title: On large solutions for fractional Hamilton-Jacobi equations

abstract:
  We study the existence of large solutions for nonlocal Dirichlet problems
posed on a bounded, smooth domain, associated to fully nonlinear elliptic
equations of order $2s$, with $s\in (1/2,1)$, and a coercive gradient term with
subcritical power $0<p<2s$.
  Due to the nonlocal nature of the diffusion, new blow-up phenomena arise
within the range $0<p<2s$, involving a continuum family of solutions and/or
solutions blowing-up to $-\infty$ on the boundary. This is in striking
difference with the local case studied by Lasry-Lions for the case subquadratic
case $1<p<2$.


title:
Chebyshev's Bias for Ramanujan's $\tau$-function via the Deep Riemann
  Hypothesis

abstract:
  The authors assume the Deep Riemann Hypothesis to prove that a weighted sum
of Ramanujan's $\tau$-function has a bias to being positive. This phenomenon is
an analogue of Chebyshev's bias.


title: Minimizing Uncertainty in Prevalence Estimates

abstract:
  Estimating prevalence, the fraction of a population with a certain medical
condition, is fundamental to epidemiology. Traditional methods rely on
classification of test samples taken at random from a population. Such
approaches to estimating prevalence are biased and have uncontrolled
uncertainty. Here, we construct a new, unbiased, minimum variance estimator for
prevalence. Recent result show that prevalence can be estimated from counting
arguments that compare the fraction of samples in an arbitrary subset of the
measurement space to what is expected from conditional probability models of
the diagnostic test. The variance of this estimator depends on both the choice
of subset and the fraction of samples falling in it. We employ a bathtub
principle to recast variance minimization as a one-dimensional optimization
problem. Using symmetry properties, we show that the resulting objective
function is well-behaved and can be numerically minimized.


title:
A Reinforcement Approach for Detecting P2P Botnet Communities in Dynamic
  Communication Graphs

abstract:
  Peer-to-peer (P2P) botnets use decentralized command and control networks
that make them resilient to disruptions. The P2P botnet overlay networks
manifest structures in mutual-contact graphs, also called communication graphs,
formed using network traffic information. It has been shown that these
structures can be detected using community detection techniques from graph
theory. These previous works, however, treat the communication graphs and the
P2P botnet structures as static. In reality, communication graphs are dynamic
as they represent the continuously changing network traffic flows. Similarly,
the P2P botnets also evolve with time, as new bots join and existing bots leave
either temporarily or permanently. In this paper we address the problem of
detecting such evolving P2P botnet communities in dynamic communication graphs.
We propose a reinforcement-based approach, suitable for large communication
graphs, that improves precision and recall of P2P botnet community detection in
dynamic communication graphs.


title:
Learning the Dynamics of Autonomous Linear Systems From Multiple
  Trajectories

abstract:
  We consider the problem of learning the dynamics of autonomous linear systems
(i.e., systems that are not affected by external control inputs) from
observations of multiple trajectories of those systems, with finite sample
guarantees. Existing results on learning rate and consistency of autonomous
linear system identification rely on observations of steady state behaviors
from a single long trajectory, and are not applicable to unstable systems. In
contrast, we consider the scenario of learning system dynamics based on
multiple short trajectories, where there are no easily observed steady state
behaviors. We provide a finite sample analysis, which shows that the dynamics
can be learned at a rate $\mathcal{O}(\frac{1}{\sqrt{N}})$ for both stable and
unstable systems, where $N$ is the number of trajectories, when the initial
state of the system has zero mean (which is a common assumption in the existing
literature). We further generalize our result to the case where the initial
state has non-zero mean. We show that one can adjust the length of the
trajectories to achieve a learning rate of
$\mathcal{O}(\sqrt{\frac{\log{N}}{N})}$ for strictly stable systems and a
learning rate of $\mathcal{O}(\frac{(\log{N})^d}{\sqrt{N}})$ for marginally
stable systems, where $d$ is some constant.


title:
A Dearth of Close-In Stellar Companions to M-dwarf TESS Objects of
  Interest

abstract:
  TESS has proven to be a powerful resource for finding planets, including
those that orbit the most prevalent stars in our galaxy: the M dwarfs.
Identification of stellar companions (both bound and unbound) has become a
standard component of the transiting planet confirmation process in order to
assess the level of light curve dilution and the possibility of the target
being a false positive. Studies of stellar companions have also enabled
investigations into stellar multiplicity in planet-hosting systems, which has
wide-ranging implications for both exoplanet detection and characterization, as
well as for the formation and evolution of planetary systems. Speckle and AO
imaging are some of the most efficient and effective tools for revealing
close-in stellar companions; we therefore present observations of 58 M-dwarf
TOIs obtained using a suite of speckle imagers at the 3.5-m WIYN telescope, the
4.3-m Lowell Discovery Telescope, and the 8.1-m Gemini North and South
telescopes. These observations, as well as near-infrared adaptive optics images
obtained for a subset (14) of these TOIs, revealed only two close-in stellar
companions. Upon surveying the literature, and cross-matching our sample with
Gaia, SUPERWIDE, and the catalog from El-Badry et al. (2021), we reveal an
additional 15 widely-separated common proper motion companions. We also
evaluate the potential for undetected close-in companions. Taking into
consideration the sensitivity of the observations, our findings suggest that
the orbital period distribution of stellar companions to planet-hosting M
dwarfs is shifted to longer periods compared to the expected distribution for
field M dwarfs.


title:
Poisson equation on Wasserstein space and diffusion approximations for
  McKean-Vlasov equation

abstract:
  We consider the fully-coupled McKean-Vlasov equation with multi-time-scale
potentials, and all the coefficients depend on the distributions of both the
slow component and the fast motion. By studying the smoothness of the solution
of the non-linear Poisson equation on Wasserstein space, we derive the
asymptotic limit as well as the quantitative error estimate of the convergence
for the slow process. Extra homogenized drift term containing derivative in the
measure argument of the solution of the Poisson equation appears in the limit,
which seems to be new and is unique for systems involving the fast
distribution.


title:
Quantum Invariants of Links and 3-Manifolds with Boundary defined via
  Virtual Links: Calculation of some examples

abstract:
  In the prequel of this paper, Kauffman and Ogasa introduced new topological
quantum invariants of compact oriented 3-manifolds with boundary where the
boundary is a disjoint union of two identical surfaces. The invariants are
constructed via surgery on manifolds of the form $F \times I$ where $I$ denotes
the unit interval. Since virtual knots and links are represented as links in
such thickened surfaces, we are able also to construct invariants in terms of
virtual link diagrams (planar diagrams with virtual crossings).
  These invariants are new, nontrivial, and calculable examples of quantum
invariants of 3-manifolds with non-vacuous boundary.
  Since virtual knots and links are represented by embeddings of circles in
thickened surfaces, we refer to embeddings of circles in the 3-sphere as {\it
classical links}. Classical links are the same as virtual links that can be
represented in a thickened 2-sphere and it is a fact that classical links, up
to isotopy, embed in the collection of virtual links taken up to isotopy. We
give a new invariant of classical links in the 3-sphere in the following sense:
Consider a link $L$ in $S^3$ of two components. The complement of a tubular
neighborhood of $L$ is a manifold whose boundary consists in two copies of a
torus. Our invariants apply to this case of bounded manifold and give new
invariants of the given link of two components. Invariants of knots are also
obtained.
  In this paper we calculate the topological quantum invariants of some
examples explicitly. We conclude from our examples that our invariant is new
and strong enough to distinguish some classical knots from one another.
  We also explain how different Our topological quantum invariants of
3-manifolds with boundary and the Reshetikhin-Turaev invariants are. (See the
body for detail).


title:
DPar2: Fast and Scalable PARAFAC2 Decomposition for Irregular Dense
  Tensors

abstract:
  Given an irregular dense tensor, how can we efficiently analyze it? An
irregular tensor is a collection of matrices whose columns have the same size
and rows have different sizes from each other. PARAFAC2 decomposition is a
fundamental tool to deal with an irregular tensor in applications including
phenotype discovery and trend analysis. Although several PARAFAC2 decomposition
methods exist, their efficiency is limited for irregular dense tensors due to
the expensive computations involved with the tensor. In this paper, we propose
DPar2, a fast and scalable PARAFAC2 decomposition method for irregular dense
tensors. DPar2 achieves high efficiency by effectively compressing each slice
matrix of a given irregular tensor, careful reordering of computations with the
compression results, and exploiting the irregularity of the tensor. Extensive
experiments show that DPar2 is up to 6.0x faster than competitors on real-world
irregular tensors while achieving comparable accuracy. In addition, DPar2 is
scalable with respect to the tensor size and target rank.


title: Energy-Efficient UAV-Mounted RIS Assisted Mobile Edge Computing

abstract:
  Unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS)
have been recently applied in the field of mobile edge computing (MEC) to
improve the data exchange environment by proactively changing the wireless
channels through maneuverable location deployment and intelligent signals
reflection, respectively. Nevertheless, they may suffer from inherent
limitations in practical scenarios. UAV-mounted RIS (U-RIS), as a promising
integrated approach, can combine the advantages of UAV and RIS to break the
limit. Inspired by this, we consider a novel U-RIS assisted MEC system, where a
U-RIS is deployed to assist the communication between the ground users and an
MEC server. The joint UAV trajectory, RIS passive beamforming and MEC resource
allocation design is developed to maximize the energy efficiency (EE) of the
system. To tackle the intractable non-convex problem, we divide it into two
subproblems and solve them iteratively based on successive convex approximation
(SCA) and the Dinkelbach method. Finally we obtain a high-performance
suboptimal solution. Simulation results show that the proposed algorithm
significantly improves the energy efficiency of the MEC system.


title: Predicting the longevity of resources shared in scientific publications

abstract:
  Research has shown that most resources shared in articles (e.g., URLs to code
or data) are not kept up to date and mostly disappear from the web after some
years (Zeng et al., 2019). Little is known about the factors that differentiate
and predict the longevity of these resources. This article explores a range of
explanatory features related to the publication venue, authors, references, and
where the resource is shared. We analyze an extensive repository of
publications and, through web archival services, reconstruct how they looked at
different time points. We discover that the most important factors are related
to where and how the resource is shared, and surprisingly little is explained
by the author's reputation or prestige of the journal. By examining the places
where long-lasting resources are shared, we suggest that it is critical to
disseminate and create standards with modern technologies. Finally, we discuss
implications for reproducibility and recognizing scientific datasets as
first-class citizens.


title: Threshold functions for incidence properties in finite vector spaces

abstract:
  The main purpose of this paper is to provide threshold functions for the
events that a random subset of the points of a finite vector space has certain
properties related to point-flat incidences. Specifically, we consider the
events that there is an $\ell$-rich $m$-flat with regard to a random set of
points in $\mathbb{F}_q^n$, the event that a random set of points is an
$m$-blocking set, and the event that there is an incidence between a random set
of points and a random set of $m$-flats. One of our key ingredients is a
stronger version of a recent result obtained by Chen and Greenhill (2021).


title:
A platform for causal knowledge representation and inference in
  industrial fault diagnosis based on cubic DUCG

abstract:
  The working conditions of large-scale industrial systems are very complex.
Once a failure occurs, it will affect industrial production, cause property
damage, and even endanger the workers' lives. Therefore, it is important to
control the operation of the system to accurately grasp the operation status of
the system and find out the failure in time. The occurrence of system failure
is a gradual process, and the occurrence of the current system failure may
depend on the previous state of the system, which is sequential. The fault
diagnosis technology based on time series can monitor the operating status of
the system in real-time, detect the abnormal operation of the system within the
allowable time interval, diagnose the root cause of the fault and predict the
status trend. In order to guide the technical personnel to troubleshoot and
solve related faults, in this paper, an industrial fault diagnosis system is
implemented based on the cubic DUCG theory. The diagnostic model of the system
is constructed based on expert knowledge and experience. At the same time, it
can perform real-time fault diagnosis based on time sequence, which solves the
problem of fault diagnosis of industrial systems without sample data.


title:
When Accuracy Meets Privacy: Two-Stage Federated Transfer Learning
  Framework in Classification of Medical Images on Limited Data: A COVID-19
  Case Study

abstract:
  COVID-19 pandemic has spread rapidly and caused a shortage of global medical
resources. The efficiency of COVID-19 diagnosis has become highly significant.
As deep learning and convolutional neural network (CNN) has been widely
utilized and been verified in analyzing medical images, it has become a
powerful tool for computer-assisted diagnosis. However, there are two most
significant challenges in medical image classification with the help of deep
learning and neural networks, one of them is the difficulty of acquiring enough
samples, which may lead to model overfitting. Privacy concerns mainly bring the
other challenge since medical-related records are often deemed patients'
private information and protected by laws such as GDPR and HIPPA. Federated
learning can ensure the model training is decentralized on different devices
and no data is shared among them, which guarantees privacy. However, with data
located on different devices, the accessible data of each device could be
limited. Since transfer learning has been verified in dealing with limited data
with good performance, therefore, in this paper, We made a trial to implement
federated learning and transfer learning techniques using CNNs to classify
COVID-19 using lung CT scans. We also explored the impact of dataset
distribution at the client-side in federated learning and the number of
training epochs a model is trained. Finally, we obtained very high performance
with federated learning, demonstrating our success in leveraging accuracy and
privacy.


title:
Unsupervised Simultaneous Learning for Camera Re-Localization and Depth
  Estimation from Video

abstract:
  We present an unsupervised simultaneous learning framework for the task of
monocular camera re-localization and depth estimation from unlabeled video
sequences. Monocular camera re-localization refers to the task of estimating
the absolute camera pose from an instance image in a known environment, which
has been intensively studied for alternative localization in GPS-denied
environments. In recent works, camera re-localization methods are trained via
supervised learning from pairs of camera images and camera poses. In contrast
to previous works, we propose a completely unsupervised learning framework for
camera re-localization and depth estimation, requiring only monocular video
sequences for training. In our framework, we train two networks that estimate
the scene coordinates using directions and the depth map from each image which
are then combined to estimate the camera pose. The networks can be trained
through the minimization of loss functions based on our loop closed view
synthesis. In experiments with the 7-scenes dataset, the proposed method
outperformed the re-localization of the state-of-the-art visual SLAM,
ORB-SLAM3. Our method also outperforms state-of-the-art monocular depth
estimation in a trained environment.


title:
Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). VIII.
  New galaxy-scale lenses from the HSC SSP

abstract:
  We conduct a search for galaxy-scale strong gravitational lens systems in
Data Release 4 of the Hyper Suprime-Cam Subaru Strategic Program (HSC SSP),
consisting of data taken up to the S21A semester. We select 103191 luminous red
galaxies from the Baryon Oscillation Spectroscopic Survey (BOSS) sample that
have deep multiband imaging from the HSC SSP and use the YattaLens algorithm to
automatically identify lens candidates with blue arc-like features. The
candidates are visually inspected and graded based on their likelihood of being
a lens. We find 8 definite lenses, 28 probable lenses, and 138 possible lenses.
The new lens candidates generally have lens redshifts in the range $0.3
\lesssim z_{\mathrm{L}} \lesssim 0.9$, a key intermediate redshift range to
study the evolution of galaxy structure. Follow-up spectroscopy will confirm
these new lenses and measure source redshifts to enable detailed lens modeling.


title:
RADiCAL: Precision-timing, Ultracompact, Radiation-hard Electromagnetic
  Calorimetry

abstract:
  To address the challenges of providing high performance calorimetry in future
hadron collider experiments under conditions of high luminosity and high
radiation (FCChh environments), we are conducting R&D on advanced calorimetry
techniques suitable for such operation, based on scintillation and
wavelength-shifting technologies and photosensor (SiPM and SiPM-like)
technology. In particular, we are focusing our attention on ultra-compact
radiation hard EM calorimeters, based on modular structures (RADiCAL modules)
consisting of alternating layers of very dense absorber and scintillating
plates, read out via radiation hard wavelength shifting (WLS) solid fiber or
capillary elements to photosensors positioned either proximately or remotely,
depending upon their radiation tolerance. The RADiCAL modules provide the
capability to measure simultaneously and with high precision the position,
energy and timing of EM showers. This paper provides an overview of the
instrumentation and photosensor R&D associated with the RADiCAL program.


title: A combinatorial view of stochastic processes: White noise

abstract:
  White noise is a fundamental and fairly well understood stochastic process
that conforms the conceptual basis for many other processes, as well as for the
modeling of time series. Here we push a fresh perspective toward white noise
that, grounded on combinatorial considerations, contributes to give new
interesting insights both for modelling and theoretical purposes. To this aim,
we incorporate the ordinal pattern analysis approach which allows us to
abstract a time series as a sequence of patterns and their associated
permutations, and introduce a simple functional over permutations that
partitions them into classes encoding their level of asymmetry. We compute the
exact probability mass function (p.m.f.) of this functional over the symmetric
group of degree $n$, thus providing the description for the case of an infinite
white noise realization. This p.m.f. can be conveniently approximated by a
continuous probability density from an exponential family, the Gaussian, hence
providing natural sufficient statistics that render a convenient and simple
statistical analysis through ordinal patterns. Such analysis is exemplified on
experimental data for the spatial increments from tracks of gold nanoparticles
in 3D diffusion.


title:
Two Stage Curvature Identification with Machine Learning: Causal
  Inference with Possibly Invalid Instrumental Variables

abstract:
  Instrumental variables regression is a popular causal inference method for
endogenous treatment. A significant concern in practical applications is the
validity and strength of instrumental variables. This paper aims to perform
causal inference when all instruments are possibly invalid. To do this, we
propose a novel methodology called two stage curvature identification (TSCI)
together with a generalized concept to measure the strengths of possibly
invalid instruments: such invalid instruments can still be used for inference
in our framework. We fit the treatment model with a general machine learning
method and propose a novel bias correction method to remove the overfitting
bias from machine learning methods. Among a collection of spaces of violation
functions, we choose the best one by evaluating invalid instrumental variables'
strength. We demonstrate our proposed TSCI methodology in a large-scale
simulation study and revisit the important economics question on the effect of
education on earnings.


title:
Unveiling the electrodynamics of the first nonlinearly charged rotating
  black hole

abstract:
  After many years of efforts, the first nonlinearly charged rotating black
hole has been finally reported by Garcia-Diaz in two recent works. This is an
important result that was pending in General Relativity, since nonlinear
generalizations of the Kerr-Newman solution were not yet known. Unfortunately,
the Lagrangian supporting this configuration cannot be expressible in terms of
the standard invariants using elementary functions. In the present work we
circumvent this problem by using the formulations of nonlinear electrodynamics
in terms of mixed electromagnetic eigenvalues, introduced by Salazar,
Garcia-Diaz and Plebanski almost four decades ago. In doing so, we prove that
the underlying theory becomes fully determined, and hence the new found
nonlinearly charged stationary axisymmetric spacetimes correspond to exact
solutions of a well-defined self-gravitating nonlinear electrodynamics whose
fundamental structural functions are provided here.


title:
Dependence of NLRs sizes on [OIII] luminosity in low redshift AGN with
  double-peaked broad Balmer emission lines

abstract:
  In the manuscript, the simple but interesting results are reported on the
upper limits of NLRs sizes of a small sample of 38 low redshift ($z<0.1$) AGN
with double-peaked broad emission lines (double-peaked BLAGN), in order to
check whether the NLRs sizes in type-1 AGN and type-2 AGN obey the similar
empirical dependence on \o3~ luminosity. In order to correct the inclination
effects on projected NLRs sizes of type-1 AGN, the accretion disk origin is
commonly applied to describe the double-peaked broad H$\alpha$ leading to the
determined inclination angles of central disk-like BLRs of the 38 double-peaked
BLAGN. Then, considering the fixed SDSS fiber radius, the upper limits of NLRs
sizes of the 38 double-peaked BLAGN can be estimated. Meanwhile, the strong
linear correlation between continuum luminosity and \o3~ luminosity is applied
to confirm that the \o3~ emissions of the 38 double-peaked BLAGN are totally
covered in the SDSS fibers. Considering the reddening corrected measured \o3~
luminosity, the upper limits of NLRs sizes of the 38 double-peaked BLAGN are
within the 99.9999\% confidence interval of the expected results from the
empirical relation between NLRs sizes and \o3~ luminosity in the type-2 AGN. In
the current stage, there are no clues to challenge the Unified model of AGN,
through the space properties of NLRs.


title:
A New Model for Massively Parallel Computation Considering both
  Communication and IO Cost

abstract:
  In the research area of parallel computation, the communication cost has been
extensively studied, while the IO cost has been neglected. For big data
computation, the assumption that the data fits in main memory no longer holds,
and external memory must be used. Therefore, it is necessary to bring the IO
cost into the parallel computation model. In this paper, we propose the first
parallel computation model which takes IO cost as well as non-uniform
communication cost into consideration. Based on the new model, we raise several
new problems which aim to minimize the IO and communication cost on the new
model. We prove the hardness of these new problems, then design and analyze the
approximate algorithms for solving them.


title:
Writable spin wave nanochannels in an artificial-spin-ice-mediated
  ferromagnetic thin film

abstract:
  Magnonics, which employs spin-waves to transmit and process information, is a
promising venue for low-power data processing. One of the major challenges is
the local control of the spin-wave propagation path. Here, we introduce the
concept of writable magnonics by taking advantage of the highly flexible
reconfigurability and rewritability of artificial spin ice systems. Using
micromagnetic simulations, we show that globally switchable spin-wave
propagation and the locally writable spin-wave nanochannels can be realized in
a ferromagnetic thin film underlying an artificial pinwheel spin ice. The
rewritable magnonics enabled by reconfigurable spin wave nanochannels provides
a unique setting to design programmable magnonic circuits and logic devices for
ultra-low power applications.


title:
Disentangleing Content and Fine-grained Prosody Information via Hybrid
  ASR Bottleneck Features for Voice Conversion

abstract:
  Non-parallel data voice conversion (VC) have achieved considerable
breakthroughs recently through introducing bottleneck features (BNFs) extracted
by the automatic speech recognition(ASR) model. However, selection of BNFs have
a significant impact on VC result. For example, when extracting BNFs from ASR
trained with Cross Entropy loss (CE-BNFs) and feeding into neural network to
train a VC system, the timbre similarity of converted speech is significantly
degraded. If BNFs are extracted from ASR trained using Connectionist Temporal
Classification loss (CTC-BNFs), the naturalness of the converted speech may
decrease. This phenomenon is caused by the difference of information contained
in BNFs. In this paper, we proposed an any-to-one VC method using hybrid
bottleneck features extracted from CTC-BNFs and CE-BNFs to complement each
other advantages. Gradient reversal layer and instance normalization were used
to extract prosody information from CE-BNFs and content information from
CTC-BNFs. Auto-regressive decoder and Hifi-GAN vocoder were used to generate
high-quality waveform. Experimental results show that our proposed method
achieves higher similarity, naturalness, quality than baseline method and
reveals the differences between the information contained in CE-BNFs and
CTC-BNFs as well as the influence they have on the converted speech.


title:
Towards Efficient and Elastic Visual Question Answering with Doubly
  Slimmable Transformer

abstract:
  Transformer-based approaches have shown great success in visual question
answering (VQA). However, they usually require deep and wide models to
guarantee good performance, making it difficult to deploy on
capacity-restricted platforms. It is a challenging yet valuable task to design
an elastic VQA model that supports adaptive pruning at runtime to meet the
efficiency constraints of diverse platforms. In this paper, we present the
Doubly Slimmable Transformer (DST), a general framework that can be seamlessly
integrated into arbitrary Transformer-based VQA models to train one single
model once and obtain various slimmed submodels of different widths and depths.
Taking two typical Transformer-based VQA approaches, i.e., MCAN and UNITER, as
the reference models, the obtained slimmable MCAN_DST and UNITER_DST models
outperform the state-of-the-art methods trained independently on two benchmark
datasets. In particular, one slimmed MCAN_DST submodel achieves a comparable
accuracy on VQA-v2, while being 0.38x smaller in model size and having 0.27x
fewer FLOPs than the reference MCAN model. The smallest MCAN_DST submodel has
9M parameters and 0.16G FLOPs in the inference stage, making it possible to be
deployed on edge devices.


title: Revisiting the Effects of Leakage on Dependency Parsing

abstract:
  Recent work by S{\o}gaard (2020) showed that, treebank size aside, overlap
between training and test graphs (termed leakage) explains more of the observed
variation in dependency parsing performance than other explanations. In this
work we revisit this claim, testing it on more models and languages. We find
that it only holds for zero-shot cross-lingual settings. We then propose a more
fine-grained measure of such leakage which, unlike the original measure, not
only explains but also correlates with observed performance variation. Code and
data are available here: https://github.com/miriamwanner/reu-nlp-project


title: ViT-FOD: A Vision Transformer based Fine-grained Object Discriminator

abstract:
  Recently, several Vision Transformer (ViT) based methods have been proposed
for Fine-Grained Visual Classification (FGVC).These methods significantly
surpass existing CNN-based ones, demonstrating the effectiveness of ViT in FGVC
tasks.However, there are some limitations when applying ViT directly to
FGVC.First, ViT needs to split images into patches and calculate the attention
of every pair, which may result in heavy redundant calculation and unsatisfying
performance when handling fine-grained images with complex background and small
objects.Second, a standard ViT only utilizes the class token in the final layer
for classification, which is not enough to extract comprehensive fine-grained
information. To address these issues, we propose a novel ViT based fine-grained
object discriminator for FGVC tasks, ViT-FOD for short. Specifically, besides a
ViT backbone, it further introduces three novel components, i.e, Attention
Patch Combination (APC), Critical Regions Filter (CRF), and Complementary
Tokens Integration (CTI). Thereinto, APC pieces informative patches from two
images to generate a new image so that the redundant calculation can be
reduced. CRF emphasizes tokens corresponding to discriminative regions to
generate a new class token for subtle feature learning. To extract
comprehensive information, CTI integrates complementary information captured by
class tokens in different ViT layers. We conduct comprehensive experiments on
widely used datasets and the results demonstrate that ViT-FOD is able to
achieve state-of-the-art performance.


title: Continual Learning and Private Unlearning

abstract:
  As intelligent agents become autonomous over longer periods of time, they may
eventually become lifelong counterparts to specific people. If so, it may be
common for a user to want the agent to master a task temporarily but later on
to forget the task due to privacy concerns. However enabling an agent to
\emph{forget privately} what the user specified without degrading the rest of
the learned knowledge is a challenging problem. With the aim of addressing this
challenge, this paper formalizes this continual learning and private unlearning
(CLPU) problem. The paper further introduces a straightforward but exactly
private solution, CLPU-DER++, as the first step towards solving the CLPU
problem, along with a set of carefully designed benchmark problems to evaluate
the effectiveness of the proposed solution.


title: Random Forest Regression for continuous affect using Facial Action Units

abstract:
  In this paper we describe our approach to the arousal and valence track of
the 3rd Workshop and Competition on Affective Behavior Analysis in-the-wild
(ABAW). We extracted facial features using OpenFace and used them to train a
multiple output random forest regressor. Our approach performed comparable to
the baseline approach.


title: Statistical analysis of circular-ribbon flares

abstract:
  Circular-ribbon flares (CFs) are a special type of solar flares owing to
their particular magnetic topology. In this paper, we conducted a comprehensive
statistical analysis of 134 CFs from 2011 September to 2017 June, including
four B-class, 82 C-class, 40 M-class, and eight X-class flares, respectively.
The flares were observed by the Atmospheric Imaging Assembly (AIA) on board the
Solar Dynamics Observatory (SDO) spacecraft. The physical properties of CFs are
derived, including the location, area ($A_{CF}$), equivalent radius ($r_{CF}$)
assuming a semi-spherical fan dome, lifetime ($\tau_{CF}$), and peak SXR flux
in 1$-$8 {\AA}. It is found that all CFs are located in active regions, with
the latitudes between -30$^\circ$ and 30$^\circ$. The distributions of areas
and lifetimes could be fitted with a log-normal function. There is a positive
correlation between the lifetime and area. The peak SXR flux in 1$-$8 {\AA} is
well in accord with a power-law distribution with an index of $-$1.42. For the
134 CFs, 57\% of them are accompanied by remote brightenings or ribbons. A
positive correlation exists between the total length ($L_{RB}$) and average
distance ($D_{RB}$) of remote brightenings. About 47\% and 51\% of the 134 CFs
are related to type III radio bursts and jets, respectively. The association
rates are independent of flare energies. About 38\% of CFs are related to
mini-filament eruptions, and the association rates increase with flare classes.
Only 28\% of CFs are related to CMEs, meaning that a majority of them are
confined rather than eruptive events. There is a positive correlation between
the CME speed and peak SXR flux in 1$-$8 {\AA}, and faster CMEs tend to be
wider.


title:
A possible trial beyond the conventional random phase approximation for
  the su(2)-Lipkin model including the case of non-closed shell system

abstract:
  Concerning the su(2)-Lipkin model, the calculation of the excitation energy
to the 1st excited-state gives rise to the following fact: The two results
based on the exact treatment and the conventional random phase approximation
(RPA) are in unexpected disagreement. In order to remove this discrepancy, the
conventional RPA is renewed. With the aim of this renewal, the terms, which
cannot be dealt with in the conventional one, are estimated. The basic
framework of this new form is not reorganized from that of the conventional
one. In addition, the su(2)-Lipkin model itself is also modified so as to be
applicable to the case with non-closed shell system. To this modification, one
more su(2)-algebra, which has been already proposed by the present authors, is
applied. Through the use of this algebra, the su(2)-Lipkin model is applicable
to the case with any total fermion number permitted in the model.


title: GraphCoCo: Graph Complementary Contrastive Learning

abstract:
  Graph Contrastive Learning (GCL) has shown promising performance in graph
representation learning (GRL) without the supervision of manual annotations.
GCL can generate graph-level embeddings by maximizing the Mutual Information
(MI) between different augmented views of the same graph (positive pairs).
However, we identify an obstacle that the optimization of InfoNCE loss only
concentrates on a few embeddings dimensions, limiting the distinguishability of
embeddings in downstream graph classification tasks. This paper proposes an
effective graph complementary contrastive learning approach named GraphCoCo to
tackle the above issue. Specifically, we set the embedding of the first
augmented view as the anchor embedding to localize "highlighted" dimensions
(i.e., the dimensions contribute most in similarity measurement). Then remove
these dimensions in the embeddings of the second augmented view to discover
neglected complementary representations. Therefore, the combination of anchor
and complementary embeddings significantly improves the performance in
downstream tasks. Comprehensive experiments on various benchmark datasets are
conducted to demonstrate the effectiveness of GraphCoCo, and the results show
that our model outperforms the state-of-the-art methods. Source code will be
made publicly available.


title:
Existence and multiplicity of solutions to Dirichlet problem for
  semilinear subelliptic equation with a free perturbation

abstract:
  This paper is concerned with existence and multiplicity results for the
semilinear subelliptic equation with free perturbation term. By using the
degenerate Rellich-Kondrachov compact embedding theorem, precise lower bound
estimates of Dirichlet eigenvalues for the finitely degenerate elliptic
operator and minimax method, we obtain the existence and multiplicity of weak
solutions for the problem.


title: A clock model for planetary conjunctions

abstract:
  This article is inspired by the great conjunction of Jupiter and Saturn on
December 21st, 2020. We will address the following questions from a
mathematical standpoint. What is a conjunction? How often do we have a
conjunction of two planets? How are great conjunctions distributed in the sky?
How long will it take for a cycle of great conjunctions to return to the same
point in the sky and time of the year? Can there be a conjunction of 3 outer
planets? How often can that happen? We will begin by setting up the geometric
framework for formulating these questions. Then we will analyze these questions
by first examining similar questions in a more mundane context: the three hands
in a clock. Even though the clock model is a simple model that ignores the more
subtle and complex elements of planets' cosmic dance, it captures the main
ideas succinctly.


title: Subjective and Objective Analysis of Streamed Gaming Videos

abstract:
  The rising popularity of online User-Generated-Content (UGC) in the form of
streamed and shared videos, has hastened the development of perceptual Video
Quality Assessment (VQA) models, which can be used to help optimize their
delivery. Gaming videos, which are a relatively new type of UGC videos, are
created when skilled gamers post videos of their gameplay. These kinds of
screenshots of UGC gameplay videos have become extremely popular on major
streaming platforms like YouTube and Twitch. Synthetically-generated gaming
content presents challenges to existing VQA algorithms, including those based
on natural scene/video statistics models. Synthetically generated gaming
content presents different statistical behavior than naturalistic videos. A
number of studies have been directed towards understanding the perceptual
characteristics of professionally generated gaming videos arising in gaming
video streaming, online gaming, and cloud gaming. However, little work has been
done on understanding the quality of UGC gaming videos, and how it can be
characterized and predicted. Towards boosting the progress of gaming video VQA
model development, we conducted a comprehensive study of subjective and
objective VQA models on UGC gaming videos. To do this, we created a novel UGC
gaming video resource, called the LIVE-YouTube Gaming video quality
(LIVE-YT-Gaming) database, comprised of 600 real UGC gaming videos. We
conducted a subjective human study on this data, yielding 18,600 human quality
ratings recorded by 61 human subjects. We also evaluated a number of
state-of-the-art (SOTA) VQA models on the new database, including a new one,
called GAME-VQP, based on both natural video statistics and CNN-learned
features. To help support work in this field, we are making the new
LIVE-YT-Gaming Database, publicly available through the link:
https://live.ece.utexas.edu/research/LIVE-YT-Gaming/index.html .


title:
Possible Ongoing Merger Discovered by Photometry and Spectroscopy in the
  Field of the Galaxy Cluster PLCK G165.7+67.0

abstract:
  We present a detailed study of the Planck-selected binary galaxy cluster PLCK
G165.7+67.0 (G165; $z$=0.348). A multiband photometric catalog is generated
that incorporates new imaging from the Large Binocular Telescope/Large
Binocular Camera and Spitzer/IRAC to existing imaging. To cope with the
different image characteristics, robust methods are applied in the extraction
of the matched-aperture photometry. Photometric redshifts are estimated for 143
galaxies in the 4 arcmin$^{2}$ field of overlap covered by all these data. We
confirm that strong lensing effects yield 30 images of 11 background galaxies,
of which we contribute photometric redshift estimates for three image
multiplicities. These constraints enable the construction of a revised lens
model that confirms the bimodal structure, and from which we measure a mass of
M$_{600 kpc}$=(2.36$\pm$0.23)$\times$10$^{14}$M$_{\odot}$. In parallel, new
spectroscopy using MMT/Binospec and archival data contributes thirteen galaxies
which meet our velocity and transverse radius criteria for cluster membership.
The two cluster components have a pair-wise velocity of $\lessapprox$100
kms$^{-1}$, favoring an orientation in the plane of the sky with a transverse
velocity of 100-1700 kms$^{-1}$. At the same time, the brightest cluster galaxy
is offset in velocity from the systemic mean value. New LOFAR and VLA radio
maps uncover the BCG and a large red galaxy in the northeastern side to be
head-tail galaxies, suggesting that this component has already traversed
southwestern side and is now exiting the cluster to the northeast.


title: HMFS: Hybrid Masking for Few-Shot Segmentation

abstract:
  We study few-shot semantic segmentation that aims to segment a target object
from a query image when provided with a few annotated support images of the
target class. Several recent methods resort to a feature masking (FM)
technique, introduced by [1], to discard irrelevant feature activations to
facilitate reliable segmentation mask prediction. A fundamental limitation of
FM is the inability to preserve the fine-grained spatial details that affect
the accuracy of segmentation mask, especially for small target objects. In this
paper, we develop a simple, effective, and efficient approach to enhance
feature masking (FM). We dub the enhanced FM as hybrid masking (HM).
Specifically, we compensate for the loss of fine-grained spatial details in FM
technique by investigating and leveraging a complementary basic input masking
method [2]. To validate the effectiveness of HM, we instantiate it into a
strong baseline [3], and coin the resulting framework as HMFS. Experimental
results on three publicly available benchmarks reveal that HMFS outperforms the
current state-of-the-art methods by visible margins.


title: Sparse Instance Activation for Real-Time Instance Segmentation

abstract:
  In this paper, we propose a conceptually novel, efficient, and fully
convolutional framework for real-time instance segmentation. Previously, most
instance segmentation methods heavily rely on object detection and perform mask
prediction based on bounding boxes or dense centers. In contrast, we propose a
sparse set of instance activation maps, as a new object representation, to
highlight informative regions for each foreground object. Then instance-level
features are obtained by aggregating features according to the highlighted
regions for recognition and segmentation. Moreover, based on bipartite
matching, the instance activation maps can predict objects in a one-to-one
style, thus avoiding non-maximum suppression (NMS) in post-processing. Owing to
the simple yet effective designs with instance activation maps, SparseInst has
extremely fast inference speed and achieves 40 FPS and 37.9 AP on the COCO
benchmark, which significantly outperforms the counterparts in terms of speed
and accuracy. Code and models are available at
https://github.com/hustvl/SparseInst.


title:
Matrix Pontryagin principle approach to controllability metrics
  maximization under sparsity constraints

abstract:
  Controllability maximization problem under sparsity constraints is a node
selection problem that selects inputs that are effective for control in order
to minimize the energy to control for desired state. In this paper we discuss
the equivalence between the sparsity constrained controllability metrics
maximization problems and their convex relaxation. The proof is based on the
matrix-valued Pontryagin maximum principle applied to the controllability
Lyapunov differential equation.


title: AIMusicGuru: Music Assisted Human Pose Correction

abstract:
  Pose Estimation techniques rely on visual cues available through observations
represented in the form of pixels. But the performance is bounded by the frame
rate of the video and struggles from motion blur, occlusions, and temporal
coherence. This issue is magnified when people are interacting with objects and
instruments, for example playing the violin. Standard approaches for
postprocessing use interpolation and smoothing functions to filter noise and
fill gaps, but they cannot model highly non-linear motion. We present a method
that leverages our understanding of the high degree of a causal relationship
between the sound produced and the motion that produces them. We use the audio
signature to refine and predict accurate human body pose motion models. We
propose MAPnet (Music Assisted Pose network) for generating a fine grain motion
model from sparse input pose sequences but continuous audio. To accelerate
further research in this domain, we also open-source MAPdat, a new multi-modal
dataset of 3D violin playing motion with music. We perform a comparison of
different standard machine learning models and perform analysis on input
modalities, sampling techniques, and audio and motion features. Experiments on
MAPdat suggest multi-modal approaches like ours as a promising direction for
tasks previously approached with visual methods only. Our results show both
qualitatively and quantitatively how audio can be combined with visual
observation to help improve any pose estimation methods.


title:
TIGRIS: An Informed Sampling-based Algorithm for Informative Path
  Planning

abstract:
  Informative path planning is an important and challenging problem in robotics
that remains to be solved in a manner that allows for wide-spread
implementation and real-world practical adoption. Among various reasons for
this, one is the lack of approaches that allow for informative path planning in
high-dimensional spaces and non-trivial sensor constraints. In this work we
present a sampling-based approach that allows us to tackle the challenges of
large and high-dimensional search spaces. This is done by performing informed
sampling in the high-dimensional continuous space and incorporating potential
information gain along edges in the reward estimation. This method rapidly
generates a global path that maximizes information gain for the given path
budget constraints. We discuss the details of our implementation for an example
use case of searching for multiple objects of interest in a large search space
using a fixed-wing UAV with a forward-facing camera. We compare our approach to
a sampling-based planner baseline and demonstrate how our contributions allow
our approach to consistently out-perform the baseline by $18.0\%$. With this we
thus present a practical and generalizable informative path planning framework
that can be used for very large environments, limited budgets, and high
dimensional search spaces, such as robots with motion constraints or
high-dimensional configuration spaces.


title: LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction

abstract:
  Precise congestion prediction from a placement solution plays a crucial role
in circuit placement. This work proposes the lattice hypergraph (LH-graph), a
novel graph formulation for circuits, which preserves netlist data during the
whole learning process, and enables the congestion information propagated
geometrically and topologically. Based on the formulation, we further developed
a heterogeneous graph neural network architecture LHNN, jointing the routing
demand regression to support the congestion spot classification. LHNN
constantly achieves more than 35% improvements compared with U-nets and Pix2Pix
on the F1 score. We expect our work shall highlight essential procedures using
machine learning for congestion prediction.


title:
Heterogeneous Ground-Air Autonomous Vehicle Networking in Austere
  Environments: Practical Implementation of a Mesh Network in the DARPA
  Subterranean Challenge

abstract:
  Implementing a wireless mesh network in a real-life scenario requires a
significant systems engineering effort to turn a network concept into a
complete system. This paper presents an evaluation of a fielded system within
the DARPA Subterranean (SubT) Challenge Final Event that contributed to a 3rd
place finish. Our system included a team of air and ground robots, deployable
mesh extender nodes, and a human operator base station. This paper presents a
real-world evaluation of a stack optimized for air and ground robotic
exploration in a RF-limited environment under practical system design
limitations. Our highly customizable solution utilizes a minimum of non-free
components with form factor options suited for UAV operations and provides
insight into network operations at all levels. We present performance metrics
based on our performance in the Final Event of the DARPA Subterranean
Challenge, demonstrating the practical successes and limitations of our
approach, as well as a set of lessons learned and suggestions for future
improvements.


title: Correlation between concurrence and mutual information

abstract:
  We investigate a two-qubit system to understand the relationship between
concurrence and mutual information, where the former determines the amount of
quantum entanglement, whereas the latter is its classical residue after
performing local projective measurement. For a given ensemble of random pure
states, in which the values of concurrence are uniformly distributed, we
calculate the joint probability of concurrence and mutual information. Although
zero mutual information is the most probable in the uniform ensemble, we find
positive correlation between the classical information and concurrence. This
result suggests that destructive measurement of classical information can be
used to assess the amount of quantum information.


title: Continuous-variable quantum sensing of a dissipative reservoir

abstract:
  We propose a continuous-variable quantum sensing scheme, in which a harmonic
oscillator is employed as the probe to estimate the parameters in the spectral
density of a quantum reservoir, within a non-Markovian dynamical framework. It
is revealed that the sensing sensitivity can be effectively boosted by (i)
optimizing the weight of the momentum-position-type coupling in the whole
probe-reservoir interaction Hamiltonian, (ii) the initial quantum squeezing
resource provided by the probe, (iii) the noncanonical equilibration induced by
the non-Markovian effect, and (iv) applying an external driving field. Our
results may have some potential applications in understanding and controlling
the decoherence of dissipative continuous-variable systems.


title:
Industrial Style Transfer with Large-scale Geometric Warping and Content
  Preservation

abstract:
  We propose a novel style transfer method to quickly create a new visual
product with a nice appearance for industrial designers' reference. Given a
source product, a target product, and an art style image, our method produces a
neural warping field that warps the source shape to imitate the geometric style
of the target and a neural texture transformation network that transfers the
artistic style to the warped source product. Our model, Industrial Style
Transfer (InST), consists of large-scale geometric warping (LGW) and
interest-consistency texture transfer (ICTT). LGW aims to explore an
unsupervised transformation between the shape masks of the source and target
products for fitting large-scale shape warping. Furthermore, we introduce a
mask smoothness regularization term to prevent the abrupt changes of the
details of the source product. ICTT introduces an interest regularization term
to maintain important contents of the warped product when it is stylized by
using the art style image. Extensive experimental results demonstrate that InST
achieves state-of-the-art performance on multiple visual product design tasks,
e.g., companies' snail logos and classical bottles (please see Fig. 1). To the
best of our knowledge, we are the first to extend the neural style transfer
method to create industrial product appearances. Project page:
\ulr{https://jcyang98.github.io/InST/home.html}. Code available at:
\url{https://github.com/jcyang98/InST}.


title: Risk Consistent Multi-Class Learning from Label Proportions

abstract:
  This study addresses a multiclass learning from label proportions (MCLLP)
setting in which training instances are provided in bags and only the
proportion of each class within the bags is provided. Most existing MCLLP
methods impose bag-wise constraints on the prediction of instances or assign
them pseudo-labels; however, none of these methods have a theoretical
consistency. To solve this problem, a risk-consistent method is proposed for
instance classification using the empirical risk minimization framework, and
its estimation error bound is derived. An approximation method is proposed for
the proposed risk estimator, to apply it to large bags, by diverting the
constraints on bags in existing research. The proposed method can be applied to
any deep model or loss and is compatible with stochastic optimization.
Experiments are conducted on benchmarks to verify the effectiveness of the
proposed method.


title:
Secure Multi-Party Delegated Authorisation For Access and Sharing of
  Electronic Health Records

abstract:
  Timely sharing of electronic health records (EHR) across providers is
essential and significance in facilitating medical researches and prompt
patients' care. With sharing, it is crucial that patients can control who can
access their data and when, and guarantee the security and privacy of their
data. In current literature, various system models, cryptographic techniques
and access control mechanisms are proposed which requires patient's consent
before sharing. However, they mostly focus on patient is available to authorize
the access of the EHR upon requested. This is impractical given that the
patient may not always be in a good state to provide this authorization, eg,
being unconscious and requires immediate medical attention. To address this
gap, this paper proposes an efficient and secure protocol for the
pre-delegation of authorization to multi-party for the access of the EHR when
patient is unavailable to do so. The solution adopts a novel approach to
combine self-sovereign identity concepts and framework with secure multi-party
computation to enable secure identity and authorization verification.
Theoretical analysis showed that it increased the efficiency of the protocol
and verification processes to ensure the security and privacy of patient's
data.


title: Extensions of vector bundles on the Fargues-Fontaine curve II

abstract:
  Given two arbitrary vector bundles on the Fargues-Fontaine curve, we
completely classify all vector bundles which arise as their extensions.


title:
Probing FeSi, a d-electron topological Kondo insulator candidate, with
  magnetic field, pressure, and microwaves

abstract:
  Recently, evidence for a conducting surface state below 19 K was reported for
the correlated d-electron small gap semiconductor FeSi. In the work reported
herein, the conducting surface state and the bulk phase of FeSi were probed via
electrical resistivity measurements as a function of temperature T, magnetic
field B to 60 T and pressure P to 7.6 GPa, and by means of a magnetic field
modulated microwave spectroscopy (MFMMS) technique. The properties of FeSi were
also compared to those of the Kondo insulator SmB6 to address the question of
whether FeSi is a d-electron analogue of an f-electron Kondo insulator and, in
addition, a topological Kondo insulator. The overall behavior of the
magnetoresistance MR of FeSi at temperatures above and below the onset
temperature (T_S) 19 K of the conducting surface state is similar to that of
SmB6. The two energy gaps, inferred from the resistivity data in the
semiconducting regime, increase with pressure up to about 7 GPa, followed by a
drop which coincides with a sharp suppression of T_S. This behavior is similar
to that reported for SmB6, except that the two energy gaps in SmB6 decrease
with pressure before dropping abruptly at T_S. The MFMMS measurements showed a
sharp feature at T_S (19 K) for FeSi, but no such feature was observed at T_S
4.5 K for SmB6. The absence of a feature at T_S for SmB6 may be due to
experimental issues and will be the subject of a future investigation.


title:
Bayesian Nonparametric Submodular Video Partition for Robust Anomaly
  Detection

abstract:
  Multiple-instance learning (MIL) provides an effective way to tackle the
video anomaly detection problem by modeling it as a weakly supervised problem
as the labels are usually only available at the video level while missing for
frames due to expensive labeling cost. We propose to conduct novel Bayesian
non-parametric submodular video partition (BN-SVP) to significantly improve MIL
model training that can offer a highly reliable solution for robust anomaly
detection in practical settings that include outlier segments or multiple types
of abnormal events. BN-SVP essentially performs dynamic non-parametric
hierarchical clustering with an enhanced self-transition that groups segments
in a video into temporally consistent and semantically coherent hidden states
that can be naturally interpreted as scenes. Each segment is assumed to be
generated through a non-parametric mixture process that allows variations of
segments within the same scenes to accommodate the dynamic and noisy nature of
many real-world surveillance videos. The scene and mixture component assignment
of BN-SVP also induces a pairwise similarity among segments, resulting in
non-parametric construction of a submodular set function. Integrating this
function with an MIL loss effectively exposes the model to a diverse set of
potentially positive instances to improve its training. A greedy algorithm is
developed to optimize the submodular function and support efficient model
training. Our theoretical analysis ensures a strong performance guarantee of
the proposed algorithm. The effectiveness of the proposed approach is
demonstrated over multiple real-world anomaly video datasets with robust
detection performance.


title:
Parameter estimation for ergodic linear SDEs from partial and discrete
  observations

abstract:
  We consider a problem of parameter estimation for the state space model
described by linear stochastic differential equations. We assume that an
unobservable Ornstein-Uhlenbeck process drives another observable process by
the linear stochastic differential equation, and these two processes depend on
some unknown parameters. We construct the quasi-likelihood estimator (QMLE) of
the unknown parameters and show asymptotic properties of the estimator.


title: Financial statements of companies in Norway

abstract:
  This document details a data set which contains all unconsolidated annual
financial statements of the universe of Norwegian private and public limited
liability companies. It also includes all financial statements of other company
types reported to the Norwegian authorities.


title: Steganalysis of Image with Adaptively Parametric Activation

abstract:
  Steganalysis as a method to detect whether image contains se-cret message, is
a crucial study avoiding the imperils from abus-ing steganography. The point of
steganalysis is to detect the weak embedding signals which is hardly learned by
convolution-al layer and easily suppressed. In this paper, to enhance
embed-ding signals, we study the insufficiencies of activation function,
filters and loss function from the aspects of reduce embedding signal loss and
enhance embedding signal capture ability. Adap-tive Parametric Activation
Module is designed to reserve nega-tive embedding signal. For embedding signal
capture ability enhancement, we add constraints on the high-pass filters to
im-prove residual diversity which enables the filters extracts rich embedding
signals. Besides, a loss function based on contrastive learning is applied to
overcome the limitations of cross-entropy loss by maximum inter-class distance.
It helps the network make a distinction between embedding signals and semantic
edges. We use images from BOSSbase 1.01 and make stegos by WOW and S-UNIWARD
for experiments. Compared to state-of-the-art methods, our method has a
competitive performance.


title:
Strongly correlated superconductivity with long-range spatial
  fluctuations

abstract:
  We review recent studies for superconductivity using diagrammatic extensions
of dynamical mean field theory. These approaches take into account
simultaneously both, the local correlation effect and spatial long-range
fluctuations, which are essential to describe unconventional superconductivity
in a quasi-two-dimensional plane. The results reproduce and predict the
experimental phase diagrams of strongly correlated system such as cuprates and
nickelates. Further studies reveal that the dynamical screening effect of the
pairing interaction vertex has dramatic consequences for the transition
temperature and may even support exotic mechanisms like odd-frequency pairing.
We also discuss the dimensionality of layered materials and how to interpret
the numerical results in two dimensions.


title: Multiple Emotion Descriptors Estimation at the ABAW3 Challenge

abstract:
  To describe complex emotional states, psychologists have proposed multiple
emotion descriptors: sparse descriptors like facial action units; continuous
descriptors like valence and arousal; and discrete class descriptors like
happiness and anger. According to Ekman and Friesen, 1969, facial action units
are sign vehicles that convey the emotion message, while discrete or continuous
emotion descriptors are the messages perceived and expressed by human.
  In this paper, we designed an architecture for multiple emotion descriptors
estimation in participating the ABAW3 Challenge. Based on the theory of Ekman
and Friesen, 1969, we designed distinct architectures to measure the sign
vehicles (i.e., facial action units) and the message (i.e., discrete emotions,
valence and arousal) given their different properties. The quantitative
experiments on the ABAW3 challenge dataset has shown the superior performance
of our approach over two baseline models.


title:
On the Achievable SINR in MU-MIMO Systems Operating in Time-Varying
  Rayleigh Fading

abstract:
  Minimizing the symbol error in the uplink of multi-user multiple input
multiple output systems is important, because the symbol error affects the
achieved signal-to-interference-plus-noise ratio (SINR) and thereby the
spectral efficiency of the system. Despite the vast literature available on
minimum mean squared error (MMSE) receivers, previously proposed receivers for
block fading channels do not minimize the symbol error in time-varying Rayleigh
fading channels. Specifically, we show that the true MMSE receiver structure
does not only depend on the statistics of the CSI error, but also on the
autocorrelation coefficient of the time-variant channel. It turns out that
calculating the average SINR when using the proposed receiver is highly
non-trivial. In this paper, we employ a random matrix theoretical approach,
which allows us to derive a quasi-closed form for the average SINR, which
allows to obtain analytical exact results that give valuable insights into how
the SINR depends on the number of antennas, employed pilot and data power and
the covariance of the time-varying channel. We benchmark the performance of the
proposed receiver against recently proposed receivers and find that the
proposed MMSE receiver achieves higher SINR than the previously proposed ones,
and this benefit increases with increasing autoregressive coefficient.


title:
Boundary stabilization and observation of a multi-dimensional unstable
  heat equation

abstract:
  In this paper, we consider the boundary stabilization and observation of the
multidimensional unstable heat equation. Since we consider the heat equation in
a general domain, the usual partial differential equation back-stepping method
is hard to apply to the considered problems. The unstable dynamics of the heat
equation are treated by combining the finite-dimensional spectral truncation
method and the dynamics compensation method. By introducing additional
finite-dimensional actuator/sensor dynamics, the unbounded
stabilization/observation turns to be a bounded one. As a result, the
controller/observer design becomes more easier. Both the full state feedback
stabilizer and the state observer are designed. The exponential stability of
the closed-loop and the wellposedness of the observer are obtained.


title: Keypoints Tracking via Transformer Networks

abstract:
  In this thesis, we propose a pioneering work on sparse keypoints tracking
across images using transformer networks. While deep learning-based keypoints
matching have been widely investigated using graph neural networks - and more
recently transformer networks, they remain relatively too slow to operate in
real-time and are particularly sensitive to the poor repeatability of the
keypoints detectors. In order to address these shortcomings, we propose to
study the particular case of real-time and robust keypoints tracking.
Specifically, we propose a novel architecture which ensures a fast and robust
estimation of the keypoints tracking between successive images of a video
sequence. Our method takes advantage of a recent breakthrough in computer
vision, namely, visual transformer networks. Our method consists of two
successive stages, a coarse matching followed by a fine localization of the
keypoints' correspondences prediction. Through various experiments, we
demonstrate that our approach achieves competitive results and demonstrates
high robustness against adverse conditions, such as illumination change,
occlusion and viewpoint differences.


abstract:
  Image manipulation has attracted a lot of interest due to its wide range of
applications. Prior work modifies images either from low-level manipulation,
such as image inpainting or through manual edits via paintbrushes and
scribbles, or from high-level manipulation, employing deep generative networks
to output an image conditioned on high-level semantic input. In this study, we
propose Semantic Image Manipulation with Background-guided Internal Learning
(SIMBIL), which combines high-level and low-level manipulation. Specifically,
users can edit an image at the semantic level by applying changes on a scene
graph. Then our model manipulates the image at the pixel level according to the
modified scene graph. There are two major advantages of our approach. First,
high-level manipulation of scene graphs requires less manual effort from the
user compared to manipulating raw image pixels. Second, our low-level internal
learning approach is scalable to images of various sizes without reliance on
external visual datasets for training. We outperform the state-of-the-art in a
quantitative and qualitative evaluation on the CLEVR and Visual Genome
datasets. Experiments show 8 points improvement on FID scores (CLEVR) and 27%
improvement on user evaluation (Visual Genome), demonstrating the effectiveness
of our approach.


title:
Specifying nonlocality of a pure bipartite state and analytical
  relations between measures for bipartite nonlocality and entanglement

abstract:
  For a multipartite quantum state, the maximal violation of all Bell
inequalities constitutes a measure of its nonlocality [Loubenets, J. Math.
Phys. 53, 022201 (2012)]. In the present article, for the maximal violation of
Bell inequalities by a pure bipartite state, possibly infinite-dimensional, we
derive a new upper bound expressed in terms of the Schmidt coefficients of a
pure state. This new upper bound allows us also to specify general analytical
relations between the maximal violation of Bell inequalities by a bipartite
state and such entanglement measures for this state as "negativity" and
"concurrence". To our knowledge, no any general analytical relations between
measures for bipartite nonlocality and entanglement have been reported in the
literature though specifically such relations for a general bipartite state are
important for the device independent entanglement certification and
quantification. As an example, we apply our new results to finding upper bounds
on nonlocality of bipartite coherent states intensively discussed last years in
the literature in view of their experimental implementations.


title: Extended triaxial projected shell model approach for odd-neutron nuclei

abstract:
  In an effort to elucidate the rich band structures observed in odd-neutron
systems, triaxial projected shell model approach is extended to include
three-quasineutron and five-quasiparticle configurations. This extension makes
it possible to investigate the high-spin states up to and including the second
band crossing. Detailed investigation has been performed for odd-mass Xe
isotopes with the extended basis, and it is shown that character of the band
crossing along the yrast line changes with shell filling of the 1h11/2 orbital.
Further, it is observed that the three-quasiparticle state that crosses the
ground-state configuration, leading to the normal band crossing phenomenon
along the yrast line, first crosses the {\gamma} band based on the ground-state
configuration at an earlier spin value. This crossing feature explains the
occurrence of the signature inversion observed in the {\gamma} bands for some
of the studied isotopes.


title:
Graph Neural Networks in Particle Physics: Implementations, Innovations,
  and Challenges

abstract:
  Many physical systems can be best understood as sets of discrete data with
associated relationships. Where previously these sets of data have been
formulated as series or image data to match the available machine learning
architectures, with the advent of graph neural networks (GNNs), these systems
can be learned natively as graphs. This allows a wide variety of high- and
low-level physical features to be attached to measurements and, by the same
token, a wide variety of HEP tasks to be accomplished by the same GNN
architectures. GNNs have found powerful use-cases in reconstruction, tagging,
generation and end-to-end analysis. With the wide-spread adoption of GNNs in
industry, the HEP community is well-placed to benefit from rapid improvements
in GNN latency and memory usage. However, industry use-cases are not perfectly
aligned with HEP and much work needs to be done to best match unique GNN
capabilities to unique HEP obstacles. We present here a range of these
capabilities, predictions of which are currently being well-adopted in HEP
communities, and which are still immature. We hope to capture the landscape of
graph techniques in machine learning as well as point out the most significant
gaps that are inhibiting potentially large leaps in research.


title:
Direct evaluation of progression or regression of disease burden in
  brain metastatic disease with Deep Neuroevolution

abstract:
  Purpose: A core component of advancing cancer treatment research is assessing
response to therapy. Doing so by hand, for example as per RECIST or RANO
criteria, is tedious, time-consuming, and can miss important tumor response
information; most notably, they exclude non-target lesions. We wish to assess
change in a holistic fashion that includes all lesions, obtaining simple,
informative, and automated assessments of tumor progression or regression. Due
to often low patient enrolments in clinical trials, we wish to make response
assessments with small training sets. Deep neuroevolution (DNE) can produce
radiology artificial intelligence (AI) that performs well on small training
sets. Here we use DNE for function approximation that predicts progression
versus regression of metastatic brain disease.
  Methods: We analyzed 50 pairs of MRI contrast-enhanced images as our training
set. Half of these pairs, separated in time, qualified as disease progression,
while the other 25 images constituted regression. We trained the parameters of
a relatively small CNN via mutations that consisted of random CNN weight
adjustments and mutation fitness. We then incorporated the best mutations into
the next generations CNN, repeating this process for approximately 50,000
generations. We applied the CNNs to our training set, as well as a separate
testing set with the same class balance of 25 progression and 25 regression
images.
  Results: DNE achieved monotonic convergence to 100% training set accuracy.
DNE also converged monotonically to 100% testing set accuracy.
  Conclusion: DNE can accurately classify brain-metastatic disease progression
versus regression. Future work will extend the input from 2D image slices to
full 3D volumes, and include the category of no change. We believe that an
approach such as our could ultimately provide a useful adjunct to RANO/RECIST
assessment.


title:
Passengers' choices of the first-arriving elevator induce the
  synchronization of elevators

abstract:
  The cluster motion of busy multiple elevators is considered to be one of the
synchronization phenomena of autonomous oscillators. In this study, we used
numerical simulations to investigate the coupling interaction between elevators
and explored differences in the elevators' dynamics during down peaks under
isolated and coupled conditions. The coupling interaction between elevators was
assumed to be induced by passengers set to ride the first-arriving elevator;
thus, we introduced the proportion of these passengers as a control parameter.
When we increased the inflow of passengers, the cluster motion was promoted and
the round-trip time increased. On the other hand, when we increased the control
parameter, the cluster motion was promoted while the round-trip time decreased.
To elucidate the relationship between the parameters and dynamics and clarify
the mechanism of the cluster motion, we established simple mathematical models
and demonstrated that the proportion of passengers set to ride the
first-arriving elevator affects the cluster motion of elevators. Finally, we
discussed the interaction that causes the in-phase synchronization of elevators
by introducing the phase as a function of the elevator's position and the
direction of motion.


title: Disturbance Observer-based Robust Control Barrier Functions

abstract:
  This paper develops a novel safe control design framework that integrates the
disturbance observer (DOB) and the control barrier function (CBF). Different
from previous robust CBF results that are based on the worst-case of
disturbances, this work utilizes a DOB to estimate the disturbances and
construct a CBF-based safe controller to compensate for the disturbances such
that the control performance can be significantly improved. The CBF-based safe
controllers are obtained by solving convex quadratic programs (QPs) online.
Application of the proposed control design method to Euler-Lagrange systems is
also presented. The effectiveness of the proposed method is illustrated via
numerical simulations.


title: Beyond Fixation: Dynamic Window Visual Transformer

abstract:
  Recently, a surge of interest in visual transformers is to reduce the
computational cost by limiting the calculation of self-attention to a local
window. Most current work uses a fixed single-scale window for modeling by
default, ignoring the impact of window size on model performance. However, this
may limit the modeling potential of these window-based models for multi-scale
information. In this paper, we propose a novel method, named Dynamic Window
Vision Transformer (DW-ViT). The dynamic window strategy proposed by DW-ViT
goes beyond the model that employs a fixed single window setting. To the best
of our knowledge, we are the first to use dynamic multi-scale windows to
explore the upper limit of the effect of window settings on model performance.
In DW-ViT, multi-scale information is obtained by assigning windows of
different sizes to different head groups of window multi-head self-attention.
Then, the information is dynamically fused by assigning different weights to
the multi-scale window branches. We conducted a detailed performance evaluation
on three datasets, ImageNet-1K, ADE20K, and COCO. Compared with related
state-of-the-art (SoTA) methods, DW-ViT obtains the best performance.
Specifically, compared with the current SoTA Swin Transformers
\cite{liu2021swin}, DW-ViT has achieved consistent and substantial improvements
on all three datasets with similar parameters and computational costs. In
addition, DW-ViT exhibits good scalability and can be easily inserted into any
window-based visual transformers.


title:
Local Measurement Based Robust Voltage Stability Index & Identification
  of Voltage Collapse Onset

abstract:
  This paper addresses the problem of real-time monitoring of long-term voltage
instability (LTVI) by using local field measurements. Existing local
measurement-based methods use Thevenin equivalent parameter estimation that is
sensitive to the noise in measurements. For solving this issue, we avoid the
Thevenin approach by projecting the power flow equations as circles to develop
local static-voltage stability indicator (LS-VSI) that is robust to measurement
noises. The proposed method is an attractive option for practical
implementation because of its decentralized nature, robustness to noise, and
realistic modeling. Next, we utilize LS-VSI to propose a new local dynamic -
VSI (LD-VSI) to identify the LTVI triggered by large disturbances and load tap
changer dynamics. LD-VSI can identify not only the onset of instability with an
alarm but also the voltage collapse point (VCP). Extensive numerical validation
comparing LS-VSI with existing methods is presented on IEEE 30-bus and larger
test systems like 2000-bus Texas synthetic grid to validate the robustness,
accuracy, and situational awareness feature. We also verify the LD-VSI behavior
on the Nordic power grid using PSSE dynamic simulation. When compared to
existing methods, we observe that LD-VSI is not only more robust to measurement
noise but also can identify VCP.


title:
The differences in SAPA Needs by Route, Traffic Volume and after
  COVID-19

abstract:
  This study aims to identify the differences in SAPA user interest due to each
route, traffic volumes, and after COVID-19 by the daily feedback from them and
to help develop SAPA plans. Food was the most common opinion. However, for the
route, some showed interest in other options. For the traffic volume, the
difference of interest was also shown in some heavy traffic areas, On the other
hand, the changes in customer needs after the COVID-19 disaster were less
changed.


title: Making SMART decisions in prophylaxis and treatment studies

abstract:
  The optimal prophylaxis, and treatment if the prophylaxis fails, for a
disease may be best evaluated using a sequential multiple assignment randomised
trial (SMART). A SMART is a multi-stage study that randomises a participant to
an initial treatment, observes some response to that treatment and then,
depending on their observed response, randomises the same participant to an
alternative treatment. Response adaptive randomisation may, in some settings,
improve the trial participants' outcomes and expedite trial conclusions,
compared to fixed randomisation. But 'myopic' response adaptive randomisation
strategies, blind to multistage dynamics, may also result in suboptimal
treatment assignments. We propose a 'dynamic' response adaptive randomisation
strategy based on Q-learning, an approximate dynamic programming algorithm.
Q-learning uses stage-wise statistical models and backward induction to
incorporate late-stage 'payoffs' (i.e. clinical outcomes) into early-stage
'actions' (i.e. treatments). Our real-world example consists of a COVID-19
prophylaxis and treatment SMART with qualitatively different binary endpoints
at each stage. Standard Q-learning does not work with such data because it
cannot be used for sequences of binary endpoints. Sequences of qualitatively
distinct endpoints may also require different weightings to ensure that the
design guides participants to regimens with the highest utility. We describe
how a simple decision-theoretic extension to Q-learning can be used to handle
sequential binary endpoints with distinct utilities. Using simulation we show
that, under a set of binary utilities, the 'dynamic' approach increases
expected participant utility compared to the fixed approach, sometimes
markedly, for all model parameters, whereas the 'myopic' approach can actually
decrease utility.


title: Efficient Answering of Historical What-if Queries

abstract:
  We introduce historical what-if queries, a novel type of what-if analysis
that determines the effect of a hypothetical change to the transactional
history of a database. For example, "how would revenue be affected if we would
have charged an additional $6 for shipping?" Such queries may lead to more
actionable insights than traditional what-if queries as their results can be
used to inform future actions, e.g., increasing shipping fees. We develop
efficient techniques for answering historical what-if queries, i.e.,
determining how a modified history affects the current database state. Our
techniques are based on reenactment, a replay technique for transactional
histories. We optimize this process using program and data slicing techniques
that determine which updates and what data can be excluded from reenactment
without affecting the result. Using an implementation of our techniques in
Mahif (a Middleware for Answering Historical what-IF queries) we demonstrate
their effectiveness experimentally.


title: Transformer Compressed Sensing via Global Image Tokens

abstract:
  Convolutional neural networks (CNN) have demonstrated outstanding Compressed
Sensing (CS) performance compared to traditional, hand-crafted methods.
However, they are broadly limited in terms of generalisability, inductive bias
and difficulty to model long distance relationships. Transformer neural
networks (TNN) overcome such issues by implementing an attention mechanism
designed to capture dependencies between inputs. However, high-resolution tasks
typically require vision Transformers (ViT) to decompose an image into
patch-based tokens, limiting inputs to inherently local contexts. We propose a
novel image decomposition that naturally embeds images into low-resolution
inputs. These Kaleidoscope tokens (KD) provide a mechanism for global
attention, at the same computational cost as a patch-based approach. To
showcase this development, we replace CNN components in a well-known CS-MRI
neural network with TNN blocks and demonstrate the improvements afforded by KD.
We also propose an ensemble of image tokens, which enhance overall image
quality and reduces model size. Supplementary material is available:
https://github.com/uqmarlonbran/TCS.git


title: Affinization of $q$-oscillator representations of $U_q(\mathfrak{gl}_n)$

abstract:
  We introduce a category $\widehat{\mathcal{O}}_\mathrm{osc}$ of
$q$-oscillator representations of the quantum affine algebra
$U_q(\widehat{\mathfrak{gl}}_n)$. We show that
$\widehat{\mathcal{O}}_\mathrm{osc}$ has a family of irreducible
representations, which naturally corresponds to finite-dimensional irreducible
representations of quantum groups of untwisted affine type $A$. It is done by
analyzing the spectral decomposition of the normalized $R$ matrix on the tensor
product of fundamental $q$-oscillator representations, and then applying fusion
construction. The category $\widehat{\mathcal{O}}_\mathrm{osc}$ can be viewed
as a quantum affine analogue of the semisimple tensor category generated by
unitarizable highest weight representations of $\mathfrak{gl}_{u+v}$ ($n=u+v$)
appearing in the $(\mathfrak{gl}_{u+v},\mathfrak{gl}_\ell)$-duality on a
bosonic Fock space.


title: On the Bohr's inequality for stable mappings

abstract:
  We consider the class of \emph{stable} harmonic mappings $f=h+\overline{g}$
introduced by Martin, Hernandez, and the class of \emph{stable} logharmonic
mappings $f=zh\overline{g}$ introduced by AbdulHadi, El-Hajj. We determine
Bohr's radius for the classes of stable univalent harmonic mappings, stable
convex harmonic mappings and stable univalent logharmonic mappings. We also
consider improved and refined versions of Bohr's inequality and discuss the
Bohr's Rogonsiski radius for these family of mappings.


title:
Kullback-Leibler control for discrete-time nonlinear systems on
  continuous spaces

abstract:
  Kullback-Leibler (KL) control enables efficient numerical methods for
nonlinear optimal control problems. The crucial assumption of KL control is the
full controllability of the transition distribution. However, this assumption
is often violated when the dynamics evolves in a continuous space.
Consequently, applying KL control to problems with continuous spaces requires
some approximation, which leads to the lost of the optimality. To avoid such
approximation, in this paper, we reformulate the KL control problem for
continuous spaces so that it does not require unrealistic assumptions. The key
difference between the original and reformulated KL control is that the former
measures the control effort by KL divergence between controlled and
uncontrolled transition distributions while the latter replaces the
uncontrolled transition by a noise-driven transition. We show that the
reformulated KL control admits efficient numerical algorithms like the original
one without unreasonable assumptions. Specifically, the associated value
function can be computed by using a Monte Carlo method based on its path
integral representation.


title: Multilingual CheckList: Generation and Evaluation

abstract:
  The recently proposed CheckList (Riberio et al,. 2020) approach to evaluation
of NLP systems has revealed high failure rates for basic capabilities for
multiple state-of-the-art and commercial models. However, the CheckList
creation process is manual which creates a bottleneck towards creation of
multilingual CheckLists catering 100s of languages. In this work, we explore
multiple approaches to generate and evaluate the quality of Multilingual
CheckList. We device an algorithm -- Automated Multilingual Checklist
Generation (AMCG) for automatically transferring a CheckList from a source to a
target language that relies on a reasonable machine translation system. We then
compare the CheckList generated by AMCG with CheckLists generated with
different levels of human intervention. Through in-depth crosslingual
experiments between English and Hindi, and broad multilingual experiments
spanning 11 languages, we show that the automatic approach can provide accurate
estimates of failure rates of a model across capabilities, as would a
human-verified CheckList, and better than CheckLists generated by humans from
scratch.


title: COSE$\nu$: A Collective Oscillation Simulation Engine for Neutrinos

abstract:
  We introduce the implementation details of the simulation code \cosenu, which
numerically solves a set of non-linear partial differential equations that
govern the dynamics of neutrino collective flavor conversions. We
systematically provide the details of both the finite difference method
supported by Kreiss-Oliger dissipation and the finite volume method with
seventh order weighted essentially non-oscillatory scheme. To ensure the
reliability of the code, we perform the comparison of the simulation results
with theoretically obtainable solutions. In order to understand and
characterize the error accumulation behavior of the implementations when
neutrino self-interactions are switched on, we also analyze the evolution of
the deviation of the conserved quantities for different values of simulation
parameters. We report the performance of our code with both CPUs and GPUs. The
public version of the \cosenu~package is available at
\url{https://github.com/COSEnu/COSEnu}.


title: Revisiting Digital Twins: Origins, Fundamentals and Practices

abstract:
  The Digital Twins (DT) has quickly become a hot topic since it was proposed.
It not only appears in all kinds of commercial propaganda, but also is widely
quoted by academic circles. However, there are misstatements and misuse of the
term DT in business and academy. This paper revisits Digital Twins and defines
it to be a more advanced system/product/service modelling and simulation
environment that combines the most modern Information Communication Technology
(ICTs) and engineering mechanisms digitization, and characterized by
system/product/service life cycle management, physically geometric
visualization, real-time sensing and measurement of system operating
conditions, predictability of system performance/safety/lifespan, complete
engineering mechanisms-based simulations. The idea of Digital Twins originates
from modelling and simulation practices of engineering informatization,
including Virtual Manufacturing (VM), Model Predictive Control (MPC), and
Building Information Model (BIM). Based on the two-element VM model, we propose
a three-element model to represent Digital Twins. Digital Twins does not have
its own unique technical characteristics; the existing practices of Digital
Twins are extensions of the engineering informatization embracing modern ICTs.
These insights clarify the origin of Digital Twins and its technical
essentials.


title: DyRep: Bootstrapping Training with Dynamic Re-parameterization

abstract:
  Structural re-parameterization (Rep) methods achieve noticeable improvements
on simple VGG-style networks. Despite the prevalence, current Rep methods
simply re-parameterize all operations into an augmented network, including
those that rarely contribute to the model's performance. As such, the price to
pay is an expensive computational overhead to manipulate these unnecessary
behaviors. To eliminate the above caveats, we aim to bootstrap the training
with minimal cost by devising a dynamic re-parameterization (DyRep) method,
which encodes Rep technique into the training process that dynamically evolves
the network structures. Concretely, our proposal adaptively finds the
operations which contribute most to the loss in the network, and applies Rep to
enhance their representational capacity. Besides, to suppress the noisy and
redundant operations introduced by Rep, we devise a de-parameterization
technique for a more compact re-parameterization. With this regard, DyRep is
more efficient than Rep since it smoothly evolves the given network instead of
constructing an over-parameterized network. Experimental results demonstrate
our effectiveness, e.g., DyRep improves the accuracy of ResNet-18 by $2.04\%$
on ImageNet and reduces $22\%$ runtime over the baseline. Code is available at:
https://github.com/hunto/DyRep.


title:
Fluid-particle interaction regimes during the evolution of turbidity
  currents from a coupled LES/DEM model

abstract:
  In this paper, fluid-particle interactions in lock-exchange turbidity
currents (TCs) over a flat bed are investigated using a model combining LES and
DEM (Large-Eddy Simulation and Discrete Element Method). The reliability of
this model is demonstrated via comparing the numerical solutions with
measurements of the front positions, fluid velocity profile, and particle
concentration profile of lock-exchange TCs. The following physical
understandings are obtained. The vorticity field plays an important role for
the current evolution by affecting the fluid lift force (i.e., in the direction
normal to the fluid-particle slip velocity) acting on the particles. At the
very beginning, a longitudinal positive lift force due to strong positive
vorticity promotes longitudinal particle transport. Afterwards, the
longitudinal lift force decreases and eventually becomes negative, with a
magnitude that even exceeds that of the positive longitudinal drag force,
because more and more of the settling particles are affected by the negative
vorticity near the bottom wall caused by surface friction. Interestingly, in
spite of the complex behavior of the fluid-particle interaction forces and
their role in TC evolution, only a very small fraction of the initial particle
gravitational potential energy is actually transformed into TC kinetic energy
(both particle and fluid).


title:
RNNPose: Recurrent 6-DoF Object Pose Refinement with Robust
  Correspondence Field Estimation and Pose Optimization

abstract:
  6-DoF object pose estimation from a monocular image is challenging, and a
post-refinement procedure is generally needed for high-precision estimation. In
this paper, we propose a framework based on a recurrent neural network (RNN)
for object pose refinement, which is robust to erroneous initial poses and
occlusions. During the recurrent iterations, object pose refinement is
formulated as a non-linear least squares problem based on the estimated
correspondence field (between a rendered image and the observed image). The
problem is then solved by a differentiable Levenberg-Marquardt (LM) algorithm
enabling end-to-end training. The correspondence field estimation and pose
refinement are conducted alternatively in each iteration to recover the object
poses. Furthermore, to improve the robustness to occlusion, we introduce a
consistency-check mechanism based on the learned descriptors of the 3D model
and observed 2D images, which downweights the unreliable correspondences during
pose optimization. Extensive experiments on LINEMOD, Occlusion-LINEMOD, and
YCB-Video datasets validate the effectiveness of our method and demonstrate
state-of-the-art performance.


title:
Application of deep learning in top pair and single top quark production
  at the LHC

abstract:
  We demonstrate the performance of a very efficient tagger applies on
hadronically decaying top quark pairs as signal based on deep neural network
algorithms and compares with the QCD multi-jet background events. A significant
enhancement of performance in boosted top quark events is observed with our
limited computing resources. We also compare modern machine learning approaches
and perform a multivariate analysis of boosted top-pair as well as single top
quark production through weak interaction at $\sqrt{s}=$14 TeV proton-proton
Collider. The most relevant known background processes are incorporated.
Through the techniques of Boosted Decision Tree (BDT), likelihood and Multlayer
Perceptron (MLP) the analysis is trained to observe the performance in
comparison with the conventional cut based and count approach.


title: Intrinsic Bias Identification on Medical Image Datasets

abstract:
  Machine learning based medical image analysis highly depends on datasets.
Biases in the dataset can be learned by the model and degrade the
generalizability of the applications. There are studies on debiased models.
However, scientists and practitioners are difficult to identify implicit biases
in the datasets, which causes lack of reliable unbias test datasets to valid
models. To tackle this issue, we first define the data intrinsic bias
attribute, and then propose a novel bias identification framework for medical
image datasets. The framework contains two major components, KlotskiNet and
Bias Discriminant Direction Analysis(bdda), where KlostkiNet is to build the
mapping which makes backgrounds to distinguish positive and negative samples
and bdda provides a theoretical solution on determining bias attributes.
Experimental results on three datasets show the effectiveness of the bias
attributes discovered by the framework.


title: Weakly-Supervised End-to-End CAD Retrieval to Scan Objects

abstract:
  CAD model retrieval to real-world scene observations has shown strong promise
as a basis for 3D perception of objects and a clean, lightweight mesh-based
scene representation; however, current approaches to retrieve CAD models to a
query scan rely on expensive manual annotations of 1:1 associations of CAD-scan
objects, which typically contain strong lower-level geometric differences. We
thus propose a new weakly-supervised approach to retrieve semantically and
structurally similar CAD models to a query 3D scanned scene without requiring
any CAD-scan associations, and only object detection information as oriented
bounding boxes. Our approach leverages a fully-differentiable top-$k$ retrieval
layer, enabling end-to-end training guided by geometric and perceptual
similarity of the top retrieved CAD models to the scan queries. We demonstrate
that our weakly-supervised approach can outperform fully-supervised retrieval
methods on challenging real-world ScanNet scans, and maintain robustness for
unseen class categories, achieving significantly improved performance over
fully-supervised state of the art in zero-shot CAD retrieval.