title:
Cancellable Template Design for Privacy-Preserving EEG Biometric
  Authentication Systems

abstract:
  As a promising candidate to complement traditional biometric modalities,
brain biometrics using electroencephalography (EEG) data has received a
widespread attention in recent years. However, compared with existing
biometrics such as fingerprints and face recognition, research on EEG
biometrics is still in its infant stage. Most of the studies focus on either
designing signal elicitation protocols from the perspective of neuroscience or
developing feature extraction and classification algorithms from the viewpoint
of machine learning. These studies have laid the ground for the feasibility of
using EEG as a biometric authentication modality, but they have also raised
security and privacy concerns as EEG data contains sensitive information.
Existing research has used hash functions and cryptographic schemes to protect
EEG data, but they do not provide functions for revoking compromised templates
as in cancellable template design. This paper proposes the first cancellable
EEG template design for privacy-preserving EEG-based authentication systems,
which can protect raw EEG signals containing sensitive privacy information
(e.g., identity, health and cognitive status). A novel cancellable EEG template
is developed based on EEG graph features and a non-invertible transform. The
proposed transformation provides cancellable templates, while taking advantage
of EEG elicitation protocol fusion to enhance biometric performance. The
proposed authentication system offers equivalent authentication performance
(8.58\% EER on a public database) as in the non-transformed domain, while
protecting raw EEG data. Furthermore, we analyze the system's capacity for
resisting multiple attacks, and discuss some overlooked but critical issues and
possible pitfalls involving hill-climbing attacks, second attacks, and
classification-based authentication systems.


title: Symmetries of the Primordial Sky

abstract:
  Quantum field theory, which is generally used to describe the origin of
large-scale gravitational perturbations during cosmic inflation, has been shown
to omit an important physical effect in curved space-time, the nonlocal
entanglement among quantized modes from their gravitational effect on causal
structure. It is argued here that in a different model of quantum gravity that
coherently preserves nonlocal directional and causal relationships, primordial
perturbations originate instead from coherent quantum distortions of emergent
inflationary horizons; and moreover, that causal constraints account for
approximate symmetries of cosmic microwave background correlations measured at
large angular separations, which are highly anomalous in the standard picture.
Thus, symmetries already apparent in the large-angle CMB pattern may be unique
signatures of the emergence of locality and causal structure from quantum
gravity.


title:
Deep Reinforcement Learning with Graph ConvNets for Distribution Network
  Voltage Control

abstract:
  This paper proposes a model-free Volt-VAR control (VVC) algorithm via the
spatio-temporal graph ConvNet-based deep reinforcement learning (STGCN-DRL)
framework, whose goal is to control smart inverters in an unbalanced
distribution system. We first identify the graph shift operator (GSO) based on
the power flow equations. Then, we develop a spatio-temporal graph ConvNet
(STGCN), testing both recurrent graph ConvNets (RGCN) and convolutional graph
ConvNets (CGCN) architectures, aimed at capturing the spatiotemporal
correlation of voltage phasors. The STGCN layer performs the feature extraction
task for the policy function and the value function of the reinforcement
learning architecture, and then we utilize the proximal policy optimization
(PPO) to search the action spaces for an optimum policy function and to
approximate an optimum value function. We further utilize the low-pass property
of voltage graph signal to introduce an GCN architecture for the the policy
whose input is a decimated state vector, i.e. a partial observation. Case
studies on the unbalanced 123-bus systems validate the excellent performance of
the proposed method in mitigating instabilities and maintaining nodal voltage
profiles within a desirable range.


title: Neuron Growth Output-Feedback Control by PDE Backstepping

abstract:
  Neurological injuries predominantly result in loss of functioning of neurons.
These neurons may regain function after particular medical therapeutics, such
as Chondroitinase ABC (ChABC), that promote axon elongation by manipulating the
extracellular matrix, the network of extracellular macromolecules, and minerals
that control the tubulin protein concentration, which is fundamental to axon
elongation. We introduce an observer for the concentration of unmeasured
tubulin along the axon, as well as in the growth cone, using the measurement of
the axon length and the tubulin flux at the growth cone. We employ this
observer in a boundary control law which actuates the tubulin concentration at
the soma (nucleus), i.e., at the end of the axon distal from the measurement
location. For this PDE system with a moving boundary, coupled with a two-state
ODE system, we establish global exponential convergence of the observer and
local exponential stabilization of the [axon, observer] system in the spatial
$\mathcal{H}_1$-norm. The results require that the axon growth speed be
bounded. For an open-loop observer, this is ensured by assumption (which
requires that tubulin influx at the soma be limited), whereas for the
output-feedback system the growth rate of the axon is ensured by assuming that
the initial conditions of all the states, including the axon length, be
sufficiently close to their setpoint values.


title:
KMT-2021-BLG-1077L: The fifth confirmed multiplanetary system detected
  by microlensing

abstract:
  The high-magnification microlensing event KMT-2021-BLG-1077 exhibits a subtle
and complex anomaly pattern in the region around the peak. We analyze the
lensing light curve of the event with the aim of revealing the nature of the
anomaly. We test various models in combination with several interpretations. We
find that the anomaly cannot be explained by the usual three-body (2L1S and
1L2S) models. The 2L2S model improves the fit compared to the three-body
models, but it still leaves noticeable residuals. On the other hand, the 3L1S
interpretation yields a model explaining all the major anomalous features in
the lensing light curve. According to the 3L1S interpretation, the estimated
mass ratios of the lens companions to the primary are $\sim 1.56 \times
10^{-3}$ and $\sim 1.75 \times 10^{-3}$, which correspond to $\sim 1.6$ and
$\sim 1.8$ times the Jupiter/Sun mass ratio, respectively, and therefore the
lens is a multiplanetary system containing two giant planets. With the
constraints of the event time-scale and angular Einstein radius, it is found
that the host of the lens system is a low-mass star of mid-to-late M spectral
type with a mass of $M_{\rm h} = 0.14^{+0.19}_{-0.07}~M_\odot$, and it hosts
two gas giant planets with masses of $M_{\rm p_1}=0.22^{+0.31}_{-0.12}~M_{\rm
J}$ and $M_{\rm p_2}=0.25^{+0.35}_{-0.13}~M_{\rm J}$. The planets lie beyond
the snow line of the host with projected separations of $a_{\perp, {\rm
p}_1}=1.26^{+1.41}_{-1.08}~{\rm AU}$ and $a_{\perp, {\rm
p}_2}=0.93^{+1.05}_{-0.80}~{\rm AU}$. The planetary system resides in the
Galactic bulge at a distance of $D_{\rm L}=8.24^{+1.02}_{-1.16}~{\rm kpc}$. The
lens of the event is the fifth confirmed multiplanetary system detected by
microlensing following OGLE-2006-BLG-109L, OGLE-2012-BLG-0026L,
OGLE-2018-BLG-1011L, and OGLE-2019-BLG-0468L.


title:
Gas Disk Sizes from CO Line Observations: A Test of Angular Momentum
  Evolution

abstract:
  The size of a disk encodes important information about its evolution.
Combining new Submillimeter Array (SMA) observations with archival Atacama
Large Millimeter Array (ALMA) data, we analyze mm continuum and CO emission
line sizes for a sample of 44 protoplanetary disks around stars with masses of
0.15--2\,$M_{\odot}$ in several nearby star-forming regions. Sizes measured
from $^{12}$CO line emission span from 50 to 1000\,au. This range could be
explained by viscous evolution models with different $\alpha$ values (mostly of
$10^{-4}-10^{-3}$) and/or a spread of initial conditions. The CO sizes for most
disks are also consistent with MHD wind models that directly remove disk
angular momentum, but very large initial disk sizes would be required to
account for the very extended CO disks in the sample. As no CO size evolution
is observed across stellar ages of 0.5--20\,Myr in this sample, determining the
dominant mechanism of disk evolution will require a more complete sample for
both younger and more evolved systems. We find that the CO emission is
universally more extended than the continuum emission by an average factor of
$2.9\pm1.2$. The ratio of the CO to continuum sizes does not show any trend
with stellar mass, mm continuum luminosity, or the properties of substructures.
The GO Tau disk has the most extended CO emission in this sample, with an
extreme CO to continuum size ratio of 7.6. Seven additional disks in the sample
show high size ratios ($\gtrsim4$) that we interpret as clear signs of
substantial radial drift.


title:
Long-time dynamical behavior for a piezoelectric system with magnetic
  effect and nonlinear dampings

abstract:
  This paper is concerned with the long-time dynamical behavior of a
piezoelectric system with magnetic effect, which has nonlinear damping terms
and external forces with a parameter. At first, we use the nonlinear semigroup
theory to prove the well-posedness of solutions. Then, we investigate the
properties of global attractors and the existence of exponential attractors.
Finally, the upper semicontinuity of global attractors has been investigated.


title: The Bell-Touchard Counting process

abstract:
  The Poisson process is one of the simplest stochastic processes defined in
continuous time, having interesting mathematical properties, leading, in many
situations, to applications mathematically treatable. One of the limitations of
the Poisson process is the rare events hypothesis; which is the hypothesis of
unitary jumps within an infinitesimal window of time. Although that restriction
may be avoided by the compound Poisson process, in most situations, we don't
have a closed expression for the probability distribution of the increments of
such processes, leaving us options such as working with probability generating
functions, numerical analysis and simulations. It is with this motivation in
mind, inspired by the recent developments of discrete distributions, that we
propose a new counting process based on the Bell-Touchard probability
distribution, naming it the Bell-Touchard process. We verify that the process
is a compound Poisson process, a multiple Poisson process and that it is closed
for convolution plus decomposition operations. Besides, we show that the
Bell-Touchard process arises naturally from the composition of two Poisson
processes. Moreover, we propose two generalizations; namely, the compound
Bell-Touchard process and the non-homogeneous Bell-Touchard process, showing
that the last one arises from the composition of a non-homogeneous Poisson
process along with a homogeneous Poisson process. We emphasize that since
previous works have been shown that the Bell-Touchard probability distribution
can be used quite effectively for modelling count data, the Bell-Touchard
process and its generalizations may contribute to the formulation of
mathematical treatable models where the rare events hypothesis is not suitable.


title:
Improving speaker de-identification with functional data analysis of f0
  trajectories

abstract:
  Due to a constantly increasing amount of speech data that is stored in
different types of databases, voice privacy has become a major concern. To
respond to such concern, speech researchers have developed various methods for
speaker de-identification. The state-of-the-art solutions utilize deep learning
solutions which can be effective but might be unavailable or impractical to
apply for, for example, under-resourced languages. Formant modification is a
simpler, yet effective method for speaker de-identification which requires no
training data. Still, remaining intonational patterns in formant-anonymized
speech may contain speaker-dependent cues. This study introduces a novel
speaker de-identification method, which, in addition to simple formant shifts,
manipulates f0 trajectories based on functional data analysis. The proposed
speaker de-identification method will conceal plausibly identifying pitch
characteristics in a phonetically controllable manner and improve formant-based
speaker de-identification up to 25%.


title: Chip-Based Laser with 1 Hertz Integrated Linewidth

abstract:
  Lasers with hertz-level linewidths on timescales up to seconds are critical
for precision metrology, timekeeping, and manipulation of quantum systems. Such
frequency stability typically relies on bulk-optic lasers and reference
cavities, where increased size is leveraged to improve noise performance, but
with the trade-off of cost, hand assembly, and limited application
environments. On the other hand, planar waveguide lasers and cavities exploit
the benefits of CMOS scalability but are fundamentally limited from achieving
hertz-level linewidths at longer times by stochastic noise and thermal
sensitivity inherent to the waveguide medium. These physical limits have
inhibited the development of compact laser systems with frequency noise
required for portable optical clocks that have performance well beyond
conventional microwave counterparts. In this work, we break this paradigm to
demonstrate a compact, high-coherence laser system at 1548 nm with a 1 s
integrated linewidth of 1.1 Hz and fractional frequency instability less than
10$^{-14}$ from 1 ms to 1 s. The frequency noise at 1 Hz offset is suppressed
by 11 orders of magnitude from that of the free-running diode laser down to the
cavity thermal noise limit near 1 Hz$^2$/Hz, decreasing to 10$^{-3}$ Hz$^2$/Hz
at 4 kHz offset. This low noise performance leverages wafer-scale integrated
lasers together with an 8 mL vacuum-gap cavity that employs micro-fabricated
mirrors with sub-angstrom roughness to yield an optical $Q$ of 11.8 billion.
Significantly, all the critical components are lithographically defined on
planar substrates and hold the potential for parallel high-volume
manufacturing. Consequently, this work provides an important advance towards
compact lasers with hertz-level linewidths for applications such as portable
optical clocks, low-noise RF photonic oscillators, and related communication
and navigation systems.


title: Searching for neutrino emissions from multi-frequency sources

abstract:
  Pinpointing the neutrino sources is crucial to unveil the mystery of
high-energy cosmic rays. The search for neutrino-source candidates from
coincident neutrino-photon signatures and electromagnetic objects with peculiar
flaring behaviors have the potential to increase our chances of finding
neutrino emitters. In this paper, we first study the temporal correlations of
astrophysical flares with neutrinos, considering a few hundreds of
multi-frequency sources from ALMA, WISE, Swift, and Fermi in the containment
regions of IceCube high-energy alerts. Furthermore, the spatial correlations
between blazars and neutrinos are investigated using the subset of 10-year
IceCube track-like neutrinos with around 250 thousand events. In the second
test, we account for 2700 blazars with different types of flaring phases in
addition to sole position. No significant neutrino emissions were found from
our analyses. Our results indicate an interesting trend showing the infrared
flaring stages of WISE blazars might be correlated with arrival times of the
neutrino alerts. Possible overflow of neutrinos associated with two of our
blazar sub-samples are also illustrated. One is characterized by a significant
flaring lag in infrared with respect to gamma-rays, like seen for TXS0506+056,
and the other is characterized by highly simultaneous infrared and gamma-ray
flares. These phenomena suggest the need to improve current multi-frequency
light-curve catalogs to pair with the advent of more sensitive neutrino
observatories.


title:
Multi-gap topological conversion of Euler class via band-node braiding:
  minimal models, $PT$-linked nodal rings, and chiral heirs

abstract:
  The past few years have seen rapid progress in characterizing topological
band structures using symmetry eigenvalue indicated methods. Recently, however,
there has been increasing theoretical and experimental interest in multi-gap
dependent topological phases that cannot be captured by this paradigm. These
topologies arise by braiding band degeneracies that reside between different
bands and carry non-Abelian charges due to the presence of either $C_2T$ or
$PT$ symmetry, culminating in different invariants such as $\mathbb{Z}$-valued
Euler class. Here, we present a universal formulation for Euler phases
motivated by their homotopy classification that is related to the
Skyrmion-profile of a single unit-vector in three-level systems, and that of
two unit-vectors in four-level systems. In addition, upon employing the
strategy of systematically building 3D models from a pair of sub-dimensional
Euler phases, we show that phase transitions between any two inequivalent Euler
phases are mediated by the presence of adjacent (in-gap) nodal rings linked
with sub-gap nodal lines, forming trajectories corresponding to the braiding or
debraiding of nodal points. The stability of the linked adjacent nodal rings is
furthermore demonstrated to be indicated by an Euler class monopole charge
matching with its $\mathbb{Z}$-valued linking numbers. We finally also
systematically address the conversion of Euler phases into descendant Chern
phases upon breaking the $C_2T$ or $PT$ symmetry. All the topological phases
discussed in this work are corroborated with explicit minimal lattice models.
These models can themselves directly serve as an extra impetus for experimental
searches or be employed for theoretical studies, thereby underpinning the
upcoming of this nascent pursuit.


title: On the number of $k$-powers in a finite word

abstract:
  This note is an attempt to attack a conjecture of Fraenkel and Simpson stated
in 1998 concerning the number of distinct squares in a finite word. By counting
the number of (right-)special factors, we give an upper bound of the number of
{\em $k$-powers} in a finite word for any integer $k\geq 3$. By {\em
$k$-power}, we mean a word of the form $\underbrace{uu...u}_{k \;
\text{times}}$.


title:
A Peek into the Political Biases in Email Spam Filtering Algorithms
  During US Election 2020

abstract:
  Email services use spam filtering algorithms (SFAs) to filter emails that are
unwanted by the user. However, at times, the emails perceived by an SFA as
unwanted may be important to the user. Such incorrect decisions can have
significant implications if SFAs treat emails of user interest as spam on a
large scale. This is particularly important during national elections. To study
whether the SFAs of popular email services have any biases in treating the
campaign emails, we conducted a large-scale study of the campaign emails of the
US elections 2020 by subscribing to a large number of Presidential, Senate, and
House candidates using over a hundred email accounts on Gmail, Outlook, and
Yahoo. We analyzed the biases in the SFAs towards the left and the right
candidates and further studied the impact of the interactions (such as reading
or marking emails as spam) of email recipients on these biases. We observed
that the SFAs of different email services indeed exhibit biases towards
different political affiliations. We present this and several other important
observations in this paper.


title:
A unified construction of vertex algebras from infinite-dimensional Lie
  algebras

abstract:
  In this paper, we give a unified construction of vertex algebras arising from
infinite-dimensional Lie algebras, including the affine Kac-Moody algebras,
Virasoro algebras, Heisenberg algebras and their higher rank analogs, orbifolds
and deformations. We define a notion of what we call quasi vertex Lie algebra
to unify these Lie algebras. Starting from any (maximal) quasi vertex Lie
algebra $\mathfrak{g}$, we construct a corresponding vertex Lie algebra
${\mathfrak{g}}_0$, and establish a canonical isomorphism between the category
of restricted $\mathfrak{g}$-modules and that of equivariant $\phi$-coordinated
quasi $V_{{\mathfrak{g}}_0}$-modules, where $V_{{\mathfrak{g}}_0}$ is the
universal enveloping vertex algebra of ${\mathfrak{g}}_0$. This unified all the
previous constructions of vertex algebras from infinite-dimensional Lie
algebras and shed light on the way to associate vertex algebras with Lie
algebras.


title:
Channel Measurement and Characterization with Modified SAGE Algorithm in
  an Indoor Corridor at 300 GHz

abstract:
  The much higher frequencies in the Terahertz (THz) band prevent the effective
utilization of channel models dedicated for microwave or millimeter-wave
frequency bands. In this paper, a measurement campaign is conducted in an
indoor corridor scenario at 306-321 GHz with a frequency-domain Vector Network
Analyzer (VNA)-based sounder. To realize high-resolution multipath component
(MPC) extraction for the direction-scan measurement campaigns in the THz band,
a novel modified space-alternating generalized expectation-maximization (SAGE)
algorithm is further proposed. Moreover, critical channel characteristics,
including the path loss, shadow fading, K-factor, delay spread, angular
spreads, cluster parameters, and cross correlations are calculated and analyzed
in the LoS case. Besides, two contrasted measurement campaigns in the NLoS case
are conducted, with and without additional reflective foils on walls to serve
as effective scatterers. Comparison results indicate that the reflective foils
are useful to improve the channel conditions in the NLoS case by nearly 6 dB,
which is potential to be utilized as alternative of intelligent reflecting
surfaces (IRS) to enhance the coverage ability of THz communications.


title:
Resilient Distribution System Restoration with Communication Recovery by
  Drone Small Cells

abstract:
  Distribution system (DS) restoration after natural disasters often faces the
challenge of communication failures to feeder automation (FA) facilities,
resulting in prolonged load pick-up process. This letter discusses the
utilization of drone small cells for wireless communication recovery of FA, and
proposes an integrated DS restoration strategy with communication recovery.
Demonstrative case studies are conducted to validate the proposed model, and
its advantages are illustrated by comparing to benchmark strategies.


title:
How Pre-trained Language Models Capture Factual Knowledge? A
  Causal-Inspired Analysis

abstract:
  Recently, there has been a trend to investigate the factual knowledge
captured by Pre-trained Language Models (PLMs). Many works show the PLMs'
ability to fill in the missing factual words in cloze-style prompts such as
"Dante was born in [MASK]." However, it is still a mystery how PLMs generate
the results correctly: relying on effective clues or shortcut patterns? We try
to answer this question by a causal-inspired analysis that quantitatively
measures and evaluates the word-level patterns that PLMs depend on to generate
the missing words. We check the words that have three typical associations with
the missing words: knowledge-dependent, positionally close, and highly
co-occurred. Our analysis shows: (1) PLMs generate the missing factual words
more by the positionally close and highly co-occurred words than the
knowledge-dependent words; (2) the dependence on the knowledge-dependent words
is more effective than the positionally close and highly co-occurred words.
Accordingly, we conclude that the PLMs capture the factual knowledge
ineffectively because of depending on the inadequate associations.


title: Topological Optimization with Big Steps

abstract:
  Using persistent homology to guide optimization has emerged as a novel
application of topological data analysis. Existing methods treat persistence
calculation as a black box and backpropagate gradients only onto the simplices
involved in particular pairs. We show how the cycles and chains used in the
persistence calculation can be used to prescribe gradients to larger subsets of
the domain. In particular, we show that in a special case, which serves as a
building block for general losses, the problem can be solved exactly in linear
time. This relies on another contribution of this paper, which eliminates the
need to examine a factorial number of permutations of simplices with the same
value. We present empirical experiments that show the practical benefits of our
algorithm: the number of steps required for the optimization is reduced by an
order of magnitude.


title:
SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with
  Adaptive Noise Spectral Shaping

abstract:
  Neural vocoder using denoising diffusion probabilistic model (DDPM) has been
improved by adaptation of the diffusion noise distribution to given acoustic
features. In this study, we propose SpecGrad that adapts the diffusion noise so
that its time-varying spectral envelope becomes close to the conditioning
log-mel spectrogram. This adaptation by time-varying filtering improves the
sound quality especially in the high-frequency bands. It is processed in the
time-frequency domain to keep the computational cost almost the same as the
conventional DDPM-based neural vocoders. Experimental results showed that
SpecGrad generates higher-fidelity speech waveform than conventional DDPM-based
neural vocoders in both analysis-synthesis and speech enhancement scenarios.
Audio demos are available at wavegrad.github.io/specgrad/.


title: Torus orbit closures in the flag variety

abstract:
  The study of torus orbit closures in the (complete) flag variety was
initiated by Klyachko and Gelfand--Serganova in the mid-1980s, but it seems
that not much has been done since then. In this chapter, we present some of the
work by Klyachko and Gelfand--Serganova and our recent work on the topology,
geometry, and combinatorics of torus orbit closures in the flag variety.


title:
An unsupervised cluster-level based method for learning node
  representations of heterogeneous graphs in scientific papers

abstract:
  Learning knowledge representation of scientific paper data is a problem to be
solved, and how to learn the representation of paper nodes in scientific paper
heterogeneous network is the core to solve this problem. This paper proposes an
unsupervised cluster-level scientific paper heterogeneous graph node
representation learning method (UCHL), aiming at obtaining the representation
of nodes (authors, institutions, papers, etc.) in the heterogeneous graph of
scientific papers. Based on the heterogeneous graph representation, this paper
performs link prediction on the entire heterogeneous graph and obtains the
relationship between the edges of the nodes, that is, the relationship between
papers and papers. Experiments results show that the proposed method achieves
excellent performance on multiple evaluation metrics on real scientific paper
datasets.


title:
Higher-order boundary layers and regularity for Stokes systems over
  rough boundaries

abstract:
  In this paper, we study the large-scale boundary regularity for the Stokes
system in periodically oscillating John domains. Our main contribution is the
construction of boundary layer correctors of arbitrary order. This is a
significant generalization of the known results restricted to the first and
second orders. As an application, we prove the large-scale regularity estimate,
as well as a Liouville theorem, of arbitrary order for the Stokes system. Our
results are also related to higher-order boundary layer tails and wall laws in
viscous fluids over rough boundaries.


title: Observable vacuum energy is finite in expanding space

abstract:
  In this work I reason that in expanding space only those quantum modes
contribute to the measured vacuum energy that do not transcend the observable
volume. Since all quantised field modes have various observable consequences,
when a gravitational horizon causally confines an observer to a finite volume
quantised modes should be restricted to the observable patch to remain
consistent with gravity.
  Within the observable patch of Friedmann-Lemaitre-Robertson-Walker (FLRW)
space the vacuum expectation value of the energy-momentum tensor can be
expressed as a sum over discrete field modes. Friedmann's first equation
provides a straightforward ultraviolet cut-off allowing only a finite number of
modes in the sum. The finite volume acts as an infrared regulator and the
calculation of the vacuum energy density is tractable without regularisation
and renormalisation.
  To test the validity of this idea I quantise a scalar field on an FLRW
background and calculate its vacuum energy density in the vacuum dominated,
conformal, holographic limit. In this limit I show that the quantum vacuum
energy density scales with the square of the Hubble parameter, consistently
with gravity. In this example quantum vacuum expands space while the horizon of
the expanding space limits the energy density of the vacuum to the observed
value.


title: Personalized Image Aesthetics Assessment with Rich Attributes

abstract:
  Personalized image aesthetics assessment (PIAA) is challenging due to its
highly subjective nature. People's aesthetic tastes depend on diversified
factors, including image characteristics and subject characters. The existing
PIAA databases are limited in terms of annotation diversity, especially the
subject aspect, which can no longer meet the increasing demands of PIAA
research. To solve the dilemma, we conduct so far, the most comprehensive
subjective study of personalized image aesthetics and introduce a new
Personalized image Aesthetics database with Rich Attributes (PARA), which
consists of 31,220 images with annotations by 438 subjects. PARA features
wealthy annotations, including 9 image-oriented objective attributes and 4
human-oriented subjective attributes. In addition, desensitized subject
information, such as personality traits, is also provided to support study of
PIAA and user portraits. A comprehensive analysis of the annotation data is
provided and statistic study indicates that the aesthetic preferences can be
mirrored by proposed subjective attributes. We also propose a conditional PIAA
model by utilizing subject information as conditional prior. Experimental
results indicate that the conditional PIAA model can outperform the control
group, which is also the first attempt to demonstrate how image aesthetics and
subject characters interact to produce the intricate personalized tastes on
image aesthetics. We believe the database and the associated analysis would be
useful for conducting next-generation PIAA study. The project page of PARA can
be found at: https://cv-datasets.institutecv.com/#/data-sets.


title:
Stochastic Backpropagation: A Memory Efficient Strategy for Training
  Video Models

abstract:
  We propose a memory efficient method, named Stochastic Backpropagation (SBP),
for training deep neural networks on videos. It is based on the finding that
gradients from incomplete execution for backpropagation can still effectively
train the models with minimal accuracy loss, which attributes to the high
redundancy of video. SBP keeps all forward paths but randomly and independently
removes the backward paths for each network layer in each training step. It
reduces the GPU memory cost by eliminating the need to cache activation values
corresponding to the dropped backward paths, whose amount can be controlled by
an adjustable keep-ratio. Experiments show that SBP can be applied to a wide
range of models for video tasks, leading to up to 80.0% GPU memory saving and
10% training speedup with less than 1% accuracy drop on action recognition and
temporal action detection.


title: Casual 6-DoF: free-viewpoint panorama using a handheld 360 camera

abstract:
  Six degrees-of-freedom (6-DoF) video provides telepresence by enabling users
to move around in the captured scene with a wide field of regard. Compared to
methods requiring sophisticated camera setups, the image-based rendering method
based on photogrammetry can work with images captured with any poses, which is
more suitable for casual users. However, existing image-based rendering methods
are based on perspective images. When used to reconstruct 6-DoF views, it often
requires capturing hundreds of images, making data capture a tedious and
time-consuming process. In contrast to traditional perspective images,
360{\deg} images capture the entire surrounding view in a single shot, thus,
providing a faster capturing process for 6-DoF view reconstruction. This paper
presents a novel method to provide 6-DoF experiences over a wide area using an
unstructured collection of 360{\deg} panoramas captured by a conventional
360{\deg} camera. Our method consists of 360{\deg} data capturing, novel depth
estimation to produce a high-quality spherical depth panorama, and
high-fidelity free-viewpoint generation. We compared our method against
state-of-the-art methods, using data captured in various environments. Our
method shows better visual quality and robustness in the tested scenes.


title:
Exploiting Single-Channel Speech for Multi-Channel End-to-End Speech
  Recognition: A Comparative Study

abstract:
  Recently, the end-to-end training approach for multi-channel ASR has shown
its effectiveness, which usually consists of a beamforming front-end and a
recognition back-end. However, the end-to-end training becomes more difficult
due to the integration of multiple modules, particularly considering that
multi-channel speech data recorded in real environments are limited in size.
This raises the demand to exploit the single-channel data for multi-channel
end-to-end ASR. In this paper, we systematically compare the performance of
three schemes to exploit external single-channel data for multi-channel
end-to-end ASR, namely back-end pre-training, data scheduling, and data
simulation, under different settings such as the sizes of the single-channel
data and the choices of the front-end. Extensive experiments on CHiME-4 and
AISHELL-4 datasets demonstrate that while all three methods improve the
multi-channel end-to-end speech recognition performance, data simulation
outperforms the other two, at the cost of longer training time. Data scheduling
outperforms back-end pre-training marginally but nearly consistently,
presumably because that in the pre-training stage, the back-end tends to
overfit on the single-channel data, especially when the single-channel data
size is small.


title:
CUSIDE: Chunking, Simulating Future Context and Decoding for Streaming
  ASR

abstract:
  History and future contextual information are known to be important for
accurate acoustic modeling. However, acquiring future context brings latency
for streaming ASR. In this paper, we propose a new framework - Chunking,
Simulating Future Context and Decoding (CUSIDE) for streaming speech
recognition. A new simulation module is introduced to recursively simulate the
future contextual frames, without waiting for future context. The simulation
module is jointly trained with the ASR model using a self-supervised loss; the
ASR model is optimized with the usual ASR loss, e.g., CTC-CRF as used in our
experiments. Experiments show that, compared to using real future frames as
right context, using simulated future context can drastically reduce latency
while maintaining recognition accuracy. With CUSIDE, we obtain new
state-of-the-art streaming ASR results on the AISHELL-1 dataset.


title: How to Report and Benchmark Emerging Field-Effect Transistors

abstract:
  Emerging low-dimensional nanomaterials have been studied for decades in
device applications as field-effect transistors (FETs). However, properly
reporting and comparing device performance has been challenging due to the
involvement and interlinking of multiple device parameters. More importantly,
the interdisciplinarity of this research community results in a lack of
consistent reporting and benchmarking guidelines. Here we report a consensus
among the authors regarding guidelines for reporting and benchmarking important
FET parameters and performance metrics. We provide an example of this reporting
and benchmarking process for a two-dimensional (2D) semiconductor FET. Our
consensus will help promote an improved approach for assessing device
performance in emerging FETs, thus aiding the field to progress more
consistently and meaningfully.


title:
Subjective intelligibility of speech sounds enhanced by ideal ratio mask
  via crowdsourced remote experiments with effective data screening

abstract:
  It is essential to perform speech intelligibility (SI) experiments with human
listeners to evaluate the effectiveness of objective intelligibility measures.
Recently crowdsourced remote testing has become popular to collect a massive
amount and variety of data with relatively small cost and in short time.
However, careful data screening is essential for attaining reliable SI data. We
compared the results of laboratory and crowdsourced remote experiments to
establish an effective data screening technique. We evaluated the SI of noisy
speech sounds enhanced by a single-channel ideal ratio mask (IRM) and
multi-channel mask-based beamformers. The results demonstrated that the SI
scores were improved by these enhancement methods. In particular, the
IRM-enhanced sounds were much better than the unprocessed and other enhanced
sounds, indicating IRM enhancement may give the upper limit of speech
enhancement performance. Moreover, tone pip tests, for which participants were
asked to report the number of audible tone pips, reduced the variability of
crowdsourced remote results so that the laboratory results became similar. Tone
pip tests could be useful for future crowdsourced experiments because of their
simplicity and effectiveness for data screening.


title: MeMOT: Multi-Object Tracking with Memory

abstract:
  We propose an online tracking algorithm that performs the object detection
and data association under a common framework, capable of linking objects after
a long time span. This is realized by preserving a large spatio-temporal memory
to store the identity embeddings of the tracked objects, and by adaptively
referencing and aggregating useful information from the memory as needed. Our
model, called MeMOT, consists of three main modules that are all
Transformer-based: 1) Hypothesis Generation that produce object proposals in
the current video frame; 2) Memory Encoding that extracts the core information
from the memory for each tracked object; and 3) Memory Decoding that solves the
object detection and data association tasks simultaneously for multi-object
tracking. When evaluated on widely adopted MOT benchmark datasets, MeMOT
observes very competitive performance.


title: Mapping Topics in 100,000 Real-life Moral Dilemmas

abstract:
  Moral dilemmas play an important role in theorizing both about ethical norms
and moral psychology. Yet thought experiments borrowed from the philosophical
literature often lack the nuances and complexity of real life. We leverage
100,000 threads -- the largest collection to date -- from Reddit's
r/AmItheAsshole to examine the features of everyday moral dilemmas. Combining
topic modeling with evaluation from both expert and crowd-sourced workers, we
discover 47 finer-grained, meaningful topics and group them into five
meta-categories. We show that most dilemmas combine at least two topics, such
as family and money. We also observe that the pattern of topic co-occurrence
carries interesting information about the structure of everyday moral concerns:
for example, the generation of moral dilemmas from nominally neutral topics,
and interaction effects in which final verdicts do not line up with the moral
concerns in the original stories in any simple way. Our analysis demonstrates
the utility of a fine-grained data-driven approach to online moral dilemmas,
and provides a valuable resource for researchers aiming to explore the
intersection of practical and theoretical ethics.


title:
CREATE: A Benchmark for Chinese Short Video Retrieval and Title
  Generation

abstract:
  Previous works of video captioning aim to objectively describe the video's
actual content, which lacks subjective and attractive expression, limiting its
practical application scenarios. Video titling is intended to achieve this
goal, but there is a lack of a proper benchmark. In this paper, we propose to
CREATE, the first large-scale Chinese shoRt vidEo retrievAl and Title
gEneration benchmark, to facilitate research and application in video titling
and video retrieval in Chinese. CREATE consists of a high-quality labeled 210K
dataset and two large-scale 3M/10M pre-training datasets, covering 51
categories, 50K+ tags, 537K manually annotated titles and captions, and 10M+
short videos. Based on CREATE, we propose a novel model ALWIG which combines
video retrieval and video titling tasks to achieve the purpose of multi-modal
ALignment WIth Generation with the help of video tags and a GPT pre-trained
model. CREATE opens new directions for facilitating future research and
applications on video titling and video retrieval in the field of Chinese short
videos.


title:
Exclusion Volumes of Convex Bodies in High Space Dimensions:
  Applications to Virial Coefficients and Continuum Percolation

abstract:
  Using the concepts of mixed volumes and quermassintegrals of convex geometry,
we derive an exact formula for the exclusion volume for a general convex body
that applies in any space dimension, including both the rotationally-averaged
exclusion volume and with the same orientation. We show that the sphere
minimizes the dimensionless exclusion volume $v_{ex}(K)/v(K)$ among all convex
bodies, whether randomly oriented or uniformly oriented, for any $d$, where
$v(K)$ is the volume of $K$. When the bodies have the same orientation, the
simplex maximizes the dimensionless exclusion volume for any $d$ with a
large-$d$ asymptotic scaling behavior of $2^{2d}/d^{3/2}$, which is to be
contrasted with the scaling of $2^d$ for the sphere. We present explicit
formulas for quermassintegrals for many nonspherical convex bodies as well as
as well as lower-dimensional bodies. These results are utilized to determine
the rotationally-averaged exclusion volume for these shapes for dimensions 2
through 12. While the sphere is the shape possessing the minimal dimensionless
exclusion volume, among the convex bodies considered that are sufficiently
compact, the simplex possesses the maximal dimensionless exclusion volume with
a scaling behavior of $2^{1.6618\ldots d}$. We also determine the corresponding
second virial coefficient $B_2(K)$ of the aforementioned hard hyperparticles
and compute estimates of the continuum percolation threshold $\eta_c$ derived
previously by the authors. We conjecture that overlapping spheres possess the
maximal value of $\eta_c$ among all identical nonzero-volume convex overlapping
bodies for $d \ge 2$, randomly or uniformly oriented, and that, among all
identical, oriented nonzero-volume convex bodies, overlapping simplices have
the minimal value of $\eta_c$ for $d\ge 2$.


title:
System Level Synthesis Beyond Finite Impulse Response Using
  Approximation by Simple Poles

abstract:
  Optimal linear feedback control design is valuable but challenging. The
system level synthesis approach uses a reparameterization to expand the class
of problems that can be solved using convex reformulations, among other
benefits. However, to solve system level synthesis problems prior work relies
on finite impulse response approximations that lead to deadbeat control, and
that can experience infeasibility and increased suboptimality, especially in
systems with large separation of time scales. This work develops a new
technique by combining system level synthesis with a new approximation based on
simple poles. The result is a new design method which does not result in
deadbeat control, is convex and tractable, always feasible, can incorporate
prior knowledge, and works well for systems with large separation of time
scales. A general suboptimality result is provided which bounds the
approximation error based on the geometry of the pole selection. The bound is
then specialized to a particularly interesting pole selection to obtain a
non-asymptotic convergence rate. An example demonstrates superior performance
of the method.


title:
First-principles method for nonlinear light propagation at oblique
  incidence

abstract:
  We have developed a computational method to describe the nonlinear light
propagation of an intense and ultrashort pulse at oblique incidence on a flat
surface. In the method, coupled equations of macroscopic light propagation and
microscopic electron dynamics are simultaneously solved using a multiscale
modeling. The microscopic electronic motion is described by first-principles
time-dependent density functional theory. The macroscopic Maxwell equations
that describe oblique light propagation are transformed into one-dimensional
wave equations. As an illustration of the method, light propagation at oblique
incidence on a silicon thin film is presented.


title: SpatioTemporal Focus for Skeleton-based Action Recognition

abstract:
  Graph convolutional networks (GCNs) are widely adopted in skeleton-based
action recognition due to their powerful ability to model data topology. We
argue that the performance of recent proposed skeleton-based action recognition
methods is limited by the following factors. First, the predefined graph
structures are shared throughout the network, lacking the flexibility and
capacity to model the multi-grain semantic information. Second, the relations
among the global joints are not fully exploited by the graph local convolution,
which may lose the implicit joint relevance. For instance, actions such as
running and waving are performed by the co-movement of body parts and joints,
e.g., legs and arms, however, they are located far away in physical connection.
Inspired by the recent attention mechanism, we propose a multi-grain contextual
focus module, termed MCF, to capture the action associated relation information
from the body joints and parts. As a result, more explainable representations
for different skeleton action sequences can be obtained by MCF. In this study,
we follow the common practice that the dense sample strategy of the input
skeleton sequences is adopted and this brings much redundancy since number of
instances has nothing to do with actions. To reduce the redundancy, a temporal
discrimination focus module, termed TDF, is developed to capture the local
sensitive points of the temporal dynamics. MCF and TDF are integrated into the
standard GCN network to form a unified architecture, named STF-Net. It is noted
that STF-Net provides the capability to capture robust movement patterns from
these skeleton topology structures, based on multi-grain context aggregation
and temporal dependency. Extensive experimental results show that our STF-Net
significantly achieves state-of-the-art results on three challenging benchmarks
NTU RGB+D 60, NTU RGB+D 120, and Kinetics-skeleton.


title: ReSTR: Convolution-free Referring Image Segmentation Using Transformers

abstract:
  Referring image segmentation is an advanced semantic segmentation task where
target is not a predefined class but is described in natural language. Most of
existing methods for this task rely heavily on convolutional neural networks,
which however have trouble capturing long-range dependencies between entities
in the language expression and are not flexible enough for modeling
interactions between the two different modalities. To address these issues, we
present the first convolution-free model for referring image segmentation using
transformers, dubbed ReSTR. Since it extracts features of both modalities
through transformer encoders, it can capture long-range dependencies between
entities within each modality. Also, ReSTR fuses features of the two modalities
by a self-attention encoder, which enables flexible and adaptive interactions
between the two modalities in the fusion process. The fused features are fed to
a segmentation module, which works adaptively according to the image and
language expression in hand. ReSTR is evaluated and compared with previous work
on all public benchmarks, where it outperforms all existing models.


title: Large-gap insulating dimer ground state in monolayer IrTe2

abstract:
  Monolayers of two-dimensional van der Waals materials exhibit novel
electronic phases distinct from their bulk due to the symmetry breaking and
reduced screening in the absence of the interlayer coupling. In this work, we
combine angle-resolved photoemission spectroscopy and scanning tunneling
microscopy/spectroscopy to demonstrate the emergence of a unique insulating 2 x
1 dimer ground state in monolayer 1T-IrTe2 that has a large band gap in
contrast to the metallic bilayer-to-bulk forms of this material.
First-principles calculations reveal that phonon and charge instabilities as
well as local bond formation collectively enhance and stabilize a
charge-ordered ground state. Our findings provide important insights into the
subtle balance of interactions having similar energy scales that occurs in the
absence of strong interlayer coupling, which offers new opportunities to
engineer the properties of 2D monolayers.


title:
BV spaces and the perimeters related to Schrodinger operators with
  inverse-square potentials and applications to the rank-one theorem

abstract:
  For $a \ge - {( \frac{{d}}{2}- 1)^2} $ and $2\sigma= {{d - 2}}-( {{{(d -
2)}^2} + 4a})^{1/2}$, let $$\begin{cases}\mathcal{H}_{a}= - \Delta + \frac{a}
{{{{ | x |}^2}}},\\ \mathcal{\widetilde{H}}_{\sigma}= 2\big( { - \Delta +
\frac{{{\sigma ^2}}} {{{{ | x |}^2}}}}\big)\end{cases}$$ be two Schr\"odinger
operators with inverse-square potentials. In this paper, on the domain $\Omega
\subset {\mathbb {R}^d}\backslash \{ 0\}, d\geq 2,$ %apart from the origin, the
${\mathcal{H} _a}$-BV space $\mathcal{B} {\mathcal{V} _{{\mathcal{H}
_a}}}(\Omega )$ and the ${\mathcal{\widetilde{H}}_{\sigma}}$-BV space
$\mathcal{B} {\mathcal{V} _{{\mathcal{\widetilde H} _\sigma}}}(\Omega )$
related to $\mathcal{H}_{a}$ and $\mathcal{\widetilde{H}}_{\sigma}$ are
introduced, respectively. We investigate a series of basic properties of
$\mathcal{B} {\mathcal{V} _{{\mathcal{H} _a}}}(\Omega )$ and $\mathcal{B}
{\mathcal{V} _{{\mathcal{\widetilde H} _{\sigma}}}}(\Omega )$. Furthermore, we
prove that ${\mathcal{\widetilde{H}}_{\sigma}}$-restricted BV functions can be
characterized equivalently via their subgraphs. As applications, we derive the
rank-one theorem for ${\mathcal{\widetilde{H}}_{\sigma}}$-restricted BV
functions.


title:
LAKe-Net: Topology-Aware Point Cloud Completion by Localizing Aligned
  Keypoints

abstract:
  Point cloud completion aims at completing geometric and topological shapes
from a partial observation. However, some topology of the original shape is
missing, existing methods directly predict the location of complete points,
without predicting structured and topological information of the complete
shape, which leads to inferior performance. To better tackle the missing
topology part, we propose LAKe-Net, a novel topology-aware point cloud
completion model by localizing aligned keypoints, with a novel
Keypoints-Skeleton-Shape prediction manner. Specifically, our method completes
missing topology using three steps: 1) Aligned Keypoint Localization. An
asymmetric keypoint locator, including an unsupervised multi-scale keypoint
detector and a complete keypoint generator, is proposed for localizing aligned
keypoints from complete and partial point clouds. We theoretically prove that
the detector can capture aligned keypoints for objects within a sub-category.
2) Surface-skeleton Generation. A new type of skeleton, named Surface-skeleton,
is generated from keypoints based on geometric priors to fully represent the
topological information captured from keypoints and better recover the local
details. 3) Shape Refinement. We design a refinement subnet where multi-scale
surface-skeletons are fed into each recursive skeleton-assisted refinement
module to assist the completion process. Experimental results show that our
method achieves the state-of-the-art performance on point cloud completion.


title: Learning Decoupling Features Through Orthogonality Regularization

abstract:
  Keyword spotting (KWS) and speaker verification (SV) are two important tasks
in speech applications. Research shows that the state-of-art KWS and SV models
are trained independently using different datasets since they expect to learn
distinctive acoustic features. However, humans can distinguish language content
and the speaker identity simultaneously. Motivated by this, we believe it is
important to explore a method that can effectively extract common features
while decoupling task-specific features. Bearing this in mind, a two-branch
deep network (KWS branch and SV branch) with the same network structure is
developed and a novel decoupling feature learning method is proposed to push up
the performance of KWS and SV simultaneously where speaker-invariant keyword
representations and keyword-invariant speaker representations are expected
respectively. Experiments are conducted on Google Speech Commands Dataset
(GSCD). The results demonstrate that the orthogonality regularization helps the
network to achieve SOTA EER of 1.31% and 1.87% on KWS and SV, respectively.


title:
An Exploration of Prompt Tuning on Generative Spoken Language Model for
  Speech Processing Tasks

abstract:
  Speech representations learned from Self-supervised learning (SSL) models can
benefit various speech processing tasks. However, utilizing SSL representations
usually requires fine-tuning the pre-trained models or designing task-specific
downstream models and loss functions, causing much memory usage and human
labor. Recently, prompting in Natural Language Processing (NLP) has been found
to be an efficient technique to leverage pre-trained language models (LMs).
Specifically, prompt tuning optimizes a limited number of task-specific
parameters with a fixed pre-trained model; as a result, only a small set of
parameters is needed to be stored for each task. Prompt tuning improves
computation and memory efficiency by leveraging the pre-trained LM's prediction
ability. Nevertheless, such a paradigm is little studied in the speech
community. We report in this paper the first exploration of the prompt tuning
paradigm for speech processing tasks based on Generative Spoken Language Model
(GSLM). Experiment results show that the prompt tuning technique achieves
competitive performance in speech classification tasks with fewer trainable
parameters than fine-tuning specialized downstream models. We further study the
technique in challenging sequence generation tasks. Prompt tuning also
demonstrates its potential, while the limitation and possible research
directions are discussed in this paper. The source code is available on
https://github.com/ga642381/SpeechPrompt.


title: On the variation of the Frobenius in a non abelian Iwasawa tower

abstract:
  For varieties with many automorphisms over a finite field, we study the
l-adic properties of the eigenvalues of the Frobenius operator on its
cohomology. The main goal of this paper is to consider towers such as y^2 =
f(x^n) and prove that the characteristic polynomials of the Frobenius on the
etale cohomology show a surprising l-adic convergence. Moreover in special
cases, we show that the eigenvalues in the tower are entirely determined by a
finite set of values. In a different direction, we provide a precise criterion
for curves with many automorphisms to be supersingular, generalizing and
unifying many old results.


title:
Bangla hate speech detection on social media using attention-based
  recurrent neural network

abstract:
  Hate speech has spread more rapidly through the daily use of technology and,
most notably, by sharing your opinions or feelings on social media in a
negative aspect. Although numerous works have been carried out in detecting
hate speeches in English, German, and other languages, very few works have been
carried out in the context of the Bengali language. In contrast, millions of
people communicate on social media in Bengali. The few existing works that have
been carried out need improvements in both accuracy and interpretability. This
article proposed encoder decoder based machine learning model, a popular tool
in NLP, to classify user's Bengali comments on Facebook pages. A dataset of
7,425 Bengali comments, consisting of seven distinct categories of hate
speeches, was used to train and evaluate our model. For extracting and encoding
local features from the comments, 1D convolutional layers were used. Finally,
the attention mechanism, LSTM, and GRU based decoders have been used for
predicting hate speech categories. Among the three encoder decoder algorithms,
the attention-based decoder obtained the best accuracy (77%).


title:
An Empirical Study of Language Model Integration for Transducer based
  Speech Recognition

abstract:
  Utilizing text-only data with an external language model (LM) in end-to-end
RNN-Transducer (RNN-T) for speech recognition is challenging. Recently, a class
of methods such as density ratio (DR) and ILM estimation (ILME) have been
developed, outperforming the classic shallow fusion (SF) method. The basic idea
behind these methods is that RNN-T posterior should first subtract the
implicitly learned ILM prior, in order to integrate the external LM. While
recent studies suggest that RNN-T only learns some low-order language model
information, the DR method uses a well-trained ILM. We hypothesize that this
setting is appropriate and may deteriorate the performance of the DR method,
and propose a low-order density ratio method (LODR) by training a low-order
weak ILM for DR. Extensive empirical experiments are conducted on both
in-domain and cross-domain scenarios on English LibriSpeech & Tedlium-2 and
Chinese WenetSpeech & AISHELL-1 datasets. It is shown that LODR consistently
outperforms SF in all tasks, while performing generally close to ILME and
better than DR in most tests.


title: Mask Atari for Deep Reinforcement Learning as POMDP Benchmarks

abstract:
  We present Mask Atari, a new benchmark to help solve partially observable
Markov decision process (POMDP) problems with Deep Reinforcement Learning
(DRL)-based approaches. To achieve a simulation environment for the POMDP
problems, Mask Atari is constructed based on Atari 2600 games with
controllable, moveable, and learnable masks as the observation area for the
target agent, especially with the active information gathering (AIG) setting in
POMDPs. Given that one does not yet exist, Mask Atari provides a challenging,
efficient benchmark for evaluating the methods that focus on the above problem.
Moreover, the mask operation is a trial for introducing the receptive field in
the human vision system into a simulation environment for an agent, which means
the evaluations are not biased from the sensing ability and purely focus on the
cognitive performance of the methods when compared with the human baseline. We
describe the challenges and features of our benchmark and evaluate several
baselines with Mask Atari.


title: ViSTA: Vision and Scene Text Aggregation for Cross-Modal Retrieval

abstract:
  Visual appearance is considered to be the most important cue to understand
images for cross-modal retrieval, while sometimes the scene text appearing in
images can provide valuable information to understand the visual semantics.
Most of existing cross-modal retrieval approaches ignore the usage of scene
text information and directly adding this information may lead to performance
degradation in scene text free scenarios. To address this issue, we propose a
full transformer architecture to unify these cross-modal retrieval scenarios in
a single $\textbf{Vi}$sion and $\textbf{S}$cene $\textbf{T}$ext
$\textbf{A}$ggregation framework (ViSTA). Specifically, ViSTA utilizes
transformer blocks to directly encode image patches and fuse scene text
embedding to learn an aggregated visual representation for cross-modal
retrieval. To tackle the modality missing problem of scene text, we propose a
novel fusion token based transformer aggregation approach to exchange the
necessary scene text information only through the fusion token and concentrate
on the most important features in each modality. To further strengthen the
visual modality, we develop dual contrastive learning losses to embed both
image-text pairs and fusion-text pairs into a common cross-modal space.
Compared to existing methods, ViSTA enables to aggregate relevant scene text
semantics with visual appearance, and hence improve results under both scene
text free and scene text aware scenarios. Experimental results show that ViSTA
outperforms other methods by at least $\bf{8.4}\%$ at Recall@1 for scene text
aware retrieval task. Compared with state-of-the-art scene text free retrieval
methods, ViSTA can achieve better accuracy on Flicker30K and MSCOCO while
running at least three times faster during the inference stage, which validates
the effectiveness of the proposed framework.


title:
The Calder\'on problem with finitely many unknowns is equivalent to
  convex semidefinite optimization

abstract:
  We consider the inverse boundary value problem of determining a coefficient
function in an elliptic partial differential equation from knowledge of the
associated Neumann-Dirichlet-operator. The unknown coefficient function is
assumed to be piecewise constant with respect to a given pixel partition, and
upper and lower bounds are assumed to be known a-priori.
  We will show that this Calder\'on problem with finitely many unknowns can be
equivalently formulated as a minimization problem for a linear cost functional
with a convex non-linear semidefinite constraint. We also prove error estimates
for noisy data, and extend the result to the practically relevant case of
finitely many measurements, where the coefficient is to be reconstructed from a
finite-dimensional Galerkin projection of the Neumann-Dirichlet-operator.
  Our result is based on previous works on Loewner monotonicity and convexity
of the Neumann-Dirichlet-operator, and the technique of localized potentials.
It connects the emerging fields of inverse coefficient problems and
semidefinite optimization.


title:
A Pixel-based Encryption Method for Privacy-Preserving Deep Learning
  Models

abstract:
  In the recent years, pixel-based perceptual algorithms have been successfully
applied for privacy-preserving deep learning (DL) based applications. However,
their security has been broken in subsequent works by demonstrating a
chosen-plaintext attack. In this paper, we propose an efficient pixel-based
perceptual encryption method. The method provides a necessary level of security
while preserving the intrinsic properties of the original image. Thereby, can
enable deep learning (DL) applications in the encryption domain. The method is
substitution based where pixel values are XORed with a sequence (as opposed to
a single value used in the existing methods) generated by a chaotic map. We
have used logistic maps for their low computational requirements. In addition,
to compensate for any inefficiency because of the logistic maps, we use a
second key to shuffle the sequence. We have compared the proposed method in
terms of encryption efficiency and classification accuracy of the DL models on
them. We have validated the proposed method with CIFAR datasets. The analysis
shows that when classification is performed on the cipher images, the model
preserves accuracy of the existing methods while provides better security.


title: Misogynistic Meme Detection using Early Fusion Model with Graph Network

abstract:
  In recent years , there has been an upsurge in a new form of entertainment
medium called memes. These memes although seemingly innocuous have transcended
onto the boundary of online harassment against women and created an unwanted
bias against them . To help alleviate this problem , we propose an early fusion
model for prediction and identification of misogynistic memes and its type in
this paper for which we participated in SemEval-2022 Task 5 . The model
receives as input meme image with its text transcription with a target vector.
Given that a key challenge with this task is the combination of different
modalities to predict misogyny, our model relies on pretrained contextual
representations from different state-of-the-art transformer-based language
models and pretrained image pretrained models to get an effective image
representation. Our model achieved competitive results on both SubTask-A and
SubTask-B with the other competition teams and significantly outperforms the
baselines.


title:
Weakly Supervised Patch Label Inference Networks for Efficient Pavement
  Distress Detection and Recognition in the Wild

abstract:
  Automatic image-based pavement distress detection and recognition are vital
for pavement maintenance and management. However, existing deep learning-based
methods largely omit the specific characteristics of pavement images, such as
high image resolution and low distress area ratio, and are not end-to-end
trainable. In this paper, we present a series of simple yet effective
end-to-end deep learning approaches named Weakly Supervised Patch Label
Inference Networks (WSPLIN) for efficiently addressing these tasks under
various application settings. To fully exploit the resolution and scale
information, WSPLIN first divides the pavement image under different scales
into patches with different collection strategies and then employs a Patch
Label Inference Network (PLIN) to infer the labels of these patches. Notably,
we design a patch label sparsity constraint based on the prior knowledge of
distress distribution, and leverage the Comprehensive Decision Network (CDN) to
guide the training of PLIN in a weakly supervised way. Therefore, the patch
labels produced by PLIN provide interpretable intermediate information, such as
the rough location and the type of distress. We evaluate our method on a
large-scale bituminous pavement distress dataset named CQU-BPDD. Extensive
results demonstrate the superiority of our method over baselines in both
performance and efficiency.


title: Mode-walk-off interferometry for position-resolved optical fiber sensing

abstract:
  Simultaneously sensing and resolving the position of measurands along an
optical fiber enables numerous opportunities, especially for application in
environments where massive sensor deployment is not feasible. Despite
significant progress in techniques based on round-trip time-of-flight
measurements, the need for bidirectional propagation imposes fundamental
barriers to their deployment in fiber communication links containing
non-reciprocal elements. In this work, we break this barrier by introducing a
position-resolved sensing technique based on the interference of two
weakly-coupled non-degenerate modes of an optical fiber, as they walk-off
through each other. We use this mode-walk-off interferometry to experimentally
measure and localize physical changes to the fiber under test (axial strain and
temperature) without the typical requirement of round-trip time-of-flight
measurements. The unidirectional propagation requirement of this method makes
it compatible with fiber links incorporating non-reciprocal elements,
uncovering a path for multiple sensing applications, including ultra-long range
distributed sensing in amplified space-division-multiplexed telecommunication
links.


title:
Video-Text Representation Learning via Differentiable Weak Temporal
  Alignment

abstract:
  Learning generic joint representations for video and text by a supervised
method requires a prohibitively substantial amount of manually annotated video
datasets. As a practical alternative, a large-scale but uncurated and narrated
video dataset, HowTo100M, has recently been introduced. But it is still
challenging to learn joint embeddings of video and text in a self-supervised
manner, due to its ambiguity and non-sequential alignment. In this paper, we
propose a novel multi-modal self-supervised framework Video-Text Temporally
Weak Alignment-based Contrastive Learning (VT-TWINS) to capture significant
information from noisy and weakly correlated data using a variant of Dynamic
Time Warping (DTW). We observe that the standard DTW inherently cannot handle
weakly correlated data and only considers the globally optimal alignment path.
To address these problems, we develop a differentiable DTW which also reflects
local information with weak temporal alignment. Moreover, our proposed model
applies a contrastive learning scheme to learn feature representations on
weakly correlated data. Our extensive experiments demonstrate that VT-TWINS
attains significant improvements in multi-modal representation learning and
outperforms various challenging downstream tasks. Code is available at
https://github.com/mlvlab/VT-TWINS.


title:
Exclusive $\pi^{-}$ Electroproduction off the Neutron in Deuterium in
  the Resonance Region

abstract:
  New results for the exclusive and quasi-free cross sections off neutrons
bound in deuterium ${\gamma}_vn(p) \rightarrow p{\pi}^{-} (p)$ are presented
over a wide final state hadron angle range with a kinematic coverage of the
invariant mass ($W$) up to 1.825 GeV and the virtual photon four-momentum
transfer squared ($Q^{2}$) from 0.4 to 1.0 GeV$^2$. The exclusive structure
functions have been extracted and their Legendre moments were obtained.
Final-state-interaction contributions have been kinematically separated from
the extracted quasi-free cross sections off bound neutrons solely based on the
analysis of the experimental data. These new results will serve as long-awaited
input for phenomenological analyses to extract the $Q^{2}$ evolution of
previously unavailable $n \to N^{*}$ electroexcitation amplitudes and to
improve state-of-the-art models of neutrino scattering off nuclei by augmenting
the already available results from free protons.


title:
Latent Sub-structural Resilience Mechanisms in Temporal Human Mobility
  Networks during Urban Flooding

abstract:
  In studying resilience in temporal human networks, relying solely on global
network measures would be inadequate; latent sub-structural network mechanisms
need to be examined to determine the extent of impact and recovery of these
networks during perturbations, such as urban flooding. In this study, we
utilized high-resolution aggregated location-based disaster related data to
construct temporal human mobility networks. Using the constructed temporal
network models, we examined characteristics such as motif distribution, motif
persistence and temporal stability, and motif attributes to reveal latent
sub-structural mechanisms related to the resilience of human mobility networks
during disaster-induced perturbations. The results show that urban flood
impacts persists in human mobility networks at the sub-structure level for
several weeks. The impact extent and recovery duration is heterogeneous across
different network types. Also, perturbation impacts persist at the
sub-structure level while global topological network properties might indicate
the network has recovered. The findings highlight the importance of examining
the microstructures and their dynamic processes and attributes in understanding
the resilience of temporal human mobility networks (and other temporal
networks), it is essential to examine the microstructures and their
perturbation impacts and recovery. The findings can also provide disaster
managers, public officials, and transportation planners with insights to better
evaluate impacts and to monitor recovery in affected communities based on the
patterns of impact and recovery in human mobility networks at both
sub-structure and global-network levels.


title: Reflection and Rotation Symmetry Detection via Equivariant Learning

abstract:
  The inherent challenge of detecting symmetries stems from arbitrary
orientations of symmetry patterns; a reflection symmetry mirrors itself against
an axis with a specific orientation while a rotation symmetry matches its
rotated copy with a specific orientation. Discovering such symmetry patterns
from an image thus benefits from an equivariant feature representation, which
varies consistently with reflection and rotation of the image. In this work, we
introduce a group-equivariant convolutional network for symmetry detection,
dubbed EquiSym, which leverages equivariant feature maps with respect to a
dihedral group of reflection and rotation. The proposed network is built
end-to-end with dihedrally-equivariant layers and trained to output a spatial
map for reflection axes or rotation centers. We also present a new dataset,
DENse and DIverse symmetry (DENDI), which mitigates limitations of existing
benchmarks for reflection and rotation symmetry detection. Experiments show
that our method achieves the state of the arts in symmetry detection on LDRS
and DENDI datasets.


title:
ESGBERT: Language Model to Help with Classification Tasks Related to
  Companies Environmental, Social, and Governance Practices

abstract:
  Environmental, Social, and Governance (ESG) are non-financial factors that
are garnering attention from investors as they increasingly look to apply these
as part of their analysis to identify material risks and growth opportunities.
Some of this attention is also driven by clients who, now more aware than ever,
are demanding for their money to be managed and invested responsibly. As the
interest in ESG grows, so does the need for investors to have access to
consumable ESG information. Since most of it is in text form in reports,
disclosures, press releases, and 10-Q filings, we see a need for sophisticated
NLP techniques for classification tasks for ESG text. We hypothesize that an
ESG domain-specific pre-trained model will help with such and study building of
the same in this paper. We explored doing this by fine-tuning BERTs pre-trained
weights using ESG specific text and then further fine-tuning the model for a
classification task. We were able to achieve accuracy better than the original
BERT and baseline models in environment-specific classification tasks.


title:
Using neural network potential to study point defect properties in
  multiple charge states of GaN with nitrogen vacancy

abstract:
  Investigation of charged defects is necessary to understand the properties of
semiconductors. While density functional theory calculations can accurately
describe the relevant physical quantities, these calculations increase the
computational loads substantially, which often limits the application of this
method to large-scale systems. In this study, we propose a new scheme of neural
network potential (NNP) to analyze the point defect behavior in multiple charge
states. The proposed scheme necessitates only minimal modifications to the
conventional scheme. We demonstrated the prediction performance of the proposed
NNP using wurzite-GaN with a nitrogen vacancy with charge states of 0, 1+, 2+,
and 3+. The proposed scheme accurately trained the total energies and atomic
forces for all the charge states. Furthermore, it fairly reproduced the phonon
band structures and thermodynamics properties of the defective structures.
Based on the results of this study, we expect that the proposed scheme can
enable us to study more complicated defective systems and lead to breakthroughs
in novel semiconductor applications.


title: The initial state of Pluto-Charon with Tidal Evolution

abstract:
  Presently, Pluto and Charon have been in tidal locking and the ratio of Pluto
and Charon's spin velocity to the orbital mean motion is $1$. We test the
effect of tidal evolution at Pluto-Charon and register the possibilities of the
initial states with $\Delta t$ model and $Q$ model. In $\Delta t$ model, the
tidal evolution equation will have an unnecessary overshoot, so we find a more
exact tidal evolution equation for the $Q$ model. Besides we consider the
influence of inclination and discuss the model with a high semi-major axis and
high eccentricity.


title: The Parking Lot Planet

abstract:
  We give conditions for an exoplanetary system to function as an ideal
amusement park/vacation resort (with its separate parking lot, of course); in
case of massive human interplanetary colonization. Our considerations stem from
the fact that an amusement park needs a parking lot of roughly the same surface
area, thus the best option for its construction would be a system with at least
2 planets close to each other for easy tourist transportation. We also discuss
the likelihood of finding such a system out there to cut down on construction
costs.


title:
TrajGen: Generating Realistic and Diverse Trajectories with Reactive and
  Feasible Agent Behaviors for Autonomous Driving

abstract:
  Realistic and diverse simulation scenarios with reactive and feasible agent
behaviors can be used for validation and verification of self-driving system
performance without relying on expensive and time-consuming real-world testing.
Existing simulators rely on heuristic-based behavior models for background
vehicles, which cannot capture the complex interactive behaviors in real-world
scenarios. To bridge the gap between simulation and the real world, we propose
TrajGen, a two-stage trajectory generation framework, which can capture more
realistic behaviors directly from human demonstration. In particular, TrajGen
consists of the multi-modal trajectory prediction stage and the reinforcement
learning based trajectory modification stage. In the first stage, we propose a
novel auxiliary RouteLoss for the trajectory prediction model to generate
multi-modal diverse trajectories in the drivable area. In the second stage,
reinforcement learning is used to track the predicted trajectories while
avoiding collisions, which can improve the feasibility of generated
trajectories. In addition, we develop a data-driven simulator I-Sim that can be
used to train reinforcement learning models in parallel based on naturalistic
driving data. The vehicle model in I-Sim can guarantee that the generated
trajectories by TrajGen satisfy vehicle kinematic constraints. Finally, we give
comprehensive metrics to evaluate generated trajectories for simulation
scenarios, which shows that TrajGen outperforms either trajectory prediction or
inverse reinforcement learning in terms of fidelity, reactivity, feasibility,
and diversity.


title:
Topology optimization of locomoting soft bodies using material point
  method

abstract:
  Topology optimization methods have widely been used in various industries,
owing to their potential for providing promising design candidates for
mechanical devices. However, their applications are usually limited to the
objects which do not move significantly due to the difficulty in
computationally efficient handling of the contact and interactions among
multiple structures or with boundaries by conventionally used simulation
techniques. In the present study, we propose a topology optimization method for
moving objects incorporating the material point method, which is often used to
simulate the motion of objects in the field of computer graphics. Several
numerical experiments demonstrate the effectiveness and the utility of the
proposed method.


title:
MMER: Multimodal Multi-task learning for Emotion Recognition in Spoken
  Utterances

abstract:
  Emotion Recognition (ER) aims to classify human utterances into different
emotion categories. Based on early-fusion and self-attention-based multimodal
interaction between text and acoustic modalities, in this paper, we propose a
multimodal multitask learning approach for ER from individual utterances in
isolation. Experiments on the IEMOCAP benchmark show that our proposed model
performs better than our re-implementation of state-of-the-art and achieves
better performance than all other unimodal and multimodal approaches in
literature. In addition, strong baselines and ablation studies prove the
effectiveness of our proposed approach. We make all our codes publicly
available on GitHub.


title: Deformable Video Transformer

abstract:
  Video transformers have recently emerged as an effective alternative to
convolutional networks for action classification. However, most prior video
transformers adopt either global space-time attention or hand-defined
strategies to compare patches within and across frames. These fixed attention
schemes not only have high computational cost but, by comparing patches at
predetermined locations, they neglect the motion dynamics in the video. In this
paper, we introduce the Deformable Video Transformer (DVT), which dynamically
predicts a small subset of video patches to attend for each query location
based on motion information, thus allowing the model to decide where to look in
the video based on correspondences across frames. Crucially, these motion-based
correspondences are obtained at zero-cost from information stored in the
compressed format of the video. Our deformable attention mechanism is optimised
directly with respect to classification performance, thus eliminating the need
for suboptimal hand-design of attention strategies. Experiments on four
large-scale video benchmarks (Kinetics-400, Something-Something-V2,
EPIC-KITCHENS and Diving-48) demonstrate that, compared to existing video
transformers, our model achieves higher accuracy at the same or lower
computational cost, and it attains state-of-the-art results on these four
datasets.


title:
Delays have Dangerous Ends: Slow HTTP/2 DoS attacks into the Wild and
  their Real-Time Detection using Event Sequence Analysis

abstract:
  The robustness principle, written by Jon Postel in an early version of TCP
implementation, states that the communicating entities should be liberal while
accepting the data. Several entities on the Internet do follow this principle.
For instance, in this work, we show that many popular web servers on the
Internet are generous as they wait for a substantial time period to receive the
remaining portion of an incomplete web request. Unfortunately, this behavior
also makes them vulnerable to a class of cyber attacks, commonly known as Slow
Rate DoS attacks. HTTP/2, the recent version of HTTP, is recently found
vulnerable to these attacks. However, the impact of Slow HTTP/2 DoS attacks on
real web servers on the Internet has not been studied yet. Also, to the best of
our knowledge, there is no defense scheme known to detect Slow Rate DoS attacks
against HTTP/2 in real-time. To bridge these gaps, we first test the behavior
of HTTP/2 supporting web servers on the Internet against Slow HTTP/2 DoS
attacks. Subsequently, we propose a scheme to detect these attacks in
real-time. We show that the proposed detection scheme can detect attacks in
real-time with high accuracy and marginal computational overhead.


title:
When Physics Meets Machine Learning: A Survey of Physics-Informed
  Machine Learning

abstract:
  Physics-informed machine learning (PIML), referring to the combination of
prior knowledge of physics, which is the high level abstraction of natural
phenomenons and human behaviours in the long history, with data-driven machine
learning models, has emerged as an effective way to mitigate the shortage of
training data, to increase models' generalizability and to ensure the physical
plausibility of results. In this paper, we survey an abundant number of recent
works in PIML and summarize them from three aspects: (1) motivations of PIML,
(2) physics knowledge in PIML, (3) methods of physics knowledge integration in
PIML. We also discuss current challenges and corresponding research
opportunities in PIML.


title: Ternary and Binary Quantization for Improved Classification

abstract:
  Dimension reduction and data quantization are two important methods for
reducing data complexity. In the paper, we study the methodology of first
reducing data dimension by random projection and then quantizing the
projections to ternary or binary codes, which has been widely applied in
classification. Usually, the quantization will seriously degrade the accuracy
of classification due to high quantization errors. Interestingly, however, we
observe that the quantization could provide comparable and often superior
accuracy, as the data to be quantized are sparse features generated with common
filters. Furthermore, this quantization property could be maintained in the
random projections of sparse features, if both the features and random
projection matrices are sufficiently sparse. By conducting extensive
experiments, we validate and analyze this intriguing property.


title:
A Discourse Aware Sequence Learning Approach for Emotion Recognition in
  Conversations

abstract:
  The expression of emotions is a crucial part of daily human communication.
Modeling the conversational and sequential context has seen much success and
plays a vital role in Emotion Recognition in Conversations (ERC). However,
existing approaches either model only one of the two or employ naive
late-fusion methodologies to obtain final utterance representations. This paper
proposes a novel idea to incorporate both these contexts and better model the
intrinsic structure within a conversation. More precisely, we propose a novel
architecture boosted by a modified LSTM cell, which we call DiscLSTM, that
better captures the interaction between conversational and sequential context.
DiscLSTM brings together the best of both worlds and provides a more intuitive
and efficient way to model the information flow between individual utterances
by better capturing long-distance conversational background through discourse
relations and sequential context through recurrence. We conduct experiments on
four benchmark datasets for ERC and show that our model achieves performance
competitive to state-of-the-art and at times performs better than other
graph-based approaches in literature, with a conversational graph that is both
sparse and avoids complicated edge relations like much of previous work. We
make all our codes publicly available on GitHub.


title:
Fine-grained Temporal Contrastive Learning for Weakly-supervised
  Temporal Action Localization

abstract:
  We target at the task of weakly-supervised action localization (WSAL), where
only video-level action labels are available during model training. Despite the
recent progress, existing methods mainly embrace a
localization-by-classification paradigm and overlook the fruitful fine-grained
temporal distinctions between video sequences, thus suffering from severe
ambiguity in classification learning and classification-to-localization
adaption. This paper argues that learning by contextually comparing
sequence-to-sequence distinctions offers an essential inductive bias in WSAL
and helps identify coherent action instances. Specifically, under a
differentiable dynamic programming formulation, two complementary contrastive
objectives are designed, including Fine-grained Sequence Distance (FSD)
contrasting and Longest Common Subsequence (LCS) contrasting, where the first
one considers the relations of various action/background proposals by using
match, insert, and delete operators and the second one mines the longest common
subsequences between two videos. Both contrasting modules can enhance each
other and jointly enjoy the merits of discriminative action-background
separation and alleviated task gap between classification and localization.
Extensive experiments show that our method achieves state-of-the-art
performance on two popular benchmarks. Our code is available at
https://github.com/MengyuanChen21/CVPR2022-FTCL.


title: Robust Meta-Reinforcement Learning with Curriculum-Based Task Sampling

abstract:
  Meta-reinforcement learning (meta-RL) acquires meta-policies that show good
performance for tasks in a wide task distribution. However, conventional
meta-RL, which learns meta-policies by randomly sampling tasks, has been
reported to show meta-overfitting for certain tasks, especially for easy tasks
where an agent can easily get high scores. To reduce effects of the
meta-overfitting, we considered meta-RL with curriculum-based task sampling.
Our method is Robust Meta Reinforcement Learning with Guided Task Sampling
(RMRL-GTS), which is an effective method that restricts task sampling based on
scores and epochs. We show that in order to achieve robust meta-RL, it is
necessary not only to intensively sample tasks with poor scores, but also to
restrict and expand the task regions of the tasks to be sampled.


title: SIERRA: Ranking Anomalous Activities in Enterprise Networks

abstract:
  An enterprise today deploys multiple security middleboxes such as firewalls,
IDS, IPS, etc. in its network to collect different kinds of events related to
threats and attacks. These events are streamed into a SIEM (Security
Information and Event Management) system for analysts to investigate and
respond quickly with appropriate actions. However, the number of events
collected for a single enterprise can easily run into hundreds of thousands per
day, much more than what analysts can investigate under a given budget
constraint (time). In this work, we look into the problem of prioritizing
suspicious events or anomalies to analysts for further investigation. We
develop SIERRA, a system that processes event logs from multiple and diverse
middleboxes to detect and rank anomalous activities. SIERRA takes an
unsupervised approach and therefore has no dependence on ground truth data.
Different from other works, SIERRA defines contexts, that help it to provide
visual explanations of highly-ranked anomalous points to analysts, despite
employing unsupervised models. We evaluate SIERRA using months of logs from
multiple security middleboxes of an enterprise network. The evaluations
demonstrate the capability of SIERRA to detect top anomalies in a network while
outperforming naive application of existing anomaly detection algorithms as
well as a state-of-the-art SIEM-based anomaly detection solution.


title:
Attack Impact Evaluation by Exact Convexification through State Space
  Augmentation

abstract:
  We address the attack impact evaluation problem for control system security.
We formulate the problem as a Markov decision process with a temporally joint
chance constraint that forces the adversary to avoid being detected throughout
the considered time period. Owing to the joint constraint, the optimal control
policy depends not only on the current state but also on the entire history,
which leads to the explosion of the search space and makes the problem
generally intractable. It is shown that whether an alarm has been triggered or
not, in addition to the current state is sufficient for specifying the optimal
decision at each time step. Augmentation of the information to the state space
induces an equivalent convex optimization problem, which is tractable using
standard solvers.


title: BRIO: Bringing Order to Abstractive Summarization

abstract:
  Abstractive summarization models are commonly trained using maximum
likelihood estimation, which assumes a deterministic (one-point) target
distribution in which an ideal model will assign all the probability mass to
the reference summary. This assumption may lead to performance degradation
during inference, where the model needs to compare several system-generated
(candidate) summaries that have deviated from the reference summary. To address
this problem, we propose a novel training paradigm which assumes a
non-deterministic distribution so that different candidate summaries are
assigned probability mass according to their quality. Our method achieves a new
state-of-the-art result on the CNN/DailyMail (47.78 ROUGE-1) and XSum (49.07
ROUGE-1) datasets. Further analysis also shows that our model can estimate
probabilities of candidate summaries that are more correlated with their level
of quality.


title: Minimum Roman Dominating Distance Energy of a Graph

abstract:
  In this correspondence, we introduced the concept of minimum roman dominating
distance energy $E_{RDd}(G)$ of a graph $G$ and computed minimum roman
dominating distance energy of some standard graphs. Also, we discussed the
properties of eigenvalues of a minimum roman dominating distance matrix
$A_{RDd}(G).$ Finally, we derived the Upper and lower bounds for $E_{RDd}(G).$


title: Photoacoustic Imaging Based on AlN MF-PMUT with Broadened Bandwidth

abstract:
  This paper reports an aluminum nitride (AlN) multi-frequency piezoelectric
micromachined ultrasound transducers (MF-PMUT) array for photoacoustic (PA)
imaging, where the broadened bandwidth is beneficial to improve imaging
resolution. Specifically, PMUT based on micro-electromechanical systems (MEMS)
technology is suitable for PA endoscopic imaging of blood vessels and bronchi
due to its miniature size. More importantly, AlN is a non-toxic material, which
makes it harmless for biomedical applications. In this work, a MF-PMUT array
are designed and fabricated for PAI. The device's vibration mode impedance and
bandwidth are analyzed. The MF-PMUT sensor provides a wider bandwidth (65%)
signal detection, which increases the resolution of PAI compared with
traditional PMUT. We conduct an experiment on agar sample to present sensor's
performance in images' axial resolution.


title:
Joint Power Allocation and Rate Control for Rate Splitting Multiple
  Access Networks with Covert Communications

abstract:
  Rate Splitting Multiple Access (RSMA) has recently emerged as a promising
technique to enhance the transmission rate for multiple access networks. Unlike
conventional multiple access schemes, RSMA requires splitting and transmitting
messages at different rates. The joint optimization of the power allocation and
rate control at the transmitter is challenging given the uncertainty and
dynamics of the environment. Furthermore, securing transmissions in RSMA
networks is a crucial problem because the messages transmitted can be easily
exposed to adversaries. This work first proposes a stochastic optimization
framework that allows the transmitter to adaptively adjust its power and
transmission rates allocated to users, and thereby maximizing the sum-rate and
fairness of the system under the presence of an adversary. We then develop a
highly effective learning algorithm that can help the transmitter to find the
optimal policy without requiring complete information about the environment in
advance. Extensive simulations show that our proposed scheme can achieve
positive covert transmission rates in the finite blocklength regime and
non-saturating rates at high SNR values. More significantly, our achievable
covert rate can be increased at high SNR values (i.e., 20 dB to 40 dB),
compared with saturating rates of a conventional multiple access scheme.


title:
Singularly Perturbed Averaging with Application to Bio-Inspired 3D
  Source Seeking

abstract:
  We analyze a class of singularly perturbed high-amplitude, high-frequency
oscillatory systems that arises in extremum seeking applications. We provide
explicit formulas for averaging and establish the convergence of the
trajectories of this class of systems to the trajectories of a suitably
averaged reduced order system by combining the higher order averaging theorem
with singular perturbation techniques. Finally, we propose a novel bio-inspired
3D source seeking algorithm and establish its singular practical stability.


title: Optimal and Robust Disclosure of Public Information

abstract:
  A policymaker discloses public information to interacting agents who also
acquire costly private information. More precise public information reduces the
precision and cost of acquired private information. Considering this effect,
what disclosure rule should the policymaker adopt? We address this question
under two alternative assumptions using a linear quadratic Gaussian game with
arbitrary quadratic material welfare and convex information costs. First, the
policymaker knows the cost of private information and adopts an optimal
disclosure rule to maximize the expected welfare. Second, the policymaker is
uncertain about the cost and adopts a robust disclosure rule to maximize the
worst-case welfare. Depending on the elasticity of marginal cost, an optimal
rule is qualitatively the same as that in the case of either a linear
information cost or exogenous private information. The worst-case welfare is
strictly increasing if and only if full disclosure is optimal under some
information costs, which provides a new rationale for central bank
transparency.


title: Adaptive Estimation of Random Vectors with Bandit Feedback

abstract:
  We consider the problem of sequentially learning to estimate, in the mean
squared error (MSE) sense, a Gaussian $K$-vector of unknown covariance by
observing only $m < K$ of its entries in each round. This reduces to learning
an optimal subset for estimating the entire vector. Towards this, we first
establish an exponential concentration bound for an estimate of the MSE for
each observable subset. We then frame the estimation problem with bandit
feedback in the best-subset identification setting. We propose a variant of the
successive elimination algorithm to cater to the adaptive estimation problem,
and we derive an upper bound on the sample complexity of this algorithm. In
addition, to understand the fundamental limit on the sample complexity of this
adaptive estimation bandit problem, we derive a minimax lower bound.


title:
Approximate Sensitivity Conditioning and Singular Perturbation Analysis
  for Power Converters

abstract:
  A feed-forward sensitivity conditioning control strategy is analyzed in this
paper and it is applied to power electronic converters. The feed-forward term
is used to improve closed loop systems, such as power converters with cascaded
inner and outer loop controllers. The impact of the feed-forward sensitivity
term is analyzed using singular perturbation theory. In addition, the
implementation of the feed-forward control term is addressed for practical
systems, where the number of inputs is generally not sufficient for exact
sensitivity conditioning. Simulation results are presented for a buck converter
with output capacitor voltage regulation and a Permanent Magnet Synchronous
Machine (PMSM), used as a generator with an active rectifier. Finally,
experimental results are presented for the buck converter, demonstrating the
advantages and feasibility in implementing the approximate sensitivity
conditioning term for closed loop power converters.