title:
Common best proximity point theorems under proximal $F$-weak dominance
  in complete metric spaces

abstract:
  Suppose that $S_1$ and $S_2$ are nonempty subsets of a complete metric space
$(\mathcal{M},d)$ and $\phi,\psi:S_1\to S_2$ are mappings. The aim of this work
is to investigate some conditions on $\phi$ and $\psi$ such that the two
functions, one that assigns to each $x\in S_1$ exactly $d(x,\phi x)$ and the
other that assigns to each $x\in S_1$ exactly $d(x,\psi x)$, attain the global
minimum value at the same point in $S_1$. We have introduced the notion of
proximally $F$-weakly dominated pair of mappings and proved two theorems that
guarantee the existence of such a point. Our work is an improvement of earlier
work in this direction. We have also provided examples in which our results are
applicable, but the earlier results are not applicable.


title:
A Contrastive Cross-Channel Data Augmentation Framework for Aspect-based
  Sentiment Analysis

abstract:
  Aspect-Based Sentiment Analysis is a fine-grained sentiment analysis task,
which focuses on detecting the sentiment polarity towards the aspect in a
sentence. However, it is always sensitive to the multi-aspect challenge, where
features of multiple aspects in a sentence will affect each other. To mitigate
this issue, we design a novel training framework, called Contrastive
Cross-Channel Data Augmentation (C3DA). A source sentence will be fed a
domain-specific generator to obtain some synthetic sentences and is
concatenated with these generated sentences to conduct supervised training and
proposed contrastive training. To be specific, considering the limited ABSA
labeled data, we also introduce some parameter-efficient approaches to complete
sentences generation. This novel generation method consists of an Aspect
Augmentation Channel (AAC) to generate aspect-specific sentences and a Polarity
Augmentation (PAC) to generate polarity-inverted sentences. According to our
extensive experiments, our C3DA framework can outperform those baselines
without any augmentations by about 1\% on accuracy and Macro-F1.


title: Optimizing differential equations to fit data and predict outcomes

abstract:
  Many scientific problems focus on observed patterns of change or on how to
design a system to achieve particular dynamics. Those problems often require
fitting differential equation models to target trajectories. Fitting such
models can be difficult because each evaluation of the fit must calculate the
distance between the model and target patterns at numerous points along a
trajectory. The gradient of the fit with respect to the model parameters can be
challenging. Recent technical advances in automatic differentiation through
numerical differential equation solvers potentially change the fitting process
into a relatively easy problem, opening up new possibilities to study dynamics.
However, application of the new tools to real data may fail to achieve a good
fit. This article illustrates how to overcome a variety of common challenges,
using the classic ecological data for oscillations in hare and lynx
populations. Models include simple ordinary differential equations (ODEs) and
neural ordinary differential equations (NODEs), which use artificial neural
networks to estimate the derivatives of differential equation systems.
Comparing the fits obtained with ODEs versus NODEs, representing small and
large parameter spaces, and changing the number of variable dimensions provide
insight into the geometry of the observed and model trajectories. To analyze
the quality of the models for predicting future observations, a
Bayesian-inspired preconditioned stochastic gradient Langevin dynamics (pSGLD)
calculation of the posterior distribution of predicted model trajectories
clarifies the tendency for various models to underfit or overfit the data.
Coupling fitted differential equation systems with pSGLD sampling provides a
powerful way to study the properties of optimization surfaces, raising an
analogy with mutation-selection dynamics on fitness landscapes.


title:
Bridging Cross-Lingual Gaps During Leveraging the Multilingual
  Sequence-to-Sequence Pretraining for Text Generation

abstract:
  For multilingual sequence-to-sequence pretrained language models
(multilingual Seq2Seq PLMs), e.g. mBART, the self-supervised pretraining task
is trained on a wide range of monolingual languages, e.g. 25 languages from
commoncrawl, while the downstream cross-lingual tasks generally progress on a
bilingual language subset, e.g. English-German, making there exists the
cross-lingual data discrepancy, namely \textit{domain discrepancy}, and
cross-lingual learning objective discrepancy, namely \textit{task discrepancy},
between the pretrain and finetune stages. To bridge the above cross-lingual
domain and task gaps, we extend the vanilla pretrain-finetune pipeline with
extra code-switching restore task. Specifically, the first stage employs the
self-supervised code-switching restore task as a pretext task, allowing the
multilingual Seq2Seq PLM to acquire some in-domain alignment information. And
for the second stage, we continuously fine-tune the model on labeled data
normally. Experiments on a variety of cross-lingual NLG tasks, including 12
bilingual translation tasks, 36 zero-shot translation tasks, and cross-lingual
summarization tasks show our model outperforms the strong baseline mBART
consistently. Comprehensive analyses indicate our approach could narrow the
cross-lingual sentence representation distance and improve low-frequency word
translation with trivial computational cost.


title: What If: Generating Code to Answer Simulation Questions

abstract:
  Many texts, especially in chemistry and biology, describe complex processes.
We focus on texts that describe a chemical reaction process and questions that
ask about the process's outcome under different environmental conditions. To
answer questions about such processes, one needs to understand the interactions
between the different entities involved in the process and to simulate their
state transitions during the process execution under different conditions. A
state transition is defined as the memory modification the program does to the
variables during the execution. We hypothesize that generating code and
executing it to simulate the process will allow answering such questions. We,
therefore, define a domain-specific language (DSL) to represent processes. We
contribute to the community a unique dataset curated by chemists and annotated
by computer scientists. The dataset is composed of process texts, simulation
questions, and their corresponding computer codes represented by the DSL.We
propose a neural program synthesis approach based on reinforcement learning
with a novel state-transition semantic reward. The novel reward is based on the
run-time semantic similarity between the predicted code and the reference code.
This allows simulating complex process transitions and thus answering
simulation questions. Our approach yields a significant boost in accuracy for
simulation questions: 88\% accuracy as opposed to 83\% accuracy of the
state-of-the-art neural program synthesis approaches and 54\% accuracy of
state-of-the-art end-to-end text-based approaches.


title:
Carbon Monitor Cities, near-real-time daily estimates of CO2 emissions
  from 1500 cities worldwide

abstract:
  Building on near-real-time and spatially explicit estimates of daily carbon
dioxide (CO2) emissions, here we present and analyze a new city-level dataset
of fossil fuel and cement emissions. Carbon Monitor Cities provides daily,
city-level estimates of emissions from January 2019 through December 2021 for
1500 cities in 46 countries, and disaggregates five sectors: power generation,
residential (buildings), industry, ground transportation, and aviation. The
goal of this dataset is to improve the timeliness and temporal resolution of
city-level emission inventories and includes estimates for both functional
urban areas and city administrative areas that are consistent with global and
regional totals. Comparisons with other datasets (i.e. CEADs, MEIC, Vulcan, and
CDP) were performed, and we estimate the overall uncertainty to be 21.7%.
Carbon Monitor Cities is a near-real-time, city-level emission dataset that
includes cities around the world, including the first estimates for many cities
in low-income countries.


title:
BLISS: Robust Sequence-to-Sequence Learning via Self-Supervised Input
  Representation

abstract:
  Data augmentations (DA) are the cores to achieving robust
sequence-to-sequence learning on various natural language processing (NLP)
tasks. However, most of the DA approaches force the decoder to make predictions
conditioned on the perturbed input representation, underutilizing supervised
information provided by perturbed input. In this work, we propose a
framework-level robust sequence-to-sequence learning approach, named BLISS, via
self-supervised input representation, which has the great potential to
complement the data-level augmentation approaches. The key idea is to supervise
the sequence-to-sequence framework with both the \textit{supervised}
("input$\rightarrow$output") and \textit{self-supervised} ("perturbed
input$\rightarrow$input") information. We conduct comprehensive experiments to
validate the effectiveness of BLISS on various tasks, including machine
translation, grammatical error correction, and text summarization. The results
show that BLISS outperforms significantly the vanilla Transformer and
consistently works well across tasks than the other five contrastive baselines.
Extensive analyses reveal that BLISS learns robust representations and rich
linguistic knowledge, confirming our claim. Source code will be released upon
publication.


title: Endomorphisms of semigroups of monotone transformations

abstract:
  In this paper, we characterize the monoid of endomorphisms of the semigroup
of all monotone full transformations of a finite chain, as well as the monoids
of endomorphisms of the semigroup of all monotone partial transformations and
of the semigroup of all monotone partial permutations of a finite chain.


title: A Logical Analysis of Dynamic Dependence

abstract:
  Many forms of dependence manifest themselves over time, with behavior of
variables in dynamical systems as a paradigmatic example. This paper studies
temporal dependence in dynamical systems from a logical perspective, by
extending a minimal modal base logic of static functional dependencies. We
define a logic for dynamical systems with single time steps, provide a complete
axiomatic proof calculus, and show the decidability of the satisfiability
problem for a substantial fragment. The system comes in two guises: modal and
first-order, that naturally complement each other. Next, we consider a timed
semantics for our logic, as an intermediate between state spaces and temporal
universes for the unfoldings of a dynamical system. We prove completeness and
decidability by combining techniques from dynamic-epistemic logic and modal
logic of functional dependencies with complex terms for objects. Also, we
extend these results to the timed logic with functional symbols and term
identity. Finally, we conclude with a brief outlook on how the system proposed
here connects with richer temporal logics of system behavior, and with dynamic
topological logic.


title:
A Robust and Scalable Attention Guided Deep Learning Framework for
  Movement Quality Assessment

abstract:
  Physical rehabilitation programs frequently begin with a brief stay in the
hospital and continue with home-based rehabilitation. Lack of feedback on
exercise correctness is a significant issue in home-based rehabilitation.
Automated movement quality assessment (MQA) using skeletal movement data
(hereafter referred to as skeletal data) collected via depth imaging devices
can assist with home-based rehabilitation by providing the necessary
quantitative feedback. This paper aims to use recent advances in deep learning
to address the problem of MQA. Movement quality score generation is an
essential component of MQA. We propose three novel skeletal data augmentation
schemes. We show that using the proposed augmentations for generating movement
quality scores result in significant performance boosts over existing methods.
Finally, we propose a novel transformer based architecture for MQA. Four novel
feature extractors are proposed and studied that allow the transformer network
to operate on skeletal data. We show that adding the attention mechanism in the
design of the proposed feature extractor allows the transformer network to pay
attention to specific body parts that make a significant contribution towards
executing a movement. We report an improvement in movement quality score
prediction of 12% on UI-PRMD dataset and 21% on KIMORE dataset compared to the
existing methods.


title:
Multimodal Few-Shot Object Detection with Meta-Learning Based
  Cross-Modal Prompting

abstract:
  We study multimodal few-shot object detection (FSOD) in this paper, using
both few-shot visual examples and class semantic information for detection.
Most of previous works focus on either few-shot or zero-shot object detection,
ignoring the complementarity of visual and semantic information. We first show
that meta-learning and prompt-based learning, the most commonly-used methods
for few-shot learning and zero-shot transferring from pre-trained
vision-language models to downstream tasks, are conceptually similar. They both
reformulate the objective of downstream tasks the same as the pre-training
tasks, and mostly without tuning the parameters of pre-trained models. Based on
this observation, we propose to combine meta-learning with prompt-based
learning for multimodal FSOD without fine-tuning, by learning transferable
class-agnostic multimodal FSOD models over many-shot base classes.
Specifically, to better exploit the pre-trained vision-language models, the
meta-learning based cross-modal prompting is proposed to generate soft prompts
and further used to extract the semantic prototype, conditioned on the few-shot
visual examples. Then, the extracted semantic prototype and few-shot visual
prototype are fused to generate the multimodal prototype for detection. Our
models can efficiently fuse the visual and semantic information at both
token-level and feature-level. We comprehensively evaluate the proposed
multimodal FSOD models on multiple few-shot object detection benchmarks,
achieving promising results.


title: A New Feasibility Condition for the AT4 family

abstract:
  Let $\Gamma$ be an antipodal distance-regular graph with diameter $4$. Then
$\Gamma$ is tight in the sense of Juri\v{s}i\'{c}, Koolen, and Terwilliger [7]
whenever $\Gamma$ is locally strongly regular with nontrivial eigenvalues $p,
-q$ $(p>-q)$. In this case, the intersection numbers of $\Gamma$ are
parametrized by $p$ and $q$. We denote $\Gamma$ by $\mathrm{AT4}(p,q,r)$, where
$r$ is the size of antipodal classes of $\Gamma$. In this paper, we give a new
feasibility condition for the $\mathrm{AT4}(p,q,r)$ family. We determine a
sufficient and necessary condition for the second subconstituent of $\Gamma$ to
be antipodal tight while $r=2$. From this, we prove the nonexistence of
$\mathrm{AT4}(q^3-2q,q,2)$ when $q\equiv3$ $(\mathrm{mod}~4)$.


title:
Degenerate r-Whitney numbers and degenerate r-Dowling polynomials via
  Boson operators

abstract:
  Dowling showed that the Whitney numbers of the first kind and of the second
kind satisfy Stirling number-like relations. Recently, Kim-Kim introduced the
degenerate r-Whitney numbers of the first kind and of the second kind, as
degenerate versions and further generalizations of the Whitney numbers of both
kinds. The normal ordering of an integral power of the number operator in terms
of boson operators is expressed with the help of the Stirling numbers of the
second kind. In this paper, it is noted that the normal ordering of a certain
quantity involving the number operator is expressed in terms of the degenerate
r-Whitney numbers of the second kind. We derive some properties, recurrence
relations, orthogonality relations and several identities on those numbers from
such normal ordering. In addition, we consider the degenerate r-Dowling
polynomials as a natural extension of the degenerate r-Whitney numbers of the
second kind and investigate their properties.


title: Enhancement of the $\textbf{W}$ boson mass in the Georgi-Machacek model

abstract:
  The recent CDF-II measurement of the mass of the $W$ boson shows a
significant tension with the Standard Model expectation. The tension can be
explained with any new physics model that contributes substantially to the
oblique $S$ and $T$ parameters. We try to explain the deviation in the
framework of the Georgi-Machacek model that keeps $\rho=1$ at the tree-level,
and find the parameter space that can accommodate the $W$ mass shift.


title: Shape-guided Object Inpainting

abstract:
  Previous works on image inpainting mainly focus on inpainting background or
partially missing objects, while the problem of inpainting an entire missing
object remains unexplored. This work studies a new image inpainting task, i.e.
shape-guided object inpainting. Given an incomplete input image, the goal is to
fill in the hole by generating an object based on the context and implicit
guidance given by the hole shape. Since previous methods for image inpainting
are mainly designed for background inpainting, they are not suitable for this
task. Therefore, we propose a new data preparation method and a novel
Contextual Object Generator (CogNet) for the object inpainting task. On the
data side, we incorporate object priors into training data by using object
instances as holes. The CogNet has a two-stream architecture that combines the
standard bottom-up image completion process with a top-down object generation
process. A predictive class embedding module bridges the two streams by
predicting the class of the missing object from the bottom-up features, from
which a semantic object map is derived as the input of the top-down stream.
Experiments demonstrate that the proposed method can generate realistic objects
that fit the context in terms of both visual appearance and semantic meanings.
Code can be found at the project page
\url{https://zengxianyu.github.io/objpaint}


title:
On Safety Testing, Validation, and Characterization with
  Scenario-Sampling: A Case Study of Legged Robots

abstract:
  The dynamic response of the legged robot locomotion is non-Lipschitz and can
be stochastic due to environmental uncertainties. To test, validate, and
characterize the safety performance of legged robots, existing solutions on
observed and inferred risk can be incomplete and sampling inefficient. Some
formal verification methods suffer from the model precision and other surrogate
assumptions. In this paper, we propose a scenario sampling based testing
framework that characterizes the overall safety performance of a legged robot
by specifying (i) where (in terms of a set of states) the robot is potentially
safe, and (ii) how safe the robot is within the specified set. The framework
can also help certify the commercial deployment of the legged robot in
real-world environment along with human and compare safety performance among
legged robots with different mechanical structures and dynamic properties. The
proposed framework is further deployed to evaluate a group of state-of-the-art
legged robot locomotion controllers from various model-based, deep neural
network involved, and reinforcement learning based methods in the literature.
Among a series of intended work domains of the studied legged robots (e.g.
tracking speed on sloped surface, with abrupt changes on demanded velocity, and
against adversarial push-over disturbances), we show that the method can
adequately capture the overall safety characterization and the subtle
performance insights. Many of the observed safety outcomes, to the best of our
knowledge, have never been reported by the existing work in the legged robot
literature.


title: On the analysis of the tin-inside-H3S M\"ossbauer experiment

abstract:
  A simple analysis is presented of the particular experiment used to prove the
bulk nature of very-high-Tc superconductivity in H3S compound under ultra-high
pressure. In the experiment, an internal magnetic field was sensed by the
synchrotron M\"ossbauer spectroscopy in tin placed inside the H2S sample. The
experiment showed peculiar anisotropy with respect to the direction of the
applied field at first sight. By considering actual experimental geometries and
parameters of the experiment, we show that this particular observation is
consistent with the expectations for a regular type-II superconductor with
Meissner expulsion and pinning.


title:
STRATA: Word Boundaries & Phoneme Recognition From Continuous Urdu
  Speech using Transfer Learning, Attention, & Data Augmentation

abstract:
  Phoneme recognition is a largely unsolved problem in NLP, especially for
low-resource languages like Urdu. The systems that try to extract the phonemes
from audio speech require hand-labeled phonetic transcriptions. This requires
expert linguists to annotate speech data with its relevant phonetic
representation which is both an expensive and a tedious task. In this paper, we
propose STRATA, a framework for supervised phoneme recognition that overcomes
the data scarcity issue for low resource languages using a seq2seq neural
architecture integrated with transfer learning, attention mechanism, and data
augmentation. STRATA employs transfer learning to reduce the network loss in
half. It uses attention mechanism for word boundaries and frame alignment
detection which further reduces the network loss by 4% and is able to identify
the word boundaries with 92.2% accuracy. STRATA uses various data augmentation
techniques to further reduce the loss by 1.5% and is more robust towards new
signals both in terms of generalization and accuracy. STRATA is able to achieve
a Phoneme Error Rate of 16.5% and improves upon the state of the art by 1.1%
for TIMIT dataset (English) and 11.5% for CSaLT dataset (Urdu).


title: Shift Invariant Algebras, Segre Products and Regular Languages

abstract:
  Motivated by results on the rationality of equivariant Hilbert series of some
hierarchical models in algebraic statistics we introduce the Segre product of
formal languages and apply it to establish rationality of equivariant Hilbert
series in new cases. To this end we show that the Segre product of two regular
languages is again regular. We also prove that every filtration of algebras
given as a tensor product of families of algebras with rational equivariant
Hilbert series has a rational equivariant Hilbert series. The term equivariant
is used broadly to include the action of the monoid of nonnegative integers by
shifting variables. Furthermore, we exhibit a filtration of shift invariant
monomial algebras that has a rational equivariant Hilbert series, but whose
presentation ideals do not stabilize.


title: Multi-organ Segmentation Network with Adversarial Performance Validator

abstract:
  CT organ segmentation on computed tomography (CT) images becomes a
significant brick for modern medical image analysis, supporting clinic
workflows in multiple domains. Previous segmentation methods include 2D
convolution neural networks (CNN) based approaches, fed by CT image slices that
lack the structural knowledge in axial view, and 3D CNN-based methods with the
expensive computation cost in multi-organ segmentation applications. This paper
introduces an adversarial performance validation network into a 2D-to-3D
segmentation framework. The classifier and performance validator competition
contribute to accurate segmentation results via back-propagation. The proposed
network organically converts the 2D-coarse result to 3D high-quality
segmentation masks in a coarse-to-fine manner, allowing joint optimization to
improve segmentation accuracy. Besides, the structural information of one
specific organ is depicted by a statistics-meaningful prior bounding box, which
is transformed into a global feature leveraging the learning process in 3D fine
segmentation. The experiments on the NIH pancreas segmentation dataset
demonstrate the proposed network achieves state-of-the-art accuracy on small
organ segmentation and outperforms the previous best. High accuracy is also
reported on multi-organ segmentation in a dataset collected by ourselves.


title:
COVIBOT: A Smart Chatbot for Assistance and E-Awareness during COVID-19
  Pandemic

abstract:
  The coronavirus pandemic has spread over the past two years in our highly
connected and information-dense society. Nonetheless, disseminating accurate
and up-to-date information on the spread of this pandemic remains a challenge.
In this context, opting for a solution based on conversational artificial
intelligence, also known under the name of the chatbot, is proving to be an
unavoidable solution, especially since it has already shown its effectiveness
in fighting the coronavirus crisis in several countries. This work proposes to
design and implement a smart chatbot on the theme of COVID-19, called COVIBOT,
which will be useful in the context of Saudi Arabia. COVIBOT is a
generative-based contextual chatbot, which is built using machine learning APIs
that are offered by the cloud-based Azure Cognitive Services. Two versions of
COVIBOT are offered: English and Arabic versions. Use cases of COVIBOT are
tested and validated using a scenario-based approach.


title:
Traffic-Aware Mean-Field Power Allocation for Ultra-Dense NB-IoT
  Networks

abstract:
  The Narrowband Internet of Things (NB-IoT) is a cellular technology
introduced by the Third Generation Partnership Project (3GPP) to provide
connectivity to a large number of low-cost IoT devices with strict energy
consumption limitations. However, in an ultra-dense small cell network
employing NB-IoT technology, inter-cell interference can be a problem, raising
serious concerns regarding the performance of NB-IoT, particularly in uplink
transmission. Thus, a power allocation method must be established to analyze
uplink performance, control and predict inter-cell interference, and avoid
excessive energy waste during transmission. Unfortunately, standard power
allocation techniques become inappropriate as their computational complexity
grows in an ultra-dense environment. Furthermore, the performance of NB-IoT is
strongly dependent on the traffic generated by IoT devices. In order to tackle
these challenges, we provide a consistent and distributed uplink power
allocation solution under spatiotemporal fluctuation incorporating NB-IoT
features such as the number of repetitions and the data rate, as well as the
IoT device's energy budget, packet size, and traffic intensity, by leveraging
stochastic geometry analysis and Mean-Field Game (MFG) theory. The
effectiveness of our approach is illustrated via extensive numerical analysis,
and many insightful discussions are presented.


title:
nigam@COLIEE-22: Legal Case Retrieval and Entailment using Cascading of
  Lexical and Semantic-based models

abstract:
  This paper describes our submission to the Competition on Legal Information
Extraction/Entailment 2022 (COLIEE-2022) workshop on case law competition for
tasks 1 and 2. Task 1 is a legal case retrieval task, which involves reading a
new case and extracting supporting cases from the provided case law corpus to
support the decision. Task 2 is the legal case entailment task, which involves
the identification of a paragraph from existing cases that entails the decision
in a relevant case. We employed the neural models Sentence-BERT and Sent2Vec
for semantic understanding and the traditional retrieval model BM25 for exact
matching in both tasks. As a result, our team ("nigam") ranked 5th among all
the teams in Tasks 1 and 2. Experimental results indicate that the traditional
retrieval model BM25 still outperforms neural network-based models.


title:
IIFNet: A Fusion based Intelligent Service for Noisy Preamble Detection
  in 6G

abstract:
  In this article, we present our vision of preamble detection in a physical
random access channel for next-generation (Next-G) networks using machine
learning techniques. Preamble detection is performed to maintain communication
and synchronization between devices of the Internet of Everything (IoE) and
next-generation nodes. Considering the scalability and traffic density, Next-G
networks have to deal with preambles corrupted by noise due to channel
characteristics or environmental constraints. We show that when injecting 15%
random noise, the detection performance degrades to 48%. We propose an
informative instance-based fusion network (IIFNet) to cope with random noise
and to improve detection performance, simultaneously. A novel sampling strategy
for selecting informative instances from feature spaces has also been explored
to improve detection performance. The proposed IIFNet is tested on a real
dataset for preamble detection that was collected with the help of a reputable
commercial company.


title: Towards a Deeper Understanding of Skeleton-based Gait Recognition

abstract:
  Gait recognition is a promising biometric with unique properties for
identifying individuals from a long distance by their walking patterns. In
recent years, most gait recognition methods used the person's silhouette to
extract the gait features. However, silhouette images can lose fine-grained
spatial information, suffer from (self) occlusion, and be challenging to obtain
in real-world scenarios. Furthermore, these silhouettes also contain other
visual clues that are not actual gait features and can be used for
identification, but also to fool the system. Model-based methods do not suffer
from these problems and are able to represent the temporal motion of body
joints, which are actual gait features. The advances in human pose estimation
started a new era for model-based gait recognition with skeleton-based gait
recognition. In this work, we propose an approach based on Graph Convolutional
Networks (GCNs) that combines higher-order inputs, and residual networks to an
efficient architecture for gait recognition. Extensive experiments on the two
popular gait datasets, CASIA-B and OUMVLP-Pose, show a massive improvement (3x)
of the state-of-the-art (SotA) on the largest gait dataset OUMVLP-Pose and
strong temporal modeling capabilities. Finally, we visualize our method to
understand skeleton-based gait recognition better and to show that we model
real gait features.


title: PAC-Bayesian Based Adaptation for Regularized Learning

abstract:
  In this paper, we propose a PAC-Bayesian \textit{a posteriori} parameter
selection scheme for adaptive regularized regression in Hilbert scales under
general, unknown source conditions. We demonstrate that our approach is
adaptive to misspecification, and achieves the optimal learning rate under
subgaussian noise. Unlike existing parameter selection schemes, the
computational complexity of our approach is independent of sample size. We
derive minimax adaptive rates for a new, broad class of Tikhonov-regularized
learning problems under general, misspecified source conditions, that notably
do not require any conventional a priori assumptions on kernel eigendecay.
Using the theory of interpolation, we demonstrate that the spectrum of the
Mercer operator can be inferred in the presence of "tight" $L^{\infty}$
embeddings of suitable Hilbert scales. Finally, we prove, that under a
$\Delta_2$ condition on the smoothness index functions, our PAC-Bayesian scheme
can indeed achieve minimax rates. We discuss applications of our approach to
statistical inverse problems and oracle-efficient contextual bandit algorithms.


title: Flux roughening in spin ice with mixed $\pm J$ interactions

abstract:
  Spin ice presents a typical example of classical spin liquid, where conserved
magnetic fluxes emerge from microscopic spin degrees of freedom. In this
letter, we investigate the effect of perturbation by magnetic charge disorder
in two-dimensional spin ice. To this aim, we develop a novel cluster update
algorithm, which enables fast relaxation of magnetic charges. The efficient
Monte Carlo calculation reveals a drastic change of spin structure factor as
doping magnetic charges: the pinch point, characterizing the spin ice, is
gradually replaced by a diffusive peak. We derive an analytical relation
connecting the flux fluctuation and the spin structure factor, and explain the
evolution of diffusive peak in terms of the roughening of magnetic fluxes.


title: Mirror symmetry for quadric hypersurfaces

abstract:
  We compute Przyjalkowski-Shramov's resolution of the Calabi-Yau
compactification of Givental's mirror Landau-Ginzburg model of the quadric
hypersurfaces. We deduce the Picard-Fuchs equation for the narrow periods,
which mirror the ambient quantum cohomology of quadric hypersurfaces. Then by
an indirect approach using the irreducibility of the narrow Picard-Fuchs
operator we deduce the Picard-Fuchs equation of the broad period, which mirrors
the quantum cohomology of quadric hypersurfaces involving primitive cohomology
classes. The result suggests a natural choice of the opposite space in
Barannikov's construction of Frobenius manifolds. Finally, we show an
isomorphism between the Frobenius manifolds associated with the quantum
cohomology of a quadric hypersurface and its mirror Landau-Ginzburg model.


title: The THDMa and possible $e^+e^-$ signatures

abstract:
  I here discuss the THDMa, a type II two Higgs doublet model that is enhanced
by an additional pseudoscalar which serves as a portal to the dark matter
sector containing a fermionic dark matter andidate. I present a recent scan of
the models parameter space where all parameters are allowed to float freely,
and discuss prospects for this model at future $e^+e^-$ colliders for cases
that are not covered in standard THDM searches.


title: Spectral properties of the Laplacian on the multislice

abstract:
  We study the graph Laplacian on a class of graphs known as multislices which
arise naturally in a physical problem concerning a system of $N$ particles each
of which may assume $r$ energy levels. It is known that for all graphs in this
large variety of graphs, the spectral gap of the graph Laplacian is $N$,
independent of $r$. We give a simple proof of this which in addition leads to
an explicit description of the eigenspaces of gap eigenfunctions for each of
these graphs.


title: Feynman Checkers with Absorption

abstract:
  We give a new elementary proof of the theorem by Ambainis et al. that for a
quantum walk, the probability amplitudes of absorption at the initial point
after 4n steps are proportional to the Catalan numbers. We also calculate the
absorption probabilities at points close to the initial one for the first time.


title: GHM Wavelet Transform for Deep Image Super Resolution

abstract:
  The GHM multi-level discrete wavelet transform is proposed as preprocessing
for image super resolution with convolutional neural networks. Previous works
perform analysis with the Haar wavelet only. In this work, 37 single-level
wavelets are experimentally analyzed from Haar, Daubechies, Biorthogonal,
Reverse Biorthogonal, Coiflets, and Symlets wavelet families. All single-level
wavelets report similar results indicating that the convolutional neural
network is invariant to choice of wavelet in a single-level filter approach.
However, the GHM multi-level wavelet achieves higher quality reconstructions
than the single-level wavelets. Three large data sets are used for the
experiments: DIV2K, a dataset of textures, and a dataset of satellite images.
The approximate high resolution images are compared using seven objective error
measurements. A convolutional neural network based approach using wavelet
transformed images has good results in the literature.


title:
ZeroIn: Characterizing the Data Distributions of Commits in Software
  Repositories

abstract:
  Modern software development is based on a series of rapid incremental changes
collaboratively made to large source code repositories by developers with
varying experience and expertise levels. The ZeroIn project is aimed at
analyzing the metadata of these dynamic phenomena, including the data on
repositories, commits, and developers, to rapidly and accurately mark the
quality of commits as they arrive at the repositories. In this context, the
present article presents a characterization of the software development
metadata in terms of distributions of data that best captures the trends in the
datasets. Multiple datasets are analyzed for this purpose, including Stack
Overflow on developers' features and GitHub data on over 452 million
repositories with 16 million commits. This characterization is intended to make
it possible to generate multiple synthetic datasets that can be used in
training and testing novel machine learning-based solutions to improve the
reliability of software even as it evolves. It is also aimed at serving the
development process to exploit the latent correlations among many key feature
vectors across the aggregate space of repositories and developers. The data
characterization of this article is designed to feed into the machine learning
components of ZeroIn, including the application of binary classifiers for early
flagging of buggy software commits and the development of graph-based learning
methods to exploit sparse connectivity among the sets of repositories, commits,
and developers.


title:
Effect of the magnetic charge on weak deflection angle and greybody
  bound of the black hole in Einstein-Gauss-Bonnet gravity

abstract:
  The objective of this paper is to analyze the weak deflection angle of
Einstein-Gauss-Bonnet gravity in the presence of plasma medium. To attain our
results, we implement the Gibbons and Werner approach and use the Gauss-Bonnet
theorem to Einstein gravity to acquire the resulting deflection angle of
photon's ray in the weak field limit. Moreover, we illustrate the behavior of
plasma medium and non-plasma mediums on the deflection of photon's ray in the
framework of Einstein-Gauss-Bonnet gravity. Similarly, we observe the graphical
influences of deflection angle on Einstein-Gauss-Bonnet gravity with the
consideration of both plasma and non-plasma mediums. Later, we observe the
rigorous bounds phenomenon of the greybody factor in contact with
Einstein-Gauss-Bonnet gravity and calculate the outcomes, analyze graphically
for specific values of parameters.


title: Enabling Relative Localization for Nanodrone Swarm Platooning

abstract:
  Nanodrone swarm is formulated by multiple light-weight and low-cost
nanodrones to perform the tasks in very challenging environments. Therefore, it
is essential to estimate the relative position of nanodrones in the swarm for
accurate and safe platooning in inclement indoor environment. However, the
vision and infrared sensors are constrained by the line-of-sight perception,
and instrumenting extra motion sensors on drone's body is constrained by the
nanodrone's form factor and energy-efficiency.
  This paper presents the design, implementation and evaluation of RFDrone, a
system that can sense the relative position of nanodrone in the swarm using
wireless signals, which can naturally identify each individual nanodrone. To do
so, each light-weight nanodrone is attached with a RF sticker (i.e., called
RFID tag), which will be localized by the external RFID reader in the inclement
indoor environment. Instead of accurately localizing each RFID-tagged
nanodrone, we propose to estimate the relative position of all the RFID-tagged
nanodrones in the swarm based on the spatial-temporal phase profiling. We
implement an end-to-end physical prototype of RFDrone. Our experimental results
show that RFDrone can accurately estimate the relative position of nanodrones
in the swarm with average relative localization accuracy of around 0.95 across
x, y and z axis, and average accuracy of around 0.93 for nanodrone swarm's
geometry estimation.


title: Comparison of classical and path-by-path solutions to SDEs

abstract:
  We consider the Stochastic Differential Equation $X_t = X_0 + \int_0^t
b(s,X_s) ds + B_t$, in $\mathbb{R}^d$. We give an example of a drift $b$ such
that there does not exist a weak solution, but there exists a solution for
almost every realization of the Brownian motion $B$. We also give an explicit
example of a drift such that the SDE has a pathwise unique weak solution, but
path-by-path uniqueness (i.e. uniqueness of solutions to the ODE for almost
every realization of the Brownian motion) is lost. These counterexamples extend
the results obtained in arXiv:2001.02869 to dimension $d=1$.


title: Analytical Benchmark Problems for Multifidelity Optimization Methods

abstract:
  The paper presents a collection of analytical benchmark problems specifically
selected to provide a set of stress tests for the assessment of multifidelity
optimization methods. In addition, the paper discusses a comprehensive ensemble
of metrics and criteria recommended for the rigorous and meaningful assessment
of the performance of multifidelity strategies and algorithms.


title:
Alternating Channel Estimation and Prediction for Cell-Free mMIMO with
  Channel Aging: A Deep Learning Based Scheme

abstract:
  In large scale dynamic wireless networks, the amount of overhead caused by
channel estimation (CE) is becoming one of the main performance bottlenecks.
This is due to the large number users whose channels should be estimated, the
user mobility, and the rapid channel change caused by the usage of the
high-frequency spectrum (e.g. millimeter wave). In this work, we propose a new
hybrid channel estimation/prediction (CEP) scheme to reduce overhead in
time-division duplex (TDD) wireless cell-free massive
multiple-input-multiple-output (mMIMO) systems. The scheme proposes sending a
pilot signal from each user only once in a given number (window) of coherence
intervals (CIs). Then minimum mean-square error (MMSE) estimation is used to
estimate the channel of this CI, while a deep neural network (DNN) is used to
predict the channels of the remaining CIs in the window. The DNN exploits the
temporal correlation between the consecutive CIs and the received pilot signals
to improve the channel prediction accuracy. By doing so, CE overhead is reduced
by at least 50 percent at the expense of negligible CE error for practical user
mobility settings. Consequently, the proposed CEP scheme improves the spectral
efficiency compared to the conventional MMSE CE approach, especially when the
number of users is large, which is demonstrated numerically.


title:
Gravitational time dilation in extended quantum systems: the case of
  light clocks in Schwarzschild spacetime

abstract:
  The precision of optical atomic clocks is approaching a regime where they
resolve gravitational time dilation on smaller scales than their own
extensions. Hence, an accurate description of quantum clocks has to take their
spatial extension into account. In this article, as a first step towards a
fully relativistic description of extended quantum clocks, we investigate a
quantized version of Einstein's light clock fixed at a constant distance from a
large massive object like the Earth. The model consists of a quantum light
field in a one-dimensional cavity in Schwarzschild spacetime, where the
distance between the mirrors is fixed by a rigid rod. By comparing a vertical
and a horizontal clock, we propose an operational way to define the clock time
when the clock resolves gravitational time dilation on scales smaller than its
extension. In particular, we show that the time measured by the vertical light
clock is equivalent to the proper time defined at its center. We also derive
fundamental bounds on the precision of these clocks for measurements of proper
time and the Schwarzschild radius.


title: On modulus inequality of the order $p$ for the inner dilatation

abstract:
  The article is devoted to mappings with bounded and finite distortion of
plane domains. Our investigations are devoted to the connection between
mappings of the Sobolev class and upper bounds for the distortion of the
modulus of families of paths. For this class, we have proved the Poletsky-type
inequality with respect to the so-called inner dilatation of the order~$p.$
Along the way, we also obtained lower bounds for the modulus distortion under
mappings. We separately considered the situations of homeomorphisms and
mappings with branch points.


title:
Quantum kinetic theory of Jeans instability in non-minimal
  matter-curvature coupling gravity

abstract:
  We present a quantum treatment of the Jeans gravitational instability in the
Newtonian limit of the non-minimal matter-curvature coupling gravity model. By
relying on Wigner functions, allowing for the representation of quantum states
in a classical phase space, we formulate a quantum kinetic treatment of this
problem, generalizing the classical kinetic approach [C. Gomes, Eur. Phys. J. C
80, 633 (2020)]. This allows us to study the interplay between non-minimal
matter-curvature coupling effects, quantum effects, and kinetic
(finite-temperature) effects, on the Jeans criterion. We study in detail
special cases of the model (general relativity, f(R) theories, pure non-minimal
coupling, etc.) and confront the model with the observed stability of Bok
globules.


title: Classification of KPI Lumps

abstract:
  A large family of nonsingular rational solutions of the
Kadomtsev-Petviashvili (KP) I equation are investigated. These solutions are
constructed via the Gramian method and are identified as points in a complex
Grassmannian. Each solution is a traveling wave moving with a uniform
background velocity but have multiple peaks which evolve at a slower time scale
in the co-moving frame. For large times, these peaks separate and form
well-defined wave patterns in the $xy$-plane. The pattern formation are
described by the roots of well-known polynomials arising in the study of
rational solutions of Painlev\'e II and IV equations. This family of solutions
are shown to be described by the classical Schur functions associated with
partitions of integers and irreducible representations of the symmetric group
of $N$ objects. It is then shown that there exists a one-to-one correspondence
between the KPI rational solutions considered in this article and partitions of
a positive integer $N$.


title: The minimal sum of squares over partitions with a nonnegative rank

abstract:
  Motivated by a question of Defant and Propp (2020) regarding the connection
between the degrees of noninvertibility of functions and those of their
iterates, we address the combinatorial optimization problem of minimizing the
sum of squares over partitions of $n$ with a nonnegative rank. Denoting the
sequence of the minima by $(m_n)_{n\in\mathbb{N}}$, we prove that
$m_n=\Theta\left(n^{4/3}\right)$. Consequently, we improve by a factor of $2$
the lower bound provided by Defant and Propp for iterates of order two.


title:
Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a
  Pedestrian Automatic Emergency Brake System

abstract:
  Integration of Machine Learning (ML) components in critical applications
introduces novel challenges for software certification and verification. New
safety standards and technical guidelines are under development to support the
safety of ML-based systems, e.g., ISO 21448 SOTIF for the automotive domain and
the Assurance of Machine Learning for use in Autonomous Systems (AMLAS)
framework. SOTIF and AMLAS provide high-level guidance but the details must be
chiseled out for each specific case. We report results from an
industry-academia collaboration on safety assurance of SMIRK, an ML-based
pedestrian automatic emergency braking demonstrator running in an
industry-grade simulator. We present the outcome of applying AMLAS on SMIRK for
a minimalistic operational design domain, i.e., a complete safety case for its
integrated ML-based component. Finally, we report lessons learned and provide
both SMIRK and the safety case under an open-source licence for the research
community to reuse.


title:
Station Reallocation and Rebalancing Strategy for Bike-Sharing Systems:
  A Case Study of Washington DC

abstract:
  Bike-sharing is becoming increasingly popular as an urban traffic mode while
increasing the affordability, flexibility, and reliability of interconnected
public transportation systems (i.e., interconnected light rail, buses,
micro-mobility, and ride-sharing modes of transportation). From the consumers
perspective, 1) finding a bike station in convenient locations where demand
usually occurs and 2) the availability of bikes at rush hours with a lesser
probability of encountering empty docks (for fixed-station bike-share systems)
are two key concerns. Some stations are more likely to be empty or full,
reflecting an imbalance in bike supply and demand. Accordingly, it is essential
to understand a bike-share system's demand pattern to select the optimal
locations and reallocate bikes to the right stations to increase the
utilization rate and reduce the number of unserved customers (i.e., potential
demand). The Capital Bikeshare in the Washington DC Metropolitan Area is one of
the prominent bike-share systems in the USA - with more than 4,300 bikes
available at 654 stations across seven jurisdictions. This study provides a
systematic analysis of a bike-sharing system's Capital Bikeshare system usage
pattern. Our study intends to create an optimization strategy formulated as a
deterministic integer programming for reallocating bike stations daily and
rebalancing the bike supply system. From an operational perspective, such a
strategy will allow overnight preparations to answer the rush-hour morning
demand and during special events in Washington D.C.


title:
Lodestar: Supporting Independent Learning and Rapid Experimentation
  Through Data-Driven Analysis Recommendations

abstract:
  Keeping abreast of current trends, technologies, and best practices in
visualization and data analysis is becoming increasingly difficult, especially
for fledgling data scientists. In this paper, we propose Lodestar, an
interactive computational notebook that allows users to quickly explore and
construct new data science workflows by selecting from a list of automated
analysis recommendations. We derive our recommendations from directed graphs of
known analysis states, with two input sources: one manually curated from online
data science tutorials, and another extracted through semi-automatic analysis
of a corpus of over 6,000 Jupyter notebooks. We evaluate Lodestar in a
formative study guiding our next set of improvements to the tool. Our results
suggest that users find Lodestar useful for rapidly creating data science
workflows.


title:
Assessing Differentially Private Variational Autoencoders under
  Membership Inference

abstract:
  We present an approach to quantify and compare the privacy-accuracy trade-off
for differentially private Variational Autoencoders. Our work complements
previous work in two aspects. First, we evaluate the the strong reconstruction
MI attack against Variational Autoencoders under differential privacy. Second,
we address the data scientist's challenge of setting privacy parameter epsilon,
which steers the differential privacy strength and thus also the
privacy-accuracy trade-off. In our experimental study we consider image and
time series data, and three local and central differential privacy mechanisms.
We find that the privacy-accuracy trade-offs strongly depend on the dataset and
model architecture. We do rarely observe favorable privacy-accuracy trade-off
for Variational Autoencoders, and identify a case where LDP outperforms CDP.


title: 3D Human Pose Estimation for Free-from and Moving Activities Using WiFi

abstract:
  This paper presents GoPose, a 3D skeleton-based human pose estimation system
that uses WiFi devices at home. Our system leverages the WiFi signals reflected
off the human body for 3D pose estimation. In contrast to prior systems that
need specialized hardware or dedicated sensors, our system does not require a
user to wear or carry any sensors and can reuse the WiFi devices that already
exist in a home environment for mass adoption. To realize such a system, we
leverage the 2D AoA spectrum of the signals reflected from the human body and
the deep learning techniques. In particular, the 2D AoA spectrum is proposed to
locate different parts of the human body as well as to enable
environment-independent pose estimation. Deep learning is incorporated to model
the complex relationship between the 2D AoA spectrums and the 3D skeletons of
the human body for pose tracking. Our evaluation results show GoPose achieves
around 4.7cm of accuracy under various scenarios including tracking unseen
activities and under NLoS scenarios.


title: Polynomial-time sparse measure recovery

abstract:
  How to recover a probability measure with sparse support from particular
moments? This problem has been the focus of research in theoretical computer
science and neural computing. However, there is no polynomial-time algorithm
for the recovery. The best algorithm for the recovery requires
$O(2^{\text{poly}(1/\epsilon)})$ for $\epsilon$-accurate recovery. We propose
the first poly-time recovery method from carefully designed moments that only
requires $O(\log(1/\epsilon)/\epsilon^2)$ computations for an
$\epsilon$-accurate recovery. This method relies on the recovery of a planted
two-layer neural network with two-dimensional inputs, a finite width, and
zero-one activation. For such networks, we establish the first global
convergence of gradient descent and demonstrate its application in sparse
measure recovery.


title: Lower bounds on the Hausdorff dimension of some Julia sets

abstract:
  We present an algorithm for a rigorous computation of lower bounds on the
Hausdorff dimensions of Julia sets for a wide class of holomorphic maps. We
apply this algorithm to obtain lower bounds on the Hausdorff dimension of the
Julia sets of some infinitely renormalizable real quadratic polynomials,
including the Feigenbaum polynomial
$p_{\,\mathrm{Feig}}(z)=z^2+c_{\,\mathrm{Feig}}$. In addition to that, we
construct a piecewise constant function on $[-2,2]$ that provides rigorous
lower bounds for the Hausdorff dimension of the Julia sets of all quadratic
polynomials $p_c(z) = z^2+c$ with $c \in [-2,2]$. Finally, we verify the
conjecture of Ludwik Jaksztas and Michel Zinsmeister that the Hausdorff
dimension of the Julia set of a quadratic polynomial $p_c(z)=z^2+c$, is a
$C^1$-smooth function of the real parameter $c$ on the interval
$c\in(c_{\,\mathrm{Feig}},-3/4)$.


title: A Family of Neyman-Pearson-Based Detectors for Noise-Type Radars

abstract:
  We derive a detector that optimizes the target detection performance of any
single-input single-output noise radar satisfying the following properties: it
transmits Gaussian noise, it retains an internal reference signal for matched
filtering, all external noise is additive white Gaussian noise, and all signals
are measured using heterodyne receivers. This class of radars, which we call
noise-type radars, includes not only many types of standard noise radars, but
also a type of quantum radar known as quantum two-mode squeezing radar. The
detector, which we derive using the Neyman-Pearson lemma, is not practical
because it requires foreknowledge of a target-dependent correlation coefficient
that cannot be known beforehand. (It is, however, a natural standard of
comparison for other detectors.) This motivates us to study the family of
Neyman-Pearson-based detectors that result when the correlation coefficient is
treated as a parameter. We derive the probability distribution of the
Neyman-Pearson-based detectors when there is a mismatch between the pre-chosen
parameter value and the true correlation coefficient. We then use this result
to generate receiver operating characteristic curves. Finally, we apply our
results to the case where the correlation coefficient is small. It turns out
that the resulting detector is not only a good one, but that it has appeared
previously in the quantum radar literature.


title: Structured Covariance Matrix Estimation for Noise-Type Radars

abstract:
  Standard noise radars, as well as noise-type radars such as quantum two-mode
squeezing radar, are characterized by a covariance matrix with a very specific
structure. This matrix has four independent parameters: the amplitude of the
received signal, the amplitude of the internal signal used for matched
filtering, the correlation between the two signals, and the relative phase
between them. In this paper, we derive estimators for these four parameters
using two techniques. The first is based on minimizing the Frobenius norm
between the structured covariance matrix and the sample covariance matrix; the
second is maximum likelihood parameter estimation. The two techniques yield the
same estimators. We then give probability density functions (PDFs) for all four
estimators. Because some of these PDFs are quite complicated, we also provide
approximate PDFs. Finally, we apply our results to the problem of target
detection and derive expressions for the receiver operating characteristic
curves of two different noise radar detectors.


title: An Overview of Query Processing on Crowdsourced Databases

abstract:
  Crowd-sourcing is a powerful solution for finding correct answers to
expensive and unanswered queries in databases, including those with uncertain
and incomplete data. Attempts to use crowd-sourcing to exploit human abilities
to process these expensive queries using human workers have helped to provide
accurate results by utilising the available data in the crowd. Crowd-sourcing
database systems (CSDBs) combine the knowledge of the crowd with a relational
database by using some variant of a relational database with minor changes.
This paper surveys the leading studies conducted in the area of query
processing with regard to both traditional and preference queries in CSDBs. The
focus of this work is on highlighting the strengths and the weakness of each
approach. A detailed discussion of current and future trends research
associated with query processing in the area of CSDBs is also presented.


title:
The Poisson algorithm: a simple method to simulate stochastic epidemic
  models with generally distributed residence times

abstract:
  Epidemic models are used to analyze the progression or outcome of an epidemic
under different control policies like vaccinations, quarantines, lockdowns, use
of face-masks, pharmaceutical interventions, etc. When these models accurately
represent real-life situations, they may become an important tool in the
decision-making process. Among these models, compartmental models are very
popular and assume individuals move along a series of compartments that
describe their current health status. Nevertheless, these models are mostly
Markovian, that is, the time in each compartment follows an exponential
distribution. In epidemic models, exponential sojourn times are most of the
times unrealistic, for instance, they imply that the probability that a patient
will recover from some disease in the next time unit is independent of the time
the patient has been sick. This is an important restriction that prevents these
models from being widely accepted and trusted by decision-makers. In spite of
the need to incorporate algorithms to tackle the problem, literature on the
topic is scarce. Here, we introduce a novel approach to simulate general
stochastic epidemic models that accepts any distribution for the sojourn times
that is efficient.


title: On the principal minors of the powers of a matrix

abstract:
  We show that if $A$ is an $n\times n$-matrix, then the diagonal entries of
each power $A^{m}$ are uniquely determined by the principal minors of $A$, and
can be written as universal (integral) polynomials in the latter. Furthermore,
if the latter all equal $1$, then so do the former. These results are inspired
by Problem B5 on the Putnam contest 2021, and shed a new light on the behavior
of minors under matrix multiplication.


title:
Gender-Wise Perception of Students Towards Blended Learning in Higher
  Education: Pakistan

abstract:
  Blended learning (BL) is a recent tread among many options that can best fit
learners' needs, regardless of time and place. This study aimed to discover
students' perceptions of BL and the challenges faced by them while using
technology. This quantitative study used data gathered from 300 students
enrolled in four public universities in the Sindh province of Pakistan. the
finding shows that students were compatible with the use of technology, and it
has a positive effect on their academic experience. The study also showed that
the use of technology encourages peer collaboration. The challenges found
include: neither teacher support nor a training program was provided to the
students for the course which needed to shift from a traditional face to face
paradigm to a blended format, a lake of space lies with skills in a laboratory
assistants for the courses with a blended format and as shortage of high tech
computer laboratories / computer units to run these courses. Therefore, it is
recommended that the authorities must develop and incorporate a comprehensive
mechanism for the effective implementation of BL in the learning
teaching-learning process heads of the departments should also provide
additional computing infrastructure to their departments.


title:
Mapping LiDAR and Camera Measurements in a Dual Top-View Grid
  Representation Tailored for Automated Vehicles

abstract:
  We present a generic evidential grid mapping pipeline designed for imaging
sensors such as LiDARs and cameras. Our grid-based evidential model contains
semantic estimates for cell occupancy and ground separately. We specify the
estimation steps for input data represented by point sets, but mainly focus on
input data represented by images such as disparity maps or LiDAR range images.
Instead of relying on an external ground segmentation only, we deduce occupancy
evidence by analyzing the surface orientation around measurements. We conduct
experiments and evaluate the presented method using LiDAR and stereo camera
data recorded in real traffic scenarios. Our method estimates cell occupancy
robustly and with a high level of detail while maximizing efficiency and
minimizing the dependency to external processing modules.


title:
AI, Ageing and Brain-Work Productivity: Technological Change in
  Professional Japanese Chess

abstract:
  Using Japanese professional chess (Shogi) players records in the novel
setting, this paper examines how and the extent to which the emergence of
technological changes influences the ageing and innate ability of players
winning probability. We gathered games of professional Shogi players from 1968
to 2019.
  The major findings are: (1) diffusion of artificial intelligence (AI) reduces
innate ability, which reduces the performance gap among same-age players; (2)
players winning rates declined consistently from 20 years and as they get
older; (3) AI accelerated the ageing declination of the probability of winning,
which increased the performance gap among different aged players; (4) the
effects of AI on the ageing declination and the probability of winning are
observed for high innate skill players but not for low innate skill ones. This
implies that the diffusion of AI hastens players retirement from active play,
especially for those with high innate abilities. Thus, AI is a substitute for
innate ability in brain-work productivity.


title: SymForce: Symbolic Computation and Code Generation for Robotics

abstract:
  We present SymForce, a fast symbolic computation and code generation library
for robotics applications like computer vision, state estimation, motion
planning, and controls. SymForce combines the development speed and flexibility
of symbolic mathematics with the performance of autogenerated, highly optimized
code in C++ or any target runtime language. SymForce provides geometry and
camera types, Lie group operations, and branchless singularity handling for
creating and analyzing complex symbolic expressions in Python, built on top of
SymPy. Generated functions can be integrated as factors into our tangent space
nonlinear optimizer, which is highly optimized for real-time production use. We
introduce novel methods to automatically compute tangent space Jacobians,
eliminating the need for bug-prone handwritten derivatives. This workflow
enables faster runtime code, faster development time, and fewer lines of
handwritten code versus the state-of-the-art. Our experiments demonstrate that
our approach can yield order of magnitude speedups on computational tasks core
to robotics. Code is available at https://github.com/symforce-org/symforce .


title:
Modeling Complex Interactions in a Disrupted Environment: Relational
  Events in the WTC Response

abstract:
  When subjected to a sudden, unanticipated threat, human groups
characteristically self-organize to identify the threat, determine potential
responses, and act to reduce its impact. Central to this process is the
challenge of coordinating information sharing and response activity within a
disrupted environment. In this paper, we consider coordination in the context
of responses to the 2001 World Trade Center disaster. Using records of
communications among 17 organizational units, we examine the mechanisms driving
communication dynamics, with an emphasis on the emergence of coordinating
roles. We employ relational event models (REMs) to identify the mechanisms
shaping communications in each unit, finding a consistent pattern of behavior
across units with very different characteristics. Using a simulation-based
"knock-out" study, we also probe the importance of different mechanisms for hub
formation. Our results suggest that, while preferential attachment and
pre-disaster role structure generally contribute to the emergence of hub
structure, temporally local conversational norms play a much larger role. We
discuss broader implications for the role of microdynamics in driving
macroscopic outcomes, and for the emergence of coordination in other settings.


title:
Bad Weather, Social Network, and Internal Migration; Case of Japanese
  Sumo Wrestlers 1946-1985

abstract:
  Post-World War II , there was massive internal migration from rural to urban
areas in Japan. The location of Sumo stables was concentrated in Tokyo. Hence,
supply of Sumo wrestlers from rural areas to Tokyo was considered as migration.
Using a panel dataset covering forty years, specifically 1946-1985, this study
investigates how weather conditions and social networks influenced the labor
supply of Sumo wrestlers. Major findings are; (1) inclemency of the weather in
local areas increased supply of Sumo wrestlers in the period 1946-1965, (2) the
effect of the bad weather conditions is greater in the locality where large
number of Sumo wrestlers were supplied in the pre-war period, (3) neither the
occurrence of bad weather conditions nor their interactions with sumo-wrestlers
influenced the supply of Sumo wrestlers in the period 1966-1985. These findings
imply that the negative shock of bad weather conditions on agriculture in the
rural areas incentivized young individuals to be apprenticed in Sumo stables in
Tokyo. Additionally, in such situations, the social networks within Sumo
wrestler communities from the same locality are important. However, once the
share of workers in agricultural sectors became very low, this mechanism did
not work.


title: Video Action Detection: Analysing Limitations and Challenges

abstract:
  Beyond possessing large enough size to feed data hungry machines (eg,
transformers), what attributes measure the quality of a dataset? Assuming that
the definitions of such attributes do exist, how do we quantify among their
relative existences? Our work attempts to explore these questions for video
action detection. The task aims to spatio-temporally localize an actor and
assign a relevant action class. We first analyze the existing datasets on video
action detection and discuss their limitations. Next, we propose a new dataset,
Multi Actor Multi Action (MAMA) which overcomes these limitations and is more
suitable for real world applications. In addition, we perform a biasness study
which analyzes a key property differentiating videos from static images: the
temporal aspect. This reveals if the actions in these datasets really need the
motion information of an actor, or whether they predict the occurrence of an
action even by looking at a single frame. Finally, we investigate the widely
held assumptions on the importance of temporal ordering: is temporal ordering
important for detecting these actions? Such extreme experiments show existence
of biases which have managed to creep into existing methods inspite of careful
modeling.


title:
On Reporting Performance and Accuracy Bugs for Deep Learning Frameworks:
  An Exploratory Study from GitHub

abstract:
  The tremendous success of Deep Learning (DL) has significantly boosted the
number of open-sourced DL frameworks hosted on GitHub. Among others,
performance and accuracy bugs are critical factors that affect the reputation
of these DL frameworks, therefore understanding the practice of discovering and
investigating them for DL is important. In this paper, we conduct an
exploratory study on the nature of reporting performance and accuracy bugs bugs
for DL frameworks, aiming to improve our knowledge on this topic. Our study
covers 10 most popular open-sourced DL frameworks on GitHub (e.g., TensorFlow,
Keras, and PyTorch), based on which we sample 664 representative performance
and accuracy bugs bug reports out of a total population of 22,522. Through
systematic analysis of these samples, our key findings are: (1) low speed is
the primary reason that a performance bug related report is submitted but we
see no consistent pattern for accuracy related ones; (2) most of the reports
are about issues encountered in the training stage; (3) only a small proportion
of the reports provide insufficient information to investigate; (4) the
majority of the performance and accuracy bugs bug reports (from 69% to 100%)
are not related to the actual bug or regarded as unclassified; (5) around 50%
of the performance and accuracy bug reports, which indeed reveal bugs, are not
resolved by direct patches. Deriving from the above, we discuss a set of
actionable implications to the researchers, maintainers, and report submitters
on this subject. To promote open science, the labeled dataset has been made
publicly available at https://tinyurl.com/4x3tap9w.


title:
Spatial Channel Covariance Estimation and Two-Timescale Beamforming for
  IRS-Assisted Millimeter Wave Systems

abstract:
  We consider the problem of spatial channel covariance matrix (CCM) estimation
for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave)
communication systems. Spatial CCM is essential for two-timescale beamforming
in IRS-assisted systems; however, estimating the spatial CCM is challenging due
to the passive nature of reflecting elements and the large size of the CCM
resulting from massive reflecting elements of the IRS. In this paper, we
propose a CCM estimation method by exploiting the low-rankness as well as the
positive semi-definite (PSD) 3-level Toeplitz structure of the CCM. Estimation
of the CCM is formulated as a semidefinite programming (SDP) problem and an
alternating direction method of multipliers (ADMM) algorithm is developed. Our
analysis shows that the proposed method is theoretically guaranteed to attain a
reliable CCM estimate with a sample complexity much smaller than the dimension
of the CCM. Thus the proposed method can help achieve a significant training
overhead reduction. Simulation results are presented to illustrate the
effectiveness of our proposed method and the performance of two-timescale
beamforming scheme based on the estimated CCM.


title: New conforming finite element divdiv complexes in three dimensions

abstract:
  In this paper, the first family of conforming finite element divdiv complexes
on cuboid grids in three dimensions is constructed. Besides, a new family of
conforming finite element divdiv complexes with enhanced smoothness on
tetrahedral grids is presented. These complexes are exact in the sense that the
range of each discrete map is the kernel space of the succeeding one.


title: Generic regularity of Level Set Flows with spherical singularity

abstract:
  Sphere is known to be the only generic compact shrinker for mean curvature
flow (MCF). In this paper, we characterize generic dynamics of MCFs with a
spherical singularity. In terms of the level set flow formulation of MCF, this
gives that generically the arrival time function of level set flow with
spherical singularity has at most $C^2$ regularity.


title:
Making Hidden Bias Visible: Designing a Feedback Ecosystem for Primary
  Care Providers

abstract:
  Implicit bias may perpetuate healthcare disparities for marginalized patient
populations. Such bias is expressed in communication between patients and their
providers. We design an ecosystem with guidance from providers to make this
bias explicit in patient-provider communication. Our end users are providers
seeking to improve their quality of care for patients who are Black,
Indigenous, People of Color (BIPOC) and/or Lesbian, Gay, Bisexual, Transgender,
and Queer (LGBTQ). We present wireframes displaying communication metrics that
negatively impact patient-centered care divided into the following categories:
digital nudge, dashboard, and guided reflection. Our wireframes provide
quantitative, real-time, and conversational feedback promoting provider
reflection on their interactions with patients. This is the first design
iteration toward the development of a tool to raise providers' awareness of
their own implicit biases.


title: Primordial black holes and dark matter mass spectrum

abstract:
  Since primordial black holes (PBHs) evaporate into all particle species in
nature, a large number of dark matter (DM) particle species with specific mass
spectrum may be emitted from PBHs. We assume that PBHs are the only source of
DMs, and DMs only interact with the standard model particles gravitationally.
We investigate whether the number of DM particle species $N_{\rm DM}$ could be
determined from initial PBH density $\beta$ and initial PBH mass $M_{\rm
BH}^{\rm in}$. It turned out that if there are too many heavy DM particle
species in DM mass spectrum, the determination of $N_{\rm DM}$ via $\beta$ and
$M_{\rm BH}^{\rm in}$ may tend to be difficult for heavy initial PBHs.


title:
QTBIPOC PD: Exploring the Intersections of Race, Gender, and Sexual
  Orientation in Participatory Design

abstract:
  As Human-Computer Interaction (HCI) research aims to be inclusive and
representative of many marginalized identities, there is still a lack of
available literature and research on intersectional considerations of race,
gender, and sexual orientation, especially when it comes to participatory
design. We aim to create a space to generate community recommendations for
effectively and appropriately engaging Queer, Transgender, Black, Indigenous,
People of Color (QTBIPOC) populations in participatory design, and discuss
methods of dissemination for recommendations. Workshop participants will engage
with critical race theory, queer theory, and feminist theory to reflect on
current exclusionary HCI and participatory design methods and practices.


title: Using HCI to Tackle Race and Gender Bias in ADHD Diagnosis

abstract:
  Attention Deficit Hyperactivity Disorder (ADHD) is a behavioral disorder that
impacts an individual's education, relationships, career, and ability to
acquire fair and just police interrogations. Yet, traditional methods used to
diagnose ADHD in children and adults are known to have racial and gender bias.
In recent years, diagnostic technology has been studied by both HCI and ML
researchers. However, these studies fail to take into consideration racial and
gender stereotypes that may impact the accuracy of their results. We highlight
the importance of taking race and gender into consideration when creating
diagnostic technology for ADHD and provide HCI researchers with suggestions for
future studies.


title:
Magnetic dynamos in white dwarfs -III: explaining the occurrence of
  strong magnetic fields in close double white dwarfs

abstract:
  The origin of strong magnetic fields in white dwarfs has been a puzzle for
decades. Recently, a dynamo mechanism operating in rapidly rotating and
crystallizing white dwarfs has been suggested to explain the occurrence rates
of strong magnetic fields in white dwarfs with close low-mass main sequence
star companions. Here we investigate whether the same mechanism may produce
strong magnetic fields in close double white dwarfs. The only known strongly
magnetic white dwarf that is part of a close double white dwarf system, the
magnetic component of NLTT 12758, is rapidly rotating and likely crystallizing
and therefore the proposed dynamo mechanism represents an excellent scenario
for the origin of its magnetic field. Presenting a revised formation scenario
for NLTT 12758, we find a natural explanation for the rapid rotation of the
magnetic component. We furthermore show that it is not surprising that strong
magnetic fields have not been detected in all other known double white dwarfs.
We therefore conclude that the incidence of magnetic fields in close double
white dwarfs supports the idea that a rotation and crystallization driven
dynamo plays a major role in the generation of strong magnetic fields in white
dwarfs.


title: Dirac series of $E_{7(-25)}$

abstract:
  By further sharpening the Helgason-Johnson bound in 1969, this paper
classifies all the irreducible unitary representations with non-zero Dirac
cohomology of the Hermitian symmetric real form $E_{7(-25)}$.


title:
Comment on "The statistics wars and intellectual conflicts of interest"
  by D. Mayo

abstract:
  While P-values are widely abused, they are a useful tool for many purposes;
banning them is analogous to banning scalpels because most people do not know
how to perform surgery. Many reported P-values are not genuine P-values, for a
variety of reasons. Perhaps the most widespread and pernicious problem is the
Type III error of testing a statistical hypothesis that has little or no
connection to the scientific hypothesis.


title:
Turing's cascade instability supports the coordination of the mind,
  brain, and behavior

abstract:
  Turing inspired a computer metaphor of the mind and brain that has been handy
and has spawned decades of empirical investigation, but he did much more and
offered behavioral and cognitive sciences another metaphor--that of the
cascade. The time has come to confront Turing's cascading instability, which
suggests a geometrical framework driven by power laws and can be studied using
multifractal formalism and multiscale probability density function analysis.
Here, we review a rapidly growing body of scientific investigations revealing
signatures of cascade instability and their consequences for a perceiving,
acting, and thinking organism. We review work related to executive functioning
(planning to act), postural control (bodily poise for turning plans into
action), and effortful perception (action to gather information in a single
modality and action to blend multimodal information). We also review findings
on neuronal avalanches in the brain, specifically about neural participation in
body-wide cascades. Turing's cascade instability blends the mind, brain, and
behavior across space and time scales and provides an alternative to the
dominant computer metaphor.


title:
Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning
  Assisted Deep Learning Approach

abstract:
  The electric vehicle (EV) and electric vehicle charging station (EVCS) have
been widely deployed with the development of large-scale transportation
electrifications. However, since charging behaviors of EVs show large
uncertainties, the forecasting of EVCS charging power is non-trivial. This
paper tackles this issue by proposing a reinforcement learning assisted deep
learning framework for the probabilistic EVCS charging power forecasting to
capture its uncertainties. Since the EVCS charging power data are not standard
time-series data like electricity load, they are first converted to the
time-series format. On this basis, one of the most popular deep learning
models, the long short-term memory (LSTM) is used and trained to obtain the
point forecast of EVCS charging power. To further capture the forecast
uncertainty, a Markov decision process (MDP) is employed to model the change of
LSTM cell states, which is solved by our proposed adaptive exploration proximal
policy optimization (AePPO) algorithm based on reinforcement learning. Finally,
experiments are carried out on the real EVCSs charging data from Caltech, and
Jet Propulsion Laboratory, USA, respectively. The results and comparative
analysis verify the effectiveness and outperformance of our proposed framework.


title:
A bijection between the sets of $(a,b,b^2)$-Generalized Motzkin paths
  avoiding $\mathbf{uvv}$-patterns and $\mathbf{uvu}$-patterns

abstract:
  A generalized Motzkin path, called G-Motzkin path for short, of length $n$ is
a lattice path from $(0, 0)$ to $(n, 0)$ in the first quadrant of the XOY-plane
that consists of up steps $\mathbf{u}=(1, 1)$, down steps $\mathbf{d}=(1, -1)$,
horizontal steps $\mathbf{h}=(1, 0)$ and vertical steps $\mathbf{v}=(0, -1)$.
An $(a,b,c)$-G-Motzkin path is a weighted G-Motzkin path such that the
$\mathbf{u}$-steps, $\mathbf{h}$-steps, $\mathbf{v}$-steps and
$\mathbf{d}$-steps are weighted respectively by $1, a, b$ and $c$. Let $\tau$
be a word on $\{\mathbf{u}, \mathbf{d}, \mathbf{v}, \mathbf{d}\}$, denoted by
$\mathcal{G}_n^{\tau}(a,b,c)$ the set of $\tau$-avoiding $(a,b,c)$-G-Motzkin
paths of length $n$ for a pattern $\tau$. In this paper, we consider the
$\mathbf{uvv}$-avoiding $(a,b,c)$-G-Motzkin paths and provide a direct
bijection $\sigma$ between $\mathcal{G}_n^{\mathbf{uvv}}(a,b,b^2)$ and
$\mathcal{G}_n^{\mathbf{uvu}}(a,b,b^2)$. Finally, the set of fixed points of
$\sigma$ is also described and counted.


title: A Flexible Bias Correction Method based on Inconsistent Estimators

abstract:
  An important challenge in statistical analysis lies in controlling the
estimation bias when handling the ever-increasing data size and model
complexity. For example, approximate methods are increasingly used to address
the analytical and/or computational challenges when implementing standard
estimators, but they often lead to inconsistent estimators. So consistent
estimators can be difficult to obtain, especially for complex models and/or in
settings where the number of parameters diverges with the sample size. We
propose a general simulation-based estimation framework that allows to
construct consistent and bias corrected estimators for parameters of increasing
dimensions. The key advantage of the proposed framework is that it only
requires to compute a simple inconsistent estimator multiple times. The
resulting Just Identified iNdirect Inference estimator (JINI) enjoys nice
properties, including consistency, asymptotic normality, and finite sample bias
correction better than alternative methods. We further provide a simple
algorithm to construct the JINI in a computationally efficient manner.
Therefore, the JINI is especially useful in settings where standard methods may
be challenging to apply, for example, in the presence of misclassification and
rounding. We consider comprehensive simulation studies and analyze an alcohol
consumption data example to illustrate the excellent performance and usefulness
of the method.


title:
MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral
  Reconstruction

abstract:
  Existing leading methods for spectral reconstruction (SR) focus on designing
deeper or wider convolutional neural networks (CNNs) to learn the end-to-end
mapping from the RGB image to its hyperspectral image (HSI). These CNN-based
methods achieve impressive restoration performance while showing limitations in
capturing the long-range dependencies and self-similarity prior. To cope with
this problem, we propose a novel Transformer-based method, Multi-stage
Spectral-wise Transformer (MST++), for efficient spectral reconstruction. In
particular, we employ Spectral-wise Multi-head Self-attention (S-MSA) that is
based on the HSI spatially sparse while spectrally self-similar nature to
compose the basic unit, Spectral-wise Attention Block (SAB). Then SABs build up
Single-stage Spectral-wise Transformer (SST) that exploits a U-shaped structure
to extract multi-resolution contextual information. Finally, our MST++,
cascaded by several SSTs, progressively improves the reconstruction quality
from coarse to fine. Comprehensive experiments show that our MST++
significantly outperforms other state-of-the-art methods. In the NTIRE 2022
Spectral Reconstruction Challenge, our approach won the First place. Code and
pre-trained models are publicly available at
https://github.com/caiyuanhao1998/MST-plus-plus.


title: Quantifiable Assurance: From IPs to Platforms

abstract:
  Hardware vulnerabilities are generally considered more difficult to fix than
software ones because they are persistent after fabrication. Thus, it is
crucial to assess the security and fix the vulnerabilities at earlier design
phases, such as Register Transfer Level (RTL) and gate level. The focus of the
existing security assessment techniques is mainly twofold. First, they check
the security of Intellectual Property (IP) blocks separately. Second, they aim
to assess the security against individual threats considering the threats are
orthogonal. We argue that IP-level security assessment is not sufficient.
Eventually, the IPs are placed in a platform, such as a system-on-chip (SoC),
where each IP is surrounded by other IPs connected through glue logic and
shared/private buses. Hence, we must develop a methodology to assess the
platform-level security by considering both the IP-level security and the
impact of the additional parameters introduced during platform integration.
Another important factor to consider is that the threats are not always
orthogonal. Improving security against one threat may affect the security
against other threats. Hence, to build a secure platform, we must first answer
the following questions: What additional parameters are introduced during the
platform integration? How do we define and characterize the impact of these
parameters on security? How do the mitigation techniques of one threat impact
others? This paper aims to answer these important questions and proposes
techniques for quantifiable assurance by quantitatively estimating and
measuring the security of a platform at the pre-silicon stages. We also touch
upon the term security optimization and present the challenges for future
research directions.


title:
Possible structural and bond reconstruction in 2D ferromagnetic
  semiconductor VSe2 under uniaxial stress

abstract:
  2D semiconducting transition metal dichalcogenides have been used to make
high-performance electronic, spintronic, and optoelectronic devices. Recently,
room-temperature ferromagnetism and semiconducting property were found in 2D
VSe$_2$ nanoflakes (mechanically exfoliated onto silicon substrates capped with
a oxide layer) and are attributed to the stable 2H-phase of VSe$_2$ in the 2D
limit. Here, our first-principles investigation show that a metastable
semiconducting H' phase can be formed from the H VSe2 monolayer and some other
similar when these 2D H-phase materials are under uniaxial stress or uniaxial
strain. For the uniaxial stress (uniaxial strain) scheme, the H' phase will
become lower in total energy than the H phase at the transition point. The
calculated phonon spectra indicate the dynamical stability of the H' structures
of VSe$_2$, VS$_2$, and CrS$_2$, and the path of phase switching between the H
and H' VSe$_2$ phases is calculated. For VSe$_2$, the H' phase has stronger
ferromagnetism and its Currier temperature can be substantially enhanced by
applying uniaxial stress or strain. Spin-resolved electronic structures, energy
band edges, and effective carrier masses for both of the H and H' phases can be
substantially changed by the applied uniaxial stress or strain, leading to huge
effective masses near the band edge of the strained H' phase. Analysis
indicated that the largest bond length difference between the H' and H phases
can reach -19\% for the Se3-Se3' bond, and there is noticeable covalence for
the Se3-Se3' bond, which switches the valence of the nearby V atoms, leading to
the enhanced ferromagnetism. Therefore, structural and bond reconstruction can
be realized by applying uniaxial stress in 2D ferromagnetic H VSe$_2$ and some
other similar. These can be useful to seeking more insights and phenomena in
such 2D materials for potential applications.


title:
A Distributed Online Algorithm for Promoting Energy Sharing Between EV
  Charging Stations

abstract:
  In recent years, electric vehicle (EV) charging station has experienced an
increasing supply-demand mismatch due to its fluctuating renewables and
unpredictable charging demand. To reduce its operating cost, this paper
proposes a distributed online algorithm to promote the energy sharing between
charging stations. We begin with the offline and centralized version of the EV
charging stations operation problem, whose objective is to minimize the
long-term time-average total cost. Then, we develop an online implementation
approach based on the Lyapunov optimization framework. Although the proposed
online algorithm runs in a prediction-free manner, we prove that by properly
choosing the parameters, the time-coupling constraints remain to be satisfied.
We also provide a theoretical bound for the optimality gap between the offline
and online optimums. Furthermore, an improved alternating direction method of
multipliers (ADMM) algorithm with iteration truncation is proposed to enable
distributed computation. The proposed algorithm can protect privacy while being
suitable for online implementation. Case studies validate the effectiveness of
the theoretical results. Comprehensive performance comparisons are carried out
to demonstrate the advantages of the proposed method.


title: Nonlinear study of local ballooning mode near the separatrix

abstract:
  Small edge-localized-mode (ELM), similar to the quasi-continuous exhaust
(QCE), has been achieved by increasing the density at the separatrix. Starting
from the Type-I ELM experimental data in EAST, we have performed a numerical
separatrix density scan to study the formation of the small ELM using BOUT++
6-field 2-fluid module. In the high separatrix density case, localized collapse
near the separatrix has been found. The corresponding ELM size, dominant mode
number, and filament transport match the experimental observations of the QCE.
Local ballooning mode near separatrix has been identified in the nonlinear
simulation. The mode is driven by the local pressure gradient and the mode
structure is constrained by the ExB shear.


title:
What Goes beyond Multi-modal Fusion in One-stage Referring Expression
  Comprehension: An Empirical Study

abstract:
  Most of the existing work in one-stage referring expression comprehension
(REC) mainly focuses on multi-modal fusion and reasoning, while the influence
of other factors in this task lacks in-depth exploration. To fill this gap, we
conduct an empirical study in this paper. Concretely, we first build a very
simple REC network called SimREC, and ablate 42 candidate designs/settings,
which covers the entire process of one-stage REC from network design to model
training. Afterwards, we conduct over 100 experimental trials on three
benchmark datasets of REC. The extensive experimental results not only show the
key factors that affect REC performance in addition to multi-modal fusion,
e.g., multi-scale features and data augmentation, but also yield some findings
that run counter to conventional understanding. For example, as a vision and
language (V&L) task, REC does is less impacted by language prior. In addition,
with a proper combination of these findings, we can improve the performance of
SimREC by a large margin, e.g., +27.12% on RefCOCO+, which outperforms all
existing REC methods. But the most encouraging finding is that with much less
training overhead and parameters, SimREC can still achieve better performance
than a set of large-scale pre-trained models, e.g., UNITER and VILLA,
portraying the special role of REC in existing V&L research.


title: Constrained optimal stopping under a regime-switching model

abstract:
  We investigate an optimal stopping problem for the expected value of a
discounted payoff on a regime-switching geometric Brownian motion under two
constraints on the possible stopping times: only at exogenous random times and
only during a specific regime. The main objectives are to show that an optimal
stopping time exists as a threshold type under some boundary conditions and to
derive expressions of the value functions and the optimal threshold. To this
end, we solve the corresponding variational inequality and show that its
solution coincides with the value functions. Some numerical results are also
introduced. Furthermore, we investigate some asymptotic behaviors.


title: Backward-Angle ($u$-channel) Production at an Electron-Ion Collider

abstract:
  In backward photoproduction of mesons, $\gamma p\rightarrow M p$, the target
proton takes most of the photon momentum, while the produced meson recoils in
the direction from which the photon came. Thus the Mandelstam $u$ is small,
while the squared momentum transfer $t$ is typically large, near the kinematic
limit. In a collider geometry, backward production transfers the struck baryon
by many units of rapidity, in a striking similarity to baryon stopping. We
explore this similarity, and point out the similarities between the Regge
theories used to model baryon stopping with those that are used for backward
production.
  We then explore how backward production can be explored at higher energies
than are available at fixed target experiments, by studying production at an
electron-ion collider. We calculate the expected $ep$ cross sections and rates,
finding that the rate for backward $\omega$ production is about 1/300 that of
forward $\omega$s. We discuss the kinematics of backward production and
consider the detector requirements for experimental study.


title:
An $n H_k$-compressed searchable partial-sums data structure for static
  sequences of sublogarithmic positive integers

abstract:
  We consider the space needed to store a searchable partial-sums data
structure with constant query time for a static sequence $S$ of $n$ positive
integers in $o \left( \frac{\log n}{(\log \log n)^2} \right)$. Arroyuelo and
Raman (2022) recently showed that such a structure can fit in $n H_0 (S) + o
(n)$ bits. Starting with Ferragina and Venturini's (2007) $n H_k$-compressed
representation of strings that supports fast random access, and augmenting it
with sublinear data structures reminiscent of those Raman, Raman and Rao (2002)
used in their succinct bitvectors, we slightly improve Arroyuelo and Raman's
bound to $n H_k (S) + o (n)$ bits for $k \in o \left( \frac{\log n}{(\log \log
n)^2} \right)$.


title:
First and second derivative H\"older estimates for generated Jacobian
  equations

abstract:
  We prove two H\"older regularity results for solutions of generated Jacobian
equations. First, that under the A3 condition and the assumption of nonnegative
$L^p$ valued data solutions are $C^{1,\alpha}$ for an $\alpha$ that is sharp.
Then, under the additional assumption of positive Dini continuous data, we
prove a $C^{2}$ estimate. Thus the equation is uniformly elliptic and when the
data is H\"older continuous solutions are in $C^{2,\alpha}$.


title:
Convergence analysis of a two-grid method for nonsymmetric positive
  definite problems

abstract:
  Multigrid is a powerful solver for large-scale linear systems arising from
discretized partial differential equations. The convergence theory of multigrid
methods for symmetric positive definite problems has been well developed over
the past decades, while, for nonsymmetric problems, such theory is still not
mature. As a foundation for multigrid analysis, two-grid convergence theory
plays an important role in motivating multigrid algorithms. Regarding two-grid
methods for nonsymmetric problems, most previous works focus on the spectral
radius of iteration matrix or rely on convergence measures that are typically
difficult to compute in practice. Moreover, the existing results are confined
to two-grid methods with exact solution of the coarse-grid system. In this
paper, we analyze the convergence of a two-grid method for nonsymmetric
positive definite problems (e.g., linear systems arising from the
discretizations of convection-diffusion equations). In the case of exact coarse
solver, we establish an elegant identity for characterizing two-grid
convergence factor, which is measured by a smoother-induced norm. The identity
can be conveniently used to derive a class of optimal restriction operators and
analyze how the convergence factor is influenced by restriction. More
generally, we present some convergence estimates for an inexact variant of the
two-grid method, in which both linear and nonlinear coarse solvers are
considered.


title:
Cognitive Architecture for Decision-Making Based on Brain Principles
  Programming

abstract:
  We describe a cognitive architecture intended to solve a wide range of
problems based on the five identified principles of brain activity, with their
implementation in three subsystems: logical-probabilistic inference,
probabilistic formal concepts, and functional systems theory. Building an
architecture involves the implementation of a task-driven approach that allows
defining the target functions of applied applications as tasks formulated in
terms of the operating environment corresponding to the task, expressed in the
applied ontology. We provide a basic ontology for a number of practical
applications as well as for the subject domain ontologies based upon it,
describe the proposed architecture, and give possible examples of the execution
of these applications in this architecture.