title:
FairNeuron: Improving Deep Neural Network Fairness with Adversary Games
  on Selective Neurons

abstract:
  With Deep Neural Network (DNN) being integrated into a growing number of
critical systems with far-reaching impacts on society, there are increasing
concerns on their ethical performance, such as fairness. Unfortunately, model
fairness and accuracy in many cases are contradictory goals to optimize. To
solve this issue, there has been a number of work trying to improve model
fairness by using an adversarial game in model level. This approach introduces
an adversary that evaluates the fairness of a model besides its prediction
accuracy on the main task, and performs joint-optimization to achieve a
balanced result. In this paper, we noticed that when performing backward
propagation based training, such contradictory phenomenon has shown on
individual neuron level. Based on this observation, we propose FairNeuron, a
DNN model automatic repairing tool, to mitigate fairness concerns and balance
the accuracy-fairness trade-off without introducing another model. It works on
detecting neurons with contradictory optimization directions from accuracy and
fairness training goals, and achieving a trade-off by selective dropout.
Comparing with state-of-the-art methods, our approach is lightweight, making it
scalable and more efficient. Our evaluation on 3 datasets shows that FairNeuron
can effectively improve all models' fairness while maintaining a stable
utility.


title: A Positive Answer to B\'ar\'any's Question on Face Numbers of Polytopes

abstract:
  Despite a full characterization of the face vectors of simple and simplicial
polytopes, the face numbers of general polytopes are poorly understood. Around
1997, B\'ar\'any asked whether for all convex $d$-polytopes $P$ and all $0 \leq
k \leq d-1$, $f_k(P) \geq \min\{f_0(P), f_{d-1}(P)\}$. We answer B\'ar\'any's
question in the affirmative and prove a stronger statement: for all convex
$d$-polytopes $P$ and all $0 \leq k \leq d-1$, \[ \frac{f_k(P)}{f_0(P)} \geq
\frac{1}{2}\biggl[{\lceil \frac{d}{2} \rceil \choose k} + {\lfloor \frac{d}{2}
\rfloor \choose k}\biggr], \qquad \frac{f_k(P)}{f_{d-1}(P)} \geq
\frac{1}{2}\biggl[{\lceil \frac{d}{2} \rceil \choose d-k-1} + {\lfloor
\frac{d}{2} \rfloor \choose d-k-1}\biggr]. \] In the former, equality holds
precisely when $k=0$ or when $k=1$ and $P$ is simple. In the latter, equality
holds precisely when $k=d-1$ or when $k=d-2$ and $P$ is simplicial.


title:
Gait Recognition in the Wild with Dense 3D Representations and A
  Benchmark

abstract:
  Existing studies for gait recognition are dominated by 2D representations
like the silhouette or skeleton of the human body in constrained scenes.
However, humans live and walk in the unconstrained 3D space, so projecting the
3D human body onto the 2D plane will discard a lot of crucial information like
the viewpoint, shape, and dynamics for gait recognition. Therefore, this paper
aims to explore dense 3D representations for gait recognition in the wild,
which is a practical yet neglected problem. In particular, we propose a novel
framework to explore the 3D Skinned Multi-Person Linear (SMPL) model of the
human body for gait recognition, named SMPLGait. Our framework has two
elaborately-designed branches of which one extracts appearance features from
silhouettes, the other learns knowledge of 3D viewpoints and shapes from the 3D
SMPL model. In addition, due to the lack of suitable datasets, we build the
first large-scale 3D representation-based gait recognition dataset, named
Gait3D. It contains 4,000 subjects and over 25,000 sequences extracted from 39
cameras in an unconstrained indoor scene. More importantly, it provides 3D SMPL
models recovered from video frames which can provide dense 3D information of
body shape, viewpoint, and dynamics. Based on Gait3D, we comprehensively
compare our method with existing gait recognition approaches, which reflects
the superior performance of our framework and the potential of 3D
representations for gait recognition in the wild. The code and dataset are
available at https://gait3d.github.io.


title:
Optimal Sublinear Sampling of Spanning Trees and Determinantal Point
  Processes via Average-Case Entropic Independence

abstract:
  We design fast algorithms for repeatedly sampling from strongly Rayleigh
distributions, which include random spanning tree distributions and
determinantal point processes. For a graph $G=(V, E)$, we show how to
approximately sample uniformly random spanning trees from $G$ in
$\widetilde{O}(\lvert V\rvert)$ time per sample after an initial
$\widetilde{O}(\lvert E\rvert)$ time preprocessing. For a determinantal point
process on subsets of size $k$ of a ground set of $n$ elements, we show how to
approximately sample in $\widetilde{O}(k^\omega)$ time after an initial
$\widetilde{O}(nk^{\omega-1})$ time preprocessing, where $\omega<2.372864$ is
the matrix multiplication exponent. We even improve the state of the art for
obtaining a single sample from determinantal point processes, from the prior
runtime of $\widetilde{O}(\min\{nk^2, n^\omega\})$ to
$\widetilde{O}(nk^{\omega-1})$.
  In our main technical result, we achieve the optimal limit on domain
sparsification for strongly Rayleigh distributions. In domain sparsification,
sampling from a distribution $\mu$ on $\binom{[n]}{k}$ is reduced to sampling
from related distributions on $\binom{[t]}{k}$ for $t\ll n$. We show that for
strongly Rayleigh distributions, we can can achieve the optimal
$t=\widetilde{O}(k)$. Our reduction involves sampling from $\widetilde{O}(1)$
domain-sparsified distributions, all of which can be produced efficiently
assuming convenient access to approximate overestimates for marginals of $\mu$.
Having access to marginals is analogous to having access to the mean and
covariance of a continuous distribution, or knowing "isotropy" for the
distribution, the key assumption behind the Kannan-Lov\'asz-Simonovits (KLS)
conjecture and optimal samplers based on it. We view our result as a moral
analog of the KLS conjecture and its consequences for sampling, for discrete
strongly Rayleigh measures.


title:
Post-Quantum Cryptography Algorithms Standardization and Performance
  Analysis

abstract:
  Quantum computer is no longer a hypothetical idea. It is the worlds most
important technology and there is a race among countries to get supremacy in
quantum technology. Its the technology that will reduce the computing time from
years to hours or even minutes. The power of quantum computing will be a great
support for the scientific community. However, it raises serious threats to
cybersecurity. Theoretically, all the cryptography algorithms are vulnerable to
attack. The practical quantum computers, when available with millions of qubits
capacity, will be able to break nearly all modern public-key cryptographic
systems. Before the quantum computers arrive with sufficient qubit capacity, we
must be ready with quantum-safe cryptographic algorithms, tools, techniques,
and deployment strategies to protect the ICT infrastructure. This paper
discusses in detail the global effort for the design, development, and
standardization of various quantum-safe cryptography algorithms along with the
performance analysis of some of the potential quantum-safe algorithms. Most of
the quantum-safe algorithms need more CPU cycles, higher runtime memory, and
large key size. The objective of the paper is to analyze the feasibility of the
various quantum-safe cryptography algorithms.


title:
Greedier is Better: Selecting Multiple Neighbors per Iteration for
  Sparse Subspace Clustering

abstract:
  Sparse subspace clustering (SSC) using greedy-based neighbor selection, such
as orthogonal matching pursuit (OMP), has been known as a popular
computationally-efficient alternative to the popular L1-minimization based
methods. This paper proposes a new SSC scheme using generalized OMP (GOMP), a
soup-up of OMP whereby multiple neighbors are identified per iteration, along
with a new stopping rule requiring nothing more than a knowledge of the ambient
signal dimension. Compared to conventional OMP, which identifies one neighbor
per iteration, the proposed GOMP method involves fewer iterations, thereby
enjoying lower algorithmic complexity; advantageously, the proposed stopping
rule is free from off-line estimation of subspace dimension and noise power.
Under the semi-random model, analytic performance guarantees, in terms of
neighbor recovery rates, are established to justify the advantage of the
proposed GOMP. The results show that, with a high probability, GOMP (i) is
halted by the proposed stopping rule, and (ii) can retrieve more true neighbors
than OMP, consequently yielding higher final data clustering accuracy. Computer
simulations using both synthetic data and real human face data are provided to
validate our analytic study and evidence the effectiveness of the proposed
approach.


title:
Detecting key Soccer match events to create highlights using Computer
  Vision

abstract:
  The research and data science community has been fascinated with the
development of automatic systems for the detection of key events in a video.
Special attention in this field is given to sports video analytics which could
help in identifying key events during a match and help in preparing a strategy
for the games going forward. For this paper, we have chosen Football (soccer)
as a sport where we would want to create highlights for a given match video,
through a computer vision model that aims to identify important events in a
Soccer match to create highlights of the match. We built the models based on
Faster RCNN and YoloV5 architectures and noticed that for the amount of data we
used for training Faster RCNN did better than YoloV5 in detecting the events in
the match though it was much slower. Within Faster RCNN using ResNet50 as a
base model gave a better class accuracy of 95.5% as compared to 92% with VGG16
as base model completely outperforming YoloV5 for our training dataset. We
tested with an original video of size 23 minutes and our model could reduce it
to 4:50 minutes of highlights capturing almost all important events in the
match.


title: FocalClick: Towards Practical Interactive Image Segmentation

abstract:
  Interactive segmentation allows users to extract target masks by making
positive/negative clicks. Although explored by many previous works, there is
still a gap between academic approaches and industrial needs: first, existing
models are not efficient enough to work on low power devices; second, they
perform poorly when used to refine preexisting masks as they could not avoid
destroying the correct part. FocalClick solves both issues at once by
predicting and updating the mask in localized areas. For higher efficiency, we
decompose the slow prediction on the entire image into two fast inferences on
small crops: a coarse segmentation on the Target Crop, and a local refinement
on the Focus Crop. To make the model work with preexisting masks, we formulate
a sub-task termed Interactive Mask Correction, and propose Progressive Merge as
the solution. Progressive Merge exploits morphological information to decide
where to preserve and where to update, enabling users to refine any preexisting
mask effectively. FocalClick achieves competitive results against SOTA methods
with significantly smaller FLOPs. It also shows significant superiority when
making corrections on preexisting masks. Code and data will be released at
github.com/XavierCHEN34/ClickSEG


title: On a rainbow extremal problem for color-critical graphs

abstract:
  There has been extensive studies on the following question: given $k$ graphs
$G_1,\dots, G_k$ over a common vertex set of size $n$, what conditions on $G_i$
ensures a `colorful' copy of $H$, i.e., a copy of $H$ containing at most one
edge from each $G_i$? A lower bound on $\sum_{i\in [k]} e(G_i)$ enforcing a
colorful copy of a given graph $H$ was considered by Keevash, Saks, Sudakov,
and Verstra\"{e}te. They defined $\operatorname{ex}_k(n,H)$ to be the maximum
total number of edges of the graphs $G_1,\dots, G_k$ on a common vertex set of
size $n$ having no colorful copy of $H$. They completely determined
$\operatorname{ex}_k(n,K_r)$ for large $n$ by showing that, depending on the
value of $k$, one of the two natural constructions is always the extremal
construction. Moreover, they conjectured the same holds for every
color-critical graphs and proved it for 3-color-critical graphs.
  We prove their conjecture for 4-color-critical graphs and for almost all
$r$-color-critical graphs when $r > 4$. Moreover, we show that for every
non-color-critical non-bipartite graphs, none of the two natural constructions
is extremal for certain values of $k$. This answers a question of Keevash,
Saks, Sudakov, and Verstra\"{e}te.


title:
On some sums involving the counting function of non-isomorphic abelian
  groups

abstract:
  Let $a(n)$ denote the number of non-isomorphic abelian groups with $n$
elements. In 1991 Ivi\'{c} proved an asymptotic formula of the sum $\sum_{n\leq
x} a(n+ a(n)).$ In this paper, we will prove a sharper asymptotic formula for
this sum.


title:
A Vergleichsstellensatz of Strassen's Type for a Noncommutative
  Preordered Semialgebra through the Semialgebra of its Fractions

abstract:
  Preordered semialgebras and semirings are two kinds of algebraic structures
occurring in real algebraic geometry frequently and usually play important
roles therein. They have many interesting and promising applications in the
fields of real algebraic geometry, probability theory, theoretical computer
science, quantum information theory, \emph{etc.}. In these applications,
Strassen's Vergleichsstellensatz and its generalized versions, which are
analogs of those Positivstellens\"atze in real algebraic geometry, play
important roles. While these Vergleichsstellens\"atze accept only a commutative
setting (for the semirings in question), we prove in this paper a
noncommutative version of one of the generalized Vergleichsstellens\"atze
proposed by Fritz [\emph{Comm. Algebra}, 49 (2) (2021), pp. 482-499]. The most
crucial step in our proof is to define the semialgebra of the fractions of a
noncommutative semialgebra, which generalizes the definitions in the
literature. Our new Vergleichsstellensatz characterizes the relaxed preorder on
a noncommutative semialgebra induced by all monotone homomorphisms to
$\mathbb{R}_+$ by three other equivalent conditions on the semialgebra of its
fractions equipped with the derived preorder, which may result in more
applications in the future.


title: Quasi-definite axial algebras of Jordan type half

abstract:
  Axial algebras are commutative nonassociative algebras generated by a finite
set of primitive idempotents which action on an algebra is semisimple, and the
fusion laws on the products between eigenvectors for these idempotents are
fulfilled. We find the sufficient conditions in terms of the Frobenius form and
of the properties of idempotents under which an axial algebra of Jordan type
half is unital and finite-dimensional.


title: Locally Spontaneous Dynamic Oxygen Migration on Biphenylene: A DFT Study

abstract:
  The dynamic oxygen migration on the interface of carbon materials, such as
graphene and carbon nanotube, has opened up a new avenue to realizing the
dynamic covalent materials. However, the understanding of dynamic behaviors of
oxygen groups on the non-honeycomb structure, such as the biphenylene sheet, is
still limited. Using both density functional theory calculations and ab initio
molecular dynamics simulations, we demonstrate that the oxygen groups on the
biphenylene, which is an allotrope of graphene and composed of four-, six- and
eight-membered rings with unequal C-C bonds, can exhibit locally spontaneous
dynamic oxygen migration through the breaking/reforming of the C-O bond. The
density of state analyses show that the p-band center of the oxygen atom is
closer to the Fermi energy level on biphenylene, compared to that of the oxygen
atom adsorbed on graphene. This contrast confirms the locally spontaneous
dynamic activity of the oxygen atom on biphenylene. This work provides
scientific guidance for the exploration of the locally/globally spontaneous
dynamic covalent materials and adds a new member to the 2D dynamic covalent
material family.


title:
Evolution of global development cooperation: An analysis of aid flows
  with stochastic block models

abstract:
  Despite considerable scholarly attention on the institutional and normative
aspects of development cooperation, its longitudinal dynamics unfolding at the
global level have rarely been investigated. Focusing on aid, we examine the
evolving global structure of development cooperation induced by aid flows in
its entirety. Representing annual aid flows between donors and recipients from
1970 to 2013 as a series of networks, we apply hierarchical stochastic block
models to extensive aid-flow data that cover not only the aid behavior of the
major OECD donors but also that of other emerging donors, including China.
Despite a considerable degree of external expansion and internal
diversification of aid relations over the years, the analysis has uncovered a
temporally persistent structure of aid networks. The latter comprises, on the
one hand, a limited number of major donors with far-reaching resources and, on
the other hand, a large number of mostly poor but globally well-connected
recipients. The results cast doubt on the efficacy of recurrent efforts for
"aid reform" in substantially changing the global aid flow pattern.


title:
Banana Sub-Family Classification and Quality Prediction using Computer
  Vision

abstract:
  India is the second largest producer of fruits and vegetables in the world,
and one of the largest consumers of fruits like Banana, Papaya and Mangoes
through retail and ecommerce giants like BigBasket, Grofers and Amazon Fresh.
However, adoption of technology in supply chain and retail stores is still low
and there is a great potential to adopt computer-vision based technology for
identification and classification of fruits. We have chosen banana fruit to
build a computer vision based model to carry out the following three use-cases
(a) Identify Banana from a given image (b) Determine sub-family or variety of
Banana (c) Determine the quality of Banana. Successful execution of these
use-cases using computer-vision model would greatly help with overall inventory
management automation, quality control, quick and efficient weighing and
billing which all are manual labor intensive currently. In this work, we
suggest a machine learning pipeline that combines the ideas of CNNs, transfer
learning, and data augmentation towards improving Banana fruit sub family and
quality image classification. We have built a basic CNN and then went on to
tune a MobileNet Banana classification model using a combination of
self-curated and publicly-available dataset of 3064 images. The results show an
overall 93.4% and 100% accuracy for sub-family/variety and for quality test
classifications respectively.


title: Covering entropy for types in tracial $\mathrm{W}^*$-algebras

abstract:
  We study embeddings of tracial $\mathrm{W}^*$-algebras into a ultraproduct of
matrix algebras through an amalgamation of free probabilistic and
model-theoretic techniques. Jung implicitly and Hayes explicitly defined
$1$-bounded entropy through the asymptotic covering numbers of Voiculescu's
microstate spaces, that is, spaces of matrix tuples
$(X_1^{(N)},X_2^{(N)},\dots)$ having approximately the same $*$-moments as the
generators $(X_1,X_2,\dots)$ of a given tracial $\mathrm{W}^*$-algebra. We
study the analogous covering entropy for microstate spaces defined through
formulas that use not only $*$-algebra operations and the trace, but also
suprema and infima, such as arise in the model theory of tracial
$\mathrm{W}^*$-algebras initiated by Farah, Hart, and Sherman. By relating the
new theory with the original $1$-bounded entropy, we show that if
$h(\mathcal{N}:\mathcal{M}) \geq 0$, then there exists an embedding of
$\mathcal{M}$ into a matrix ultraproduct $\mathcal{Q} = \prod_{n \to
\mathcal{U}} M_n(\mathbb{C})$ such that $h(\mathcal{N}:\mathcal{Q})$ is
arbitrarily close to $h(\mathcal{N}:\mathcal{Q})$. We deduce if all embeddings
of $\mathcal{M}$ into $\mathcal{Q}$ are automorphically equivalent, then
$\mathcal{M}$ is strongly $1$-bounded and in fact has $h(\mathcal{M}) = 0$.


title:
PAGP: A physics-assisted Gaussian process framework with active learning
  for forward and inverse problems of partial differential equations

abstract:
  In this work, a Gaussian process regression(GPR) model incorporated with
given physical information in partial differential equations(PDEs) is
developed: physics-assisted Gaussian processes(PAGP). The targets of this model
can be divided into two types of problem: finding solutions or discovering
unknown coefficients of given PDEs with initial and boundary conditions. We
introduce three different models: continuous time, discrete time and hybrid
models. The given physical information is integrated into Gaussian process
model through our designed GP loss functions. Three types of loss function are
provided in this paper based on two different approaches to train the standard
GP model. The first part of the paper introduces the continuous time model
which treats temporal domain the same as spatial domain. The unknown
coefficients in given PDEs can be jointly learned with GP hyper-parameters by
minimizing the designed loss function. In the discrete time models, we first
choose a time discretization scheme to discretize the temporal domain. Then the
PAGP model is applied at each time step together with the scheme to approximate
PDE solutions at given test points of final time. To discover unknown
coefficients in this setting, observations at two specific time are needed and
a mixed mean square error function is constructed to obtain the optimal
coefficients. In the last part, a novel hybrid model combining the continuous
and discrete time models is presented. It merges the flexibility of continuous
time model and the accuracy of the discrete time model. The performance of
choosing different models with different GP loss functions is also discussed.
The effectiveness of the proposed PAGP methods is illustrated in our numerical
section.


title: Nonabelian embedding tensors

abstract:
  In this paper, first we introduce the notion of a nonabelian embedding
tensor, which is a nonabelian generalization of an embedding tensor. Then we
introduce the notion of a Leibniz-Lie algebra, which is the underlying
algebraic structure of a nonabelian embedding tensor, and can also be viewed as
a nonabelian generalization of a Leibniz algebra. Next using the derived
bracket, we construct a differential graded Lie algebra, whose Maurer-Cartan
elements are exactly nonabelian embedding tensors. Consequently, we obtain the
differential graded Lie algebra that governs deformations of a nonabelian
embedding tensor. Finally, we define the cohomology of a nonabelian embedding
tensor and use the second cohomology group to characterize linear deformations.


title: SqueezeNeRF: Further factorized FastNeRF for memory-efficient inference

abstract:
  Neural Radiance Fields (NeRF) has emerged as the state-of-the-art method for
novel view generation of complex scenes, but is very slow during inference.
Recently, there have been multiple works on speeding up NeRF inference, but the
state of the art methods for real-time NeRF inference rely on caching the
neural network output, which occupies several giga-bytes of disk space that
limits their real-world applicability. As caching the neural network of
original NeRF network is not feasible, Garbin et al. proposed "FastNeRF" which
factorizes the problem into 2 sub-networks - one which depends only on the 3D
coordinate of a sample point and one which depends only on the 2D camera
viewing direction. Although this factorization enables them to reduce the cache
size and perform inference at over 200 frames per second, the memory overhead
is still substantial. In this work, we propose SqueezeNeRF, which is more than
60 times memory-efficient than the sparse cache of FastNeRF and is still able
to render at more than 190 frames per second on a high spec GPU during
inference.


title:
Hypergraph-based Source Codes for Function Computation Under Maximal
  Distortion

abstract:
  This work investigates functional source coding problems with maximal
distortion, motivated by approximate function computation in many modern
applications. The maximal distortion treats imprecise reconstruction of a
function value as good as perfect computation if it deviates less than a
tolerance level, while treating reconstruction that differs by more than that
level as a failure. Using a geometric understanding of the maximal distortion,
we propose a hypergraph-based source coding scheme for function computation
that is constructive in the sense that it gives an explicit procedure for
defining auxiliary random variables. Moreover, we find that the
hypergraph-based coding scheme achieves the optimal rate-distortion function in
the setting of coding for computing with side information and the Berger-Tung
sum-rate inner bound in the setting of distributed source coding for computing.
It also achieves the El Gamal-Cover inner bound for multiple description coding
for computing and is optimal for successive refinement and cascade multiple
description problems for computing. Lastly, the benefit of complexity reduction
of finding a forward test channel is shown for a class of Markov sources.


title: Learning to Anticipate Future with Dynamic Context Removal

abstract:
  Anticipating future events is an essential feature for intelligent systems
and embodied AI. However, compared to the traditional recognition task, the
uncertainty of future and reasoning ability requirement make the anticipation
task very challenging and far beyond solved. In this filed, previous methods
usually care more about the model architecture design or but few attention has
been put on how to train an anticipation model with a proper learning policy.
To this end, in this work, we propose a novel training scheme called Dynamic
Context Removal (DCR), which dynamically schedules the visibility of observed
future in the learning procedure. It follows the human-like curriculum learning
process, i.e., gradually removing the event context to increase the
anticipation difficulty till satisfying the final anticipation target. Our
learning scheme is plug-and-play and easy to integrate any reasoning model
including transformer and LSTM, with advantages in both effectiveness and
efficiency. In extensive experiments, the proposed method achieves
state-of-the-art on four widely-used benchmarks. Our code and models are
publicly released at https://github.com/AllenXuuu/DCR.


title:
Density results for the zeros of zeta applied to the error term in the
  prime number theorem

abstract:
  We improve the unconditional explicit bounds for the error term in the prime
counting function $\psi(x)$. In particular, we prove that, for all $x>2$, we
have \[ \left| \psi(x)-x \right| < 9.22106 \, x \, (\log x)^{3/2}
\exp(-0.8476836\sqrt{\log x}), \] and that, for all $\log x \ge 3\,000$, \[
\left| \psi(x)-x \right| < 4.47\cdot 10^{-15} x. \] This compares to results of
Platt \& Trudgian (2021) who obtained $4.51\cdot 10^{-13} x $. Our approach
represents a significant refinement of ideas of Pintz which had been applied by
Platt and Trudgian. Improvements are obtained by splitting the zeros into
additional regions, carefully estimating all of the consequent terms, and a
significant use of computational methods. Results concerning $\pi(x)$ will
appear in a follow up work.


title:
Solid-state heteronuclear multiple-quantum spectroscopy under a
  magic-angle spinning frequency of 150 kHz

abstract:
  We hereby demonstrate that 1H detected 15N-1H heteronuclear multiple-quantum
spectroscopy can be carried out at a magic angle spinning frequency of 150 kHz.
While the 15N-1H multiple-quantum coherences can be directly excited from the
dipolar order created by the method of adiabatic demagnetization in the
rotating frame, it is technically more advantageous to acquire the chemical
shift evolution of the heteronuclear multiple-quantum coherence by two separate
chemical shift evolution periods for 1H and 15N. We also show that the
heteronuclear multiple-quantum correlation spectrum can be obtained by shearing
the corresponding heteronuclear single-quantum correlation spectrum.


title: Discrete equational theories

abstract:
  We introduce discrete equational theories where operations are induced by
those having discrete arities. We characterize the corresponding monads as
monads preserving surjections. Using it, we prove Birkhoff type theorems for
categories of algebras of discrete theories. This extends known results from
metric spaces to general symmetric monoidal closed categories.


title:
Contextual Attention Mechanism, SRGAN Based Inpainting System for
  Eliminating Interruptions from Images

abstract:
  The new alternative is to use deep learning to inpaint any image by utilizing
image classification and computer vision techniques. In general, image
inpainting is a task of recreating or reconstructing any broken image which
could be a photograph or oil/acrylic painting. With the advancement in the
field of Artificial Intelligence, this topic has become popular among AI
enthusiasts. With our approach, we propose an initial end-to-end pipeline for
inpainting images using a complete Machine Learning approach instead of a
conventional application-based approach. We first use the YOLO model to
automatically identify and localize the object we wish to remove from the
image. Using the result obtained from the model we can generate a mask for the
same. After this, we provide the masked image and original image to the GAN
model which uses the Contextual Attention method to fill in the region. It
consists of two generator networks and two discriminator networks and is also
called a coarse-to-fine network structure. The two generators use fully
convolutional networks while the global discriminator gets hold of the entire
image as input while the local discriminator gets the grip of the filled region
as input. The contextual Attention mechanism is proposed to effectively borrow
the neighbor information from distant spatial locations for reconstructing the
missing pixels. The third part of our implementation uses SRGAN to resolve the
inpainted image back to its original size. Our work is inspired by the paper
Free-Form Image Inpainting with Gated Convolution and Generative Image
Inpainting with Contextual Attention.


title:
Thinking inside The Box: Learning Hypercube Representations for Group
  Recommendation

abstract:
  As a step beyond traditional personalized recommendation, group
recommendation is the task of suggesting items that can satisfy a group of
users. In group recommendation, the core is to design preference aggregation
functions to obtain a quality summary of all group members' preferences. Such
user and group preferences are commonly represented as points in the vector
space (i.e., embeddings), where multiple user embeddings are compressed into
one to facilitate ranking for group-item pairs. However, the resulted group
representations, as points, lack adequate flexibility and capacity to account
for the multi-faceted user preferences. Also, the point embedding-based
preference aggregation is a less faithful reflection of a group's
decision-making process, where all users have to agree on a certain value in
each embedding dimension instead of a negotiable interval. In this paper, we
propose a novel representation of groups via the notion of hypercubes, which
are subspaces containing innumerable points in the vector space. Specifically,
we design the hypercube recommender (CubeRec) to adaptively learn group
hypercubes from user embeddings with minimal information loss during preference
aggregation, and to leverage a revamped distance metric to measure the affinity
between group hypercubes and item points. Moreover, to counteract the
long-standing issue of data sparsity in group recommendation, we make full use
of the geometric expressiveness of hypercubes and innovatively incorporate
self-supervision by intersecting two groups. Experiments on four real-world
datasets have validated the superiority of CubeRec over state-of-the-art
baselines.


title:
Nonlinear gradient mappings and stochastic optimization: A general
  framework with applications to heavy-tail noise

abstract:
  We introduce a general framework for nonlinear stochastic gradient descent
(SGD) for the scenarios when gradient noise exhibits heavy tails. The proposed
framework subsumes several popular nonlinearity choices, like clipped,
normalized, signed or quantized gradient, but we also consider novel
nonlinearity choices. We establish for the considered class of methods strong
convergence guarantees assuming a strongly convex cost function with Lipschitz
continuous gradients under very general assumptions on the gradient noise. Most
notably, we show that, for a nonlinearity with bounded outputs and for the
gradient noise that may not have finite moments of order greater than one, the
nonlinear SGD's mean squared error (MSE), or equivalently, the expected cost
function's optimality gap, converges to zero at rate~$O(1/t^\zeta)$, $\zeta \in
(0,1)$. In contrast, for the same noise setting, the linear SGD generates a
sequence with unbounded variances. Furthermore, for the nonlinearities that can
be decoupled component wise, like, e.g., sign gradient or component-wise
clipping, we show that the nonlinear SGD asymptotically (locally) achieves a
$O(1/t)$ rate in the weak convergence sense and explicitly quantify the
corresponding asymptotic variance. Experiments show that, while our framework
is more general than existing studies of SGD under heavy-tail noise, several
easy-to-implement nonlinearities from our framework are competitive with state
of the art alternatives on real data sets with heavy tail noises.


title:
SFCW GPR tree roots detection enhancement by time frequency analysis in
  tropical areas

abstract:
  Accurate monitoring of tree roots using ground penetrating radar (GPR) is
very useful in assessing the trees health. In high moisture tropical areas such
as Singapore, tree fall due to root rot can cause loss of lives and properties.
The tropical complex soil characteristics due to the high moisture content
tends to affect penetration depth of the signal. This limits the depth range of
the GPR. Typically, a wide band signal is used to increase the penetration
depth and to improve the resolution of the GPR. However, this broad band
frequency tends to be noisy and selective frequency filtering is required for
noise reduction. Therefore, in this paper, we adapt the stepped frequency
continuous wave (SFCW) GPR and propose the use of a Joint time frequency
analysis (JTFA) method called short time Fourier transform (STFT), to reduce
noise and enhance tree root detection. The proposed methodology is illustrated
and tested with controlled experiments and real tree roots testing. The results
show promising prospects of the method for tree roots detection in tropical
areas.


title:
Normal ordering of degenerate integral powers of number operator and its
  applications

abstract:
  The normal ordering of an integral power of the number operator in terms of
boson operators is expressed with the help of the Stirling numbers of the
second kind. As a `degenerate version' of this, we consider the normal ordering
of a degenerate integral power of the number operator in terms of boson
operators, which is represented by means of the degenerate Stirling numbers of
the second kind. As an application of this normal ordering, we derive two
equations defining the degenerate Stirling numbers of the second kind and a
Dobinski-like formula for the degenerate Bell polynomials.


title:
Effect of electron-phonon interaction on the room temperature
  thermoelectric properties of bulk $MoSe_2$

abstract:
  We study the thermoelectric properties of bulk $MoSe_2$ within relaxation
time approximation including electron-phonon and ionized impurity interactions
using first-principles calculations and at room temperatures. The anisotropy of
this two-dimensional layered metal dichalcogenide is studied by calculations of
electron mobility in the cross-plane and the in-plane directions. We show that
the cross-plane mobility is two orders of magnitude smaller than the in-plane
one. The inclusion of van der Waals interactions further lowers the carrier
mobility in the cross-plane direction but minimally affects the in-plane one.
The results for in-plane electrical mobility and conductivity are in close
agreement with experimentally reported values indicating the accuracy of the
calculations and the dominance of the electron-phonon scattering rates at low
carrier concentrations and the combination of electron-phonon and ionized
impurity scattering at high carrier concentrations. The Seebeck coefficient
calculations show that this coefficient is primarily dictated by the band
structure. The details of relaxation times and inclusion of van der Waals
interactions only slightly change the Seebeck coefficient.


title: Fine-Grained Predicates Learning for Scene Graph Generation

abstract:
  The performance of current Scene Graph Generation models is severely hampered
by some hard-to-distinguish predicates, e.g., "woman-on/standing on/walking
on-beach" or "woman-near/looking at/in front of-child". While general SGG
models are prone to predict head predicates and existing re-balancing
strategies prefer tail categories, none of them can appropriately handle these
hard-to-distinguish predicates. To tackle this issue, inspired by fine-grained
image classification, which focuses on differentiating among
hard-to-distinguish object classes, we propose a method named Fine-Grained
Predicates Learning (FGPL) which aims at differentiating among
hard-to-distinguish predicates for Scene Graph Generation task. Specifically,
we first introduce a Predicate Lattice that helps SGG models to figure out
fine-grained predicate pairs. Then, utilizing the Predicate Lattice, we propose
a Category Discriminating Loss and an Entity Discriminating Loss, which both
contribute to distinguishing fine-grained predicates while maintaining learned
discriminatory power over recognizable ones. The proposed model-agnostic
strategy significantly boosts the performances of three benchmark models
(Transformer, VCTree, and Motif) by 22.8\%, 24.1\% and 21.7\% of Mean Recall
(mR@100) on the Predicate Classification sub-task, respectively. Our model also
outperforms state-of-the-art methods by a large margin (i.e., 6.1\%, 4.6\%, and
3.2\% of Mean Recall (mR@100)) on the Visual Genome dataset.


title: Romanov V48: unusual intermediate polar in the period gap

abstract:
  We studied Romanov V48, which had been considered as a possible polar below
the period minimum (orbital period of 0.0420991 d). We analyzed the publicly
available Zwicky Transient Facility data and found that this object is an
intermediate polar with an orbital period of 0.102312 d (in the period gap) and
a spin period of 0.0420991 d. The amplitude of the spin variation was very
large (0.6 mag) and the profile has been confirmed to vary with the beat period
of 0.071534 d. The ratio between the spin and orbital periods was large and the
system resembles the intermediate polar DW Cnc below the period gap. Infrared
emission from Romanov V48, however, could not be explained by radiation from
the secondary. The infrared excess and the shape of the spectral energy
distribution resembled those of polars, suggesting that the emission mechanism
in Romanov V48 is similar to those of polars. Romanov V48 may be an
intermediate object between intermediate polars and polars.


title: Local theory of functions on tropical curves in $\mathbb R^n$

abstract:
  We first develop the local theory of functions on $\mathbb R^n$ defined by
tropical Laurent polynomials. We study the structure of the semiring of
functions, where two functions are identified when they coincide on a
neighborhood of a fixed point. We see that this semiring is closely related to
the semiring of functions defined by Boolean Laurent polynomials. Then we
develop the local theory of functions on tropical curves. We construct a
contravariant functor from the category of 1-dimensional tropical fans to the
category of certain homomorphisms of semirings. As an application, we discuss
about the smoothness of 1-dimensional tropical fans at the origin.


title: Holomorphic Koszul-Brylinski homology via Dolbeault cohomology

abstract:
  We use the Dolbeault cohomology to investigate the Koszul-Brylinski homology
on holomorphic Poisson manifolds. We obtain the Leray-Hirsch theorem for
Hochschild homology and the Mayer-Vietoris sequence, K\"{u}nneth theorem for
holomorphic Koszul-Brylinski homology. In particular, we show some relations of
holomorphic Koszul-Brylinski homologies around blow-up transforms on general
cases (\emph{not necessarily compact}) by our previous works on the Dolbeault
cohomology.


title:
Probing Structured Pruning on Multilingual Pre-trained Models: Settings,
  Algorithms, and Efficiency

abstract:
  Structured pruning has been extensively studied on monolingual pre-trained
language models and is yet to be fully evaluated on their multilingual
counterparts. This work investigates three aspects of structured pruning on
multilingual pre-trained language models: settings, algorithms, and efficiency.
Experiments on nine downstream tasks show several counter-intuitive phenomena:
for settings, individually pruning for each language does not induce a better
result; for algorithms, the simplest method performs the best; for efficiency,
a fast model does not imply that it is also small. To facilitate the comparison
on all sparsity levels, we present Dynamic Sparsification, a simple approach
that allows training the model once and adapting to different model sizes at
inference. We hope this work fills the gap in the study of structured pruning
on multilingual pre-trained models and sheds light on future research.


title: Distributed Transition Systems with Tags for Privacy Analysis

abstract:
  We present a logical framework that formally models how a given private
information P stored on a given database D, can get captured progressively, by
an agent/adversary querying the database repeatedly.Named DLTTS (Distributed
Labeled Tagged Transition System), the frame-work borrows ideas from several
domains: Probabilistic Automata of Segala, Probabilistic Concurrent Systems,
and Probabilistic labelled transition systems. To every node on a DLTTS is
attached a tag that represents the 'current' knowledge of the adversary,
acquired from the responses of the answering mechanism of the DBMS to his/her
queries, at the nodes traversed earlier, along any given run; this knowledge is
completed at the same node, with further relational deductions, possibly in
combination with 'public' information from other databases given in advance. A
'blackbox' mechanism is also part of a DLTTS, and it is meant as an oracle; its
role is to tell if the private information has been deduced by the adversary at
the current node, and if so terminate the run. An additional special feature is
that the blackbox also gives information on how 'close',or how 'far', the
knowledge of the adversary is, from the private information P , at the current
node. A metric is defined for that purpose, on the set of all 'type compatible'
tuples from the given database, the data themselves being typed with the
headers of the base. Despite the transition systems flavor of our framework,
this metric is not 'behavioral' in the sense presented in some other works. It
is exclusively database oriented,and allows to define new notions of adjacency
and of -indistinguishabilty between databases, more generally than those
usually based on the Hamming metric (and a restricted notion of adjacency).
Examples are given all along to illustrate how our framework works.
Keywords:Database, Privacy, Transition System, Probability, Distribution.


title:
Short review on the refractive index of air as a function of
  temperature, pressure, humidity and ionization

abstract:
  The empirical law of Gladstone-Dale is insufficient for high-precision
studies using the refractivity of a gas: this is not exactly proportional to
its density, and the gas may not be properly described as perfect. An optical
Mariotte temperature allows making a comparative analysis of the results given
by various authors. The effect of hygrometry on the refractivity at visible
wavelengths is historically traced and its small effect on the astronomical
refraction angle numerically shown. Finally at infrared and radio wavelengths,
the effects of the humidity in the lower atmosphere can be strong; as for the
ionosphere, its curvature plays an essential role for the astronomical
refraction angle unlike in the visible.


title:
Interactive Evolutionary Multi-Objective Optimization via
  Learning-to-Rank

abstract:
  In practical multi-criterion decision-making, it is cumbersome if a decision
maker (DM) is asked to choose among a set of trade-off alternatives covering
the whole Pareto-optimal front. This is a paradox in conventional evolutionary
multi-objective optimization (EMO) that always aim to achieve a well balance
between convergence and diversity. In essence, the ultimate goal of
multi-objective optimization is to help a decision maker (DM) identify
solution(s) of interest (SOI) achieving satisfactory trade-offs among multiple
conflicting criteria. Bearing this in mind, this paper develops a framework for
designing preference-based EMO algorithms to find SOI in an interactive manner.
Its core idea is to involve human in the loop of EMO. After every several
iterations, the DM is invited to elicit her feedback with regard to a couple of
incumbent candidates. By collecting such information, her preference is
progressively learned by a learning-to-rank neural network and then applied to
guide the baseline EMO algorithm. Note that this framework is so general that
any existing EMO algorithm can be applied in a plug-in manner. Experiments on
$48$ benchmark test problems with up to 10 objectives fully demonstrate the
effectiveness of our proposed algorithms for finding SOI.


title:
Properties of optical ducts, their chromatism and its effects on
  astronomical refraction

abstract:
  The fundamental quadrature governing light rays in a spherically symmetrical
medium is first recalled. A rigorous discussion of some qualitative properties
of its solutions follows, using the Young-Kattawar diagram which leads to a
geometric formulation of the ray curvature. The case of an optical duct is
deepened, analyzing transfer curves for different positions of the observer
with respect to the duct. New analytical expressions for their wavelength
dependence are derived, and their numerical consequences are coherent with
computer simulations.


title:
Consensual Aggregation on Random Projected High-dimensional Features for
  Regression

abstract:
  In this paper, we present a study of a kernel-based consensual aggregation on
randomly projected high-dimensional features of predictions for regression. The
aggregation scheme is composed of two steps: the high-dimensional features of
predictions, given by a large number of regression estimators, are randomly
projected into a smaller subspace using Johnson-Lindenstrauss Lemma in the
first step, and a kernel-based consensual aggregation is implemented on the
projected features in the second step. We theoretically show that the
performance of the aggregation scheme is close to the performance of the
aggregation implemented on the original high-dimensional features, with high
probability. Moreover, we numerically illustrate that the aggregation scheme
upholds its performance on very large and highly correlated features of
predictions given by different types of machines. The aggregation scheme allows
us to flexibly merge a large number of redundant machines, plainly constructed
without model selection or cross-validation. The efficiency of the proposed
method is illustrated through several experiments evaluated on different types
of synthetic and real datasets.


title: A collocation IGA-BEM for 3D potential problems on unbounded domains

abstract:
  In this paper the numerical solution of potential problems defined on 3D
unbounded domains is addressed with Boundary Element Methods (BEMs), since in
this way the problem is studied only on the boundary, and thus any finite
approximation of the infinite domain can be avoided. The isogeometric analysis
(IGA) setting is considered and in particular B-splines and NURBS functions are
taken into account. In order to exploit all the possible benefits from using
spline spaces, an important point is the development of specific cubature
formulas for weakly and nearly singular integrals. Our proposal for this aim is
based on spline quasi-interpolation and on the use of a spline product formula.
Besides that, a robust singularity extraction procedure is introduced as a
preliminary step and an efficient function-by-function assembly phase is
adopted. A selection of numerical examples confirms that the numerical
solutions reach the expected convergence orders.


title: Face recognition in a transformed domain

abstract:
  This paper proposes the use of a discrete cosine transform (DCT) instead of
the eigenfaces method (Karhunen-Loeve Transform) for biometric identification
based on frontal face images. Experimental results show better recognition
accuracies and reduced computational burden. This paper includes results with
different classifiers and a combination of them.


title:
A New Nonlinear speaker parameterization algorithm for speaker
  identification

abstract:
  In this paper we propose a new parameterization algorithm based on nonlinear
prediction, which is an extension of the classical LPC parameters. The
parameters performances are estimated by two different methods: the
Arithmetic-Harmonic Sphericity (AHS) and the Auto-Regressive Vector Model
(ARVM). Two different methods are proposed for the parameterization based on
the Neural Predictive Coding (NPC): classical neural networks initialization
and linear initialization. We applied these two parameters to speaker
identification. The fist parameters obtained smaller rates. We show for the
first parameters how they can be combined with the classical parameters (LPCC,
MFCC, etc.) in order to improve the results of only one classical
parameterization (MFCC provides 97.55% and MFCC+NPC 98.78%). For the linear
initialization, we obtain 100% which is great improvement. This study opens a
new way towards different parameterization schemes that offer better accuracy
on speaker recognition tasks.


title: Efficient Test-Time Model Adaptation without Forgetting

abstract:
  Test-time adaptation (TTA) seeks to tackle potential distribution shifts
between training and testing data by adapting a given model w.r.t. any testing
sample. This task is particularly important for deep models when the test
environment changes frequently. Although some recent attempts have been made to
handle this task, we still face two practical challenges: 1) existing methods
have to perform backward computation for each test sample, resulting in
unbearable prediction cost to many applications; 2) while existing TTA
solutions can significantly improve the test performance on out-of-distribution
data, they often suffer from severe performance degradation on in-distribution
data after TTA (known as catastrophic forgetting). In this paper, we point out
that not all the test samples contribute equally to model adaptation, and
high-entropy ones may lead to noisy gradients that could disrupt the model.
Motivated by this, we propose an active sample selection criterion to identify
reliable and non-redundant samples, on which the model is updated to minimize
the entropy loss for test-time adaptation. Furthermore, to alleviate the
forgetting issue, we introduce a Fisher regularizer to constrain important
model parameters from drastic changes, where the Fisher importance is estimated
from test samples with generated pseudo labels. Extensive experiments on
CIFAR-10-C, ImageNet-C, and ImageNet-R verify the effectiveness of our proposed
method.


title:
Cloning Outfits from Real-World Images to 3D Characters for
  Generalizable Person Re-Identification

abstract:
  Recently, large-scale synthetic datasets are shown to be very useful for
generalizable person re-identification. However, synthesized persons in
existing datasets are mostly cartoon-like and in random dress collocation,
which limits their performance. To address this, in this work, an automatic
approach is proposed to directly clone the whole outfits from real-world person
images to virtual 3D characters, such that any virtual person thus created will
appear very similar to its real-world counterpart. Specifically, based on UV
texture mapping, two cloning methods are designed, namely registered clothes
mapping and homogeneous cloth expansion. Given clothes keypoints detected on
person images and labeled on regular UV maps with clear clothes structures,
registered mapping applies perspective homography to warp real-world clothes to
the counterparts on the UV map. As for invisible clothes parts and irregular UV
maps, homogeneous expansion segments a homogeneous area on clothes as a
realistic cloth pattern or cell, and expand the cell to fill the UV map.
Furthermore, a similarity-diversity expansion strategy is proposed, by
clustering person images, sampling images per cluster, and cloning outfits for
3D character generation. This way, virtual persons can be scaled up densely in
visual similarity to challenge model learning, and diversely in population to
enrich sample distribution. Finally, by rendering the cloned characters in
Unity3D scenes, a more realistic virtual dataset called ClonedPerson is
created, with 5,621 identities and 887,766 images. Experimental results show
that the model trained on ClonedPerson has a better generalization performance,
superior to that trained on other popular real-world and synthetic person
re-identification datasets. The ClonedPerson project is available at
https://github.com/Yanan-Wang-cs/ClonedPerson.


title:
Study of the front-end signal for the 3-inch PMTs instrumentation in
  JUNO

abstract:
  25,600 3-inch PMTs will be installed in Jiangmen Underground Neutrino
Observatory (JUNO) to achieve more precise energy calibration and to extend the
physics detection potential. Performances of all bare PMTs have been
characterized and these PMTs are being instrumented with the high voltage
divider, underwater front-end cable, and connector. In this paper, we present a
dedicated study on signal quality at different stages of the instrumentation.
An optimized high voltage ratio was confirmed and finalized which improved the
PMT transit time spread by 25%. The signal charge was attenuated by 22.5%
(13.0%) in the 10 m (5 m) cable and it required the addition of 45 V (23 V) to
compensate for the loss of PMT gain. There was a 1% overshoot following the PMT
signal and no sign of reflection in the connector. A group of 16 3-inch PMTs
with the full instrumentation was installed in the JUNO prototype detector
together with a few 8-inch and 20-inch PMTs, which showed good stability and
demonstrated a photon detection system with multiple types of PMTs.


title: Albanese maps for open algebraic spaces

abstract:
  We show that for each algebraic space that is separated and of finite type
over a field, and whose affinization is connected and reduced, there is a
universal morphism to a para-abelian variety. The latter are schemes that
acquire the structure of an abelian variety after some ground field extension.
This generalizes classical results of Serre on universal morphisms from
algebraic varieties to abelian varieties. Our proof relies on corresponding
facts for the proper case, together with the theory of Macaulayfication,
removal of singularities by alterations, pseudo-rational singularities,
ind-objects, and Bockstein maps. It turns out that the formation of the
Albanese variety commutes with base-change up to universal homeomorphisms. We
also give a detailed analysis of Albanese maps for algebraic curves and
algebraic groups, with special emphasis on imperfect ground fields.


title:
Phase-resolved ocean wave forecast with simultaneous current estimation
  through data assimilation

abstract:
  In Wang & Pan (J. Fluid Mech., vol. 918, A19, 2021), the authors developed
the first ensemble-based data assimilation (DA) capability for the
reconstruction and forecast of ocean surface waves, namely the EnKF-HOS method
coupling an ensemble Kalman filter (EnKF) and the high-order spectral (HOS)
method. In this work, we continue to enrich the method by allowing it to
simultaneously estimate the ocean current field, which is in general not known
a priori and can (slowly) vary in both space and time. To achieve this goal, we
incorporate the effect of ocean current (as unknown parameters) on waves to
build the HOS-C method as the forward prediction model, and obtain a
simultaneous estimation of (current) parameters and (wave) states via an
iterative EnKF (IEnKF) method that is necessary to handle the complexity in
this DA problem. The new algorithm, named IEnKF-HOS-C method, is first tested
in synthetic problems with various forms (steady/unsteady, uniform/non-uniform)
of current. It is shown that the IEnKF-HOS-C method is able to not only
estimate the current field accurately, but also boost the prediction accuracy
of the wave field (even) relative to the state-of-the-art EnKF-HOS method.
Finally, using real data from a shipborne radar, we show that the IEnKF-HOS-C
method successfully recovers the current speed that matches the in situ
measurement by a floating buoy.


title:
Sign-switching of superexchange mediated by few electrons under
  non-uniform magnetic field

abstract:
  Long range interaction between distant spins is an important building block
for the realization of large quantum-dot network in which couplings between
pairs of spins can be selectively addressed. Recent experiments on coherent
logical states oscillation between remote spins facilitated by intermediate
electron states has paved the first step for large scale quantum information
processing. Reaching this ultimate goal requires extensive studies on the
superexchange interaction on different quantum-dot spatial arrangements and
electron configurations. Here, we consider a linear triple-quantum-dot with two
anti-parallel spins in the outer dots forming the logical states while various
number of electrons in the middle dot forming a mediator, which facilitates the
superexchange interaction. We show that the superexchange is enhanced when the
number of mediating electrons increases. In addition, we show that forming a
four-electron triplet in the mediator dot further enhance the superexchange
strength. Our work can be a guide to scale up the quantum-dot array with
controllable and dense connectivity.


title: Mockingbird lattices

abstract:
  We study combinatorial and order theoretic structures arising from the
fragment of combinatory logic spanned by the basic combinator ${\bf M}$. This
basic combinator, named as the Mockingbird by Smullyan, is defined by the
rewrite rule ${\bf M} x_1 \to x_1 x_1$. We prove that the reflexive and
transitive closure of this rewrite relation is a partial order on terms on
${\bf M}$ and that all connected components of its rewrite graph are Hasse
diagrams of lattices. This last result is based on the introduction of lattices
on some forests. We enumerate the elements, the edges of the Hasse diagrams,
and the intervals of these lattices with the help of formal power series on
terms and on forests.


title:
Fault Diagnosis of Discrete-Event Systems under Non-Deterministic
  Observations with Output Fairness

abstract:
  In this paper, we revisit the fault diagnosis problem of discrete-event
systems (DES) under non-deterministic observations. Non-deterministic
observation is a general observation model that includes the case of
intermittent loss of observations. In this setting, upon the occurrence of an
event, the sensor reading may be non-deterministic such that a set of output
symbols are all possible. Existing works on fault diagnosis under
non-deterministic observations require to consider all possible observation
realizations. However, this approach includes the case where some possible
outputs are permanently disabled. In this work, we introduce the concept of
output fairness by requiring that, for any output symbols, if it has infinite
chances to be generated, then it will indeed be generated infinite number of
times. We use an assume-guarantee type of linear temporal logic formulas to
formally describe this assumption. A new notion called output-fair
diagnosability (OF-diagnosability) is proposed. An effective approach is
provided for the verification of OF-diagnosability. We show that the proposed
notion of OF-diagnosability is weaker than the standard definition of
diagnosability under non-deterministic observations, and it better captures the
physical scenario of observation non-determinism or intermittent loss of
observations.


title: Data-Driven Approach for Log Instruction Quality Assessment

abstract:
  In the current IT world, developers write code while system operators run the
code mostly as a black box. The connection between both worlds is typically
established with log messages: the developer provides hints to the (unknown)
operator, where the cause of an occurred issue is, and vice versa, the operator
can report bugs during operation. To fulfil this purpose, developers write log
instructions that are structured text commonly composed of a log level (e.g.,
"info", "error"), static text ("IP {} cannot be reached"), and dynamic
variables (e.g. IP {}). However, as opposed to well-adopted coding practices,
there are no widely adopted guidelines on how to write log instructions with
good quality properties. For example, a developer may assign a high log level
(e.g., "error") for a trivial event that can confuse the operator and increase
maintenance costs. Or the static text can be insufficient to hint at a specific
issue. In this paper, we address the problem of log quality assessment and
provide the first step towards its automation. We start with an in-depth
analysis of quality log instruction properties in nine software systems and
identify two quality properties: 1) correct log level assignment assessing the
correctness of the log level, and 2) sufficient linguistic structure assessing
the minimal richness of the static text necessary for verbose event
description. Based on these findings, we developed a data-driven approach that
adapts deep learning methods for each of the two properties. An extensive
evaluation on large-scale open-source systems shows that our approach correctly
assesses log level assignments with an accuracy of 0.88, and the sufficient
linguistic structure with an F1 score of 0.99, outperforming the baselines. Our
study shows the potential of the data-driven methods in assessing instructions
quality and aid developers in comprehending and writing better code.


title:
The $\chi_\mathrm{eff}-z$ correlation of field binary black hole mergers
  and how 3G gravitational-wave detectors can constrain it

abstract:
  Understanding the origin of merging binary black holes is currently one of
the most pressing quests in astrophysics. We show that if isolated binary
evolution dominates the formation mechanism of merging binary black holes, one
should expect a correlation between the effective spin parameter,
$\chi_\mathrm{eff}$, and the redshift of the merger, $z$, of binary black
holes. This correlation comes from tidal spin-up systems preferentially forming
and merging at higher redshifts due to the combination of weaker orbital
expansion from low metallicity stars given their reduced wind mass loss rate,
delayed expansion and have smaller maximal radii during the supergiant phase
compared to stars at higher metallicity. As a result, these tightly bound
systems merge with short inspiral times. Given our fiducial model of isolated
binary evolution, we show that the origin of a $\chi_\mathrm{eff}-z$
correlation in the detectable LIGO--Virgo binary black hole population is
different from the intrinsic population, which will become accessible only in
the future by third-generation gravitational-wave detectors such as Einstein
Telescope and Cosmic Explorer. Finally, we compare our model predictions with
population predictions based on the current catalog of binary black hole
mergers and find that current data favor a positive correlation of
$\chi_\mathrm{eff}-z$ as predicted by our model of isolated binary evolution.


title: Towards Robust Adaptive Object Detection under Noisy Annotations

abstract:
  Domain Adaptive Object Detection (DAOD) models a joint distribution of images
and labels from an annotated source domain and learns a domain-invariant
transformation to estimate the target labels with the given target domain
images. Existing methods assume that the source domain labels are completely
clean, yet large-scale datasets often contain error-prone annotations due to
instance ambiguity, which may lead to a biased source distribution and severely
degrade the performance of the domain adaptive detector de facto. In this
paper, we represent the first effort to formulate noisy DAOD and propose a
Noise Latent Transferability Exploration (NLTE) framework to address this
issue. It is featured with 1) Potential Instance Mining (PIM), which leverages
eligible proposals to recapture the miss-annotated instances from the
background; 2) Morphable Graph Relation Module (MGRM), which models the
adaptation feasibility and transition probability of noisy samples with
relation matrices; 3) Entropy-Aware Gradient Reconcilement (EAGR), which
incorporates the semantic information into the discrimination process and
enforces the gradients provided by noisy and clean samples to be consistent
towards learning domain-invariant representations. A thorough evaluation on
benchmark DAOD datasets with noisy source annotations validates the
effectiveness of NLTE. In particular, NLTE improves the mAP by 8.4\% under 60\%
corrupted annotations and even approaches the ideal upper bound of training on
a clean source dataset.


title:
Negative-chirality order in $S=1/2$ kagome antiferromagnet
  CdCu$_{3}$(OH)$_{6}$(NO$_{3}$)$_{2}\cdot $H$_{2}$O

abstract:
  The neutron diffraction and nuclear magnetic resonance (NMR) measurements
have been used to microscopically analyze the magnetic structure in the $S =
1/2$ kagome antiferromagnet CdCu$_{3}$(OH)$_{6}$(NO$_{3}$)$_{2}\cdot $H$_{2}$O.
Below the magnetic ordering temperature $T_N\simeq 4$ K, magnetic Bragg
reflections at (110) and (100) were found in the neutron diffraction pattern,
which suggests a $q=0$ magnetic structure. Furthermore, the vector spin
chirality for the $q=0$ structure was successfully identified from the internal
field direction obtained by the $^{14}$N-NMR measurement. Our findings point to
a chirality-ordered magnetic structure with negative vector chirality and
$\langle 100 \rangle$ anisotropy.


title:
The Interplay Between Disorder and Collective Coherent Response:
  Superradiance and Spectral Motional Narrowing in the Time Domain

abstract:
  The interplay between static and dynamic disorder and collective optical
response in molecular ensembles is an important characteristic of nanoplasmonic
and nanophotonic molecular systems. Here we investigate the cooperative
superradiant response of a molecular ensemble of quantum emitters under the
influence of environmental disorder, including inhomogeneous broadening (as
induced by static random distribution of the molecular transition frequencies)
and motional narrowing (as induced by stochastic modulation of these excitation
energies). The effect of inhomogeneous broadening is to destroy the coherence
of the collective molecular excitation and suppress superradiant emission.
However, fast stochastic modulation of the molecular excitation energy can
effectively restore the coherence of the quantum emitters and lead to a
recovery of superradiant emission, which is an unexpected manifestation of
motional narrowing. For a light scattering process as induced by an
off-resonant incident pulse, stochastic modulation leads to inelastic
fluorescence emission at the average excitation energy at long times and
suggests that dynamic disorder effects can actually lead to collective
excitation of the molecular ensemble.


title: Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction

abstract:
  Stock market plays an important role in the economic development. Due to the
complex volatility of the stock market, the research and prediction on the
change of the stock price, can avoid the risk for the investors. The
traditional time series model ARIMA can not describe the nonlinearity, and can
not achieve satisfactory results in the stock prediction. As neural networks
are with strong nonlinear generalization ability, this paper proposes an
attention-based CNN-LSTM and XGBoost hybrid model to predict the stock price.
The model constructed in this paper integrates the time series model, the
Convolutional Neural Networks with Attention mechanism, the Long Short-Term
Memory network, and XGBoost regressor in a non-linear relationship, and
improves the prediction accuracy. The model can fully mine the historical
information of the stock market in multiple periods. The stock data is first
preprocessed through ARIMA. Then, the deep learning architecture formed in
pretraining-finetuning framework is adopted. The pre-training model is the
Attention-based CNN-LSTM model based on sequence-to-sequence framework. The
model first uses convolution to extract the deep features of the original stock
data, and then uses the Long Short-Term Memory networks to mine the long-term
time series features. Finally, the XGBoost model is adopted for fine-tuning.
The results show that the hybrid model is more effective and the prediction
accuracy is relatively high, which can help investors or institutions to make
decisions and achieve the purpose of expanding return and avoiding risk. Source
code is available at
https://github.com/zshicode/Attention-CLX-stock-prediction.


title:
There Are a Thousand Hamlets in a Thousand People's Eyes: Enhancing
  Knowledge-grounded Dialogue with Personal Memory

abstract:
  Knowledge-grounded conversation (KGC) shows great potential in building an
engaging and knowledgeable chatbot, and knowledge selection is a key ingredient
in it. However, previous methods for knowledge selection only concentrate on
the relevance between knowledge and dialogue context, ignoring the fact that
age, hobby, education and life experience of an interlocutor have a major
effect on his or her personal preference over external knowledge. Without
taking the personalization issue into account, it is difficult to select the
proper knowledge and generate persona-consistent responses. In this work, we
introduce personal memory into knowledge selection in KGC to address the
personalization issue. We propose a variational method to model the underlying
relationship between one's personal memory and his or her selection of
knowledge, and devise a learning scheme in which the forward mapping from
personal memory to knowledge and its inverse mapping is included in a closed
loop so that they could teach each other. Experiment results show that our
method outperforms existing KGC methods significantly on both automatic
evaluation and human evaluation.


title:
Bridging the Gap of AutoGraph between Academia and Industry: Analysing
  AutoGraph Challenge at KDD Cup 2020

abstract:
  Graph structured data is ubiquitous in daily life and scientific areas and
has attracted increasing attention. Graph Neural Networks (GNNs) have been
proved to be effective in modeling graph structured data and many variants of
GNN architectures have been proposed. However, much human effort is often
needed to tune the architecture depending on different datasets. Researchers
naturally adopt Automated Machine Learning on Graph Learning, aiming to reduce
the human effort and achieve generally top-performing GNNs, but their methods
focus more on the architecture search. To understand GNN practitioners'
automated solutions, we organized AutoGraph Challenge at KDD Cup 2020,
emphasizing on automated graph neural networks for node classification. We
received top solutions especially from industrial tech companies like Meituan,
Alibaba and Twitter, which are already open sourced on Github. After detailed
comparisons with solutions from academia, we quantify the gaps between academia
and industry on modeling scope, effectiveness and efficiency, and show that (1)
academia AutoML for Graph solutions focus on GNN architecture search while
industrial solutions, especially the winning ones in the KDD Cup, tend to
obtain an overall solution (2) by neural architecture search only, academia
solutions achieve on average 97.3% accuracy of industrial solutions (3)
academia solutions are cheap to obtain with several GPU hours while industrial
solutions take a few months' labors. Academic solutions also contain much fewer
parameters.


title:
A Weakly Supervised Propagation Model for Rumor Verification and Stance
  Detection with Multiple Instance Learning

abstract:
  The diffusion of rumors on microblogs generally follows a propagation tree
structure, that provides valuable clues on how an original message is
transmitted and responded by users over time. Recent studies reveal that rumor
detection and stance detection are two different but relevant tasks which can
jointly enhance each other, e.g., rumors can be debunked by cross-checking the
stances conveyed by their relevant microblog posts, and stances are also
conditioned on the nature of the rumor. However, most stance detection methods
require enormous post-level stance labels for training, which are
labor-intensive given a large number of posts. Enlightened by Multiple Instance
Learning (MIL) scheme, we first represent the diffusion of claims with
bottom-up and top-down trees, then propose two tree-structured weakly
supervised frameworks to jointly classify rumors and stances, where only the
bag-level labels concerning claim's veracity are needed. Specifically, we
convert the multi-class problem into a multiple MIL-based binary classification
problem where each binary model focuses on differentiating a target stance or
rumor type and other types. Finally, we propose a hierarchical attention
mechanism to aggregate the binary predictions, including (1) a bottom-up or
top-down tree attention layer to aggregate binary stances into binary veracity;
and (2) a discriminative attention layer to aggregate the binary class into
finer-grained classes. Extensive experiments conducted on three Twitter-based
datasets demonstrate promising performance of our model on both claim-level
rumor detection and post-level stance classification compared with
state-of-the-art methods.


title:
Cluster Synchronization of Kuramoto Oscillators and Brain Functional
  Connectivity

abstract:
  The recent progress of functional magnetic resonance imaging techniques has
unveiled that human brains exhibit clustered correlation patterns of their
spontaneous activities. It is important to understand the mechanism of cluster
synchronization phenomena since it may reflect the underlying brain functions
and brain diseases. In this paper, we investigate cluster synchronization
conditions for networks of Kuramoto oscillators. The key analytical tool that
we use is the method of averaging, and we provide a unified framework of
stability analysis for cluster synchronization. The main results show that
cluster synchronization is achieved if (i) the inter-cluster coupling strengths
are sufficiently weak and/or (ii) the natural frequencies are largely different
among clusters. Moreover, we apply our theoretical findings to empirical brain
networks. Discussions on how to understand brain functional connectivity and
further directions to investigate neuroscientific questions are provided.


title:
Asymptotics and sign patterns for coefficients in expansions of Habiro
  elements

abstract:
  We prove asymptotics and study sign patterns for coefficients in expansions
of elements in the Habiro ring which satisfy a strange identity. As an
application, we prove asymptotics and discuss positivity for the generalized
Fishburn numbers which arise from the Kontsevich-Zagier series associated to
the colored Jones polynomial for a family of torus knots. This extends Zagier's
result on asymptotics for the Fishburn numbers.


title: An Automated Conversion Between Selected Robot Kinematic Representations

abstract:
  This paper presents a methodology that forms an automated tool for robot
kinematic representation conversion, called the RobKin Interpreter. It is a set
of analytical algorithms that can analyze an input robot representation,
express the joints globally in matrix form, and map to other representations
such as standard Denavit-Hartenberg parameters, Roll-Pitch-Yaw angles with
translational displacement, and Product of Exponentials with a possibility to
generate a URDF (Universal Robot Description Format) file from any of them. It
works for revolute and prismatic joints and can interpret even arbitrary
kinematic structures that do not have orthogonally placed joints.


title: IterVM: Iterative Vision Modeling Module for Scene Text Recognition

abstract:
  Scene text recognition (STR) is a challenging problem due to the imperfect
imagery conditions in natural images. State-of-the-art methods utilize both
visual cues and linguistic knowledge to tackle this challenging problem.
Specifically, they propose iterative language modeling module (IterLM) to
repeatedly refine the output sequence from the visual modeling module (VM).
Though achieving promising results, the vision modeling module has become the
performance bottleneck of these methods. In this paper, we newly propose
iterative vision modeling module (IterVM) to further improve the STR accuracy.
Specifically, the first VM directly extracts multi-level features from the
input image, and the following VMs re-extract multi-level features from the
input image and fuse them with the high-level (i.e., the most semantic one)
feature extracted by the previous VM. By combining the proposed IterVM with
iterative language modeling module, we further propose a powerful scene text
recognizer called IterNet. Extensive experiments demonstrate that the proposed
IterVM can significantly improve the scene text recognition accuracy,
especially on low-quality scene text images. Moreover, the proposed scene text
recognizer IterNet achieves new state-of-the-art results on several public
benchmarks. Codes will be available at https://github.com/VDIGPKU/IterNet.


title:
Super-resolved multi-temporal segmentation with deep
  permutation-invariant networks

abstract:
  Multi-image super-resolution from multi-temporal satellite acquisitions of a
scene has recently enjoyed great success thanks to new deep learning models. In
this paper, we go beyond classic image reconstruction at a higher resolution by
studying a super-resolved inference problem, namely semantic segmentation at a
spatial resolution higher than the one of sensing platform. We expand upon
recently proposed models exploiting temporal permutation invariance with a
multi-resolution fusion module able to infer the rich semantic information
needed by the segmentation task. The model presented in this paper has recently
won the AI4EO challenge on Enhanced Sentinel 2 Agriculture.


title: DeFTA: A Plug-and-Play Decentralized Replacement for FedAvg

abstract:
  Federated learning (FL) is identified as a crucial enabler for large-scale
distributed machine learning (ML) without the need for local raw dataset
sharing, substantially reducing privacy concerns and alleviating the isolated
data problem. In reality, the prosperity of FL is largely due to a centralized
framework called FedAvg, in which workers are in charge of model training and
servers are in control of model aggregation. However, FedAvg's centralized
worker-server architecture has raised new concerns, be it the low scalability
of the cluster, the risk of data leakage, and the failure or even defection of
the central server. To overcome these problems, we propose Decentralized
Federated Trusted Averaging (DeFTA), a decentralized FL framework that serves
as a plug-and-play replacement for FedAvg, instantly bringing better security,
scalability, and fault-tolerance to the federated learning process after
installation. In principle, it fundamentally resolves the above-mentioned
issues from an architectural perspective without compromises or tradeoffs,
primarily consisting of a new model aggregating formula with theoretical
performance analysis, and a decentralized trust system (DTS) to greatly improve
system robustness. Note that since DeFTA is an alternative to FedAvg at the
framework level, \textit{prevalent algorithms published for FedAvg can be also
utilized in DeFTA with ease}. Extensive experiments on six datasets and six
basic models suggest that DeFTA not only has comparable performance with FedAvg
in a more realistic setting, but also achieves great resilience even when 66%
of workers are malicious. Furthermore, we also present an asynchronous variant
of DeFTA to endow it with more powerful usability.


title: DAGAM: Data Augmentation with Generation And Modification

abstract:
  Text classification is a representative downstream task of natural language
processing, and has exhibited excellent performance since the advent of
pre-trained language models based on Transformer architecture. However, in
pre-trained language models, under-fitting often occurs due to the size of the
model being very large compared to the amount of available training data. Along
with significant importance of data collection in modern machine learning
paradigm, studies have been actively conducted for natural language data
augmentation. In light of this, we introduce three data augmentation schemes
that help reduce underfitting problems of large-scale language models.
Primarily we use a generation model for data augmentation, which is defined as
Data Augmentation with Generation (DAG). Next, we augment data using text
modification techniques such as corruption and word order change (Data
Augmentation with Modification, DAM). Finally, we propose Data Augmentation
with Generation And Modification (DAGAM), which combines DAG and DAM techniques
for a boosted performance. We conduct data augmentation for six benchmark
datasets of text classification task, and verify the usefulness of DAG, DAM,
and DAGAM through BERT-based fine-tuning and evaluation, deriving better
results compared to the performance with original datasets.


title: Federated Reinforcement Learning with Environment Heterogeneity

abstract:
  We study a Federated Reinforcement Learning (FedRL) problem in which $n$
agents collaboratively learn a single policy without sharing the trajectories
they collected during agent-environment interaction. We stress the constraint
of environment heterogeneity, which means $n$ environments corresponding to
these $n$ agents have different state transitions. To obtain a value function
or a policy function which optimizes the overall performance in all
environments, we propose two federated RL algorithms, \texttt{QAvg} and
\texttt{PAvg}. We theoretically prove that these algorithms converge to
suboptimal solutions, while such suboptimality depends on how heterogeneous
these $n$ environments are. Moreover, we propose a heuristic that achieves
personalization by embedding the $n$ environments into $n$ vectors. The
personalization heuristic not only improves the training but also allows for
better generalization to new environments.


title:
PVI-DSO: Leveraging Planar Regularities for Direct Sparse
  Visual-Inertial Odometry

abstract:
  The monocular Visual-Inertial Odometry (VIO) based on the direct method can
leverage all the available pixels in the image to estimate the camera motion
and reconstruct the environment. The denser map reconstruction provides more
information about the environment, making it easier to extract structure and
planar regularities. In this paper, we propose a monocular direct sparse
visual-inertial odometry, which exploits the plane regularities (PVI-DSO). Our
system detects coplanar information from 3D meshes generated from 3D point
clouds and uses coplanar parameters to introduce coplanar constraints. In order
to reduce computation and improve compactness, the plane-distance cost is
directly used as the prior information of plane parameters. We conduct ablation
experiments on public datasets and compare our system with other
state-of-the-art algorithms. The experimental results verified leveraging the
plane information can improve the accuracy of the VIO system based on the
direct method.


title: Failure Identification from Unstable Log Data using Deep Learning

abstract:
  The reliability of cloud platforms is of significant relevance because
society increasingly relies on complex software systems running on the cloud.
To improve it, cloud providers are automating various maintenance tasks, with
failure identification frequently being considered. The precondition for
automation is the availability of observability tools, with system logs
commonly being used. The focus of this paper is log-based failure
identification. This problem is challenging because of the instability of the
log data and the incompleteness of the explicit logging failure coverage within
the code. To address the two challenges, we present CLog as a method for
failure identification. The key idea presented herein based is on our
observation that by representing the log data as sequences of subprocesses
instead of sequences of log events, the effect of the unstable log data is
reduced. CLog introduces a novel subprocess extraction method that uses
context-aware neural network and clustering methods to extract meaningful
subprocesses. The direct modeling of log event contexts allows the
identification of failures with respect to the abrupt context changes,
addressing the challenge of insufficient logging failure coverage. Our
experimental results demonstrate that the learned subprocesses representations
reduce the instability in the input, allowing CLog to outperform the baselines
on the failure identification subproblems - 1) failure detection by 9-24% on F1
score and 2) failure type identification by 7% on the macro averaged F1 score.
Further analysis shows the existent negative correlation between the
instability in the input event sequences and the detection performance in a
model-agnostic manner.


title:
Global HRTF Interpolation via Learned Affine Transformation of
  Hyper-conditioned Features

abstract:
  Estimating Head-Related Transfer Functions (HRTFs) of arbitrary source points
is essential in immersive binaural audio rendering. Computing each individual's
HRTFs is challenging, as traditional approaches require expensive time and
computational resources, while modern data-driven approaches are data-hungry.
Especially for the data-driven approaches, existing HRTF datasets differ in
spatial sampling distributions of source positions, posing a major problem when
generalizing the method across multiple datasets. To alleviate this, we propose
a deep learning method based on a novel conditioning architecture. The proposed
method can predict an HRTF of any position by interpolating the HRTFs of known
distributions. Experimental results show that the proposed architecture
improves the model's generalizability across datasets with various coordinate
systems. Additional demonstrations using coarsened HRTFs demonstrate that the
model robustly reconstructs the target HRTFs from the coarsened data.


title:
Monotone Improvement of Information-Geometric Optimization Algorithms
  with a Surrogate Function

abstract:
  A surrogate function is often employed to reduce the number of objective
function evaluations for optimization. However, the effect of using a surrogate
model in evolutionary approaches has not been theoretically investigated. This
paper theoretically analyzes the information-geometric optimization framework
using a surrogate function. The value of the expected objective function under
the candidate sampling distribution is used as the measure of progress of the
algorithm. We assume that the surrogate function is maintained so that the
population version of the Kendall's rank correlation coefficient between the
surrogate function and the objective function under the candidate sampling
distribution is greater than or equal to a predefined threshold. We prove that
information-geometric optimization using such a surrogate function leads to a
monotonic decrease in the expected objective function value if the threshold is
sufficiently close to one. The acceptable threshold value is analyzed for the
case of the information-geometric optimization instantiated with Gaussian
distributions, i.e., the rank-$\mu$ update CMA-ES, on a convex quadratic
objective function. As an alternative to the Kendall's rank correlation
coefficient, we investigate the use of the Pearson correlation coefficient
between the weights assigned to candidate solutions based on the objective
function and the surrogate function.


title:
Representation Selective Self-distillation and wav2vec 2.0 Feature
  Exploration for Spoof-aware Speaker Verification

abstract:
  Text-to-speech and voice conversion studies are constantly improving to the
extent where they can produce synthetic speech almost indistinguishable from
bona fide human speech. In this regrad, the importance of countermeasures (CM)
against synthetic voice attacks of the automatic speaker verification (ASV)
systems emerges. Nonetheless, most end-to-end spoofing detection networks are
black box systems, and the answer to what is an effective representation for
finding artifacts still remains veiled. In this paper, we examine which feature
space can effectively represent synthetic artifacts using wav2vec 2.0, and
study which architecture can effectively utilize the space. Our study allows us
to analyze which attribute of speech signals is advantageous for the CM
systems. The proposed CM system achieved 0.31% equal error rate (EER) on
ASVspoof 2019 LA evaluation set for the spoof detection task. We further
propose a simple yet effective spoofing aware speaker verification (SASV)
methodology, which takes advantage of the disentangled representations from our
countermeasure system. Evaluation performed with the SASV Challenge 2022
database show 1.08% of SASV EER. Quantitative analysis shows that using the
explored feature space of wav2vec 2.0 advantages both spoofing CM and SASV.


title: Application of Perron Trees to Geometric Maximal Operators

abstract:
  We characterize the $L^p(\mathbb{R}^2)$ boundeness of the geometric maximal
operator $M_{a,b}$ associated to the basis $\mathcal{B}_{a,b}$ ($a,b > 0$)
which is composed of rectangles $R$ whose eccentricity and orientation is of
the form $$\left( e_R ,\omega_R \right) = \left( \frac{1}{n^a} ,
\frac{\pi}{4n^b} \right)$$ for some $n \in \mathbb{N}^*$. The proof involves
\textit{generalized Perron trees}, as constructed in \cite{KATHRYN JAN}.


title:
The Swiss Army Knife for Image-to-Image Translation: Multi-Task
  Diffusion Models

abstract:
  Recently, diffusion models were applied to a wide range of image analysis
tasks. We build on a method for image-to-image translation using denoising
diffusion implicit models and include a regression problem and a segmentation
problem for guiding the image generation to the desired output. The main
advantage of our approach is that the guidance during the denoising process is
done by an external gradient. Consequently, the diffusion model does not need
to be retrained for the different tasks on the same dataset. We apply our
method to simulate the aging process on facial photos using a regression task,
as well as on a brain magnetic resonance (MR) imaging dataset for the
simulation of brain tumor growth. Furthermore, we use a segmentation model to
inpaint tumors at the desired location in healthy slices of brain MR images. We
achieve convincing results for all problems.


title:
Unconstrained Proximal Operator: the Optimal Parameter for the
  Douglas-Rachford Type Primal-Dual Methods

abstract:
  In this work, we propose an alternative parametrized form of the proximal
operator, of which the parameter no longer needs to be positive. That is, the
parameter can be a non-zero scalar, a full-rank square matrix, or, more
generally, a bijective bounded linear operator. We demonstrate that the
positivity requirement is essentially due to a quadratic form. We prove several
key characterizations for the new form in a generic way (with an operator
parameter). We establish the optimal choice of parameter for the
Douglas-Rachford type methods by solving a simple unconstrained optimization
problem. The optimality is in the sense that a non-ergodic worst-case
convergence rate bound is minimized. We provide closed-form optimal choices for
scalar and orthogonal matrix parameters under zero initialization.
Additionally, a simple self-contained proof of a sharp linear convergence rate
for a $ (1/L) $-cocoercive fixed-point sequence with $ L\in(0,1) $ is provided
(as a preliminary result).
  To our knowledge, an operator parameter is new. To show its practical use, we
design a dedicated parameter for the 2-by-2 block-structured semidefinite
program (SDP). Such a structured SDP is strongly related to the quadratically
constrained quadratic program (QCQP), and we therefore expect the proposed
parameter to be of great potential use. At last, two well-known applications
are investigated. Numerical results show that the theoretical optimal
parameters are close to the practical optimums, except they are not a priori
knowledge. We then demonstrate that, by exploiting problem model structures,
the theoretical optimums can be well approximated. Such approximations turn out
to work very well, and in some cases almost reach the underlying limits.


title: Modular pre-processing for automated reasoning in dependent type theory

abstract:
  The power of modern automated theorem provers can be put at the service of
interactive theorem proving. But this requires in particular bridging the
expressivity gap between the logics these provers are respectively based on.
This paper presents the implementation of a modular suite of pre-processing
transformations, which incrementally bring certain formulas expressed in the
Calculus of Inductive Constructions closer to the first-order logic of
Satifiability Modulo Theory solvers. These transformations address issues
related to the axiomatization of inductive types, to polymorphic definitions or
to the different implementations of a same theory signature. This suite is
implemented as a plugin for the Coq proof assistant, and integrated to the
SMTCoq toolchain.


title: Dynamics of skew-products tangent to the identity

abstract:
  We study the local dynamics of generic skew-products tangent to the identity,
i.e. maps of the form $P(z,w)=(p(z), q(z,w))$ with $dP_0=\mathrm{id}$. More
precisely, we focus on maps with non-degenerate second differential at the
origin; such maps have local normal form
$P(z,w)=(z-z^2+O(z^3),w+w^2+bz^2+O(\|(z,w)\|^3))$. We prove the existence of
parabolic domains, and prove that inside these parabolic domains the orbits
converge non-tangentially if and only if $b \in (\frac{1}{4},+\infty)$.
Furthermore, we prove the existence of a type of parabolic implosion, in which
the renormalization limits are different from previously known cases. This has
a number of consequences: under a diophantine condition on coefficients of $P$,
we prove the existence of wandering domains with rank 1 limit maps. We also
give explicit examples of quadratic skew-products with countably many grand
orbits of wandering domains, and we give an explicit example of a skew-product
map with a Fatou component exhibiting historic behaviour. Finally, we construct
various topological invariants, which allow us to answer a question of Abate.


title:
Gas phase Elemental abundances in Molecular cloudS (GEMS) VI. A sulphur
  journey across star-forming regions: study of thioformaldehyde emission

abstract:
  In the context of the IRAM 30m Large Program GEMS, we present a study of
thioformaldehyde in several starless cores located in star-forming filaments of
Taurus, Perseus, and Orion. We investigate the influence of the environmental
conditions on the abundances of these molecules in the cores, and the effect of
time evolution. We have modelled the observed lines of H2CS, HDCS, and D2CS
using the radiative transfer code RADEX. We have also used the chemical code
Nautilus to model the evolution of these species depending on the
characteristics of the starless cores. We derive column densities and
abundances for all the cores. We also derive deuterium fractionation ratios,
Dfrac, to determine and compare the evolutionary stage between different parts
of each star-forming region. Our results indicate that the north region of the
B213 filament in Taurus is more evolved than the south, while the north-eastern
part of Perseus presents an earlier evolutionary stage than the south-western
zone. Model results also show that Dfrac decreases with the cosmic-ray
ionisation rate, while it increases with density and with the degree of sulphur
depletion. In particular, we only reproduce the observations when the initial
sulphur abundance in the starless cores is at least one order of magnitude
lower than the solar elemental sulphur abundance. The progressive increase in
HDCS/H2CS and D2CS/H2CS with time makes these ratios powerful tools for
deriving the chemical evolutionary stage of starless cores. However, they
cannot be used to derive the temperature of these regions, since both ratios
present a similar evolution at two different temperature ranges (7-11 K and
15-19 K). Regarding chemistry, (deuterated) thioformaldehyde is mainly formed
through gas-phase reactions (double-replacement and neutral-neutral
displacement reactions), while surface chemistry plays an important role as a
destruction mechanism.