title: Zero-Query Transfer Attacks on Context-Aware Object Detectors

abstract:
  Adversarial attacks perturb images such that a deep neural network produces
incorrect classification results. A promising approach to defend against
adversarial attacks on natural multi-object scenes is to impose a
context-consistency check, wherein, if the detected objects are not consistent
with an appropriately defined context, then an attack is suspected. Stronger
attacks are needed to fool such context-aware detectors. We present the first
approach for generating context-consistent adversarial attacks that can evade
the context-consistency check of black-box object detectors operating on
complex, natural scenes. Unlike many black-box attacks that perform repeated
attempts and open themselves to detection, we assume a "zero-query" setting,
where the attacker has no knowledge of the classification decisions of the
victim system. First, we derive multiple attack plans that assign incorrect
labels to victim objects in a context-consistent manner. Then we design and use
a novel data structure that we call the perturbation success probability
matrix, which enables us to filter the attack plans and choose the one most
likely to succeed. This final attack plan is implemented using a
perturbation-bounded adversarial attack algorithm. We compare our zero-query
attack against a few-query scheme that repeatedly checks if the victim system
is fooled. We also compare against state-of-the-art context-agnostic attacks.
Against a context-aware defense, the fooling rate of our zero-query approach is
significantly higher than context-agnostic approaches and higher than that
achievable with up to three rounds of the few-query scheme.


title: Auto-correlative weak-value amplification under strong noise background

abstract:
  In the general optical metro-logical protocols based on the weak-value
amplification(WVA) approach, it is beneficial to choose the pre-selected state
and the post-selected one to be nearly orthogonal for improving the
sensitivity. However, the orthogonality of the post-selection decreases the
probability of detecting photons and makes the weak measurement difficult,
especially when there is strong noise background and the pointer is drowned in
noise. In this article, we investigate a modified weak measurement protocol
with a temporal pointer, namely, the auto-correlative weak-value amplification
(AWVA) approach. We find it can significantly improve the precision of optical
metrology under Gaussian white noise, especially with a negative
signal-to-noise ratio. With the AWVA approach, a small longitudinal time delay
(tiny phase shift) $\tau$ of a Gaussian pulse is measured by implementing two
auto-correlative weak measurements. The small quantities are obtained by
measuring the auto-correlation coefficient of the pulses instead of fitting the
shift of the mean value of the probe. Simulation results show that the AWVA
approach outperforms the standard WVA technique in the time domain, remarkably
increasing the precision of weak measurement under strong noise background.


title:
Unified Performance Assessment of Optical Wireless Communication over
  Multi-Layer Underwater Channels

abstract:
  In this paper, we model the multi-layer vertical underwater link as a
cascaded channel and unify the performance analysis for the underwater optical
communication (UWOC) system using generalized Gamma (GG), exponential GG (EGG),
exponentiated Weibull (EW), and Gamma-Gamma ({\Gamma}{\Gamma}) oceanic
turbulence models. We derive unified analytical expressions for probability
density function (PDF) and cumulative distribution function (CDF) for the
signal-to-noise ratios (SNR) considering independent and non-identical
(i.ni.d.) turbulent models and zero bore-sight model for pointing errors. We
develop performance metrics of the considered UWOC system using outage
probability, average bit error rate (BER), and ergodic capacity with asymptotic
expressions for outage probability and average BER. We develop the diversity
order of the proposed system to provide a better insight into the system
performance at a high SNR. We also integrate a terrestrial OWC (TOWC) subjected
to the combined effect of generalized Malaga atmospheric turbulence,
fog-induced random path gain, and pointing errors to communicate with the UWOC
link using the fixed-gain amplify-and-forward (AF) relaying. We analyze the
performance of the mixed TWOC and multi-layer UWOC system by deriving PDF, CDF,
outage probability, and average BER using the bivariate Fox H-function. We use
Monte-Carlo simulation results to validate our exact and asymptotic expressions
and demonstrate the performance of the considered underwater UWOC system using
measurement-based parametric data available for turbulent oceanic channels.


title:
AutoPoly: Predicting a Polygonal Mesh Construction Sequence from a
  Silhouette Image

abstract:
  Polygonal modeling is a core task of content creation in Computer Graphics.
The complexity of modeling, in terms of the number and the order of operations
and time required to execute them makes it challenging to learn and execute.
Our goal is to automatically derive a polygonal modeling sequence for a given
target. Then, one can learn polygonal modeling by observing the resulting
sequence and also expedite the modeling process by starting from the
auto-generated result. As a starting point for building a system for 3D
modeling in the future, we tackle the 2D shape modeling problem and present
AutoPoly, a hybrid method that generates a polygonal mesh construction sequence
from a silhouette image. The key idea of our method is the use of the Monte
Carlo tree search (MCTS) algorithm and differentiable rendering to separately
predict sequential topological actions and geometric actions. Our hybrid method
can alter topology, whereas the recently proposed inverse shape estimation
methods using differentiable rendering can only handle a fixed topology. Our
novel reward function encourages MCTS to select topological actions that lead
to a simpler shape without self-intersection. We further designed two deep
learning-based methods to improve the expansion and simulation steps in the
MCTS search process: an $n$-step "future action prediction" network (nFAP-Net)
to generate candidates for potential topological actions, and a shape warping
network (WarpNet) to predict polygonal shapes given the predicted rendered
images and topological actions. We demonstrate the efficiency of our method on
2D polygonal shapes of multiple man-made object categories.


title:
Equivariance Allows Handling Multiple Nuisance Variables When Analyzing
  Pooled Neuroimaging Datasets

abstract:
  Pooling multiple neuroimaging datasets across institutions often enables
improvements in statistical power when evaluating associations (e.g., between
risk factors and disease outcomes) that may otherwise be too weak to detect.
When there is only a {\em single} source of variability (e.g., different
scanners), domain adaptation and matching the distributions of representations
may suffice in many scenarios. But in the presence of {\em more than one}
nuisance variable which concurrently influence the measurements, pooling
datasets poses unique challenges, e.g., variations in the data can come from
both the acquisition method as well as the demographics of participants
(gender, age). Invariant representation learning, by itself, is ill-suited to
fully model the data generation process. In this paper, we show how bringing
recent results on equivariant representation learning (for studying symmetries
in neural networks) instantiated on structured spaces together with simple use
of classical results on causal inference provides an effective practical
solution. In particular, we demonstrate how our model allows dealing with more
than one nuisance variable under some assumptions and can enable analysis of
pooled scientific datasets in scenarios that would otherwise entail removing a
large portion of the samples.


title:
Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape
  Laplacian

abstract:
  We propose a framework that can deform an object in a 2D image as it exists
in 3D space. Most existing methods for 3D-aware image manipulation are limited
to (1) only changing the global scene information or depth, or (2) manipulating
an object of specific categories. In this paper, we present a 3D-aware image
deformation method with minimal restrictions on shape category and deformation
type. While our framework leverages 2D-to-3D reconstruction, we argue that
reconstruction is not sufficient for realistic deformations due to the
vulnerability to topological errors. Thus, we propose to take a supervised
learning-based approach to predict the shape Laplacian of the underlying volume
of a 3D reconstruction represented as a point cloud. Given the deformation
energy calculated using the predicted shape Laplacian and user-defined
deformation handles (e.g., keypoints), we obtain bounded biharmonic weights to
model plausible handle-based image deformation. In the experiments, we present
our results of deforming 2D character and clothed human images. We also
quantitatively show that our approach can produce more accurate deformation
weights compared to alternative methods (i.e., mesh reconstruction and point
cloud Laplacian methods).


title: Best Arm Identification in Restless Markov Multi-Armed Bandits

abstract:
  We study the problem of identifying the best arm in a multi-armed bandit
environment when each arm is a time-homogeneous and ergodic discrete-time
Markov process on a common, finite state space. The state evolution on each arm
is governed by the arm's transition probability matrix (TPM). A decision entity
that knows the set of arm TPMs but not the exact mapping of the TPMs to the
arms, wishes to find the index of the best arm as quickly as possible, subject
to an upper bound on the error probability. The decision entity selects one arm
at a time sequentially, and all the unselected arms continue to undergo state
evolution ({\em restless} arms). For this problem, we derive the first-known
problem instance-dependent asymptotic lower bound on the growth rate of the
expected time required to find the index of the best arm, where the asymptotics
is as the error probability vanishes. Further, we propose a sequential policy
that, for an input parameter $R$, forcibly selects an arm that has not been
selected for $R$ consecutive time instants. We show that this policy achieves
an upper bound that depends on $R$ and is monotonically non-increasing as
$R\to\infty$. The question of whether, in general, the limiting value of the
upper bound as $R\to\infty$ matches with the lower bound, remains open. We
identify a special case in which the upper and the lower bounds match. Prior
works on best arm identification have dealt with (a) independent and
identically distributed observations from the arms, and (b) rested Markov arms,
whereas our work deals with the more difficult setting of restless Markov arms.


title:
HAZMAT. VIII. A Spectroscopic Analysis of the Ultraviolet Evolution of K
  Stars: Additional Evidence for K Dwarf Rotational Stalling in the First
  Gigayear

abstract:
  Efforts to discover and characterize habitable zone planets have primarily
focused on Sun-like stars and M dwarfs. K stars, however, provide an appealing
compromise between these two alternatives that has been relatively unexplored.
Understanding the ultraviolet (UV) environment around such stars is critical to
our understanding of their planets, as the UV can drastically alter the
photochemistry of a planet's atmosphere. Here we present near-UV and far-UV
\textit{Hubble Space Telescope}'s Cosmic Origins Spectrograph observations of
39 K stars at three distinct ages: 40 Myr, 650 Myr, and $\approx$5 Gyr. We find
that the K star (0.6 -- 0.8 M$_{\odot}$) UV flux remains constant beyond 650
Myr before falling off by an order of magnitude by field age. This is distinct
from early M stars (0.3 -- 0.6 M$_{\odot}$), which begin to decline after only
a few hundred Myr. However, the rotation-UV activity relation for K stars is
nearly identical to that of early M stars. These results may be a consequence
of the spin-down stalling effect recently reported for K dwarfs, in which the
spin-down of K stars halts for over a Gyr when their rotation periods reach
$\approx$10 d, rather than the continuous spin down that G stars experience.
These results imply that exoplanets orbiting K dwarfs may experience a stronger
UV environment than thought, weakening the case for K stars as hosts of
potential "super-habitable" planets.


title:
No-go theorems for deterministic purification and probabilistic
  enhancement of coherence

abstract:
  The manipulation of quantum coherence is one of the principal issues in the
resource theory of coherence, with two critical topics being the purification
and enhancement of coherence. Here, we present two no-go theorems for the
deterministic purification of coherence and the probabilistic enhancement of
coherence, respectively. Specifically, we prove that a quantum state cannot be
deterministically purified if it can be expressed as a convex combination of an
incoherent state and a coherent state. Besides, we give an easy-to-verified
sufficient and necessary condition to determine whether a state can be
probabilistically enhanced via a stochastic strictly incoherent operation
(sSIO). Our findings provide two feasibility criteria for the deterministic
purification and the probabilistic enhancement of coherence, respectively.
These results have repercussions on the understanding of quantum coherence in
real quantum systems.


title: Enabling hand gesture customization on wrist-worn devices

abstract:
  We present a framework for gesture customization requiring minimal examples
from users, all without degrading the performance of existing gesture sets. To
achieve this, we first deployed a large-scale study (N=500+) to collect data
and train an accelerometer-gyroscope recognition model with a cross-user
accuracy of 95.7% and a false-positive rate of 0.6 per hour when tested on
everyday non-gesture data. Next, we design a few-shot learning framework which
derives a lightweight model from our pre-trained model, enabling knowledge
transfer without performance degradation. We validate our approach through a
user study (N=20) examining on-device customization from 12 new gestures,
resulting in an average accuracy of 55.3%, 83.1%, and 87.2% on using one,
three, or five shots when adding a new gesture, while maintaining the same
recognition accuracy and false-positive rate from the pre-existing gesture set.
We further evaluate the usability of our real-time implementation with a user
experience study (N=20). Our results highlight the effectiveness, learnability,
and usability of our customization framework. Our approach paves the way for a
future where users are no longer bound to pre-existing gestures, freeing them
to creatively introduce new gestures tailored to their preferences and
abilities.


title:
On bifurcation of statistical properties of partially hyperbolic
  endomorphisms

abstract:
  We give an example of a path-wise connected open set of $C^\infty$ partially
hyperbolic endomorphisms on the $2$-torus, on which the SRB measure exists for
each system and varies smoothly depending on the system, while the sign of its
central Lyapunov exponent does change.


title: Semi-Supervised Image-to-Image Translation using Latent Space Mapping

abstract:
  Recent image-to-image translation works have been transferred from supervised
to unsupervised settings due to the expensive cost of capturing or labeling
large amounts of paired data. However, current unsupervised methods using the
cycle-consistency constraint may not find the desired mapping, especially for
difficult translation tasks. On the other hand, a small number of paired data
are usually accessible. We therefore introduce a general framework for
semi-supervised image translation. Unlike previous works, our main idea is to
learn the translation over the latent feature space instead of the image space.
Thanks to the low dimensional feature space, it is easier to find the desired
mapping function, resulting in improved quality of translation results as well
as the stability of the translation model. Empirically we show that using
feature translation generates better results, even using a few bits of paired
data. Experimental comparisons with state-of-the-art approaches demonstrate the
effectiveness of the proposed framework on a variety of challenging
image-to-image translation tasks


title:
Temporal profile of biphotons generated from a hot atomic vapor and
  spectrum of electromagnetically induced transparency

abstract:
  We systematically studied the temporal profile of biphotons, i.e., pairs of
time-correlated single photons, generated from a hot atomic vapor via the
spontaneous four-wave mixing process. The measured temporal width of biphoton
wave packet or two-photon correlation function against the coupling power was
varied from about 70 to 580 ns. We derived an analytical expression of the
biphoton's spectral profile in the Doppler-broadened medium. The analytical
expression reveals that the spectral profile is mainly determined by the effect
of electromagnetically induced transparency (EIT), and behaves like a
Lorentzian function with a linewidth approximately equal to the EIT linewidth.
Consequently, the biphoton's temporal profile influenced by the Doppler
broadening is an exponential-decay function, which was consistent with the
experimental data. Employing a weak input probe field of classical light, we
further measured the EIT spectra under the same experimental conditions as
those in the biphoton measurements. The theoretical predictions of the biphoton
wave packets calculated with the parameters determined by the classical-light
EIT spectra are consistent with the experimental data. The consistency
demonstrates that in the Doppler-broadened medium, the classical-light EIT
spectrum is a good indicator for the biphoton's temporal profile. Besides, the
measured biphoton's temporal widths well approximated to the predictions of the
analytical formula based on the biphoton's EIT effect. This study provides an
analytical way to quantitatively understand the biphoton's spectral and
temporal profiles in the Doppler-broadened medium.


title: Fine-tuning Image Transformers using Learnable Memory

abstract:
  In this paper we propose augmenting Vision Transformer models with learnable
memory tokens. Our approach allows the model to adapt to new tasks, using few
parameters, while optionally preserving its capabilities on previously learned
tasks. At each layer we introduce a set of learnable embedding vectors that
provide contextual information useful for specific datasets. We call these
"memory tokens". We show that augmenting a model with just a handful of such
tokens per layer significantly improves accuracy when compared to conventional
head-only fine-tuning, and performs only slightly below the significantly more
expensive full fine-tuning. We then propose an attention-masking approach that
enables extension to new downstream tasks, with a computation reuse. In this
setup in addition to being parameters efficient, models can execute both old
and new tasks as a part of single inference at a small incremental cost.


title:
Multiferroic van der Waals heterostructure FeCl$_2$/Sc$_2$CO$_2$:
  Nonvolatile electrically switchable electronic and spintronic properties

abstract:
  Multiferroic van der Waals (vdW) heterostrucutres offers an exciting route
towards novel nanoelectronics and spintronics device technology. Here we
investigate the electronic and transport properties of multiferroic vdW
heterostructure composed of ferromagnetic FeCl$_2$ monolayer and ferroelectric
Sc$_2$CO$_2$ monolayer using first-principles density functional theory and
quantum transport simulations. We show that FeCl$_2$/Sc$_2$CO$_2$
heterostructure can be reversibly switched from semiconducting to half-metallic
behavior by electrically modulating the ferroelectric polarization states of
Sc$_2$CO$_2$. Intriguingly, the half-metallic phase exhibits a Type-III broken
gap band alignment, which can be beneficial for tunnelling field-effect
transistor application. We perform a quantum transport simulation, based on a
\emph{proof-of-concept} two-terminal nanodevice, to demonstrate
all-electric-controlled valving effects uniquely enabled by the nonvolatile
ferroelectric switching of the heterostructure. These findings unravels the
potential of FeCl$_2$/Sc$_2$CO$_2$ vdW heterostructures as a building block for
designing a next generation of ultimately compact information processing, data
storage and spintronics devices.


title:
Robust Structured Declarative Classifiers for 3D Point Clouds: Defending
  Adversarial Attacks with Implicit Gradients

abstract:
  Deep neural networks for 3D point cloud classification, such as PointNet,
have been demonstrated to be vulnerable to adversarial attacks. Current
adversarial defenders often learn to denoise the (attacked) point clouds by
reconstruction, and then feed them to the classifiers as input. In contrast to
the literature, we propose a family of robust structured declarative
classifiers for point cloud classification, where the internal constrained
optimization mechanism can effectively defend adversarial attacks through
implicit gradients. Such classifiers can be formulated using a bilevel
optimization framework. We further propose an effective and efficient
instantiation of our approach, namely, Lattice Point Classifier (LPC), based on
structured sparse coding in the permutohedral lattice and 2D convolutional
neural networks (CNNs) that is end-to-end trainable. We demonstrate
state-of-the-art robust point cloud classification performance on ModelNet40
and ScanNet under seven different attackers. For instance, we achieve 89.51%
and 83.16% test accuracy on each dataset under the recent JGBA attacker that
outperforms DUP-Net and IF-Defense with PointNet by ~70%. Demo code is
available at https://zhang-vislab.github.io.


title:
A quantum-inspired tensor network method for constrained combinatorial
  optimization problems

abstract:
  Combinatorial optimization is of general interest for both theoretical study
and real-world applications. Fast-developing quantum algorithms provide a
different perspective on solving combinatorial optimization problems. In this
paper, we propose a quantum inspired algorithm for general locally constrained
combinatorial optimization problems by encoding the constraints directly into a
tensor network state. The optimal solution can be efficiently solved by
borrowing the imaginary time evolution from a quantum many-body system. We
demonstrate our algorithm with the open-pit mining problem numerically. Our
computational results show the effectiveness of this construction and potential
applications in further studies for general combinatorial optimization
problems.


title:
A Space-Time Neural Network for Analysis of Stress Evolution under DC
  Current Stressing

abstract:
  The electromigration (EM)-induced reliability issues in very large scale
integration (VLSI) circuits have attracted increased attention due to the
continuous technology scaling. Traditional EM models often lead to overly
pessimistic prediction incompatible with the shrinking design margin in future
technology nodes. Motivated by the latest success of neural networks in solving
differential equations in physical problems, we propose a novel mesh-free model
to compute EM-induced stress evolution in VLSI circuits. The model utilizes a
specifically crafted space-time physics-informed neural network (STPINN) as the
solver for EM analysis. By coupling the physics-based EM analysis with dynamic
temperature incorporating Joule heating and via effect, we can observe stress
evolution along multi-segment interconnect trees under constant, time-dependent
and space-time-dependent temperature during the void nucleation phase. The
proposed STPINN method obviates the time discretization and meshing required in
conventional numerical stress evolution analysis and offers significant
computational savings. Numerical comparison with competing schemes demonstrates
a 2x ~ 52x speedup with a satisfactory accuracy.


title: CP$^2$ Skyrmions and Skyrmion Crystals in Realistic Quantum Magnets

abstract:
  Magnetic skyrmions are nanoscale topological textures that have been recently
observed in different families of quantum magnets. These textures are known as
CP$^1$ skyrmions because the target manifold of the magnetization field is the
2D sphere $S^2 \cong$ CP$^{1}$. Here we report the emergence of magnetic CP$^2$
skyrmions in realistic spin-$1$ models. Unlike CP$^1$ skyrmions, CP$^2$
skyrmions can also arise as metastable textures of quantum paramagnets, opening
a new road to discover emergent topological solitons in non-magnetic materials.
The quantum phase diagram of the spin-$1$ models also includes magnetic
field-induced CP$^2$ skyrmion crystals that can be detected with regular
momentum- (diffraction) and real-space (Lorentz transmission electron
microscopy) experimental techniques.


title:
MFA-Conformer: Multi-scale Feature Aggregation Conformer for Automatic
  Speaker Verification

abstract:
  In this paper, we present Multi-scale Feature Aggregation Conformer
(MFA-Conformer), an easy-to-implement, simple but effective backbone for
automatic speaker verification based on the Convolution-augmented Transformer
(Conformer). The architecture of the MFA-Conformer is inspired by recent
state-of-the-art models in speech recognition and speaker verification.
Firstly, we introduce a convolution sub-sampling layer to decrease the
computational cost of the model. Secondly, we adopt Conformer blocks which
combine Transformers and convolution neural networks (CNNs) to capture global
and local features effectively. Finally, the output feature maps from all
Conformer blocks are concatenated to aggregate multi-scale representations
before final pooling. We evaluate the MFA-Conformer on the widely used
benchmarks. The best system obtains 0.64%, 1.29% and 1.63% EER on VoxCeleb1-O,
SITW.Dev, and SITW.Eval set, respectively. MFA-Conformer significantly
outperforms the popular ECAPA-TDNN systems in both recognition performance and
inference speed. Last but not the least, the ablation studies clearly
demonstrate that the combination of global and local feature learning can lead
to robust and accurate speaker embedding extraction. We will release the code
for future works to do comparison.


title: Analysis of EEG frequency bands for Envisioned Speech Recognition

abstract:
  The use of Automatic speech recognition (ASR) interfaces have become
increasingly popular in daily life for use in interaction and control of
electronic devices. The interfaces currently being used are not feasible for a
variety of users such as those suffering from a speech disorder, locked-in
syndrome, paralysis or people with utmost privacy requirements. In such cases,
an interface that can identify envisioned speech using electroencephalogram
(EEG) signals can be of great benefit. Various works targeting this problem
have been done in the past. However, there has been limited work in identifying
the frequency bands ($\delta, \theta, \alpha, \beta, \gamma$) of the EEG signal
that contribute towards envisioned speech recognition. Therefore, in this work,
we aim to analyze the significance of different EEG frequency bands and signals
obtained from different lobes of the brain and their contribution towards
recognizing envisioned speech. Signals obtained from different lobes and
bandpass filtered for different frequency bands are fed to a spatio-temporal
deep learning architecture with Convolutional Neural Network (CNN) and Long
Short-Term Memory (LSTM). The performance is evaluated on a publicly available
dataset comprising of three classification tasks - digit, character and images.
We obtain a classification accuracy of $85.93\%$, $87.27\%$ and $87.51\%$ for
the three tasks respectively. The code for the implementation has been made
available at https://github.com/ayushayt/ImaginedSpeechRecognition.


title:
Exploring Intra- and Inter-Video Relation for Surgical Semantic Scene
  Segmentation

abstract:
  Automatic surgical scene segmentation is fundamental for facilitating
cognitive intelligence in the modern operating theatre. Previous works rely on
conventional aggregation modules (e.g., dilated convolution, convolutional
LSTM), which only make use of the local context. In this paper, we propose a
novel framework STswinCL that explores the complementary intra- and inter-video
relations to boost segmentation performance, by progressively capturing the
global context. We firstly develop a hierarchy Transformer to capture
intra-video relation that includes richer spatial and temporal cues from
neighbor pixels and previous frames. A joint space-time window shift scheme is
proposed to efficiently aggregate these two cues into each pixel embedding.
Then, we explore inter-video relation via pixel-to-pixel contrastive learning,
which well structures the global embedding space. A multi-source contrast
training objective is developed to group the pixel embeddings across videos
with the ground-truth guidance, which is crucial for learning the global
property of the whole data. We extensively validate our approach on two public
surgical video benchmarks, including EndoVis18 Challenge and CaDIS dataset.
Experimental results demonstrate the promising performance of our method, which
consistently exceeds previous state-of-the-art approaches. Code will be
available at https://github.com/YuemingJin/STswinCL.


title:
Identification and classification of exfoliated graphene flakes from
  microscopy images using a hierarchical deep convolutional neural network

abstract:
  Identification of the mechanically exfoliated graphene flakes and
classification of the thickness is important in the nanomanufacturing of
next-generation materials and devices that overcome the bottleneck of Moore's
Law. Currently, identification and classification of exfoliated graphene flakes
are conducted by human via inspecting the optical microscope images. The
existing state-of-the-art automatic identification by machine learning is not
able to accommodate images with different backgrounds while different
backgrounds are unavoidable in experiments. This paper presents a deep learning
method to automatically identify and classify the thickness of exfoliated
graphene flakes on Si/SiO2 substrates from optical microscope images with
various settings and background colors. The presented method uses a
hierarchical deep convolutional neural network that is capable of learning new
images while preserving the knowledge from previous images. The deep learning
model was trained and used to classify exfoliated graphene flakes into
monolayer (1L), bi-layer (2L), tri-layer (3L), four-to-six-layer (4-6L),
seven-to-ten-layer (7-10L), and bulk categories. Compared with existing machine
learning methods, the presented method possesses high accuracy and efficiency
as well as robustness to the backgrounds and resolutions of images. The results
indicated that our deep learning model has accuracy as high as 99% in
identifying and classifying exfoliated graphene flakes. This research will shed
light on scaled-up manufacturing and characterization of graphene for advanced
materials and devices.


title:
NeuraGen-A Low-Resource Neural Network based approach for Gender
  Classification

abstract:
  Human voice is the source of several important information. This is in the
form of features. These Features help in interpreting various features
associated with the speaker and speech. The speaker dependent work
researchersare targeted towards speaker identification, Speaker verification,
speaker biometric, forensics using feature, and cross-modal matching via speech
and face images. In such context research, it is a very difficult task to come
across clean, and well annotated publicly available speech corpus as data set.
Acquiring volunteers to generate such dataset is also very expensive, not to
mention the enormous amount of effort and time researchers spend to gather such
data. The present paper work, a Neural Network proposal as NeuraGen focused
which is a low-resource ANN architecture. The proposed tool used to classify
gender of the speaker from the speech recordings. We have used speech
recordings collected from the ELSDSR and limited TIMIT datasets, from which we
extracted 8 speech features, which were pre-processed and then fed into
NeuraGen to identify the gender. NeuraGen has successfully achieved accuracy of
90.7407% and F1 score of 91.227% in train and 20-fold cross validation dataset.


title:
Constructing Effective Customer Feedback Systems -- A Design Science
  Study Leveraging Blockchain Technology

abstract:
  Organizations have to adjust to changes in the ecosystem, and customer
feedback systems (CFS) provide important information to adapt products and
services to changing customer preferences. However, current systems are limited
to single-dimensional rating scales and are subject to self-selection biases.
This work contributes design principles for CFS and implements a CFS that
advances current systems by means of contextualized feedback according to
specific organizational objectives. It also uses blockchain-based incentives to
support CFS use. We apply Design Science Research (DSR) methodology and report
on a longitudinal DSR journey considering multiple stakeholder values. We
conducted expert interviews, design workshops, demonstrations, and a four-day
experiment in an organizational setup, involving 132 customers of a major Swiss
library. This validates the identified design principles and the implemented
software artifact both qualitatively and quantitatively. Based on this
evaluation, the design principles are revisited and conclusions for the
construction of successful CFS are drawn. The findings of this work advance the
knowledge on the design of CFS and provide a guideline to managers and decision
makers for designing effective CFS.


title:
Survival analysis for user disengagement prediction:
  question-and-answering communities' case

abstract:
  We used survival analysis to model user disengagement in three distinct
questions-and-answering communities in this work. We used the complete
historical data of {Politics, Data Science, Computer Science} Stack Exchange
communities from their inception until May 2021, which include the information
about all users who were members of one of these three communities.
Furthermore, formulating the user disengagement prediction as a survival
analysis task, we utilised two survival analysis techniques to model and
predict the probabilities of members of each community becoming disengaged. Our
main finding is that the likelihood of users with even a few contributions
staying active is noticeably higher than the users who were making no
contributions; this distinction may widen as time passes. Moreover, the results
of our experiments indicate that users with more favourable views towards the
content shared on the platform may stay engaged longer. Finally, the observed
pattern holds for all three communities, regardless of their themes.


title: The Bushell-Okrasi\'nski inequality

abstract:
  We present an expository account of the Bushell-Okrasi\'nski inequality, the
motivation behind it, its history, and several generalizations. This inequality
originally appeared in studies of nonlinear Volterra equations but very soon
gained interest of its own. The basic result has quickly been generalized and
extended in different directions strengthening the assertion, generalizing the
kernel and nonlinearity, providing the optimal prefactor, finding conditions
under which it becomes an equality, and formulating variations valid for other
than Lebesgue integrals. We review all of these aspects.


title:
$L_2$-Gain Analysis of Coupled Linear 2D PDEs using Linear PI
  Inequalities

abstract:
  In this paper, we present a new method for estimating the $L_2$-gain of
systems governed by 2nd order linear Partial Differential Equations (PDEs) in
two spatial variables, using semidefinite programming. It has previously been
shown that, for any such PDE, an equivalent Partial Integral Equation (PIE) can
be derived. These PIEs are expressed in terms of Partial Integral (PI)
operators mapping states in $L_2[\Omega]$, and are free of the boundary and
continuity constraints appearing in PDEs. In this paper, we extend the 2D PIE
representation to include input and output signals in $\mathbb{R}^n$, deriving
a bijective map between solutions of the PDE and the PIE, along with the
necessary formulae to convert between the two representations. Next, using the
algebraic properties of PI operators, we prove that an upper bound on the
$L_2$-gain of PIEs can be verified by testing feasibility of a Linear PI
Inequality (LPI), defined by a positivity constraint on a PI operator mapping
$\mathbb{R}^n\times L_2[\Omega]$. Finally, we use positive matrices to
parameterize the cone of positive PI operators on $\mathbb{R}^n\times
L_2[\Omega]$, allowing feasibility of the $L_2$-gain LPI to be tested using
semidefinite programming. We implement this test in the MATLAB toolbox
PIETOOLS, and demonstrate that this approach allows an upper bound on the
$L_2$-gain of PDEs to be estimated with little conservatism.


title: Efficient Reflectance Capture with a Deep Gated Mixture-of-Experts

abstract:
  We present a novel framework to efficiently acquire near-planar anisotropic
reflectance in a pixel-independent fashion, using a deep gated
mixtureof-experts. While existing work employs a unified network to handle all
possible input, our network automatically learns to condition on the input for
enhanced reconstruction. We train a gating module to select one out of a number
of specialized decoders for reflectance reconstruction, based on photometric
measurements, essentially trading generality for quality. A common, pre-trained
latent transform module is also appended to each decoder, to offset the burden
of the increased number of decoders. In addition, the illumination conditions
during acquisition can be jointly optimized. The effectiveness of our framework
is validated on a wide variety of challenging samples using a near-field
lightstage. Compared with the state-of-the-art technique, our results are
improved at the same input bandwidth, and our bandwidth can be reduced to about
1/3 for equal-quality results.


title: Eigencontours: Novel Contour Descriptors Based on Low-Rank Approximation

abstract:
  Novel contour descriptors, called eigencontours, based on low-rank
approximation are proposed in this paper. First, we construct a contour matrix
containing all object boundaries in a training set. Second, we decompose the
contour matrix into eigencontours via the best rank-M approximation. Third, we
represent an object boundary by a linear combination of the M eigencontours. We
also incorporate the eigencontours into an instance segmentation framework.
Experimental results demonstrate that the proposed eigencontours can represent
object boundaries more effectively and more efficiently than existing
descriptors in a low-dimensional space. Furthermore, the proposed algorithm
yields meaningful performances on instance segmentation datasets.


title: Efficient Convex Optimization Requires Superlinear Memory

abstract:
  We show that any memory-constrained, first-order algorithm which minimizes
$d$-dimensional, $1$-Lipschitz convex functions over the unit ball to
$1/\mathrm{poly}(d)$ accuracy using at most $d^{1.25 - \delta}$ bits of memory
must make at least $\tilde{\Omega}(d^{1 + (4/3)\delta})$ first-order queries
(for any constant $\delta \in [0, 1/4]$). Consequently, the performance of such
memory-constrained algorithms are a polynomial factor worse than the optimal
$\tilde{O}(d)$ query bound for this problem obtained by cutting plane methods
that use $\tilde{O}(d^2)$ memory. This resolves a COLT 2019 open problem of
Woodworth and Srebro.


title:
Growth, structure, micro-structure and magneto transport of an easy
  route synthesized bulk polycrystalline TiSe2

abstract:
  This article reports an easy route synthesis of bulk polycrystalline TiSe2.
Phase purity and microstructure are determined through powder X-ray diffraction
(PXRD) and field emission scanning electron microscopy (FESEM) respectively.
Vibrational modes of TiSe2 as being analyzed by Raman spectroscopy, show the
occurrence of both Ag and Eg modes. Charge density wave (CDW) is observed in
transport measurements of TiSe2 with hysteresis in cooling and warming
measurements at around 180K. Further, studied TiSe2 showed negative
magnetoresistance (MR) below the CDW and a small positive MR above the CDW.


title:
Applications of the Numerov method to simple quantum systems using
  Python

abstract:
  Numerov's numerical method is developed in a didactic way by using Python in
its {\it Jupyter Notebook} version 6.0.3 for three different quantum physical
systems: the hydrogen atom, a molecule governed by the Morse potential and for
a quantum dot. After a brief introduction to the Numerov method, the complete
code to calculate the eigenfunctions and eigenvalues of the hydrogen atom is
presented. The necessary code changes to calculate the other two examples are
also provided in the sequel.


title:
Limitations on concentration measurements and gradient discerning times
  in cellular systems

abstract:
  This work reports on two results. At first we revisit the Berg and Purcell
calculation that provides a lower bound to the error in concentration
measurement by cells, by considering the realistic case when the cell starts
measuring the moment it comes in contact with the chemoattractants, instead of
measuring after equilibrating with the chemotactic concentration as done in the
classic Berg and Purcell paper. We find that the error in concentration
measurement is still the same as evaluated by Berg and Purcell. We next derive
a lower bound on measurement time below which it is not possible for the cell
to discern extra-cellular chemotactic gradients through spatial sensing
mechanisms. This bound is independent of diffusion rate and concentration of
the chemoattracts and is instead set by detachment rate of ligands from the
cell receptors. The result could help explain experimental observations.


title: Iterated function systems over arbitrary shift spaces

abstract:
  The orbit of a point $x\in X$ in a classical iterated function system (IFS)
can be defined as $\{f_u(x)=f_{u_n}\circ\cdots \circ f_{u_1}(x):$ $u=u_1\cdots
u_n$ is a word of a full shift $\Sigma$ on finite symbols and $f_{u_i}$ is a
continuous self map on $X$ $\}$. One also can associate to
$\sigma=\sigma_1\sigma_2\cdots\in\Sigma$ a non-autonomous system
$(X,\,f_\sigma)$ where the trajectory of $x\in X$ is defined as
$x,\,f_{\sigma_1}(x),\,f_{\sigma_1\sigma_2}(x),\ldots$.Here instead of the full
shift, we consider an arbitrary shift space $\Sigma$. Then we investigate basic
properties related to this IFS and the associated non-autonomous systems. In
particular, we look for sufficient conditions that guarantees that in a
transitive IFS one may have a transitive $(X,\,f_\sigma)$ for some
$\sigma\in\Sigma$ and how abundance are such $\sigma$'s.


title: Spatio-temporal sampling of near-petahertz vortex fields

abstract:
  Measuring the field of visible light with high spatial resolution has been
challenging, as many established methods only detect a focus-averaged signal.
Here, we introduce a near-field method for optical field sampling that
overcomes that limitation by employing the localization of the enhanced
near-field of a nanometric needle tip. A probe field perturbs the photoemission
from the tip, which is induced by a pump pulse, generating a field-dependent
current modulation that can easily be captured with our electronic detection
scheme. The approach provides reliable characterization of near-petahertz
fields. We show that not only the spiral wave-front of visible femtosecond
light pulses carrying orbital angular momentum (OAM) can be resolved, but also
the field evolution with time in the focal plane. Additionally, our method is
polarization sensitive, which makes it applicable to vectorial field
reconstruction.


title: Interactive Multi-Class Tiny-Object Detection

abstract:
  Annotating tens or hundreds of tiny objects in a given image is laborious yet
crucial for a multitude of Computer Vision tasks. Such imagery typically
contains objects from various categories, yet the multi-class interactive
annotation setting for the detection task has thus far been unexplored. To
address these needs, we propose a novel interactive annotation method for
multiple instances of tiny objects from multiple classes, based on a few
point-based user inputs. Our approach, C3Det, relates the full image context
with annotator inputs in a local and global manner via late-fusion and
feature-correlation, respectively. We perform experiments on the Tiny-DOTA and
LCell datasets using both two-stage and one-stage object detection
architectures to verify the efficacy of our approach. Our approach outperforms
existing approaches in interactive annotation, achieving higher mAP with fewer
clicks. Furthermore, we validate the annotation efficiency of our approach in a
user study where it is shown to be 2.85x faster and yield only 0.36x task load
(NASA-TLX, lower is better) compared to manual annotation. The code is
available at
https://github.com/ChungYi347/Interactive-Multi-Class-Tiny-Object-Detection.


title: Selective inference for k-means clustering

abstract:
  We consider the problem of testing for a difference in means between clusters
of observations identified via k-means clustering. In this setting, classical
hypothesis tests lead to an inflated Type I error rate. To overcome this
problem, we take a selective inference approach. We propose a finite-sample
p-value that controls the selective Type I error for a test of the difference
in means between a pair of clusters obtained using k-means clustering, and show
that it can be efficiently computed. We apply our proposal in simulation, and
on hand-written digits data and single-cell RNA-sequencing data.


title:
What to expect from dynamical modelling of cluster haloes II.
  Investigating dynamical state indicators with Random Forest

abstract:
  We investigate the importances of various dynamical features in predicting
the dynamical state (DS) of galaxy clusters, based on the Random Forest (RF)
machine learning approach. We use a large sample of galaxy clusters from the
Three Hundred Project of hydrodynamical zoomed-in simulations, and construct
dynamical features from the raw data as well as from the corresponding mock
maps in the optical, X-ray, and Sunyaev-Zel'dovich (SZ) channels. Instead of
relying on the impurity based feature importance of the RF algorithm, we
directly use the out-of-bag (OOB) scores to evaluate the importances of
individual features and different feature combinations. Among all the features
studied, we find the virial ratio, $\eta$, to be the most important single
feature. The features calculated directly from the simulations and in
3-dimensions carry more information on the DS than those constructed from the
mock maps. Compared with the features based on X-ray or SZ maps, features
related to the centroid positions are more important. Despite the large number
of investigated features, a combination of up to three features of different
types can already saturate the score of the prediction. Lastly, we show that
the most sensitive feature $\eta$ is strongly correlated with the well-known
half-mass bias in dynamical modelling. Without a selection in DS, cluster halos
have an asymmetric distribution in $\eta$, corresponding to an overall positive
half-mass bias. Our work provides a quantitative reference for selecting the
best features to discriminate the DS of galaxy clusters in both simulations and
observations.


title:
Vision Transformers in Medical Computer Vision -- A Contemplative
  Retrospection

abstract:
  Recent escalation in the field of computer vision underpins a huddle of
algorithms with the magnificent potential to unravel the information contained
within images. These computer vision algorithms are being practised in medical
image analysis and are transfiguring the perception and interpretation of
Imaging data. Among these algorithms, Vision Transformers are evolved as one of
the most contemporary and dominant architectures that are being used in the
field of computer vision. These are immensely utilized by a plenty of
researchers to perform new as well as former experiments. Here, in this article
we investigate the intersection of Vision Transformers and Medical images and
proffered an overview of various ViTs based frameworks that are being used by
different researchers in order to decipher the obstacles in Medical Computer
Vision. We surveyed the application of Vision transformers in different areas
of medical computer vision such as image-based disease classification,
anatomical structure segmentation, registration, region-based lesion Detection,
captioning, report generation, reconstruction using multiple medical imaging
modalities that greatly assist in medical diagnosis and hence treatment
process. Along with this, we also demystify several imaging modalities used in
Medical Computer Vision. Moreover, to get more insight and deeper
understanding, self-attention mechanism of transformers is also explained
briefly. Conclusively, we also put some light on available data sets, adopted
methodology, their performance measures, challenges and their solutions in form
of discussion. We hope that this review article will open future directions for
researchers in medical computer vision.


title: MAT: Mask-Aware Transformer for Large Hole Image Inpainting

abstract:
  Recent studies have shown the importance of modeling long-range interactions
in the inpainting problem. To achieve this goal, existing approaches exploit
either standalone attention techniques or transformers, but usually under a low
resolution in consideration of computational cost. In this paper, we present a
novel transformer-based model for large hole inpainting, which unifies the
merits of transformers and convolutions to efficiently process high-resolution
images. We carefully design each component of our framework to guarantee the
high fidelity and diversity of recovered images. Specifically, we customize an
inpainting-oriented transformer block, where the attention module aggregates
non-local information only from partial valid tokens, indicated by a dynamic
mask. Extensive experiments demonstrate the state-of-the-art performance of the
new model on multiple benchmark datasets. Code is released at
https://github.com/fenglinglwb/MAT.


title:
Approximate Information States for Worst-case Control of Uncertain
  Systems

abstract:
  In this paper, we investigate a worst-case control problem with a partially
observed state. We consider a non-stochastic formulation, where noises and
disturbances in our dynamics are uncertain variables which take values in
finite sets. In such problems, the optimal control strategy can be derived
using a dynamic program (DP) with respect to the memory. The computational
complexity of DP can be improved using a conditional range of the state instead
of the memory. We present a more general definition of an information state
which is sufficient to construct a DP without loss of optimality, and show that
the conditional range is an example of an information state. Next, we extend
this notion to define an approximate information state and an approximate DP.
We also bound the maximum loss of optimality when using an approximate
information state to derive the control strategy. Finally, we illustrate our
results in a numerical example.


title:
Sparse Image based Navigation Architecture to Mitigate the need of
  precise Localization in Mobile Robots

abstract:
  Traditional simultaneous localization and mapping (SLAM) methods focus on
improvement in the robot's localization under environment and sensor
uncertainty. This paper, however, focuses on mitigating the need for exact
localization of a mobile robot to pursue autonomous navigation using a sparse
set of images. The proposed method consists of a model architecture - RoomNet,
for unsupervised learning resulting in a coarse identification of the
environment and a separate local navigation policy for local identification and
navigation. The former learns and predicts the scene based on the short term
image sequences seen by the robot along with the transition image scenarios
using long term image sequences. The latter uses sparse image matching to
characterise the similarity of frames achieved vis-a-vis the frames viewed by
the robot during the mapping and training stage. A sparse graph of the image
sequence is created which is then used to carry out robust navigation purely on
the basis of visual goals. The proposed approach is evaluated on two robots in
a test environment and demonstrates the ability to navigate in dynamic
environments where landmarks are obscured and classical localization methods
fail.


title:
Gravitational waves detectable in laser interferometers from axion-SU(2)
  inflation

abstract:
  Chromo-natural inflation (CNI) is an inflationary model where an axion
coupled with SU$(2)$ gauge fields acts as the inflaton. In CNI, the gauge
fields have nonzero vacuum expectation values (VEVs), which results in the
enhancement of gravitational waves (GWs). The original CNI is ruled out by the
Planck observations due to the overproduction of GWs. In this work, we consider
an inflationary model where the gauge fields acquire nonzero VEVs after the CMB
modes exit the horizon. Moreover, we add to the model another field that
dominates the universe and drives inflation after the axion starts to oscillate
and the gauge field VEVs vanish. By performing numerical simulations, we find a
parameter space where the enhanced GWs do not violate the CMB constraints and
can be detected by the future GWs observations such as LISA, BBO, and ET.


title: Finding Structure and Causality in Linear Programs

abstract:
  Linear Programs (LP) are celebrated widely, particularly so in machine
learning where they have allowed for effectively solving probabilistic
inference tasks or imposing structure on end-to-end learning systems. Their
potential might seem depleted but we propose a foundational, causal perspective
that reveals intriguing intra- and inter-structure relations for LP components.
We conduct a systematic, empirical investigation on general-, shortest path-
and energy system LPs.


title:
A Multi-size Kernel based Adaptive Convolutional Neural Network for
  Bearing Fault Diagnosis

abstract:
  Bearing fault identification and analysis is an important research area in
the field of machinery fault diagnosis. Aiming at the common faults of rolling
bearings, we propose a data-driven diagnostic algorithm based on the
characteristics of bearing vibrations called multi-size kernel based adaptive
convolutional neural network (MSKACNN). Using raw bearing vibration signals as
the inputs, MSKACNN provides vibration feature learning and signal
classification capabilities to identify and analyze bearing faults. Ball mixing
is a ball bearing production quality problem that is difficult to identify
using traditional frequency domain analysis methods since it requires high
frequency resolutions of the measurement signals and results in a long
analyzing time. The proposed MSKACNN is shown to improve the efficiency and
accuracy of ball mixing diagnosis. To further demonstrate the effectiveness of
MSKACNN in bearing fault identification, a bearing vibration data acquisition
system was developed, and vibration signal acquisition was performed on rolling
bearings under five different fault conditions including ball mixing. The
resulting datasets were used to analyze the performance of our proposed model.
To validate the adaptive ability of MSKACNN, fault test data from the Case
Western Reserve University Bearing Data Center were also used. Test results
show that MSKACNN can identify the different bearing conditions with high
accuracy with high generalization ability. We presented an implementation of
the MSKACNN as a lightweight module for a real-time bearing fault diagnosis
system that is suitable for production.


title:
Applying Syntax$\unicode{x2013}$Prosody Mapping Hypothesis and Prosodic
  Well-Formedness Constraints to Neural Sequence-to-Sequence Speech Synthesis

abstract:
  End-to-end text-to-speech synthesis (TTS), which generates speech sounds
directly from strings of texts or phonemes, has improved the quality of speech
synthesis over the conventional TTS. However, most previous studies have been
evaluated based on subjective naturalness and have not objectively examined
whether they can reproduce pitch patterns of phonological phenomena such as
downstep, rhythmic boost, and initial lowering that reflect syntactic
structures in Japanese. These phenomena can be linguistically explained by
phonological constraints and the syntax$\unicode{x2013}$prosody mapping
hypothesis (SPMH), which assumes projections from syntactic structures to
phonological hierarchy. Although some experiments in psycholinguistics have
verified the validity of the SPMH, it is crucial to investigate whether it can
be implemented in TTS. To synthesize linguistic phenomena involving syntactic
or phonological constraints, we propose a model using phonological symbols
based on the SPMH and prosodic well-formedness constraints. Experimental
results showed that the proposed method synthesized similar pitch patterns to
those reported in linguistics experiments for the phenomena of initial lowering
and rhythmic boost. The proposed model efficiently synthesizes phonological
phenomena in the test data that were not explicitly included in the training
data.


title:
Decomposed Temporal Dynamic CNN: Efficient Time-Adaptive Network for
  Text-Independent Speaker Verification Explained with Speaker Activation Map

abstract:
  Temporal dynamic models for text-independent speaker verification extract
consistent speaker information regardless of phonemes by using temporal dynamic
CNN (TDY-CNN) in which kernels adapt to each time bin. However, TDY-CNN shows
limitations that the model is too large and does not guarantee the diversity of
adaptive kernels. To address these limitations, we propose decomposed temporal
dynamic CNN (DTDY-CNN) that makes adaptive kernel by combining static kernel
and dynamic residual based on matrix decomposition. The baseline model using
DTDY-CNN maintained speaker verification performance while reducing the number
of model parameters by 35% compared to the model using TDY-CNN. In addition,
detailed behaviors of temporal dynamic models on extraction of speaker
information was explained using speaker activation maps (SAM) modified from
gradient-weighted class activation mapping (Grad-CAM). In DTDY-CNN, the static
kernel activates voiced features of utterances, and the dynamic residual
activates unvoiced high-frequency features of phonemes. DTDY-CNN effectively
extracts speaker information from not only formant frequencies and harmonics
but also detailed unvoiced phonemes' information, thus explaining its
outstanding performance on text-independent speaker verification.


title:
Can we estimate the intensities of great geomagnetic
  storms($\Delta$SYM-H$\le -$200 nT) by Burton equation or by O'Brien and
  McPherron equation?

abstract:
  We input solar wind parameters responsible for the main phases of 15 great
geomagnetic storms (GGSs: $\Delta$SYM-H$\le-$200 nT) into the empirical
formulae created by \cite{Burton1975}(hereafter Burton equation), and by
\cite{OBrien2000}(hereafter OM equation) to evaluate whether \textbf{two
equations} can correctly estimate the intensities of GGSs. The results show
that the intensities of most GGSs estimated by OM equation are much smaller
than the observed intensities. The RMS error between the intensities estimated
by OM equation and the observed intensities is \textbf{203} nT, implying that
the estimated storm intensity deviates significantly from the observed one. The
RMS error between the intensities estimated by Burton equation and the observed
intensities is 130.8 nT. The relative error caused by Burton equation for the
storms with intensities $\Delta$SYM-H$<$-400 nT is larger than 27\%, implying
that the absolute error will be large for the storms with $\Delta$SYM-H$<$-400
nT. The results indicate that the two equations cannot work effectively in the
estimation of GGSs. On the contrary, the intensity of a GGS estimated by the
empirical formula created by \cite{WangCB2003} can always be very close to the
observed one if we select the right weight for solar wind dynamic pressure,
proving that solar wind dynamic pressure is an important factor for GGS
intensity, but it is overlooked in the ring current injection terms of Burton
equation or OM equation. This is the reason why the two equations cannot work
effectively in the estimation of GGSs.


title:
Targeting the Major Groove of the Palindromic d(GGCGCC)2 Sequence by
  Oligopeptide Derivatives of Anthraquinone Intercalators

abstract:
  GC-rich sequences are recurring motifs in oncogenes and retroviruses, and
could be targeted by non-covalent major-groove therapeutic ligands. We
considered the palindromic sequence d(G1G2C3G4C5C6)2, and designed several
oligopeptide derivatives of the anti-cancer intercalator mitoxantrone. The
stability of their complexes with a 18-mer oligonucleotide encompassing this
sequence in its center was validated using polarizable molecular dynamics. We
report the most salient structural features of two novel compounds, having a
dialkylammonium group as a side-chain on both arms. The anthraquinone ring is
intercalated in the central d(CpG)2 sequence with its long axis perpendicular
to that of the two base-pairs. On each strand, this enables each ammonium group
to bind in-register to O6/N7 of the two facing G bases upstream. We
subsequently designed tris-intercalating derivatives, each dialkylammonium
substituted with a connector to an N9-aminoacridine intercalator extending our
target range from six- to a ten-base pair palindromic sequence,
d(C1G2G3G4C5G6C7C8C9G10)2. The structural features of the complex of the most
promising derivative are reported. The present design strategy paves the way
for designing intercalator-oligopeptide derivatives with an even higher
selectivity, targeting an increased number of DNA bases, going beyond ten.


title: Role of Longitudinal Waves in Alfv\'en-wave-driven Solar Wind

abstract:
  We revisit the role of longitudinal waves in driving the solar wind. We study
how the the $p$-mode-like vertical oscillation on the photosphere affects the
properties of solar winds under the framework of Alfv\'en-wave-driven winds. We
perform a series of one-dimensional magnetohydrodynamical numerical simulations
from the photosphere to beyond several tens of solar radii. We find that the
mass-loss rate drastically increases with the longitudinal wave amplitude at
the photosphere up to $\sim 4$ times, in contrast to the classical
understanding that the acoustic wave hardly affects the energetics of the solar
wind. The addition of the longitudinal fluctuation induces the
longitudinal-to-transverse wave mode conversion in the chromosphere, which
results in the enhanced Alfv\'enic Poynting flux in the corona. Consequently,
the coronal heating is promoted to give higher coronal density by the
chromospheric evaporation, leading to the increased mass-loss rate. This study
clearly shows the importance of the longnitudinal oscillation in the
photosphere and the mode conversion in the chromosphere in determining the
basic properties of the wind from solar-like stars.


title:
First-principles Study of the RKKY Interaction and the Quadrupole Order
  in the Pr 1-2-20 systems PrT2Al20 (T=Ti, V)

abstract:
  Electronic states and quadrupole orders in the Pr 1-2-20 systems PrT2Al20
(T=Ti, V) are investigated on the basis of the first-principles calculations.
The effective 196 orbital model is derived to reproduce the first-principles
electronic structures of LaT2Al20 (T=Ti, V) without contribution from the Pr 4f
electrons which are considered to be well localized and is employed to
calculate the Ruderman-Kittel-Kasuya-Yosida (RKKY) interactions between
quadrupole and octupole moments of the Pr ions. Within the random phase
approximation for the RKKY Hamiltonian, the most divergent susceptibility is
found to be the quadrupole one for the wave vector Q = (0,0,0) in the case of
PrTi2Al20 while that for Q = (pi/a,0,pi/a) in the case of PrV2Al20 as
consistent with experimental observations in the both cases which exhibit the
ferro-quadrupole (FQ) and the antiferro-quadrupole (AFQ) orders, respectively.
We also discuss the ordered states using the mean-field approximation and find
that, in the case of PrTi2Al20, the 1st-order phase transition to the O20 FQ
order with a tiny discontinuity takes place as predicted by the Landau theory.
In the case of PrV2Al20, the system exhibits two distinct O22 AFQ orders, AFQ-I
and AFQ-II, and shows subsequent two phase transitions, the 2nd-order one from
normal to AFQ-I and the 1st-order one from AFQ-I to AFQ-II, that may be
responsible for the double transitions observed by specific heat measurements.


title: Sliding Dumbbell Method to search for the CME in heavy ion collisions

abstract:
  The deconfined state of matter created in the hot and dense medium in
ultra-relativistic heavy-ion collisions causes the back-to-back charge
separation along the direction of magnetic field produced due to the energetic
spectator protons, a phenomenon known as the Chiral Magnetic Effect (CME). A
new technique, the Sliding Dumbbell Method (SDM), is developed to search for
the back-to-back charge separation on an event-by-event basis. The SDM is
applied to AMPT generated Au+Au collision events with string melting
configuration at $\sqrt{s}_{\mathrm{NN}}$ = 200 GeV. The varying percentages
i.e., from $\sim$0.5\% to $\sim$8\%, of the CME like signals are externally
injected in events by flipping charges of pairs of the particles perpendicular
to the reaction plane. A significant enhancement of the CME sensitive
3-particle $\gamma$ correlator is reported for the events sub sample exhibiting
the highest back-to-back charge separation for a given collision centrality.
For the first time, we report the linear dependence of $\sqrt {|\gamma|}$
correlator for the same sign charge pairs on the positive charge asymmetry
($\langle A{^+}\rangle$) across the dumbbell for the CME enriched sub samples.
The CME fraction in the observed $\Delta\gamma$ (difference between opposite
and same sign $\gamma$ correlator) is also presented for different collision
centralities with different percentages of externally injected CME like signal.


title:
Mel Frequency Spectral Domain Defenses against Adversarial Attacks on
  Speech Recognition Systems

abstract:
  A variety of recent works have looked into defenses for deep neural networks
against adversarial attacks particularly within the image processing domain.
Speech processing applications such as automatic speech recognition (ASR) are
increasingly relying on deep learning models, and so are also prone to
adversarial attacks. However, many of the defenses explored for ASR simply
adapt the image-domain defenses, which may not provide optimal robustness. This
paper explores speech specific defenses using the mel spectral domain, and
introduces a novel defense method called 'mel domain noise flooding' (MDNF).
MDNF applies additive noise to the mel spectrogram of a speech utterance prior
to re-synthesising the audio signal. We test the defenses against strong
white-box adversarial attacks such as projected gradient descent (PGD) and
Carlini-Wagner (CW) attacks, and show better robustness compared to a
randomized smoothing baseline across strong threat models.


title:
A review on a general multi-species BGK model: modelling, theory and
  numerics

abstract:
  In this article we focus on kinetic equations for gas mixtures since in
applications one often has to deal with mixtures instead of a single gas. In
particular we consider an approximation of the Boltzmann equation, the
Bathnagar-Gross-Krook (BGK) equation. This equation is used in many
applications because it is very efficient in numerical simulations. In this
article, we recall a general BGK equation for gas mixtures which has free
parameters. Specific choices of these free parameters lead to special cases in
the literature. For this model, we provide an overview concerning modelling,
theoretical results and numerics.


title:
Semantic Line Detection Using Mirror Attention and Comparative Ranking
  and Matching

abstract:
  A novel algorithm to detect semantic lines is proposed in this paper. We
develop three networks: detection network with mirror attention (D-Net) and
comparative ranking and matching networks (R-Net and M-Net). D-Net extracts
semantic lines by exploiting rich contextual information. To this end, we
design the mirror attention module. Then, through pairwise comparisons of
extracted semantic lines, we iteratively select the most semantic line and
remove redundant ones overlapping with the selected one. For the pairwise
comparisons, we develop R-Net and M-Net in the Siamese architecture.
Experiments demonstrate that the proposed algorithm outperforms the
conventional semantic line detector significantly. Moreover, we apply the
proposed algorithm to detect two important kinds of semantic lines
successfully: dominant parallel lines and reflection symmetry axes. Our codes
are available at https://github.com/dongkwonjin/Semantic-Line-DRM.


title:
Regularity of extremal solutions of semilinear elliptic equations with
  m-convex nonlinearities

abstract:
  We consider the Gelfand problem in a bounded smooth domain $\Omega\subset
\mathbb{R}^N$ with the Dirichlet boundary condition. We are interested in the
boundedness of the extremal solution $u^*$. When the dimension $N\ge10$, it is
known that a singular extremal solution can be constructed for the nonlinearity
$f(u)=e^u$ and $\Omega=B_1$. When $3\le N\le 9$, Cabr\'e, Figalli, Ros-Oton,
and Serra (2020) proved the following surprising result: the extremal solution
$u^*$ is bounded if the nonlinearity $f$ is positive, nondecreasing, and
convex.
  In this paper, we succeed in generalizing their result to general $m$-convex
nonlinearities. Moreover, we give a unified viewpoint on the results of
previous studies by considering $m$-convexity. We provide a closedness result
for stable solutions with $m$-convex nonlinearities. As a consequence, we
provide a Liouville-type result and by using a blow-up argument, we prove the
boundedness of extremal solutions.


title: Accelerating Code Search with Deep Hashing and Code Classification

abstract:
  Code search is to search reusable code snippets from source code corpus based
on natural languages queries. Deep learning-based methods of code search have
shown promising results. However, previous methods focus on retrieval accuracy
but lacked attention to the efficiency of the retrieval process. We propose a
novel method CoSHC to accelerate code search with deep hashing and code
classification, aiming to perform an efficient code search without sacrificing
too much accuracy. To evaluate the effectiveness of CoSHC, we apply our method
to five code search models. Extensive experimental results indicate that
compared with previous code search baselines, CoSHC can save more than 90% of
retrieval time meanwhile preserving at least 99% of retrieval accuracy.


title: Topological Atomic Spinwave Lattices by Dissipative Couplings

abstract:
  Recent experimental advance in creating dissipative couplings provides a new
route for engineering exotic lattice systems and exploring topological
dissipation. Using the spatial lattice of atomic spinwaves in a vacuum vapor
cell, where purely dissipative couplings arise from diffusion of atoms, we
experimentally realize a dissipative version of the Su-Schrieffer-Heeger (SSH)
model. We construct the dissipation spectra of the topological or trivial
lattices via electromagnetically-induced-transparency (EIT) spectroscopy. The
topological dissipation spectrum is found to exhibit edge modes at dissipation
rates within a dissipative gap, decoupled from the bulk. We also validate
chiral symmetry of the dissipative SSH couplings. This work paves the way for
realizing topology-enabled quantum correlations and non-Hermitian topological
quantum optics via dissipative couplings.


title: Identification of new classical Be stars from the LAMOST MRS survey

abstract:
  Be stars are B-type main-sequence stars that display broad Balmer emission
lines in their spectra. Identification of Be population is essential to further
examine the formation and evolutionary models. We report the detection of
classical Be (CBe) stars from observations with the Large sky Area Multi-Object
fiber Spectroscopic Telescope Medium Resolution Survey of Date Release 7
(LAMOST MRS DR7). We used a deep convolutional neural network, the ResNet, with
an 18-layer module to examine the morphology of the H alpha profile. We
identified 1,162 candidate Be stars from the collection of 2,260,387 spectra
for 789,918 stars in the database. The ResNet network achieves a Be star
classification accuracy of 99.5%. Among the detections, 151 of these are prior
known Be stars cross-matched from the literature. By applying a three-step
test, we identified 183 new CBe stars. We find that 41 CBe stars are members of
known open clusters. Based upon an investigation of the kinematics of the
identified CBe stars from the Gaia EDR3 astrometric solutions, we identified 16
new runaways. These new identifications will provide a reference for future
follow-ups to further investigate their physical properties.


title:
Design strategies for controlling neuron-connected robots using
  reinforcement learning

abstract:
  Despite the growing interest in robot control utilizing the computation of
biological neurons, context-dependent behavior by neuron-connected robots
remains a challenge. Context-dependent behavior here is defined as behavior
that is not the result of a simple sensory-motor coupling, but rather based on
an understanding of the task goal. This paper proposes design principles for
training neuron-connected robots based on task goals to achieve
context-dependent behavior. First, we employ deep reinforcement learning (RL)
to enable training that accounts for goal achievements. Second, we propose a
neuron simulator as a probability distribution based on recorded neural data,
aiming to represent physiologically valid neural dynamics while avoiding
complex modeling with high computational costs. Furthermore, we propose to
update the simulators during the training to bridge the gap between the
simulation and the real settings. The experiments showed that the robot
gradually learned context-dependent behaviors in pole balancing and robot
navigation tasks. Moreover, the learned policies were valid for neural
simulators based on novel neural data, and the task performance increased by
updating the simulators during training. These results suggest the
effectiveness of the proposed design principle for the context-dependent
behavior of neuron-connected robots.


title:
Simulating challenging correlated molecules and materials on the
  Sycamore quantum processor

abstract:
  Simulating complex molecules and materials is an anticipated application of
quantum devices. With strong quantum advantage demonstrated in artificial
tasks, we examine how such advantage translates into modeling physical problems
of correlated electronic structure. We simulate static and dynamical electronic
structure on a superconducting quantum processor derived from Google's Sycamore
architecture for two representative correlated electron problems: the
nitrogenase iron-sulfur molecular clusters, and $\alpha$-ruthenium trichloride,
a proximate spin-liquid material. To do so, we simplify the electronic
structure into low-energy spin models that fit on the device. With extensive
error mitigation and assistance from classically simulated data, we achieve
quantitatively meaningful results deploying about 1/5 of the gate resources
used in artificial quantum advantage experiments on a similar architecture.
This increases to over 1/2 of the gate resources when choosing a model that
suits the hardware. Our work serves to convert artificial measures of quantum
advantage into a physically relevant setting.


title:
A Two-phase Framework with a B\'{e}zier Simplex-based Interpolation
  Method for Computationally Expensive Multi-objective Optimization

abstract:
  This paper proposes a two-phase framework with a B\'{e}zier simplex-based
interpolation method (TPB) for computationally expensive multi-objective
optimization. The first phase in TPB aims to approximate a few Pareto optimal
solutions by optimizing a sequence of single-objective scalar problems. The
first phase in TPB can fully exploit a state-of-the-art single-objective
derivative-free optimizer. The second phase in TPB utilizes a B\'{e}zier
simplex model to interpolate the solutions obtained in the first phase. The
second phase in TPB fully exploits the fact that a B\'{e}zier simplex model can
approximate the Pareto optimal solution set by exploiting its simplex structure
when a given problem is simplicial. We investigate the performance of TPB on
the 55 bi-objective BBOB problems. The results show that TPB performs
significantly better than HMO-CMA-ES and some state-of-the-art meta-model-based
optimizers.


title:
Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose
  Estimation

abstract:
  The advances in monocular 3D human pose estimation are dominated by
supervised techniques that require large-scale 2D/3D pose annotations. Such
methods often behave erratically in the absence of any provision to discard
unfamiliar out-of-distribution data. To this end, we cast the 3D human pose
learning as an unsupervised domain adaptation problem. We introduce MRP-Net
that constitutes a common deep network backbone with two output heads
subscribing to two diverse configurations; a) model-free joint localization and
b) model-based parametric regression. Such a design allows us to derive
suitable measures to quantify prediction uncertainty at both pose and joint
level granularity. While supervising only on labeled synthetic samples, the
adaptation process aims to minimize the uncertainty for the unlabeled target
images while maximizing the same for an extreme out-of-distribution dataset
(backgrounds). Alongside synthetic-to-real 3D pose adaptation, the
joint-uncertainties allow expanding the adaptation to work on in-the-wild
images even in the presence of occlusion and truncation scenarios. We present a
comprehensive evaluation of the proposed approach and demonstrate
state-of-the-art performance on benchmark datasets.


title:
Hydrogenation induced magnetic and electronic transitions in monolayer
  electride Gd$_2$C: A first-principles study

abstract:
  The recently synthesized two-dimensional electride Gd$_2$C was proposed to be
a ferromagnetic metal that possesses multiple pairs of Weyl points and may
display a large anomalous Hall conductivity [Liu \textit{et al.}, Phys. Rev.
Lett. \textbf{125}, 187203 (2020)]. In view of its layered structure, here we
carry out first-principles studies on the magnetic and electronic properties of
Gd$_2$C in the ultrathin monolayer limit. We find that monolayer Gd$_2$C
remains ferromagnetic like the bulk form and the hydrogenation can effectively
tune its magnetism and electronic structure. With one-sided coverage of
hydrogen atoms, monolayer Gd$_2$C becomes a half-metal with one spin channel
around the Fermi level. For two-sided hydrogenation, monolayer Gd$_2$C
transforms to an antiferromagnetic insulator with a band gap of 0.8 eV. Our
studies show that monolayer electride Gd$_2$C can perform multiple magnetic and
electronic transitions with different levels of hydrogenation and may be also
adopted to construct a planar heterojunction with selective area adsorption of
hydrogen atoms, which has promising applications in future electronic and
spintronic devices.


title: A mathematical model for planning oil products distribution via pipeline

abstract:
  Compared to other transportation modes, road, railroad and vessel,
multiproduct pipelines are the safest and most economical way of conveying
petroleum products over long distances day and night. During the last two
decades, the operational multiproduct pipeline scheduling has gained increasing
attention, where most of the contributions are based on continuous time
representation. In this paper, we present a new discrete-time mixed integer
linear programming model for the shortterm scheduling of multiproduct pipelines
featuring multiple refineries and distribution centers. The model optimally
determines the sequence and planning of product injections at input nodes, the
sequence of product deliveries to output nodes, and traces the size and the
products location along the pipeline at any time. The proposed model also
allows to execute simultaneous injections at the input nodes and to tackle
simultaneous injections and deliveries at an intermediate node that can act as
both the input and output node. Solutions to two benchmark example problems
illustrate that the proposed model presents significant reductions in the
pipeline operational cost.


title:
Frequency Dynamic Convolution: Frequency-Adaptive Pattern Recognition
  for Sound Event Detection

abstract:
  2D convolution is widely used in sound event detection (SED) to recognize 2D
patterns of sound events in time-frequency domain. However, 2D convolution
enforces translation-invariance on sound events along both time and frequency
axis while sound events exhibit frequency-dependent patterns. In order to
improve physical inconsistency in 2D convolution on SED, we propose frequency
dynamic convolution which applies kernel that adapts to frequency components of
input. Frequency dynamic convolution outperforms the baseline model by 6.3% in
DESED dataset in terms of polyphonic sound detection score (PSDS). It also
significantly outperforms dynamic convolution and temporal dynamic convolution
on SED. In addition, by comparing class-wise F1 scores of baseline model and
frequency dynamic convolution, we showed that frequency dynamic convolution is
especially more effective for detection of non-stationary sound events. From
this result, we verified that frequency dynamic convolution is superior in
recognizing frequency-dependent patterns as non-stationary sound events show
more intricate time-frequency patterns.


title:
Kernel Modulation: A Parameter-Efficient Method for Training
  Convolutional Neural Networks

abstract:
  Deep Neural Networks, particularly Convolutional Neural Networks (ConvNets),
have achieved incredible success in many vision tasks, but they usually require
millions of parameters for good accuracy performance. With increasing
applications that use ConvNets, updating hundreds of networks for multiple
tasks on an embedded device can be costly in terms of memory, bandwidth, and
energy. Approaches to reduce this cost include model compression and
parameter-efficient models that adapt a subset of network layers for each new
task. This work proposes a novel parameter-efficient kernel modulation (KM)
method that adapts all parameters of a base network instead of a subset of
layers. KM uses lightweight task-specialized kernel modulators that require
only an additional 1.4% of the base network parameters. With multiple tasks,
only the task-specialized KM weights are communicated and stored on the
end-user device. We applied this method in training ConvNets for Transfer
Learning and Meta-Learning scenarios. Our results show that KM delivers up to
9% higher accuracy than other parameter-efficient methods on the Transfer
Learning benchmark.


title:
A Wavelet, AR and SVM based hybrid method for short-term wind speed
  prediction

abstract:
  Wind speed modelling and prediction has been gaining importance because of
its significant roles in various stages of wind energy management. In this
paper, we propose a hybrid model, based on wavelet transform to improve the
accuracy of the short-term forecast. The wind speed time series are split into
various frequency components using wavelet decomposition technique, and each
frequency components are modelled separately. Since the components associated
with the high- frequency range shows stochastic nature, we modelled them with
autoregressive (AR) method and rest of low-frequency components modelled with
support vector machine (SVM). The results of the hybrid method show a promising
improvement in accuracy of wind speed prediction compared to that of
stand-alone AR or SVM model.


title:
A new experiment to detect dark matter in sub-MeV range using
  semiconductor superlattice superstructures (SSS)

abstract:
  About $26\%$ of the matter in our Universe is made up of Dark Matter (DM),
which interacts with Standard Model (SM) matter only through gravitational or
weak interactions. Many proposals have been made by scientists about the
possible candidates of DM - WIMPs, axions, ALPs, black holes etc. And range of
its mass could be extremely broad - from Planck scale to as light as $10^{-22}$
eV. Experiments to detect DM are extremely challenging, as DM does not exhibit
appreciable interactions with ordinary matter. May be due to such elusive
nature, so far it has not been possible to detect DM, though many experiments
are going on worldwide to do so. With about 40 orders of magnitude variation in
their mass, it is possible that their gravitational interaction too is very
weak, and many creative proposals have been made to detect possible DM
candidates, with vast variation of techniques and target materials. In this
work, we propose a new experiment to detect sub-MeV range DM particles, using
the semiconductor superlattice superstructure (SSS) as the target material.
Such materials have band gap of the order of few hundreds of milli eV, and are
suitable for detecting sub-MeV range particles scattering off electrons. The
photons emitted as a result of excitation of SSS lie in the micrometer range
and may be detected via quantum cascade lasers (QCL).


title: Mesoscopic System: Semiclassical vs Quantum

abstract:
  We explain a hierarchy of Friedel sum rule like formulas that help us
understand response of mesoscopic systems to applied electric and magnetic
fields. The formulas can be derived fully quantum mechanically and then there
is a mathematical way to argue a semiclassical limit which may make the
formulas practically relevant. But the mathematical prescription is not
physically relevant in presence of a Fano resonance. A proper understanding of
what exactly is the physical semiclassical limit is therefor not known and
require future work.


title: Pointwise Assouad dimension for measures

abstract:
  We introduce a pointwise variant of the Assouad dimension for measures on
metric spaces, and study its properties in relation to the global Assouad
dimension. We show that, in general, the value of the pointwise Assouad
dimension differs from the global counterpart, but in many classical cases, it
exhibits similar exact dimensionality properties as the classical local
dimension, namely it equals the global Assouad dimension at almost every point.
We also compute the Assouad dimension of invariant measures with place
dependent probabilities supported on strongly separated self-conformal sets.


title: Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes

abstract:
  A novel algorithm to detect road lanes in the eigenlane space is proposed in
this paper. First, we introduce the notion of eigenlanes, which are data-driven
descriptors for structurally diverse lanes, including curved, as well as
straight, lanes. To obtain eigenlanes, we perform the best rank-M approximation
of a lane matrix containing all lanes in a training set. Second, we generate a
set of lane candidates by clustering the training lanes in the eigenlane space.
Third, using the lane candidates, we determine an optimal set of lanes by
developing an anchor-based detection network, called SIIC-Net. Experimental
results demonstrate that the proposed algorithm provides excellent detection
performance for structurally diverse lanes. Our codes are available at
https://github.com/dongkwonjin/Eigenlanes.


title: Pseudodifferential operators on Mixed-Norm $\alpha$-modulation spaces

abstract:
  Mixed-norm $\alpha$-modulation spaces were introduced recently by Cleanthous
and Georgiadis [Trans.\ Amer.\ Math.\ Soc.\ 373 (2020), no. 5, 3323-3356]. The
mixed-norm spaces $M^{s,\alpha}_{\vec{p},q}(\mathbb{R}^n)$, $\alpha\in [0,1]$,
form a family of smoothness spaces that contain the mixed-norm Besov spaces as
special cases. In this paper we prove that a pseudodifferential operator
$\sigma(x,D)$ with symbol in the H\"ormander class $S^b_{\rho}$ extends to a
bounded operator $\sigma(x,D)\colon M^{s,\alpha}_{\vec{p},q}(\mathbb{R}^n)
\rightarrow M^{s-b,\alpha}_{\vec{p},q}(\mathbb{R}^n)$ provided $0<\alpha\leq
\rho\leq 1$,
  $\vec{p}\in (0,\infty)^n$, and $0<q<\infty$. The result extends the known
result that pseudodifferential operators with symbol in the class $S^b_{1}$
maps the mixed-norm Besov space $B^s_{\vec{p},q}(\mathbb{R}^n)$ into
$B^{s-b}_{\vec{p},q}(\mathbb{R}^n)$.


title:
A Practical and Scalable Decoder for Topological Quantum Error
  Correction with Digital Annealer

abstract:
  Quantum error correction is one of the most important milestones for
realization of large-scale quantum computation. To achieve this, it is
essential not only to integrate a large number of qubits with high fidelity,
but also to build a scalable classical system that can perform error
correction. Here, we propose an efficient and scalable decoder for quantum
error correction using Fujitsu Digital Annealer (DA). Specifically, the error
correction problem of stabilizer codes is mapped into an Ising-type
optimization problem, so-called quadratic unconstrained binary optimization
(QUBO) problem, which is solved by DA. In particular, we implement the proposed
DA decoder for the surface code and perform detailed numerical experiments for
various code distances to see its performance and scalability. We observe that
computational scaling for the DA decoder has a lower order of polynomial than
the decoding methods using simulated annealing (SA) and minimum-weight perfect
matching (MWPM) algorithm under all tested conditions. Furthermore, the
threshold behavior of the logical error probability for the DA decoder is
analyzed and the resultant threshold value is about 9.7% which is very close to
that obtained by the MWPM decoder. This result clearly shows the high potential
of the DA decoder for quantum error correction.


title:
Suboptimal Safety-Critical Control for Continuous Systems Using
  Prediction-Correction Online Optimization

abstract:
  This paper investigates the control barrier function (CBF) based
safety-critical control for continuous nonlinear control affine systems using
more efficient online algorithms by the time-varying optimization method. The
idea of the algorithms is that when quadratic programming (QP) or other convex
optimization algorithms needed in the CBF-based method is not computation
affordable, the alternative suboptimal feasible solutions can be obtained more
economically. By using the barrier-based interior point method, the constrained
CBF-QP problems are transformed into unconstrained ones with suboptimal
solutions tracked by two continuous descent-based algorithms. Considering the
lag effect of tracking and exploiting the system information, the prediction
method is added to the algorithms, which achieves exponential convergence to
the time-varying suboptimal solutions. The convergence and robustness of the
designed methods as well as the safety criteria of the algorithms are studied
theoretically. The effectiveness is illustrated by simulations on the
anti-swing and obstacle avoidance tasks.


title: Better Integrators for Functional Renormalization Group Calculations

abstract:
  We analyze a variety of integration schemes for the momentum space functional
renormalization group calculation with the goal of finding an optimized scheme.
Using the square lattice $t-t'$ Hubbard model as a testbed we define and
benchmark the quality. Most notably we define an error estimate of the solution
for the ordinary differential equation circumventing the issues introduced by
the divergences at the end of the FRG flow. Using this measure to control for
accuracy we find a threefold reduction in number of required integration steps
achievable by choice of integrator. We herewith publish a set of recommended
choices for the functional renormalization group, shown to decrease the
computational cost for FRG calculations and representing a valuable basis for
further investigations.


title: Higher order moments for SPDE with monotone nonlinearities

abstract:
  This paper introduces a new $p$-dependent coercivity condition through which
$L^p$-moments for solutions can be obtained for a large class of SPDEs in the
variational framework. If $p=2$, our condition reduces to the classically
coercivity condition, which only yields second moments for the solution. The
abstract result is shown to be optimal. Moreover, the results are applied to
obtain $L^p$-moments of solutions for several classical SPDEs such as
stochastic heat equations with Dirichlet and Neumann boundary conditions,
Burgers' equation and the Navier-Stokes equations in two spatial dimensions.
Furthermore, we can recover recent results for systems of SPDEs and higher
order SPDEs using our unifying coercivity condition.


title: Information criteria for sparse methods in causal inference

abstract:
  For propensity score analysis and sparse estimation, we develop an
information criterion for determining the regularization parameters needed in
variable selection. First, for Gaussian distribution-based causal inference
models, we extend Stein's unbiased risk estimation theory, which leads to a
generalized Cp criterion that has almost no weakness in conventional sparse
estimation, and derive an inverse-probability-weighted sparse estimation
version of the criterion without resorting to asymptotics. Next, for general
causal inference models that are not necessarily Gaussian distribution-based,
we extend the asymptotic theory on LASSO for propensity score analysis, with
the intention of implementing doubly robust sparse estimation. From the
asymptotic theory, an AIC-type information criterion for
inverse-probability-weighted sparse estimation is given, and then a criterion
with double robustness in itself is derived for doubly robust sparse
estimation. Numerical experiments compare the proposed criterion with the
existing criterion derived from a formal argument and verify that the proposed
criterion is superior in almost all cases, that the difference is not
negligible in many cases, and that the results of variable selection differ
significantly. Real data analysis confirms that the difference between variable
selection and estimation by these criteria is actually large. Finally,
generalizations to general sparse estimation using group LASSO, elastic net,
and non-convex regularization are made in order to indicate that the proposed
criterion is highly extensible.


title:
Learning-based Point Cloud Registration for 6D Object Pose Estimation in
  the Real World

abstract:
  In this work, we tackle the task of estimating the 6D pose of an object from
point cloud data. While recent learning-based approaches to addressing this
task have shown great success on synthetic datasets, we have observed them to
fail in the presence of real-world data. We thus analyze the causes of these
failures, which we trace back to the difference between the feature
distributions of the source and target point clouds, and the sensitivity of the
widely-used SVD-based loss function to the range of rotation between the two
point clouds. We address the first challenge by introducing a new normalization
strategy, Match Normalization, and the second via the use of a loss function
based on the negative log likelihood of point correspondences. Our two
contributions are general and can be applied to many existing learning-based 3D
object registration frameworks, which we illustrate by implementing them in two
of them, DCP and IDAM. Our experiments on the real-scene TUD-L, LINEMOD and
Occluded-LINEMOD datasets evidence the benefits of our strategies. They allow
for the first time learning-based 3D object registration methods to achieve
meaningful results on real-world data. We therefore expect them to be key to
the future development of point cloud registration methods.


title: Hybrid Routing Transformer for Zero-Shot Learning

abstract:
  Zero-shot learning (ZSL) aims to learn models that can recognize unseen image
semantics based on the training of data with seen semantics. Recent studies
either leverage the global image features or mine discriminative local patch
features to associate the extracted visual features to the semantic attributes.
However, due to the lack of the necessary top-down guidance and semantic
alignment for ensuring the model attending to the real attribute-correlation
regions, these methods still encounter a significant semantic gap between the
visual modality and the attribute modality, which makes their prediction on
unseen semantics unreliable. To solve this problem, this paper establishes a
novel transformer encoder-decoder model, called hybrid routing transformer
(HRT). In HRT encoder, we embed an active attention, which is constructed by
both the bottom-up and the top-down dynamic routing pathways to generate the
attribute-aligned visual feature. While in HRT decoder, we use static routing
to calculate the correlation among the attribute-aligned visual features, the
corresponding attribute semantics, and the class attribute vectors to generate
the final class label predictions. This design makes the presented transformer
model a hybrid of 1) top-down and bottom-up attention pathways and 2) dynamic
and static routing pathways. Comprehensive experiments on three widely-used
benchmark datasets, namely CUB, SUN, and AWA2, are conducted. The obtained
experimental results demonstrate the effectiveness of the proposed method.