title:
Thermodynamics of chromosome inversions and 100 million years of
  Lachancea evolution

abstract:
  Gene sequences of a deme evolve over time as new chromosome inversions appear
in a population via mutations, some of which will replace an existing sequence.
The underlying biochemical processes that generates these and other mutations
are governed by the laws of thermodynamics, although the connection between
thermodynamics and the generation and propagation of mutations are often
neglected. Here, chromosome inversions are modeled as a specific example of
mutations in an evolving system. The thermodynamic concepts of chemical
potential, energy, and temperature are linked to the input parameters that
include inversion rate, recombination loss rate and deme size. An energy
barrier to existing gene sequence replacement is a natural consequence of the
model. Finally, the model calculations are compared to the observed chromosome
inversion distribution of the Lachancea genus of yeast. The model introduced in
this work should be applicable to other types of mutations in evolving systems.


title: Programming of linear virtual element methods in three dimensions

abstract:
  We present a simple and efficient MATLAB implementation of the linear virtual
element method for the three dimensional Poisson equation. The purpose of this
software is primarily educational, to demonstrate how the key components of the
method can be translated into code.


title:
An Energy-Efficient and Runtime-Reconfigurable FPGA-Based Accelerator
  for Robotic Localization Systems

abstract:
  Simultaneous Localization and Mapping (SLAM) estimates agents' trajectories
and constructs maps, and localization is a fundamental kernel in autonomous
machines at all computing scales, from drones, AR, VR to self-driving cars. In
this work, we present an energy-efficient and runtime-reconfigurable FPGA-based
accelerator for robotic localization. We exploit SLAM-specific data locality,
sparsity, reuse, and parallelism, and achieve >5x performance improvement over
the state-of-the-art. Especially, our design is reconfigurable at runtime
according to the environment to save power while sustaining accuracy and
performance.


title:
Functional Connectivity Based Classification of ADHD Using Different
  Atlases

abstract:
  These days, computational diagnosis strategies of neuropsychiatric disorders
are gaining attention day by day. It's critical to determine the brain's
functional connectivity based on Functional-Magnetic-Resonance-Imaging(fMRI) to
diagnose the disorder. It's known as a chronic disease, and millions of
children amass the symptoms of this disease, so there is much vacuum for the
researcher to formulate a model to improve the accuracy to diagnose ADHD
accurately. In this paper, we consider the functional connectivity of a brain
extracted using various time templates/Atlases. Local-Binary Encoding-Method
(LBEM) algorithm is utilized for feature extraction, while Hierarchical-
Extreme-Learning-Machine (HELM) is used to classify the extracted features. To
validate our approach, fMRI data of 143 normal and 100 ADHD affected children
is used for experimental purpose. Our experimental results are based on
comparing various Atlases given as CC400, CC200, and AAL. Our model achieves
high performance with CC400 as compared to other Atlases


title:
Rydberg atom-based field sensing enhancement using a split-ring
  resonator

abstract:
  We investigate the use of a split-ring resonator (SRR) incorporated with an
atomic-vapor cell to improve the sensitivity and the minimal detectable
electric (E) field of Rydberg atom-based sensors. In this approach, a
sub-wavelength SRR is placed around an atomic vapor-cell filled with cesium
atoms for E-field measurements at 1.3~GHz. The SRR provides a factor of 100 in
the enhancement of the E-field measurement sensitivity. Using
electromagnetically induced transparency (EIT) with Aulter-Townes splitting,
E-field measurements down to 5~mV/m are demonstrated with the SRR, while in the
absence of the SRR, the minimal detectable field is 500~mV/m. We demonstrate
that by combining EIT with a heterodyne Rydberg atom-based mixer approach, the
SRR allows for the a sensitivity of 5.5~$\mu$V/m$\sqrt{{\rm Hz}}$, which is
two-orders of magnitude improvement in sensitivity than when the SRR is not
used.


title:
R2-D2: Repetitive Reprediction Deep Decipher for Semi-Supervised Deep
  Learning

abstract:
  Most recent semi-supervised deep learning (deep SSL) methods used a similar
paradigm: use network predictions to update pseudo-labels and use pseudo-labels
to update network parameters iteratively. However, they lack theoretical
support and cannot explain why predictions are good candidates for
pseudo-labels in the deep learning paradigm. In this paper, we propose a
principled end-to-end framework named deep decipher (D2) for SSL. Within the D2
framework, we prove that pseudo-labels are related to network predictions by an
exponential link function, which gives a theoretical support for using
predictions as pseudo-labels. Furthermore, we demonstrate that updating
pseudo-labels by network predictions will make them uncertain. To mitigate this
problem, we propose a training strategy called repetitive reprediction (R2).
Finally, the proposed R2-D2 method is tested on the large-scale ImageNet
dataset and outperforms state-of-the-art methods by 5 percentage points.


title:
GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for
  Parameter Space Exploration of Unstructured-Mesh Ocean Simulations

abstract:
  We propose GNN-Surrogate, a graph neural network-based surrogate model to
explore the parameter space of ocean climate simulations. Parameter space
exploration is important for domain scientists to understand the influence of
input parameters (e.g., wind stress) on the simulation output (e.g.,
temperature). The exploration requires scientists to exhaust the complicated
parameter space by running a batch of computationally expensive simulations.
Our approach improves the efficiency of parameter space exploration with a
surrogate model that predicts the simulation outputs accurately and
efficiently. Specifically, GNN-Surrogate predicts the output field with given
simulation parameters so scientists can explore the simulation parameter space
with visualizations from user-specified visual mappings. Moreover, our
graph-based techniques are designed for unstructured meshes, making the
exploration of simulation outputs on irregular grids efficient. For efficient
training, we generate hierarchical graphs and use adaptive resolutions. We give
quantitative and qualitative evaluations on the MPAS-Ocean simulation to
demonstrate the effectiveness and efficiency of GNN-Surrogate. Source code is
publicly available at https://github.com/trainsn/GNN-Surrogate.


title:
Estimating the Feasibility of 21cm-Ly$\alpha$ Synergies using the
  Hydrogen Epoch of Reionization Array

abstract:
  Cross-correlating 21cm and Ly$\alpha$ intensity maps of the Epoch of
Reionization (EoR) promises to be a powerful tool for exploring the properties
of the first galaxies. Next-generation intensity mapping experiments such as
the Hydrogen Epoch of Reionization Array (HERA) and SPHEREx will individually
probe reionization through the power spectra of the 21cm and Ly$\alpha$ lines
respectively, but will be limited by bright foregrounds and instrumental
systematics. Cross-correlating these measurements could reduce systematics,
potentially tightening constraints on the inferred astrophysical parameters. In
this study, we present forecasts of cross-correlation taking into account the
effects of exact uv-sampling and foreground filtering to estimate the
feasibility of HERAxSPHEREx making a detection of the 21cm-Ly$\alpha$
cross-power spectrum. We also project the sensitivity of a cross-power spectrum
between HERA and the proposed next-generation Cosmic Dawn Intensity Mapper. By
isolating the sources of uncertainty, we explore the impacts of experimental
limitations such as foreground filtering and Ly$\alpha$ thermal noise
uncertainty have on making a detection of the cross-power spectrum. We then
implement this strategy in a simulation of the cross-power spectrum and
observational error to identify redshifts where fiducial 21cmFAST models
predict the highest signal-to-noise detection ($z \sim 8$). We conclude that
detection of the SPHEREx-HERA cross-correlation will require an optimistic
level of 21cm foreground filtering, as well as deeper thermal noise
integrations due to a lack of overlapping sensitive modes but for CDIM with its
larger range of scales and lower noise forecast detection levels, may be
possible even with stricter 21cm foreground filtering.


title:
On spherically symmetric vacuum solutions and horizons in covariant
  $f(T)$ gravity theory

abstract:
  In this paper we study properties that the vacuum must possess in the minimal
extension to the teleparallel equivalent of general relativity (TEGR) where the
action is supplemented with a quadratic torsion term. No assumption is made
about the weakness of the quadratic term although in the weak-field regime the
validity of our previously derived perturbative solution is confirmed.
Regarding the exact nature of the vacuum, it is found that if the center of
symmetry is to be regular, the mathematical conditions on the tetrad at the
isotropy point mimic those of general relativity. With respect to horizons it
is found that, under very mild assumptions, a smooth horizon cannot exist
unless the quadratic torsion coupling, $\alpha$, vanishes, which is the TEGR
limit (with the Schwarzschild tetrad as its solution). This analysis is then
supplemented with computational work utilizing asymptotically Schwarzschild
boundary data. It is verified that in no case studied does a smooth horizon
form. For $\alpha > 0$ naked singularities occur which break down the equations
of motion before a horizon can form. For $\alpha < 0$ there is a limited range
of $\alpha$ where a vacuum horizon might exist but, if present, the horizon is
singular. Therefore physically acceptable black hole horizons are problematic
in the studied theory at least within the realm of vacuum static spherical
symmetry. These results also imply that static spherical matter distributions
generally must have extra restrictions on their spatial extent and
stress-energy bounds so as to render the vacuum solution invalid in the
singular region and make the solutions finite.


title:
Deep Interest Highlight Network for Click-Through Rate Prediction in
  Trigger-Induced Recommendation

abstract:
  In many classical e-commerce platforms, personalized recommendation has been
proven to be of great business value, which can improve user satisfaction and
increase the revenue of platforms. In this paper, we present a new
recommendation problem, Trigger-Induced Recommendation (TIR), where users'
instant interest can be explicitly induced with a trigger item and follow-up
related target items are recommended accordingly. TIR has become ubiquitous and
popular in e-commerce platforms. In this paper, we figure out that although
existing recommendation models are effective in traditional recommendation
scenarios by mining users' interests based on their massive historical
behaviors, they are struggling in discovering users' instant interests in the
TIR scenario due to the discrepancy between these scenarios, resulting in
inferior performance. To tackle the problem, we propose a novel recommendation
method named Deep Interest Highlight Network (DIHN) for Click-Through Rate
(CTR) prediction in TIR scenarios. It has three main components including 1)
User Intent Network (UIN), which responds to generate a precise probability
score to predict user's intent on the trigger item; 2) Fusion Embedding Module
(FEM), which adaptively fuses trigger item and target item embeddings based on
the prediction from UIN; and (3) Hybrid Interest Extracting Module (HIEM),
which can effectively highlight users' instant interest from their behaviors
based on the result of FEM. Extensive offline and online evaluations on a
real-world e-commerce platform demonstrate the superiority of DIHN over
state-of-the-art methods.


title: Toward a traceable, explainable, and fairJD/Resume recommendation system

abstract:
  In the last few decades, companies are interested to adopt an online
automated recruitment process in an international recruitment environment. The
problem is that the recruitment of employees through the manual procedure is a
time and money consuming process. As a result, processing a significant number
of applications through conventional methods can lead to the recruitment of
clumsy individuals. Different JD/Resume matching model architectures have been
proposed and reveal a high accuracy level in selecting relevant candidatesfor
the required job positions. However, the development of an automatic
recruitment system is still one of the main challenges. The reason is that the
development of a fully automated recruitment system is a difficult task and
poses different challenges. For example, providing a detailed matching
explanation for the targeted stakeholders is needed to ensure a transparent
recommendation. There are several knowledge bases that represent skills and
competencies (e.g, ESCO, O*NET) that are used to identify the candidate and the
required job skills for a matching purpose. Besides, modernpre-trained language
models are fine-tuned for this context such as identifying lines where a
specific feature was introduced. Typically, pre-trained language models use
transfer-based machine learning models to be fine-tuned for a specific field.
In this proposal, our aim is to explore how modern language models (based on
transformers) can be combined with knowledge bases and ontologies to enhance
the JD/Resume matching process. Our system aims at using knowledge bases and
features to support the explainability of the JD/Resume matching. Finally,
given that multiple software components, datasets, ontology, andmachine
learning models will be explored, we aim at proposing a fair, ex-plainable, and
traceable architecture for a Resume/JD matching purpose.


title: Partially-coherent waves with binominal coherence

abstract:
  Comprehensive analysis of non-diffracting optical waves with binominal
two-point coherence function (BCF) is presented. This coherence function
consist of two terms, each depending on either separation of points or central
point. We established the necessary and sufficient conditions for non-negative
definiteness of the binominal coherence functions. Spectral analysis, including
calculations of the eigenvalues and eigenfunctions for the general BCF case is
presented. We considered two simple BCF examples of and analyzed the conditions
leading to the maximum irradiance contrast.


title: Volatility forecasting with machine learning and intraday commonality

abstract:
  We apply machine learning models to forecast intraday realized volatility
(RV), by exploiting commonality in intraday volatility via pooling stock data
together, and by incorporating a proxy for the market volatility. Neural
networks dominate linear regressions and tree models in terms of performance,
due to their ability to uncover and model complex latent interactions among
variables. Our findings remain robust when we apply trained models to new
stocks that have not been included in the training set, thus providing new
empirical evidence for a universal volatility mechanism among stocks. Finally,
we propose a new approach to forecasting one-day-ahead RVs using past intraday
RVs as predictors, and highlight interesting diurnal effects that aid the
forecasting mechanism. The results demonstrate that the proposed methodology
yields superior out-of-sample forecasts over a strong set of traditional
baselines that only rely on past daily RVs.


title: Understanding and Shifting Preferences for Battery Electric Vehicles

abstract:
  Identifying personalized interventions for an individual is an important
task. Recent work has shown that interventions that do not consider the
demographic background of individual consumers can, in fact, produce the
reverse effect, strengthening opposition to electric vehicles. In this work, we
focus on methods for personalizing interventions based on an individual's
demographics to shift the preferences of consumers to be more positive towards
Battery Electric Vehicles (BEVs). One of the constraints in building models to
suggest interventions for shifting preferences is that each intervention can
influence the effectiveness of later interventions. This, in turn, requires
many subjects to evaluate effectiveness of each possible intervention. To
address this, we propose to identify personalized factors influencing BEV
adoption, such as barriers and motivators. We present a method for predicting
these factors and show that the performance is better than always predicting
the most frequent factors. We then present a Reinforcement Learning (RL) model
that learns the most effective interventions, and compare the number of
subjects required for each approach.


title:
Simulating User-Level Twitter Activity with XGBoost and Probabilistic
  Hybrid Models

abstract:
  The Volume-Audience-Match simulator, or VAM was applied to predict future
activity on Twitter related to international economic affairs. VAM was applied
to do timeseries forecasting to predict the: (1) number of total activities,
(2) number of active old users, and (3) number of newly active users over the
span of 24 hours from the start time of prediction. VAM then used these volume
predictions to perform user link predictions. A user-user edge was assigned to
each of the activities in the 24 future timesteps. VAM considerably
outperformed a set of baseline models in both the time series and
user-assignment tasks


title:
High-performance automatic categorization and attribution of inventory
  catalogs

abstract:
  Techniques of machine learning for automatic text categorization are applied
and adapted for the problem of inventory catalog data attribution, with
different approaches explored and optimal solution addressing the tradeoff
between accuracy and performance is selected.


title: Constructing a NFT Price Index and Applications

abstract:
  We are witnessing the emergence of a new digital art market, the art market
3.0. Blockchain technology has taken on a new sector which is still not well
known, Non-Fungible tokens (NFT). In this paper we propose a new methodology to
build a NFT Price Index that represents this new market on the whole. In
addition, this index will allow us to have a look on the dynamics and
performances of NFT markets, and to diagnose them.


title: Ensemble and Multimodal Approach for Forecasting Cryptocurrency Price

abstract:
  Since the birth of Bitcoin in 2009, cryptocurrencies have emerged to become a
global phenomenon and an important decentralized financial asset. Due to this
decentralization, the value of these digital currencies against fiat currencies
is highly volatile over time. Therefore, forecasting the crypto-fiat currency
exchange rate is an extremely challenging task. For reliable forecasting, this
paper proposes a multimodal AdaBoost-LSTM ensemble approach that employs all
modalities which derive price fluctuation such as social media sentiments,
search volumes, blockchain information, and trading data. To better support
investment decision making, the approach forecasts also the fluctuation
distribution. The conducted extensive experiments demonstrated the
effectiveness of relying on multimodalities instead of only trading data.
Further experiments demonstrate the outperformance of the proposed approach
compared to existing tools and methods with a 19.29% improvement.


title:
Stock Embeddings: Learning Distributed Representations for Financial
  Assets

abstract:
  Identifying meaningful relationships between the price movements of financial
assets is a challenging but important problem in a variety of financial
applications. However with recent research, particularly those using machine
learning and deep learning techniques, focused mostly on price forecasting, the
literature investigating the modelling of asset correlations has lagged
somewhat. To address this, inspired by recent successes in natural language
processing, we propose a neural model for training stock embeddings, which
harnesses the dynamics of historical returns data in order to learn the nuanced
relationships that exist between financial assets. We describe our approach in
detail and discuss a number of ways that it can be used in the financial
domain. Furthermore, we present the evaluation results to demonstrate the
utility of this approach, compared to several important benchmarks, in two
real-world financial analytics tasks.


title: Private Quantiles Estimation in the Presence of Atoms

abstract:
  We address the differentially private estimation of multiple quantiles (MQ)
of a dataset, a key building block in modern data analysis. We apply the recent
non-smoothed Inverse Sensitivity (IS) mechanism to this specific problem and
establish that the resulting method is closely related to the current
state-of-the-art, the JointExp algorithm, sharing in particular the same
computational complexity and a similar efficiency. However, we demonstrate both
theoretically and empirically that (non-smoothed) JointExp suffers from an
important lack of performance in the case of peaked distributions, with a
potentially catastrophic impact in the presence of atoms. While its smoothed
version would allow to leverage the performance guarantees of IS, it remains an
open challenge to implement. As a proxy to fix the problem we propose a simple
and numerically efficient method called Heuristically Smoothed JointExp
(HSJointExp), which is endowed with performance guarantees for a broad class of
distributions and achieves results that are orders of magnitude better on
problematic datasets.


title:
Status and initial physics performance studies of the MPD experiment at
  NICA

abstract:
  The Nuclotron-base Ion Collider fAcility (NICA) is under construction at the
Joint Institute for Nuclear Research (JINR), with commissioning of the facility
expected in late 2022. The Multi-Purpose Detector (MPD) has been designed to
operate at NICA and its components are currently in production. The detector is
expected to be ready for data taking with the first beams from NICA. This
document provides an overview of the landscape of the investigation of the QCD
phase diagram in the region of maximum baryonic density, where NICA and MPD
will be able to provide significant and unique input. It also provides a
detailed description of the MPD set-up, including its various subsystems as
well as its support and computing infrastructures. Selected performance studies
for particular physics measurements at MPD are presented and discussed in the
context of existing data and theoretical expectations.


title: Hybrid cooling of the Cassiopeia A neutron star

abstract:
  The observed rapid cooling of the neutron star Cassiopeia A is usually
interpreted as being caused by transitions of neutrons and protons in the
star's core from the normal state to the superfluid and superconducting state.
However, this so-called "minimal" cooling paradigm faces the problem of
numerically simulating the observed anomalously fast drop in the neutron star
surface temperature using theoretical neutrino energy losses from superfluid
neutrons. As a solution to this problem, I propose a somewhat more complex
cooling model, in which, in addition to superfluid neutrons, direct Urca
processes from a very small central part of the neutron star core are also
involved. Numerical simulations of the cooling trajectory in this scenario show
excellent agreement with observations of the Cassiopeia A neutron star. The
proposed cooling scenario unambiguously relates the used equation of state and
the mass of the neutron star. For a neutron star constructed according to BSk25
equation of state, the most appropriate are the mass $M=1.62M_{\sun}$ and the
radius $R=12.36$ km. If BSk24 equation of state is used, then the most suitable
solution is $M=1.60M_{\sun}$ and $R=12.55$ km.


title:
Deep Reinforcement Learning Based Multi-Access Edge Computing Schedule
  for Internet of Vehicle

abstract:
  As intelligent transportation systems been implemented broadly and unmanned
arial vehicles (UAVs) can assist terrestrial base stations acting as
multi-access edge computing (MEC) to provide a better wireless network
communication for Internet of Vehicles (IoVs), we propose a UAVs-assisted
approach to help provide a better wireless network service retaining the
maximum Quality of Experience(QoE) of the IoVs on the lane. In the paper, we
present a Multi-Agent Graph Convolutional Deep Reinforcement Learning
(M-AGCDRL) algorithm which combines local observations of each agent with a
low-resolution global map as input to learn a policy for each agent. The agents
can share their information with others in graph attention networks, resulting
in an effective joint policy. Simulation results show that the M-AGCDRL method
enables a better QoE of IoTs and achieves good performance.


title:
Energy-Efficient Parking Analytics System using Deep Reinforcement
  Learning

abstract:
  Advances in deep vision techniques and ubiquity of smart cameras will drive
the next generation of video analytics. However, video analytics applications
consume vast amounts of energy as both deep learning techniques and cameras are
power-hungry. In this paper, we focus on a parking video analytics platform and
propose RL-CamSleep, a deep reinforcement learning-based technique, to actuate
the cameras to reduce the energy footprint while retaining the system's
utility. Our key insight is that many video-analytics applications do not
always need to be operational, and we can design policies to activate video
analytics only when necessary. Moreover, our work is complementary to existing
work that focuses on improving hardware and software efficiency. We evaluate
our approach on a city-scale parking dataset having 76 streets spread across
the city. Our analysis demonstrates how streets have various parking patterns,
highlighting the importance of an adaptive policy. Our approach can learn such
an adaptive policy that can reduce the average energy consumption by 76.38% and
achieve an average accuracy of more than 98% in performing video analytics.


title:
Multimodal Emotion Recognition using Transfer Learning from Speaker
  Recognition and BERT-based models

abstract:
  Automatic emotion recognition plays a key role in computer-human interaction
as it has the potential to enrich the next-generation artificial intelligence
with emotional intelligence. It finds applications in customer and/or
representative behavior analysis in call centers, gaming, personal assistants,
and social robots, to mention a few. Therefore, there has been an increasing
demand to develop robust automatic methods to analyze and recognize the various
emotions. In this paper, we propose a neural network-based emotion recognition
framework that uses a late fusion of transfer-learned and fine-tuned models
from speech and text modalities. More specifically, we i) adapt a residual
network (ResNet) based model trained on a large-scale speaker recognition task
using transfer learning along with a spectrogram augmentation approach to
recognize emotions from speech, and ii) use a fine-tuned bidirectional encoder
representations from transformers (BERT) based model to represent and recognize
emotions from the text. The proposed system then combines the ResNet and
BERT-based model scores using a late fusion strategy to further improve the
emotion recognition performance. The proposed multimodal solution addresses the
data scarcity limitation in emotion recognition using transfer learning, data
augmentation, and fine-tuning, thereby improving the generalization performance
of the emotion recognition models. We evaluate the effectiveness of our
proposed multimodal approach on the interactive emotional dyadic motion capture
(IEMOCAP) dataset. Experimental results indicate that both audio and text-based
models improve the emotion recognition performance and that the proposed
multimodal solution achieves state-of-the-art results on the IEMOCAP benchmark.


title: Probing Pretrained Models of Source Code

abstract:
  Deep learning models are widely used for solving challenging code processing
tasks, such as code generation or code summarization. Traditionally, a specific
model architecture was carefully built to solve a particular code processing
task. However, recently general pretrained models such as CodeBERT or CodeT5
have been shown to outperform task-specific models in many applications. While
pretrained models are known to learn complex patterns from data, they may fail
to understand some properties of source code. To test diverse aspects of code
understanding, we introduce a set of diagnosting probing tasks. We show that
pretrained models of code indeed contain information about code syntactic
structure and correctness, the notions of identifiers, data flow and
namespaces, and natural language naming. We also investigate how probing
results are affected by using code-specific pretraining objectives, varying the
model size, or finetuning.


title: Minkowski tensor-based shape analysis methods on the sphere

abstract:
  Recently, Minkowski Tensors (MT) have gained popularity for morphological
analysis tasks. As opposed to the scalar Minkowski functionals (MF; in 2D given
by area, perimeter and Euler characteristic), MT can characterize symmetry and
orientation of a body. This has been used for a variety of tasks, e.g. to
detect interstellar bubbles by tracing back the origins of filaments in
HII-regions, or to search for alignment of structures in the CMB. I present a
marching-square-based method for calculating MT and MF on the sphere for maps
in the Healpix format. MT are calculated for a local neighborhood and can then
be summed up/averaged over a larger region, using their additivity property.
This provides the possibility of localized analyses looking for CMB
anisotropies and non-Gaussianities at varying scales.


title:
Fairness constraint in Structural Econometrics and Application to fair
  estimation using Instrumental Variables

abstract:
  A supervised machine learning algorithm determines a model from a learning
sample that will be used to predict new observations. To this end, it
aggregates individual characteristics of the observations of the learning
sample. But this information aggregation does not consider any potential
selection on unobservables and any status-quo biases which may be contained in
the training sample. The latter bias has raised concerns around the so-called
\textit{fairness} of machine learning algorithms, especially towards
disadvantaged groups. In this chapter, we review the issue of fairness in
machine learning through the lenses of structural econometrics models in which
the unknown index is the solution of a functional equation and issues of
endogeneity are explicitly accounted for. We model fairness as a linear
operator whose null space contains the set of strictly {\it fair} indexes. A
{\it fair} solution is obtained by projecting the unconstrained index into the
null space of this operator or by directly finding the closest solution of the
functional equation into this null space. We also acknowledge that policymakers
may incur a cost when moving away from the status quo. Achieving
\textit{approximate fairness} is obtained by introducing a fairness penalty in
the learning procedure and balancing more or less heavily the influence between
the status quo and a full fair solution.


title: Cyclical Focal Loss

abstract:
  The cross-entropy softmax loss is the primary loss function used to train
deep neural networks. On the other hand, the focal loss function has been
demonstrated to provide improved performance when there is an imbalance in the
number of training samples in each class, such as in long-tailed datasets. In
this paper, we introduce a novel cyclical focal loss and demonstrate that it is
a more universal loss function than cross-entropy softmax loss or focal loss.
We describe the intuition behind the cyclical focal loss and our experiments
provide evidence that cyclical focal loss provides superior performance for
balanced, imbalanced, or long-tailed datasets. We provide numerous experimental
results for CIFAR-10/CIFAR-100, ImageNet, balanced and imbalanced 4,000
training sample versions of CIFAR-10/CIFAR-100, and ImageNet-LT and Places-LT
from the Open Long-Tailed Recognition (OLTR) challenge. Implementing the
cyclical focal loss function requires only a few lines of code and does not
increase training time. In the spirit of reproducibility, our code is available
at \url{https://github.com/lnsmith54/CFL}.


title:
The Response Shift Paradigm to Quantify Human Trust in AI
  Recommendations

abstract:
  Explainability, interpretability and how much they affect human trust in AI
systems are ultimately problems of human cognition as much as machine learning,
yet the effectiveness of AI recommendations and the trust afforded by end-users
are typically not evaluated quantitatively. We developed and validated a
general purpose Human-AI interaction paradigm which quantifies the impact of AI
recommendations on human decisions. In our paradigm we confronted human users
with quantitative prediction tasks: asking them for a first response, before
confronting them with an AI's recommendations (and explanation), and then
asking the human user to provide an updated final response. The difference
between final and first responses constitutes the shift or sway in the human
decision which we use as metric of the AI's recommendation impact on the human,
representing the trust they place on the AI. We evaluated this paradigm on
hundreds of users through Amazon Mechanical Turk using a multi-branched
experiment confronting users with good/poor AI systems that had good, poor or
no explainability. Our proof-of-principle paradigm allows one to quantitatively
compare the rapidly growing set of XAI/IAI approaches in terms of their effect
on the end-user and opens up the possibility of (machine) learning trust.


title:
On the strong convergence of the trajectories of a Tikhonov regularized
  second order dynamical system with asymptotically vanishing damping

abstract:
  This paper deals with a second order dynamical system with vanishing damping
that contains a Tikhonov regularization term, in connection to the minimization
problem of a convex Fr\'echet differentiable function $g$.
  We show that for appropriate Tikhonov regularization parameters the value of
the objective function in a generated trajectory converges fast to the global
minimum of the objective function and a trajectory generated by the dynamical
system converges weakly to a minimizer of the objective function. We also
obtain the fast convergence of the velocities towards zero and some integral
estimates. Nevertheless, our main goal is to extend and improve some recent
results obtained in \cite{ABCR} and \cite{AL-nemkoz} concerning the strong
convergence of the generated trajectories to an element of minimal norm from
the $\argmin$ set of the objective function $g$. Our analysis also reveals that
the damping coefficient and the Tikhonov regularization coefficient are
strongly correlated.


title: A Summary of the ComParE COVID-19 Challenges

abstract:
  The COVID-19 pandemic has caused massive humanitarian and economic damage.
Teams of scientists from a broad range of disciplines have searched for methods
to help governments and communities combat the disease. One avenue from the
machine learning field which has been explored is the prospect of a digital
mass test which can detect COVID-19 from infected individuals' respiratory
sounds. We present a summary of the results from the INTERSPEECH 2021
Computational Paralinguistics Challenges: COVID-19 Cough, (CCS) and COVID-19
Speech, (CSS).


title:
PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal
  Traffic Forecasting

abstract:
  The complex spatial-temporal correlations in transportation networks make the
traffic forecasting problem challenging. Since transportation system inherently
possesses graph structures, much research efforts have been put with graph
neural networks. Recently, constructing adaptive graphs to the data has shown
promising results over the models relying on a single static graph structure.
However, the graph adaptations are applied during the training phases, and do
not reflect the data used during the testing phases. Such shortcomings can be
problematic especially in traffic forecasting since the traffic data often
suffers from the unexpected changes and irregularities in the time series. In
this study, we propose a novel traffic forecasting framework called Progressive
Graph Convolutional Network (PGCN). PGCN constructs a set of graphs by
progressively adapting to input data during the training and the testing
phases. Specifically, we implemented the model to construct progressive
adjacency matrices by learning trend similarities among graph nodes. Then, the
model is combined with the dilated causal convolution and gated activation unit
to extract temporal features. With residual and skip connections, PGCN performs
the traffic prediction. When applied to four real-world traffic datasets of
diverse geometric nature, the proposed model achieves state-of-the-art
performance with consistency in all datasets. We conclude that the ability of
PGCN to progressively adapt to input data enables the model to generalize in
different study sites with robustness.


title:
Optical transmittance measurements of sheet resistors for the $\rm
  {CF}_4$ scintillating light in a gaseous time-projection chamber

abstract:
  A gaseous time-projection chamber (TPC) with a reconstructable $z$ coordinate
for nuclear recoil tracks has been developed for dark-matter searches and
$\alpha$ particle imagings in a low radioactivity background. A TPC with a
sheet-resistor field cage shows a potential to detect charge and photons
produced by tracks if the sheet resistor has optical transmittance for visible
light, and determine a drift length from a time difference between these
signals. In this study, an optical transmittance of sheet resistors was
measured to be $24.5\pm0.1_{\rm stat}\pm0.6_{\rm syst}\%$ for the $\rm {CF}_4$
gas scintillating light using an $\alpha$-particle source. The number of
photoelectrons is observed to be $\rm\sim20~p.e.$ for 5.3~MeV$\alpha$ with the
existence of a sheet resistor in the $\rm CF_4$ gas. Then, it is discussed how
many photoelectrons to be observable by using multi-alkali-cathode phototubes
and SiPMs for near-infrared light in order to detect lower energy tracks.


title: Positivity of Narayana polynomials and Eulerian polynomials

abstract:
  Gamma-positivity appears frequently in finite geometries, combinatorics and
number theory. Motivated by the recent work of Sagan and Tirrell (Adv. Math.,
374 (2020), 107387), we study the relationships between gamma-positivity and
alternating gamma-positivity. As applications, we derive several alternatingly
gamma-positive polynomials related to Narayana polynomials and Eulerian
polynomials. In particular, we show the alternating gamma-positivity and
Hurwitz stability of a combination of the modified Narayana polynomials of
types A and B. By using colored $2\times n$ Young diagrams, we present a
unified combinatorial interpretations of three identities involving Narayana
numbers of type B. A general result of this paper is that every gamma-positive
polynomial is also alternatingly semi-gamma-positive. At the end of this paper,
we pose two conjectures, one concerns the Boros-Moll polynomials and the other
concerns the enumerators of permutations by descents and excedances.


title:
Out of Distribution Data Detection Using Dropout Bayesian Neural
  Networks

abstract:
  We explore the utility of information contained within a dropout based
Bayesian neural network (BNN) for the task of detecting out of distribution
(OOD) data. We first show how previous attempts to leverage the randomized
embeddings induced by the intermediate layers of a dropout BNN can fail due to
the distance metric used. We introduce an alternative approach to measuring
embedding uncertainty, justify its use theoretically, and demonstrate how
incorporating embedding uncertainty improves OOD data identification across
three tasks: image classification, language classification, and malware
detection.


title: The Fibonacci Sequence is Normal Base 10

abstract:
  In this paper, we show that the concatenation of the Fibonacci sequence is
\textit{normal} in base $10$, meaning every string of a given length, $k$,
occurs as frequently as every other string of length $k$ (there are as many
$1$'s as $2$'s and as many $704$'s and $808$'s). Although we know that almost
every number is normal, we can name very few of them. It is still unclear if
$e$, $\pi$, or $\sqrt{2}$ are normal. We show that concatenating the Fibonacci
sequence behind a decimal creates a normal number in every base of the form
$5^x\times2^y$. We then provide evidence that potentially extends our result to
all integer bases, and claim that the Fibonacci concatenation is
\textit{absolutely normal}.


title:
Multiwavelength analysis and the C IV {\lambda}1549 {\AA} emission line
  behavior from 2008 to 2020 of the FSRQ B2 1633+382

abstract:
  The flat-spectrum radio quasar B2 1633+382 (4C 38.41) has been monitored for
several years and has presented correlated variability in multiple wavelengths.
In this article, we are performing different analyses for multiple frequencies,
from gamma-rays to radio, as well as, the C IV ${\lambda}$1549 {\AA} emission
line and the ${\lambda}$1350 {\AA} continuum. Using the non-thermal dominance
parameter, we separated the C IV and the continuum light curves for when the
dominant source of continuum is the accretion disk or the jet. We found a
correlation at a delay consistent with zero between the line and the continuum
dominated by disk emission indicating a very small broad-line region (BLR).
From the resulting delay between the 15 GHz and gamma-rays, we estimated the
distance of the gamma-ray emission region from the jet apex to be $\sim$37 pc.
The C IV flux decreases when the continuum and gamma-rays increase at some of
the high activity periods. The C IV profile presents a larger variable
component in its blue wing. The relation between the luminosities of C IV and
the continuum does not completely follow the relation for a quasar sample. Our
results lead us to propose an outflow of BLR material in the jet-flow
direction, a gamma-ray production through magnetic reconnection for the flaring
event of mid-2011, and that there is not enough BLR material close to the radio
core to be easily ionized by the non-thermal continuum.


title: Drunk Angel and Hiding Devil

abstract:
  The angel game is played on $2$-dimensional infinite grid by $2$ players, the
angel and the devil. In each turn, the angel of power $c$ moves from her
current point $(x, y)$ to a point $(x', y')$ which $\max\{|x - x'|, |y - y'|\}
\leq c$ while the devil destroys a point in his turn. The angel can no longer
land on the destroyed points. The angel wins if she has a strategy to escape
from the devil forever and the devil wins if he can cage the angel in his
destroyed points by a finite number of turns. It was proved that the angel of
power $2$ always wins. In this paper, we rise the problem when the angel is
drunk. She randomly moves to any point in the range of her power in each turn.
In our game version, the devil must cage the angel by a given finite number of
turns, otherwise the angel wins. We present a strategy for the devil that: if
the devil plays with this strategy, then for given $c \in \mathbb{N}$ and
$\epsilon > 0$, the devil can cage the angel of power $c$ with probability
greater than $1 - \epsilon$ if and only if the game is played on an
$n$-dimensional infinite grid when $n \leq 2$. The numerical simulation results
are presented in the last section.


title: Enhanced two-component superconductivity in CoSi2/TiSi2 heterojunctions

abstract:
  We report enhanced two-component superconductivity in (CoSi2/Si)/TiSi2
superconductor/normal-metal (S/N) heterojunctions. An enhanced superconducting
transition temperature about twice that of CoSi2 and an upper critical field
about 20 times bigger than that of epitaxial CoSi2/Si films were found. The
tunneling spectra of three-terminal S/N junctions show pronounced zero-bias
conductance peaks (ZBCPs) that signify penetration of odd-frequency,
spin-triplet and even-parity Cooper pairs in TiSi2 from triplet dominant
pairing in CoSi2/Si driven by symmetry reduction at the CoSi2/Si interface.
Both the enhancement of the superconducting transition temperature and the
ZBCPs are found to be more pronounced if TiSi2 is made more diffusive.


title:
Integrable nonlinear Klein-Gordon systems with $\mathcal{PT}$
  nonlocality and/or space-time exchange nonlocality

abstract:
  In additional to the parity ($\mathcal{P}$) symmetric, time reversal
($\mathcal{T}$) symmetric, and $\mathcal{PT}$ symmetric nonlocal integrable
systems, some other types of nonlocal integrable Klein-Gordon models with the
space-time exchange nonlocality and the moving nolocality are proposed. The Lax
pairs of the established nonlinear nonlocal Klein-Gordon equations are
explicitly given. A special soliton solution, composed of
$\mathcal{PT}$-symmetric part and $\mathcal{PT}$-antisymmetric part, is
illustrated with the shape change.


title: Joint Learning of Frequency and Spatial Domains for Dense Predictions

abstract:
  Current artificial neural networks mainly conduct the learning process in the
spatial domain but neglect the frequency domain learning. However, the learning
course performed in the frequency domain can be more efficient than that in the
spatial domain. In this paper, we fully explore frequency domain learning and
propose a joint learning paradigm of frequency and spatial domains. This
paradigm can take full advantage of the preponderances of frequency learning
and spatial learning; specifically, frequency and spatial domain learning can
effectively capture global and local information, respectively. Exhaustive
experiments on two dense prediction tasks, i.e., self-supervised depth
estimation and semantic segmentation, demonstrate that the proposed joint
learning paradigm can 1) achieve performance competitive to those of
state-of-the-art methods in both depth estimation and semantic segmentation
tasks, even without pretraining; and 2) significantly reduce the number of
parameters compared to other state-of-the-art methods, which provides more
chance to develop real-world applications. We hope that the proposed method can
encourage more research in cross-domain learning.


title: Enhanced Multi-Objective A* Using Balanced Binary Search Trees

abstract:
  This work addresses a Multi-Objective Shortest Path Problem (MO-SPP) on a
graph where the goal is to find a set of Pareto-optimal solutions from a start
node to a destination in the graph. A family of approaches based on MOA* have
been developed to solve MO-SPP in the literature. Typically, these approaches
maintain a "frontier" set at each node during the search process to keep track
of the non-dominated, partial paths to reach that node. This search process
becomes computationally expensive when the number of objectives increases as
the number of Pareto-optimal solutions becomes large. In this work, we
introduce a new method to efficiently maintain these frontiers for multiple
objectives by incrementally constructing balanced binary search trees within
the MOA* search framework. We first show that our approach correctly finds the
Pareto-optimal front, and then provide extensive simulation results for
problems with three, four and five objectives to show that our method runs
faster than existing techniques by up to an order of magnitude.


title:
Molecular dynamics study of the linear viscoelastic shear and bulk
  relaxation moduli of poly(tetramethylene oxide) (PTMO)

abstract:
  Here we report the linear viscoelastic properties of amorphous
poly(tetramethylene oxide) (PTMO), which is one of the key components in
synthesizing segmented polyurethane (PU) elastomers. The temperature and
molecular weight dependent viscoelastic behavior is investigated in detail by
computing the shear relaxation modulus G(t) and the bulk relaxation modulus
K(t), using the Green-Kubo relationship with correlation function. Our results
provide new data for PTMO melt from the united atom model and also extend the
existing knowledge of viscoelastic properties of polymers in general. The
predicted viscoelastic behavior range is shifted on a master curve using the
time-temperature superposition principle (TTSP) with horizontal and vertical
shift factors. The emerging shift factors agree with the Williams-Landel-Ferry
(WLF) equation. For the validation of the united-atom model of PTMO using the
TraPPE-UA force field we explored the transport properties and observed a
position-dependent diffusion dynamics throughout the polymer chain, which
subsequently influences the scaling laws for chain dynamics. These findings are
discussed in terms of emerging experimental evidence on position dependent
displacement for different chain portions along the chain length.


title:
REFUGE2 Challenge: Treasure for Multi-Domain Learning in Glaucoma
  Assessment

abstract:
  Glaucoma is the second leading cause of blindness and is the leading cause of
irreversible blindness disease in the world. Early screening for glaucoma in
the population is significant. Color fundus photography is the most cost
effective imaging modality to screen for ocular diseases. Deep learning network
is often used in color fundus image analysis due to its powful feature
extraction capability. However, the model training of deep learning method
needs a large amount of data, and the distribution of data should be abundant
for the robustness of model performance. To promote the research of deep
learning in color fundus photography and help researchers further explore the
clinical application signification of AI technology, we held a REFUGE2
challenge. This challenge released 2,000 color fundus images of four models,
including Zeiss, Canon, Kowa and Topcon, which can validate the stabilization
and generalization of algorithms on multi-domain. Moreover, three sub-tasks
were designed in the challenge, including glaucoma classification, cup/optic
disc segmentation, and macular fovea localization. These sub-tasks technically
cover the three main problems of computer vision and clinicly cover the main
researchs of glaucoma diagnosis. Over 1,300 international competitors joined
the REFUGE2 challenge, 134 teams submitted more than 3,000 valid preliminary
results, and 22 teams reached the final. This article summarizes the methods of
some of the finalists and analyzes their results. In particular, we observed
that the teams using domain adaptation strategies had high and robust
performance on the dataset with multi-domain. This indicates that UDA and other
multi-domain related researches will be the trend of deep learning field in the
future, and our REFUGE2 datasets will play an important role in these
researches.


title:
Modeling GRB 170202A fireball from continuous observations with the
  Zadko and the Virgin Island Robotic Telescopes

abstract:
  We present coordinated observations of GRB 170202A carried out by the Zadko
and the Virgin Island Robotic Telescopes. The observations started 59s after
the event trigger, and provided nearly continuous coverage for two days due to
the unique location of these telescopes. We clearly detected an early rise in
optical emission, followed by late optical flares. By complementing these data
with archival observations, we show GRB 170202A is well described by the
standard fireball model if multiple reverse shocks are taken into account. Its
fireball is evidenced to expand within a constant density interstellar medium,
with most burst parameters consistent with the usual ranges found in
literature. The electron and magnetic energy parameters (\epsilon_e,
\epsilon_B) are orders of magnitude smaller than commonly assumed values. We
argue that the global fit of the fireball model achieved by our study should be
possible for any burst, pending the availability of a sufficiently
comprehensive data set. This conclusion emphasizes the crucial importance of
coordinated observation campaigns of GRBs, such as the one central to this
work, to answer outstanding questions about the underlying physics driving
these phenomena.


title: Worst-Case to Average-Case Reductions via Additive Combinatorics

abstract:
  We present a new framework for designing worst-case to average-case
reductions. For a large class of problems, it provides an explicit
transformation of algorithms running in time $T$ that are only correct on a
small (subconstant) fraction of their inputs into algorithms running in time
$\widetilde{O}(T)$ that are correct on all inputs.
  Using our framework, we obtain such efficient worst-case to average-case
reductions for fundamental problems in a variety of computational models;
namely, algorithms for matrix multiplication, streaming algorithms for the
online matrix-vector multiplication problem, and static data structures for all
linear problems as well as for the multivariate polynomial evaluation problem.
  Our techniques crucially rely on additive combinatorics. In particular, we
show a local correction lemma that relies on a new probabilistic version of the
quasi-polynomial Bogolyubov-Ruzsa lemma.


title:
The removal method and generation mechanism of spikes in Insight-HXMT/HE
  telescope

abstract:
  Spikes are some obvious sharp increases that appear on the raw light curves
of High Energy X-ray telescope(HE) onboard Insight-HXMT, which could have
influences on the data products like energy and power spectra. They are
considered to be fake triggers generated by large signals. In this paper, we
study the characteristic of the spikes and propose two methods to remove spikes
from the raw data. According to the different influences on energy and power
spectra, the best parameters for removing the spikes is selected and used in
the Insight-HXMT data analysis software. The generation mechanism of spikes is
also studied using the backup HE detectors on ground and the spikes can be
reduced by the electronic design.


title:
Well-posedness of frame hydrodynamics for biaxial nematic liquid
  crystals

abstract:
  We consider the hydrodynamics for the biaxial nematic phase characterized by
a field of orthonormal frame, which can be derived from a
molecular-theory-based tensor model. In dimension two and three, we establish
the local well-posedness and the blow-up criterion for smooth solutions to the
frame hydrodynamic model. Furthermore, we prove the global existence of weak
solutions in $\mathbb{R}^2$ which are nonsmooth at finitely many singular
times.


title:
Hydrodynamic derivation of the Schr\"odinger equation and spacetime
  curvature of a quantum particle

abstract:
  We report a hydrodynamic derivation of the Schr\"odinger equation. The
derivation only assumes that spin represents the angular momentum associated
with the rotation of a charged fluid within a massive particle. The rotation
velocity can be evaluated through the current density of the fourth Maxwell
equation, leading to a quantum correction of the classical fluid energy. The
Schr\"odinger equation then follows in Madelung form by application of the
Poisson operator of the Euler equations for an ideal fluid to the total fluid
energy including the quantum effect of internal spin. The hydrodynamic
representation is then used to obtain the stress-energy-momentum tensor for a
quantum particle. We find that the trace of the quantum modification to the
stress-energy-momentum tensor expresses the energy density of an oscillator
with frequency given by the vorticity of the internal rotation velocity.
Finally, the stress-energy-momentum tensor is used to determine the
relationship between the Ricci scalar curvature arising from the Einstein field
equations and the fluid density associated with the hydrodynamic representation
of the quantum particle in a static spherically symmetric configuration.


title: Monopole deformations of 3d Seiberg-like dualities with adjoint matters

abstract:
  We propose new 3d $\mathcal{N}=2$ Seiberg-like dualities by considering
various monopole superpotential deformations on 3d $\mathcal{N}=2$ $U(N_c)$
SQCDs with fundamental and adjoint matter fields. We provide nontrivial
evidence of these new dualities by comparing the superconformal indices, from
which we analyze the change of the moduli space due to the monopole
deformation. In addition, we perform the $F$-maximization to check the
relevance of the monopole deformation for some examples, one of which is found
to exhibit nontrivial symmetry enhancement in the IR. We prove such enhancement
of the global symmetry using the superconformal index.


title: Phase-locking matter-wave interferometer of vortex states

abstract:
  Matter-wave interferometer of ultracold atoms with different linear momenta
has been extensively studied in theory and experiment. The vortex matter-wave
interferometer with different angular momenta is applicable as a quantum sensor
for measuring the rotation, interatomic interaction, geometric phase, etc. Here
we report the first experimental realization of a vortex matter-wave
interferometer by coherently transferring the optical angular momentum to an
ultracold Bose condensate. After producing a lossless interferometer with atoms
only populating the two spin states, we demonstrate that the phase difference
between the interferences in the two spin states is locked on $\pi$. We also
demonstrate the robustness of this out-of-phase relation, which is independent
of the angular-momentum difference between the two interfering vortex states,
constituent of Raman optical fields and expansion of the condensate. The
experimental results agree well with the calculation from the unitary evolution
of wave packet in quantum mechanics. This work opens a new way to build a
quantum sensor and measure the atomic correlation in quantum gases.


title:
An Active and Contrastive Learning Framework for Fine-Grained Off-Road
  Semantic Segmentation

abstract:
  Off-road semantic segmentation with fine-grained labels is necessary for
autonomous vehicles to understand driving scenes, as the coarse-grained road
detection can not satisfy off-road vehicles with various mechanical properties.
Fine-grained semantic segmentation in off-road scenes usually has no unified
category definition due to ambiguous nature environments, and the cost of
pixel-wise labeling is extremely high. Furthermore, semantic properties of
off-road scenes can be very changeable due to various precipitations,
temperature, defoliation, etc. To address these challenges, this research
proposes an active and contrastive learning-based method that does not rely on
pixel-wise labels, but only on patch-based weak annotations for model learning.
There is no need for predefined semantic categories, the contrastive
learning-based feature representation and adaptive clustering will discover the
category model from scene data. In order to actively adapt to new scenes, a
risk evaluation method is proposed to discover and select hard frames with
high-risk predictions for supplemental labeling, so as to update the model
efficiently. Experiments conducted on our self-developed off-road dataset and
DeepScene dataset demonstrate that fine-grained semantic segmentation can be
learned with only dozens of weakly labeled frames, and the model can
efficiently adapt across scenes by weak supervision, while achieving almost the
same level of performance as typical fully supervised baselines.


title:
End-to-end contextual asr based on posterior distribution adaptation for
  hybrid ctc/attention system

abstract:
  End-to-end (E2E) speech recognition architectures assemble all components of
traditional speech recognition system into a single model. Although it
simplifies ASR system, it introduces contextual ASR drawback: the E2E model has
worse performance on utterances containing infrequent proper nouns. In this
work, we propose to add a contextual bias attention (CBA) module to attention
based encoder decoder (AED) model to improve its ability of recognizing the
contextual phrases. Specifically, CBA utilizes the context vector of source
attention in decoder to attend to a specific bias embedding. Jointly learned
with the basic AED parameters, CBA can tell the model when and where to bias
its output probability distribution. At inference stage, a list of bias phrases
is preloaded and we adapt the posterior distributions of both CTC and attention
decoder according to the attended bias phrase of CBA. We evaluate the proposed
method on GigaSpeech and achieve a consistent relative improvement on recall
rate of bias phrases ranging from 15% to 28% compared to the baseline model.
Meanwhile, our method shows a strong anti-bias ability as the performance on
general tests only degrades 1.7% even 2,000 bias phrases are present.


title:
Magnetic reconnection in the era of exascale computing and multiscale
  experiments

abstract:
  Astrophysical plasmas have the remarkable ability to preserve magnetic
topology, which inevitably gives rise to the accumulation of magnetic energy
within stressed regions including current sheets. This stored energy is often
released explosively through the process of magnetic reconnection, which
produces a reconfiguration of the magnetic field, along with high-speed flows,
thermal heating, and nonthermal particle acceleration. Either collisional or
kinetic dissipation mechanisms are required to overcome the topological
constraints, both of which have been predicted by theory and validated with in
situ spacecraft observations or laboratory experiments. However, major
challenges remain in understanding magnetic reconnection in large systems, such
as the solar corona, where the collisionality is weak and the kinetic scales
are vanishingly small in comparison to macroscopic scales. The plasmoid
instability or formation of multiple plasmoids in long reconnecting current
sheets is one possible multiscale solution for bridging this vast range of
scales, and new laboratory experiments are poised to study these regimes. In
conjunction with these efforts, we anticipate that the coming era of exascale
computing, together with the next generation of observational capabilities,
will enable new progress on a range of challenging problems, including the
energy build-up and onset of reconnection, partially ionized regimes, the
influence of magnetic turbulence, and particle acceleration.

categories: physics.plasm-ph astro-ph.HE astro-ph.IM astro-ph.SR physics.space-ph


title:
Reviews in motion: a large scale, longitudinal study of review
  recommendations on Yelp

abstract:
  The United Nations Consumer Protection Guidelines lists "access ... to
adequate information ... to make informed choices" as a core consumer
protection right. However, problematic online reviews and imperfections in
algorithms that detect those reviews pose obstacles to the fulfillment of this
right. Research on reviews and review platforms often derives insights from a
single web crawl, but the decisions those crawls observe may not be static. A
platform may feature a review one day and filter it from view the next day. An
appreciation for these dynamics is necessary to understand how a platform
chooses which reviews consumers encounter and which reviews may be unhelpful or
suspicious. We introduce a novel longitudinal angle to the study of reviews. We
focus on "reclassification," wherein a platform changes its filtering decision
for a review. To that end, we perform repeated web crawls of Yelp to create
three longitudinal datasets. These datasets highlight the platform's dynamic
treatment of reviews. We compile over 12.5M reviews--more than 2M
unique--across over 10k businesses. Our datasets are available for researchers
to use.
  Our longitudinal approach gives us a unique perspective on Yelp's classifier
and allows us to explore reclassification. We find that reviews routinely move
between Yelp's two main classifier classes ("Recommended" and "Not
Recommended")--up to 8% over eight years--raising concerns about prior works'
use of Yelp's classes as ground truth. These changes have impacts on small
scales; for example, a business going from a 3.5 to 4.5 star rating despite no
new reviews. Some reviews move multiple times: we observed up to five
reclassifications in eleven months. Our data suggests demographic disparities
in reclassifications, with more changes in lower density and low-middle income
areas.


title: KINet: Keypoint Interaction Networks for Unsupervised Forward Modeling

abstract:
  Object-centric representation is an essential abstraction for physical
reasoning and forward prediction. Most existing approaches learn this
representation through extensive supervision (e.g., object class and bounding
box) although such ground-truth information is not readily accessible in
reality. To address this, we introduce KINet (Keypoint Interaction Network) --
an end-to-end unsupervised framework to reason about object interactions in
complex systems based on a keypoint representation. Using visual observations,
our model learns to associate objects with keypoint coordinates and discovers a
graph representation of the system as a set of keypoint embeddings and their
relations. It then learns an action-conditioned forward model using contrastive
estimation to predict future keypoint states. By learning to perform physical
reasoning in the keypoint space, our model automatically generalizes to
scenarios with a different number of objects, and novel object geometries.
Experiments demonstrate the effectiveness of our model to accurately perform
forward prediction and learn plannable object-centric representations which can
also be used in downstream model-based control tasks.


title:
Intrinsic spin-momentum dynamics of surface electromagnetic waves in
  complex dispersive system

abstract:
  Spin-momentum locking is an intrinsic property of surface electromagnetic
fields and its study has led to the discovery of photonic spin lattices and
diverse applications. Previously, dispersion was ignored in the spin-momentum
locking, giving rise to abnormal phenomena contradictory to the physical
realities. Here, we formulate four dispersive spin-momentum equations for
surface waves, revealing universally that the transverse spin vector is locked
with the momentum. The locking property obeys the right-hand rule in the
dielectric but the left-hand rule in the dispersive metal/magnetic materials.
In addition to the dispersion, the structural features can affect the
spin-momentum locking significantly. Remarkably, an extraordinary longitudinal
spin originating from the coupling polarization ellipticity is uncovered even
for the purely polarized state. We further demonstrate the spin-momentum
locking properties with diverse photonic topological lattices by engineering
the rotating symmetry. The findings open up opportunities for designing robust
nanodevices with practical importance in chiral quantum optics.


title: On Variance Estimation of Random Forests

abstract:
  Ensemble methods, such as random forests, are popular in applications due to
their high predictive accuracy. Existing literature views a random forest
prediction as an infinite-order incomplete U-statistic to quantify its
uncertainty. However, these methods focus on a small subsampling size of each
tree, which is theoretically valid but practically limited. This paper develops
an unbiased variance estimator based on incomplete U-statistics, which allows
the tree size to be comparable with the overall sample size, making statistical
inference possible in a broader range of real applications. Simulation results
demonstrate that our estimators enjoy lower bias and a more accurate coverage
rate without additional computational costs. We also propose a local smoothing
procedure to reduce the variation of our estimator, which shows improved
numerical performance when the number of trees is relatively small. Further, we
investigate the ratio consistency of our proposed variance estimator under
specific scenarios. In particular, we develop a new "double U-statistic"
formulation to analyze the Hoeffding decomposition of the estimator's variance.


title: LG-LSQ: Learned Gradient Linear Symmetric Quantization

abstract:
  Deep neural networks with lower precision weights and operations at inference
time have advantages in terms of the cost of memory space and accelerator
power. The main challenge associated with the quantization algorithm is
maintaining accuracy at low bit-widths. We propose learned gradient linear
symmetric quantization (LG-LSQ) as a method for quantizing weights and
activation functions to low bit-widths with high accuracy in integer neural
network processors. First, we introduce the scaling simulated gradient (SSG)
method for determining the appropriate gradient for the scaling factor of the
linear quantizer during the training process. Second, we introduce the
arctangent soft round (ASR) method, which differs from the straight-through
estimator (STE) method in its ability to prevent the gradient from becoming
zero, thereby solving the discrete problem caused by the rounding process.
Finally, to bridge the gap between full-precision and low-bit quantization
networks, we propose the minimize discretization error (MDE) method to
determine an accurate gradient in backpropagation. The ASR+MDE method is a
simple alternative to the STE method and is practical for use in different
uniform quantization methods. In our evaluation, the proposed quantizer
achieved full-precision baseline accuracy in various 3-bit networks, including
ResNet18, ResNet34, and ResNet50, and an accuracy drop of less than 1% in the
quantization of 4-bit weights and 4-bit activations in lightweight models such
as MobileNetV2 and ShuffleNetV2.


title:
Non-equatorial circular geodesics for the Painleve-Gullstrand form of
  Lense-Thirring spacetime

abstract:
  Herein we explore the non-equatorial circular geodesics (both timelike and
null) in the Painleve-Gullstrand variant of the Lense-Thirring spacetime
recently introduced by the current authors. Even though the spacetime is not
spherically symmetric, shells of circular geodesics still exist. While the
radial motion is (by construction) utterly trivial, determining the allowed
locations of these circular geodesics is decidedly non-trivial, and the
stability analysis is equally tricky. Regarding the angular motion, these
circular orbits will be seen to exhibit both precession and nutation --
typically with incommensurate frequencies. Thus circular geodesic motion,
though integrable in the technical sense, is generically surface-filling, with
the orbits completely covering an equatorial band which is a segment of a
sphere, and whose extent is governed by an interplay between the orbital
angular momentum and the Carter constant. The situation is qualitatively
similar to that for the (exact) Kerr spacetime -- but we now see that any
physical model having the same slow-rotation weak-field limit as general
relativity will still possess non-equatorial circular geodesics.


title: A class of twisted generalized Reed-Solomon codes

abstract:
  Let $\mathbb{F}_q$ be a finite field of size $q$ and $\mathbb{F}_q^*$ the set
of non-zero elements of $\mathbb{F}_q$. In this paper, we study a class of
twisted generalized Reed-Solomon code $C_\ell(D, k, \eta, \vec{v})\subset
\mathbb{F}_q^n$ generated by the following matrix \[ \left(\begin{array}{cccc}
v_{1} & v_{2} & \cdots & v_{n} \\ v_{1} \alpha_{1} & v_{2} \alpha_{2} & \cdots
& v_{n} \alpha_{n} \\ \vdots & \vdots & \ddots & \vdots \\ v_{1}
\alpha_{1}^{\ell-1} & v_{2} \alpha_{2}^{\ell-1} & \cdots & v_{n}
\alpha_{n}^{\ell-1} \\ v_{1} \alpha_{1}^{\ell+1} & v_{2} \alpha_{2}^{\ell+1} &
\cdots & v_{n} \alpha_{n}^{\ell+1} \\ \vdots & \vdots & \ddots & \vdots \\
v_{1} \alpha_{1}^{k-1} & v_{2} \alpha_{2}^{k-1} & \cdots & v_{n}
\alpha_{n}^{k-1} \\ v_{1}\left(\alpha_{1}^{\ell}+\eta\alpha_{1}^{q-{2}}\right)
& v_{2}\left(\alpha_{2}^{\ell}+ \eta \alpha_{2}^{q-2}\right) &\cdots &
v_{n}\left(\alpha_{n}^{\ell}+\eta\alpha_{n}^{q-2}\right) \end{array}\right) \]
where $0\leq \ell\leq k-1,$ the evaluation set
$D=\{\alpha_{1},\alpha_{2},\cdots, \alpha_{n}\}\subseteq \mathbb{F}_q^*$,
scaling vector $\vec{v}=(v_1,v_2,\cdots,v_n)\in (\mathbb{F}_q^*)^n$ and
$\eta\in\mathbb{F}_q^*$. The minimum distance and dual code of $C_\ell(D, k,
\eta, \vec{v})$ will be determined. For the special case $\ell=k-1,$ a
sufficient and necessary condition for $C_{k-1}(D, k, \eta, \vec{v})$ to be
self-dual will be given. We will also show that the code is MDS or near-MDS.
Moreover, a complete classification when the code is near-MDS or MDS will be
presented.


title:
Modulated non-collinear magnetic structure of
  (Co$_{0.97}$$^{57}$Fe$_{0.03}$)$_{4}$Nb$_{2}$O$_{9}$ as revealed by
  M\"ossbauer spectroscopy

abstract:
  In this work, we present detailed $^{57}$Fe M\"ossbauer spectroscopy
investigations of (Co$_{0.97}$$^{57}$Fe$_{0.03}$)$_{4}$Nb$_{2}$O$_{9}$ compound
to study its possible magnetic structures. We have shown that the previously
reported magnetic structures can not satisfactorily describe our low
temperature M\"ossbauer spectra. Therefore, in combination with theoretical
calculations, we have proposed a modulated helicoidal magnetic structure that
can be used to simulate the whole series of our low temperature M\"ossbauer
spectra. Our results suggest that the combination of previously reported
different magnetic structures are only approximations of the average magnetic
structure from our modulated helicoidal model. We anticipate that the proposed
modulated non-collinear magnetic structure might shed light on the
understanding of the complex magnetoelectric effects observed in this system.


title: Is Selling Complete Information (Approximately) Optimal?

abstract:
  We study the problem of selling information to a data-buyer who faces a
decision problem under uncertainty. We consider the classic Bayesian
decision-theoretic model pioneered by [Blackwell, 1951, 1953]. Initially, the
data buyer has only partial information about the payoff-relevant state of the
world. A data seller offers additional information about the state of the
world. The information is revealed through signaling schemes, also referred to
as experiments. In the single-agent setting, any mechanism can be represented
as a menu of experiments. [Bergemann et al., 2018] present a complete
characterization of the revenue-optimal mechanism in a binary state and binary
action environment. By contrast, no characterization is known for the case with
more actions. In this paper, we consider more general environments and study
arguably the simplest mechanism, which only sells the fully informative
experiment. In the environment with binary state and $m\geq 3$ actions, we
provide an $O(m)$-approximation to the optimal revenue by selling only the
fully informative experiment and show that the approximation ratio is tight up
to an absolute constant factor. An important corollary of our lower bound is
that the size of the optimal menu must grow at least linearly in the number of
available actions, so no universal upper bound exists for the size of the
optimal menu in the general single-dimensional setting.
  For multi-dimensional environments, we prove that even in arguably the
simplest matching utility environment with 3 states and 3 actions, the ratio
between the optimal revenue and the revenue by selling only the fully
informative experiment can grow immediately to a polynomial of the number of
agent types. Nonetheless, if the distribution is uniform, we show that selling
only the fully informative experiment is indeed the optimal mechanism.


title:
How Well Do Self-Supervised Methods Perform in Cross-Domain Few-Shot
  Learning?

abstract:
  Cross-domain few-shot learning (CDFSL) remains a largely unsolved problem in
the area of computer vision, while self-supervised learning presents a
promising solution. Both learning methods attempt to alleviate the dependency
of deep networks on the requirement of large-scale labeled data. Although
self-supervised methods have recently advanced dramatically, their utility on
CDFSL is relatively unexplored. In this paper, we investigate the role of
self-supervised representation learning in the context of CDFSL via a thorough
evaluation of existing methods. It comes as a surprise that even with shallow
architectures or small training datasets, self-supervised methods can perform
favorably compared to the existing SOTA methods. Nevertheless, no single
self-supervised approach dominates all datasets indicating that existing
self-supervised methods are not universally applicable. In addition, we find
that representations extracted from self-supervised methods exhibit stronger
robustness than the supervised method. Intriguingly, whether self-supervised
representations perform well on the source domain has little correlation with
their applicability on the target domain. As part of our study, we conduct an
objective measurement of the performance for six kinds of representative
classifiers. The results suggest Prototypical Classifier as the standard
evaluation recipe for CDFSL.


title: Regularity of solutions for singular fractional differential equation

abstract:
  In this work, we study the regularity of positive solutions for nonlinear
fractional differential equation with a singular weight. We define the new
Banach space and use this space to show the regularity. We also give an example
with a singular weight which may not be in $L^1.$


title:
Why, How and Where of Delays in Software Security Patch Management: An
  Empirical Investigation in the Healthcare Sector

abstract:
  Numerous security attacks that resulted in devastating consequences can be
traced back to a delay in applying a security patch. Despite the criticality of
timely patch application, not much is known about why and how delays occur when
applying security patches in practice, and how the delays can be mitigated.
Based on longitudinal data collected from 132 delayed patching tasks over a
period of four years and observations of patch meetings involving eight teams
from two organisations in the healthcare domain, and using quantitative and
qualitative data analysis approaches, we identify a set of reasons relating to
technology, people and organisation as key explanations that cause delays in
patching. Our findings also reveal that the most prominent cause of delays is
attributable to coordination delays in the patch management process and a
majority of delays occur during the patch deployment phase. Towards mitigating
the delays, we describe a set of strategies employed by the studied
practitioners. This research serves as the first step towards understanding the
practical reasons for delays and possible mitigation strategies in
vulnerability patch management. Our findings provide useful insights for
practitioners to understand what and where improvement is needed in the patch
management process and guide them towards taking timely actions against
potential attacks. Also, our findings help researchers to invest effort into
designing and developing computer-supported tools to better support a timely
security patch management process.


title:
Characterizing the temporal rotation and radial twist of the
  interference pattern of vortex beam

abstract:
  We experimentally and theoretically characterize the temporal rotation and
radial twist of the interference pattern of vortex beam with its conjugate
copy. To quantitatively study the temporal rotation and radial twist, we
controllably modify the conjugate beam with a frequency or wavefront curvature
difference using a movable Mach-Zehnder interferometer. The effects of the
physical parameters (i.e., the topological charge, frequency difference and
wavefront curvature difference of the vortex beams) on the temporal rotation as
well as radial twist are systematically explored. We further measure two
parameters, the rotation velocity $\Omega$ and twist coefficient $\alpha$,
respectively, to characterize the degree of the temporal rotation and radial
twist of the interference pattern. The theory of the interference model on
vortex beams have good agreements with the experimental results. This work is
favorite to study the detailed structure of the interference pattern and
manipulate matter with superimposed vortex beams.


title:
Strong suppression of near-field thermal transport between twisted
  bilayer graphene near the magic angle

abstract:
  Twisted bilayer graphene (TBLG) has recently emerged as a versatile platform
for studying a variety of exotic transport phenomena. Here, we present a
theoretical study of near-field thermal radiation between suspended TBLG with a
focus on the magic angle. Within the chirally symmetric continuum model, we
observe a suppressed heat flow when approaching the magic angle owing to a
reduced Drude weight, with greater suppressions at lower temperatures and
larger gap sizes. When the chemical potential lies in the energy gap near the
charge neutrality point, more than 100-fold heat-flow variation can be achieved
at 50 K within 0.25{\deg}of twist. By reducing the electron scattering rate,
the radiation spectrum near the magic angle dramatically narrows, leading to
over 10,000-fold of suppression. In addition, supported TBLG is briefly
considered to facilitate experimental measurement. With rationally tailored
substrates, the heat-flow contrast can still exceed 1000. We also discuss
lattice relaxation effect in terms of the interlayer coupling energy, finding
that a stronger coupling leads to a smaller heat-flow contrast and more
prominent multiband transport. Our results highlight the great potential of
magic-angle TBLG in thermal transport, especially for controlling thermal
radiation.


title:
DARL1N: Distributed multi-Agent Reinforcement Learning with One-hop
  Neighbors

abstract:
  Most existing multi-agent reinforcement learning (MARL) methods are limited
in the scale of problems they can handle. Particularly, with the increase of
the number of agents, their training costs grow exponentially. In this paper,
we address this limitation by introducing a scalable MARL method called
Distributed multi-Agent Reinforcement Learning with One-hop Neighbors (DARL1N).
DARL1N is an off-policy actor-critic method that breaks the curse of
dimensionality by decoupling the global interactions among agents and
restricting information exchanges to one-hop neighbors. Each agent optimizes
its action value and policy functions over a one-hop neighborhood,
significantly reducing the learning complexity, yet maintaining expressiveness
by training with varying numbers and states of neighbors. This structure allows
us to formulate a distributed learning framework to further speed up the
training procedure. Comparisons with state-of-the-art MARL methods show that
DARL1N significantly reduces training time without sacrificing policy quality
and is scalable as the number of agents increases.


title:
A Comprehensive Survey with Quantitative Comparison of Image Analysis
  Methods for Microorganism Biovolume Measurements

abstract:
  With the acceleration of urbanization and living standards, microorganisms
play increasingly important roles in industrial production, bio-technique, and
food safety testing. Microorganism biovolume measurements are one of the
essential parts of microbial analysis. However, traditional manual measurement
methods are time-consuming and challenging to measure the characteristics
precisely. With the development of digital image processing techniques, the
characteristics of the microbial population can be detected and quantified. The
changing trend can be adjusted in time and provided a basis for the
improvement. The applications of the microorganism biovolume measurement method
have developed since the 1980s. More than 62 articles are reviewed in this
study, and the articles are grouped by digital image segmentation methods with
periods. This study has high research significance and application value, which
can be referred to microbial researchers to have a comprehensive understanding
of microorganism biovolume measurements using digital image analysis methods
and potential applications.


title:
Effective Urban Region Representation Learning Using Heterogeneous Urban
  Graph Attention Network (HUGAT)

abstract:
  Revealing the hidden patterns shaping the urban environment is essential to
understand its dynamics and to make cities smarter. Recent studies have
demonstrated that learning the representations of urban regions can be an
effective strategy to uncover the intrinsic characteristics of urban areas.
However, existing studies lack in incorporating diversity in urban data
sources. In this work, we propose heterogeneous urban graph attention network
(HUGAT), which incorporates heterogeneity of diverse urban datasets. In HUGAT,
heterogeneous urban graph (HUG) incorporates both the geo-spatial and temporal
people movement variations in a single graph structure. Given a HUG, a set of
meta-paths are designed to capture the rich urban semantics as composite
relations between nodes. Region embedding is carried out using heterogeneous
graph attention network (HAN). HUGAT is designed to consider multiple learning
objectives of city's geo-spatial and mobility variations simultaneously. In our
extensive experiments on NYC data, HUGAT outperformed all the state-of-the-art
models. Moreover, it demonstrated a robust generalization capability across the
various prediction tasks of crime, average personal income, and bike flow as
well as the spatial clustering task.


title: TURNER: The Uncertainty-based Retrieval Framework for Chinese NER

abstract:
  Chinese NER is a difficult undertaking due to the ambiguity of Chinese
characters and the absence of word boundaries. Previous work on Chinese NER
focus on lexicon-based methods to introduce boundary information and reduce
out-of-vocabulary (OOV) cases during prediction. However, it is expensive to
obtain and dynamically maintain high-quality lexicons in specific domains,
which motivates us to utilize more general knowledge resources, e.g., search
engines. In this paper, we propose TURNER: The Uncertainty-based Retrieval
framework for Chinese NER. The idea behind TURNER is to imitate human behavior:
we frequently retrieve auxiliary knowledge as assistance when encountering an
unknown or uncertain entity. To improve the efficiency and effectiveness of
retrieval, we first propose two types of uncertainty sampling methods for
selecting the most ambiguous entity-level uncertain components of the input
text. Then, the Knowledge Fusion Model re-predict the uncertain samples by
combining retrieved knowledge. Experiments on four benchmark datasets
demonstrate TURNER's effectiveness. TURNER outperforms existing lexicon-based
approaches and achieves the new SOTA.


title: Clustering by Hill-Climbing: Consistency Results

abstract:
  We consider several hill-climbing approaches to clustering as formulated by
Fukunaga and Hostetler in the 1970's. We study both continuous-space and
discrete-space (i.e., medoid) variants and establish their consistency.


title: On The "Majority is Least Stable" Conjecture

abstract:
  We show that the "majority is least stable" conjecture is true for $n=1$ and
$3$ and false for all odd $n\geq 5$.


title: Graph Auto-Encoder Via Neighborhood Wasserstein Reconstruction

abstract:
  Graph neural networks (GNNs) have drawn significant research attention
recently, mostly under the setting of semi-supervised learning. When
task-agnostic representations are preferred or supervision is simply
unavailable, the auto-encoder framework comes in handy with a natural graph
reconstruction objective for unsupervised GNN training. However, existing graph
auto-encoders are designed to reconstruct the direct links, so GNNs trained in
this way are only optimized towards proximity-oriented graph mining tasks, and
will fall short when the topological structures matter. In this work, we
revisit the graph encoding process of GNNs which essentially learns to encode
the neighborhood information of each node into an embedding vector, and propose
a novel graph decoder to reconstruct the entire neighborhood information
regarding both proximity and structure via Neighborhood Wasserstein
Reconstruction (NWR). Specifically, from the GNN embedding of each node, NWR
jointly predicts its node degree and neighbor feature distribution, where the
distribution prediction adopts an optimal-transport loss based on the
Wasserstein distance. Extensive experiments on both synthetic and real-world
network datasets show that the unsupervised node representations learned with
NWR have much more advantageous in structure-oriented graph mining tasks, while
also achieving competitive performance in proximity-oriented ones.


title:
Post-quantum Multi-stage Secret Sharing Schemes using Inhomogeneous
  Linear Recursion and Ajtai's Function

abstract:
  Secret sharing was firstly proposed in 1979 by Shamir and Blakley
respectively. To avoid deficiencies of original schemes, researchers presented
improvement schemes, among which the multi-secret sharing scheme (MSS) is
significant. There are three categories of MSSs, however, we focus on
multi-stage secret sharing scheme (MSSS) recovering secrets with any order in
this work. By observing inhomogeneous linear recursions (ILRs) in the
literature, we conclude a general formula and divide ILRs into two types
according to different variables in them. Utilizing these two kinds of ILRs, we
propose four verifiable MSSSs with Ajtai's function, which is a lattice-based
function. Our schemes have the following advantages. Firstly, our schemes can
detect cheat of the dealer and participants, and are multi-use. Secondly, we
have several ways to restore secrets. Thirdly, we can turn our schemes into
other types of MSSs due to the universality of our method. Fourthly, since we
utilize a lattice-based function to mask shares, our schemes can resist the
attack from the quantum computer with computational security. Finally, although
our schemes need more memory consumption than some known schemes, we need much
less time consumption, which makes our schemes more suitable facing limited
computing power.


title:
Trusted AI in Multi-agent Systems: An Overview of Privacy and Security
  for Distributed Learning

abstract:
  Motivated by the advancing computational capacity of distributed end-user
equipments (UEs), as well as the increasing concerns about sharing private
data, there has been considerable recent interest in machine learning (ML) and
artificial intelligence (AI) that can be processed on on distributed UEs.
Specifically, in this paradigm, parts of an ML process are outsourced to
multiple distributed UEs, and then the processed ML information is aggregated
on a certain level at a central server, which turns a centralized ML process
into a distributed one, and brings about significant benefits. However, this
new distributed ML paradigm raises new risks of privacy and security issues. In
this paper, we provide a survey of the emerging security and privacy risks of
distributed ML from a unique perspective of information exchange levels, which
are defined according to the key steps of an ML process, i.e.: i) the level of
preprocessed data, ii) the level of learning models, iii) the level of
extracted knowledge and, iv) the level of intermediate results. We explore and
analyze the potential of threats for each information exchange level based on
an overview of the current state-of-the-art attack mechanisms, and then discuss
the possible defense methods against such threats. Finally, we complete the
survey by providing an outlook on the challenges and possible directions for
future research in this critical area.


title: On the Implicit Bias Towards Minimal Depth of Deep Neural Networks

abstract:
  We study the implicit bias of stochastic gradient descent to favor low-depth
solutions when training deep neural networks. Recent results in the literature
suggest that penultimate layer representations learned by a classifier over
multiple classes exhibit a clustering property, called neural collapse. First,
we empirically show that neural collapse generally strengthens when increasing
the number of layers. In addition, we demonstrate that neural collapse extends
beyond the penultimate layer and emerges in intermediate layers as well, making
the higher layers essentially redundant. We characterize a notion of effective
depth which measures the minimal layer that enjoys neural collapse. In this
regard, we hypothesize and empirically show that gradient descent implicitly
selects neural networks of small effective depths. Finally, we theoretically
and empirically show that the effective depth of a trained neural network
monotonically increases when training with extended portions of random labels
and connecting it with generalization.


title: Reflectarrays and metasurface reflectors as diffraction gratings

abstract:
  Reconfigurable reflectors have a significant potential in future
telecommunication systems, and approaches to the design and realization of full
and tunable reflection control are now actively studied. Reflectarrays, being
the classical approach to realization of scanning reflectors, are based on the
phased-array theory (the so-called generalized reflection law) and the physical
optics approximation of the reflection response. To overcome the limitations of
the reflectarray technology, researchers actively study inhomogeneous
metasurfaces, using the theory of diffraction gratings. In order to make these
devices tunable and fully realize their potential, it is necessary to unify the
two approaches and study reconfigurable reflectors from a unified point of
view. Here, we offer a basic tutorial on reflectarrays and reflecting
metasufaces, explaining their common fundamental properties that stem from the
diffraction theory. This tutorial is suitable for graduate and post-graduate
students and hopefully will help to develop more deeper understanding of both
phased arrays and diffraction gratings.


title:
Minimax Rate of Distribution Estimation on Unknown Submanifold under
  Adversarial Losses

abstract:
  Statistical inference from high-dimensional data with low-dimensional
structures has recently attracted lots of attention. In machine learning, deep
generative modeling approaches implicitly estimate distributions of complex
objects by creating new samples from the underlying distribution, and have
achieved great success in generating synthetic realistic-looking images and
texts. A key step in these approaches is the extraction of latent features or
representations (encoding) that can be used for accurately reconstructing the
original data (decoding). In other words, low-dimensional manifold structure is
implicitly assumed and utilized in the distribution modeling and estimation. To
understand the benefit of low-dimensional manifold structure in generative
modeling, we build a general minimax framework for distribution estimation on
unknown submanifold under adversarial losses, with suitable smoothness
assumptions on the target distribution and the manifold. The established
minimax rate elucidates how various problem characteristics, including
intrinsic dimensionality of the data and smoothness levels of the target
distribution and the manifold, affect the fundamental limit of high-dimensional
distribution estimation. To prove the minimax upper bound, we construct an
estimator based on a mixture of locally fitted generative models, which is
motivated by the partition of unity technique from differential geometry and is
necessary to cover cases where the underlying data manifold does not admit a
global parametrization. We also propose a data-driven adaptive estimator that
is shown to simultaneously attain within a logarithmic factor of the optimal
rate over a large collection of distribution classes.


title:
Update on the method using nonlocal/noncontact differential conductance
  experimental data for direct probing Andreev reflections and for extracting
  the superconducting gap

abstract:
  In our previous work, high-temperature superconductivity (HTSC) was probed
for the first time by using electrical differential conductance Gdiff(V) =
dI/dV data as obtained from current-voltage V(I) measurements on a hydrogenated
graphitic fiber [1]. While our nonlocal method for finding the superconducting
(SC) gap was recently applied to a conventional SC (Pb [2]), the nonlocal Gdiff
method was first proposed in [1]. Herein, we are bringing forth an update on
the topic.


title:
Algebraicity criteria, invariant subvarities and transcendence problems
  from arithmetic dynamics

abstract:
  We introduce an algebraicity criteria. It has the following form: under
certain conditions, an analytic subvariety of some algebriac variety over a
global field $K$, if it contains many $K$-points, then it is algebraic over
$K.$ This gives a way to show the transcendence of points via the transcendence
of analytic subvarieties. Such a situation often appears when we have a
dynamical system, because we can often produce infinitely many points from one
point via iterates.
  Combing this criteria and the study of invariant subvarieties, we get some
results on the transcendence in arithmetic dynamics. We get a characterization
of products of B\"ottcher coordinates or products of multiplicative canonical
heights for polynomial dynamical pairs to be algebraic. For this, we studied
the invariant subvarieties for product of endomorphisms. In particular, we
partially generate Medvedev-Scanlon's classification of invariant subvarieties
of split polynomial maps to separable endomorphisms on $(\mathbb{P}^1)^N$ in
any characteristic. We also get some hight dimensional partial generalization
via introducing of a notion of independence. We then study dominant
endomorphisms $f$ on $\mathbb{A}^N$ over a number field of algebraic degree
$d\geq 2$. We show that in most of the cases (e.g. when such an endomorphism
extends to an endomorphism on $\mathbb{P}^N$), there are many analytic curves
centered at infinity which are periodic. We show that for most of them, it is
algebraic if and only if it contains one algebraic point. We also study the
periodic curves. We show that for most of $f$, all periodic curves has degree
at most $2$. When $N=2$, we get a more precise classification result. We show
that under a condition which is satisfied for a general $f$, if $f$ has
infinitely many periodic curves, then $f$ is homogenous up to a changing of
origin.