title:
UNMAS: Multi-Agent Reinforcement Learning for Unshaped Cooperative
  Scenarios

abstract:
  Multi-agent reinforcement learning methods such as VDN, QMIX, and QTRAN that
adopt centralized training with decentralized execution (CTDE) framework have
shown promising results in cooperation and competition. However, in some
multi-agent scenarios, the number of agents and the size of action set actually
vary over time. We call these unshaped scenarios, and the methods mentioned
above fail in performing satisfyingly. In this paper, we propose a new method
called Unshaped Networks for Multi-Agent Systems (UNMAS) that adapts to the
number and size changes in multi-agent systems. We propose the self-weighting
mixing network to factorize the joint action-value. Its adaption to the change
in agent number is attributed to the nonlinear mapping from each-agent Q value
to the joint action-value with individual weights. Besides, in order to address
the change in action set, each agent constructs an individual action-value
network that is composed of two streams to evaluate the constant
environment-oriented subset and the varying unit-oriented subset. We evaluate
UNMAS on various StarCraft II micro-management scenarios and compare the
results with several state-of-the-art MARL algorithms. The superiority of UNMAS
is demonstrated by its highest winning rates especially on the most difficult
scenario 3s5z_vs_3s6z. The agents learn to perform effectively cooperative
behaviors while other MARL algorithms fail in. Animated demonstrations and
source code are provided in https://sites.google.com/view/unmas.


title: Structured Local Radiance Fields for Human Avatar Modeling

abstract:
  It is extremely challenging to create an animatable clothed human avatar from
RGB videos, especially for loose clothes due to the difficulties in motion
modeling. To address this problem, we introduce a novel representation on the
basis of recent neural scene rendering techniques. The core of our
representation is a set of structured local radiance fields, which are anchored
to the pre-defined nodes sampled on a statistical human body template. These
local radiance fields not only leverage the flexibility of implicit
representation in shape and appearance modeling, but also factorize cloth
deformations into skeleton motions, node residual translations and the dynamic
detail variations inside each individual radiance field. To learn our
representation from RGB data and facilitate pose generalization, we propose to
learn the node translations and the detail variations in a conditional
generative latent space. Overall, our method enables automatic construction of
animatable human avatars for various types of clothes without the need for
scanning subject-specific templates, and can generate realistic images with
dynamic details for novel poses. Experiment show that our method outperforms
state-of-the-art methods both qualitatively and quantitatively.


title:
Multilayer patent citation networks: A comprehensive analytical
  framework for studying explicit technological relationships

abstract:
  The use of patent citation networks as research tools is becoming
increasingly commonplace in the field of innovation studies. However, these
networks rarely consider the contexts in which these citations are generated
and are generally restricted to a single jurisdiction. Here, we propose and
explore the use of a multilayer network framework that can naturally
incorporate citation metadata and stretch across jurisdictions, allowing for a
complete view of the global technological landscape that is accessible through
patent data. Taking a conservative approach that links citation network layers
through triadic patent families, we first observe that these layers contain
complementary, rather than redundant, information about technological
relationships. To probe the nature of this complementarity, we extract network
communities from both the multilayer network and analogous single-layer
networks, then directly compare their technological composition with
established technological similarity networks. We find that while technologies
are more splintered across communities in the multilayer case, the extracted
communities match much more closely the established networks. We conclude that
by capturing citation context, a multilayer representation of patent citation
networks is, conceptually and empirically, better able to capture the
significant nuance that exists in real technological relationships when
compared to traditional, single-layer approaches. We suggest future avenues of
research that take advantage of novel computational tools designed for use with
multilayer networks.


title: Instability for rank one factors of product actions

abstract:
  We provide a counterexample to a standard interpretation of the
Katok-Spatzier conjecture, and pose questions which may serve as reasonable
replacements.


title:
DNN-Driven Compressive Offloading for Edge-Assisted Semantic Video
  Segmentation

abstract:
  Deep learning has shown impressive performance in semantic segmentation, but
it is still unaffordable for resource-constrained mobile devices. While
offloading computation tasks is promising, the high traffic demands overwhelm
the limited bandwidth. Existing compression algorithms are not fit for semantic
segmentation, as the lack of obvious and concentrated regions of interest
(RoIs) forces the adoption of uniform compression strategies, leading to low
compression ratios or accuracy. This paper introduces STAC, a DNN-driven
compression scheme tailored for edge-assisted semantic video segmentation. STAC
is the first to exploit DNN's gradients as spatial sensitivity metrics for
spatial adaptive compression and achieves superior compression ratio and
accuracy. Yet, it is challenging to adapt this content-customized compression
to videos. Practical issues include varying spatial sensitivity and huge
bandwidth consumption for compression strategy feedback and offloading. We
tackle these issues through a spatiotemporal adaptive scheme, which (1) takes
partial strategy generation operations offline to reduce communication load,
and (2) propagates compression strategies and segmentation results across
frames through dense optical flow, and adaptively offloads keyframes to
accommodate video content. We implement STAC on a commodity mobile device.
Experiments show that STAC can save up to 20.95% of bandwidth without losing
accuracy, compared to the state-of-the-art algorithm.


title:
Leveraging Clinically Relevant Biometric Constraints To Supervise A Deep
  Learning Model For The Accurate Caliper Placement To Obtain Sonographic
  Measurements Of The Fetal Brain

abstract:
  Multiple studies have demonstrated that obtaining standardized fetal brain
biometry from mid-trimester ultrasonography (USG) examination is key for the
reliable assessment of fetal neurodevelopment and the screening of central
nervous system (CNS) anomalies. Obtaining these measurements is highly
subjective, expertise-driven, and requires years of training experience,
limiting quality prenatal care for all pregnant mothers. In this study, we
propose a deep learning (DL) approach to compute 3 key fetal brain biometry
from the 2D USG images of the transcerebellar plane (TC) through the accurate
and automated caliper placement (2 per biometry) by modeling it as a landmark
detection problem. We leveraged clinically relevant biometric constraints
(relationship between caliper points) and domain-relevant data augmentation to
improve the accuracy of a U-Net DL model (trained/tested on: 596 images, 473
subjects/143 images, 143 subjects). We performed multiple experiments
demonstrating the effect of the DL backbone, data augmentation,
generalizability and benchmarked against a recent state-of-the-art approach
through extensive clinical validation (DL vs. 7 experienced clinicians). For
all cases, the mean errors in the placement of the individual caliper points
and the computed biometry were comparable to error rates among clinicians. The
clinical translation of the proposed framework can assist novice users from
low-resource settings in the reliable and standardized assessment of fetal
brain sonograms.


title:
Closed $k$-Schur Katalan functions as $K$-homology Schubert
  representatives of the affine Grassmannian

abstract:
  Recently, Blasiak-Morse-Seelinger introduced symmetric functions called
Katalan functions, and proved that the $K$-theoretic $k$-Schur functions due to
Lam-Schilling-Shimozono form a subfamily of the Katalan functions. They
conjectured that another subfamily of Katalan functions called the closed
$k$-Schur Katalan functions are identified with the Schubert structure sheaves
in the $K$-homology of the affine Grassmannian. The main result is a proof of
the conjecture.
  We also study a $K$-theoretic Peterson isomorphism that Ikeda, Iwao, and
Maeno constructed, in a non-geometric manner, based on the unipotent solution
of the relativistic Toda lattice of Ruijsenaars. We prove that the map sends a
Schubert class of the quantum $K$-theory ring of the flag variety to a closed
$K$-$k$-Schur Katalan function up to an explicit factor related to a
translation element with respect to an anti-dominant coroot. In fact, we prove
the above map coincides with a map whose existence was conjectured by Lam, Li,
Mihalcea, Shimozono, and proved by Kato, and more recently by Chow and Leung.

categories: math.CO math-ph math.AG math.KT math.MP math.RT


title:
Does Coding in Pythonic Zen Peak Performance? Preliminary Experiments of
  Nine Pythonic Idioms at Scale

abstract:
  In the field of data science, and for academics in general, the Python
programming language is a popular choice, mainly because of its libraries for
storing, manipulating, and gaining insight from data. Evidence includes the
versatile set of machine learning, data visualization, and manipulation
packages used for the ever-growing size of available data. The Zen of Python is
a set of guiding design principles that developers use to write acceptable and
elegant Python code. Most principles revolve around simplicity. However, as the
need to compute large amounts of data, performance has become a necessity for
the Python programmer. The new idea in this paper is to confirm whether writing
the Pythonic way peaks performance at scale. As a starting point, we conduct a
set of preliminary experiments to evaluate nine Pythonic code examples by
comparing the performance of both Pythonic and Non-Pythonic code snippets. Our
results reveal that writing in Pythonic idioms may save memory and time. We
show that incorporating list comprehension, generator expression, zip, and
itertools.zip_longest idioms can save up to 7,000 MB and up to 32.25 seconds.
The results open more questions on how they could be utilized in a real-world
setting. The replication package includes all scripts, and the results are
available at https://doi.org/10.5281/zenodo.5712349


title:
Optimization of Directional Landmark Deployment for Visual Observer on
  SE(3)

abstract:
  An optimization method is proposed in this paper for novel deployment of
given number of directional landmarks (location and pose) within a given region
in the 3-D task space. This new deployment technique is built on the geometric
models of both landmarks and the monocular camera. In particular, a new concept
of Multiple Coverage Probability (MCP) is defined to characterize the
probability of at least n landmarks being covered simultaneously by a camera at
a fixed position. The optimization is conducted with respect to the position
and pose of the given number of landmarks to maximize MCP through globally
exploration of the given 3-D space. By adopting the elimination genetic
algorithm, the global optimal solutions can be obtained, which are then applied
to improve the convergent performance of the visual observer on SE(3) as a
demonstration example. Both simulation and experimental results are presented
to validate the effectiveness of the proposed landmark deployment optimization
method.


title:
Equivariant Point Cloud Analysis via Learning Orientations for Message
  Passing

abstract:
  Equivariance has been a long-standing concern in various fields ranging from
computer vision to physical modeling. Most previous methods struggle with
generality, simplicity, and expressiveness -- some are designed ad hoc for
specific data types, some are too complex to be accessible, and some sacrifice
flexible transformations. In this work, we propose a novel and simple framework
to achieve equivariance for point cloud analysis based on the message passing
(graph neural network) scheme. We find the equivariant property could be
obtained by introducing an orientation for each point to decouple the relative
position for each point from the global pose of the entire point cloud.
Therefore, we extend current message passing networks with a module that learns
orientations for each point. Before aggregating information from the neighbors
of a point, the networks transforms the neighbors' coordinates based on the
point's learned orientations. We provide formal proofs to show the equivariance
of the proposed framework. Empirically, we demonstrate that our proposed method
is competitive on both point cloud analysis and physical modeling tasks. Code
is available at https://github.com/luost26/Equivariant-OrientedMP .


title:
Enhancing Neural Mathematical Reasoning by Abductive Combination with
  Symbolic Library

abstract:
  Mathematical reasoning recently has been shown as a hard challenge for neural
systems. Abilities including expression translation, logical reasoning, and
mathematics knowledge acquiring appear to be essential to overcome the
challenge. This paper demonstrates that some abilities can be achieved through
abductive combination with discrete systems that have been programmed with
human knowledge. On a mathematical reasoning dataset, we adopt the recently
proposed abductive learning framework, and propose the ABL-Sym algorithm that
combines the Transformer neural models with a symbolic mathematics library.
ABL-Sym shows 9.73% accuracy improvement on the interpolation tasks and 47.22%
accuracy improvement on the extrapolation tasks, over the state-of-the-art
approaches. Online demonstration: http://math.polixir.ai


title: Network structure and fragmentation of the Argentinean interbank markets

abstract:
  This paper studies the network structure and fragmentation of the Argentinean
interbank market. Both the unsecured (CALL) and the secured (REPO) markets are
examined, applying complex network analysis. Results indicate that, although
the secured market has less participants, its nodes are more densely connected
than in the unsecured market. The interrelationships in the unsecured market
are less stable, making its structure more volatile and vulnerable to negative
shocks. The analysis identifies two 'hidden' underlying sub-networks within the
REPO market: one based on the transactions collateralized by Treasury bonds
(REPO-T) and other based on the operations collateralized by Central Bank (CB)
securities (REPO-CB). The changes in monetary policy stance and monetary
conditions seem to have a substantially smaller impact in the former than in
the latter 'sub-market'. The connectivity levels within the REPO-T market and
its structure remain relatively unaffected by the (in some period pronounced)
swings in the other segment of the market. Hence, the REPO market shows signs
of fragmentation in its inner structure, according to the type of collateral
asset involved in the transactions, so the average REPO interest rate reflects
the interplay between these two partially fragmented sub-markets. This mixed
structure of the REPO market entails one of the main sources of differentiation
with respect to the CALL market.


title:
Holographic $\mathrm{QCD}_{3}$ and Chern-Simons theory from anisotropic
  supergravity

abstract:
  Based on the gauge-gravity duality, we study the three-dimensional QCD
($\mathrm{QCD}_{3}$) and Chern-Simons theory by constructing the anisotropic
black D3-brane solution in IIB supergravity. The deformed bulk geometry is
obtained by performing a double Wick rotation and dimension reduction which
becomes an anisotropic bubble configuration exhibiting confinement in the dual
theory. And its anisotropy also reduces to a Chern-Simons term due to the
presence of the dissolved D7-branes or the axion field in bulk. Using the
bubble geometry, we investigate the the ground-state energy density, quark
potential, entanglement entropy and the baryon vertex according to the standard
methods in the AdS/CFT dictionary. Our calculation shows that the ground-state
energy illustrates degenerate to the Chern-Simons coupling coefficient which is
in agreement with the properties of the gauge Chern-Simons theory. The behavior
of the quark tension, entanglement entropy and the embedding of the baryon
vertex further implies strong anisotropy may destroy the confinement.
Afterwards, we additionally introduce various D7-branes as flavor and
Chern-Simons branes to include the fundamental matter and effective
Chern-Simons level in the dual theory. By counting their orientation, we
finally obtain the associated topological phase in the dual theory and the
critical mass for the phase transition. Interestingly the formula of the
critical mass reveals the flavor or chiral symmetry would be restored if the
anisotropy increases greatly. As all of the analysis is consistent with
characteristics of quark-gluon plasma, we therefore believe our framework
provides a remarkable way to understand the features of Chern-Simons theory,
the strong coupled nuclear matter and its deconfinement condition with
anisotropy.


title:
The effect of $f$-$c$ hybridization on the $\gamma\rightarrow\alpha$
  phase transition of cerium studied by lanthanum doping

abstract:
  The hybridization between the localized 4$f$ level ($f$) with conduction
($c$) states in $\gamma$-Ce upon cooling has been previously revealed in single
crystalline thin films experimentally and theoretically, whereas its influence
on the $\gamma\rightarrow\alpha$ phase transition was not explicitly verified,
due to the fact that the phase transition happened in the bulk-layer, leaving
the surface in the $\gamma$ phase. Here in our work, we circumvent this issue
by investigating the effect of alloying addition of La on Ce, by means of
crystal structure, electronic transport and ARPES measurements, together with a
phenomenological periodic Anderson model and a modified Anderson impurity
model. Our current researches indicate that the weakening of $f$-$c$
hybridization is the major factor in the suppression of
$\gamma\rightarrow\alpha$ phase transition by La doping. The consistency of our
results with the effects of other rare earth and actinide alloying additions on
the $\gamma\rightarrow\alpha$ phase transition of Ce is also discussed. Our
work demonstrates the importance of the interaction of $f$ and $c$ electrons in
understanding the unconventional phase transition in Ce, which is intuitive for
further researches on other rare earth and actinide metals and alloys with
similar phase transition behaviors.


title: A nonlocal Stokes system with volume constraints

abstract:
  In this paper, we introduce a nonlocal model for linear steady Stokes system
with physical no-slip boundary condition. We use the idea of volume constraint
to enforce the no-slip boundary condition and prove that the nonlocal model is
well-posed. We also show that and the solution of the nonlocal system converges
to the solution of the original Stokes system as the nonlocality vanishes.


title: Equilibrium in Two-Player Stochastic Games with Shift-Invariant Payoffs

abstract:
  We show that every two-player stochastic game with finite state and action
sets and bounded, Borel-measurable, and shift-invariant payoffs, admits an
$\ep$-equilibrium for all $\varepsilon>0$.


title: ARCS: Accurate Rotation and Correspondence Search

abstract:
  This paper is about the old Wahba problem in its more general form, which we
call "simultaneous rotation and correspondence search". In this generalization
we need to find a rotation that best aligns two partially overlapping $3$D
point sets, of sizes $m$ and $n$ respectively with $m\geq n$. We first propose
a solver, $\texttt{ARCS}$, that i) assumes noiseless point sets in general
position, ii) requires only $2$ inliers, iii) uses $O(m\log m)$ time and $O(m)$
space, and iv) can successfully solve the problem even with, e.g., $m,n\approx
10^6$ in about $0.1$ seconds. We next robustify $\texttt{ARCS}$ to noise, for
which we approximately solve consensus maximization problems using ideas from
robust subspace learning and interval stabbing. Thirdly, we refine the
approximately found consensus set by a Riemannian subgradient descent approach
over the space of unit quaternions, which we show converges globally to an
$\varepsilon$-stationary point in $O(\varepsilon^{-4})$ iterations, or locally
to the ground-truth at a linear rate in the absence of noise. We combine these
algorithms into $\texttt{ARCS+}$, to simultaneously search for rotations and
correspondences. Experiments show that $\texttt{ARCS+}$ achieves
state-of-the-art performance on large-scale datasets with more than $10^6$
points with a $10^4$ time-speedup over alternative methods.
\url{https://github.com/liangzu/ARCS}


title:
Multi-source wideband doa estimation method by frequency focusing and
  error weighting

abstract:
  In this paper, a new multi-source wideband direction of arrival (MSW-DOA)
estimation method is proposed for the signal with non-uniform distribution
using the sub-array of uniform linear array. Different from conventional
methods, based on the free far-field model, the proposed method mainly makes
two contributions. One is that the sub-array decomposition is adopted to
improve the accuracy of MSW-DOA estimation by minimizing the weighted error,
and the other one is that the frequency focusing procedure is optimized
according to the presence probability of sound sources for reducing the
influence of the sub-bands with low signal to noise ratio (SNR). Simulation
results show that the proposed method can effectively improve the performance
of wideband DOA estimation in the case of multiple sound sources.


title: Conjugate Gradient Method for Generative Adversarial Networks

abstract:
  While the generative model has many advantages, it is not feasible to
calculate the Jensen-Shannon divergence of the density function of the data and
the density function of the model of deep neural networks; for this reason,
various alternative approaches have been developed. Generative adversarial
networks (GANs) can be used to formulate this problem as a discriminative
problem with two models, a generator and a discriminator whose learning can be
formulated in the context of game theory and the local Nash equilibrium. Since
this optimization is more difficult than minimization of a single objective
function, we propose to apply the conjugate gradient method to solve the local
Nash equilibrium problem in GANs. We give a proof and convergence analysis
under mild assumptions showing that the proposed method converges to a local
Nash equilibrium with three different learning-rate schedules including a
constant learning rate. Furthermore, we demonstrate the convergence of a simple
toy problem to a local Nash equilibrium and compare the proposed method with
other optimization methods in experiments using real-world data, finding that
the proposed method outperforms stochastic gradient descent (SGD) and momentum
SGD.


title:
Free-space optomechanical liquid probes using a twin-microbottle
  resonator

abstract:
  Cavity optomechanics provides high-performance sensor technology, and the
scheme is also applicable to liquid samples for biological and rheological
applications. However, previously reported methods using fluidic capillary
channels and liquid droplets are based on fixed-by-design structures and
therefore do not allow an active free-space approach to the samples. Here, we
demonstrate an alternate technique using a probe-based architecture with a
twin-microbottle resonator. The probe consists of two microbottle
optomechanical resonators, where one bottle (for detection) is immersed in
liquid and the other bottle (for readout) is placed in air, which retains
excellent detection performance through the high optical-Q (~10^7) of the
readout bottle. The scheme allows the detection of thermomechanical motion of
the detection bottle as well as its optomechanical sideband drive. This
technique could lead to in-situ metrology at the target location in arbitrary
media, and could be extended to ultrasensitive biochips and rheometers.


title: Laser-guided acoustic tweezers

abstract:
  Acoustic tweezers can manipulate microscopic objects and cells independently
of the optical, magnetic and electrical properties of the objects or their
medium. However, because ultrasonic waves are attenuated within few
millimeters, existing devices must synthesize intricate and powerful acoustic
fields in a very narrow footprint immediately close to the manipulated object.
Here we show that the design of microscale acoustic tweezers can be
considerably simplified by taking advantage of the nonlinear nature of the
acoustic trapping force. In our experiment, a featureless piezoelectric crystal
coated with a photoacoustic conversion layer is hit by an electric pulse and a
spatially modulated laser pulse to generate synchronized electro- and photo-
acoustic waves. Interference between these waves creates a hybrid acoustic
trapping force 30 times stronger than the laser pulse alone but with the same
spatial information. By disentangling the tradeoff between spatial resolution
and trapping force that has long held back the development of acoustic
tweezers, this field hybridization strategy opens new avenues for cell
manipulation in organ on chip and organ printing.


title:
EnCBP: A New Benchmark Dataset for Finer-Grained Cultural Background
  Prediction in English

abstract:
  While cultural backgrounds have been shown to affect linguistic expressions,
existing natural language processing (NLP) research on culture modeling is
overly coarse-grained and does not examine cultural differences among speakers
of the same language. To address this problem and augment NLP models with
cultural background features, we collect, annotate, manually validate, and
benchmark EnCBP, a finer-grained news-based cultural background prediction
dataset in English. Through language modeling (LM) evaluations and manual
analyses, we confirm that there are noticeable differences in linguistic
expressions among five English-speaking countries and across four states in the
US. Additionally, our evaluations on nine syntactic (CoNLL-2003), semantic
(PAWS-Wiki, QNLI, STS-B, and RTE), and psycholinguistic tasks (SST-5, SST-2,
Emotion, and Go-Emotions) show that, while introducing cultural background
information does not benefit the Go-Emotions task due to text domain conflicts,
it noticeably improves deep learning (DL) model performance on other tasks. Our
findings strongly support the importance of cultural background modeling to a
wide variety of NLP tasks and demonstrate the applicability of EnCBP in
culture-related research.


title:
NOC-REK: Novel Object Captioning with Retrieved Vocabulary from External
  Knowledge

abstract:
  Novel object captioning aims at describing objects absent from training data,
with the key ingredient being the provision of object vocabulary to the model.
Although existing methods heavily rely on an object detection model, we view
the detection step as vocabulary retrieval from an external knowledge in the
form of embeddings for any object's definition from Wiktionary, where we use in
the retrieval image region features learned from a transformers model. We
propose an end-to-end Novel Object Captioning with Retrieved vocabulary from
External Knowledge method (NOC-REK), which simultaneously learns vocabulary
retrieval and caption generation, successfully describing novel objects outside
of the training dataset. Furthermore, our model eliminates the requirement for
model retraining by simply updating the external knowledge whenever a novel
object appears. Our comprehensive experiments on held-out COCO and Nocaps
datasets show that our NOC-REK is considerably effective against SOTAs.


title:
MolGenSurvey: A Systematic Survey in Machine Learning Models for
  Molecule Design

abstract:
  Molecule design is a fundamental problem in molecular science and has
critical applications in a variety of areas, such as drug discovery, material
science, etc. However, due to the large searching space, it is impossible for
human experts to enumerate and test all molecules in wet-lab experiments.
Recently, with the rapid development of machine learning methods, especially
generative methods, molecule design has achieved great progress by leveraging
machine learning models to generate candidate molecules. In this paper, we
systematically review the most relevant work in machine learning models for
molecule design. We start with a brief review of the mainstream molecule
featurization and representation methods (including 1D string, 2D graph, and 3D
geometry) and general generative methods (deep generative and combinatorial
optimization methods). Then we summarize all the existing molecule design
problems into several venues according to the problem setup, including input,
output types and goals. Finally, we conclude with the open challenges and point
out future opportunities of machine learning models for molecule design in
real-world applications.


title:
Space-Time Block Coded Reconfigurable Intelligent Surface-Based Received
  Spatial Modulation

abstract:
  Reconfigurable intelligent surface (RIS) structures reflect the incident
signals by adjusting phase adaptively according to the channel condition where
doing transmission in order to increase signal quality at the receiver.
Besides, the spatial modulation (SM) technique is a possible candidate for
future energy-efficient wireless communications due to providing better
throughput, low-cost implementation and good error performance. Also,
Alamouti's space-time block coding (ASBC) is an important space and time coding
technique in terms of diversity gain and simplified ML detection. In this
paper, we proposed the RIS assisted received spatial modulation (RSM) scheme
with ASBC, namely RIS-RSM-ASBC. The termed RIS is portioned by two parts in the
proposed system model. Each one is utilized as an access point (AP) to transmit
its Alamouti coded information while reflecting passive signals to the selected
received antenna. The optimal maximum likelihood (ML) detector is designed for
the proposed RIS-RSM-ASBC scheme. Extensive computer simulations are conducted
to corroborate theoretical derivations. Results show that RIS-RSM-ASBC system
is highly reliable and provides data rate enhancement in contrast to
conventional RIS assisted transmit SM (RIS-TSM), RIS assisted transmit
quadrature SM (RIS-TQSM), RIS assisted received SM (RIS-RSM), RIS assisted
transmit space shift keying with ASBC (RIS-TSSK-ASBC) and RIS-TSSK-VBLAST
schemes.


title: A virtualized skein relation for a multivariable polynomial invariant

abstract:
  The virtual skein relation for the Jones polynomial of the virtual link
diagram was introduced by N. Kamada, S. Nakabo, and S. Satoh. H. A. Dye, L. H.
Kauffman, and Y. Miyazawa introduced multivariable polynomial, an invariant of
virtual links, which is a refinement of Jones polynomial. In this paper, we
give a skein relation for the multivariable polynomials among positive,
negative, and virtual crossings with some restrictions. We apply this relation
to study some properties of virtual links obtained by replacing a real crossing
by a virtual crossing.


title:
Strong quantum nonlocality and unextendibility without entanglement in
  $N$-partite systems with odd $N$

abstract:
  A set of orthogonal product states is strongly nonlocal if it is locally
irreducible in every bipartition, which shows the phenomenon of strong quantum
nonlocality without entanglement
[\href{https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.122.040403}{Phys.
Rev. Lett. \textbf{122}, 040403 (2019)}]. Although such phenomenon has been
shown to any three-, four-, and five-partite systems, the existence of strongly
nonlocal orthogonal product sets in multipartite systems remains unknown. In
this paper, by using a general decomposition of the $N$-dimensional hypercubes
for odd $N\geq 3$, we show strongly nonlocal orthogonal product sets in
$N$-partite systems for odd $N\geq 3$. Moreover, basing on this decomposition,
we give explicit construction of unextendible product bases in $N$-partite
systems for odd $N\geq 3$.


title: Black-box Selective Inference via Bootstrapping

abstract:
  We propose a method for selective inference after a model selection procedure
that is potentially a black box. In the conditional post-selection inference
framework, a crucial quantity in determining the post-selection distribution of
a test statistic is the probability of selecting the model conditional on the
statistic. By repeatedly running the model selection procedure on bootstrapped
datasets, we can generate training data with binary responses indicating the
selection event as well as specially designed covariates, which are then used
to learn the selection probability. We prove that the constructed confidence
intervals are asymptotically valid if we can learn the selection probability
sufficiently well around a neighborhood of the target parameter. The validity
of the proposed algorithm is verified by several examples.


title:
Is the mm/sub-mm dust polarization a robust tracer of the magnetic field
  topology in protostellar envelopes ? A model exploration

abstract:
  High resolution (sub-)millimeter polarization observations have opened a new
era in the understanding of how B-fields are organized in star forming regions,
unveiling an intricate interplay between the B-fields and the gas in
protostellar cores. However, to assess the role of the B-field in the process
of solar-type star formation, it is key to be able to understand to what extent
these polarized dust emission are good tracers of the B-field in the youngest
protostellar objects. We present a thorough investigation of the fidelity and
limitations of using dust polarized emission to map the B-field topologies in
low-mass protostars. To assess the importance of these effects, we performed
the analysis of B-field properties in 27 realizations of MHD models of
star-forming cores. Assuming a uniform population of dust grains which sizes
follow the standard MRN, we analyze the synthetic polarized dust emission maps
produced if these grains align with the local B-field thanks to B-RATs. We find
that (sub-)millimeter polarized dust emission is a robust tracer of the B-field
topologies in inner protostellar envelopes and is successful at capturing the
details of the B-field spatial distribution down to radii ~100 au. Measurements
of the los averaged B-field orientation using the polarized dust emission are
precise to < 15{\deg} in about 75 - 95% of the independent lines of sight
peering through protostellar envelopes. Large discrepancies between the
integrated B-field mean orientation and the orientation reconstructed from the
polarized dust emission are mostly observed in (i) lines of sight where the
B-field is highly disorganized and (ii) lines of sight probing large column
densities. Our analysis shows that high opacity of the thermal dust emission
and low polarization fractions could be used to avoid utilizing the small
fraction of measurements affected by large errors.


title:
Catching Both Gray and Black Swans: Open-set Supervised Anomaly
  Detection

abstract:
  Despite most existing anomaly detection studies assume the availability of
normal training samples only, a few labeled anomaly examples are often
available in many real-world applications, such as defect samples identified
during random quality inspection, lesion images confirmed by radiologists in
daily medical screening, etc. These anomaly examples provide valuable knowledge
about the application-specific abnormality, enabling significantly improved
detection of similar anomalies in some recent models. However, those anomalies
seen during training often do not illustrate every possible class of anomaly,
rendering these models ineffective in generalizing to unseen anomaly classes.
This paper tackles open-set supervised anomaly detection, in which we learn
detection models using the anomaly examples with the objective to detect both
seen anomalies (`gray swans') and unseen anomalies (`black swans'). We propose
a novel approach that learns disentangled representations of abnormalities
illustrated by seen anomalies, pseudo anomalies, and latent residual anomalies
(i.e., samples that have unusual residuals compared to the normal data in a
latent space), with the last two abnormalities designed to detect unseen
anomalies. Extensive experiments on nine real-world anomaly detection datasets
show superior performance of our model in detecting seen and unseen anomalies
under diverse settings. Code and data are available at:
https://github.com/choubo/DRA.


title: ANNA: Enhanced Language Representation for Question Answering

abstract:
  Pre-trained language models have brought significant improvements in
performance in a variety of natural language processing tasks. Most existing
models performing state-of-the-art results have shown their approaches in the
separate perspectives of data processing, pre-training tasks, neural network
modeling, or fine-tuning. In this paper, we demonstrate how the approaches
affect performance individually, and that the language model performs the best
results on a specific question answering task when those approaches are jointly
considered in pre-training models. In particular, we propose an extended
pre-training task, and a new neighbor-aware mechanism that attends neighboring
tokens more to capture the richness of context for pre-training language
modeling. Our best model achieves new state-of-the-art results of 95.7\% F1 and
90.6\% EM on SQuAD 1.1 and also outperforms existing pre-trained language
models such as RoBERTa, ALBERT, ELECTRA, and XLNet on the SQuAD 2.0 benchmark.


title: Stratified Transformer for 3D Point Cloud Segmentation

abstract:
  3D point cloud segmentation has made tremendous progress in recent years.
Most current methods focus on aggregating local features, but fail to directly
model long-range dependencies. In this paper, we propose Stratified Transformer
that is able to capture long-range contexts and demonstrates strong
generalization ability and high performance. Specifically, we first put forward
a novel key sampling strategy. For each query point, we sample nearby points
densely and distant points sparsely as its keys in a stratified way, which
enables the model to enlarge the effective receptive field and enjoy long-range
contexts at a low computational cost. Also, to combat the challenges posed by
irregular point arrangements, we propose first-layer point embedding to
aggregate local information, which facilitates convergence and boosts
performance. Besides, we adopt contextual relative position encoding to
adaptively capture position information. Finally, a memory-efficient
implementation is introduced to overcome the issue of varying point numbers in
each window. Extensive experiments demonstrate the effectiveness and
superiority of our method on S3DIS, ScanNetv2 and ShapeNetPart datasets. Code
is available at https://github.com/dvlab-research/Stratified-Transformer.


title:
Automated Progressive Learning for Efficient Training of Vision
  Transformers

abstract:
  Recent advances in vision Transformers (ViTs) have come with a voracious
appetite for computing power, high-lighting the urgent need to develop
efficient training methods for ViTs. Progressive learning, a training scheme
where the model capacity grows progressively during training, has started
showing its ability in efficient training. In this paper, we take a practical
step towards efficient training of ViTs by customizing and automating
progressive learning. First, we develop a strong manual baseline for
progressive learning of ViTs, by introducing momentum growth (MoGrow) to bridge
the gap brought by model growth. Then, we propose automated progressive
learning (AutoProg), an efficient training scheme that aims to achieve lossless
acceleration by automatically increasing the training overload on-the-fly; this
is achieved by adaptively deciding whether, where and how much should the model
grow during progressive learning. Specifically, we first relax the optimization
of the growth schedule to sub-network architecture optimization problem, then
propose one-shot estimation of the sub-network performance via an elastic
supernet. The searching overhead is reduced to minimal by recycling the
parameters of the supernet. Extensive experiments of efficient training on
ImageNet with two representative ViT models, DeiT and VOLO, demonstrate that
AutoProg can accelerate ViTs training by up to 85.1% with no performance drop.
Code: https://github.com/changlin31/AutoProg


title:
ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural
  Representations

abstract:
  Precise representations of 3D faces are beneficial to various computer vision
and graphics applications. Due to the data discretization and model linearity,
however, it remains challenging to capture accurate identity and expression
clues in current studies. This paper presents a novel 3D morphable face model,
namely ImFace, to learn a nonlinear and continuous space with implicit neural
representations. It builds two explicitly disentangled deformation fields to
model complex shapes associated with identities and expressions, respectively,
and designs an improved learning strategy to extend embeddings of expressions
to allow more diverse changes. We further introduce a Neural Blend-Field to
learn sophisticated details by adaptively blending a series of local fields. In
addition to ImFace, an effective preprocessing pipeline is proposed to address
the issue of watertight input requirement in implicit representations, enabling
them to work with common facial surfaces for the first time. Extensive
experiments are performed to demonstrate the superiority of ImFace.


title:
Statistical Inference for Heterogeneous Treatment Effects Discovered by
  Generic Machine Learning in Randomized Experiments

abstract:
  Researchers are increasingly turning to machine learning (ML) algorithms to
investigate causal heterogeneity in randomized experiments. Despite their
promise, ML algorithms may fail to accurately ascertain heterogeneous treatment
effects under practical settings with many covariates and small sample size. In
addition, the quantification of estimation uncertainty remains a challenge. We
develop a general approach to statistical inference for heterogeneous treatment
effects discovered by a generic ML algorithm. We apply the Neyman's repeated
sampling framework to a common setting, in which researchers use an ML
algorithm to estimate the conditional average treatment effect and then divide
the sample into several groups based on the magnitude of the estimated effects.
We show how to estimate the average treatment effect within each of these
groups, and construct a valid confidence interval. In addition, we develop
nonparametric tests of treatment effect homogeneity across groups, and
rank-consistency of within-group average treatment effects. The validity of our
methodology does not rely on the properties of ML algorithms because it is
solely based on the randomization of treatment assignment and random sampling
of units. Finally, we generalize our methodology to the cross-fitting procedure
by accounting for the additional uncertainty induced by the random splitting of
data.


title: Encode-in-Style: Latent-based Video Encoding using StyleGAN2

abstract:
  We propose an end-to-end facial video encoding approach that facilitates
data-efficient high-quality video re-synthesis by optimizing low-dimensional
edits of a single Identity-latent. The approach builds on StyleGAN2 image
inversion and multi-stage non-linear latent-space editing to generate videos
that are nearly comparable to input videos. It economically captures face
identity, head-pose, and complex facial motions at fine levels, and thereby
bypasses training and person modeling which tend to hamper many re-synthesis
approaches. The approach is designed with maximum data efficiency, where a
single W+ latent and 35 parameters per frame enable high-fidelity video
rendering. This pipeline can also be used for puppeteering (i.e., motion
transfer).


title: Multi-View Substructure Learning for Drug-Drug Interaction Prediction

abstract:
  Drug-drug interaction (DDI) prediction provides a drug combination strategy
for systemically effective treatment. Previous studies usually model drug
information constrained on a single view such as the drug itself, leading to
incomplete and noisy information, which limits the accuracy of DDI prediction.
In this work, we propose a novel multi- view drug substructure network for DDI
prediction (MSN-DDI), which learns chemical substructures from both the
representations of the single drug (intra-view) and the drug pair (inter-view)
simultaneously and utilizes the substructures to update the drug representation
iteratively. Comprehensive evaluations demonstrate that MSN-DDI has almost
solved DDI prediction for existing drugs by achieving a relatively improved
accuracy of 19.32% and an over 99% accuracy under the transductive setting.
More importantly, MSN-DDI exhibits better generalization ability to unseen
drugs with a relatively improved accuracy of 7.07% under more challenging
inductive scenarios. Finally, MSN-DDI improves prediction performance for
real-world DDI applications to new drugs.


title: Binding of Curvature-Inducing Proteins onto Biomembranes

abstract:
  We review the theoretical analyses and simulations of the interactions
between curvature-inducing proteins and biomembranes. Laterally isotropic
proteins induce spherical budding, whereas anisotropic proteins, such as
Bin/Amphiphysin/Rvs (BAR) superfamily proteins, induce tubulation. Both types
of proteins can sense the membrane curvature. We describe the theoretical
analyses of various transitions of protein binding accompanied by a change in
various properties, such as the number of buds, the radius of membrane tubes,
and the nematic order of anisotropic proteins. Moreover, we explain the
membrane-mediated interactions and protein assembly. Many types of membrane
shape transformations (spontaneous tubulation, formation of polyhedral
vesicles, polygonal tubes, periodic bumps, and network structures, etc.) have
been demonstrated by coarse-grained simulations. Furthermore, traveling waves
and Turing patterns under the coupling of reaction-diffusion dynamics and
membrane deformation are described.


title: A measure model for the spread of viral infections with mutations

abstract:
  Genetic variations in the COVID-19 virus are one of the main causes of the
COVID-19 pandemic outbreak in 2020 and 2021. In this article, we aim to
introduce a new type of model, a system coupled with ordinary differential
equations (ODEs), and measure differential equation (MDE), stemming from the
classical SIR model for the variants distribution. Specifically, we model the
evolution of susceptible $S$ and removed $R$ populations by ODEs and the
infected $I$ population by an MDE comprised of a probability vector field (PVF)
and a source term. In addition, the ODEs for $S$ and $R$ contain terms that are
related to the measure $I$. We establish analytically the well-posedness of the
coupled ODE-MDE system by using generalized Wasserstein distance. We give two
examples to show that the proposed ODE-MDE model coincides with the classical
SIR model in the case of constant or time-dependent


title: On the inner horizon instability of non-singular black holes

abstract:
  Regular black holes represent a conservative model in which the classical
singularity is replaced by a non-singular core without necessarily modifying
the spacetime outside the trapping horizon. Given the possible lack of
phenomenological signatures, it is crucial to study the consistency of the
model. In this short work, we review the physical mechanism leading to the
instability of the central core, arguing that non-perturbative backreaction is
non-negligible and must be taken into account to provide a meaningful
description of physical black holes.


title: REGTR: End-to-end Point Cloud Correspondences with Transformers

abstract:
  Despite recent success in incorporating learning into point cloud
registration, many works focus on learning feature descriptors and continue to
rely on nearest-neighbor feature matching and outlier filtering through RANSAC
to obtain the final set of correspondences for pose estimation. In this work,
we conjecture that attention mechanisms can replace the role of explicit
feature matching and RANSAC, and thus propose an end-to-end framework to
directly predict the final set of correspondences. We use a network
architecture consisting primarily of transformer layers containing self and
cross attentions, and train it to predict the probability each point lies in
the overlapping region and its corresponding position in the other point cloud.
The required rigid transformation can then be estimated directly from the
predicted correspondences without further post-processing. Despite its
simplicity, our approach achieves state-of-the-art performance on 3DMatch and
ModelNet benchmarks. Our source code can be found at
https://github.com/yewzijian/RegTR .


title: Certified Mergeable Replicated Data Types

abstract:
  Replicated data types (RDTs) are data structures that permit concurrent
modification of multiple, potentially geo-distributed, replicas without
coordination between them. RDTs are designed in such a way that conflicting
operations are eventually deterministically reconciled ensuring convergence.
Constructing correct RDTs remains a difficult endeavour due to the complexity
of reasoning about independently evolving states of the replicas. With the
focus on the correctness of RDTs (and rightly so), existing approaches to RDTs
are less efficient compared to their sequential counterparts in terms of time
and space complexity of local operations. This is unfortunate since RDTs are
often used in a local-first setting where the local operations far outweigh
remote communication.
  In this paper, we present Peepul, a pragmatic approach to building and
verifying efficient RDTs. To make reasoning about correctness easier, we cast
RDTs in the mould of a distributed version control system, and equip it with a
three-way merge function for reconciling conflicting versions. Further, we go
beyond just verifying convergence, and provide a methodology to verify
arbitrarily complex specifications. We develop a replication-aware simulation
relation to relate RDT specifications to their efficient purely functional
implementations. We implement Peepul as an F* library that discharges proof
obligations to an SMT solver. The verified efficient RDTs are extracted as
OCaml code and used in Irmin, a Git-like distributed database.


title:
Quantum particle creation in gravastar formation; Radiation with
  Gibbons-Hawking temperature

abstract:
  We study the quantum particle creation in a toy model of spherically
symmetric gravitational collapse whose final product is not a black hole but a
gravastar. Precedent studies revealed that even in the case of the
gravitational collapse to form a horizonless ultra-compact object, the thermal
radiation named the transient Hawking radiation is generated at the late stage
of the gravitational collapse, and a sudden stop of collapsing motion to form a
horizonless ultra-compact object causes one or two bursts of quantum particle
creation. The very different behavior of the model studied in this paper from
the precedent ones is the quantum radiation with the thermal spectrum from the
gravastar between two bursts. The temperature of the radiation is not the same
as the Hawking one determined by the gravitational mass of the system but the
Gibbons-Hawking one of the de Sitter spacetime inside the gravastar.


title: Optimistic Online Convex Optimization in Dynamic Environments

abstract:
  In this paper, we study the optimistic online convex optimization problem in
dynamic environments. Existing works have shown that Ader enjoys an
$O\left(\sqrt{\left(1+P_T\right)T}\right)$ dynamic regret upper bound, where
$T$ is the number of rounds, and $P_T$ is the path length of the reference
strategy sequence. However, Ader is not environment-adaptive. Based on the fact
that optimism provides a framework for implementing environment-adaptive, we
replace Greedy Projection (GP) and Normalized Exponentiated Subgradient (NES)
in Ader with Optimistic-GP and Optimistic-NES respectively, and name the
corresponding algorithm ONES-OGP. We also extend the doubling trick to the
adaptive trick, and introduce three characteristic terms naturally arise from
optimism, namely $M_T$, $\widetilde{M}_T$ and $V_T+1_{L^2\rho\left(\rho+2
P_T\right)\leqslant\varrho^2 V_T}D_T$, to replace the dependence of the dynamic
regret upper bound on $T$. We elaborate ONES-OGP with adaptive trick and its
subgradient variation version, all of which are environment-adaptive.


title: Faces of Directed Edge Polytopes

abstract:
  Given a finite quiver (directed graph) without loops and multiedges, the
convex hull of the column vector of the incidence matrix is called the directed
edge polytope and is an interesting example of lattice polytopes. In this
paper, we give a complete characterization of facets of the directed edge
polytope of an arbitrary finite quiver without loops and multiedges in terms of
the connectivity and the existence of a rank function. Our result can be
regarded as an extension of the result of Higashitani et al. on facets of
symmetric edge polytopes to directed edge polytopes. When the quiver in
question has a rank function, we obtain a characterization of faces of
arbitrary dimensions.


title:
Comparative Performance of Novel Nodal-to-Edge finite elements over
  Conventional Nodal element for Electromagnetic Analysis

abstract:
  In nodal based finite element method (FEM), degrees of freedom are associated
with the nodes of the element whereas, for edge FEM, degrees of freedom are
assigned to the edges of the element. Edge element is constructed based on
Whitney spaces. Nodal elements impose both tangential and normal continuity of
vector or scalar fields across interface boundaries. But in edge elements only
tangential continuity is imposed across interface boundaries, which is
consistent with electromagnetic field problems. Therefore, the required
continuities in the electromagnetic analysis are directly obtained with edge
elements, whereas in nodal elements they are attained through potential
formulations. Hence, while using edge elements, field variables are directly
calculated, but with nodal elements, post-processing is required to obtain the
field variables from the potentials. Here, we present the finite element
formulations with the edge element as well as with nodal elements. Thereafter,
we have demonstrated the relative performances of different nodal and edge
elements through a series of examples. All possible complexities like curved
boundaries, non-convex domains, sharp corners, non-homogeneous domains have
been addressed in those examples. The robustness of edge elements in predicting
the singular eigen values for the domains with sharp edges and corners is
evident in the analysis. A better coarse mesh accuracy has been observed for
all the edge elements as compared to the respective nodal elements. Edge
elements are also not susceptible to mesh distortion.


title:
Translation Consistent Semi-supervised Segmentation for 3D Medical
  Images

abstract:
  3D medical image segmentation methods have been successful, but their
dependence on large amounts of voxel-level annotated data is a disadvantage
that needs to be addressed given the high cost to obtain such annotation.
Semi-supervised learning (SSL) solve this issue by training models with a large
unlabelled and a small labelled dataset. The most successful SSL approaches are
based on consistency learning that minimises the distance between model
responses obtained from perturbed views of the unlabelled data. These
perturbations usually keep the spatial input context between views fairly
consistent, which may cause the model to learn segmentation patterns from the
spatial input contexts instead of the segmented objects. In this paper, we
introduce the Translation Consistent Co-training (TraCoCo) which is a
consistency learning SSL method that perturbs the input data views by varying
their spatial input context, allowing the model to learn segmentation patterns
from visual objects. Furthermore, we propose the replacement of the commonly
used mean squared error (MSE) semi-supervised loss by a new Cross-model
confident Binary Cross entropy (CBC) loss, which improves training convergence
and keeps the robustness to co-training pseudo-labelling mistakes. We also
extend CutMix augmentation to 3D SSL to further improve generalisation. Our
TraCoCo shows state-of-the-art results for the Left Atrium (LA) and Brain Tumor
Segmentation (BRaTS19) datasets with different backbones. Our code is available
at https://github.com/yyliu01/TraCoCo.


title: Pontryagin calculus in Riemannian geometry

abstract:
  In this contribution, we study systems with a finite number of degrees of
freedom as in robotics. A key idea is to consider the mass tensor associated to
the kinetic energy as a metric in a Riemannian configuration space. We apply
Pontryagin's framework to derive an optimal evolution of the control forces and
torques applied to the mechanical system. This equation under covariant form
uses explicitly the Riemann curvature tensor. This contribution is dedicated to
the memory of Claude Vall{\'e}e (1945-2014).


title: Self-supervised curriculum learning for speaker verification

abstract:
  Self-supervised learning is one of the emerging approaches to machine
learning today, and has been successfully applied to vision, speech and natural
processing tasks. There is a range of frameworks within self-supervised
learning literature, but the speaker recognition literature has particularly
adopted self-supervision via contrastive loss functions. Our work adapts the
DINO framework for speaker recognition, in which the model is trained without
exploiting negative utterance pairs. We introduce a curriculum learning
strategy to the self-supervised framework, which guides effective training of
speaker recognition models. In particular, we propose two curriculum strategies
where one gradually increases the number of speakers in training dataset, and
the other gradually applies augmentations to more utterances within a
mini-batch as the training proceeds. A range of experiments conducted on the
VoxCeleb1 evaluation protocol demonstrate the effectiveness of both the DINO
framework on speaker verification and our proposed curriculum learning
strategies. We report the state-of-the-art equal error rate of 4.47% with a
single-phase training.


title: Non-iterative Gaussianization

abstract:
  In this work, we propose a non-iterative Gaussian transformation strategy
based on copula function, which doesn't require some commonly seen restrictive
assumptions in the previous studies such as the elliptically symmetric
distribution assumption and the linear independent component analysis
assumption. Theoretical properties guarantee the proposed strategy can exactly
transfer any random variable vector with a continuous multivariate distribution
to a variable vector that follows a multivariate Gaussian distribution.
Simulation studies also demonstrate the outperformance of such a strategy
compared to some other methods like Box-Cox Gaussianization and radial
Gaussianization. An application for probability density estimation for image
synthesis is also shown.


title:
Distributed Finite-Sum Constrained Optimization subject to Nonlinearity
  on the Node Dynamics

abstract:
  Motivated by recent development in networking and parallel data-processing,
we consider a distributed and localized finite-sum (or fixed-sum) allocation
technique to solve resource-constrained convex optimization problems over
multi-agent networks (MANs). Such networks include (smart) agents representing
an intelligent entity capable of communication, processing, and
decision-making. In particular, we consider problems subject to practical
nonlinear constraints on the dynamics of the agents in terms of their
communications and actuation capabilities (referred to as the node dynamics),
e.g., networks of mobile robots subject to actuator saturation and quantized
communication. The considered distributed sum-preserving optimization solution
further enables adding purposeful nonlinear constraints, for example,
sign-based nonlinearities, to reach convergence in predefined-time or robust to
impulsive noise and disturbances in faulty environments. Moreover, convergence
can be achieved under minimal network connectivity requirements among the
agents; thus, the solution is applicable over dynamic networks where the
channels come and go due to the agent's mobility and limited range. This paper
discusses how various nonlinearity constraints on the optimization problem
(e.g., collaborative allocation of resources) can be addressed for different
applications via a distributed setup (over a network).


title: Asymptotic geometry and delta-points

abstract:
  We study Daugavet- and $\Delta$-points in Banach spaces. A norm one element
$x$ is a Daugavet-point (respectively a $\Delta$-point) if in every slice of
the unit ball (respectively in every slice of the unit ball containing $x$) you
can find another element of distance as close to $2$ from $x$ as desired.
  In this paper we look for criteria and properties ensuring that a norm one
element is not a Daugavet- or $\Delta$-point. We show that asymptotically
uniformly smooth spaces and reflexive asymptotically uniformly convex spaces do
not contain $\Delta$-points.
  We also show that the same conclusion holds true for the James tree space as
well as for its predual.
  Finally we prove that there exists a superreflexive Banach space with a
Daugavet- or $\Delta$-point provided there exists such a space satisfying a
weaker condition.


title:
Stable mass transfer can explain massive binary black hole mergers with
  a high spin component

abstract:
  Recent gravitational wave observations showed that binary black hole (BBH)
mergers with massive components are more likely to have high effective spins.
In the model of isolated binary evolution, BH spins mainly originate from the
angular momenta of the collapsing cores before BH formation. Both observations
and theories indicate that BHs tend to possess relatively low spins, the origin
of fast-spinning BHs remains a puzzle. We investigate an alternative process
that stable Case A mass transfer may significantly increase BH spins during the
evolution of massive BH binaries. We present detailed binary evolution
calculations and find that this process can explain observed high spins of some
massive BBH mergers under the assumption of mildly super-Eddington accretion.


title:
Acceleration of multiple precision solver for ill-conditioned algebraic
  equations with lower precision eigensolver

abstract:
  There are some types of ill-conditioned algebraic equations that have
difficulty in obtaining accurate roots and coefficients that must be expressed
with a multiple precision floating-point number. When all their roots are
simple, the problem solved via eigensolver (eigenvalue method) is
well-conditioned if the corresponding companion matrix has its small condition
number. However, directly solving them with Newton or simultaneous iteration
methods (direct iterative method for short) should be considered as
ill-conditioned because of increasing density of its root distribution.
Although a greater number of mantissa of floating-point arithmetic is necessary
in the direct iterative method than eigenvalue method, the total computational
costs cannot obviously be determined. In this study, we target Wilkinson's
example and Chebyshev quadrature problem as examples of ill-conditioned
algebraic equations, and demonstrate some concrete numerical results to prove
that the direct iterative method can perform better than standard eigensolver.


title:
Uni6D: A Unified CNN Framework without Projection Breakdown for 6D Pose
  Estimation

abstract:
  As RGB-D sensors become more affordable, using RGB-D images to obtain
high-accuracy 6D pose estimation results becomes a better option.
State-of-the-art approaches typically use different backbones to extract
features for RGB and depth images. They use a 2D CNN for RGB images and a
per-pixel point cloud network for depth data, as well as a fusion network for
feature fusion. We find that the essential reason for using two independent
backbones is the "projection breakdown" problem. In the depth image plane, the
projected 3D structure of the physical world is preserved by the 1D depth value
and its built-in 2D pixel coordinate (UV). Any spatial transformation that
modifies UV, such as resize, flip, crop, or pooling operations in the CNN
pipeline, breaks the binding between the pixel value and UV coordinate. As a
consequence, the 3D structure is no longer preserved by a modified depth image
or feature. To address this issue, we propose a simple yet effective method
denoted as Uni6D that explicitly takes the extra UV data along with RGB-D
images as input. Our method has a Unified CNN framework for 6D pose estimation
with a single CNN backbone. In particular, the architecture of our method is
based on Mask R-CNN with two extra heads, one named RT head for directly
predicting 6D pose and the other named abc head for guiding the network to map
the visible points to their coordinates in the 3D model as an auxiliary module.
This end-to-end approach balances simplicity and accuracy, achieving comparable
accuracy with state of the arts and 7.2x faster inference speed on the
YCB-Video dataset.


title:
Joint Active and Passive Beamforming Design for IRS-Aided
  Radar-Communication

abstract:
  In this paper, we study an intelligent reflecting surface (IRS)-aided
radar-communication (Radcom) system, where the IRS is leveraged to help Radcom
base station (BS) transmit the joint of communication signals and radar signals
for serving communication users and tracking targets simultaneously. The
objective of this paper is to minimize the total transmit power at the Radcom
BS by jointly optimizing the active beamformers, including communication
beamformers and radar beamformers, at the Radcom BS and the phase shifts at the
IRS, subject to the minimum signal-to-interference-plus-noise ratio (SINR)
required by communication users, the minimum SINR required by the radar, and
the cross-correlation pattern design. In particular, we consider two cases,
namely, case I and case II, based on the presence or absence of the radar
cross-correlation design and the interference introduced by the IRS on the
Radcom BS. For case I where the cross correlation design and the interference
are not considered, we prove that the dedicated radar signals are not needed,
which significantly reduces implementation complexity and simplifies algorithm
design. Then, a penalty-based algorithm is proposed to solve the resulting
non-convex optimization problem. Whereas for case II considering the
cross-correlation design and the interference, we unveil that the dedicated
radar signals are needed in general to enhance the system performance. Since
the resulting optimization problem is more challenging to solve as compared
with the case I, the semidefinite relaxation (SDR) based alternating
optimization (AO) algorithm is proposed. Simulation results demonstrate the
effectiveness of proposed algorithms and also show the superiority of the
proposed scheme over various benchmark schemes.


title:
Robust Unlearnable Examples: Protecting Data Against Adversarial
  Learning

abstract:
  The tremendous amount of accessible data in cyberspace face the risk of being
unauthorized used for training deep learning models. To address this concern,
methods are proposed to make data unlearnable for deep learning models by
adding a type of error-minimizing noise. However, such conferred unlearnability
is found fragile to adversarial training. In this paper, we design new methods
to generate robust unlearnable examples that are protected from adversarial
training. We first find that the vanilla error-minimizing noise, which
suppresses the informative knowledge of data via minimizing the corresponding
training loss, could not effectively minimize the adversarial training loss.
This explains the vulnerability of error-minimizing noise in adversarial
training. Based on the observation, robust error-minimizing noise is then
introduced to reduce the adversarial training loss. Experiments show that the
unlearnability brought by robust error-minimizing noise can effectively protect
data from adversarial training in various scenarios. The code is available at
\url{https://github.com/fshp971/robust-unlearnable-examples}.


title: A Combinatorial Proof of a generalization of a Theorem of Frobenius

abstract:
  In this article, we shall generalize a theorem due to Frobenius in group
theory, which asserts that if $p$ is a prime and $p^{r}$ divides the order of a
finite group, then the number of subgroups of order $p^{r}$ is $\equiv$ 1(mod
$p$). Interestingly, our proof is purely combinatorial and does not use much
group theory.


title:
Asymptotic analysis of k-hop connectivity in the 1D unit disk random
  graph model

abstract:
  We propose an algorithm for the closed-form recursive computation of joint
moments and cumulants of all orders for k-hop counts in the 1D unit disk random
graph model with Poisson distributed vertices. Our approach uses decompositions
of k-hop counts into multiple Poisson stochastic integrals. As a consequence,
using the Stein method we derive Berry-Esseen bounds for the asymptotic
convergence of renormalized k-hop path counts to the normal distribution as the
density of Poisson vertices tends to infinity. Computer codes for the recursive
symbolic computation of moments and cumulants are provided in appendix.


title:
Neutrino phenomenology, muon and electron (g-2) under $U(1)$ gauged
  symmetries in an extended inverse seesaw model

abstract:
  The proposed work is an extension of the Standard Model, where we have
introduced two anomaly free gauge symmetries i.e. $U(1)_{B-L}$ and
$U(1)_{L_e-L_\mu}$ in an inverse seesaw framework. For this purpose, we have
included three right handed neutrinos $N_{iR}$, three neutral fermions $S_{iL}
(i=1,2,3)$ and two scalar singlet bosons ($\chi_1,\chi_2$). We get a definite
structure for neutrino mass matrix due to the aforementioned gauge symmetries.
Thus, our model is able to predict the neutrino oscillation results which are
in accordance with the experimental data and mostly supports normal ordering.
The outcome comprises of active neutrino mass, mixing angles, mass square
differences, CP violating phase etc. We also discuss neutrinoless double beta
decay effective mass parameter $\langle m_{ee}\rangle$ which gives a strong
evidence on the Majorana nature of neutrinos. Its predicted value is found to
be well below the current experimental bounds of KamLAND-Zen, CUORE etc.
Furthermore, as the extended gauge symmetries are local, hence, are associated
with the corresponding gauge bosons i.e. ($Z_1, Z_2$), which make our model
feasible to explain current results of electron and muon $(g-2)$ through
neutral current interactions.


title: Reference-based Video Super-Resolution Using Multi-Camera Video Triplets

abstract:
  We propose the first reference-based video super-resolution (RefVSR) approach
that utilizes reference videos for high-fidelity results. We focus on RefVSR in
a triple-camera setting, where we aim at super-resolving a low-resolution
ultra-wide video utilizing wide-angle and telephoto videos. We introduce the
first RefVSR network that recurrently aligns and propagates temporal reference
features fused with features extracted from low-resolution frames. To
facilitate the fusion and propagation of temporal reference features, we
propose a propagative temporal fusion module. For learning and evaluation of
our network, we present the first RefVSR dataset consisting of triplets of
ultra-wide, wide-angle, and telephoto videos concurrently taken from triple
cameras of a smartphone. We also propose a two-stage training strategy fully
utilizing video triplets in the proposed dataset for real-world 4x video
super-resolution. We extensively evaluate our method, and the result shows the
state-of-the-art performance in 4x super-resolution.


title:
Zeta-Payne: a fully automated spectrum analysis algorithm for the Milky
  Way Mapper program of the SDSS-V survey

abstract:
  The Sloan Digital Sky Survey has recently initiated its 5th survey generation
(SDSS-V), with a central focus on stellar spectroscopy. In particular, SDSS-V
Milky Way Mapper program will deliver multi-epoch optical and near-infrared
spectra for more than 5 million stars across the entire sky, covering a large
range in stellar mass, surface temperature, evolutionary stage, and age. About
10% of those spectra will be of hot stars of OBAF spectral types, for whose
analysis no established survey pipelines exist. Here we present the spectral
analysis algorithm, Zeta-Payne, developed specifically to obtain stellar labels
from SDSS-V spectra of stars with these spectral types and drawing on machine
learning tools. We provide details of the algorithm training, its test on
artificial spectra, and its validation on two control samples of real stars.
Analysis with Zeta-Payne leads to only modest internal uncertainties in the
near-IR with APOGEE (optical with BOSS): 3-10% (1-2%) for Teff, 5-30% (5-25%)
for v*sin(i), 1.7-6.3 km/s(0.7-2.2 km/s) for RV, $<0.1$ dex ($<0.05$ dex) for
log(g), and 0.4-0.5 dex (0.1 dex) for [M/H] of the star, respectively. We find
a good agreement between atmospheric parameters of OBAF-type stars when
inferred from their high- and low-resolution optical spectra. For most stellar
labels the APOGEE spectra are (far) less informative than the BOSS spectra of
these stars, while log(g), v*sin(i), and [M/H] are in most cases too uncertain
for meaningful astrophysical interpretation. This makes BOSS low-resolution
optical spectra better for stellar labels of OBAF-type stars, unless the latter
are subject to high levels of extinction.


title:
Semi-supervised anomaly detection algorithm based on KL divergence
  (SAD-KL)

abstract:
  The unlabeled data are generally assumed to be normal data in detecting
abnormal data via semisupervised learning. This assumption, however, causes
inevitable detection error when distribution of unlabeled data is different
from distribution of labeled normal dataset. To deal the problem caused by
distribution gap between labeled and unlabeled data, we propose a
semi-supervised anomaly detection algorithm using KL divergence (SAD-KL). The
proposed SAD-KL is composed of two steps: (1) estimating KL divergence of
probability density functions (PDFs) of the local outlier factors (LOFs) of the
labeled normal data and the unlabeled data (2) estimating detection probability
and threshold for detecting normal data in unlabeled data by using the KL
divergence. We show that the PDFs of the LOFs follow Burr distribution and use
them for detection. Once the threshold is computed, the SAD-KL runs iteratively
until the labeling change rate is lower than the predefined threshold.
Experiments results show that the SAD-KL shows superior detection probability
over the existing algorithms even though it takes less learning time.


title:
Improving Matrix-vector Multiplication via Lossless Grammar-Compressed
  Matrices

abstract:
  As nowadays Machine Learning (ML) techniques are generating huge data
collections, the problem of how to efficiently engineer their storage and
operations is becoming of paramount importance. In this article we propose a
new lossless compression scheme for real-valued matrices which achieves
efficient performance in terms of compression ratio and time for linear-algebra
operations. Experiments show that, as a compressor, our tool is clearly
superior to gzip and it is usually within 20% of xz in terms of compression
ratio. In addition, our compressed format supports matrix-vector
multiplications in time and space proportional to the size of the compressed
representation, unlike gzip and xz that require the full decompression of the
compressed matrix. To our knowledge our lossless compressor is the first one
achieving time and space complexities which match the theoretical limit
expressed by the $k$-th order statistical entropy of the input.
  To achieve further time/space reductions, we propose column-reordering
algorithms hinging on a novel column-similarity score. Our experiments on
various data sets of ML matrices show that, with a modest preprocessing time,
our column reordering can yield a further reduction of up to 16% in the peak
memory usage during matrix-vector multiplication.
  Finally, we compare our proposal against the state-of-the-art Compressed
Linear Algebra (CLA) approach showing that ours runs always at least twice
faster (in a multi-thread setting) and achieves better compressed space
occupancy for most of the tested data sets. This experimentally confirms the
provably effective theoretical bounds we show for our compressed-matrix
approach.


title:
Specialized Document Embeddings for Aspect-based Similarity of Research
  Papers

abstract:
  Document embeddings and similarity measures underpin content-based
recommender systems, whereby a document is commonly represented as a single
generic embedding. However, similarity computed on single vector
representations provides only one perspective on document similarity that
ignores which aspects make two documents alike. To address this limitation,
aspect-based similarity measures have been developed using document
segmentation or pairwise multi-class document classification. While
segmentation harms the document coherence, the pairwise classification approach
scales poorly to large scale corpora. In this paper, we treat aspect-based
similarity as a classical vector similarity problem in aspect-specific
embedding spaces. We represent a document not as a single generic embedding but
as multiple specialized embeddings. Our approach avoids document segmentation
and scales linearly w.r.t.the corpus size. In an empirical study, we use the
Papers with Code corpus containing 157,606 research papers and consider the
task, method, and dataset of the respective research papers as their aspects.
We compare and analyze three generic document embeddings, six specialized
document embeddings and a pairwise classification baseline in the context of
research paper recommendations. As generic document embeddings, we consider
FastText, SciBERT, and SPECTER. To compute the specialized document embeddings,
we compare three alternative methods inspired by retrofitting, fine-tuning, and
Siamese networks. In our experiments, Siamese SciBERT achieved the highest
scores. Additional analyses indicate an implicit bias of the generic document
embeddings towards the dataset aspect and against the method aspect of each
research paper. Our approach of aspect-based document embeddings mitigates
potential risks arising from implicit biases by making them explicit.


title:
UNICON: Combating Label Noise Through Uniform Selection and Contrastive
  Learning

abstract:
  Supervised deep learning methods require a large repository of annotated
data; hence, label noise is inevitable. Training with such noisy data
negatively impacts the generalization performance of deep neural networks. To
combat label noise, recent state-of-the-art methods employ some sort of sample
selection mechanism to select a possibly clean subset of data. Next, an
off-the-shelf semi-supervised learning method is used for training where
rejected samples are treated as unlabeled data. Our comprehensive analysis
shows that current selection methods disproportionately select samples from
easy (fast learnable) classes while rejecting those from relatively harder
ones. This creates class imbalance in the selected clean set and in turn,
deteriorates performance under high label noise. In this work, we propose
UNICON, a simple yet effective sample selection method which is robust to high
label noise. To address the disproportionate selection of easy and hard
samples, we introduce a Jensen-Shannon divergence based uniform selection
mechanism which does not require any probabilistic modeling and hyperparameter
tuning. We complement our selection method with contrastive learning to further
combat the memorization of noisy labels. Extensive experimentation on multiple
benchmark datasets demonstrates the effectiveness of UNICON; we obtain an 11.4%
improvement over the current state-of-the-art on CIFAR100 dataset with a 90%
noise rate. Our code is publicly available


title:
On The Two Types Of Affine Structures For Degenerating Kummer Surfaces
  -Non-Archimedean VS Gromov-Hausdorff Limits-

abstract:
  Kontsevich and Soibelman constructed integral affine manifolds with
singularities (IAMS, for short) for maximal degenerations of polarized
Calabi-Yau manifolds in a non-Archimedean way. On the other hand, for each
maximally degenerating family of polarized Calabi-Yau manifolds, we can
consider the Gromov-Hausdorff limit of the fibers. It is expected that this
Gromov-Hausdorff limit carries an IAMS-structure. Kontsevich and Soibelman
conjectured that these two types of IAMS are the same. This conjecture is
believed in the mirror symmetry context. In this paper, we prove the above
conjecture for maximal degenerations of polarized Kummer surfaces.


title: Gradient-Matching Coresets for Rehearsal-Based Continual Learning

abstract:
  The goal of continual learning (CL) is to efficiently update a machine
learning model with new data without forgetting previously-learned knowledge.
Most widely-used CL methods rely on a rehearsal memory of data points to be
reused while training on new data. Curating such a rehearsal memory to maintain
a small, informative subset of all the data seen so far is crucial to the
success of these methods. We devise a coreset selection method for
rehearsal-based continual learning. Our method is based on the idea of gradient
matching: The gradients induced by the coreset should match, as closely as
possible, those induced by the original training dataset. Inspired by the
neural tangent kernel theory, we perform this gradient matching across the
model's initialization distribution, allowing us to extract a coreset without
having to train the model first. We evaluate the method on a wide range of
continual learning scenarios and demonstrate that it improves the performance
of rehearsal-based CL methods compared to competing memory management
strategies such as reservoir sampling.


title:
Construction of the Temperley-Lieb algebra from bond algebra: From the
  transverse-field Ising to the XXZ

abstract:
  We show that the Temperley-Lieb algebra is constructed from the generators of
the transverse-field Ising-type bond algebra. It is also shown that, when we
take the representation of the generators to the one-dimensional
transverse-field Ising model, then the Temperley-Lieb algebra constructed on
three consecutive bond generators becomes the XXZ model. Furthermore, we obtain
several integrable spin systems from other representations for the bond
generators.


title:
Effect of Diffraction on Wigner Distributions of Optical Fields and how
  to Use It in Optical Resonator Theory. II -- Unstable Resonators

abstract:
  The second part of the article is devoted to field transfers by diffraction
that are represented by fractional Fourier transformations whose orders are
complex numbers. The corresponding effects on the Wigner distributions
associated with optical fields are still represented by $4\times4$ matrices
operating on the scaled phase-space, but unlike matrices involved in the first
part, those matrices decompose into two matrices that essentially represent
2--dimensional hyperbolic rotations, not elliptical rotations. The result is
applied to the theory of unstable resonators.


title:
Two-Dimensional Drift Analysis: Optimizing Two Functions Simultaneously
  Can Be Hard

abstract:
  In this paper we show how to use drift analysis in the case of two random
variables $X_1, X_2$, when the drift is approximatively given by $A\cdot
(X_1,X_2)^T$ for a matrix $A$. The non-trivial case is that $X_1$ and $X_2$
impede each other's progress, and we give a full characterization of this case.
As application, we develop and analyze a minimal example TwoLinear of a dynamic
environment that can be hard. The environment consists of two linear function
$f_1$ and $f_2$ with positive weights $1$ and $n$, and in each generation
selection is based on one of them at random. They only differ in the set of
positions that have weight $1$ and $n$. We show that the $(1+1)$-EA with
mutation rate $\chi/n$ is efficient for small $\chi$ on TwoLinear, but does not
find the shared optimum in polynomial time for large $\chi$.


title: Average order in wreath products

abstract:
  We obtain an exact formula for the average order of elements of a wreath
product of two finite groups. Then focussing our attention on $p$-groups for
primes $p$, we give an estimate for the average order of a wreath product $A\wr
B$ in terms of maximum order of elements of $A$ and average order of $B$ and an
exact formula for the distribution of orders of elements of $A\wr B.$ Finally,
we show how wreath products can be used to find several rational numbers which
are limits of average orders of a sequence of $p$-groups with cardinalities
going to infinity.


title: A Formal Analysis of the Count-Min Sketch with Conservative Updates

abstract:
  Count-Min Sketch with Conservative Updates (CMS-CU) is a popular algorithm to
approximately count items' appearances in a data stream. Despite CMS-CU's
widespread adoption, the theoretical analysis of its performance is still
wanting because of its inherent difficulty. In this paper, we propose a novel
approach to study CMS-CU and derive new upper bounds on the expected value and
the CCDF of the estimation error under an i.i.d. request process. Our formulas
can be successfully employed to derive improved estimates for the precision of
heavy-hitter detection methods and improved configuration rules for CMS-CU. The
bounds are evaluated both on synthetic and real traces.


title:
CenterLoc3D: Monocular 3D Vehicle Localization Network for Roadside
  Surveillance Cameras

abstract:
  Monocular 3D vehicle localization is an important task in Intelligent
Transportation System (ITS) and Cooperative Vehicle Infrastructure System
(CVIS), which is usually achieved by monocular 3D vehicle detection. However,
depth information cannot be obtained directly by monocular cameras due to the
inherent imaging mechanism, resulting in more challenging monocular 3D tasks.
Most of the current monocular 3D vehicle detection methods leverage 2D
detectors and additional geometric modules, which reduces the efficiency. In
this paper, we propose a 3D vehicle localization network CenterLoc3D for
roadside monocular cameras, which directly predicts centroid and eight vertexes
in image space, and the dimension of 3D bounding boxes without 2D detectors. To
improve the precision of 3D vehicle localization, we propose a weighted-fusion
module and a loss with spatial constraints embedded in CenterLoc3D. Firstly,
the transformation matrix between 2D image space and 3D world space is solved
by camera calibration. Secondly, vehicle type, centroid, eight vertexes, and
the dimension of 3D vehicle bounding boxes are obtained by CenterLoc3D.
Finally, centroid in 3D world space can be obtained by camera calibration and
CenterLoc3D for 3D vehicle localization. To the best of our knowledge, this is
the first application of 3D vehicle localization for roadside monocular
cameras. Hence, we also propose a benchmark for this application including a
dataset (SVLD-3D), an annotation tool (LabelImg-3D), and evaluation metrics.
Through experimental validation, the proposed method achieves high accuracy and
real-time performance.


title: MeMC: A package for monte-carlo simulations of spherical shells

abstract:
  The MeMC is an open-source software package for monte-carlo simulation of
elastic shells. It is designed as a tool to interpret the force-distance data
generated by indentation of biological nano-vesicles by atomic force
microscopes. The code is written in c++ and python. The code is customizable --
new modules can be added in a straightforward manner.


title: Poisson-Lie Group structures on semidirect products

abstract:
  We look at the Poisson structure on the total space of the dual bundle to the
Lie algebroid arising from a matched pair of Lie groups. This dual bundle, with
the natural semidirect product group structure, becomes a Poisson-Lie group as
suggested by a recent work of Stachura. Moreover, when we start from matched
pairs given by the Iwasawa decomposition of simple Lie groups, the associated
Lie bialgebra is coboundary.


title:
Investigating Active-learning-based Training Data Selection for Speech
  Spoofing Countermeasure

abstract:
  Training a spoofing countermeasure (CM) that generalizes to various unseen
data is desired but challenging. While methods such as data augmentation and
self-supervised learning are applicable, the imperfect CM performance on
diverse test sets still calls for additional strategies. This study took the
initiative and investigated CM training using active learning (AL), a framework
that iteratively selects useful data from a large pool set and fine-tunes the
CM. This study compared a few methods to measure the data usefulness and the
impact of using different pool sets collected from various sources. The results
showed that the AL-based CMs achieved better generalization than our strong
baseline on multiple test tests. Furthermore, compared with a top-line CM that
simply used the whole data pool set for training, the AL-based CMs achieved
similar performance using less training data. Although no single best
configuration was found for AL, the rule of thumb is to include diverse spoof
and bona fide data in the pool set and to avoid any AL data selection method
that selects the data that the CM feels confident in.


title:
An algebraic convergence rate for the optimal control of Mckean-Vlasov
  dynamics

abstract:
  We establish an algebraic rate of convergence in the large number of players
limit of the value functions of N-particle stochastic control problems towards
the value function of the corresponding McKean-Vlasov problem also known as
mean field control. The rate is obtained in the presence of both idiosyncratic
and common noises and in a setting where the value function for the
McKean-Vlasov problem need not be smooth. Our approach relies crucially on
uniform in N Lipschitz and semi-concavity estimates for the N-particle value
functions as well as a certain concentration inequality.


title:
A Comment on the "Photon position operator with commuting components" by
  Margaret Hawton

abstract:
  We show that the position operator with commuting components proposed by M.
Hawton [M. Hawton, Phys. Rev. A {\bf 59}, 954 (1999)] and developed in
subsequent papers, including the recent ones, does not have the properties
required for a photon position operator. Depending on the exact interpretation
of the results it either concerns a triplet of massless spin zero particles
rather than the photon, or does not have the required covariance properties
under space rotations.


title: Pyramid Feature Alignment Network for Video Deblurring

abstract:
  Video deblurring remains a challenging task due to various causes of
blurring. Traditional methods have considered how to utilize neighboring frames
by the single-scale alignment for restoration. However, they typically suffer
from misalignment caused by severe blur. In this work, we aim to better utilize
neighboring frames with high efficient feature alignment. We propose a Pyramid
Feature Alignment Network (PFAN) for video deblurring. First, the multi-scale
feature of blurry frames is extracted with the strategy of Structure-to-Detail
Downsampling (SDD) before alignment. This downsampling strategy makes the edges
sharper, which is helpful for alignment. Then we align the feature at each
scale and reconstruct the image at the corresponding scale. This strategy
effectively supervises the alignment at each scale, overcoming the problem of
propagated errors from the above scales at the alignment stage. To better
handle the challenges of complex and large motions, instead of aligning
features at each scale separately, lower-scale motion information is used to
guide the higher-scale motion estimation. Accordingly, a Cascade Guided
Deformable Alignment (CGDA) is proposed to integrate coarse motion into
deformable convolution for finer and more accurate alignment. As demonstrated
in extensive experiments, our proposed PFAN achieves superior performance with
competitive speed compared to the state-of-the-art methods.


title:
Visual Mechanisms Inspired Efficient Transformers for Image and Video
  Quality Assessment

abstract:
  Visual (image, video) quality assessments can be modelled by visual features
in different domains, e.g., spatial, frequency, and temporal domains.
Perceptual mechanisms in the human visual system (HVS) play a crucial role in
generation of quality perception. This paper proposes a general framework for
no-reference visual quality assessment using efficient windowed transformer
architectures. A lightweight module for multi-stage channel attention is
integrated into Swin (shifted window) Transformer. Such module can represent
appropriate perceptual mechanisms in image quality assessment (IQA) to build an
accurate IQA model. Meanwhile, representative features for image quality
perception in the spatial and frequency domains can also be derived from the
IQA model, which are then fed into another windowed transformer architecture
for video quality assessment (VQA). The VQA model efficiently reuses attention
information across local windows to tackle the issue of expensive time and
memory complexities of original transformer. Experimental results on both
large-scale IQA and VQA databases demonstrate that the proposed quality
assessment models outperform other state-of-the-art models by large margins.
The complete source code will be published on Github.


title:
Large coupling in a FitzHug-Nagumo neural network: quantitative and
  strong convergence results

abstract:
  We consider a spatially extended mesoscopic FitzHugh-Nagumo model with
interactions and prove that in the regime where strong and local interactions
dominate, the probability density of the potential throughout the network
concentrates into a Dirac distribution whose center of mass solves the
classical non-local reaction-diffusion FitzHugh-Nagumo system. In order to
refine our comprehension of this regime, we focus on the blow-up profile of
this concentration phenomenon. Our main purpose here consists in proving that
it is Gaussian by deriving two quantitative and strong convergence estimates:
the first one in a L1 functional framework and the second in a weighted L2
functional setting. We develop original relative entropy techniques to prove
the first result whereas our second result relies on propagation of regularity.


title:
A Paired Phase and Magnitude Reconstruction for Advanced
  Diffusion-Weighted Imaging

abstract:
  Multi-shot interleaved echo planer imaging can obtain diffusion-weighted
images (DWI) with high spatial resolution and low distortion, but suffers from
ghost artifacts introduced by phase variations between shots. In this work, we
aim at solving the challenging reconstructions under severe motions between
shots and low signal-to-noise ratio. An explicit phase model with paired phase
and magnitude priors is proposed to regularize the reconstruction (PAIR). The
former prior is derived from the smoothness of the shot phase and enforced with
low-rankness in the k-space domain. The latter explores similar edges among
multi-b-value and multi-direction DWI with weighted total variation in the
image domain. Extensive simulation and in vivo results show that PAIR can
remove ghost image artifacts very well under the high number of shots (8 shots)
and significantly suppress the noise under the ultra-high b-value (4000 s/mm2).
The explicit phase model PAIR with complementary priors has a good performance
on challenging reconstructions under severe motions between shots and low
signal-to-noise ratio. PAIR has great potential in the advanced clinical DWI
applications and brain function research.


title: First steps towards $q$-deformed Clifford analysis

abstract:
  We consider the extension of the Jackson calculus into higher dimensions and
specifically into Clifford analysis.


title: An Effective Dereverberation Algorithm by Fusing MVDR and MCLP

abstract:
  In the scenario with reverberation, the experience of human-machine
interaction will become worse. In order to solve this problem, many methods for
the dereverberation have emerged. At present, how to update the parameters of
the Kalman filter in the existing dereverberation methods based on multichannel
linear prediction (MCLP) is a challenging task, especially, accurate power
spectral density (PSD) estimation of target speech. In this paper, minimum
variance distortionless response (MVDR) beamformer and MCLP are effectively
fused in the dereverberation, where the PSD of target speech used for Kalman
filter is modified in the MCLP. In order to construct a MVDR beamformer, the
PSD of late reverberation and the PSD of the noise are estimated simultaneously
by the blocking-based PSD estimator. Thus, the PSD of target speech used for
Kalman filter can be obtained by subtracting the PSD of late reverberation and
the PSD of the noise from the PSD of observation signal. Compared to the
reference methods, the proposed method shows an outstanding performance.