title: A Palm Calculus Approach to the Distribution of the Age of Information

abstract:
  A key metric to express the timeliness of status updates in latency-sensitive
networked systems is the age of information (AoI), i.e., the time elapsed since
the generation of the last received informative status message. This metric
allows studying a number of applications including updates of sensory and
control information in cyber-physical systems and vehicular networks as well
as, job and resource allocation in cloud clusters. State-of-the-art approaches
to analyzing the AoI rely on queueing models that are composed of one or many
queuing systems endowed with service order, e.g., FIFO, LIFO, or
last-generated-first-out order. A major difficulty arising in these analysis
methods is capturing the AoI under message reordering when the delivery is
non-preemptive and non-FIFO, i.e., when messages can overtake each other and
the reception of informative messages may obsolete some messages that are
underway. In this paper, we derive an exact formulation for the distribution of
AoI in non-preemptive, non-FIFO systems where the main ingredients of our
analysis are Palm calculus and time inversion. Owing to the rationality of the
Laplace-Stieltjes transforms that are used in our approach, we obtain
computable exact expressions for the distribution of AoI.


title: From graphs to DAGs: a low-complexity model and a scalable algorithm

abstract:
  Learning directed acyclic graphs (DAGs) is long known a critical challenge at
the core of probabilistic and causal modeling. The NoTears approach of (Zheng
et al., 2018), through a differentiable function involving the matrix
exponential trace $\mathrm{tr}(\exp(\cdot))$, opens up a way to learning DAGs
via continuous optimization, though with a $O(d^3)$ complexity in the number
$d$ of nodes. This paper presents a low-complexity model, called LoRAM for
Low-Rank Additive Model, which combines low-rank matrix factorization with a
sparsification mechanism for the continuous optimization of DAGs. The main
contribution of the approach lies in an efficient gradient approximation method
leveraging the low-rank property of the model, and its straightforward
application to the computation of projections from graph matrices onto the DAG
matrix space. The proposed method achieves a reduction from a cubic complexity
to quadratic complexity while handling the same DAG characteristic function as
NoTears, and scales easily up to thousands of nodes for the projection problem.
The experiments show that the LoRAM achieves efficiency gains of orders of
magnitude compared to the state-of-the-art at the expense of a very moderate
accuracy loss in the considered range of sparse matrices, and with a low
sensitivity to the rank choice of the model's low-rank component.


title:
Self-Supervised Audio-and-Text Pre-training with Extremely Low-Resource
  Parallel Data

abstract:
  Multimodal pre-training for audio-and-text has recently been proved to be
effective and has significantly improved the performance of many downstream
speech understanding tasks. However, these state-of-the-art pre-training
audio-text models work well only when provided with large amount of parallel
audio-and-text data, which brings challenges on many languages that are rich in
unimodal corpora but scarce of parallel cross-modal corpus. In this paper, we
investigate whether it is possible to pre-train an audio-text multimodal model
with extremely low-resource parallel data and extra non-parallel unimodal data.
Our pre-training framework consists of the following components: (1)
Intra-modal Denoising Auto-Encoding (IDAE), which is able to reconstruct input
text (audio) representations from a noisy version of itself. (2) Cross-modal
Denoising Auto-Encoding (CDAE), which is pre-trained to reconstruct the input
text (audio), given both a noisy version of the input text (audio) and the
corresponding translated noisy audio features (text embeddings). (3) Iterative
Denoising Process (IDP), which iteratively translates raw audio (text) and the
corresponding text embeddings (audio features) translated from previous
iteration into the new less-noisy text embeddings (audio features). We adapt a
dual cross-modal Transformer as our backbone model which consists of two
unimodal encoders for IDAE and two cross-modal encoders for CDAE and IDP. Our
method achieves comparable performance on multiple downstream speech
understanding tasks compared with the model pre-trained on fully parallel data,
demonstrating the great potential of the proposed method. Our code is available
at: \url{https://github.com/KarlYuKang/Low-Resource-Multimodal-Pre-training}.


title:
Deep Embeddings for Robust User-Based Amateur Vocal Percussion
  Classification

abstract:
  Vocal Percussion Transcription (VPT) is concerned with the automatic
detection and classification of vocal percussion sound events, allowing music
creators and producers to sketch drum lines on the fly. Classifier algorithms
in VPT systems learn best from small user-specific datasets, which usually
restrict modelling to small input feature sets to avoid data overfitting. This
study explores several deep supervised learning strategies to obtain
informative feature sets for amateur vocal percussion classification. We
evaluated the performance of these sets on regular vocal percussion
classification tasks and compared them with several baseline approaches
including feature selection methods and a speech recognition engine. These
proposed learning models were supervised with several label sets containing
information from four different levels of abstraction: instrument-level,
syllable-level, phoneme-level, and boxeme-level. Results suggest that
convolutional neural networks supervised with syllable-level annotations
produced the most informative embeddings for classification, which can be used
as input representations to fit classifiers with. Finally, we used
back-propagation-based saliency maps to investigate the importance of different
spectrogram regions for feature learning.


title: Self-tuning inflation

abstract:
  We develop an inflationary model without small parameters on the basis of
multidimensional $f(R)$ gravity with a minimally coupled scalar field. The
model is described by two stages. The first one begins at energy scales about
the D-dimensional Planck mass and ends with the de Sitter metric of our space
and the maximally symmetric extra dimensions. In the following, the quantum
fluctuations produce a wide set of inhomogeneous extra metrics in causally
disconnected regions quickly generated in the de Sitter space. We find a
specific extra space metric that leads to the effective Starobinsky model that
fits the observational data.


title: Gaussian Processes for Missing Value Imputation

abstract:
  Missing values are common in many real-life datasets. However, most of the
current machine learning methods can not handle missing values. This means that
they should be imputed beforehand. Gaussian Processes (GPs) are non-parametric
models with accurate uncertainty estimates that combined with sparse
approximations and stochastic variational inference scale to large data sets.
Sparse GPs can be used to compute a predictive distribution for missing data.
Here, we present a hierarchical composition of sparse GPs that is used to
predict missing values at each dimension using all the variables from the other
dimensions. We call the approach missing GP (MGP). MGP can be trained
simultaneously to impute all observed missing values. Specifically, it outputs
a predictive distribution for each missing value that is then used in the
imputation of other missing values. We evaluate MGP in one private clinical
data set and four UCI datasets with a different percentage of missing values.
We compare the performance of MGP with other state-of-the-art methods for
imputing missing values, including variants based on sparse GPs and deep GPs.
The results obtained show a significantly better performance of MGP.


title:
Neutron spectroscopy evidence for a possible magnetic-field-induced
  gapless quantum-spin-liquid phase in a Kitaev material $\alpha$-RuCl$_3$

abstract:
  As one of the most promising Kitaev quantum-spin-liquid (QSL) candidates,
$\alpha$-RuCl$_3$ has received a great amount of attention. However, its ground
state exhibits a long-range zigzag magnetic order, which defies the QSL phase.
Nevertheless, the magnetic order is fragile and can be completely suppressed by
applying an external magnetic field. Here, we explore the evolution of magnetic
excitations of $\alpha$-RuCl$_3$ under an in-plane magnetic field, by carrying
out inelastic neutron scattering measurements on high-quality single crystals.
Under zero field, there exist spin-wave excitations near the $M$ point and a
continuum near the $\mit\Gamma$ point, which are believed to be associated with
the zigzag magnetic order and fractional excitations of the Kitaev QSL state,
respectively. By increasing the magnetic field, the spin-wave excitations
gradually give way to the continuous excitations. On the verge of the critical
field $\mu_0H_{\rm c}=7.5$ T, the former vanish and only the latter is left,
indicating the emergence of a pure QSL state. By further increasing the field
strength, the excitations near the $\mit\Gamma$ point become more intense. By
following the gap evolution of the excitations near the $\mit\Gamma$ point, we
are able to establish a phase diagram composed of three interesting phases,
including a gapped zigzag order phase at low fields, possibly-gapless QSL phase
near $\mu_0H_{\rm c}$, and gapped partially polarized phase at high fields.
These results demonstrate that an in-plane magnetic field can drive
$\alpha$-RuCl$_3$ into a long-sought QSL state near the critical field.


title: Maximum principal ratio of the signless Laplacian of graphs

abstract:
  Let $G$ be a connected graph and $Q(G)$ be the signless Laplacian of $G$. The
principal ratio $\gamma(G)$ of $Q(G)$ is the ratio of the maximum and minimum
entries of the Perron vector of $Q(G)$. In this paper, we consider the maximum
principal ratio $\gamma(G)$ among all connected graphs of order $n$, and show
that for sufficiently large $n$ the extremal graph is a kite graph obtained by
identifying an end vertex of a path to any vertex of a complete graph.


title:
Deep Conditional Representation Learning for Drum Sample Retrieval by
  Vocalisation

abstract:
  Imitating musical instruments with the human voice is an efficient way of
communicating ideas between music producers, from sketching melody lines to
clarifying desired sonorities. For this reason, there is an increasing interest
in building applications that allow artists to efficiently pick target samples
from big sound libraries just by imitating them vocally. In this study, we
investigated the potential of conditional autoencoder models to learn
informative features for Drum Sample Retrieval by Vocalisation (DSRV). We
assessed the usefulness of their embeddings using four evaluation metrics, two
of them relative to their acoustic properties and two of them relative to their
perceptual properties via human listeners' similarity ratings. Results suggest
that models conditioned on both sound-type labels (drum vs imitation) and
drum-type labels (kick vs snare vs closed hi-hat vs opened hi-hat) learn the
most informative embeddings for DSRV. We finally looked into individual
differences in vocal imitation style via the Mantel test and found salient
differences among participants, highlighting the importance of user information
when designing DSRV systems.


title:
Large deviations in chaotic systems: exact results and dynamical phase
  transition

abstract:
  Despite their potentially significant and dramatic consequences, large
deviations in chaotic dynamics have only been partially studied, with few
existing analytic results. We study large deviations of series of finite
lengths $N$ generated by chaotic maps. The distributions generally display an
expoential decay with $N$, associated with large-deviation (rate) functions. We
obtain the exact rate functions analytically for the doubling, tent, and
logistic maps, and numerically for the cat map. In the latter case, we uncover
a remarkable singularity of the rate function that we interpret as a second
order dynamical phase transition. Furthermore, we develop a numerical tool for
efficiently simulating atypical realizations of sequences if the chaotic map is
not invertible, and we apply it to the tent and logistic maps. Our research
lays the groundwork for the study of unusual trends of long duration in chaotic
systems, such as heatwaves or droughts in climate models, or unusual mean
growth rate of a pandemic over a long period of time.


title:
Counting in the 2020s: Binned Representations and Inclusive Performance
  Measures for Deep Crowd Counting Approaches

abstract:
  The data distribution in popular crowd counting datasets is typically heavy
tailed and discontinuous. This skew affects all stages within the pipelines of
deep crowd counting approaches. Specifically, the approaches exhibit
unacceptably large standard deviation wrt statistical measures (MSE, MAE). To
address such concerns in a holistic manner, we make two fundamental
contributions. Firstly, we modify the training pipeline to accommodate the
knowledge of dataset skew. To enable principled and balanced minibatch
sampling, we propose a novel smoothed Bayesian binning approach. More
specifically, we propose a novel cost function which can be readily
incorporated into existing crowd counting deep networks to encourage bin-aware
optimization. As the second contribution, we introduce additional performance
measures which are more inclusive and throw light on various comparative
performance aspects of the deep networks. We also show that our binning-based
modifications retain their superiority wrt the newly proposed performance
measures. Overall, our contributions enable a practically useful and
detail-oriented characterization of performance for crowd counting approaches.


title:
Fashionformer: A simple, Effective and Unified Baseline for Human
  Fashion Segmentation and Recognition

abstract:
  Human fashion understanding is one important computer vision task since it
has the comprehensive information that can be used for real-world applications.
In this work, we focus on joint human fashion segmentation and attribute
recognition. Contrary to the previous works that separately model each task as
a multi-head prediction problem, our insight is to bridge these two tasks with
one unified model via vision transformer modeling to benefit each task. In
particular, we introduce the object query for segmentation and the attribute
query for attribute prediction. Both queries and their corresponding features
can be linked via mask prediction. Then we adopt a two-stream query learning
framework to learn the decoupled query representations. For attribute stream,
we design a novel Multi-Layer Rendering module to explore more fine-grained
features. The decoder design shares the same spirits with DETR, thus we name
the proposed method Fahsionformer. Extensive experiments on three human fashion
datasets including Fashionpedia, ModaNet and Deepfashion illustrate the
effectiveness of our approach. In particular, our method with the same backbone
achieve relative 10% improvements than previous works in case of \textit{a
joint metric ( AP$^{\text{mask}}_{\text{IoU+F}_1}$) for both segmentation and
attribute recognition}. To the best of our knowledge, we are the first unified
end-to-end vision transformer framework for human fashion analysis. We hope
this simple yet effective method can serve as a new flexible baseline for
fashion analysis. Code will be available at
https://github.com/xushilin1/FashionFormer.


title:
Panoptic-PartFormer: Learning a Unified Model for Panoptic Part
  Segmentation

abstract:
  Panoptic Part Segmentation (PPS) aims to unify panoptic segmentation and part
segmentation into one task. Previous work mainly utilizes separated approaches
to handle thing, stuff, and part predictions individually without performing
any shared computation and task association. In this work, we aim to unify
these tasks at the architectural level, designing the first end-to-end unified
method named Panoptic-PartFormer. In particular, motivated by the recent
progress in Vision Transformer, we model things, stuff, and part as object
queries and directly learn to optimize the all three predictions as unified
mask prediction and classification problem. We design a decoupled decoder to
generate part feature and thing/stuff feature respectively. Then we propose to
utilize all the queries and corresponding features to perform reasoning jointly
and iteratively. The final mask can be obtained via inner product between
queries and the corresponding features. The extensive ablation studies and
analysis prove the effectiveness of our framework. Our Panoptic-PartFormer
achieves the new state-of-the-art results on both Cityscapes PPS and Pascal
Context PPS datasets with at least 70% GFlops and 50% parameters decrease. In
particular, we get 3.4% relative improvements with ResNet50 backbone and 10%
improvements after adopting Swin Transformer on Pascal Context PPS dataset. To
the best of our knowledge, we are the first to solve the PPS problem via
\textit{a unified and end-to-end transformer model. Given its effectiveness and
conceptual simplicity, we hope our Panoptic-PartFormer can serve as a good
baseline and aid future unified research for PPS. Our code and models will be
available at https://github.com/lxtGH/Panoptic-PartFormer.


title:
Video K-Net: A Simple, Strong, and Unified Baseline for Video
  Segmentation

abstract:
  This paper presents Video K-Net, a simple, strong, and unified framework for
fully end-to-end video panoptic segmentation. The method is built upon K-Net, a
method that unifies image segmentation via a group of learnable kernels. We
observe that these learnable kernels from K-Net, which encode object
appearances and contexts, can naturally associate identical instances across
video frames. Motivated by this observation, Video K-Net learns to
simultaneously segment and track "things" and "stuff" in a video with simple
kernel-based appearance modeling and cross-temporal kernel interaction. Despite
the simplicity, it achieves state-of-the-art video panoptic segmentation
results on Citscapes-VPS and KITTI-STEP without bells and whistles. In
particular on KITTI-STEP, the simple method can boost almost 12\% relative
improvements over previous methods. We also validate its generalization on
video semantic segmentation, where we boost various baselines by 2\% on the
VSPW dataset. Moreover, we extend K-Net into clip-level video framework for
video instance segmentation where we obtain 40.5\% for ResNet50 backbone and
51.5\% mAP for Swin-base on YouTube-2019 validation set. We hope this simple
yet effective method can serve as a new flexible baseline in video
segmentation. Both code and models are released at
https://github.com/lxtGH/Video-K-Net


title:
Tunable Magnetic Scattering and Ferroelectric Switching at the
  LaAlO$_3$/EuTiO$_3$/Sr$_{0.99}$Ca$_{0.01}$TiO$_3$ Interface

abstract:
  Ferroelectric and ferromagnetic orders rarely coexist, and magnetoelectric
coupling is even more scarce. A possible avenue for combining these orders is
by interface design, where orders formed at the constituent materials can
overlap and interact. Using a combination of magneto-transport and scanning
SQUID measurements, we explore the interactions between ferroelectricity,
magnetism, and the 2D electron system (2DES) formed at the novel
LaAlO$_3$/EuTiO$_3$/Sr$_{0.99}$Ca$_{0.01}$TiO$_3$ heterostructure. We find that
the electrons at the interface experience magnetic scattering appearing along
with a diverging Curie-Weiss-type behaviour in the EuTiO$_3$ layer. The 2DES is
also affected by the switchable ferroelectric polarization at the
Sr$_{0.99}$Ca$_{0.01}$TiO$_3$ bulk. While the 2DES interacts with both
magnetism and ferroelectricity, we show that the presence of the conducting
electrons has no effect on magnetization in the EuTiO$_3$ layer. Our results
provide a first step towards realizing a new multiferroic system where
magnetism and ferroelectricity can interact via an intermediate conducting
layer.


title: Flag matroids with coefficients

abstract:
  This paper is a direct generalization of Baker-Bowler theory to flag
matroids, including its moduli interpretation as developed by Baker and the
second author for matroids. More explicitly, we extend the notion of flag
matroids to flag matroids over any tract, provide cryptomorphic descriptions in
terms of basis axioms (Grassmann-Pl\"ucker functions), circuit/vector axioms
and dual pairs, including additional characterizations in the case of perfect
tracts. We establish duality of flag matroids and construct minors. Based on
the theory of ordered blue schemes, we introduce flag matroid bundles and
construct their moduli space, which leads to algebro-geometric descriptions of
duality and minors. Taking rational points recovers flag varieties in several
geometric contexts: over (topological) fields, in tropical geometry, and as a
generalization of the MacPhersonian.


title:
Amalgamation of Indirect Gamification into Development and Operations
  (DEVOPS) Course Teaching

abstract:
  In DevOps Course, the Installation and configuration of the various software
tools are useful in the context of application development. To teach DevOps
Tools precisely to achieve the learning outcome is a skilled task for a
trainer. From the perspective of the learner, the learning environment has to
be encouraging and exciting. The author used the third-party Competition as an
indirect Gamification technique to achieve the learning outcomes of the Course.
The author encouraged the students to participate in the Hacktoberfest
Competition to use the practical skills learned in the Course. The Under
Graduate and Post Graduate Students have gone through the regular sessions of
DevOps. For the participation purpose, both groups of students communicated on
one platform. The students who succeeded faster during the GitHub Pull Request
submission shared their experiences with other participants. The PG students
participated 68.75% higher than UG students. The active participation of the
small number of UG students became a motivational factor for the PG students.
The Gaussian distribution on the marks obtained by the experimental group shows
the absence of outliers. The research shows that the effectiveness of indirect
Gamification depends on the age group, level, course content, and learning
environment. The participation of a faculty member in the Competition during
the learning activity boosts the desire of the Student to complete the task.
The experimental group of 15 Students has outperformed in terms of the marks
obtained compared to the control group of 52 students.


title:
Measuring the randomness of micro and nanostructure spatial
  distributions: Effects of Scanning Electron Microscope image processing and
  analysis

abstract:
  The quantitative characterization of the degree of randomness and aggregation
of surface micro and nanostructures is critical to evaluate their effects on
targeted functionalities. To this end, the methods of Point Pattern Analysis
(PPA), largely used in ecology and medical imaging, seem to provide a powerful
toolset. However, the application of these techniques requires the extraction
of the point pattern of nanostructures from their microscope images. In this
work, we address the issue of the impact that Scanning Electron Microscope
(SEM) image processing may have on the fundamental metric of PPA, i.e. the
Nearest Neighbour Index (NNI). Using as examples two typical SEM images of
polymer micro- and nanostructures taken from secondary and backscattered
electrons, we report the effects of the a) noise filtering and b) binarization
threshold on the value of NNI as well as the impact of the image finite size
effects. Based on these results, we draw conclusions for the safe choice of SEM
settings to provide accurate measurement of nanostructure randomness through
NNI estimation.


title: Expressiveness and Approximation Properties of Graph Neural Networks

abstract:
  Characterizing the separation power of graph neural networks (GNNs) provides
an understanding of their limitations for graph learning tasks. Results
regarding separation power are, however, usually geared at specific GNN
architectures, and tools for understanding arbitrary GNN architectures are
generally lacking. We provide an elegant way to easily obtain bounds on the
separation power of GNNs in terms of the Weisfeiler-Leman (WL) tests, which
have become the yardstick to measure the separation power of GNNs. The crux is
to view GNNs as expressions in a procedural tensor language describing the
computations in the layers of the GNNs. Then, by a simple analysis of the
obtained expressions, in terms of the number of indexes and the nesting depth
of summations, bounds on the separation power in terms of the WL-tests readily
follow. We use tensor language to define Higher-Order Message-Passing Neural
Networks (or k-MPNNs), a natural extension of MPNNs. Furthermore, the tensor
language point of view allows for the derivation of universality results for
classes of GNNs in a natural way. Our approach provides a toolbox with which
GNN architecture designers can analyze the separation power of their GNNs,
without needing to know the intricacies of the WL-tests. We also provide
insights in what is needed to boost the separation power of GNNs.


title: FOSTER: Feature Boosting and Compression for Class-Incremental Learning

abstract:
  The ability to learn new concepts continually is necessary in this
ever-changing world. However, deep neural networks suffer from catastrophic
forgetting when learning new categories. Many works have been proposed to
alleviate this phenomenon, whereas most of them either fall into the
stability-plasticity dilemma or take too much computation or storage overhead.
Inspired by the gradient boosting algorithm to gradually fit the residuals
between the target and the current approximation function, we propose a novel
two-stage learning paradigm FOSTER, empowering the model to learn new
categories adaptively. Specifically, we first dynamically expand new modules to
fit the residuals of the target and the original model. Next, we remove
redundant parameters and feature dimensions through an effective distillation
strategy to maintain the single backbone model. We validate our method FOSTER
on CIFAR-100, ImageNet-100/1000 under different settings. Experimental results
show that our method achieves state-of-the-art performance.


title: A note on "exotic integrals"

abstract:
  We consider Bernoulli measures $\mu_p$ on the interval $[0,1]$. For the
standard Lebesgue measure the digits $0$ and $1$ in the binary representation
of real numbers appear with an equal probability $1/2$. For the Bernoulli
measures, the digits $0$ and $1$ appear with probabilities $p$ and $1-p$,
respectively. We provide explicit expressions for various $\mu_p$-integrals. In
particular, integrals of polynomials are expressed in terms of the determinants
of special Hessenberg matrices, which, in turn, are constructed from the Pascal
matrices of binomial coefficients. This allows us to find closed-form
expressions for the Fourier coefficients of $\mu_p$ in the Legendre polynomial
basis. At the same time, the trigonometric Fourier coefficients are values of
some special entire function, which admits an explicit infinite product
expansion and satisfies interesting properties.


title:
Optimization of IoT-Enabled Physical Location Monitoring Using DT and
  VAR

abstract:
  This study shows an enhancement of IoT that gets sensor data and performs
real-time face recognition to screen physical areas to find strange situations
and send an alarm mail to the client to make remedial moves to avoid any
potential misfortune in the environment. Sensor data is pushed onto the local
system and GoDaddy Cloud whenever the camera detects a person to optimize the
physical location monitoring system by reducing the bandwidth requirement and
storage cost onto the cloud using edge computation. The study reveals that
decision tree (DT) and random forest give reasonably similar macro average
f1-scores to predict a person using sensor data. Experimental results show that
DT is the most reliable predictive model for the cloud datasets of three
different physical locations to predict a person using timestamp with an
accuracy of 83.99%, 88.92%, and 80.97%. This study also explains multivariate
time series prediction using vector auto regression that gives reasonably good
root mean squared error to predict temperature, humidity, light-dependent
resistor, and gas time series.


title:
Effective Out-of-Distribution Detection in Classifier Based on
  PEDCC-Loss

abstract:
  Deep neural networks suffer from the overconfidence issue in the open world,
meaning that classifiers could yield confident, incorrect predictions for
out-of-distribution (OOD) samples. Thus, it is an urgent and challenging task
to detect these samples drawn far away from training distribution based on the
security considerations of artificial intelligence. Many current methods based
on neural networks mainly rely on complex processing strategies, such as
temperature scaling and input preprocessing, to obtain satisfactory results. In
this paper, we propose an effective algorithm for detecting out-of-distribution
examples utilizing PEDCC-Loss. We mathematically analyze the nature of the
confidence score output by the PEDCC (Predefined Evenly-Distribution Class
Centroids) classifier, and then construct a more effective scoring function to
distinguish in-distribution (ID) and out-of-distribution. In this method, there
is no need to preprocess the input samples and the computational burden of the
algorithm is reduced. Experiments demonstrate that our method can achieve
better OOD detection performance.


title:
Near-Optimal Trajectory Design and Restoration Areas Allocation for
  UAV-Enabled Grassland Restoration

abstract:
  Grassland restoration is a critical means to safeguard grassland ecological
degradation. This work considers the maximization restoration areas problem for
UAV-enabled grassland restoration method in a restoration process, which is a
precise restoration scheme. We first formulate the problem as a multivariable
combinatorial optimization problem. By analyzing the characteristics of the
optimization problem, it can be decomposed into the two stages: UAV trajectory
design and restoration areas allocation. On this basis, the problem can be
regarded as a composite problem of traveling salesman problem (TSP) and
multidimensional knapsack problem (MKP). Unlike the single combinatorial
optimization problem, the coupling relationship between them makes the problem
difficult to directly solve by employing the single stage traditional methods.
To effectively solve without ignoring the dependence between the two stages, we
develop a cooperative optimization algorithm based on heuristic algorithm and
population-based incremental learning (PBIL) incorporated with a
maximum-residual-energy-based local search (MRELS) strategy, called CHAPBILM,
to deal with this problem under constraint conditions of the UAV energy, the
total seeds weight, and the number of restored areas. The simulation studies
demonstrate that the proposed cooperative optimization method is effective for
UAV-enabled grassland restoration problem and can significantly outperform the
noncooperative optimization methods, which also verifies the dependency
relationship between UAV trajectory design and restoration areas allocation.


title: Linear Complexity Randomized Self-attention Mechanism

abstract:
  Recently, random feature attentions (RFAs) are proposed to approximate the
softmax attention in linear time and space complexity by linearizing the
exponential kernel. In this paper, we first propose a novel perspective to
understand the bias in such approximation by recasting RFAs as self-normalized
importance samplers. This perspective further sheds light on an \emph{unbiased}
estimator for the whole softmax attention, called randomized attention (RA). RA
constructs positive random features via query-specific distributions and enjoys
greatly improved approximation fidelity, albeit exhibiting quadratic
complexity. By combining the expressiveness in RA and the efficiency in RFA, we
develop a novel linear complexity self-attention mechanism called linear
randomized attention (LARA). Extensive experiments across various domains
demonstrate that RA and LARA significantly improve the performance of RFAs by a
substantial margin.


title: NAN: Noise-Aware NeRFs for Burst-Denoising

abstract:
  Burst denoising is now more relevant than ever, as computational photography
helps overcome sensitivity issues inherent in mobile phones and small cameras.
A major challenge in burst-denoising is in coping with pixel misalignment,
which was so far handled with rather simplistic assumptions of simple motion,
or the ability to align in pre-processing. Such assumptions are not realistic
in the presence of large motion and high levels of noise. We show that Neural
Radiance Fields (NeRFs), originally suggested for physics-based novel-view
rendering, can serve as a powerful framework for burst denoising. NeRFs have an
inherent capability of handling noise as they integrate information from
multiple images, but they are limited in doing so, mainly since they build on
pixel-wise operations which are suitable to ideal imaging conditions. Our
approach, termed NAN, leverages inter-view and spatial information in NeRFs to
better deal with noise. It achieves state-of-the-art results in burst denoising
and is especially successful in coping with large movement and occlusions,
under very high levels of noise. With the rapid advances in accelerating NeRFs,
it could provide a powerful platform for denoising in challenging environments.


title:
Generalized Calogero-Sutherland models for Kondo physics in Luttinger
  liquid

abstract:
  We propose a series of exactly solvable quantum impurity models with
inverse-square interactions that describe a single spin-$1/2$ Kondo impurity
embedded in a Luttinger liquid, both in the continuum and on the lattice. The
model wave functions take the form of Jastrow products and can have either
fermionic or bosonic baths depending on the powers of the Jastrow factors. We
show that they are exact ground states of an open boundary, two-component
Calogero-Sutherland model in the continuum (or a long-range interacting $t-J$
model on the lattice) coupled to a localized spin-$1/2$ impurity. The
connection of these wave functions to chiral correlators of certain conformal
field theories is discussed.


title: Active Learning with Label Comparisons

abstract:
  Supervised learning typically relies on manual annotation of the true labels.
When there are many potential classes, searching for the best one can be
prohibitive for a human annotator. On the other hand, comparing two candidate
labels is often much easier. We focus on this type of pairwise supervision and
ask how it can be used effectively in learning, and in particular in active
learning. We obtain several insightful results in this context. In principle,
finding the best of $k$ labels can be done with $k-1$ active queries. We show
that there is a natural class where this approach is sub-optimal, and that
there is a more comparison-efficient active learning scheme. A key element in
our analysis is the "label neighborhood graph" of the true distribution, which
has an edge between two classes if they share a decision boundary. We also show
that in the PAC setting, pairwise comparisons cannot provide improved sample
complexity in the worst case. We complement our theoretical results with
experiments, clearly demonstrating the effect of the neighborhood graph on
sample complexity.


title:
Kripke Contexts, Double Boolean Algebras with Operators and
  Corresponding Modal Systems

abstract:
  The notion of a context in formal concept analysis and that of an
approximation space in rough set theory are unified in this study to define a
Kripke context. For any context (G,M,I), a relation on the set G of objects and
a relation on the set M of properties are included, giving a structure of the
form ((G,R), (M,S), I). A Kripke context gives rise to complex algebras based
on the collections of protoconcepts and semiconcepts of the underlying context.
On abstraction, double Boolean algebras (dBas) with operators and topological
dBas are defined. Representation results for these algebras are established in
terms of the complex algebras of an appropriate Kripke context. As a natural
next step, logics corresponding to classes of these algebras are formulated. A
sequent calculus is proposed for contextual dBas, modal extensions of which
give logics for contextual dBas with operators and topological contextual dBas.
The representation theorems for the algebras result in a protoconcept-based
semantics for these logics.


title: Dynamical Minimal Flavour Violating Inverse Seesaw

abstract:
  The Inverse Seesaw mechanism is dynamically realised within the Minimal
Lepton Flavour Violation context. Lepton number, whose breaking is
spontaneously realised, is generalised to a global Abelian factor of the whole
flavour symmetry, that also plays the role of the Peccei-Quinn symmetry. The
associated Goldstone boson is a Majoraxion that solves the Strong CP problem
and represents a Dark Matter candidate.
  Three distinct scenarios are identified in terms of flavour symmetry and
transformation properties of the exotic neutral leptons that enrich the
Standard Model spectrum. The associated phenomenology is studied, focusing on
the deviations from unitarity of the PMNS mixing matrix. The strongest
constraints arise from the determination of the number of active neutrinos
through the invisible width of the $Z$, the comparison of the measured $W$
boson mass with its prediction in terms of the Fermi constant from muon decay,
and the null searches for the radiative rare muon decay and $\mu\to e$
conversion in nuclei. The heavy neutral leptons may have masses of a few TeV,
leaving open the possibility for a direct detection at future colliders.
  The impact of the recent measurement of the $W$ mass at the CDF II detector
has also been considered, which, in one of the scenarios, points to a sharp
prediction for the masses of the heavy neutral leptons at about $2-3$ TeV.


title:
Optimal long-time decay rate of solutions of complete
  monotonicity-preserving schemes for nonlinear time-fractional evolutionary
  equations

abstract:
  The solution of the nonlinear initial-value problem
$\mathcal{D}_{t}^{\alpha}y(t)=-\lambda y(t)^{\gamma}$ for $t>0$ with $y(0)>0$,
where $\mathcal{D}_{t}^{\alpha}$ is a Caputo derivative of order $\alpha\in
(0,1)$ and $\lambda, \gamma$ are positive parameters, is known to exhibit
$O(t^{\alpha/\gamma})$ decay as $t\to\infty$. No corresponding result for any
discretisation of this problem has previously been proved. In the present paper
it is shown that for the class of complete monotonicity-preserving
($\mathcal{CM}$-preserving) schemes (which includes the L1 and
Gr\"unwald-Letnikov schemes) on uniform meshes $\{t_n:=nh\}_{n=0}^\infty$, the
discrete solution also has $O(t_{n}^{-\alpha/\gamma})$ decay as
$t_{n}\to\infty$. This result is then extended to $\mathcal{CM}$-preserving
discretisations of certain time-fractional nonlinear subdiffusion problems such
as the time-fractional porous media and $p$-Laplace equations. For the L1
scheme, the $O(t_{n}^{-\alpha/\gamma})$ decay result is shown to remain valid
on a very general class of nonuniform meshes. Our analysis uses a discrete
comparison principle with discrete subsolutions and supersolutions that are
carefully constructed to give tight bounds on the discrete solution. Numerical
experiments are provided to confirm our theoretical analysis.


title:
Is my Driver Observation Model Overconfident? Input-guided Calibration
  Networks for Reliable and Interpretable Confidence Estimates

abstract:
  Driver observation models are rarely deployed under perfect conditions. In
practice, illumination, camera placement and type differ from the ones present
during training and unforeseen behaviours may occur at any time. While
observing the human behind the steering wheel leads to more intuitive
human-vehicle-interaction and safer driving, it requires recognition algorithms
which do not only predict the correct driver state, but also determine their
prediction quality through realistic and interpretable confidence measures.
Reliable uncertainty estimates are crucial for building trust and are a serious
obstacle for deploying activity recognition networks in real driving systems.
In this work, we for the first time examine how well the confidence values of
modern driver observation models indeed match the probability of the correct
outcome and show that raw neural network-based approaches tend to significantly
overestimate their prediction quality. To correct this misalignment between the
confidence values and the actual uncertainty, we consider two strategies.
First, we enhance two activity recognition models often used for driver
observation with temperature scaling-an off-the-shelf method for confidence
calibration in image classification. Then, we introduce Calibrated Action
Recognition with Input Guidance (CARING)-a novel approach leveraging an
additional neural network to learn scaling the confidences depending on the
video representation. Extensive experiments on the Drive&Act dataset
demonstrate that both strategies drastically improve the quality of model
confidences, while our CARING model out-performs both, the original
architectures and their temperature scaling enhancement, leading to best
uncertainty estimates.


title:
Generalized Drazin-Riesz invertible elements in a semi-simple Banach
  algebra

abstract:
  We extend the notion of generalized Drazin-Riesz inverse introduced for
bounded linear operators in \cite{Ziv} to elements in a complex unital
semi-simple Banach algebra. Several characterizations and properties of
generalized Drazin-Riesz invertible elements are given. In particular, we
extend those of \cite{AbZg1,Djor,Ziv}.


title: Simple Baselines for Image Restoration

abstract:
  Although there have been significant advances in the field of image
restoration recently, the system complexity of the state-of-the-art (SOTA)
methods is increasing as well, which may hinder the convenient analysis and
comparison of methods. In this paper, we propose a simple baseline that exceeds
the SOTA methods and is computationally efficient. To further simplify the
baseline, we reveal that the nonlinear activation functions, e.g. Sigmoid,
ReLU, GELU, Softmax, etc. are not necessary: they could be replaced by
multiplication or removed. Thus, we derive a Nonlinear Activation Free Network,
namely NAFNet, from the baseline. SOTA results are achieved on various
challenging benchmarks, e.g. 33.69 dB PSNR on GoPro (for image deblurring),
exceeding the previous SOTA 0.38 dB with only 8.4% of its computational costs;
40.30 dB PSNR on SIDD (for image denoising), exceeding the previous SOTA 0.28
dB with less than half of its computational costs. The code and the pretrained
models will be released at https://github.com/megvii-research/NAFNet.


title: FedCorr: Multi-Stage Federated Learning for Label Noise Correction

abstract:
  Federated learning (FL) is a privacy-preserving distributed learning paradigm
that enables clients to jointly train a global model. In real-world FL
implementations, client data could have label noise, and different clients
could have vastly different label noise levels. Although there exist methods in
centralized learning for tackling label noise, such methods do not perform well
on heterogeneous label noise in FL settings, due to the typically smaller sizes
of client datasets and data privacy requirements in FL. In this paper, we
propose $\texttt{FedCorr}$, a general multi-stage framework to tackle
heterogeneous label noise in FL, without making any assumptions on the noise
models of local clients, while still maintaining client data privacy. In
particular, (1) $\texttt{FedCorr}$ dynamically identifies noisy clients by
exploiting the dimensionalities of the model prediction subspaces independently
measured on all clients, and then identifies incorrect labels on noisy clients
based on per-sample losses. To deal with data heterogeneity and to increase
training stability, we propose an adaptive local proximal regularization term
that is based on estimated local noise levels. (2) We further finetune the
global model on identified clean clients and correct the noisy labels for the
remaining noisy clients after finetuning. (3) Finally, we apply the usual
training on all clients to make full use of all local data. Experiments
conducted on CIFAR-10/100 with federated synthetic label noise, and on a
real-world noisy dataset, Clothing1M, demonstrate that $\texttt{FedCorr}$ is
robust to label noise and substantially outperforms the state-of-the-art
methods at multiple noise levels.


title:
Parallelized integrated nested Laplace approximations for fast Bayesian
  inference

abstract:
  There is a growing demand for performing larger-scale Bayesian inference
tasks, arising from greater data availability and higher-dimensional model
parameter spaces. In this work we present parallelization strategies for the
methodology of integrated nested Laplace approximations (INLA), a popular
framework for performing approximate Bayesian inference on the class of Latent
Gaussian models. Our approach makes use of nested OpenMP parallelism, a
parallel line search procedure using robust regression in INLA's optimization
phase and the state-of-the-art sparse linear solver PARDISO. We leverage
mutually independent function evaluations in the algorithm as well as advanced
sparse linear algebra techniques. This way we can flexibly utilize the power of
today's multi-core architectures. We demonstrate the performance of our new
parallelization scheme on a number of different real-world applications. The
introduction of parallelism leads to speedups of a factor 10 and more for all
larger models. Our work is already integrated in the current version of the
open-source R-INLA package, making its improved performance conveniently
available to all users.


title: Scale Invariant Semantic Segmentation with RGB-D Fusion

abstract:
  In this paper, we propose a neural network architecture for scale-invariant
semantic segmentation using RGB-D images. We utilize depth information as an
additional modality apart from color images only. Especially in an outdoor
scene which consists of different scale objects due to the distance of the
objects from the camera. The near distance objects consist of significantly
more pixels than the far ones. We propose to incorporate depth information to
the RGB data for pixel-wise semantic segmentation to address the different
scale objects in an outdoor scene. We adapt to a well-known
DeepLab-v2(ResNet-101) model as our RGB baseline. Depth images are passed
separately as an additional input with a distinct branch. The intermediate
feature maps of both color and depth image branch are fused using a novel
fusion block. Our model is compact and can be easily applied to the other RGB
model. We perform extensive qualitative and quantitative evaluation on a
challenging dataset Cityscapes. The results obtained are comparable to the
state-of-the-art. Additionally, we evaluated our model on a self-recorded real
dataset. For the shake of extended evaluation of a driving scene with ground
truth we generated a synthetic dataset using popular vehicle simulation project
CARLA. The results obtained from the real and synthetic dataset shows the
effectiveness of our approach.


title: Reasoning with Multi-Structure Commonsense Knowledge in Visual Dialog

abstract:
  Visual Dialog requires an agent to engage in a conversation with humans
grounded in an image. Many studies on Visual Dialog focus on the understanding
of the dialog history or the content of an image, while a considerable amount
of commonsense-required questions are ignored. Handling these scenarios depends
on logical reasoning that requires commonsense priors. How to capture relevant
commonsense knowledge complementary to the history and the image remains a key
challenge. In this paper, we propose a novel model by Reasoning with
Multi-structure Commonsense Knowledge (RMK). In our model, the external
knowledge is represented with sentence-level facts and graph-level facts, to
properly suit the scenario of the composite of dialog history and image. On top
of these multi-structure representations, our model can capture relevant
knowledge and incorporate them into the vision and semantic features, via
graph-based interaction and transformer-based fusion. Experimental results and
analysis on VisDial v1.0 and VisDialCK datasets show that our proposed model
effectively outperforms comparative methods.


title:
Enhancing the Robustness, Efficiency, and Diversity of Differentiable
  Architecture Search

abstract:
  Differentiable architecture search (DARTS) has attracted much attention due
to its simplicity and significant improvement in efficiency. However, the
excessive accumulation of the skip connection makes it suffer from long-term
weak stability and low robustness. Many works attempt to restrict the
accumulation of skip connections by indicators or manual design, however, these
methods are susceptible to thresholds and human priors. In this work, we
suggest a more subtle and direct approach that removes skip connections from
the operation space. Then, by introducing an adaptive channel allocation
strategy, we redesign the DARTS framework to automatically refill the skip
connections in the evaluation stage, resolving the performance degradation
caused by the absence of skip connections. Our method, dubbed
Adaptive-Channel-Allocation-DARTS (ACA-DRATS), could eliminate the
inconsistency in operation strength and significantly expand the architecture
diversity. We continue to explore smaller search space under our framework, and
offer a direct search on the entire ImageNet dataset. Experiments show that
ACA-DRATS improves the search stability and significantly speeds up DARTS by
more than ten times while yielding higher accuracy.


title:
Chaotic Excitations of Rogue Waves in Stable Parametric Region for
  Highly-Energetic Pair Plasmas

abstract:
  We have studied the Rogue wave existence and propagation in Ion-acoustic mode
for the highly energetic case using kappa distributed electrons in accordance
with the Korteweg de Vries equation that is modified KdV and extended KdV
equation. We have used reductive perturbation method. We first examined the
linear dispersive behaviour in Ionacoustic mode. Obtaining the Nonlinear
Schrodinger equation, we simulated Rogue wave and examined dynamics of it, and
its response to small perturbations. We discussed the possibility of generation
of Rogue wave as well as the stability of this against various parameters like
wave number, spatial and time component. This study is quite helpful for
understanding some prominent points of the nonlinearity of IA waves and Rogue
wave generation of the highly energetic case in space plasma also in a
laboratory plasma.


title:
Nilpotent Symmetries of a Modified Massive Abelian 3-Form Theory:
  Augmented Superfield Approach

abstract:
  We derive the off-shell nilpotent and absolutely anticommuting (anti-)BRST
symmetry transformations for any arbitrary D-dimensional St$\ddot
u$ckelberg-modified massive Abelian 3-form theory within the framework of
augmented version of superfield approach (AVSA) to Becchi-Rouet-Stora-Tyutin
(BRST) formalism where, in addition to the horizontality condition (HC), we
exploit the theoretical strength of the gauge invariant restriction (GIR) to
deduce the proper transformations for the gauge, associated (anti-)ghost
fields, auxiliary fields, St$\ddot u$ckelberg compensating field, etc. In fact,
it is an elegant and delicate combination of HC and GIR (within the ambit of
AVSA) that is crucial for all our discussions and derivations. One of the
highlights of our present endeavor is the deduction of a new set of (anti-)BRST
invariant Curci-Ferrari (CF)-type restrictions which are not found in the
massless version of our present theory where only the HC plays an important
role in the derivation of all the (anti-)BRST transformations and a very
specific set of CF-type restrictions. The alternative ways of the derivation of
the full set of the latter, from various theoretical considerations, are also
interesting results of our present investigation.


title:
Rates of mixing for the measure of maximal entropy of dispersing
  billiard maps

abstract:
  In a recent work, Baladi and Demers constructed a measure of maximal entropy
for finite horizon dispersing billiard maps and proved that it is unique,
mixing and moreover Bernoulli. We show that this measure enjoys natural
probabilistic properties for H\"older continuous observables, such as at least
polynomial decay of correlations and the Central Limit Theorem.
  The results of Baladi and Demers are subject to a condition of sparse
recurrence to singularities. We use a similar and slightly stronger condition,
and it has a direct effect on our rate of decay of correlations. For billiard
tables with bounded complexity (a property conjectured to be generic), we show
that the sparse recurrence condition is always satisfied and the correlations
decay super-polynomially.


title: EPTAS for the dual of splittable bin packing with cardinality constraint

abstract:
  The problem considered is the splittable bin packing with cardinality
constraint. It is a variant of the bin packing problem where items are allowed
to be split into parts but the number of parts in each bin is at most a given
upper bound. Two versions of the splittable bin packing with cardinality
constraint have been studied in the literature. Among these variants we
consider the dual one where the objective is to minimize the maximum bin size
while packing (may be fractional) the items to a given set of bins. We exhibit
an EPTAS for the dual problem when the cardinality upper bound is part of the
input. This result answers an open question raised by Epstein, Levin, and van
Stee.


title:
DISK: Domain-constrained Instance Sketch for Math Word Problem
  Generation

abstract:
  A math word problem (MWP) is a coherent narrative which reflects the
underlying logic of math equations. Successful MWP generation can automate the
writing of mathematics questions. Previous methods mainly generate MWP text
based on inflexible pre-defined templates. In this paper, we propose a neural
model for generating MWP text from math equations. Firstly, we incorporate a
matching model conditioned on the domain knowledge to retrieve a MWP instance
which is most consistent with the ground-truth, where the domain is a latent
variable extracted with a domain summarizer. Secondly, by constructing a
Quantity Cell Graph (QCG) from the retrieved MWP instance and reasoning over
it, we improve the model's comprehension of real-world scenarios and derive a
domain-constrained instance sketch to guide the generation. Besides, the QCG
also interacts with the equation encoder to enhance the alignment between math
tokens (e.g., quantities and variables) and MWP text. Experiments and empirical
analysis on educational MWP set show that our model achieves impressive
performance in both automatic evaluation metrics and human evaluation metrics.


title: MA-Dreamer: Coordination and communication through shared imagination

abstract:
  Multi-agent RL is rendered difficult due to the non-stationary nature of
environment perceived by individual agents. Theoretically sound methods using
the REINFORCE estimator are impeded by its high-variance, whereas
value-function based methods are affected by issues stemming from their ad-hoc
handling of situations like inter-agent communication. Methods like MADDPG are
further constrained due to their requirement of centralized critics etc. In
order to address these issues, we present MA-Dreamer, a model-based method that
uses both agent-centric and global differentiable models of the environment in
order to train decentralized agents' policies and critics using model-rollouts
a.k.a `imagination'. Since only the model-training is done off-policy,
inter-agent communication/coordination and `language emergence' can be handled
in a straight-forward manner. We compare the performance of MA-Dreamer with
other methods on two soccer-based games. Our experiments show that in long-term
speaker-listener tasks and in cooperative games with strong
partial-observability, MA-Dreamer finds a solution that makes effective use of
coordination, whereas competing methods obtain marginal scores and fail
outright, respectively. By effectively achieving coordination and communication
under more relaxed and general conditions, out method opens the door to the
study of more complex problems and population-based training.


title:
Using gamma-ray observation of dwarf spheroidal galaxy to test a dark
  matter model that can interpret the W-boson mass anomaly

abstract:
  A recent result from Fermilab suggests that the measured W-boson mass
deviates from the prediction of the Standard Model (SM) with a significance of
$>7\sigma$, and there may exist new physics beyond the SM. It is proposed that
the inert two Higgs doublet model (i2HDM) can well explain the new W-boson
mass. The preferred dark matter mass is between 54 and 74 GeV. More
interestingly, it is found that part of the parameter space of this model can
explain both the Galactic center GeV gamma-ray excess detected by Fermi-LAT and
the GeV antiproton excess detected by AMS-02 through a $SS\rightarrow WW^*$
annihilation. In this paper, we aim to test this model using the Fermi-LAT
observation of Milky Way dwarf spheroidal (dSph) galaxy. We mainly focus on the
four nearest confirmed dSphs, which are among the dSphs with the largest
J-factors. We find that our constraints are above the favored parameters and
can not exclude such a model, suggesting i2HDM is a promising model that can
interpret the W-boson mass anomaly, GeV excess, and antiproton excess.


title:
Impacts of gravitational-wave standard siren observations from Einstein
  Telescope and Cosmic Explorer on weighing neutrinos in interacting dark
  energy models

abstract:
  The multi-messenger gravitational-wave (GW) observation for binary neutron
star merger events could provide a rather useful tool to explore the evolution
of the universe. In particular, for the third-generation GW detectors, i.e.,
the Einstein Telescope (ET) and the Cosmic Explorer (CE), proposed to be built
in Europe and the U.S., respectively, lots of GW standard sirens with known
redshifts could be obtained, which would exert great impacts on the
cosmological parameter estimation. The total neutrino mass could be measured by
cosmological observations, but such a measurement is model-dependent and
currently only gives an upper limit. In this work, we wish to investigate
whether the GW standard sirens observed by ET and CE could help improve the
constraint on the neutrino mass, in particular in the interacting dark energy
(IDE) models. We find that the GW standard siren observations from ET and CE
can only slightly improve the constraint on the neutrino mass in the IDE
models, compared to the current limit. The improvements in the IDE models are
weaker than in the standard cosmological model. Although the limit on neutrino
mass can only be slightly updated, the constraints on other cosmological
parameters can be significantly improved by using the GW observations.


title: Maximal Inequalities and Some Applications

abstract:
  A maximal inequality is an inequality which involves the (absolute) supremum
$\sup_{s\leq t}|X_{s}|$ or the running maximum $\sup_{s\leq t}X_{s}$ of a
stochastic process $(X_t)_{t\geq 0}$. We discuss maximal inequalities for
several classes of stochastic processes with values in an Euclidean space:
Martingales, L\'evy processes, L\'evy-type - including Feller processes,
(compound) pseudo Poisson processes, stable-like processes and solutions to
SDEs driven by a L\'evy process -, strong Markov processes and Gaussian
processes. Using the Burkholder-Davis-Gundy inequalities we als discuss some
relations between maximal estimates in probability and the Hardy-Littlewood
maximal functions from analysis.


title: Coreset of Hyperspectral Images on Small Quantum Computer

abstract:
  Machine Learning (ML) techniques are employed to analyze and process big
Remote Sensing (RS) data, and one well-known ML technique is a Support Vector
Machine (SVM). An SVM is a quadratic programming (QP) problem, and a D-Wave
quantum annealer (D-Wave QA) promises to solve this QP problem more efficiently
than a conventional computer. However, the D-Wave QA cannot solve directly the
SVM due to its very few input qubits. Hence, we use a coreset ("core of a
dataset") of given EO data for training an SVM on this small D-Wave QA. The
coreset is a small, representative weighted subset of an original dataset, and
any training models generate competitive classes by using the coreset in
contrast to by using its original dataset. We measured the closeness between an
original dataset and its coreset by employing a Kullback-Leibler (KL)
divergence measure. Moreover, we trained the SVM on the coreset data by using
both a D-Wave QA and a conventional method. We conclude that the coreset
characterizes the original dataset with very small KL divergence measure. In
addition, we present our KL divergence results for demonstrating the closeness
between our original data and its coreset. As practical RS data, we use
Hyperspectral Image (HSI) of Indian Pine, USA.


title:
Setup of Compton polarimeters for measuring entangled annihilation
  photons

abstract:
  An experimental setup for studying the Compton scattering of annihilation
photons in various (entangled and decoherent) quantum states is presented. Two
entangled $\gamma$-quanta with an energy of 511 keV and mutually orthogonal
polarizations are produced by positron-electron annihilation in a thin aluminum
plate and are emitted in opposite directions. To measure both photons, the
setup provides two equivalent arms of Compton polarimeters. A Compton
polarimeter consists of a plastic scintillation scatterer and an array of
NaI(Tl) detectors for measuring photons deflected at an angle of 90$^\circ$.
The intermediate scatterer of the GAGG scintillator with SiPM readout is
inserted into one of the arms to create a tagged decoherence process prior to
the measurement of annihilation photons in polarimeters. The performance of
Compton scatterers and NaI(Tl) counters is discussed. The polarization
modulation factor and the analyzing power of Compton polarimeters are evaluated
from the angular distributions of scattered gammas. The Compton scattering of
photons in entangled and decoherent states is compared reliably for the first
time.


title:
A Monte Carlo Simulation Study of L-band Emission upon Gamma Radiolysis
  of Water

abstract:
  Several studies have confirmed visible light and ultraviolet emission during
water molecule radiolysis.However radiofrequency (RF) emissions have been
scarcely investigated.This simulation study has revealed that the gamma
radiolysis of water creates excited hydrogen atoms which emit radio
recombination Lband (1 GHz to 2 GHz) radio waves of sufficient strength that a
radio-imaging device can detect.The physical and physicochemical stages of
radiolysis of water have been modeled via application of Monte Carlo simulation
techniques up to 1ps from the onset of gamma photon interaction with water
molecules.


title:
ClioQuery: Interactive Query-Oriented Text Analytics for Comprehensive
  Investigation of Historical News Archives

abstract:
  Historians and archivists often find and analyze the occurrences of query
words in newspaper archives, to help answer fundamental questions about
society. But much work in text analytics focuses on helping people investigate
other textual units, such as events, clusters, ranked documents, entity
relationships, or thematic hierarchies. Informed by a study into the needs of
historians and archivists, we thus propose ClioQuery, a text analytics system
uniquely organized around the analysis of query words in context. ClioQuery
applies text simplification techniques from natural language processing to help
historians quickly and comprehensively gather and analyze all occurrences of a
query word across an archive. It also pairs these new NLP methods with more
traditional features like linked views and in-text highlighting to help
engender trust in summarization techniques. We evaluate ClioQuery with two
separate user studies, in which historians explain how ClioQuery's novel text
simplification features can help facilitate historical research. We also
evaluate with a separate quantitative comparison study, which shows that
ClioQuery helps crowdworkers find and remember historical information. Such
results suggest possible new directions for text analytics in other
query-oriented settings.


title:
A reflection equivalence for Gorenstein-projective quiver
  representations

abstract:
  For $\Lambda$ a selfinjective algebra, and $Q$ a finite quiver without
oriented cycles, the algebra $\Lambda Q$ is a Gorenstein algebra and the
category ${\rm Gproj}\Lambda Q$ of Gorenstein-projective $\Lambda Q$-modules is
a Frobenius category. For a sink $v$ of $Q$, we define a functor $F(v) :
\underline{\rm Gproj}\Lambda Q\to \underline{\rm Gproj}\Lambda Q(v)$ between
the stable categories modulo projectives, where $Q(v)$ is obtained from $Q$ by
changing the direction of each arrow ending in $v$. The functor is given by an
explicit construction on the level of objects and homomorphisms. Our main
result states that $F(v)$ is an equivalence of categories. In the case where
the underlying graph of $Q$ is a tree, we deduce that the stable category
$\underline{\rm Gproj}\Lambda Q$ does not depend on the orientation of $Q$.
Moreover, if $Q$ is a quiver of type $\mathbb A_3$ and $\Lambda=k[T]/(T^n)$ the
bounded polynomial algebra, we use the symmetry of the octahedron in the
octahedral axiom to verify that the composition of twelve reflections yields
the identity on objects.


title: Rough convergence of sequences in a S-metric space

abstract:
  Phu[12] introduced the idea of rough convergence of sequences in a normed
linear space. Here using the idea of Phu we have brought the idea of rough
convergence of sequences in a S-metric space and discussed some of its basic
properties.


title:
Effect of iron vacancies on the magnetic order and spin dynamics of the
  spin ladder BaFe$_{2-\delta}$S$_{1.5}$Se$_{1.5}$

abstract:
  Quasi-one-dimensional iron chalcogenides possess various magnetic states
depending on the lattice distortion, electronic correlations, and presence of
defects. We present neutron diffraction and inelastic neutron scattering
experiments on the spin ladder compound BaFe$_{2-\delta}$S$_{1.5}$Se$_{1.5}$
with $\sim$6% iron vacancies. The data reveal that long-range magnetic order is
absent, while the characteristic magnetic excitations that correspond to both
the stripe- and block-type antiferromagnetic correlations are observed.
First-principles calculations support the existence of both stripe and
block-type antiferromagnetic short-range order in the experimental sample. The
disappearance of long-range magnetic order may be due to the competition
between these two magnetic orders, which is greatly enhanced for a certain
concentration of iron vacancies, which we calculate to be about 6%, consistent
with the measured iron vacancy concentration. Our results highlight how iron
vacancies in the iron-based spin ladder system strongly influence the magnetic
ground state.


title: Verification of Strong K-Step Opacity for Discrete-Event Systems

abstract:
  In this paper, we revisit the verification of strong K-step opacity (K-SSO)
for partially-observed discrete-event systems modeled as nondeterministic
finite-state automata. As a stronger version of the standard K-step opacity,
K-SSO requires that an intruder cannot make sure whether or not a secret state
has been visited within the last K observable steps. To efficiently verify
K-SSO, we propose a new concurrent-composition structure, which is a variant of
our previously- proposed one. Based on this new structure, we design an
algorithm for deciding K-SSO and prove that the proposed algorithm not only
reduces the time complexity of the existing algorithms, but also does not
depend on the value of K. Furthermore, a new upper bound on the value of K in
K-SSO is derived, which also reduces the existing upper bound on K in the
literature. Finally, we illustrate the proposed algorithm by a simple example.


title: On the Cleaning Lemma of Quantum Coding Theory

abstract:
  The term "Cleaning Lemma" refers to a family of similar propositions that
have been used in Quantum Coding Theory to estimate the minimum distance of a
code in terms of its length and dimension. We show that the mathematical core
is a simple fact of linear algebra of inner product spaces; moreover, it admits
a further reduction to a combinatorial, lattice-theoretical level. Several
concrete variants of the Cleaning Lemma and some additional propositions are
derived as corollaries of the proposed approach.


title:
Activity and Rotation of Nearby Field M Dwarfs in the TESS Southern
  Continuous Viewing Zone

abstract:
  The evolution of magnetism in late-type dwarfs remains murky, as we can only
weakly predict levels of activity for M dwarfs of a given mass and age. We
report results from our spectroscopic survey of M dwarfs in the Southern
Continuous Viewing Zone (CVZ) of the Transiting Exoplanet Survey Satellite
(TESS). As the TESS CVZs overlap with those of the James Webb Space Telescope,
our targets constitute a legacy sample for studies of nearby M dwarfs. For 122
stars, we obtained at least one $R\approx 2000$ optical spectrum with which we
measure chromospheric $\mathrm{H}\alpha$ emission, a proxy for magnetic field
strength. The fraction of active stars is consistent with what is expected for
field M dwarfs; as in previous studies, we find that late-type M dwarfs remain
active for longer than their early type counterparts. While the TESS light
curves for $\approx$20% of our targets show modulations consistent with
rotation, TESS systematics are not well enough understood for confident
measurements of rotation periods ($P_{\mathrm{rot}}$) longer than half the
length of an observing sector. We report periods for 12 stars for which we
measure $P_{\mathrm{rot}} {\lower0.8ex\hbox{$\buildrel <\over\sim$}}$ 15 d or
find confirmation for the TESS-derived $P_{\mathrm{rot}}$ in the literature.
Our sample of 21 $P_{\mathrm{rot}}$, which includes periods from the
literature, is consistent with our targets being spun-down field stars.
Finally, we examine the $\mathrm{H}\alpha$-to-bolometric luminosity
distribution for our sample. Two stars are rotating fast enough to be
magnetically saturated, but are not, hinting at the possibility that fast
rotators may appear inactive in $\mathrm{H}\alpha$.


title: Educational Inequality

abstract:
  This chapter provides new evidence on educational inequality and reviews the
literature on the causes and consequences of unequal education. We document
large achievement gaps between children from different socio-economic
backgrounds, show how patterns of educational inequality vary across countries,
time, and generations, and establish a link between educational inequality and
social mobility. We interpret this evidence from the perspective of economic
models of skill acquisition and investment in human capital. The models account
for different channels underlying unequal education and highlight how
endogenous responses in parents' and children's educational investments
generate a close link between economic inequality and educational inequality.
Given concerns over the extended school closures during the Covid-19 pandemic,
we also summarize early evidence on the impact of the pandemic on children's
education and on possible long-run repercussions for educational inequality.


title:
Prospects for detecting axion-like particles via the decay $Z\rightarrow
  af\bar{f}$ at future $Z$ factories

abstract:
  We investigate the prospects for detecting axion-like particles (ALPs, dubbed
as "a") via the decay $Z\rightarrow a f\bar{f}$ at future $Z$ factories.
Considering the decay channels $a\rightarrow \mu^+ \mu^-$ and $a\rightarrow b
\bar{b}$ , four types of signals $\mu^+ \mu^- /E$, $b b /E$, $e^+ e^- \mu^+
\mu^-$ and $e^+ e^- b b$ are explored. We demonstrate that these channels are
promising for detecting ALPs at $Z$ factories and obtain the sensitivity bounds
on the couplings $g_{aZZ}$ and $g_{a\gamma Z}$.


title: Second order Sobolev embeddings and fourth-order pq-Laplacians

abstract:
  Motivated by the study of Sobolev embeddings of second order on intervals and
their approximation numbers, we examine the non-linear $pq$-biharmonic
eigenvalue problem on the unit segment subject to Navier boundary conditions
for general $1<p,q<\infty$. By establishing existence, uniqueness and symmetry
of periodic solutions, we fully describe the eigenvalues and eigenfunctions. In
the case $q=p'$, these are given explicitly in terms of generalized
trigonometric functions. However, remarkably, this is not so the case when
$q\not=p'$, as the eigenfunctions are related to a different family of
functions which are solution of a coupled system of integral equations. After
determining these, we give an explicit description of the s-numbers of the
Sobolev embedding of second order with homogeneous Dirichlet boundary
conditions.


title:
An Efficient Pattern Mining Convolution Neural Network (CNN) algorithm
  with Grey Wolf Optimization (GWO)

abstract:
  Automation of feature analysis in the dynamic image frame dataset deals with
complexity of intensity mapping with normal and abnormal class. The
threshold-based data clustering and feature analysis requires iterative model
to learn the component of image frame in multi-pattern for different image
frame data type. This paper proposed a novel model of feature analysis method
with the CNN based on Convoluted Pattern of Wavelet Transform (CPWT) feature
vectors that are optimized by Grey Wolf Optimization (GWO) algorithm.
Initially, the image frame gets normalized by applying median filter to the
image frame that reduce the noise and apply smoothening on it. From that, the
edge information represents the boundary region of bright spot in the image
frame. Neural network-based image frame classification performs repeated
learning of the feature with minimum training of dataset to cluster the image
frame pixels. Features of the filtered image frame was analyzed in different
pattern of feature extraction model based on the convoluted model of wavelet
transformation method. These features represent the different class of image
frame in spatial and textural pattern of it. Convolutional Neural Network (CNN)
classifier supports to analyze the features and classify the action label for
the image frame dataset. This process enhances the classification with minimum
number of training dataset. The performance of this proposed method can be
validated by comparing with traditional state-of-art methods.


title:
SplitNets: Designing Neural Architectures for Efficient Distributed
  Computing on Head-Mounted Systems

abstract:
  We design deep neural networks (DNNs) and corresponding networks' splittings
to distribute DNNs' workload to camera sensors and a centralized aggregator on
head mounted devices to meet system performance targets in inference accuracy
and latency under the given hardware resource constraints. To achieve an
optimal balance among computation, communication, and performance, a
split-aware neural architecture search framework, SplitNets, is introduced to
conduct model designing, splitting, and communication reduction simultaneously.
We further extend the framework to multi-view systems for learning to fuse
inputs from multiple camera sensors with optimal performance and systemic
efficiency. We validate SplitNets for single-view system on ImageNet as well as
multi-view system on 3D classification, and show that the SplitNets framework
achieves state-of-the-art (SOTA) performance and system latency compared with
existing approaches.


title: Moment sequences and difference equations

abstract:
  We recall the definition and the properties of a moment sequence and then
analyze cases when a difference equation with constant coefficients and
suitably chosen initial conditions has a solution that is a moment sequence. We
give one general simple result and give many examples illustrating the theory.


title:
Generative Adversarial Networks for Image Augmentation in Agriculture: A
  Systematic Review

abstract:
  In agricultural image analysis, optimal model performance is keenly pursued
for better fulfilling visual recognition tasks (e.g., image classification,
segmentation, object detection and localization), in the presence of challenges
with biological variability and unstructured environments. Large-scale,
balanced and ground-truthed image datasets, however, are often difficult to
obtain to fuel the development of advanced, high-performance models. As
artificial intelligence through deep learning is impacting analysis and
modeling of agricultural images, data augmentation plays a crucial role in
boosting model performance while reducing manual efforts for data preparation,
by algorithmically expanding training datasets. Beyond traditional data
augmentation techniques, generative adversarial network (GAN) invented in 2014
in the computer vision community, provides a suite of novel approaches that can
learn good data representations and generate highly realistic samples. Since
2017, there has been a growth of research into GANs for image augmentation or
synthesis in agriculture for improved model performance. This paper presents an
overview of the evolution of GAN architectures followed by a systematic review
of their application to agriculture
(https://github.com/Derekabc/GANs-Agriculture), involving various vision tasks
for plant health, weeds, fruits, aquaculture, animal farming, plant phenotyping
as well as postharvest detection of fruit defects. Challenges and opportunities
of GANs are discussed for future research.


title: Cache-Aided Massive MIMO with Linear Precoding in Multi-cell Systems

abstract:
  In this paper, we propose a novel joint caching and massive multiple-input
multiple-output (MIMO) transmission scheme, referred to as \emph{cache-aided
massive MIMO}, for multi-cell downlink transmission to multiple cache-enabled
receivers. With the proposed scheme, users who have cached (a portion of) the
files that they request are offloaded and, hence, (partially) inactive during
downlink transmission. The other users either benefit from the cache-enabled
offloading for mitigating pilot contamination or exploit the cached but
unrequested files to cancel interference during uplink channel estimation and
downlink file reception. Moreover, by redesigning the transmit precoders based
on the cache status of the users and channel state information, we gain
additional degrees of freedom for massive MIMO transmission. For a given cache
status, we analyze the equivalent content delivery rates (ECDRs), i.e., the
average rates of delivering a requested file via both caching and massive MIMO
transmission to the requesting user, for cache-aided massive MIMO employing
re-designed maximum ratio transmission (MRT), zero-forcing (ZF) precoding, and
regularized zero-forcing (RZF) precoding. Based on the derived results, the
impact of (random) uncoded caching and coded caching on the performance of the
re-designed precoding schemes is investigated. Simulation results validate our
derivations and show that caching is beneficial for precoded downlink
transmission as it enhances the transmit power allocation, mitigates intra- and
inter-cell interference, and reduces the impairment caused by pilot
contamination. Compared with conventional massive MIMO without caching and with
cache-oblivious precoding, the proposed cache-aided massive MIMO scheme
achieves a significantly higher ECDR even when the number of users approaches
the number of transmit antennas.


title:
Recurrence relations of coefficients involving hypergeometric function
  with an application

abstract:
  For $a,b,p\in \mathbb{R}$, $-c\notin \mathbb{N\cup }\left\{ 0\right\} $ and $
\theta \in \left[ -1,1\right] $, let \begin{equation*} U_{\theta }\left(
x\right) =\left( 1-\theta x\right) ^{p}F\left( a,b;c;x\right)
=\sum_{n=0}^{\infty }u_{n}\left( \theta \right) x^{n}. \end{equation*}% In this
paper, we prove that the coefficients $u_{n}\left( \theta \right) $ for $n\geq
0$ satisfies a 3-order recurrence relation. In particular, $ u_{n}\left(
1\right) $ satisfies a 2-order recurrence relation. These offer a new way to
study for hypergeometric function. As an example, we present the necessary and
sufficient conditions such that a hypergeometric mean value is Schur m-power
convex or concave on $\mathbb{R}_{+}^{2}$.


title:
Impact of LHC probes of SUSY and recent measurement of $(g-2)_{\mu}$ on
  $\mathbb{Z}_3$-NMSSM

abstract:
  It is well known that excessively heavy supersymmetric particles (sparticles)
are disfavored to explain the $(g-2)_\mu$ anomaly, but few people realize that
moderately light sparticles are also disfavored by the LHC probes of
supersymmetry. We take the Next-to-Minimal Supersymmetric Standard Model as an
example to illustrate the latter point. It is found that, if the theory is
required to explain the anomaly at $2\sigma$ level and meanwhile keep
consistent with the LHC results, the following lower bounds may be set: $\tan
\beta \gtrsim 20$, $|M_1| \gtrsim 275~{\rm GeV}$, $M_2 \gtrsim 300~{\rm GeV}$,
$\mu \gtrsim 460~{\rm GeV}$, $m_{\tilde{\mu}_L} \gtrsim 310~{\rm GeV}$, and
$m_{\tilde{\mu}_R} \gtrsim 350~{\rm GeV}$, where $M_1$ and $M_2$ denote gaugino
masses, $\mu$ represents the Higgsino mass, and $m_{\tilde{\mu}_L}$ and
$m_{\tilde{\mu}_R}$ are the mass of Smuons with $L$ and $R$ denoting their
dominant chiral component. This observation has significant impacts on dark
matter (DM) physics, e.g., the popular $Z$- and Higgs-funnel regions have been
excluded, and the Bino-dominated neutralino DM has to co-annihilate with the
Wino-dominated electroweakinos (in most cases) and/or Smuons (in few cases) to
obtain the correct density. It is also inferred that these conclusions should
apply to the Minimal Supersymmetric Standard Model since the underlying physics
for the bounds are the same.


title: Data Augmentation for Biomedical Factoid Question Answering

abstract:
  We study the effect of seven data augmentation (da) methods in factoid
question answering, focusing on the biomedical domain, where obtaining training
instances is particularly difficult. We experiment with data from the BioASQ
challenge, which we augment with training instances obtained from an artificial
biomedical machine reading comprehension dataset, or via back-translation,
information retrieval, word substitution based on word2vec embeddings, or
masked language modeling, question generation, or extending the given passage
with additional context. We show that da can lead to very significant
performance gains, even when using large pre-trained Transformers, contributing
to a broader discussion of if/when da benefits large pre-trained models. One of
the simplest da methods, word2vec-based word substitution, performed best and
is recommended. We release our artificial training instances and code.


title: Reduced critical slowing down for statistical physics simulations

abstract:
  Wang-Landau simulations offer the possibility to integrate explicitly over a
collective coordinate and stochastically over the remainder of configuration
space. We propose to choose the so-called "slow mode", which is responsible for
large autocorrelation times and thus critical slowing down, for collective
integration. We study this proposal for the Ising model and the LLR method as
simulation algorithm. We firstly show that in a phase with spontaneously broken
global symmetry, autocorrelation times grow exponentially with system size for
the standard heatbath update. Identifying the magnetisation as collective
coordinate, we present evidence that critical slowing down is absent for almost
all observables.


title:
Neutron-neutral particle mixing in partial compositeness model and its
  observable consequences

abstract:
  In this work, we explore the possibility that the mixing between neutron
($n$) and elementary neutral particle ($\eta$), which violates both the baryon
number ($B$) and the lepton number ($L$) by one unit but conserves their
difference $(B-L)$, may give rise to non-trivial effects that are different
from the Standard Model predictions. We focus on two different scenarios,
namely the pure and impure oscillation, roughly corresponding to whether the
decay products of neutron are responsible for the $n$-$\bar{n}$ oscillation. In
the scenario where the neutral particle oscillation is pure oscillation, the
Majorana phase, which leads to CP-violating effects, can be observable. In the
scenario where the neutral particle oscillation is impure, we analyze the
testable implications on the masses and lifetimes of the mass eigenstates,
arising from the $n$-$\bar{n}$ oscillation mediated by $\eta$. In this
scenario, we also explore the compatibility between the interpretation of the
neutron lifetime anomaly and the interpretation of the $n$-$\bar{n}$
oscillation experiments in terms of the $n$-$\eta$ mixing. In both scenarios,
we present the lower bounds imposed by the results of the searches for
$n$-$\bar{n}$ oscillations on the masses of the color triplet bosons and point
out that they could be within the reach of a direct detection at the LHC or
future high-energy experiments. Furthermore, we show that the Majorana phase
can also be manifested as a part of the Mukunda-Simon geometric phase and the
measurement of such a geometric phase may provide another opportunity for the
study of the new physical effects.


title: Transverse Peierls Transition

abstract:
  In the present paper, we have discussed a new type of spontaneous symmetry
breaking phases caused by the softening of the transverse acoustic phonon modes
through the electron phonon coupling. These new phases include the shear
density wave and self-twisting wave, which are caused by the softening of
linearly and circularly polarized acoustic phonon modes, respectively. We
propose that two of the topological semimetal systems in the quantum limit,
where the electrons only occupy the lowest Landau bands under external magnetic
field, will be the perfect systems to realize these new phases. Exotic physical
effects will be induced in these new phases, including the 3D quantum Hall
effect, chiral standing acoustic wave, magneto-acoustic effects and chiral
phonon correction to Einstein-de Hass effect.


title: Image Harmonization by Matching Regional References

abstract:
  To achieve visual consistency in composite images, recent image harmonization
methods typically summarize the appearance pattern of global background and
apply it to the global foreground without location discrepancy. However, for a
real image, the appearances (illumination, color temperature, saturation, hue,
texture, etc) of different regions can vary significantly. So previous methods,
which transfer the appearance globally, are not optimal. Trying to solve this
issue, we firstly match the contents between the foreground and background and
then adaptively adjust every foreground location according to the appearance of
its content-related background regions. Further, we design a residual
reconstruction strategy, that uses the predicted residual to adjust the
appearance, and the composite foreground to reserve the image details.
Extensive experiments demonstrate the effectiveness of our method. The source
code will be available publicly.


title:
TOV: The Original Vision Model for Optical Remote Sensing Image
  Understanding via Self-supervised Learning

abstract:
  Do we on the right way for remote sensing image understanding (RSIU) by
training models via supervised data-dependent and task-dependent way, instead
of human vision in a label-free and task-independent way? We argue that a more
desirable RSIU model should be trained with intrinsic structure from data
rather that extrinsic human labels to realize generalizability across a wide
range of RSIU tasks. According to this hypothesis, we proposed \textbf{T}he
\textbf{O}riginal \textbf{V}ision model (TOV) in remote sensing filed. Trained
by massive unlabeled optical data along a human-like self-supervised learning
(SSL) path that is from general knowledge to specialized knowledge, TOV model
can be easily adapted to various RSIU tasks, including scene classification,
object detection, and semantic segmentation, and outperforms dominant ImageNet
supervised pretrained method as well as two recently proposed SSL pretrained
methods on majority of 12 publicly available benchmarks. Moreover, we analyze
the influences of two key factors on the performance of building TOV model for
RSIU, including the influence of using different data sampling methods and the
selection of learning paths during self-supervised optimization. We believe
that a general model which is trained by a label-free and task-independent way
may be the next paradigm for RSIU and hope the insights distilled from this
study can help to foster the development of an original vision model for RSIU.


title:
Closing the Gap between Weighted and Unweighted Matching in the Sliding
  Window Model

abstract:
  We consider the Maximum-weight Matching (MWM) problem in the streaming
sliding window model of computation. In this model, the input consists of a
sequence of weighted edges on a given vertex set $V$ of size $n$. The objective
is to maintain an approximation of a maximum-weight matching in the graph
spanned by the $L$ most recent edges, for some integer $L$, using space
$\tilde{O}(n)$.
  Crouch et al. [ESA'13] gave a $(3+\varepsilon)$-approximation for
(unweighted) Maximum Matching (MM), and, very recently, Biabani et al.
[ISAAC'21] gave a $(3.5+\varepsilon)$-approximation for MWM. In this paper, we
give a $(3 + \varepsilon)$-approximation for MWM, thereby closing the gap
between MWM and MM.
  Biabani et al.'s work makes use of the smooth histogram technique introduced
by Braverman and Ostrovsky [FOCS'07]. Rather than designing sliding window
algorithms directly, this technique reduces the problem to designing so-called
lookahead algorithms that have certain smoothness properties. Biabani et al.
showed that the one-pass MWM streaming algorithm by Paz and Schwartzman
[SODA'17] constitutes a lookahead algorithm with approximation factor $3.5 +
\varepsilon$, which yields their result.
  We first give a hard instance, showing that Paz and Schwartzman's algorithm
is indeed no better than a $3.5$-approximation lookahead algorithm, which
implies that Biabani et al.'s analysis is tight. To obtain our improvement, we
give an alternative and more complex definition of lookahead algorithms that
still maintains the connection to the sliding window model. Our new definition,
however, reflects further smoothness properties of Paz and Schwartzman's
algorithm, which we exploit in order to improve upon Biabani et al.'s analysis,
thereby establishing our result.


title: Rethinking Exponential Averaging of the Fisher

abstract:
  In optimization for Machine learning (ML), it is typical that
curvature-matrix (CM) estimates rely on an exponential average (EA) of local
estimates (giving EA-CM algorithms). This approach has little principled
justification, but is very often used in practice. In this paper, we draw a
connection between EA-CM algorithms and what we call a "Wake of Quadratic
regularized models". The outlined connection allows us to understand what EA-CM
algorithms are doing from an optimization perspective. Generalizing from the
established connection, we propose a new family of algorithms, "KL-Divergence
Wake-Regularized Models" (KLD-WRM). We give three different practical
instantiations of KLD-WRM, and show numerical results where we outperform
K-FAC.


title: A note on log-algebraicity on elliptic curves

abstract:
  We analyze log-algebraic power series identities for formal groups of
elliptic curves over $\mathbb{Q}$ which arise from modular parametrizations. We
further investigate applications to special values of elliptic curve
$L$-functions.


title:
The crossover from the Macroscopic Fluctuation Theory to the
  Kardar-Parisi-Zhang equation controls the large deviations beyond Einstein's
  diffusion

abstract:
  We study the crossover from the macroscopic fluctuation theory (MFT) which
describes 1D stochastic diffusive systems at late times, to the weak noise
theory (WNT) which describes the Kardar-Parisi-Zhang (KPZ) equation at early
times. We focus on the example of the diffusion in a time-dependent random
field, observed in an atypical direction which induces an asymmetry. The
crossover is described by a non-linear system which interpolates between the
derivative and the standard non-linear Schrodinger equations in imaginary time.
We solve this system using the inverse scattering method for mixed-time
boundary conditions introduced by us to solve the WNT. We obtain the rate
function which describes the large deviations of the sample-to-sample
fluctuations of the cumulative distribution of the tracer position. It exhibits
a crossover as the asymmetry is varied, recovering both MFT and KPZ limits. We
sketch how it is consistent with extracting the asymptotics of a Fredholm
determinant formula, recently derived for sticky Brownian motions. The
crossover mechanism studied here should generalize to a larger class of models
described by the MFT. Our results apply to study extremal diffusion beyond
Einstein's theory.

categories: cond-mat.stat-mech cond-mat.dis-nn math-ph math.MP math.PR nlin.SI


title:
Dual-Function Radar-Communication System Aided by Intelligent Reflecting
  Surfaces

abstract:
  We propose a novel design of a dual-function radar communication (DFRC)
system aided by an Intelligent Reflecting Surface (IRS). We consider a scenario
with one target and multiple communication receivers, where there is no
line-of-sight between the radar and the target. The radar precoding matrix and
the IRS weights are optimally designed to maximize the weighted sum of the
signal-to-noise ratio (SNR) at the radar receiver and the SNR at the
communication receivers subject to power constraints and constant modulus
constraints on the IRS weights. The problem is decoupled into two sub-problems,
namely, waveform design and IRS weight design, and is solved via alternating
optimization. The former subproblem is solved via linear programming, and the
latter via manifold optimization with a quartic polynomial objective. The key
contribution of this paper lies in solving the IRS weight design sub-problem
that is based on the optimization of a quartic objective function in the IRS
weights, and is subject to unit modulus-constraint on the IRS weights.
Simulation results are provided to show the convergence behavior of the
proposed algorithm under different system configurations, and the effectiveness
of using IRS to improve radar and communication performance.


title:
Regret Analysis of Online Gradient Descent-based Iterative Learning
  Control with Model Mismatch

abstract:
  In Iterative Learning Control (ILC), a sequence of feedforward control
actions is generated at each iteration on the basis of partial model knowledge
and past measurements with the goal of steering the system toward a desired
reference trajectory. This is framed here as an online learning task, where the
decision-maker takes sequential decisions by solving a sequence of optimization
problems having only partial knowledge of the cost functions. Having
established this connection, the performance of an online gradient-descent
based scheme using inexact gradient information is analyzed in the setting of
dynamic and static regret, standard measures in online learning. Fundamental
limitations of the scheme and its integration with adaptation mechanisms are
further investigated, followed by numerical simulations on a benchmark ILC
problem.


title:
Machine Learning-Based CSI Feedback With Variable Length in FDD Massive
  MIMO

abstract:
  To fully unlock the benefits of multiple-input multiple-output (MIMO)
networks, downlink channel state information (CSI) is required at the base
station (BS). In frequency division duplex (FDD) systems, the CSI is acquired
through a feedback signal from the user equipment (UE). However, this may lead
to an important overhead in FDD massive MIMO systems. Focusing on these
systems, in this study, we propose a novel strategy to design the CSI feedback.
Our strategy allows to optimally design the feedback with variable length,
while reducing the parameter number at the UE. Specifically, principal
component analysis (PCA) is used to compress the channel into a latent space
with adaptive dimensionality. To quantize this compressed channel, the feedback
bits are smartly allocated to the latent space dimensions by minimizing the
normalized mean squared error (NMSE) distortion. Finally, the quantization
codebook is determined with k-means clustering. Numerical simulations show that
our strategy improves the zeroforcing beamforming sum rate by 26.8%, compared
with the popular CsiNet. The number of model parameters is reduced by 24.9
times, thus causing a significantly smaller offloading overhead. At the same
time, PCA is characterized by a lightweight unsupervised training, requiring
eight times fewer training samples than CsiNet.