluated.


title: Error Correction for Reliable Quantum Computing

abstract:
  Quantum computers herald the arrival of a new era in which previously
intractable computational problems will be solved efficiently. However, quantum
technology is held down by decoherence, a phenomenon that is omnipresent in the
quantum paradigm and that renders quantum information useless when left
unchecked. The science of quantum error correction, a discipline that seeks to
combine and protect quantum information from the effects of decoherence using
structures known as codes, has arisen to meet this challenge. Stabilizer codes,
a particular subclass of quantum codes, have enabled fast progress in the field
of quantum error correction by allowing parallels to be drawn with the widely
studied field of classical error correction. This has resulted in the
construction of the quantum counterparts of well-known capacity-approaching
classical codes like sparse codes and quantum turbo codes. However, quantum
codes obtained in this manner do not entirely evoke the stupendous error
correcting abilities of their classical counterparts. This occurs because
classical strategies ignore important differences between the quantum and
classical paradigms, an issue that needs to be addressed if quantum error
correction is to succeed in its battle with decoherence. In this dissertation
we study a phenomenon exclusive to the quantum paradigm, known as degeneracy,
and its effects on the performance of sparse quantum codes. Furthermore, we
also analyze and present methods to improve the performance of a specific
family of sparse quantum codes in various different scenarios.


title:
Decoherence and Quantum Error Correction for Quantum Computing and
  Communications

abstract:
  Quantum technologies have shown immeasurable potential to effectively solve
several information processing tasks such as prime number factorization,
unstructured database search or complex macromolecule simulation. As a result
of such capability to solve certain problems that are not classically
tractable, quantum machines have the potential revolutionize the modern world
via applications such as drug design, process optimization, unbreakable
communications or machine learning. However, quantum information is prone to
suffer from errors caused by the so-called decoherence, which describes the
loss in coherence of quantum states associated to their interactions with the
surrounding environment. This decoherence phenomenon is present in every
quantum information task, be it transmission, processing or even storage of
quantum information. Consequently, the protection of quantum information via
quantum error correction codes (QECC) is of paramount importance to construct
fully operational quantum computers. Understanding environmental decoherence
processes and the way they are modeled is fundamental in order to construct
effective error correction methods capable of protecting quantum information.
In this thesis, the nature of decoherence is studied and mathematically
modelled; and QECCs are designed and optimized so that they exhibit better
error correction capabilities.


title: Homological projective duality for the Segre cubic

abstract:
  The Segre cubic and Castelnuovo-Richmond quartic are two projectively dual
hypersurfaces in $\mathbb{P}^4$, with a long and rich history starting in the
19th century. We will explain how Kuznetsov's theory of homological projective
duality lifts this projective duality to a relationship between the derived
category of a small resolution of the Segre cubic and a small resolution of the
Coble fourfold, the double cover of $\mathbb{P}^4$ ramified along the
Castelnuovo-Richmond quartic.
  Homological projective duality then provides a description of the derived
categories of linear sections, which we will describe to illustrate the theory.
The case of the Segre cubic and Coble fourfold is non-trivial enough to exhibit
interesting behavior, whilst being easy enough to explain the general machinery
in this special and very classical case.


title:
Fingerprinting Deep Neural Networks Globally via Universal Adversarial
  Perturbations

abstract:
  In this paper, we propose a novel and practical mechanism which enables the
service provider to verify whether a suspect model is stolen from the victim
model via model extraction attacks. Our key insight is that the profile of a
DNN model's decision boundary can be uniquely characterized by its
\textit{Universal Adversarial Perturbations (UAPs)}. UAPs belong to a
low-dimensional subspace and piracy models' subspaces are more consistent with
victim model's subspace compared with non-piracy model. Based on this, we
propose a UAP fingerprinting method for DNN models and train an encoder via
\textit{contrastive learning} that takes fingerprint as inputs, outputs a
similarity score. Extensive studies show that our framework can detect model IP
breaches with confidence $> 99.99 \%$ within only $20$ fingerprints of the
suspect model. It has good generalizability across different model
architectures and is robust against post-modifications on stolen models.


title:
CoFED: Cross-Silo Heterogeneous Federated Multitask Learning via
  Cotraining

abstract:
  Federated learning (FL) is a machine learning technique that enables
participants to collaboratively train high-quality models without exchanging
their private data. Participants utilizing cross-silo FL (CS-FL) settings are
independent organizations with different task needs, and they are concerned not
only with data privacy but also with independently training their unique models
due to intellectual property considerations. Most existing FL schemes are
incapable of satisfying the above scenarios. In this paper, we propose a
communication-efficient FL (CoFED) scheme based on the pseudolabeling of
unlabeled data via a process such as cotraining. To the best of our knowledge,
this is the first FL scheme that is simultaneously compatible with
heterogeneous tasks, heterogeneous models, and heterogeneous training
algorithms. Experimental results show that CoFED achieves better performance
than competing approaches with a lower communication cost. This is especially
true for non-independent and identically distributed (IID) settings and
heterogeneous models, where the proposed method achieves a 35% performance
improvement.


title:
Two-Stage Architectural Fine-Tuning with Neural Architecture Search
  using Early-Stopping in Image Classification

abstract:
  Deep neural networks (NN) perform well in various tasks (e.g., computer
vision) because of the convolutional neural networks (CNN). However, the
difficulty of gathering quality data in the industry field hinders the
practical use of NN. To cope with this issue, the concept of transfer learning
(TL) has emerged, which leverages the fine-tuning of NNs trained on large-scale
datasets in data-scarce situations. Therefore, this paper suggests a two-stage
architectural fine-tuning method for image classification, inspired by the
concept of neural architecture search (NAS). One of the main ideas of our
proposed method is a mutation with base architectures, which reduces the search
cost by using given architectural information. Moreover, an early-stopping is
also considered which directly reduces NAS costs. Experimental results verify
that our proposed method reduces computational and searching costs by up to
28.2% and 22.3%, compared to existing methods.


title:
Analysis of a kinetic model for electron heat transport in inertial
  confinement fusion plasmas

abstract:
  To determine the electron heat flux density on macroscopic scales, the most
widely used approach is to solve a diffusion equation through a multi-group
technique. This method is however restricted to transport induced by
temperature gradients without accounting for other sources of fast electrons
because the electric field induced by the charge separation is indirectly
treated. In addition, significant discrepancies are reported on the underlying
distribution function when compared to complete kinetic calculations. These
limitations motivate the research for alternative reduced kinetic models. The
physical content of one of them is here deepened, its precision improved and
the benefit of its usage compared to other models discussed.


title:
Pressure dependence of atomic dynamics in barocaloric ammonium sulfate:
  II. Vibrations

abstract:
  Ammonium sulfate is a giant inverse barocaloric material that is cheaply and
commercially available. Exploiting its potential for cooling applications
requires an understanding of the mechanism driving the entropy change. Here we
report an investigation by inelastic neutron scattering and density functional
theory of the phonons under working conditions of temperature and pressure. We
find excellent agreement between the experimental and calculated results. The
ammonium librational modes that are crucial to the entropy change are
identifiable by their negative Gr\"{u}neisen parameter. Our results connect the
differences in structure across the phase transition to those in the atomic
dynamics, suggesting a route towards designing new caloric materials.


title:
Scalable spin squeezing from spontaneous breaking of a continuous
  symmetry

abstract:
  Spontaneous symmetry breaking (SSB) is a property of Hamiltonian equilibrium
states which, in the thermodynamic limit, retain a finite average value of an
order parameter even after a field coupled to it is adiabatically turned off.
In the case of quantum spin models with continuous symmetry, we show that this
adiabatic process is also accompanied by the suppression of the fluctuations of
the symmetry generator -- namely, the collective spin component along an axis
of symmetry. In systems of $S=1/2$ spins or qubits, the combination of the
suppression of fluctuations along one direction and of the persistence of
transverse magnetization leads to spin squeezing -- a much sought-after
property of quantum states, both for the purpose of entanglement detection as
well as for metrological uses. Focusing on the case of XXZ models spontaneously
breaking a U(1) (or even SU(2)) symmetry, we show that the adiabatically
prepared states have nearly minimal spin uncertainty; that the minimum phase
uncertainty that one can achieve with these states scales as $N^{-3/4}$ with
the number of spins $N$; and that this scaling is attained after an adiabatic
preparation time scaling linearly with $N$. Our findings open the door to the
adiabatic preparation of strongly spin-squeezed states in a large variety of
quantum many-body devices including e.g. optical lattice clocks.


title:
Eccentricity of the long inspiraling compact binaries sheds light on
  dark sirens

abstract:
  We investigate the parameter estimation of gravitational waves emitted by the
eccentric compact binaries in the mid-frequency (0.1--10 Hz) band. Based on the
configuration of one cluster of DECIGO (B-DECIGO), we simulate five types of
typical compact binaries in GWTC-3 with component mass ranging from
$\mathcal{O}(1\sim100)~M_{\odot}$. For each type of binaries, we assign
discrete eccentricities from 0 to 0.4 at 0.1 Hz in $10^3$ random orientations.
The multiple harmonics induced by eccentricity can break the degeneracy between
parameters. We find that with eccentricity $e_0=0.4$, these typical binaries
can achieve $\mathcal{O}(10^2-10^4)$ improvement for the distance inference in
the near face-on orientations, compared to the circular case. More importantly,
a nonvanishing eccentricity ($0.01\sim0.4$) can significantly improve the
source localization of the typical binaries with component mass greater than
$30~M_{\odot}$, most by a factor of $1.5\sim{3.5}$ orders of magnitude. Our
result shows the remarkable significance of eccentricity for dark sirens in the
mid-band as precise probes of the Universe.

categories: gr-qc astro-ph.CO astro-ph.HE astro-ph.IM


title:
Semiclassical approach to $S$ matrix energy correlations and time delay
  in chaotic systems

abstract:
  The $M$-dimensional scattering matrix $S(E)$ which connects incoming to
outgoing waves in a chaotic systyem is always unitary, but shows complicated
dependence on the energy. This is partly encoded in correlators constructed
from traces of powers of $S(E+\epsilon)S^\dagger(E-\epsilon)$, averaged over
$E$, and by the statistical properties of the time delay operator,
$Q(E)=-i\hbar S^\dagger dS/dE$. Using a semiclassical approach for systems with
broken time reversal symmetry, we derive two kind of expressions for the energy
correlators: one as a power series in $1/M$ whose coefficients are rational
functions of $\epsilon$, and another as a power series in $\epsilon$ whose
coefficients are rational functions of $M$. From the latter we extract an
explicit formula for $\rm{Tr}(Q^n)$ which is valid for all $n$ and is in
agreement with random matrix theory predictions.


title:
Understanding the three-dimensional quantum Hall effect in generic
  multi-Weyl semimetals

abstract:
  The quantum Hall effect in three-dimensional Weyl semimetal (WSM) receives
significant attention for the emergence of the Fermi loop where the underlying
two-dimensional Hall conductivity, namely, sheet Hall conductivity, shows
quantized plateaus. Considering the tilted lattice models for multi Weyl
semimetals (mWSMs), we systematically study the Landau levels (LLs) and
magneto-Hall conductivity in the presence of parallel and perpendicular (with
respect to the Weyl node's separation) magnetic field, i.e., $\mathbf{
B}\parallel z$ and $\mathbf{B}\parallel x$, to explore the impact of tilting
and non-linearity in the dispersion. We make use of two (single) node
low-energy models to qualitatively explain the emergence of mid-gap chiral
(linear crossing of chiral) LLs on the lattice for $\mathbf{ B}\parallel z$
($\mathbf{ B}\parallel x$). Remarkably, we find that the sheet Hall
conductivity becomes quantized for $\mathbf{ B}\parallel z$ even when two Weyl
nodes project onto a single Fermi point in two opposite surfaces, forming a
Fermi loop with $k_z$ as the good quantum number. On the other hand, the Fermi
loop, connecting two distinct Fermi points in two opposite surfaces, with $k_x$
being the good quantum number, causes the quantization in sheet Hall
conductivity for $\mathbf{ B}\parallel x$. The quantization is almost lost
(perfectly remained) in the type-II phase for $\mathbf{ B}\parallel x$
($\mathbf{ B}\parallel z$). Interestingly, the jump profiles between the
adjacent quantized plateaus change with the topological charge for both of the
above cases. The momentum-integrated three-dimensional Hall conductivity is not
quantized; however, it bears the signature of chiral LLs as resulting in the
linear dependence on $\mu$ for small $\mu$. The linear zone (its slope) reduces
(increases) as the tilt (topological charge) of the underlying WSM increases.


title:
Multimodal interference and bound in the continuum modes in
  indirectly-patterned hyperbolic cavities

abstract:
  A conventional optical cavity supports modes which are confined because they
are unable to leak out of the cavity. Bound state in continuum (BIC) cavities
are an unconventional alternative, where light can leak out, but is confined by
multimodal destructive interference. BICs are a general wave phenomenon, of
particular interest to optics, but BICs and multimodal interference have never
been demonstrated at the nanoscale. Here, we demonstrate the first nanophotonic
cavities based on BIC-like multimodal interference. This novel confinement
mechanism for deep sub-wavelength light shows orders of magnitude improvement
in several confinement metrics. Specifically, we obtain cavity volumes below
100x100x3nm^3 with quality factors about 100, with extreme cases having
23x23x3nm^3 volumes or quality factors above 400. Key to our approach, is the
use of pristine crystalline hyperbolic dispersion media (HyM) which can support
large momentum excitations with relatively low losses. Making a HyM cavity is
complicated by the additional modes that appear in a HyM. Ordinarily, these
serve as additional channels for leakage, reducing cavity performance. But, in
our experiments, we find a BIC-like cavity confinement enhancement effect,
which is intimately related to the ray-like nature of HyM excitations. In fact,
the quality factors of our cavities exceed the maximum that is possible in the
absence of higher order modes. The alliance of HyM with BICs in our work yields
a radically novel way to confine light and is expected to have far reaching
consequences wherever strong optical confinement is utilized, from ultra-strong
light-matter interactions, to mid-IR nonlinear optics and a range of sensing
applications.


title:
Oscillator strengths in the framework of equation of motion multilevel
  CC3

abstract:
  We present an efficient implementation of the equation of motion oscillator
strengths for the closed-shell multilevel coupled cluster singles and doubles
with perturbative triples method (MLCC3) in the electronic structure program
eT. The orbital space is split into an active part treated with CC3 and an
inactive part computed at the coupled cluster singles and doubles (CCSD) level
of theory. Asymptotically, the CC3 contribution scales as $O(n_\text{V}
n^3_\text{v} n^3_\text{o})$ floating-point operations (FLOP), where $n_V$ is
the total number of virtual orbitals while $n_\text{v}$ and $n_\text{o}$ are
the number of active virtual and occupied orbitals, respectively. The CC3
contribution, thus, only scales linearly with the full system size and can
become negligible compared to the cost of CCSD. We demonstrate the capabilities
of our implementation by calculating the UV-VIS spectrum of azobenzene and a
core excited state of betaine 30 with more than 1000 molecular orbitals.


title: On the evaluation of (meta-)solver approaches

abstract:
  Meta-solver approaches exploits a number of individual solvers to potentially
build a better solver. To assess the performance of meta-solvers, one can
simply adopt the metrics typically used for individual solvers (e.g., runtime
or solution quality), or employ more specific evaluation metrics (e.g., by
measuring how close the meta-solver gets to its virtual best performance). In
this paper, based on some recently published works, we provide an overview of
different performance metrics for evaluating (meta-)solvers, by underlying
their strengths and weaknesses.


title: Fourier PlenOctrees for Dynamic Radiance Field Rendering in Real-time

abstract:
  Implicit neural representations such as Neural Radiance Field (NeRF) have
focused mainly on modeling static objects captured under multi-view settings
where real-time rendering can be achieved with smart data structures, e.g.,
PlenOctree. In this paper, we present a novel Fourier PlenOctree (FPO)
technique to tackle efficient neural modeling and real-time rendering of
dynamic scenes captured under the free-view video (FVV) setting. The key idea
in our FPO is a novel combination of generalized NeRF, PlenOctree
representation, volumetric fusion and Fourier transform. To accelerate FPO
construction, we present a novel coarse-to-fine fusion scheme that leverages
the generalizable NeRF technique to generate the tree via spatial blending. To
tackle dynamic scenes, we tailor the implicit network to model the Fourier
coefficients of timevarying density and color attributes. Finally, we construct
the FPO and train the Fourier coefficients directly on the leaves of a union
PlenOctree structure of the dynamic sequence. We show that the resulting FPO
enables compact memory overload to handle dynamic objects and supports
efficient fine-tuning. Extensive experiments show that the proposed method is
3000 times faster than the original NeRF and achieves over an order of
magnitude acceleration over SOTA while preserving high visual quality for the
free-viewpoint rendering of unseen dynamic scenes.