title:
Optimizing Transmission Infrastructure Investments to Support Line
  De-energization for Mitigating Wildfire Ignition Risk

abstract:
  Wildfires pose a growing risk to public safety in regions like the western
United States, and, historically, electric power systems have ignited some of
the most destructive wildfires. To reduce wildfire ignition risks, power system
operators preemptively de-energize high-risk power lines during extreme
wildfire conditions as part of "Public Safety Power Shutoff" (PSPS) events.
While capable of substantially reducing acute wildfire risks, PSPS events can
also result in significant amounts of load shedding as the partially
de-energized system may not be able to supply all customer demands. We
investigate the extent to which infrastructure investments can support system
operations during PSPS events by enabling reduced load shedding and wildfire
ignition risk. We consider the installation of grid-scale batteries, solar PV,
and line hardening or maintenance measures (e.g., undergrounding or increased
vegetation management). Optimally selecting the locations, types, and sizes of
these infrastructure investments requires considering the line de-energizations
associated with PSPS events. Accordingly, this paper proposes a multi-period
optimization formulation that locates and sizes infrastructure investments
while simultaneously choosing line de-energizations to minimize wildfire
ignition risk and load shedding. The proposed formulation is evaluated using
two geolocated test cases along with realistic infrastructure investment
parameters and actual wildfire risk data from the US Geological Survey. We
evaluate the performance of investment choices by simulating de-energization
decisions for the entire 2021 wildfire season with optimized infrastructure
placements. With investment decisions varying significantly for different test
cases, budgets, and operator priorities, the numerical results demonstrate the
proposed formulation's value in tailoring investment choices to different
settings.


title:
Characterizing Disorder Within Atomic Environments Through
  Physics-preserving Graph Neural Networks

abstract:
  Quantifying the level of atomic disorder within materials is critical to
understanding how evolving local structural environments dictate performance
and durability. Here, we leverage graph neural networks to define a physically
interpretable metric for local disorder. This metric encodes the diversity of
the local atomic configurations as a continuous spectrum between the solid and
liquid phases, quantified against a distribution of thermal perturbations. We
apply this novel methodology to three prototypical examples with varying levels
of disorder: (1) solid-liquid interfaces, (2) polycrystalline microstructures,
and (3) grain boundaries. Using elemental aluminum as a case study, we show how
our paradigm can track the spatio-temporal evolution of interfaces,
incorporating a mathematically defined description of the spatial boundary
between order and disorder. We further show how to extract physics-preserved
gradients from our continuous disorder fields, which may be used to understand
and predict materials performance and failure. Overall, our framework provides
an intuitive and generalizable pathway to quantify the relationship between
complex local atomic structure and coarse-grained materials phenomena.


title: Existentially closed measure-preserving actions of free groups

abstract:
  This paper is motivated by the study of probability measure-preserving (pmp)
actions of free groups using continuous model theory. Such an action is treated
as a metric structure that consists of the measure algebra of the probability
measure space expanded by a family of its automorphisms. We prove that the
existentially closed pmp actions of a given free group form an elementary
class, and therefore the theory of pmp $\mathbb{F}_k$-actions has a model
companion. We show this model companion is stable and has quantifier
elimination. We also prove that the action of $\mathbb{F}_k$ on its profinite
completion with the Haar measure is metrically generic and therefore, as we
show, it is existentially closed.
  We deduce our main result from a more general theorem, which gives a set of
sufficient conditions for the existence of a model companion for the theory of
$\mathbb{F}_k$-actions on a separably categorical, stable metric structure.


title:
A user-friendly tool to convert photon counting data to the open-source
  Photon-HDF5 file format

abstract:
  Photon-HDF5 is an open-source and open file format for storing
photon-counting data from single molecule microscopy experiments, introduced to
simplify data exchange and increase the reproducibility of data analysis. Part
of the Photon-HDF5 ecosystem, is phconvert, an extensible python library that
allows converting proprietary formats into Photon-HDF5 files. However, its use
requires some proficiency with command line instructions, the python
programming language, and the YAML markup format. This creates a significant
barrier for potential users without that expertise, but who want to benefit
from the advantages of releasing their files in an open format. In this work,
we present a GUI that lowers this barrier, thus simplifying the use of
Photon-HDF5. This tool uses the phconvert python library to convert data files
originally saved in proprietary data formats to Photon-HDF5 files, without
users having to write a single line of code. Because reproducible analyses
depend on essential experimental information, such as laser power or sample
description, the GUI also includes (currently limited) functionality to
associate valid metadata with the converted file, without having to write any
YAML. Finally, the GUI includes several productivity-enhancing features such as
whole-directory batch conversion and the ability to re-run a failed batch, only
converting the files that could not be converted in the previous run.


title:
Evaluation of April Tag and WhyCode Fiducial Systems for Autonomous
  Precision Drone Landing with a Gimbal-Mounted Camera

abstract:
  Fiducial markers provide a computationally cheap way for drones to determine
their location with respect to a landing pad and execute precision landings.
However, most existing work in this field uses a fixed, downward facing camera
that does not leverage the common gimbal-mounted camera setup found on many
drones. Such rigid systems cannot easily track detected markers, and may lose
sight of the markers in non-ideal conditions (e.g. wind gusts). This paper
evaluates April Tag and WhyCode fiducial systems for drone landing with a
gimbal-mounted, monocular camera, with the advantage that the drone system can
track the marker over time. However, since the orientation of the camera
changes, we must know the orientation of the marker, which is unreliable in
monocular fiducial systems. Additionally, the system must be fast. We propose 2
methods for mitigating the orientation ambiguity of WhyCode, and 1 method for
increasing the runtime detection rate of April Tag. We evaluate our 3 systems
against 2 default systems in terms of marker orientation ambiguity, and
detection rate. We test rates of marker detection in a ROS framework on a
Raspberry Pi 4, and we rank the systems in terms of their performance. Our
first WhyCode variant significantly reduces orientation ambiguity with an
insignificant reduction in detection rate. Our second WhyCode variant does not
show significantly different orientation ambiguity from the default WhyCode
system, but does provide additional functionality in terms of multi-marker
WhyCode bundle arrangements. Our April Tag variant does not show performance
improvements on a Raspberry Pi 4.


title:
Active learning in open experimental environments: selecting the right
  information channel(s) based on predictability in deep kernel learning

abstract:
  Active learning methods are rapidly becoming the integral component of
automated experiment workflows in imaging, materials synthesis, and
computation. The distinctive aspect of many experimental scenarios is the
presence of multiple information channels, including both the intrinsic
modalities of the measurement system and the exogenous environment and noise
signals. One of the key tasks in experimental studies is hence establishing
which of these channels is predictive of the behaviors of interest. Here we
explore the problem of discovery of the optimal predictive channel for
structure-property relationships (in microscopy) using deep kernel learning for
modality selection in an active experiment setting. We further pose that this
approach can be directly applicable to similar active learning tasks in
automated synthesis and the discovery of quantitative structure-activity
relations in molecular systems.


title:
Failing gracefully: Decryption failures and the Fujisaki-Okamoto
  transform

abstract:
  In known security reductions for the Fujisaki-Okamoto transformation,
decryption failures are handled via a reduction solving the rather unnatural
task of finding failing plaintexts given the private key, resulting in a Grover
search bound. Moreover, they require an implicit rejection mechanism for
invalid ciphertexts to achieve a reasonable security bound in the QROM. We
present a reduction that has neither of these deficiencies: We introduce two
security games related to finding decryption failures, one capturing the
computationally hard task of using the public key to find a decryption failure,
and one capturing the statistically hard task of searching the random oracle
for key-independent failures like, e.g., large randomness. As a result, our
security bounds in the QROM are tighter than previous ones with respect to the
generic random oracle search attacks: The attacker can only partially compute
the search predicate, namely for said key-independent failures. In addition,
our entire reduction works for the explicit-reject variant of the
transformation and improves significantly over all of its known reductions.
Besides being the more natural variant of the transformation, security of the
explicit reject mechanism is also relevant for side channel attack resilience
of the implicit-rejection variant. Along the way, we prove several technical
results characterizing preimage extraction and certain search tasks in the QROM
that might be of independent interest.


title:
Adversarial Attacks on Deep Learning-based Video Compression and
  Classification Systems

abstract:
  Video compression plays a crucial role in enabling video streaming and
classification systems and maximizing the end-user quality of experience (QoE)
at a given bandwidth budget. In this paper, we conduct the first systematic
study for adversarial attacks on deep learning based video compression and
downstream classification systems. We propose an adaptive adversarial attack
that can manipulate the Rate-Distortion (R-D) relationship of a video
compression model to achieve two adversarial goals: (1) increasing the network
bandwidth or (2) degrading the video quality for end-users. We further devise
novel objectives for targeted and untargeted attacks to a downstream video
classification service. Finally, we design an input-invariant perturbation that
universally disrupts video compression and classification systems in real time.
Unlike previously proposed attacks on video classification, our adversarial
perturbations are the first to withstand compression. We empirically show the
resilience of our attacks against various defenses, i.e., adversarial training,
video denoising, and JPEG compression. Our extensive experimental results on
various video datasets demonstrate the effectiveness of our attacks. Our video
quality and bandwidth attacks deteriorate peak signal-to-noise ratio by up to
5.4dB and the bit-rate by up to 2.4 times on the standard video compression
datasets while achieving over 90% attack success rate on a downstream
classifier.


title: Study of Electroweak Phase Transition in Exotic Higgs Decays at the CEPC

abstract:
  A strong first-order electroweak phase transition (EWPT) can be induced by
light new physics weakly coupled to the Higgs. This study focuses on a scenario
in which the first-order EWPT is driven by a light scalar $s$ with a mass
between 15-60 GeV. A search for exotic decays of the Higgs boson into a pair of
spin-zero particles, $h \to ss$, where the $s$-boson decays into $b$-quarks
promptly is presented. The search is performed in events where the Higgs boson
is produced in association with a $Z$ boson, giving rise to a signature of two
charged leptons (electrons or muons) and multiple jets from $b$-quark decays.
The analysis is considering a scenario of analysing 5000 fb$^{-1}$ $e^+ e^-$
collision data at $\sqrt{s} = 240 $ GeV from the Circular Electron Positron
Collider (CEPC). This study with $4b$ final state conclusively tests the
expected sensitivity of probing the light scalars in the CEPC experiment. The
sensitivity reach is significantly larger than that can be achieved at the LHC.


title: Negative Inner-Loop Learning Rates Learn Universal Features

abstract:
  Model Agnostic Meta-Learning (MAML) consists of two optimization loops: the
outer loop learns a meta-initialization of model parameters that is shared
across tasks, and the inner loop task-specific adaptation step. A variant of
MAML, Meta-SGD, uses the same two loop structure, but also learns the
learning-rate for the adaptation step. Little attention has been paid to how
the learned learning-rate of Meta-SGD affects feature reuse. In this paper, we
study the effect that a learned learning-rate has on the per-task feature
representations in Meta-SGD. The learned learning-rate of Meta-SGD often
contains negative values. During the adaptation phase, these negative learning
rates push features away from task-specific features and towards task-agnostic
features.
  We performed several experiments on the Mini-Imagenet dataset. Two neural
networks were trained, one with MAML, and one with Meta-SGD. The feature
quality for both models was tested as follows: strip away the linear
classification layer, pass labeled and unlabeled samples through this encoder,
classify the unlabeled samples according to their nearest neighbor. This
process was performed: 1) after training and using the meta-initialization
parameters; 2) after adaptation, and validated on that task; and 3) after
adaptation, and validated on a different task. The MAML trained model improved
on the task it was adapted to, but had worse performance on other tasks. The
Meta-SGD trained model was the opposite; it had worse performance on the task
it was adapted to, but improved on other tasks. This confirms the hypothesis
that Meta-SGD's negative learning rates cause the model to learn task-agnostic
features rather than simply adapt to task specific features.


title:
A Class of Two-Timescale Stochastic EM Algorithms for Nonconvex Latent
  Variable Models

abstract:
  The Expectation-Maximization (EM) algorithm is a popular choice for learning
latent variable models. Variants of the EM have been initially introduced,
using incremental updates to scale to large datasets, and using Monte Carlo
(MC) approximations to bypass the intractable conditional expectation of the
latent data for most nonconvex models. In this paper, we propose a general
class of methods called Two-Timescale EM Methods based on a two-stage approach
of stochastic updates to tackle an essential nonconvex optimization task for
latent variable models. We motivate the choice of a double dynamic by invoking
the variance reduction virtue of each stage of the method on both sources of
noise: the index sampling for the incremental update and the MC approximation.
We establish finite-time and global convergence bounds for nonconvex objective
functions. Numerical applications on various models such as deformable template
for image analysis or nonlinear models for pharmacokinetics are also presented
to illustrate our findings.


title: A Mass Formula For Artin--Schreier Curves Over Finite Fields

abstract:
  We study a mass formula for Artin--Schreier curves of genus $g$ defined over
a finite field $k$ of characteristic $p$. For an odd prime $p$ and for small
$g$, we determine the number of $k$-isomorphism classes of Artin-Schreier
curves of genus $g$, weighted by the order of the centralizer of the
Artin-Schreier involution in the automorphism group. This extends earlier
results by several authors in characteristic $p=2$.
  Keywords: Artin-Schreier curve, finite field, automorphism, Mass formula,
moduli space, arithmetic statistics


title:
Trackers Bounce Back: Measuring Evasion of Partitioned Storage in the
  Wild

abstract:
  This work presents a systematic study of navigational tracking, the latest
development in the cat-and-mouse game between browsers and online trackers.
Navigational tracking allows trackers to 'aggregate users' activities and
behaviors across sites by modifying their navigation requests. This technique
is particularly important because it circumvents the increasing efforts by
browsers to partition or block third-party storage, which was previously
necessary for most cross-website tracking. While previous work has studied
specific navigational tracking techniques (i.e. "bounce tracking"), our work is
the first effort to systematically study and measure the entire category of
navigational tracking techniques. We describe and measure the frequency of two
different navigational tracking techniques on the Web, and find that
navigational tracking is present on slightly more than ten percent of all
navigations that we made. Our contributions include identifying 214 domains
belonging to at least 104 organizations tracking users across sites through
link decoration techniques using direct or indirect navigation flows. We
identify a further 23 domains belonging to at least 16 organizations tracking
users through bounce tracking (i.e. bouncing users through unrelated third
parties to generate user profiles). We also improve on prior techniques for
differenting user identifiers from non-sensitive information, which is
necessary to detect one class of navigational tracking. We discuss how our
findings can used to protect users from navigational tracking, and commit to
releasing both our complete dataset and our measurement pipeline


title: Evo-SETI: A Mathematical Tool for Cladistics, Evolution, and SETI

abstract:
  The discovery of new exoplanets makes us wonder where each new exoplanet
stands along its way to develop life as we know it on Earth. Our Evo-SETI
Theory is a mathematical way to face this problem. We describe cladistics and
evolution by virtue of a few statistical equations based on lognormal
probability density functions (pdf) in the time. We call b-lognormal a
lognormal pdf starting at instant b (birth). Then, the lifetime of any living
being becomes a suitable b-lognormal in the time. Next, our "Peak-Locus
Theorem" translates cladistics: each species created by evolution is a
b-lognormal whose peak lies on the exponentially growing number of living
species. This exponential is the mean value of a stochastic process called
"Geometric Brownian Motion" (GBM). Past mass extinctions were all-lows of this
GBM. In addition, the Shannon Entropy (with a reversed sign) of each
b-lognormal is the measure of how evolved that species is, and we call it
EvoEntropy. The "molecular clock" is re-interpreted as the EvoEntropy straight
line in the time whenever the mean value is exactly the GBM exponential. We
were also able to extend the Peak-Locus Theorem to any mean value other than
the exponential. For example, we derive in this paper for the first time the
EvoEntropy corresponding to the Markov-Korotayev (2007) "cubic" evolution: a
curve of logarithmic increase.


title: Provably Fair Federated Learning via Bounded Group Loss

abstract:
  In federated learning, fair prediction across various protected groups (e.g.,
gender, race) is an important constraint for many applications. Unfortunately,
prior work studying group fair federated learning lacks formal convergence or
fairness guarantees. Our work provides a new definition for group fairness in
federated learning based on the notion of Bounded Group Loss (BGL), which can
be easily applied to common federated learning objectives. Based on our
definition, we propose a scalable algorithm that optimizes the empirical risk
and global fairness constraints, which we evaluate across common fairness and
federated learning benchmarks. Our resulting method and analysis are the first
we are aware of to provide formal theoretical guarantees for training a fair
federated learning model.


title: Sync and swarm: solvable model of non-identical swarmalators

abstract:
  We study a one-dimensional (1D) model of non-identical swarmalators,
generalizations of phase oscillators that both sync in time and swarm in space.
The model produces three collective states: asynchrony, sync clusters, and
vortex-like phase-waves. These states occur in many real-world swarmalator
systems such as biological microswimmers, chemical nanomotors, and groups of
drones. A generalized Ott-Antonsen ansatz provides the first analytic
description of these states and conditions for their existence. Our work shows
how tools from coupled oscillator theory may be used in studies of active
matter, swarm robotics, and related disciplines.


title:
Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty
  Quantification

abstract:
  A critical limitation of current methods based on Neural Radiance Fields
(NeRF) is that they are unable to quantify the uncertainty associated with the
learned appearance and geometry of the scene. This information is paramount in
real applications such as medical diagnosis or autonomous driving where, to
reduce potentially catastrophic failures, the confidence on the model outputs
must be included into the decision-making process. In this context, we
introduce Conditional-Flow NeRF (CF-NeRF), a novel probabilistic framework to
incorporate uncertainty quantification into NeRF-based approaches. For this
purpose, our method learns a distribution over all possible radiance fields
modelling which is used to quantify the uncertainty associated with the
modelled scene. In contrast to previous approaches enforcing strong constraints
over the radiance field distribution, CF-NeRF learns it in a flexible and fully
data-driven manner by coupling Latent Variable Modelling and Conditional
Normalizing Flows. This strategy allows to obtain reliable uncertainty
estimation while preserving model expressivity. Compared to previous
state-of-the-art methods proposed for uncertainty quantification in NeRF, our
experiments show that the proposed method achieves significantly lower
prediction errors and more reliable uncertainty values for synthetic novel view
and depth-map estimation.


title:
Between SC and LOGDCFL: Families of Languages Accepted by
  Logarithmic-Space Deterministic Auxiliary Depth-k Storage Automata

abstract:
  The closure of deterministic context-free languages under logarithmic-space
many-one reductions ($\mathrm{L}$-m-reductions), known as LOGDCFL, has been
studied in depth from an aspect of parallel computability because it is nicely
situated between $\mathrm{L}$ and $\mathrm{AC}^1\cap\mathrm{SC}^2$. By changing
a memory device from pushdown stacks to access-controlled storage tapes, we
introduce a computational model of one-way deterministic depth-$k$ storage
automata ($k$-sda's) whose tape cells are freely modified during the first $k$
accesses and then become blank forever. These $k$-sda's naturally induce the
language family $k\mathrm{SDA}$. Similarly to $\mathrm{LOGDCFL}$, we study the
closure $\mathrm{LOG}k\mathrm{SDA}$ of all languages in $k\mathrm{SDA}$ under
$\mathrm{L}$-m-reductions. We first demonstrate that $\mathrm{DCFL}\subseteq
k\mathrm{SDA}\subseteq \mathrm{SC}^k$ by significantly extending Cook's early
result (1979) of $\mathrm{DCFL}\subseteq \mathrm{SC}^2$. The entire hierarch of
$\mathrm{LOG}k\mathrm{SDA}$ for all $k\geq1$ therefore lies between
$\mathrm{LOGDCFL}$ and $\mathrm{SC}$. As an immediate consequence, we obtain
the same simulation bounds for Hibbard's limited automata. We further
characterize the closure $\mathrm{LOG}k\mathrm{SDA}$ in terms of a new machine
model, called logarithmic-space deterministic auxiliary depth-$k$ storage
automata that run in polynomial time. These machines are as powerful as a
polynomial-time two-way multi-head deterministic depth-$k$ storage automata. We
also provide "generic" $\mathrm{LOG}k\mathrm{SDA}$-complete languages under
$\mathrm{L}$-m-reductions by constructing a universal simulator working for all
two-way $k$-sda's.


title: Analysis and Adaptation of YOLOv4 for Object Detection in Aerial Images

abstract:
  The recent and rapid growth in Unmanned Aerial Vehicles (UAVs) deployment for
various computer vision tasks has paved the path for numerous opportunities to
make them more effective and valuable. Object detection in aerial images is
challenging due to variations in appearance, pose, and scale. Autonomous aerial
flight systems with their inherited limited memory and computational power
demand accurate and computationally efficient detection algorithms for
real-time applications. Our work shows the adaptation of the popular YOLOv4
framework for predicting the objects and their locations in aerial images with
high accuracy and inference speed. We utilized transfer learning for faster
convergence of the model on the VisDrone DET aerial object detection dataset.
The trained model resulted in a mean average precision (mAP) of 45.64% with an
inference speed reaching 8.7 FPS on the Tesla K80 GPU and was highly accurate
in detecting truncated and occluded objects. We experimentally evaluated the
impact of varying network resolution sizes and training epochs on the
performance. A comparative study with several contemporary aerial object
detectors proved that YOLOv4 performed better, implying a more suitable
detection algorithm to incorporate on aerial platforms.


title: Clustering in laboratory and numerical turbulent swirling flows

abstract:
  We study the three-dimensional clustering of velocity stagnation points, of
nulls of the vorticity and of the Lagrangian acceleration, and of inertial
particles in turbulent flows at fixed Reynolds numbers, but under different
large-scale flow geometries. To this end, we combine direct numerical
simulations of homogeneous and isotropic turbulence and of the Taylor-Green
flow, with particle tracking velocimetry in a von K\'arm\'an experiment. While
flows have different topologies (as nulls cluster differently), particles
behave similarly in all cases, indicating large neutrally buoyant particles
behave and cluster as inertial particles.


title:
Learning Morphological Feature Perturbations for Calibrated
  Semi-Supervised Segmentation

abstract:
  We propose MisMatch, a novel consistency-driven semi-supervised segmentation
framework which produces predictions that are invariant to learnt feature
perturbations. MisMatch consists of an encoder and a two-head decoders. One
decoder learns positive attention to the foreground regions of interest (RoI)
on unlabelled images thereby generating dilated features. The other decoder
learns negative attention to the foreground on the same unlabelled images
thereby generating eroded features. We then apply a consistency regularisation
on the paired predictions. MisMatch outperforms state-of-the-art
semi-supervised methods on a CT-based pulmonary vessel segmentation task and a
MRI-based brain tumour segmentation task. In addition, we show that the
effectiveness of MisMatch comes from better model calibration than its
supervised learning counterpart.


title:
Cost Function Learning in Memorized Social Networks with Cognitive
  Behavioral Asymmetry

abstract:
  This paper investigates the cost function learning in social information
networks, wherein the influence of humans' memory on information consumption is
explicitly taken into account. We first propose a model for social
information-diffusion dynamics with a focus on systematic modeling of
asymmetric cognitive bias, represented by confirmation bias and novelty bias.
Building on the proposed social model, we then propose the M$^{3}$IRL: a model
and maximum-entropy based inverse reinforcement learning framework for learning
the cost functions of target individuals in the memorized social networks.
Compared with the existing Bayesian IRL, maximum entropy IRL, relative entropy
IRL and maximum causal entropy IRL, the characteristics of M$^{3}$IRL are
significantly different here: no dependency on the Markov Decision Process
principle, the need of only a single finite-time trajectory sample, and bounded
decision variables. Finally, the effectiveness of the proposed social
information-diffusion model and the M$^{3}$IRL algorithm are validated by the
online social media data.


title:
The Ion Fluorescence Chamber (IFC): A new concept for directional dark
  matter and topologically imaging neutrinoless double beta decay searches

abstract:
  We introduce a novel particle detection concept for large-volume, fine
granularity particle detection: The Ion Fluorescence Chamber (IFC). In
electronegative gases such as SF$_6$ and SeF$_6$, ionizing particles create
ensembles of positive and negative ions. In the IFC, positive ions are drifted
to a chemically active cathode where they react with a custom organic turn-on
fluorescent monolayer encoding a long-lived 2D image. The negative ions are
sensed electrically with course resolution at the anode, inducing an optical
microscope to travel to and scan the corresponding cathode location for the
fluorescent image. This concept builds on technologies developed for barium
tagging in neutrinoless double beta decay, combining the ultra-fine imaging
capabilities of an emulsion detector with the monolithic sensing of a time
projection chamber. The result is a high precision imaging detector over
arbitrarily large volumes without the challenges of ballooning channel count or
system complexity. After outlining the concept, we discuss R\&D to be
undertaken to demonstrate it, and explore application to both directional dark
matter searches in SF$_6$ and searches for neutrinoless double beta decay in
large $^{82}$SeF$_6$ chambers.


title:
Spectral graph fractional Fourier transform for directed graphs and its
  application

abstract:
  In graph signal processing, most studies assume that the underlying network
is undirected. Although the digraph model is rarely adopted, it is more
appropriate for many applications, especially for real world networks. In this
paper, we present a general framework for extending the graph signal processing
to directed graphs in graph fractional domain. For this purpose, we consider a
new definition for fractional Laplacian matrix on directed graph and generalize
the spectral graph fractional Fourier transform to directed graph (DGFRFT).
Based on our new transform, we then define filtering.


title:
Emulating Quantum Dynamics with Neural Networks via Knowledge
  Distillation

abstract:
  High-fidelity quantum dynamics emulators can be used to predict the time
evolution of complex physical systems. Here, we introduce an efficient training
framework for constructing machine learning-based emulators. Our approach is
based on the idea of knowledge distillation and uses elements of curriculum
learning. It works by constructing a set of simple, but rich-in-physics
training examples (a curriculum). These examples are used by the emulator to
learn the general rules describing the time evolution of a quantum system
(knowledge distillation). The goal is not only to obtain high-quality
predictions, but also to examine the process of how the emulator learns the
physics of the underlying problem. This allows us to discover new facts about
the physical system, detect symmetries, and measure relative importance of the
contributing physical processes. We illustrate this approach by training an
artificial neural network to predict the time evolution of quantum wave
packages propagating through a potential landscape. We focus on the question of
how the emulator learns the rules of quantum dynamics from the curriculum of
simple training examples and to which extent it can generalize the acquired
knowledge to solve more challenging cases.


title: A Quantum Algorithm for Network Reliability

abstract:
  Building a network that is resilient to a component failure is vital. Our
access to electricity and telecommunications or the internet of things all
hinge on an uninterrupted service provided by a robust network. Calculating the
network reliability $R$ is $\sharp$P-complete and intractable to calculate
exactly for medium and large networks. Here, we present an explicit,
circuit-level implementation of a quantum algorithm that computes $R$. Our
algorithm requires $O(EV/\epsilon)$ gate operations and $O(E)$ qubits, where
$V$ and $E$ are the number of nodes and edges in the graph and $\epsilon$ is
the uncertainty in the reliability estimation. This constitutes a significant
polynomial speedup over the best classical approaches currently known. We
further provide quantum gate counts, relevant for both pre-fault-tolerant and
fault-tolerant regimes, sufficient to compute $R$.


title: Relationformer: A Unified Framework for Image-to-Graph Generation

abstract:
  A comprehensive representation of an image requires understanding objects and
their mutual relationship, especially in image-to-graph generation, e.g., road
network extraction, blood-vessel network extraction, or scene graph generation.
Traditionally, image-to-graph generation is addressed with a two-stage approach
consisting of object detection followed by a separate relation prediction,
which prevents simultaneous object-relation interaction. This work proposes a
unified one-stage transformer-based framework, namely Relationformer, that
jointly predicts objects and their relations. We leverage direct set-based
object prediction and incorporate the interaction among the objects to learn an
object-relation representation jointly. In addition to existing [obj]-tokens,
we propose a novel learnable token, namely [rln]-token. Together with
[obj]-tokens, [rln]-token exploits local and global semantic reasoning in an
image through a series of mutual associations. In combination with the
pair-wise [obj]-token, the [rln]-token contributes to a computationally
efficient relation prediction. We achieve state-of-the-art performance on
multiple, diverse and multi-domain datasets that demonstrate our approach's
effectiveness and generalizability.


title: Belle II Executive Summary

abstract:
  Belle II is a Super $B$ Factory experiment, expected to record 50 ab$^{-1}$
of $e^+e^-$ collisions at the SuperKEKB accelerator over the next decade. The
large samples of $B$ mesons, charm hadrons, and tau leptons produced in the
clean experimental environment of $e^+e^-$ collisions will provide the basis of
a broad and unique flavor-physics program. Belle II will pursue physics beyond
the Standard Model in many ways, for example: improving the precision of weak
interaction parameters, particularly Cabibbo-Kobayashi-Maskawa (CKM) matrix
elements and phases, and thus more rigorously test the CKM paradigm, measuring
lepton-flavor-violating parameters, and performing unique searches for
missing-mass dark matter events. Many key measurements will be made with
world-leading precision.


title:
Inferring topological transitions in pattern-forming processes with
  self-supervised learning

abstract:
  The identification and classification of transitions in topological and
microstructural regimes in pattern-forming processes is critical for
understanding and fabricating microstructurally precise novel materials in many
application domains. Unfortunately, relevant microstructure transitions may
depend on process parameters in subtle and complex ways that are not captured
by the classic theory of phase transition. While supervised machine learning
methods may be useful for identifying transition regimes, they need labels
which require prior knowledge of order parameters or relevant structures.
Motivated by the universality principle for dynamical systems, we instead use a
self-supervised approach to solve the inverse problem of predicting process
parameters from observed microstructures using neural networks. This approach
does not require labeled data about the target task of predicting
microstructure transitions. We show that the difficulty of performing this
prediction task is related to the goal of discovering microstructure regimes,
because qualitative changes in microstructural patterns correspond to changes
in uncertainty for our self-supervised prediction problem. We demonstrate the
value of our approach by automatically discovering transitions in
microstructural regimes in two distinct pattern-forming processes: the spinodal
decomposition of a two-phase mixture and the formation of concentration
modulations of binary alloys during physical vapor deposition of thin films.
This approach opens a promising path forward for discovering and understanding
unseen or hard-to-detect transition regimes, and ultimately for controlling
complex pattern-forming processes.

categories: cond-mat.mtrl-sci cond-mat.dis-nn cs.CV cs.LG


title: Conjugate Gradient Adaptive Learning with Tukey's Biweight M-Estimate

abstract:
  We propose a novel M-estimate conjugate gradient (CG) algorithm, termed
Tukey's biweight M-estimate CG (TbMCG), for system identification in impulsive
noise environments. In particular, the TbMCG algorithm can achieve a faster
convergence while retaining a reduced computational complexity as compared to
the recursive least-squares (RLS) algorithm. Specifically, the Tukey's biweight
M-estimate incorporates a constraint into the CG filter to tackle impulsive
noise environments. Moreover, the convergence behavior of the TbMCG algorithm
is analyzed. Simulation results confirm the excellent performance of the
proposed TbMCG algorithm for system identification and active noise control
applications.


title: Incentive Compatibility in Two-Stage Repeated Stochastic Games

abstract:
  We address the problem of mechanism design for two-stage repeated stochastic
games -- a novel setting using which many emerging problems in next-generation
electricity markets can be readily modeled. Repeated playing affords the
players a large class of strategies that adapt a player's actions to all past
observations and inferences obtained therefrom. In other settings such as
iterative auctions or dynamic games where a large strategy space of this sort
manifests, it typically has an important implication for mechanism design: It
may be impossible to obtain truth-telling as a dominant strategy equilibrium.
Consequently, in such scenarios, it is common to settle for mechanisms that
render truth-telling only a Nash equilibrium, or variants thereof, even though
Nash equilibria are known to be poor models of real-world behavior owing to
each player having to make overly specific assumptions about the behaviors of
the other players in order for them to employ their Nash equilibrium strategy.
In general, the lesser the burden of speculation in an equilibrium, the more
plausible it is that it models real-world behavior. Guided by this maxim, we
introduce a new notion of equilibrium called Dominant Strategy Non-Bankrupting
Equilibrium (DNBE) which requires the players to make very little assumptions
about the behavior of the other players to employ their equilibrium strategy.
Consequently, a mechanism that renders truth-telling a DNBE as opposed to only
a Nash equilibrium could be quite effective in molding real-world behavior
along the desired lines. Finally, we present a mechanism for two-stage repeated
stochastic games that renders truth-telling a Dominant Strategy Non-Bankrupting
Equilibrium. The mechanism also guarantees individual rationality and maximizes
social welfare.


title:
CausalDeepCENT: Deep Learning for Causal Prediction of Individual Event
  Times

abstract:
  Deep learning (DL) has recently drawn much attention in image analysis,
natural language process, and high-dimensional medical data analysis. Under the
causal direct acyclic graph (DAG) interpretation, the input variables without
incoming edges from parent nodes in the DL architecture maybe assumed to be
randomized and independent of each other. As in a regression setting, including
the input variables in the DL algorithm would reduce the bias from the
potential confounders. However, failing to include a potential latent causal
structure among the input variables affecting both treatment assignment and the
output variable could be additional significant source of bias. The primary
goal of this study is to develop new DL algorithms to estimate causal
individual event times for time-to-event data, equivalently to estimate the
causal time-to-event distribution with or without right censoring, accounting
for the potential latent structure among the input variables. Once the causal
individual event times are estimated, it would be straightforward to estimate
the causal average treatment effects as the differences in the averages of the
estimated causal individual event times. A connection is made between the
proposed method and the targeted maximum likelihood estimation (TMLE).
Simulation studies are performed to assess improvement in prediction abilities
of the proposed methods by using the mean square error (MSE)-based method and
rank-based $C$-Index metric. The simulation results indicate that improvement
on the prediction accuracy could be substantial particularly when there is a
collider among the input variables. The proposed method is illustrated with a
publicly available and influential breast cancer data set. The proposed method
has been implemented by using PyTorch and uploaded at
https://github.com/yicjia/CausalDeepCENT.


title: On super polyharmonic property of high-order fractional Laplacian

abstract:
  Let $0<\alpha<2$, $p\geq 1$, $m\in\mathbb{N}_+$. Consider $u$ to be the
positive solution of the PDE \begin{equation}\label{abstract PDE}
  (-\Delta)^{\frac{\alpha}{2}+m} u(x)=u^p(x) \quad\text{in }\mathbb{R}^n.
\end{equation} Cao, Dai and Qin( Transactions of the American mathematical
society, 2021) showed that, under the condition $u\in\mathcal{L}_\alpha$, the
PDE possesses super polyharmonic property $(-\Delta)^{k+\frac{\alpha}{2}}u\geq
0$ for $k=0,1,...,m-1$. In this paper, we show another kind of super
polyharmonic property $(-\Delta)^k u> 0$ for $k=1,...m$ under different
conditions $(-\Delta)^mu\in\mathcal{L}_\alpha$ and $(-\Delta)^m u\geq 0$. Both
kinds of super polyharmonic properties can lead to the equivalence between the
PDE and the integral equation
$u(x)=\int_{\mathbb{R}^n}\frac{u^p(y)}{|x-y|^{n-2m-\alpha}}dy$.


title:
SwinTextSpotter: Scene Text Spotting via Better Synergy between Text
  Detection and Text Recognition

abstract:
  End-to-end scene text spotting has attracted great attention in recent years
due to the success of excavating the intrinsic synergy of the scene text
detection and recognition. However, recent state-of-the-art methods usually
incorporate detection and recognition simply by sharing the backbone, which
does not directly take advantage of the feature interaction between the two
tasks. In this paper, we propose a new end-to-end scene text spotting framework
termed SwinTextSpotter. Using a transformer encoder with dynamic head as the
detector, we unify the two tasks with a novel Recognition Conversion mechanism
to explicitly guide text localization through recognition loss. The
straightforward design results in a concise framework that requires neither
additional rectification module nor character-level annotation for the
arbitrarily-shaped text. Qualitative and quantitative experiments on
multi-oriented datasets RoIC13 and ICDAR 2015, arbitrarily-shaped datasets
Total-Text and CTW1500, and multi-lingual datasets ReCTS (Chinese) and VinText
(Vietnamese) demonstrate SwinTextSpotter significantly outperforms existing
methods. Code is available at https://github.com/mxin262/SwinTextSpotter.


title:
Coordinated Pose Control of Mobile Manipulation with an Unstable Bikebot
  Platform

abstract:
  Bikebot manipulation has advantages of the single-track robot mobility and
manipulation dexterity. We present a coordinated pose control of mobile
manipulation with the stationary bikebot. The challenges of the bikebot
manipulation include the limited steering balance capability of the unstable
bikebot and kinematic redundancy of the manipulator. We first present the
steering balance model to analyze and explore the maximum steering capability
to balance the stationary platform. A balancing equilibrium manifold is then
proposed to describe the necessary condition to fulfill the simultaneous
platform balance and posture control of the end-effector. A coordinated
planning and control design is presented to determine the balance-prioritized
posture control under kinematic and dynamic constraints. Extensive experiments
are conducted to demonstrate the mechatronic design for autonomous plant
inspection in agricultural applications. The results confirm the feasibility to
use the bikebot manipulation for a plant inspection with end-effector position
and orientation errors about 5 mm and 0.3 degs, respectively.


title: On the Hasse Principle for conic bundles over even degree extensions

abstract:
  Let $k$ be a number field and let $\pi \colon X \rightarrow \mathbb{P}_k^1$
be a smooth conic bundle. We show that if $X/k$ has four geometric singular
fibers with $X(\mathbb{A}_k)\neq \emptyset$ or non-trivial Brauer group, then
$X$ satisfies the Hasse principle over any even degree extension $L/k$.
Furthermore for arbitrary $X$ we show that, conditional on Schinzel's
hypothesis, $X$ satisfies the Hasse principle over all but finitely many
quadratic extensions of $k$. We prove these results by showing the Brauer-Manin
obstruction vanishes and then apply fibration method results of
Colliot-Th\'el\`ene, following Colliot-Th\'el\`ene and Sansuc.


title:
Unsupervised Learning of 3D Semantic Keypoints with Mutual
  Reconstruction

abstract:
  Semantic 3D keypoints are category-level semantic consistent points on 3D
objects. Detecting 3D semantic keypoints is a foundation for a number of 3D
vision tasks but remains challenging, due to the ambiguity of semantic
information, especially when the objects are represented by unordered 3D point
clouds. Existing unsupervised methods tend to generate category-level keypoints
in implicit manners, making it difficult to extract high-level information,
such as semantic labels and topology. From a novel mutual reconstruction
perspective, we present an unsupervised method to generate consistent semantic
keypoints from point clouds explicitly. To achieve this, the proposed model
predicts keypoints that not only reconstruct the object itself but also
reconstruct other instances in the same category. To the best of our knowledge,
the proposed method is the first to mine 3D semantic consistent keypoints from
a mutual reconstruction view. Experiments under various evaluation metrics as
well as comparisons with the state-of-the-arts demonstrate the efficacy of our
new solution to mining semantic consistent keypoints with mutual
reconstruction.


title:
Volkit: A Performance-Portable Computer Vision Library for 3D Volumetric
  Data

abstract:
  We present volkit, an open source library with high performance
implementations of image manipulation and computer vision algorithms that focus
on 3D volumetric representations. Volkit implements a cross-platform,
performance-portable API targeting both CPUs and GPUs that defers data and
resource movement and hides them from the application developer using a managed
API. We use volkit to process medical and simulation data that is rendered in
VR and consequently integrated the library into the C++ virtual reality
software CalVR. The paper presents case studies and performance results and by
that demonstrates the library's effectiveness and the efficiency of this
approach.


title: Thompson Sampling on Asymmetric $\alpha$-Stable Bandits

abstract:
  In algorithm optimization in reinforcement learning, how to deal with the
exploration-exploitation dilemma is particularly important. Multi-armed bandit
problem can optimize the proposed solutions by changing the reward distribution
to realize the dynamic balance between exploration and exploitation. Thompson
Sampling is a common method for solving multi-armed bandit problem and has been
used to explore data that conform to various laws. In this paper, we consider
the Thompson Sampling approach for multi-armed bandit problem, in which rewards
conform to unknown asymmetric $\alpha$-stable distributions and explore their
applications in modelling financial and wireless data.


title:
Convergence Error Analysis of Reflected Gradient Langevin Dynamics for
  Globally Optimizing Non-Convex Constrained Problems

abstract:
  Non-convex optimization problems have various important applications, whereas
many algorithms have been proven only to converge to stationary points.
Meanwhile, gradient Langevin dynamics (GLD) and its variants have attracted
increasing attention as a framework to provide theoretical convergence
guarantees for a global solution in non-convex settings. The studies on GLD
initially treated unconstrained convex problems and very recently expanded to
convex constrained non-convex problems by Lamperski (2021). In this work, we
can deal with non-convex problems with some kind of non-convex feasible region.
This work analyzes reflected gradient Langevin dynamics (RGLD), a global
optimization algorithm for smoothly constrained problems, including non-convex
constrained ones, and derives a convergence rate to a solution with
$\epsilon$-sampling error. The convergence rate is faster than the one given by
Lamperski (2021) for convex constrained cases. Our proofs exploit the Poisson
equation to effectively utilize the reflection for the faster convergence rate.


title:
Nature of quantum spin liquids of the S=1/2 Heisenberg antiferromagnet
  on the triangular lattice: A parallel DMRG study

abstract:
  We study the ground-state properties of the quantum spin liquid (QSL) phases
of the spin-$1/2$ antiferromagnetic Heisenberg model on the triangular lattice
with nearest- ($J_1$), next-nearest- ($J_2$), and third-neighbor ($J_3$)
interactions by using density-matrix renormalization group (DMRG) method. By
combining parallel DMRG with $SU(2)$ spin rotational symmetry, we are able to
obtain accurate results on large cylinders with length up to $L_x=48$ and
circumference $L_y=6 - 12$. Our results suggest that the QSL phase of the
$J_1$-$J_2$ Heisenberg model is gapped which is characterized by the absence of
gapless mode, short-range spin-spin and dimer-dimer correlations. In the
presence of $J_3$ interaction, we find that a new critical QSL with a single
gapless mode emerges. While both spin-spin and scalar chiral-chiral
correlations are short-ranged, dimer-dimer correlations are quasi-long-ranged
which decays as a power-law at long distances.


title: A Walking Space Robot for On-Orbit Satellite Servicing: The ReCoBot

abstract:
  A key factor in the economic efficiency of satellites is their availability
in orbit. Replacing standardized building blocks, such as empty fuel tanks or
outdated electronic modules, could greatly extend the satellites' lifetime.
This, however, requires flexible robots that can locomote on the surface of
these satellites for optimal accessibility and manipulation. This paper
introduces ReCoBot, a 7-axis walking space manipulator for locomotion and
manipulation. The robot can connect to compatible structures with its symmetric
ends and provides interfaces for manual teleoperation and motion planning with
a constantly changing base and tip. We build on open-source robotics software
and easily available components to evaluate the overall concept with an early
stage demonstrator. The proposed manipulator has a length of 1.20 m and a
weight of 10.4 kg and successfully locomotes over a satellite mockup in our lab
environment.


title:
Shared Control in Human Robot Teaming: Toward Context-Aware
  Communication

abstract:
  In the field of Human-Robot Interaction (HRI), many researchers study shared
control systems. Shared control is when a person and agent both contribute to
the performance of a task in a collaborative way, often by providing control
inputs for a robot. One of the most important things in shared control is the
nature of the communication between the person and robot, which could help the
human-robot team handle a variety of challenging situations. In this paper, we
identify key challenges in shared control, in which better communication design
could be useful, including when encountering novel situations and contexts,
resolving tensions between preferences and performance, and alleviating
cognitive burden and interruptions. Through the use of four exemplar shared
control scenarios, we explore how well-designed human-robot communication
strategies could help address each challenge. We hope this paper will draw
attention to these challenges and assist researchers in advancing the
development of shared control systems.


title:
Efficient DOA Estimation Method for Reconfigurable Intelligent Surfaces
  Aided UAV Swarm

abstract:
  The conventional direction of arrival (DOA) estimation methods are performed
with multiple receiving channels. In this paper, a changeling DOA estimation
problem is addressed in a different scenario with only one full-functional
receiving channel. A new unmanned aerial vehicle (UAV) swarm system using
multiple lifted reconfigurable intelligent surface (RIS) is proposed for the
DOA estimation. The UAV movement degrades the DOA estimation performance
significantly, and the existing atomic norm minimization (ANM) methods cannot
be used in the scenario with array perturbation. Specifically, considering the
position perturbation of UAVs, a new atomic norm-based DOA estimation method is
proposed, where an atomic norm is defined with the parameter of the position
perturbation. Then, a customized semi-definite programming (SDP) method is
derived to solve the atomic norm-based method, where different from the
traditional SDP method, an additional transforming matrix is formulated.
Moreover, a gradient descent method is applied to refine the estimated DOA and
the position perturbation further. Simulation results show that the proposed
method achieves much better DOA estimation performance in the RIS-aided UAV
swarm system with only one receiving channel than various benchmark schemes.


title:
Online Guaranteed Reachable Set Approximation for Systems with Changed
  Dynamics and Control Authority

abstract:
  This work presents a method of efficiently computing inner and outer
approximations of forward reachable sets for nonlinear control systems with
changed dynamics and diminished control authority, given an a priori computed
reachable set for the nominal system. The method functions by shrinking or
inflating a precomputed reachable set based on prior knowledge of the system's
trajectory deviation growth dynamics, depending on whether an inner
approximation or outer approximation is desired. These dynamics determine an
upper bound on the minimal deviation between two trajectories emanating from
the same point that are generated on the nominal system using nominal control
inputs, and by the impaired system based on the diminished set of control
inputs, respectively. The dynamics depend on the given Hausdorff distance bound
between the nominal set of admissible controls and the possibly unknown
impaired space of admissible controls, as well as a bound on the rate change
between the nominal and off-nominal dynamics. Because of its computational
efficiency compared to direct computation of the off-nominal reachable set,
this procedure can be applied to on-board fault-tolerant path planning and
failure recovery. In addition, the proposed algorithm does not require
convexity of the reachable sets unlike our previous work, thereby making it
suitable for general use. We raise a number of implementational considerations
for our algorithm, and we present three illustrative examples, namely an
application to the heading dynamics of a ship, a lower triangular dynamical
system, and a system of coupled linear subsystems.


title: An Exploratory Study on Refactoring Documentation in Issues Handling

abstract:
  Understanding the practice of refactoring documentation is of paramount
importance in academia and industry. Issue tracking systems are used by most
software projects enabling developers, quality assurance, managers, and users
to submit feature requests and other tasks such as bug fixing and code review.
Although recent studies explored how to document refactoring in commit
messages, little is known about how developers describe their refactoring needs
in issues. In this study, we aim at exploring developer-reported refactoring
changes in issues to better understand what developers consider to be
problematic in their code and how they handle it. Our approach relies on text
mining 45,477 refactoring-related issues and identifying refactoring patterns
from a diverse corpus of 77 Java projects by investigating issues associated
with 15,833 refactoring operations and developers' explicit refactoring
intention. Our results show that (1) developers mostly use move refactoring
related terms/phrases to target refactoring-related issues; and (2) developers
tend to explicitly mention the improvement of specific quality attributes and
focus on duplicate code removal. We envision our findings enabling tool
builders to support developers with automated documentation of refactoring
changes in issues.


title:
Reconfigurable Intelligent Surface Aided Sparse DOA Estimation Method
  With Non-ULA

abstract:
  The direction of arrival (DOA) estimation problem is addressed in this
letter. A reconfigurable intelligent surface (RIS) aided system for the DOA
estimation is proposed. Unlike traditional DOA estimation systems, a low-cost
system with only one complete functional receiver is given by changing the
phases of the reflected signals at the RIS elements to realize the multiple
measurements. Moreover, an atomic norm-based method is proposed for the DOA
estimation by exploiting the target sparsity in the spatial domain and solved
by a semi-definite programming (SDP) method. Furthermore, the RIS elements can
be any geometry array for practical consideration, so a transformation matrix
is formulated and different from the conventional SDP method. Simulation
results show that the proposed method can estimate the DOA more accurately than
the existing methods in the non-uniform linear RIS array.


title:
UAV Trajectory and Beamforming Optimization for Integrated Periodic
  Sensing and Communication

abstract:
  Unmanned aerial vehicle (UAV) is expected to bring transformative improvement
to the integrated sensing and communication (ISAC) system. However, due to
shared spectrum resources, it is challenging to achieve a critical trade-off
between these two integrated functionalities. To address this issue, we propose
in this paper a new integrated \emph{periodic} sensing and communication
mechanism for the UAV-enable ISAC system. Specifically, the user achievable
rate is maximized via jointly optimizing UAV trajectory, transmit precoder, and
sensing start instant, subject to the sensing frequency and beam pattern gain
constraints. Despite that this problem is highly non-convex and involves an
infinite number of variables, we obtain the optimal transmit precoder and
derive the optimal achievable rate in closed-form for any given UAV location to
facilitate the UAV trajectory design. Furthermore, we first prove the
structural symmetry between optimal solutions in different ISAC frames without
location constraints and then propose a high-quality UAV trajectory and sensing
optimization algorithm for the general location-constrained case. Simulation
results corroborate the effectiveness of the proposed design and also unveil a
more flexible trade-off in ISAC systems over benchmark schemes.


title: Local Partial Zero-Forcing Combining for Cell-Free Massive MIMO Systems

abstract:
  Cell-free massive multiple-input multiple-output (MIMO) provides more uniform
spectral efficiency (SE) for users (UEs) than cellular technology. The main
challenge to achieve the benefits of cell-free massive MIMO is to realize
signal processing in a scalable way. In this paper, we consider scalable
fullpilot zero-forcing (FZF), partial FZF (PFZF), protective weak PFZF
(PWPFZF), and local regularized ZF (LRZF) combining by exploiting channel
statistics. We derive closed-form expressions of the uplink SE for FZF, PFZF,
and PWPFZF combining with large-scale fading decoding over independent Rayleigh
fading channels, taking channel estimation errors and pilot contamination into
account. Moreover, we investigate the impact of the number of pilot sequences,
antennas per AP, and APs on the performance. Numerical results show that LRZF
provides the highest SE. However, PWPFZF is preferable when the number of pilot
sequences is large and the number of antennas per AP is small. The reason is
that PWPFZF has lower computational complexity and the SE expression can be
computed in closed-form. Furthermore, we investigate the performance of PWPFZF
combining with fractional power control and the numerical results show that it
improves the performance of weak UEs and realizes uniformly good service for
all UEs in a scalable fashion.


title:
Booting 10K Serverless Functions within One Second via RDMA-based Remote
  Fork

abstract:
  We present MITOSIS,an OS primitive to support fast remote fork with minimal
resource provisioning for serverless computing. MITOSIS is the first to fork
over 10,000 new containers from only one instance across multiple machines
within a second.It also allows transparent and efficient intermediate data
sharing between producer and consumer serverless functions even across
machines. The key enabler for this high speed is an RDMA-OS co-design, which
imitates local fork with RDMA's remote memory read capability bypassing extra
software overhead for starting and running containers. We also introduce
techniques for lightweight connection and memory management based on advanced
RDMA transport and fast containerization in a distributed setting. We have
implemented MITOSIS on Linux and integrated it to FN, a popular serverless
computing framework. Under load spikes in real serverless workloads, MITOSIS
reduces the FN tail latency by 95.2% with orders of magnitude smaller memory
usage.


title:
Robust enhancement of high-harmonic generation from all-dielectric
  metasurfaces enabled by polarization-insensitive bound states in the
  continuum

abstract:
  The emerging all-dielectric platform exhibits high-quality ($Q$) resonances
governed by the physics of bound states in the continuum (BIC) that drives
highly efficient nonlinear optical processes. Here we demonstrate the robust
enhancement of third-(THG) and fifth-harmonic generation (FHG) from
all-dielectric metasurfaces composed of four silicon nanodisks. Through the
symmetry breaking, the genuine BIC transforms into the high-$Q$ quasi-BIC
resonance with tight field confinement for record high THG efficiency of
$3.9\times10^{-4}$ W$^{-2}$ and FHG efficiency of $4.8\times10^{-10}$ W$^{-4}$
using a moderate pump intensity of 1 GW/cm$^{2}$. Moreover, the quasi-BIC and
the resonantly enhanced harmonics exhibit polarization-insensitive
characteristics due to the special $C_{4}$ arrangement of meta-atoms. Our
results suggest the way for smart design of efficient and robust nonlinear
nanophotonic devices.


title:
Has Your FaaS Application Been Decommissioned Yet? -- A Case Study on
  the Idle Timeout in Function as a Service Infrastructure

abstract:
  Function as a Service (FaaS) is a new cloud technology with automated
resource management. Different from traditional cloud computing, each FaaS
cloud function can only run a fixed period of time before being decommissioned.
Furthermore, FaaS cloud providers often update their platforms (e.g., idle
timeout). These changes and their associated impact are not transparent and
could potentially impact the execution of the cloud functions. Hence, in this
paper, we develop a methodology to characterize the cloud function idle timeout
which is the duration a FaaS cloud provider keeps a cloud function instance
alive without serving active traffic. Our study was conducted on three popular
FaaS platforms, namely AWS Lambda, IBM and Azure Cloud Function. Moreover, we
also report how long a cloud function instance can be kept alive when a user
regularly polls the instance. Experimental results show that the idle timeout
period has evolved from 01/2020 till 01/2022.


title:
A Track-Wise Ensemble Event Independent Network for Polyphonic Sound
  Event Localization and Detection

abstract:
  Polyphonic sound event localization and detection (SELD) aims at detecting
types of sound events with corresponding temporal activities and spatial
locations. In this paper, a track-wise ensemble event independent network with
a novel data augmentation method is proposed. The proposed model is based on
our previous proposed Event-Independent Network V2 and is extended by conformer
blocks and dense blocks. The track-wise ensemble model with track-wise output
format is proposed to solve an ensemble model problem for track-wise output
format that track permutation may occur among different models. The data
augmentation approach contains several data augmentation chains, which are
composed of random combinations of several data augmentation operations. The
method also utilizes log-mel spectrograms, intensity vectors, and Spatial
Cues-Augmented Log-Spectrogram (SALSA) for different models. We evaluate our
proposed method in the Task of the L3DAS22 challenge and obtain the top ranking
solution with a location-dependent F-score to be 0.699. Source code is
released.


title:
Reinforcement Learned Distributed Multi-Robot Navigation with Reciprocal
  Velocity Obstacle Shaped Rewards

abstract:
  The challenges to solving the collision avoidance problem lie in adaptively
choosing optimal robot velocities in complex scenarios full of interactive
obstacles. In this paper, we propose a distributed approach for multi-robot
navigation which combines the concept of reciprocal velocity obstacle (RVO) and
the scheme of deep reinforcement learning (DRL) to solve the reciprocal
collision avoidance problem under limited information. The novelty of this work
is threefold: (1) using a set of sequential VO and RVO vectors to represent the
interactive environmental states of static and dynamic obstacles, respectively;
(2) developing a bidirectional recurrent module based neural network, which
maps the states of a varying number of surrounding obstacles to the actions
directly; (3) developing a RVO area and expected collision time based reward
function to encourage reciprocal collision avoidance behaviors and trade off
between collision risk and travel time. The proposed policy is trained through
simulated scenarios and updated by the actor-critic based DRL algorithm. We
validate the policy in complex environments with various numbers of
differential drive robots and obstacles. The experiment results demonstrate
that our approach outperforms the state-of-art methods and other learning based
approaches in terms of the success rate, travel time, and average speed. Source
code of this approach is available at https://github.com/hanruihua/rl_rvo_nav.


title: Temporal Correlation of Internet Observatories and Outposts

abstract:
  The Internet has become a critical component of modern civilization requiring
scientific exploration akin to endeavors to understand the land, sea, air, and
space environments. Understanding the baseline statistical distributions of
traffic are essential to the scientific understanding of the Internet.
Correlating data from different Internet observatories and outposts can be a
useful tool for gaining insights into these distributions. This work compares
observed sources from the largest Internet telescope (the CAIDA darknet
telescope) with those from a commercial outpost (the GreyNoise honeyfarm).
Neither of these locations actively emit Internet traffic and provide distinct
observations of unsolicited Internet traffic (primarily botnets and scanners).
Newly developed GraphBLAS hyperspace matrices and D4M associative array
technologies enable the efficient analysis of these data on significant scales.
The CAIDA sources are well approximated by a Zipf-Mandelbrot distribution. Over
a 6-month period 70\% of the brightest (highest frequency) sources in the CAIDA
telescope are consistently detected by coeval observations in the GreyNoise
honeyfarm. This overlap drops as the sources dim (reduce frequency) and as the
time difference between the observations grows. The probability of seeing a
CAIDA source is proportional to the logarithm of the brightness. The temporal
correlations are well described by a modified Cauchy distribution. These
observations are consistent with a correlated high frequency beam of sources
that drifts on a time scale of a month.


title:
SDOAnet: An Efficient Deep Learning-Based DOA Estimation Network for
  Imperfect Array

abstract:
  Direction of arrival (DOA) estimation is a fundamental problem in both
conventional radar and wireless communication applications and emerging
integrated sensing and communication (ISAC) systems. Due to many imperfect
factors in the low-cost systems, including the antenna position perturbations,
the inconsistent gains/phases, the mutual coupling effect, the nonlinear
amplifier effect, etc., the performance of the DOA estimation often degrades
significantly. To characterize the realistic array more accurately, a novel
deep learning (DL)-based DOA estimation method named super-resolution DOA
network (SDOAnet) is proposed in this paper. Different from the existing
DL-based DOA methods, our proposed SDOAnet employs the sampled received
signals, instead of the covariance matrices of the received signals, as the
input of the convolution layers for extracting data features. Moreover, the
output of SDOAnet is a vector that is independent of the DOA of targets but can
be used to estimate their spatial spectrum. As a result, the same training
network can be applied with any number of targets, which significantly reduce
the implementation complexity. At last, the convergence speed of our SDOAnet
with a low-dimension network structure is much faster than existing DL-based
methods. Simulation results show that the proposed SDOAnet outperforms the
existing DOA estimation methods with the effect of the imperfect array. The
code about the SDOAnet is available online
https://github.com/chenpengseu/SDOAnet.git.


title:
DuReader_retrieval: A Large-scale Chinese Benchmark for Passage
  Retrieval from Web Search Engine

abstract:
  In this paper, we present DuReader_retrieval, a large-scale Chinese dataset
for passage retrieval. DuReader_retrieval contains more than 90K queries and
over 8M unique passages from Baidu search. To ensure the quality of our
benchmark and address the shortcomings in other existing datasets, we (1)
reduce the false negatives in development and testing sets by pooling the
results from multiple retrievers with human annotations, (2) and remove the
semantically similar questions between training with development and testing
sets. We further introduce two extra out-of-domain testing sets for
benchmarking the domain generalization capability. Our experiment results
demonstrate that DuReader_retrieval is challenging and there is still plenty of
room for the community to improve, e.g. the generalization across domains,
salient phrase and syntax mismatch between query and paragraph and robustness.
DuReader_retrieval will be publicly available at
https://github.com/baidu/DuReader/tree/master/DuReader-Retrieval


title:
DirecFormer: A Directed Attention in Transformer Approach to Robust
  Action Recognition

abstract:
  Human action recognition has recently become one of the popular research
topics in the computer vision community. Various 3D-CNN based methods have been
presented to tackle both the spatial and temporal dimensions in the task of
video action recognition with competitive results. However, these methods have
suffered some fundamental limitations such as lack of robustness and
generalization, e.g., how does the temporal ordering of video frames affect the
recognition results? This work presents a novel end-to-end Transformer-based
Directed Attention (DirecFormer) framework for robust action recognition. The
method takes a simple but novel perspective of Transformer-based approach to
understand the right order of sequence actions. Therefore, the contributions of
this work are three-fold. Firstly, we introduce the problem of ordered temporal
learning issues to the action recognition problem. Secondly, a new Directed
Attention mechanism is introduced to understand and provide attentions to human
actions in the right order. Thirdly, we introduce the conditional dependency in
action sequence modeling that includes orders and classes. The proposed
approach consistently achieves the state-of-the-art (SOTA) results compared
with the recent action recognition methods, on three standard large-scale
benchmarks, i.e. Jester, Kinetics-400 and Something-Something-V2.


title:
Solving the Schrodinger Poisson System using the coordinate Adaptive
  Moving Mesh method

abstract:
  In this paper, we implement the Adaptive Moving Mesh method (AMM) to the
solution of initial value problems involving the Schr\"odinger equation, and
more specifically the Schr\"odinger-Poisson system of equations. This method is
based on the solution of the problem on a discrete domain, whose resolution is
coordinate and time-dependent, and allows to dynamically assign numerical
resolution in terms of desired refinement criteria. We apply the method to
solve various test problems involving stationary solutions of the SP system,
and toy scenarios related to the disruption of subhalo s made of ultralight
bosonic dark matter traveling on top of host galaxies.


title: Quartic Index Form Equations and Monogenizations of Quartic Orders

abstract:
  Some upper bounds for the number of monogenizations of quartic orders are
established by considering certain classical Diophantine equations, namely
index form equations in quartic number fields, and cubic and quartic Thue
equations.


title: Explicit Quantum Circuits for Block Encodings of Certain Sparse Matrices

abstract:
  Many standard linear algebra problems can be solved on a quantum computer by
using recently developed quantum linear algebra algorithms that make use of
block encoding and quantum eigenvalue / singular value transformations. Block
encoding embeds a properly scaled matrix of interest $A$ in a larger unitary
transformation $U$ that can be decomposed into a product of simpler unitaries
and implemented efficiently on a quantum computer. Although quantum algorithms
can potentially achieve exponential speedup in solving linear algebra problems
compared to the best classical algorithm, such gain in efficiency ultimately
hinges on our ability to construct an efficient quantum circuit for the block
encoding of $A$, which is difficult in general, and not trivial even for well
structured sparse matrices. In this paper, we give a few examples on how
efficient quantum circuits can be explicitly constructed for some well
structured sparse matrices, and discuss a few strategies used in these
constructions. We are particularly interested in sparse stochastic matrices
that represent random walks on a graph, and show how the block encodings of
such matrices yield efficient quantum walks.


title:
On some $\Sigma^{B}_{0}$-generalizations of the pigeonhole and the
  modular counting principles over $V^{0}$

abstract:
  Ajtai's discovery of $V^{0}\not \vdash ontoPHP^{n+1}_{n}$, where
$ontoPHP^{n+1}_{n}$ is a $\Sigma^{B}_{0}$ formalization of the statement "there
does not exist a bijection between $(n+1)$ pigeons and $n$ holes," was a
significant breakthrough in proof complexity, and there have been many
interesting generalizations and variations of this result.
  In this paper, we first focus on the following result: for any $p \geq 2$,
$V^{0}+Count^{p}_{k} \not \vdash injPHP^{n+1}_{n}$, where $Count^{p}_{k}$
denotes a $\Sigma^{B}_{0}$ formalization of the modular counting principle mod
$p$ and $injPHP^{n+1}_{n}$ denotes that of the pigeonhole principle for
injections.
  We try to make this result uniform for $p$.
  We give three types of (first-order and propositional) formulae which at
first glance seem to be generalized versions of counting principles. In
particular, we see two of them, $UCP^{l,d}_{n}$ and $GCP$, actually serve as
uniform versions of $Count^{p}_{n}$ ($p \geq 2$).
  Then we conjecture that $V^{0}+UCP^{l,d}_{k} \not \vdash injPHP^{n+1}_{n}$,
and give a sufficient condition to prove it.
  We also compare the strengths of oddtown theorem and Fisher's inequality with
those of the modular counting principles over $V^0$ by stating some conjectures
and giving sufficient conditions to prove them.


title:
On the entropy projection and the robustness of high order entropy
  stable discontinuous Galerkin schemes for under-resolved flows

abstract:
  High order entropy stable schemes provide improved robustness for
computational simulations of fluid flows. However, additional stabilization and
positivity preserving limiting can still be required for variable-density flows
with under-resolved features. We demonstrate numerically that entropy stable DG
methods which incorporate an "entropy projection" are less likely to require
additional limiting to retain positivity for certain types of flows. We
conclude by investigating potential explanations for this observed improvement
in robustness.


title: Plane Triangulations Without Spanning 2-Trees

abstract:
  A 2-tree is a graph that can be formed by starting with a triangle and
iterating the operation of making a new vertex adjacent to two adjacent
vertices of the existing graph. Leizhen Cai asked in 1995 whether every maximal
planar graph contains a spanning 2-tree. We answer this question in the
negative by constructing an infinite class of maximal planar graphs that have
no spanning 2-tree.


title:
Stabilization for small mass in a quasilinear
  parabolic--elliptic--elliptic attraction-repulsion chemotaxis system with
  density-dependent sensitivity: repulsion-dominant case

abstract:
  This paper deals with the quasilinear attraction-repulsion chemotaxis system
\begin{align*}
  \begin{cases}
  u_t=\nabla\cdot \big((u+1)^{m-1}\nabla u
  -\chi u(u+1)^{p-2}\nabla v
  +\xi u(u+1)^{q-2}\nabla w\big),\\[]
  0=\Delta v+\alpha u-\beta v,\\[]
  0=\Delta w+\gamma u-\delta w
  \end{cases} \end{align*} in a bounded domain $\Omega \subset \mathbb{R}^n$
$(n \in \mathbb{N})$ with smooth boundary $\partial \Omega$, where $m, p, q \in
\mathbb{R}$, $\chi, \xi, \alpha, \beta, \gamma, \delta>0$ are constants. In the
case that $m=1$ and $p=q=2$, when $\chi\alpha-\xi\gamma<0$ and $\beta=\delta$,
Tao-Wang (Math. Models Methods Appl. Sci.; 2013; 23; 1-36) proved that global
bounded classical solutions toward the spatially constant equilibrium
$(\overline{u_0}, \frac{\alpha}{\beta}\overline{u_0},
\frac{\gamma}{\delta}\overline{u_0})$ via the reduction to the Keller-Segel
system by using the transformation $z:=\chi v-\xi w$, where $\overline{u_0}$ is
the spatial average of the initial data $u_0$. However, since the above system
involves nonlinearities, the method is no longer valid. The purpose of this
paper is to establish that global bounded classical solutions converge to the
spatially constant equilibrium $(\overline{u_0},
\frac{\alpha}{\beta}\overline{u_0}, \frac{\gamma}{\delta}\overline{u_0})$.


title:
Intersection Queries for Flat Semi-Algebraic Objects in Three Dimensions
  and Related Problems

abstract:
  Let $\mathcal{T}$ be a set of $n$ planar semi-algebraic regions in
$\mathbb{R}^3$ of constant complexity (e.g., triangles, disks), which we call
\emph{plates}. We wish to preprocess $\mathcal{T}$ into a data structure so
that for a query object $\gamma$, which is also a plate, we can quickly answer
various intersection queries, such as detecting whether $\gamma$ intersects any
plate of $\mathcal{T}$, reporting all the plates intersected by $\gamma$, or
counting them. We also consider two simpler cases of this general setting: (i)
the input objects are plates and the query objects are constant-degree
algebraic arcs in $\mathbb{R}^3$ (arcs, for short), or (ii) the input objects
are arcs and the query objects are plates in $\mathbb{R}^3$. Besides being
interesting in their own right, the data structures for these two special cases
form the building blocks for handling the general case.
  By combining the polynomial-partitioning technique with additional tools from
real algebraic geometry, we obtain a variety of results with different storage
and query-time bounds, depending on the complexity of the input and query
objects. For example, if $\mathcal{T}$ is a set of plates and the query objects
are arcs, we obtain a data structure that uses $O^*(n^{4/3})$ storage (where
the $O^*(\cdot)$ notation hides subpolynomial factors) and answers an
intersection query in $O^*(n^{2/3})$ time. Alternatively, by increasing the
storage to $O^*(n^{3/2})$, the query time can be decreased to $O^*(n^{\rho})$,
where $\rho = (2t-3)/3(t-1) < 2/3$ and $t \ge 3$ is the number of parameters
needed to represent the query arcs.


title: Temporal-offset dual-comb vibrometer with picometer axial precision

abstract:
  We demonstrate a dual-comb vibrometer where the pulses of one frequency-comb
are split into pulse pairs. We introduce a delay between the two pulses of each
pulse pair in front of the sample, and after the corresponding two consecutive
reflections at the vibrating sample surface, the initially introduced delay is
cancelled by a modified Sagnac geometry. The remaining phase difference between
the two pulses corresponds to the change in the axial position of the surface
during the two consecutive reflections. The Sagnac geometry reduces the effect
of phase jitter since both pulses propagate through nearly the same optical
path (in opposite directions), and spurious signals are eliminated by time
gating. We determine the amplitude of a surface vibration on a
surface-acoustic-wave device with an axial precision of 4 pm. This technique
enables highly accurate determination of extremely small displacements.


title: Diagnosing Triggered Star Formation in the Galactic H II region Sh 2-142

abstract:
  Stars are formed by gravitational collapse, spontaneously or, in some cases
under the constructive influence of nearby massive stars, out of molecular
cloud cores. Here we present an observational diagnosis of such triggered
formation processes in the prominent \ion{H}{2} region Sh\,2-142, which is
associated with the young star cluster NGC\,7380, and with some bright-rimmed
clouds as the signpost of photoionization of molecular cloud surfaces. Using
near- (2MASS) and mid-infrared (WISE) colors, we identified candidate young
stars at different evolutionary stages, including embedded infrared sources
having spectral energy distributions indicative of active accretion. We have
also used data from our optical observations to be used in SEDs, and from Gaia
EDR3 to study the kinematics of young objects. With this young stellar sample,
together with the latest CO line emission data (spectral resolution $\sim
0.16$~km~s$^{-1}$, sensitivity $\sim 0.5$~K), a positional and ageing sequence
relative to the neighboring cloud complex, and to the bright-rimmed clouds, is
inferred. The propagating stellar birth may be responsible, at least partially,
for the formation of the cluster a few million years ago, and for the ongoing
activity now witnessed in the cloud complex.


title:
ChartQA: A Benchmark for Question Answering about Charts with Visual and
  Logical Reasoning

abstract:
  Charts are very popular for analyzing data. When exploring charts, people
often ask a variety of complex reasoning questions that involve several logical
and arithmetic operations. They also commonly refer to visual features of a
chart in their questions. However, most existing datasets do not focus on such
complex reasoning questions as their questions are template-based and answers
come from a fixed-vocabulary. In this work, we present a large-scale benchmark
covering 9.6K human-written questions as well as 23.1K questions generated from
human-written chart summaries. To address the unique challenges in our
benchmark involving visual and logical reasoning over charts, we present two
transformer-based models that combine visual features and the data table of the
chart in a unified way to answer questions. While our models achieve the
state-of-the-art results on the previous datasets as well as on our benchmark,
the evaluation also reveals several challenges in answering complex reasoning
questions.


title:
Extremal spectral radius of nonregular graphs with prescribed maximum
  degree

abstract:
  Let $G$ be a graph attaining the maximum spectral radius among all connected
nonregular graphs of order $n$ with maximum degree $\Delta$. Let $\lambda_1(G)$
be the spectral radius of $G$. A nice conjecture due to Liu, Shen and Wang [On
the largest eigenvalue of non-regular graphs, J. Combin. Theory Ser. B, 97
(2007) 1010--1018] asserts that \[ \lim_{n\to\infty}
\frac{n^2(\Delta-\lambda_1(G))}{\Delta-1} = \pi^2 \] for each fixed $\Delta$.
Concerning an important structural property of the extremal graphs $G$, Liu and
Li present another conjecture which states that $G$ has degree sequence
$\Delta,\ldots,\Delta,\delta$. Here, $\delta=\Delta-1$ or $\delta=\Delta-2$
depending on the parity of $n\Delta$. In this paper, we make progress on the
two conjectures. To be precise, we disprove the first conjecture for all
$\Delta\geq 3$ by showing that the limit superior is at most $\pi^2/2$. For
small $\Delta$, we determine the precise asymptotic behavior of
$\Delta-\lambda_1(G)$. In particular, we show that $\lim\limits_{n\to\infty}
n^2 (\Delta - \lambda_1(G)) /(\Delta - 1) = \pi^2/4$ if $\Delta=3$; and
$\lim\limits_{n\to\infty} n^2 (\Delta - \lambda_1(G)) /(\Delta - 2) = \pi^2/2$
if $\Delta = 4$. We also confirm the second conjecture for $\Delta = 3$ and
$\Delta = 4$ by determining the precise structure of extremal graphs.
Particularly, we show that the extremal graphs for $\Delta\in\{3,4\}$ must have
a path-like structure built from specific blocks.


title: Asymmetric Fuglede-Putnam Theorem for Unbounded M-Hyponormal Operators

abstract:
  A closed densely defined operator $ T $ on a Hilbert space $ \mathcal{H} $ is
callled $M$-hyponormal if $\mathcal{D}(T) \subset \mathcal{D}(T^{*}) $ and
there exists $ M > 0 $ for which $ \parallel(T-zI)^{*}x \parallel \leq M
\parallel(T-zI)x \parallel $ for all $ z \in \mathbb{C}$ and for all $ x\in
\mathcal{D}(T)$. In this paper, we prove that if bounded linear operator $ A :
\mathcal{H} \rightarrow \mathcal{K}$ is such that $ AB^*\subseteq TA $, where $
B $ is a closed subnormal (resp. a closed $ M $-hyponormal) on $\mathcal{H}$, $
T $ is a closed $ M $-hyponormal (resp. a closed subnormal) on $\mathcal{H}$,
then (i) $ AB\subseteq T^*A, $ (ii) $ {\overline{ran(A^{*})}} $ reduces $ B $
to the normal operator $ B\vert_{{\overline{ran(A^{*})}}}, $ and (iii) $
{\overline{ran(A)}} $ reduces $ T $ to the normal operator $
T\vert_{{\overline{ran(A)}}}.$


title: HIPA: Hierarchical Patch Transformer for Single Image Super Resolution

abstract:
  Transformer-based architectures start to emerge in single image super
resolution (SISR) and have achieved promising performance. Most existing Vision
Transformers divide images into the same number of patches with a fixed size,
which may not be optimal for restoring patches with different levels of texture
richness. This paper presents HIPA, a novel Transformer architecture that
progressively recovers the high resolution image using a hierarchical patch
partition. Specifically, we build a cascaded model that processes an input
image in multiple stages, where we start with tokens with small patch sizes and
gradually merge to the full resolution. Such a hierarchical patch mechanism not
only explicitly enables feature aggregation at multiple resolutions but also
adaptively learns patch-aware features for different image regions, e.g., using
a smaller patch for areas with fine details and a larger patch for textureless
regions. Meanwhile, a new attention-based position encoding scheme for
Transformer is proposed to let the network focus on which tokens should be paid
more attention by assigning different weights to different tokens, which is the
first time to our best knowledge. Furthermore, we also propose a new
multi-reception field attention module to enlarge the convolution reception
field from different branches. The experimental results on several public
datasets demonstrate the superior performance of the proposed HIPA over
previous methods quantitatively and qualitatively.


title:
Jackknife Partially Linear Model Averaging for the Conditional Quantile
  Prediction

abstract:
  Estimating the conditional quantile of the interested variable with respect
to changes in the covariates is frequent in many economical applications as it
can offer a comprehensive insight. In this paper, we propose a novel
semiparametric model averaging to predict the conditional quantile even if all
models under consideration are potentially misspecified. Specifically, we first
build a series of non-nested partially linear sub-models, each with different
nonlinear component. Then a leave-one-out cross-validation criterion is applied
to choose the model weights. Under some regularity conditions, we have proved
that the resulting model averaging estimator is asymptotically optimal in terms
of minimizing the out-of-sample average quantile prediction error. Our
modelling strategy not only effectively avoids the problem of specifying which
a covariate should be nonlinear when one fits a partially linear model, but
also results in a more accurate prediction than traditional model-based
procedures because of the optimality of the selected weights by the
cross-validation criterion. Simulation experiments and an illustrative
application show that our proposed model averaging method is superior to other
commonly used alternatives.


title:
Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue
  Comprehension

abstract:
  Comprehending a dialogue requires a model to capture diverse kinds of key
information in the utterances, which are either scattered around or implicitly
implied in different turns of conversations. Therefore, dialogue comprehension
requires diverse capabilities such as paraphrasing, summarizing, and
commonsense reasoning. Towards the objective of pre-training a zero-shot
dialogue comprehension model, we develop a novel narrative-guided pre-training
strategy that learns by narrating the key information from a dialogue input.
However, the dialogue-narrative parallel corpus for such a pre-training
strategy is currently unavailable. For this reason, we first construct a
dialogue-narrative parallel corpus by automatically aligning movie subtitles
and their synopses. We then pre-train a BART model on the data and evaluate its
performance on four dialogue-based tasks that require comprehension.
Experimental results show that our model not only achieves superior zero-shot
performance but also exhibits stronger fine-grained dialogue comprehension
capabilities. The data and code are available at
https://github.com/zhaochaocs/Diana


title:
Meta-X$_{NLG}$: A Meta-Learning Approach Based on Language Clustering
  for Zero-Shot Cross-Lingual Transfer and Generation

abstract:
  Recently, the NLP community has witnessed a rapid advancement in multilingual
and cross-lingual transfer research where the supervision is transferred from
high-resource languages (HRLs) to low-resource languages (LRLs). However, the
cross-lingual transfer is not uniform across languages, particularly in the
zero-shot setting. Towards this goal, one promising research direction is to
learn shareable structures across multiple tasks with limited annotated data.
The downstream multilingual applications may benefit from such a learning setup
as most of the languages across the globe are low-resource and share some
structures with other languages. In this paper, we propose a novel
meta-learning framework (called Meta-X$_{NLG}$) to learn shareable structures
from typologically diverse languages based on meta-learning and language
clustering. This is a step towards uniform cross-lingual transfer for unseen
languages. We first cluster the languages based on language representations and
identify the centroid language of each cluster. Then, a meta-learning algorithm
is trained with all centroid languages and evaluated on the other languages in
the zero-shot setting. We demonstrate the effectiveness of this modeling on two
NLG tasks (Abstractive Text Summarization and Question Generation), 5 popular
datasets and 30 typologically diverse languages. Consistent improvements over
strong baselines demonstrate the efficacy of the proposed framework. The
careful design of the model makes this end-to-end NLG setup less vulnerable to
the accidental translation problem, which is a prominent concern in zero-shot
cross-lingual NLG tasks.


title:
Modelling observable signatures of jet-ISM interaction: thermal emission
  and gas kinematics

abstract:
  Relativistic jets are believed to have a substantial impact on the gas
dynamics and evolution of the interstellar medium (ISM) of their host galaxies.
In this paper, we aim to draw a link between the simulations and the observable
signatures of jet-ISM interactions by analyzing the emission morphology and gas
kinematics resulting from jet-induced shocks in simulated disc and spherical
systems. We find that the jet-induced laterally expanding forward shock of the
energy bubble sweeping through the ISM causes large-scale outflows, creating
shocked emission and high-velocity dispersion in the entire nuclear regions
($\sim2$ kpcs) of their hosts. The jetted systems exhibit larger velocity
widths (> 800 km/s), broader Position-Velocity maps and distorted symmetry in
the disc's projected velocities than systems without a jet. We also investigate
the above quantities at different inclination angles of the observer with
respect to the galaxy. Jets inclined to the gas disc of its host are found to
be confined for longer times, and consequently couple more strongly with the
disc gas. This results in prominent shocked emission and high-velocity widths,
not only along the jet's path, but also in the regions perpendicular to them.
Strong interaction of the jet with a gas disc can also distort its morphology.
However, after the jets escape their initial confinement, the jet-disc coupling
is weakened, thereby lowering the shocked emission and velocity widths.


title:
Similarity and Content-based Phonetic Self Attention for Speech
  Recognition

abstract:
  Transformer-based speech recognition models have achieved great success due
to the self-attention (SA) mechanism that utilizes every frame in the feature
extraction process. Especially, SA heads in lower layers capture various
phonetic characteristics by the query-key dot product, which is designed to
compute the pairwise relationship between frames. In this paper, we propose a
variant of SA to extract more representative phonetic features. The proposed
phonetic self-attention (phSA) is composed of two different types of phonetic
attention; one is similarity-based and the other is content-based. In short,
similarity-based attention utilizes the correlation between frames while
content-based attention only considers each frame without being affected by
others. We identify which parts of the original dot product are related to two
different attention patterns and improve each part by simple modifications. Our
experiments on phoneme classification and speech recognition show that
replacing SA with phSA for lower layers improves the recognition performance
without increasing the latency and the parameter size.


title: High-harmonic spectroscopy of two-dimensional materials

abstract:
  Recent advancements in the generation of mid-infrared and terahertz laser
pulses have enabled us to observe strong-field driven non-perturbative
high-harmonic generation (HHG) from semiconductors, dielectrics, and
semimetals. HHG has added another dimension to time-resolved ultrafast electron
dynamics in materials with unprecedented temporal resolution. Present thesis
discusses how HHG is an emerging method to probe static and dynamical
properties in two-dimensional materials. In this thesis, two-dimensional
materials with hexagonal symmetry are studied.
  We have demonstrated that the high-harmonic spectrum encodes the fingerprints
of electronic band structure and interband coupling between different bands.
Furthermore, by analysing gapped and gapless graphene, we show how electron
dynamics in a semimetal and a semiconductor are different as the harmonic
spectrum depends differently on the polarisation of the driving laser. To
explore the role of defects in HHG, spin-polarised vacancy defects in hexagonal
boron nitride are considered. It has been found that electron-electron
interaction is crucial for electron dynamics in a defected solid. In all cases,
we present how different symmetries of the lattice can be extracted from the
harmonic spectrum. Finally, a light-driven method is proposed for observing
valley-polarisation in pristine graphene, using a tailored laser pulse. Also, a
recipe is discussed to write and read valley-selective electron excitations in
materials with zero bandgap and zero Berry curvature.


title:
Read Top News First: A Document Reordering Approach for Multi-Document
  News Summarization

abstract:
  A common method for extractive multi-document news summarization is to
re-formulate it as a single-document summarization problem by concatenating all
documents as a single meta-document. However, this method neglects the relative
importance of documents. We propose a simple approach to reorder the documents
according to their relative importance before concatenating and summarizing
them. The reordering makes the salient content easier to learn by the
summarization model. Experiments show that our approach outperforms previous
state-of-the-art methods with more complex architectures.