title: Monotonicity of the quantum 2-Wasserstein distance

abstract:
  We study a quantum analogue of the 2-Wasserstein distance as a measure of
proximity on the set $\Omega_N$ of density matrices of dimension $N$. We show
that such (semi-)distances do not induce Riemannian metrics on the tangent
bundle of $\Omega_N$ and are typically not unitary invariant. Nevertheless, we
prove that for $N=2$ dimensional Hilbert space the quantum 2-Wasserstein
distance (unique up to rescaling) is monotonous with respect to any
single-qubit quantum operation. Furthermore, for any $N \geq 3$ and the quantum
cost matrix proportional to a projector we demonstrate the monotonicity under
arbitrary mixed unitary channels. Finally, we provide numerical evidence which
allows us to conjecture that the unitary invariant quantum 2-Wasserstein
semi-distance is monotonous with respect to all CPTP maps in any dimension $N$.


title:
SSR-HEF: Crowd Counting with Multi-Scale Semantic Refining and Hard
  Example Focusing

abstract:
  Crowd counting based on density maps is generally regarded as a regression
task.Deep learning is used to learn the mapping between image content and crowd
density distribution. Although great success has been achieved, some
pedestrians far away from the camera are difficult to be detected. And the
number of hard examples is often larger. Existing methods with simple Euclidean
distance algorithm indiscriminately optimize the hard and easy examples so that
the densities of hard examples are usually incorrectly predicted to be lower or
even zero, which results in large counting errors. To address this problem, we
are the first to propose the Hard Example Focusing(HEF) algorithm for the
regression task of crowd counting. The HEF algorithm makes our model rapidly
focus on hard examples by attenuating the contribution of easy examples.Then
higher importance will be given to the hard examples with wrong estimations.
Moreover, the scale variations in crowd scenes are large, and the scale
annotations are labor-intensive and expensive. By proposing a multi-Scale
Semantic Refining (SSR) strategy, lower layers of our model can break through
the limitation of deep learning to capture semantic features of different
scales to sufficiently deal with the scale variation. We perform extensive
experiments on six benchmark datasets to verify the proposed method. Results
indicate the superiority of our proposed method over the state-of-the-art
methods. Moreover, our designed model is smaller and faster.


title: A new entanglement measure based dual entropy

abstract:
  Quantum entropy is an important measure for describing the uncertainty of a
quantum state, more uncertainty in subsystems implies stronger quantum
entanglement between subsystems. Our goal in this work is to quantify bipartite
entanglement using both von Neumann entropy and its complementary dual. We
first propose a type of dual entropy from Shannon entropy. We define
$S^{t}$-entropy entanglement based on von Neumann entropy and its complementary
dual. This implies an analytic formula for two-qubit systems. We show that the
monogamy properties of the $S^{t}$-entropy entanglement and the entanglement of
formation are inequivalent for high-dimensional systems. We finally prove a new
type of entanglement polygon inequality in terms of $S^{t}$-entropy
entanglement for quantum entangled networks. These results show new features of
multipartite entanglement in quantum information processing.


title: Towards Fine-grained Causal Reasoning and QA

abstract:
  Understanding causality is key to the success of NLP applications, especially
in high-stakes domains. Causality comes in various perspectives such as enable
and prevent that, despite their importance, have been largely ignored in the
literature. This paper introduces a novel fine-grained causal reasoning dataset
and presents a series of novel predictive tasks in NLP, such as causality
detection, event causality extraction, and Causal QA. Our dataset contains
human annotations of 25K cause-effect event pairs and 24K question-answering
pairs within multi-sentence samples, where each can have multiple causal
relationships. Through extensive experiments and analysis, we show that the
complex relations in our dataset bring unique challenges to state-of-the-art
methods across all three tasks and highlight potential research opportunities,
especially in developing "causal-thinking" methods.


title:
How transpiration by forests and other vegetation determines alternate
  moisture regimes

abstract:
  The terrestrial water cycle links the soil and atmosphere moisture reservoirs
through four fluxes: precipitation, evaporation, runoff and atmospheric
moisture convergence. Each of these fluxes is essential for human and ecosystem
well-being. However, predicting how the water cycle responds to changes in
vegetation cover, remains a challenge (Lawrence and Vandecar, 2015; Ellison et
al., 2017; te Wierik et al., 2021). Recently, rainfall was shown to decrease
disproportionally with reduced forest transpiration following deforestation
(Baudena et al., 2021). Here, combining these findings with the law of matter
conservation, we show that in a sufficiently wet atmosphere forest
transpiration can control atmospheric moisture convergence such that increased
transpiration enhances atmospheric moisture import. Conversely, in a drier
atmosphere increased transpiration reduces atmospheric moisture convergence and
runoff. This previously unrecognized dichotomy can explain the seemingly random
observations of runoff and soil moisture sometimes increasing and sometimes
reducing in response to re-greening (e.g., Zheng et al., 2021). Evaluating the
transition between the two regimes is crucial both for characterizing the risk
posed by deforestation as well as for motivating and guiding global ecosystem
restoration.


title:
Initialisation and Grammar Design in Grammar-Guided Evolutionary
  Computation

abstract:
  Grammars provide a convenient and powerful mechanism to define the space of
possible solutions for a range of problems. However, when used in grammatical
evolution (GE), great care must be taken in the design of a grammar to ensure
that the polymorphic nature of the genotype-to-phenotype mapping does not
impede search. Additionally, recent work has highlighted the importance of the
initialisation method on GE's performance. While recent work has shed light on
the matters of initialisation and grammar design with respect to GE, their
impact on other methods, such as random search and context-free grammar genetic
programming (CFG-GP), is largely unknown. This paper examines GE, random search
and CFG-GP under a range of benchmark problems using several different
initialisation routines and grammar designs. The results suggest that CFG-GP is
less sensitive to initialisation and grammar design than both GE and random
search: we also demonstrate that observed cases of poor performance by CFG-GP
are managed through simple adjustment of tuning parameters. We conclude that
CFG-GP is a strong base from which to conduct grammar-guided evolutionary
search, and that future work should focus on understanding the parameter space
of CFG-GP for better application.


title:
A model explaining the new CDF II W boson mass linking to muon $g-2$ and
  dark matter

abstract:
  We propose a model to explain the W boson mass anomaly reported by CDFII
collaboration that would suggest new physics. We introduce exotic fermions; one
isospin doublet vector-like lepton, one isospin singlet singly-charged
vector-like lepton and an isospin doublet inert scalar. These new fermions also
provide us with sizable anomalous magnetic moment (muon $g-2$) due to no chiral
suppression and nonzero mass difference between the real and imaginary parts of
neutral inert scalar bosons. This mass difference and doublet vector-like
leptons affect the oblique parameters, especially $\Delta T$, whose shift from
zero are the direct consequence of this W boson mass anomaly. We search for the
allowed parameter region to explain both the phenomenologies. In addition, we
discuss a dark matter candidate assuming the real part of the inert scalar
boson to be the one. We find that lighter DM mass is favored.


title: End-to-End Sensitivity-Based Filter Pruning

abstract:
  In this paper, we present a novel sensitivity-based filter pruning algorithm
(SbF-Pruner) to learn the importance scores of filters of each layer
end-to-end. Our method learns the scores from the filter weights, enabling it
to account for the correlations between the filters of each layer. Moreover, by
training the pruning scores of all layers simultaneously our method can account
for layer interdependencies, which is essential to find a performant sparse
sub-network. Our proposed method can train and generate a pruned network from
scratch in a straightforward, one-stage training process without requiring a
pretrained network. Ultimately, we do not need layer-specific hyperparameters
and pre-defined layer budgets, since SbF-Pruner can implicitly determine the
appropriate number of channels in each layer. Our experimental results on
different network architectures suggest that SbF-Pruner outperforms advanced
pruning methods. Notably, on CIFAR-10, without requiring a pretrained baseline
network, we obtain 1.02% and 1.19% accuracy gain on ResNet56 and ResNet110,
compared to the baseline reported for state-of-the-art pruning algorithms. This
is while SbF-Pruner reduces parameter-count by 52.3% (for ResNet56) and 54%
(for ResNet101), which is better than the state-of-the-art pruning algorithms
with a high margin of 9.5% and 6.6%.


title:
Super Resolution for Turbulent Flows in 2D: Stabilized Physics Informed
  Neural Networks

abstract:
  We propose a new design of a neural network for solving a zero shot super
resolution problem for turbulent flows. We embed Luenberger-type observer into
the network's architecture to inform the network of the physics of the process,
and to provide error correction and stabilization mechanisms. In addition, to
compensate for decrease of observer's performance due to the presence of
unknown destabilizing forcing, the network is designed to estimate the
contribution of the unknown forcing implicitly from the data over the course of
training. By running a set of numerical experiments, we demonstrate that the
proposed network does recover unknown forcing from data and is capable of
predicting turbulent flows in high resolution from low resolution noisy
observations.


title: SOTVerse: A User-defined Task Space of Single Object Tracking

abstract:
  Single object tracking (SOT) research falls into a cycle - trackers perform
well on most benchmarks but quickly fail in challenging scenarios, causing
researchers to doubt the insufficient data content and take more effort
constructing larger datasets with more challenging situations. However,
isolated experimental environments and limited evaluation methods more
seriously hinder the SOT research. The former causes existing datasets can not
be exploited comprehensively, while the latter neglects challenging factors in
the evaluation process. In this article, we systematize the representative
benchmarks and form a single object tracking metaverse (SOTVerse) - a
user-defined SOT task space to break through the bottleneck. We first propose a
3E Paradigm to describe tasks by three components (i.e., environment,
evaluation, and executor). Then, we summarize task characteristics, clarify the
organization standards, and construct SOTVerse with 12.56 million frames.
Specifically, SOTVerse automatically labels challenging factors per frame,
allowing users to generate user-defined spaces efficiently via construction
rules. Besides, SOTVerse provides two mechanisms with new indicators and
successfully evaluates trackers under various subtasks. Consequently, SOTVerse
firstly provides a strategy to improve resource utilization in the computer
vision area, making research more standardized and scientific. The SOTVerse,
toolkit, evaluation server, and results are available at
http://metaverse.aitestunion.com.


title: Universal approximation property of invertible neural networks

abstract:
  Invertible neural networks (INNs) are neural network architectures with
invertibility by design. Thanks to their invertibility and the tractability of
Jacobian, INNs have various machine learning applications such as probabilistic
modeling, generative modeling, and representation learning. However, their
attractive properties often come at the cost of restricting the layer designs,
which poses a question on their representation power: can we use these models
to approximate sufficiently diverse functions? To answer this question, we have
developed a general theoretical framework to investigate the representation
power of INNs, building on a structure theorem of differential geometry. The
framework simplifies the approximation problem of diffeomorphisms, which
enables us to show the universal approximation properties of INNs. We apply the
framework to two representative classes of INNs, namely Coupling-Flow-based
INNs (CF-INNs) and Neural Ordinary Differential Equations (NODEs), and
elucidate their high representation power despite the restrictions on their
architectures.


title: Generalized uncertainty principle and Asymptotic Safe gravity

abstract:
  We present a procedure to link the deformation parameter $\beta$ of the
generalized uncertainty principle (GUP) to the two free parameters $\om$ and
$\gamma$ of the running Newtonian coupling constant of the Asymptotic Safe
gravity (ASG) program. To this aim, we compute the Hawking temperature of a
black hole in two different ways. The first way involves the use of the GUP in
place of the Heisenberg uncertainty relations, and therefore we get a deformed
Hawking temperature containing the parameter $\beta$. The second way involves
the deformation of the Schwarzschild metric due to the Newtonian coupling
constant running according to the AS gravity prescription. The comparison of
the two techniques yields a relation between $\beta$ and $\om$, $\gamma$. As a
particular case, we discuss also the so called $\xi$-model. The relations
between $\beta$ and $\om$, $\xi$ allow us to transfer upper bounds from one
parameter to the others.


title: Safe Reinforcement Learning Using Black-Box Reachability Analysis

abstract:
  Reinforcement learning (RL) is capable of sophisticated motion planning and
control for robots in uncertain environments. However, state-of-the-art deep RL
approaches typically lack safety guarantees, especially when the robot and
environment models are unknown. To justify widespread deployment, robots must
respect safety constraints without sacrificing performance. Thus, we propose a
Black-box Reachability-based Safety Layer (BRSL) with three main components:
(1) data-driven reachability analysis for a black-box robot model, (2) a
trajectory rollout planner that predicts future actions and observations using
an ensemble of neural networks trained online, and (3) a differentiable
polytope collision check between the reachable set and obstacles that enables
correcting unsafe actions. In simulation, BRSL outperforms other
state-of-the-art safe RL methods on a Turtlebot 3, a quadrotor, and a
trajectory-tracking point mass with an unsafe set adjacent to the area of
highest reward.


title:
The gravitational wave and short gamma-ray burst GW170817/SHB170817A,
  not your everyday binary neutron star merger

abstract:
  This event, so far unique, beautifully confirmed the standard views on the
gravitational waves produced by a merger of two neutron stars, but its
electromagnetic multi-wavelenth observations disagreed with the numerous
initial versions of the "standard fireball model(s)" of gamma ray bursts.
Contrariwise, they provided strong evidence in favour of the "cannonball"
model. Most uncontroversially, a cannonball was observed at radio wavelengths,
with an overwhelming statistical significance ($>\! 17\,\sigma$), and
travelling in the plane of the sky, as expected, at an apparent superluminal
velocity $V_{app}\sim 4\, c$.


title: Some properties of differentiable p-adic functions

abstract:
  In this paper, using the tools from the lineability theory, we distinguish
certain subsets of $p$-adic differentiable functions. Specifically, we show
that the following sets of functions are large enough to contain an infinite
dimensional algebraic structure: (i) continuously differentiable but not
strictly differentiable functions, (ii) strictly differentiable functions of
order $r$ but not strictly differentiable of order $r+1$, (iii) strictly
differentiable functions with zero derivative that are not Lipschitzian of any
order $\alpha >1$, (iv) differentiable functions with unbounded derivative, and
(v) continuous functions that are differentiable on a full set with respect to
the Haar measure but not differentiable on its complement having cardinality
the continuum.


title:
Deep CardioSound-An Ensembled Deep Learning Model for Heart Sound
  MultiLabelling

abstract:
  Heart sound diagnosis and classification play an essential role in detecting
cardiovascular disorders, especially when the remote diagnosis becomes standard
clinical practice. Most of the current work is designed for single category
based heard sound classification tasks. To further extend the landscape of the
automatic heart sound diagnosis landscape, this work proposes a deep multilabel
learning model that can automatically annotate heart sound recordings with
labels from different label groups, including murmur's timing, pitch, grading,
quality, and shape. Our experiment results show that the proposed method has
achieved outstanding performance on the holdout data for the multi-labelling
task with sensitivity=0.990, specificity=0.999, F1=0.990 at the segments level,
and an overall accuracy=0.969 at the patient's recording level.


title: An interpretable machine learning approach for ferroalloys consumptions

abstract:
  This paper is devoted to a practical method for ferroalloys consumption
modeling and optimization. We consider the problem of selecting the optimal
process control parameters based on the analysis of historical data from
sensors. We developed approach, which predicts results of chemical reactions
and give ferroalloys consumption recommendation. The main features of our
method are easy interpretation and noise resistance. Our approach is based on
k-means clustering algorithm, decision trees and linear regression. The main
idea of the method is to identify situations where processes go similarly. For
this, we propose using a k-means based dataset clustering algorithm and a
classification algorithm to determine the cluster. This algorithm can be also
applied to various technological processes, in this article, we demonstrate its
application in metallurgy. To test the application of the proposed method, we
used it to optimize ferroalloys consumption in Basic Oxygen Furnace steelmaking
when finishing steel in a ladle furnace. The minimum required element content
for a given steel grade was selected as the predictive model's target variable,
and the required amount of the element to be added to the melt as the optimized
variable. Keywords: Clustering, Machine Learning, Linear Regression,
Steelmaking, Optimization, Gradient Boosting, Artificial Intelligence, Decision
Trees, Recommendation services


title:
Using Restart Sequences to Determine Beneficial First Passage Under
  Restart

abstract:
  First Passage (FP) processes are utilized widely to model phenomena in many
areas of mathematical applications, from biology to computer science.
Introducing a mechanism to restart the parent process can alter the first
passage characteristics, and the properties of the resulting First Passage
Under Restart (FPUR) process have been subject to much recent investigation.
Here we seek to more fully characterize whether a discrete FP process can have
its mean hitting time reduced through the implementation of restart by
analyzing a construct we call the restart sequence, which is determined solely
by the distribution of the FP process.


title: On graphic degree sequences and matching numbers

abstract:
  In this paper we study the relationship between the degree sequence of a
graph and its matching number. First, we prove that whether a degree sequence
can be extended with a new degree $\delta$ is equivalent to the existence of a
realization of the original degree sequence with a matching of size $\delta/2$
that covers the largest $\delta$ degrees. Second, we estimate the minimum size
of both the maximal and the maximum matching, as determined by the degree
sequence, for any realization. Furthermore, we also estimate the maximum value
of the matching number over all possible realizations for the degree sequence.
Along this line we answer a question raised by Biedl, Demaine et al.
(DOI:10.1016/j.disc.2004.05.003). These results are all closely connected to
the recently introduced family of Degree Preserving Growth graph-dynamics
(DOI:10.1038/s41567-021-01417-7).


title:
Singular quadratic eigenvalue problems: Linearization and weak condition
  numbers

abstract:
  The numerical solution of singular eigenvalue problems is complicated by the
fact that small perturbations of the coefficients may have an arbitrarily bad
effect on eigenvalue accuracy. However, it has been known for a long time that
such perturbations are exceptional and standard eigenvalue solvers, such as the
QZ algorithm, tend to yield good accuracy despite the inevitable presence of
roundoff error. Recently, Lotz and Noferini quantified this phenomenon by
introducing the concept of $\delta$-weak eigenvalue condition numbers. In this
work, we consider singular quadratic eigenvalue problems and two popular
linearizations. Our results show that a correctly chosen linearization
increases $\delta$-weak eigenvalue condition numbers only marginally,
justifying the use of these linearizations in numerical solvers also in the
singular case. We propose a very simple but often effective algorithm for
computing well-conditioned eigenvalues of a singular quadratic eigenvalue
problems by adding small random perturbations to the coefficients. We prove
that the eigenvalue condition number is, with high probability, a reliable
criterion for detecting and excluding spurious eigenvalues created from the
singular part.


title: Finding Hall blockers by matrix scaling

abstract:
  For a given nonnegative matrix $A=(A_{ij})$, the matrix scaling problem asks
whether $A$ can be scaled to a doubly stochastic matrix $XAY$ for some positive
diagonal matrices $X,Y$. The Sinkhorn algorithm is a simple iterative
algorithm, which repeats row-normalization $A_{ij} \leftarrow
A_{ij}/\sum_{j}A_{ij}$ and column-normalization $A_{ij} \leftarrow
A_{ij}/\sum_{i}A_{ij}$ alternatively. By this algorithm, $A$ converges to a
doubly stochastic matrix in limit if and only if the bipartite graph associated
with $A$ has a perfect matching. This property can decide the existence of a
perfect matching in a given bipartite graph $G$, which is identified with the
$0,1$-matrix $A_G$. Linial, Samorodnitsky, and Wigderson showed that a
polynomial number of the Sinkhorn iterations for $A_G$ decides whether $G$ has
a perfect matching.
  In this paper, we show an extension of this result: If $G$ has no perfect
matching, then a polynomial number of the Sinkhorn iterations identifies a Hall
blocker -- a certificate of the nonexistence of a perfect matching. Our
analysis is based on an interpretation of the Sinkhorn algorithm as alternating
KL-divergence minimization (Csisz\'{a}r and Tusn\'{a}dy 1984, Gietl and Reffel
2013) and its limiting behavior for a nonscalable matrix (Aas 2014). We also
relate the Sinkhorn limit with parametric network flow, principal partition of
polymatroids, and the Dulmage-Mendelsohn decomposition of a bipartite graph.


title: A Novel CFD-DEM Coarse-Graining Method Based on the Voronoi Tessellation

abstract:
  In unresolved flow CFD-DEM simulations, the porosity values for each CFD cell
are determined using a coarse-graining algorithm. While this approach enables
coupled simulations of representative numbers of particles, the influence of
the porosity local to the particles on the fluid-particle interaction force is
not captured. This contribution considers a two-grid coarse-graining method
that determines a local porosity for each particle using a radical Voronoi
tessellation of the system. A relatively fine, regular point cloud is used to
map these local porosity data to the CFD cells. The method is evaluated using
two different cases considering both disperse and tightly packed particles. The
data show that the method conserves porosity data, is reasonably
grid-independent and can generate a relatively smooth porosity field. The new
method can more accurately predict the fluid-particle interactive force for
polydisperse particle system than alternative methods that have been
implemented in unresolved CFD-DEM codes.


title:
On certain Semigroup of Order-decreasing Full Contraction Mappings of a
  Finite Chain

abstract:
  Let $\mathcal{CT}_n$ be the semigroup of full contraction mappings on
$[n]=\{1,2,\ldots,n\}$, and let $\mathcal{OCT}_n$ and $\mathcal{ODCT}_n$ be the
subsemigroups consisting of all order-preserving full contraction and
subsemigroup of order-decreasing and order-preserving full contraction
mappings, respectively. In this paper, we show that the semigroup
$\mathcal{ODCT}_n$ is left adequate. We further study the rank properties and
as well obtain the rank of the semigroup, $\mathcal{ODCT}_n$. Moreover, we
obtain a characterization of natural partial order for the semigroup
$\mathcal{OCT}_n$ and its subsemigroup $\mathcal{ODCT}_n$, respectively.


title:
Decision-making with E-admissibility given a finite assessment of
  choices

abstract:
  Given information about which options a decision-maker definitely rejects
from given finite sets of options, we study the implications for
decision-making with E-admissibility. This means that from any finite set of
options, we reject those options that no probability mass function compatible
with the given information gives the highest expected utility. We use the
mathematical framework of choice functions to specify choices and rejections,
and specify the available information in the form of conditions on such
functions. We characterise the most conservative extension of the given
information to a choice function that makes choices based on E-admissibility,
and provide an algorithm that computes this extension by solving linear
feasibility problems.


title: Experimentally realized memristive memory augmented neural network

abstract:
  Lifelong on-device learning is a key challenge for machine intelligence, and
this requires learning from few, often single, samples. Memory augmented neural
network has been proposed to achieve the goal, but the memory module has to be
stored in an off-chip memory due to its size. Therefore the practical use has
been heavily limited. Previous works on emerging memory-based implementation
have difficulties in scaling up because different modules with various
structures are difficult to integrate on the same chip and the small sense
margin of the content addressable memory for the memory module heavily limited
the degree of mismatch calculation. In this work, we implement the entire
memory augmented neural network architecture in a fully integrated memristive
crossbar platform and achieve an accuracy that closely matches standard
software on digital hardware for the Omniglot dataset. The successful
demonstration is supported by implementing new functions in crossbars in
addition to widely reported matrix multiplications. For example, the
locality-sensitive hashing operation is implemented in crossbar arrays by
exploiting the intrinsic stochasticity of memristor devices. Besides, the
content-addressable memory module is realized in crossbars, which also supports
the degree of mismatches. Simulations based on experimentally validated models
show such an implementation can be efficiently scaled up for one-shot learning
on the Mini-ImageNet dataset. The successful demonstration paves the way for
practical on-device lifelong learning and opens possibilities for novel
attention-based algorithms not possible in conventional hardware.


title:
Stateless and Rule-Based Verification For Compliance Checking
  Applications

abstract:
  Underlying computational model has an important role in any computation. The
state and transition (such as in automata) and rule and value (such as in Lisp
and logic programming) are two comparable and counterpart computational models.
Both of deductive and model checking verification techniques are relying on a
notion of state and as a result, their underlying computational models are
state dependent. Some verification problems (such as compliance checking by
which an under compliance system is verified against some regulations and
rules) have not a strong notion of state nor transition. Behalf of it, these
systems have a strong notion of value symbols and declarative rules defined on
them. SARV (Stateless And Rule-Based Verification) is a verification framework
that designed to simplify the overall process of verification for stateless and
rule-based verification problems (e.g. compliance checking). In this paper, a
formal logic-based framework for creating intelligent compliance checking
systems is presented. We define and introduce this framework, report a case
study and present results of an experiment on it. The case study is about
protocol compliance checking for smart cities. Using this solution, a Rescue
Scenario use case and its compliance checking are sketched and modeled. An
automation engine for and a compliance solution with SARV are introduced. Based
on 300 data experiments, the SARV-based compliance solution outperforms famous
machine learning methods on a 3125-records software quality dataset.


title:
The Importance of Landscape Features for Performance Prediction of
  Modular CMA-ES Variants

abstract:
  Selecting the most suitable algorithm and determining its hyperparameters for
a given optimization problem is a challenging task. Accurately predicting how
well a certain algorithm could solve the problem is hence desirable. Recent
studies in single-objective numerical optimization show that supervised machine
learning methods can predict algorithm performance using landscape features
extracted from the problem instances.
  Existing approaches typically treat the algorithms as black-boxes, without
consideration of their characteristics. To investigate in this work if a
selection of landscape features that depends on algorithms properties could
further improve regression accuracy, we regard the modular CMA-ES framework and
estimate how much each landscape feature contributes to the best algorithm
performance regression models. Exploratory data analysis performed on this data
indicate that the set of most relevant features does not depend on the
configuration of individual modules, but the influence that these features have
on regression accuracy does. In addition, we have shown that by using
classifiers that take the features relevance on the model accuracy, we are able
to predict the status of individual modules in the CMA-ES configurations.


title:
ML_LTU at SemEval-2022 Task 4: T5 Towards Identifying Patronizing and
  Condescending Language

abstract:
  This paper describes the system used by the Machine Learning Group of LTU in
subtask 1 of the SemEval-2022 Task 4: Patronizing and Condescending Language
(PCL) Detection. Our system consists of finetuning a pretrained
Text-to-Text-Transfer Transformer (T5) and innovatively reducing its
out-of-class predictions. The main contributions of this paper are 1) the
description of the implementation details of the T5 model we used, 2) analysis
of the successes & struggles of the model in this task, and 3) ablation studies
beyond the official submission to ascertain the relative importance of data
split. Our model achieves an F1 score of 0.5452 on the official test set.


title:
Interacting with Non-Cooperative User: A New Paradigm for Proactive
  Dialogue Policy

abstract:
  Proactive dialogue system is able to lead the conversation to a goal topic
and has advantaged potential in bargain, persuasion and negotiation. Current
corpus-based learning manner limits its practical application in real-world
scenarios. To this end, we contribute to advance the study of the proactive
dialogue policy to a more natural and challenging setting, i.e., interacting
dynamically with users. Further, we call attention to the non-cooperative user
behavior -- the user talks about off-path topics when he/she is not satisfied
with the previous topics introduced by the agent. We argue that the targets of
reaching the goal topic quickly and maintaining a high user satisfaction are
not always converge, because the topics close to the goal and the topics user
preferred may not be the same. Towards this issue, we propose a new solution
named I-Pro that can learn Proactive policy in the Interactive setting.
Specifically, we learn the trade-off via a learned goal weight, which consists
of four factors (dialogue turn, goal completion difficulty, user satisfaction
estimation, and cooperative degree). The experimental results demonstrate I-Pro
significantly outperforms baselines in terms of effectiveness and
interpretability.


title:
ERGO: Event Relational Graph Transformer for Document-level Event
  Causality Identification

abstract:
  Document-level Event Causality Identification (DECI) aims to identify causal
relations between event pairs in a document. It poses a great challenge of
across-sentence reasoning without clear causal indicators. In this paper, we
propose a novel Event Relational Graph TransfOrmer (ERGO) framework for DECI,
which improves existing state-of-the-art (SOTA) methods upon two aspects.
First, we formulate DECI as a node classification problem by constructing an
event relational graph, without the needs of prior knowledge or tools. Second,
ERGO seamlessly integrates event-pair relation classification and global
inference, which leverages a Relational Graph Transformer (RGT) to capture the
potential causal chain. Besides, we introduce edge-building strategies and
adaptive focal loss to deal with the massive false positives caused by common
spurious correlation. Extensive experiments on two benchmark datasets show that
ERGO significantly outperforms previous SOTA methods (13.1% F1 gains on
average). We have conducted extensive quantitative analysis and case studies to
provide insights for future research directions (Section 4.8).


title:
Performance and Construction of Polar Codes: The Perspective of Bit
  Error Probability

abstract:
  Most existing works of polar codes focus on the analysis of block error
probability. However, in many scenarios, bit error probability is also
important for evaluating the performance of channel codes. In this paper, we
establish a new framework to analyze the bit error probability of polar codes.
Specifically, by revisiting the error event of bit-channel, we first introduce
the conditional bit error probability as a metric to evaluate the reliability
of bit-channel for both systematic and non-systematic polar codes. Guided by
the concept of polar subcode, we then derive an upper bound on the conditional
bit error probability of each bit-channel, and accordingly, an upper bound on
the bit error probability of polar codes. Based on these, two types of
construction metrics aiming at minimizing the bit error probability of polar
codes are proposed, which are of linear computational complexity and explicit
forms. Simulation results show that the polar codes constructed by the proposed
methods can outperform those constructed by the conventional methods.


title:
Political Communities on Twitter: Case Study of the 2022 French
  Presidential Election

abstract:
  With the significant increase in users on social media platforms, a new means
of political campaigning has appeared. Twitter and Facebook are now notable
campaigning tools during elections. Indeed, the candidates and their parties
now take to the internet to interact and spread their ideas. In this paper, we
aim to identify political communities formed on Twitter during the 2022 French
presidential election and analyze each respective community. We create a
large-scale Twitter dataset containing 1.2 million users and 62.6 million
tweets that mention keywords relevant to the election. We perform community
detection on a retweet graph of users and propose an in-depth analysis of the
stance of each community. Finally, we attempt to detect offensive tweets and
automatic bots, comparing across communities in order to gain insight into each
candidate's supporter demographics and online campaign strategy.


title:
Transfer Learning for Instance Segmentation of Waste Bottles using Mask
  R-CNN Algorithm

abstract:
  This paper proposes a methodological approach with a transfer learning scheme
for plastic waste bottle detection and instance segmentation using the
\textit{mask region proposal convolutional neural network} (Mask R-CNN).
Plastic bottles constitute one of the major pollutants posing a serious threat
to the environment both in oceans and on land. The automated identification and
segregation of bottles can facilitate plastic waste recycling. We prepare a
custom-made dataset of 192 bottle images with pixel-by pixel-polygon annotation
for the automatic segmentation task. The proposed transfer learning scheme
makes use of a Mask R-CNN model pre-trained on the Microsoft COCO dataset. We
present a comprehensive scheme for fine-tuning the base pre-trained Mask-RCNN
model on our custom dataset. Our final fine-tuned model has achieved 59.4
\textit{mean average precision} (mAP), which corresponds to the MS COCO metric.
The results indicate a promising application of deep learning for detecting
waste bottles.


title:
Non-relativistic limit of the Euler-HMP$_N$ approximation model arising
  in radiation hydrodynamics

abstract:
  In this paper, we are concerned with the non-relativistic limit of a class of
computable approximation models for radiation hydrodynamics. The models consist
of the compressible Euler equations coupled with moment closure approximations
to the radiative transfer equation. They are first-order partial differential
equations with source terms. As hyperbolic relaxation systems, they are showed
to satisfy the structural stability condition proposed by W.-A. Yong (1999).
Base on this, we verify the non-relativistic limit by combining an energy
method with a formal asymptotic analysis.


title: INSTA-BNN: Binary Neural Network with INSTAnce-aware Threshold

abstract:
  Binary Neural Networks (BNNs) have emerged as a promising solution for
reducing the memory footprint and compute costs of deep neural networks. BNNs,
on the other hand, suffer from information loss because binary activations are
limited to only two values, resulting in reduced accuracy. To improve the
accuracy, previous studies have attempted to control the distribution of binary
activation by manually shifting the threshold of the activation function or
making the shift amount trainable. During the process, they usually depended on
statistical information computed from a batch. We argue that using statistical
data from a batch fails to capture the crucial information for each input
instance in BNN computations, and the differences between statistical
information computed from each instance need to be considered when determining
the binary activation threshold of each instance. Based on the concept, we
propose the Binary Neural Network with INSTAnce-aware threshold (INSTA-BNN),
which decides the activation threshold value considering the difference between
statistical data computed from a batch and each instance. The proposed
INSTA-BNN outperforms the baseline by 2.5% and 2.3% on the ImageNet
classification task with comparable computing cost, achieving 68.0% and 71.7%
top-1 accuracy on ResNet-18 and MobileNetV1 based models, respectively.


title:
Non-invertible self-duality defects of Cardy-Rabinovici model and mixed
  gravitational anomaly

abstract:
  We study properties of self-duality symmetry in the Cardy-Rabinovici model.
The Cardy-Rabinovici model is the $4$d $U(1)$ gauge theory with electric and
magnetic matters, and it enjoys the $SL(2,\mathbb{Z})$ self-duality at
low-energies. $SL(2,\mathbb{Z})$ self-duality does not realize in a naive way,
but we notice that the $ST^{p}$ duality transformation becomes the legitimate
duality operation by performing the gauging of $\mathbb{Z}_N$ $1$-form symmetry
with including the level-$p$ discrete topological term. Due to such
complications in its realization, the fusion rule of duality defects becomes a
non-group-like structure, and thus the self-duality symmetry is realized as a
non-invertible symmetry. Moreover, for some fixed points of the self-duality,
the duality symmetry turns out to have a mixed gravitational anomaly detected
on a $K3$ surface, and we can rule out the trivially gapped phase as a
consequence of anomaly matching. We also uncover how the conjectured phase
diagram of the Cardy-Rabinovici model satisfies this new anomaly matching
condition.


title:
COTS: Collaborative Two-Stream Vision-Language Pre-Training Model for
  Cross-Modal Retrieval

abstract:
  Large-scale single-stream pre-training has shown dramatic performance in
image-text retrieval. Regrettably, it faces low inference efficiency due to
heavy attention layers. Recently, two-stream methods like CLIP and ALIGN with
high inference efficiency have also shown promising performance, however, they
only consider instance-level alignment between the two streams (thus there is
still room for improvement). To overcome these limitations, we propose a novel
COllaborative Two-Stream vision-language pretraining model termed COTS for
image-text retrieval by enhancing cross-modal interaction. In addition to
instance level alignment via momentum contrastive learning, we leverage two
extra levels of cross-modal interactions in our COTS: (1) Token-level
interaction - a masked visionlanguage modeling (MVLM) learning objective is
devised without using a cross-stream network module, where variational
autoencoder is imposed on the visual encoder to generate visual tokens for each
image. (2) Task-level interaction - a KL-alignment learning objective is
devised between text-to-image and image-to-text retrieval tasks, where the
probability distribution per task is computed with the negative queues in
momentum contrastive learning. Under a fair comparison setting, our COTS
achieves the highest performance among all two-stream methods and comparable
performance (but with 10,800X faster in inference) w.r.t. the latest
single-stream methods. Importantly, our COTS is also applicable to
text-to-video retrieval, yielding new state-ofthe-art on the widely-used
MSR-VTT dataset.


title:
Scalable and Real-time Multi-Camera Vehicle Detection,
  Re-Identification, and Tracking

abstract:
  Multi-camera vehicle tracking is one of the most complicated tasks in
Computer Vision as it involves distinct tasks including Vehicle Detection,
Tracking, and Re-identification. Despite the challenges, multi-camera vehicle
tracking has immense potential in transportation applications including speed,
volume, origin-destination (O-D), and routing data generation. Several recent
works have addressed the multi-camera tracking problem. However, most of the
effort has gone towards improving accuracy on high-quality benchmark datasets
while disregarding lower camera resolutions, compression artifacts and the
overwhelming amount of computational power and time needed to carry out this
task on its edge and thus making it prohibitive for large-scale and real-time
deployment. Therefore, in this work we shed light on practical issues that
should be addressed for the design of a multi-camera tracking system to provide
actionable and timely insights. Moreover, we propose a real-time city-scale
multi-camera vehicle tracking system that compares favorably to computationally
intensive alternatives and handles real-world, low-resolution CCTV instead of
idealized and curated video streams. To show its effectiveness, in addition to
integration into the Regional Integrated Transportation Information System
(RITIS), we participated in the 2021 NVIDIA AI City multi-camera tracking
challenge and our method is ranked among the top five performers on the public
leaderboard.


title: Detecting Violence in Video Based on Deep Features Fusion Technique

abstract:
  With the rapid growth of surveillance cameras in many public places to
mon-itor human activities such as in malls, streets, schools and, prisons,
there is a strong demand for such systems to detect violence events
automatically. Au-tomatic analysis of video to detect violence is significant
for law enforce-ment. Moreover, it helps to avoid any social, economic and
environmental damages. Mostly, all systems today require manual human
supervisors to de-tect violence scenes in the video which is inefficient and
inaccurate. in this work, we interest in physical violence that involved two
persons or more. This work proposed a novel method to detect violence using a
fusion tech-nique of two significantly different convolutional neural networks
(CNNs) which are AlexNet and SqueezeNet networks. Each network followed by
separate Convolution Long Short Term memory (ConvLSTM) to extract ro-bust and
richer features from a video in the final hidden state. Then, making a fusion
of these two obtained states and fed to the max-pooling layer. Final-ly,
features were classified using a series of fully connected layers and soft-max
classifier. The performance of the proposed method is evaluated using three
standard benchmark datasets in terms of detection accuracy: Hockey Fight
dataset, Movie dataset and Violent Flow dataset. The results show an accuracy
of 97%, 100%, and 96% respectively. A comparison of the results with the state
of the art techniques revealed the promising capability of the proposed method
in recognizing violent videos.


title:
Dynamics of a ring resonator under simultaneous index and loss
  modulation

abstract:
  We report a complete steady-state solution to the time rate equations
governing the dynamics of a sinusoidally driven ring modulator. Compared to
previous works that provide comprehensive solutions for pure index (phase) or
loss (loss) modulation, this work presents an exact solution with their
simultaneous modulation. Furthermore, the optimum modulation parameters for
efficient microwave-to-optical conversion are discussed under such concurrent
modulation. As an application, we report selective downward frequency
conversion from the ring modulator which is not possible from pure loss or
index modulation. The work allows for more accurate modeling of the ring
modulator. It provides an insight to explore the interplay between phase and
amplitude modulation in MRMs for unique applications, and fully understand the
limitations of such devices.


title: Ram pressure stripping in the z~0.5 galaxy cluster MS 0451.6-0305

abstract:
  The pressure exerted by the ambient hot X-ray gas on cluster galaxies can
lead to the presence of ram pressure stripped (RPS) galaxies, characterized by
asymmetric shapes, and, in some cases, tails of blue stars and/or X-ray gas,
with increased star formation. We searched for such galaxies in the cluster MS
0451.6-0305 at z~0.5, based on Hubble Space Telescope (HST) imaging covering a
region of about 6x6 Mpc^2, an eight magnitude ground-based catalogue with
photometric redshifts, and a spectroscopic redshift catalogue. We defined as
cluster members a spectroscopic redshift sample of 359 galaxies within 4sigma_v
of the mean cluster velocity, and a photometric redshift sample covering the
[0.48,0.61] range. We searched for RPS galaxies and tested the error on their
classification with a Zooniverse collaboration, and computed the phase space
diagram for the spectroscopic sample. We ran the LePhare stellar population
synthesis code to analyze and compare the properties of RPS and non-RPS
galaxies. We find 56 and 273 RPS candidates in the spectroscopic and
photometric redshift samples, respectively, distributed throughout the cluster
and tending to avoid high density regions. The phase space diagram gives the
percentages of virialized, backsplash, and infall galaxies. RPS galaxy
candidates typically show rather high star formation rates, young ages, and
relatively low masses. This study confirms that RPS galaxies host, on average,
younger stellar populations and strongly form stars when compared with non-RPS
counterparts. The fact that RPS candidates with spectroscopic and with
photometric redshifts have comparable properties shows that large samples of
such objects could be gathered based on multi-band photometry only, a promising
result in view of future very large imaging surveys.


title: Wi-Fi and Bluetooth Contact Tracing Without User Intervention

abstract:
  A custom Wi-Fi and Bluetooth indoor contact tracing system is created to find
detailed paths of infected individuals without any user intervention. The
system tracks smartphones, but it does not require smartphone applications,
connecting to the routers, or any other extraneous devices on the users. A
custom Turtlebot3 is used for site surveying, where it simulates mobile device
movement and packet transmission. Transmit power, receive power, and round trip
time are collected by a custom ESP32C3 router. MAC randomization is defeated to
identify unique smartphones. Subsequently, the wireless parameters above are
converted to signal path loss and time of flight. Bidirectional long short term
memory takes the wireless parameters and predicts the detailed paths of the
users within 1 m. Public health authorities can use the contact tracing website
to find the detailed paths of the suspected cases using the smartphone models
and initial positions of confirm cases. The system can also track indirect
contact transmissions originating from surfaces and droplets due to having
absolute positions of users.


title:
Stretching Sentence-pair NLI Models to Reason over Long Documents and
  Clusters

abstract:
  Natural Language Inference (NLI) has been extensively studied by the NLP
community as a framework for estimating the semantic relation between sentence
pairs. While early work identified certain biases in NLI models, recent
advancements in modeling and datasets demonstrated promising performance. In
this work, we further explore the direct zero-shot applicability of NLI models
to real applications, beyond the sentence-pair setting they were trained on.
First, we analyze the robustness of these models to longer and out-of-domain
inputs. Then, we develop new aggregation methods to allow operating over full
documents, reaching state-of-the-art performance on the ContractNLI dataset.
Interestingly, we find NLI scores to provide strong retrieval signals, leading
to more relevant evidence extractions compared to common similarity-based
methods. Finally, we go further and investigate whole document clusters to
identify both discrepancies and consensus among sources. In a test case, we
find real inconsistencies between Wikipedia pages in different languages about
the same topic.


title:
A Chebyshev type alternation theorem for best approximation by a sum of
  two algebras

abstract:
  Let $X$ be a compact metric space, $C(X)$ be the space of continuous
real-valued functions on $X$, and $A_1$, $A_2$ be two closed subalgebras of
$C(X)$ containing constant functions. We consider the problem of approximation
of a function $f\in C(X)$ by elements from $A_1+A_2$. We prove a Chebyshev type
alternation theorem for a function $u_0\in A_1+A_2$ to be a best approximation
to $f$.


title: Semantic-Aware Pretraining for Dense Video Captioning

abstract:
  This report describes the details of our approach for the event
dense-captioning task in ActivityNet Challenge 2021. We present a
semantic-aware pretraining method for dense video captioning, which empowers
the learned features to recognize high-level semantic concepts. Diverse video
features of different modalities are fed into an event captioning module to
generate accurate and meaningful sentences. Our final ensemble model achieves a
10.00 METEOR score on the test set.


title:
Asymptotic of the Discrete Volume-Preserving Fractional Mean Curvature
  Flow via a Nonlocal Quantitative Alexandrov Theorem

abstract:
  We characterize the long time behaviour of a discrete-in-time approximation
of the volume preserving fractional mean curvature flow. In particular, we
prove that the discrete flow starting from any bounded set of finite fractional
perimeter converges exponentially fast to a single ball. As an intermediate
result we establish a quantitative Alexandrov type estimate in the fractional
setting for normal deformations of a ball. Finally, we provide existence for
flat flows as limit points of the discrete flow when the time discretization
parameter tends to zero.


title: On the stability of low-mass planets with supercritical hydrospheres

abstract:
  Short-period and low-mass water-rich planets are subject to strong
irradiation from their host star, resulting in hydrospheres in supercritical
state. In this context, we explore the role of irradiation on small terrestrial
planets that are moderately wet in the low-mass regime (0.2--1$M_{\oplus}$). We
investigate their bulk properties for water contents in the 0.01--5\% range by
making use of an internal structure model that is coupled to an atmosphere
model. This coupling allows us to take into account both the compression of the
interior due to the weight of the hydrosphere and the possibility of
atmospheric instability in the low-mass regime. We show that even for low
masses and low water contents, these planets display inflated atmospheres. For
extremely low planetary masses and high irradiation temperatures, we find that
steam atmospheres become gravitationally unstable when the ratio $\eta$ of
their scale height to planetary radius exceeds a critical value of $\sim 0.1$.
This result is supported by observational data, as all currently detected
exoplanets exhibit values of $\eta$ smaller than 0.013. Depending on their
water content, our results show that highly irradiated and low-mass planets up
to $0.9M_{\oplus}$ with significative hydrospheres are not in stable form and
should loose their volatile envelope.


title:
A Framework to capture and reproduce the Absolute State of Jupyter
  Notebooks

abstract:
  Jupyter Notebooks are an enormously popular tool for creating and narrating
computational research projects. They also have enormous potential for creating
reproducible scientific research artifacts. Capturing the complete state of a
notebook has additional benefits; for instance, the notebook execution may be
split between local and remote resources, where the latter may have more
powerful processing capabilities or store large or access-limited data. There
are several challenges for making notebooks fully reproducible when examined in
detail. The notebook code must be replicated entirely, and the underlying
Python runtime environments must be identical. More subtle problems arise in
replicating referenced data, external library dependencies, and runtime
variable states. This paper presents solutions to these problems using
Juptyer's standard extension mechanisms to create an archivable system state
for a running notebook. We show that the overhead for these additional
mechanisms, which involve interacting with the underlying Linux kernel, does
not introduce substantial execution time overheads, demonstrating the
approach's feasibility.


title: Quantum control of hole spin qubits in double quantum dots

abstract:
  Hole spin qubits in semiconductor quantum dots (QDs) are promising candidates
for quantum information processing due to their weak hyperfine coupling to
nuclear spins and rapid operation time due to the strong spin orbit coupling.
We study the coherent control on two heavy hole spin qubits in a double quantum
dot, and construct one qubit and two qubit gates by an all electrical protocol.
Using fast quasiadiabatic driving via spin-orbit coupling, we can reduce
significantly charge noise for qubit manipulation and quantum state transfer,
as compared to other protocols, and achieve high robustness for the qubit
initialization. In particular, the schemes for the one-qubit NOT gate and the
two qubit CNOT, SWAP-like gates of hole spins in a double QD achieve fidelities
above $99\%$, exhibiting the capability of hole spins to encode qubits for
quantum computing.


title:
Formalizing $\varphi$-calculus: a purely object-oriented calculus of
  decorated objects

abstract:
  Many calculi exist for modelling various features of object-oriented
languages. Many of them are based on $\lambda$-calculus and focus either on
statically typed class-based languages or dynamic prototype-based languages. We
formalize untyped calculus of decorated objects, informally presented by
Bugayenko, which is defined in terms of objects and relies on decoration as a
primary mechanism of object extension. It is not based on $\lambda$-calculus,
yet with only four basic syntactic constructions is just as complete. We prove
the calculus is confluent (i.e. possesses Church-Rosser property), and
introduce an abstract machine for call-by-name evaluation. Finally, we provide
a sound translation to $\lambda$-calculus with records.


title:
A non-autonomous variational problem describing a nonlinear Timoshenko
  beam

abstract:
  We study the non-autonomous variational problem:
  \begin{equation*}
  \inf_{(\phi,\theta)}
  \bigg\{\int_0^1 \bigg(\frac{k}{2}\phi'^2 +
  \frac{(\phi-\theta)^2}{2}-V(x,\theta)\bigg)\text{d}x\bigg\}
  \end{equation*}
  where $k>0$, $V$ is a bounded continuous function,
  $(\phi,\theta)\in H^1([0,1])\times L^2([0,1])$ and $\phi(0)=0$
  in the sense of traces. The peculiarity of the problem is its setting in the
product of spaces of different regularity order. Problems with this form arise
in elastostatics, when studying the equilibria of a nonlinear Timoshenko beam
under distributed load, and in classical dynamics
  of coupled particles in time-depending external fields. We prove the
existence and qualitative properties of global minimizers and study, under
additional assumptions on $V$, the existence and regularity of local
minimizers.


title:
ORCNet: A context-based network to simultaneously segment the ocular
  region components

abstract:
  Accurate extraction of the Region of Interest is critical for successful
ocular region-based biometrics. In this direction, we propose a new
context-based segmentation approach, entitled Ocular Region Context Network
(ORCNet), introducing a specific loss function, i.e., he Punish Context Loss
(PC-Loss). The PC-Loss punishes the segmentation losses of a network by using a
percentage difference value between the ground truth and the segmented masks.
We obtain the percentage difference by taking into account Biederman's semantic
relationship concepts, in which we use three contexts (semantic, spatial, and
scale) to evaluate the relationships of the objects in an image. Our proposal
achieved promising results in the evaluated scenarios: iris, sclera, and ALL
(iris + sclera) segmentations, utperforming the literature baseline techniques.
The ORCNet with ResNet-152 outperforms the best baseline (EncNet with
ResNet-152) on average by 2.27%, 28.26% and 6.43% in terms of F-Score, Error
Rate and Intersection Over Union, respectively. We also provide (for research
purposes) 3,191 manually labeled masks for the MICHE-I database, as another
contribution of our work.


title:
Model-Based Deep Learning of Joint Probabilistic and Geometric Shaping
  for Optical Communication

abstract:
  Autoencoder-based deep learning is applied to jointly optimize geometric and
probabilistic constellation shaping for optical coherent communication. The
optimized constellation shaping outperforms the 256 QAM Maxwell-Boltzmann
probabilistic distribution with extra 0.05 bits/4D-symbol mutual information
for 64 GBd transmission over 170 km SMF link.


title: Distinguishing parity-switching mechanisms in a superconducting qubit

abstract:
  Single-charge tunneling is a decoherence mechanism affecting superconducting
qubits, yet the origin of excess quasiparticle excitations (QPs) responsible
for this tunneling in superconducting devices is not fully understood. We
measure the flux dependence of charge-parity (or simply, ``parity'') switching
in an offset-charge-sensitive transmon qubit to identify the contributions of
photon-assisted parity switching and QP generation to the overall
parity-switching rate. The parity-switching rate exhibits a
qubit-state-dependent peak in the flux dependence, indicating a cold
distribution of excess QPs which are predominantly trapped in the low-gap film
of the device. Moreover, we find that the photon-assisted process contributes
significantly to both parity switching and the generation of excess QPs by
fitting to a model that self-consistently incorporates photon-assisted parity
switching as well as inter-film QP dynamics.


title:
Qtrade AI at SemEval-2022 Task 11: An Unified Framework for Multilingual
  NER Task

abstract:
  This paper describes our system, which placed third in the Multilingual Track
(subtask 11), fourth in the Code-Mixed Track (subtask 12), and seventh in the
Chinese Track (subtask 9) in the SemEval 2022 Task 11: MultiCoNER Multilingual
Complex Named Entity Recognition. Our system's key contributions are as
follows: 1) For multilingual NER tasks, we offer an unified framework with
which one can easily execute single-language or multilingual NER tasks, 2) for
low-resource code-mixed NER task, one can easily enhance his or her dataset
through implementing several simple data augmentation methods and 3) for
Chinese tasks, we propose a model that can capture Chinese lexical semantic,
lexical border, and lexical graph structural information. Finally, our system
achieves macro-f1 scores of 77.66, 84.35, and 74.00 on subtasks 11, 12, and 9,
respectively, during the testing phase.


title:
Relation between Black Hole Mass and Bulge Luminosity in Hard X-ray
  selected Type 1 AGNs

abstract:
  Using $I$-band images of 35 nearby ($z<0.1$) type 1 active galactic nuclei
(AGNs) obtained with Hubble Space Telescope, selected from the 70-month
Swift-BAT X-ray source catalog, we investigate the photometric properties of
the host galaxies. With a careful treatment of the point-spread function (PSF)
model and imaging decomposition, we robustly measure the $I$-band brightness
and the effective radius of bulges in our sample. Along with black hole (BH)
mass estimates from single-epoch spectroscopic data, we present the relation
between BH mass and $I$-band bulge luminosity ($M_{\rm BH}-M_{I,\rm bul}$
relation) of our sample AGNs. We find that our sample lies offset from the
$M_{\rm BH}-M_{I,\rm bul}$ relation of inactive galaxies by 0.4 dex, i.e., at a
given bulge luminosity, the BH mass of our sample is systematically smaller
than that of inactive galaxies. We also demonstrate that the zero point offset
in the $M_{\rm BH}-M_{I,\rm bul}$ relation with respect to inactive galaxies is
correlated with the Eddington ratio. Based on the Kormendy relation, we find
that the mean surface brightness of ellipticals and classical bulges in our
sample is comparable to that of normal galaxies, revealing that bulge
brightness is not enhanced in our sample. As a result, we conclude that the
deviation in the $M_{\rm BH}-M_{I,\rm bul}$ relation from inactive galaxies is
possibly because the scaling factor in the virial BH mass estimator depends on
the Eddington ratio.


title: Balayage of charge distributions and subharmonic functions onto a strip

abstract:
  We consider two balayage constructions on the complex plane $\mathbb C$ with
real axis $\mathbb R$ for $0\leq b\in \mathbb R$. Let $u\not\equiv -\infty$ be
a subharmonic function on $\mathbb C$ of order
$$\operatorname{ord}[u]:=\limsup_{z\to \infty} \frac{\ln \max\{1,u(z)\}}{\ln
|z|}\leq 1,$$ $U=u-v$ be the difference of subharmonic functions $u$ and
$v\not\equiv -\infty$ on $\mathbb C$ with $\operatorname{ord}[v]\leq 1$, i.e.,
$\delta$-subharmonic function on $\mathbb C$ of order
$\operatorname{ord}[U]\leq 1$. Then there is a $\delta$-subharmonic function
$V\not\equiv \pm\infty$ on $\mathbb C$ of order $\operatorname{ord}[V]\leq 1$
such that $V$ is harmonic on $\bigl\{ z \in \mathbb C\bigm| |\Re z|> b\bigr\}$
and $U(z)\equiv V(z)$ for all $z\in \bigl\{ z \in \mathbb C\bigm| |\Re z|\leq
b\bigr\}\setminus E$ where $E\subset \mathbb C$ is polar.
  If $u$ is a subharmonic function of finite type under order $1$, i.e.,
$$\limsup_{z\to \infty} \frac{u(z)}{|z|}<+\infty,$$ then there exist
subharmonic functions $u_{\mathbb R}$ and $u_b$ of finite type under order $1$
that are harmonic respectively on $\mathbb C\setminus \mathbb R$ and $\bigl\{ z
\in \mathbb C\bigm| |\Re z|> b\bigr\}$ such that $$\begin{cases} u(z)\equiv
u_{\mathbb R}(z)+u_b(z) \text{ for all $z\in {\mathbb R}\bigcup \bigl\{ z \in
\mathbb C\bigm| |\Re z|\leq b\bigr\}$},\\ u(z)\leq u_{\mathbb R}(z) + u_b(z)
\text{ for each $z\in \mathbb C$.}\end{cases}$$ At the same time, we trace
special relationships between the various logarithmic characteristics of the
Riesz mass and charge distributions of subharmonic and $\delta$-subharmonic
functions.


title: Two new families of bivariate APN functions

abstract:
  In this work, we present two new families of quadratic APN functions. The
first one (F1) is constructed via biprojective polynomials. This family
includes one of the two APN families introduced by G\"olo\v{g}lu in 2022. Then,
following a similar approach as in Li \emph{et al.} (2022), we give another
family (F2) obtained by adding certain terms to F1. As a byproduct, this second
family includes one of the two families introduced by Li \emph{et al.} (2022).
Moreover, we show that for $n=12$, from our constructions, we can obtain APN
functions that are CCZ-inequivalent to any other known APN function over
$\mathbb{F}_{2^{12}}$.


title: Gradient Estimate of subelliptic harmonic maps with potential

abstract:
  In this paper, we investigate subelliptic harmonic maps with potential from
noncompact complete sub-Riemannian manifolds corresponding to totally geodesic
Riemannian foliations. Under some suitable conditions, we give the gradient
estimates of these maps and establish a Liouville type result.


title:
Improving Pre-trained Language Models with Syntactic Dependency
  Prediction Task for Chinese Semantic Error Recognition

abstract:
  Existing Chinese text error detection mainly focuses on spelling and simple
grammatical errors. These errors have been studied extensively and are
relatively simple for humans. On the contrary, Chinese semantic errors are
understudied and more complex that humans cannot easily recognize. The task of
this paper is Chinese Semantic Error Recognition (CSER), a binary
classification task to determine whether a sentence contains semantic errors.
The current research has no effective method to solve this task. In this paper,
we inherit the model structure of BERT and design several syntax-related
pre-training tasks so that the model can learn syntactic knowledge. Our
pre-training tasks consider both the directionality of the dependency structure
and the diversity of the dependency relationship. Due to the lack of a
published dataset for CSER, we build a high-quality dataset for CSER for the
first time named Corpus of Chinese Linguistic Semantic Acceptability (CoCLSA).
The experimental results on the CoCLSA show that our methods outperform
universal pre-trained models and syntax-infused models.


title: The State of the Molecular Gas in Post-Starburst Galaxies

abstract:
  The molecular gas in galaxies traces both the fuel for star formation and the
processes that can either enhance or suppress star formation. Observations of
the molecular gas state can thus point to when and why galaxies stop forming
stars. In this study, we present ALMA observations of the molecular gas in
galaxies evolving through the post-starburst phase. These galaxies have low
current star formation rates, regardless of the SFR tracer used, yet their
optical spectra show evidence for recent bursts of star formation that have
ended within the last 600 Myr. We present CO (3--2) observations for three
post-starburst galaxies, and dense gas HCN/HCO$^+$/HNC (1--0) observations for
six (four new) post-starburst galaxies. The post-starbursts have low excitation
as traced by the CO spectral line energy distribution (SLED) up to CO (3--2),
more similar to early-type than starburst galaxies. The low excitation
indicates that lower density rather than high temperatures may suppress star
formation during the post-starburst phase. One galaxy displays a blueshifted
molecular gas outflow traced by CO (3--2). MaNGA observations show that the
ionized gas velocity is disturbed relative to the stellar velocity field, with
a blueshifted component aligned with the molecular gas outflow, suggestive of a
multiphase outflow. Low ratios of HCO$^+$/CO, indicating low fractions of dense
molecular gas relative to the total molecular gas, are seen throughout
post-starburst phase, except for the youngest post-starburst galaxy considered
here.


title: Sensitivity of sparse codes to image distortions

abstract:
  Sparse coding has been proposed as a theory of visual cortex and as an
unsupervised algorithm for learning representations. We show empirically with
the MNIST dataset that sparse codes can be very sensitive to image distortions,
a behavior that may hinder invariant object recognition. A locally linear
analysis suggests that the sensitivity is due to the existence of linear
combinations of active dictionary elements with high cancellation. A nearest
neighbor classifier is shown to perform worse on sparse codes than original
images. For a linear classifier with a sufficiently large number of labeled
examples, sparse codes are shown to yield higher accuracy than original images,
but no higher than a representation computed by a random feedforward net.
Sensitivity to distortions seems to be a basic property of sparse codes, and
one should be aware of this property when applying sparse codes to invariant
object recognition.


title: Convergence of the Discrete Minimum Energy Path

abstract:
  The minimum energy path (MEP) describes the mechanism of reaction, and the
energy barrier along the path can be used to calculate the reaction rate in
thermal systems. The nudged elastic band (NEB) method is one of the most
commonly used schemes to compute MEPs numerically. It approximates an MEP by a
discrete set of configuration images, where the discretization size determines
both computational cost and accuracy of the simulations. In this paper, we
consider a discrete MEP to be a stationary state of the NEB method and prove an
optimal convergence rate of the discrete MEP with respect to the number of
images. Numerical simulations for the transitions of some several proto-typical
model systems are performed to support the theory.


title:
Nuclear matter calculations with the phenomenological three-nucleon
  interaction

abstract:
  Employing the concept of three-body radial distribution function and using
the two-body correlation functions, calculated based on the lowest order
constrained variational method, we investigated the effect of the three-body
force (TBF) on the nuclear matter properties, for Argonne and Urbana
$\it{v_{14}}$ potentials. As such, the results for nuclear matter density,
incompressibility, energy per nucleon, and symmetry energy are presented at the
saturation point. The inclusion of a phenomenological TBF resulted in closer
values of the saturation density, incompressibility, and symmetry energy to the
empirical ones for the symmetric nuclear matter. This is especially the case
for the Urbana $\it{v_{14}}$ potential. In addition, an empirically-verified
parabolic approximation of the interaction energy was utilized to perform an
approximate study of the nuclear matter with neutron excess. Hence, at
densities higher than about 0.3~fm$^{-3}$ and for proton-to-neutron density
ratios close to the symmetric nuclear matter, the inclusion of TBF resulted in
an extra attraction for the Argonne as compared to the Urbana $\it{v_{14}}$
potential.


title: Mixture of Experts for Biomedical Question Answering

abstract:
  Biomedical Question Answering (BQA) has attracted increasing attention in
recent years due to its promising application prospect. It is a challenging
task because the biomedical questions are professional and usually vary widely.
Existing question answering methods answer all questions with a homogeneous
model, leading to various types of questions competing for the shared
parameters, which will confuse the model decision for each single type of
questions. In this paper, in order to alleviate the parameter competition
problem, we propose a Mixture-of-Expert (MoE) based question answering method
called MoEBQA that decouples the computation for different types of questions
by sparse routing. To be specific, we split a pretrained Transformer model into
bottom and top blocks. The bottom blocks are shared by all the examples, aiming
to capture the general features. The top blocks are extended to an MoE version
that consists of a series of independent experts, where each example is
assigned to a few experts according to its underlying question type. MoEBQA
automatically learns the routing strategy in an end-to-end manner so that each
expert tends to deal with the question types it is expert in. We evaluate
MoEBQA on three BQA datasets constructed based on real examinations. The
results show that our MoE extension significantly boosts the performance of
question answering models and achieves new state-of-the-art performance. In
addition, we elaborately analyze our MoE modules to reveal how MoEBQA works and
find that it can automatically group the questions into human-readable
clusters.


title: Explaining the 96 GeV Di-photon Anomaly in a Generic 2HDM Type-III

abstract:
  Motivated by results recently reported by the CMS Collaboration about an
excess in the di-photon spectrum at about 96 GeV, especially when combined with
another long-standing anomaly at the same value in the $b\bar b$ invariant mass
spectrum in four-jet events collected at LEP, we show that a possible
explanation to both phenomena can be found at 1$\sigma$ level in a generic
2-Higgs Doublet Model (2HDM) of Type-III in presence of a specific Yukawa
texture, wherein Lepton Flavour Violating (LFV) (neutral) currents are induced
at tree level. Bounds from Higgs data play a major role in limiting the
parameter space of this scenario, yet we find solutions with $m_H = 125$ GeV
and $m_h = 96$ GeV consistent with current theoretical and experimental bounds.


title: The Importance of Credo in Multiagent Learning

abstract:
  We propose a model for multi-objective optimization, a credo, for agents in a
system that are configured into multiple groups (i.e., teams). Our model of
credo regulates how agents optimize their behavior for the component groups
they belong to. We evaluate credo in the context of challenging social dilemmas
with reinforcement learning agents. Our results indicate that the interests of
teammates, or the entire system, are not required to be fully aligned for
globally beneficial outcomes. We identify two scenarios without full common
interest that achieve high equality and significantly higher mean population
rewards compared to when the interests of all agents are aligned.


title: Dipole Anisotropy in Gravitational Wave Source Distribution

abstract:
  Our local motion with respect to the cosmic frame of rest is believed to be
dominantly responsible for the observed dipole anisotropy in the Cosmic
Microwave Background Radiation (CMBR). We study the effect of this motion on
the sky distribution of gravitational wave (GW) sources. We determine the
resulting dipole anisotropy in GW source number counts, mass weighted number
counts, which we refer to as mass intensity, and mean mass per source. The mass
M dependence of the number density n(M) distribution of BBH is taken directly
from data. We also test the anisotropy in the observable mean mass per source
along the direction of the CMB dipole


title: Spectroscopic Line Modeling of the Fastest Rotating O-type Stars

abstract:
  We present a spectroscopic analysis of the most rapidly rotating stars
currently known, VFTS 102 ($v_{e} \sin i = 649 \pm 52$ km s$^{-1}$; O9: Vnnne+)
and VFTS 285 ($v_{e} \sin i = 610 \pm 41$ km s$^{-1}$; O7.5: Vnnn), both
members of the 30 Dor complex in the Large Magellanic Cloud. This study is
based on high resolution ultraviolet spectra from HST/COS and optical spectra
from VLT X-shooter plus archival VLT GIRAFFE spectra. We utilize numerical
simulations of their photospheres, rotationally distorted shape, and gravity
darkening to calculate model spectral line profiles and predicted monochromatic
absolute fluxes. We use a guided grid search to investigate parameters that
yield best fits for the observed features and fluxes. These fits produce
estimates of the physical parameters for these stars (plus a Galactic
counterpart, $\zeta$ Oph) including the equatorial rotational velocity,
inclination, radius, mass, gravity, temperature, and reddening. We find that
both stars appear to be radial velocity constant. VFTS 102 is rotating at
critical velocity, has a modest He enrichment, and appears to share the motion
of the nearby OB association LH 99. These properties suggest that the star was
spun up through a close binary merger. VFTS 285 is rotating at $95\%$ of
critical velocity, has a strong He enrichment, and is moving away from the R136
cluster at the center of 30 Dor. It is mostly likely a runaway star ejected by
a supernova explosion that released the components of the natal binary system.


title: The comparative statics of persuasion

abstract:
  In the canonical persuasion model, comparative statics has been an open
question. We answer it, delineating which shifts of the sender's interim payoff
lead her optimally to choose a more informative signal. Our first theorem
identifies an ordinal notion of 'increased convexity' that we show
characterises those shifts of the sender's interim payoff that lead her
optimally to choose no less informative signals. To strengthen this conclusion
to 'more informative' requires further assumptions: our second theorem
identifies the necessary and sufficient condition on the sender's interim
payoff, which strictly generalises the 'S'-shape commonly imposed in the
literature. (Note: preliminary and incomplete.)


title: Kernel similarity matching with Hebbian neural networks

abstract:
  Recent works have derived neural networks with online correlation-based
learning rules to perform \textit{kernel similarity matching}. These works
applied existing linear similarity matching algorithms to nonlinear features
generated with random Fourier methods. In this paper attempt to perform kernel
similarity matching by directly learning the nonlinear features. Our algorithm
proceeds by deriving and then minimizing an upper bound for the sum of squared
errors between output and input kernel similarities. The construction of our
upper bound leads to online correlation-based learning rules which can be
implemented with a 1 layer recurrent neural network. In addition to generating
high-dimensional linearly separable representations, we show that our upper
bound naturally yields representations which are sparse and selective for
specific input patterns. We compare the approximation quality of our method to
neural random Fourier method and variants of the popular but non-biological
"Nystr{\"o}m" method for approximating the kernel matrix. Our method appears to
be comparable or better than randomly sampled Nystr{\"o}m methods when the
outputs are relatively low dimensional (although still potentially higher
dimensional than the inputs) but less faithful when the outputs are very high
dimensional.


title: Guiding Attention using Partial-Order Relationships for Image Captioning

abstract:
  The use of attention models for automated image captioning has enabled many
systems to produce accurate and meaningful descriptions for images. Over the
years, many novel approaches have been proposed to enhance the attention
process using different feature representations. In this paper, we extend this
approach by creating a guided attention network mechanism, that exploits the
relationship between the visual scene and text-descriptions using spatial
features from the image, high-level information from the topics, and temporal
context from caption generation, which are embedded together in an ordered
embedding space. A pairwise ranking objective is used for training this
embedding space which allows similar images, topics and captions in the shared
semantic space to maintain a partial order in the visual-semantic hierarchy and
hence, helps the model to produce more visually accurate captions. The
experimental results based on MSCOCO dataset shows the competitiveness of our
approach, with many state-of-the-art models on various evaluation metrics.


title: Superconducting quantum fluctuations in one dimension

abstract:
  We review some recent developments in the field of quasi-one-dimensional
superconductivity. We demonstrate that low temperature properties of
superconducting nanowires are essentially determined by quantum fluctuations.
Smooth (Gaussian) fluctuations of the superconducting phase (also associated
with plasma modes propagating along the wire) may significantly affect the
electron density of states in such nanowires and induce persistent current
noise in superconducting nanorings. Further interesting phenomena such as,
e.g., non-vanishing resistance and shot noise of the voltage in current-biased
superconducting nanowires, are caused by non-Gaussian fluctuations of the order
parameter -- quantum phase slips (QPS). Such phenomena may be interpreted in
terms of tunneling of fluxons playing the role of effective quantum "particles"
dual to Cooper pairs and obeying complicated full counting statistics which
reduces to Poissonian one in the low frequency limit. We also demonstrate that
QPS effects may be particularly pronounced in thinnest wires and rings where
quantum phase slips remain unbound and determine a non-perturbative length
scale $L_c$ beyond which the supercurrent gets suppressed by quantum
fluctuations. Accordingly, for $T \to 0$ such nanowires should become
insulating at scales exceeding $L_c$, whereas at shorter length scales they may
still exhibit superconducting properties. We argue that certain non-trivial
features associated with quantum fluctuations of the order parameter may be
sensitive to specific circuit topology and may be observed in structures like,
e.g., a system of capacitively coupled superconducting nanowires.


title: Signal Reception With Generic Three-State Receptors in Synaptic MC

abstract:
  Synaptic communication is studied by communication engineers for two main
reasons. One is to enable novel neuroengineering applications that require
interfacing with neurons. The other reason is to draw inspiration for the
design of synthetic molecular communication systems. Both of these goals
require understanding of how the chemical synaptic signal is sensed and
transduced at the synaptic receiver (Rx). While signal reception in synaptic
molecular communication (SMC) depends heavily on the kinetics of the receptors
employed by the synaptic Rxs, existing channel models for SMC either
oversimplify the receptor kinetics or employ complex, high-dimensional kinetic
schemes limited to specific types of receptors. Both approaches do not
facilitate a comparative analysis of different types of natural synapses. In
this paper, we propose a novel deterministic channel model for SMC which
employs a generic three-state receptor model that captures the characteristics
of the most important receptor types in SMC. The model is based on a transfer
function expansion of Fick's diffusion equation and accounts for release,
diffusion, and degradation of neurotransmitters as well as their reversible
binding to finitely many generic postsynaptic receptors. The proposed SMC model
is the first that allows studying the impact of the characteristic dynamics of
the main postsynaptic receptor types on synaptic signal transmission. Numerical
results indicate that the proposed model indeed exhibits a wide range of
biologically plausible dynamics when specialized to specific natural receptor
types.


title:
Gagliardo-Nirenberg inequality in anisotropic Lebesgue spaces and energy
  equality in the Navier-Stokes equations

abstract:
  In this paper, it is shown that an analogue in mixed norm spaces of
Gagliardo-Nirenberg inequality is valid. As an application, some new criteria
for energy conservation of Leray-Hopf weak solutions to the 3D Navier-Stokes
equations are established in these spaces, which extend the corresponding known
results.


title:
Finite element methods respecting the discrete maximum principle for
  convection-diffusion equations

abstract:
  Convection-diffusion-reaction equations model the conservation of scalar
quantities. From the analytic point of view, solution of these equations
satisfy under certain conditions maximum principles, which represent physical
bounds of the solution. That the same bounds are respected by numerical
approximations of the solution is often of utmost importance in practice. The
mathematical formulation of this property, which contributes to the physical
consistency of a method, is called Discrete Maximum Principle (DMP). In many
applications, convection dominates diffusion by several orders of magnitude. It
is well known that standard discretizations typically do not satisfy the DMP in
this convection-dominated regime. In fact, in this case, it turns out to be a
challenging problem to construct discretizations that, on the one hand, respect
the DMP and, on the other hand, compute accurate solutions. This paper presents
a survey on finite element methods, with a main focus on the
convection-dominated regime, that satisfy a local or a global DMP. The concepts
of the underlying numerical analysis are discussed. The survey reveals that for
the steady-state problem there are only a few discretizations, all of them
nonlinear, that at the same time satisfy the DMP and compute reasonably
accurate solutions, e.g., algebraically stabilized schemes. Moreover, most of
these discretizations have been developed in recent years, showing the enormous
progress that has been achieved lately. Methods based on algebraic
stabilization, nonlinear and linear ones, are currently as well the only finite
element methods that combine the satisfaction of the global DMP and accurate
numerical results for the evolutionary equations in the convection-dominated
situation.


title: Knowledge Equivalence in Digital Twins of Intelligent Systems

abstract:
  A digital twin contains up-to-date data-driven models of the physical world
being studied and can use simulation to optimise the physical world. However,
the analysis made by the digital twin is valid and reliable only when the model
is equivalent to the physical world. Maintaining such an equivalent model is
challenging, especially when the physical systems being modelled are
intelligent and autonomous. The paper focuses in particular on digital twin
models of intelligent systems where the systems are knowledge-aware but with
limited capability. The digital twin improves the acting of the physical system
at a meta-level by accumulating more knowledge in the simulated environment.
The modelling of such an intelligent physical system requires replicating the
knowledge-awareness capability in the virtual space. Novel equivalence
maintaining techniques are needed, especially in synchronising the knowledge
between the model and the physical system. This paper proposes the notion of
knowledge equivalence and an equivalence maintaining approach by knowledge
comparison and updates. A quantitative analysis of the proposed approach
confirms that compared to state equivalence, knowledge equivalence maintenance
can tolerate deviation thus reducing unnecessary updates and achieve more
Pareto efficient solutions for the trade-off between update overhead and
simulation reliability.


title: Towards PAC Multi-Object Detection and Tracking

abstract:
  Accurately detecting and tracking multi-objects is important for
safety-critical applications such as autonomous navigation. However, it remains
challenging to provide guarantees on the performance of state-of-the-art
techniques based on deep learning. We consider a strategy known as conformal
prediction, which predicts sets of labels instead of a single label; in the
classification and regression settings, these algorithms can guarantee that the
true label lies within the prediction set with high probability. Building on
these ideas, we propose multi-object detection and tracking algorithms that
come with probably approximately correct (PAC) guarantees. They do so by
constructing both a prediction set around each object detection as well as
around the set of edge transitions; given an object, the detection prediction
set contains its true bounding box with high probability, and the edge
prediction set contains its true transition across frames with high
probability. We empirically demonstrate that our method can detect and track
objects with PAC guarantees on the COCO and MOT-17 datasets.


title:
Polling Latent Opinions: A Method for Computational Sociolinguistics
  Using Transformer Language Models

abstract:
  Text analysis of social media for sentiment, topic analysis, and other
analysis depends initially on the selection of keywords and phrases that will
be used to create the research corpora. However, keywords that researchers
choose may occur infrequently, leading to errors that arise from using small
samples. In this paper, we use the capacity for memorization, interpolation,
and extrapolation of Transformer Language Models such as the GPT series to
learn the linguistic behaviors of a subgroup within larger corpora of Yelp
reviews. We then use prompt-based queries to generate synthetic text that can
be analyzed to produce insights into specific opinions held by the populations
that the models were trained on. Once learned, more specific sentiment queries
can be made of the model with high levels of accuracy when compared to
traditional keyword searches. We show that even in cases where a specific
keyphrase is limited or not present at all in the training corpora, the GPT is
able to accurately generate large volumes of text that have the correct
sentiment.


title:
Operator semigroups in the mixed topology and the infinitesimal
  description of Markov processes

abstract:
  We define a class of not necessarily linear $C_0$-semigroups $(P_t)_{t\geq0}$
on $C_b(E)$ (more generally, on $C_\kappa(E):=\frac{1}{\kappa} C_b(E)$, for
some continuous weight function $\kappa$) equipped with the mixed topology
$\tau_1^\mathscr{M}$ for a large class of topological state spaces $E$. In the
linear case we prove that such $(P_t)_{t\geq0}$ can be characterized as
integral operators given by certain measure kernels. One main result is that
transition semigroups of Markov processes are $C_0$-semigroups on $(C_b(E),
\tau_{1}^{\mathscr M})$, if they leave $C_b(E)$ invariant and are jointly
weakly continuous in space and time. In particular, they are infinitesimally
generated by their generator $(L, D(L))$ and thus reconstructable through an
Euler formula from their strong derivative at zero in $(C_b(E),
\tau_{1}^{\mathscr M})$. This solves a long standing open problem on Markov
processes. Our results apply to a large number of Markov processes given as the
laws of solutions to SDEs and SPDEs. We also introduce the notion of a Markov
core operator $(L_0, D(L_0))$ for $(L, D(L))$ and prove that uniqueness of the
Fokker-Planck-Kolmogorov equations given by $(L_0,D(L_0))$ for all Dirac
initial conditions implies that $(L_0,D(L_0))$ is a Markov core operator for
$(L,D(L))$. As a consequence we can identify the Kolmogorov operator of a large
number of SDEs on finite and infinite dimensional state spaces as such.
Furthermore, if each $P_t$ is merely convex, we prove that $(P_t)_{t \geq 0}$
gives rise to viscosity solutions to the Cauchy problem of its associated (non
linear) infinitesimal generators. We also show that value functions of finite
or infinite dimensional optimal control problems are examples of convex
$C_0$-semigroups on $\left( C_b(E),\tau_{1}^{\mathscr M}\right)$.


title: Big-means: Less is More for K-means Clustering

abstract:
  K-means clustering plays a vital role in data mining. However, its
performance drastically drops when applied to huge amounts of data. We propose
a new heuristic that is built on the basis of regular K-means for faster and
more accurate big data clustering using the "less is more" and MSSC
decomposition approaches. The main advantage of the proposed algorithm is that
it naturally turns the K-means local search into global one through the process
of decomposition of the MSSC problem. On one hand, decomposition of the MSSC
problem into smaller subproblems reduces the computational complexity and
allows for their parallel processing. On the other hand, the MSSC decomposition
provides a new method for the natural data-driven shaking of the incumbent
solution while introducing a new neighborhood structure for the solution of the
MSSC problem. This leads to a new heuristic that improves K-means in big data
conditions. The scalability of the algorithm to big data can be easily adjusted
by choosing the appropriate number of subproblems and their size. The proposed
algorithm is both scalable and accurate. In our experiments it outperforms all
recent state-of-the-art algorithms for the MSSC in terms of time as well as the
solution quality.


title: Patch-wise Contrastive Style Learning for Instagram Filter Removal

abstract:
  Image-level corruptions and perturbations degrade the performance of CNNs on
different downstream vision tasks. Social media filters are one of the most
common resources of various corruptions and perturbations for real-world visual
analysis applications. The negative effects of these distractive factors can be
alleviated by recovering the original images with their pure style for the
inference of the downstream vision tasks. Assuming these filters substantially
inject a piece of additional style information to the social media images, we
can formulate the problem of recovering the original versions as a reverse
style transfer problem. We introduce Contrastive Instagram Filter Removal
Network (CIFR), which enhances this idea for Instagram filter removal by
employing a novel multi-layer patch-wise contrastive style learning mechanism.
Experiments show our proposed strategy produces better qualitative and
quantitative results than the previous studies. Moreover, we present the
results of our additional experiments for proposed architecture within
different settings. Finally, we present the inference outputs and quantitative
comparison of filtered and recovered images on localization and segmentation
tasks to encourage the main motivation for this problem.


title: An abstract decomposition of measures and its many applications

abstract:
  We consider a little-known abstract decomposition result, due to Dellacherie,
and show that it yields many decompositions of measures, several of which are
new. Then, we investigate how the outputs of the decomposition depend on its
inputs, in particular characterising the two elements of the decomposition as
projections in the sense of Riesz spaces and of metric spaces.


title:
Quantum Anomalous Hall and Valley Quantum Anomalous Hall Effects in
  Two-Dimensional d0 Orbital XY Monolayers

abstract:
  We propose a new family of the d0 orbital XY (X = K, Rb, Cs; Y = N, P, As,
Sb, Bi) monolayers with abundant and novel topology and valley properties. The
KN, RbN, RbP, RbAs, CsP, CsAs, and CsSb monolayers possess remarkable quantum
anomalous Hall effect (QAHE). CsSb monolayer also exhibits extraordinary valley
QAHE with giant splitting. Moreover, the topological properties of XY
monolayers can be effciently tuned by the in-plane strain, owing to the
strain-induced band inversion between the px;y and pz orbitals. Our findings
suggest that the d0 orbital XY monolayers can be good candidates for promising
applications in the spintronics and multifunctional topological-based devices.


title: Epistemic uncertainty from an averaged Hamilton-Jacobi formalism

abstract:
  In recent years, the non-relativistic quantum dynamics derived from three
assumptions; i) probability current conservation, ii) average energy
conservation, and iii) an epistemic momentum uncertainty [A. Budiyono and D.
Rohrlich, \href{https://doi.org/10.1038/s41467-017-01375-w}{Nat. Commun 8, 1306
(2017)}]. Here we show that, these assumptions can be derived from a natural
extension of classical statistical mechanics.

categories: quant-ph math-ph math.MP physics.atom-ph


title:
Detection limit of next-generation of multi-element germanium detectors
  in the context of Environmental science

abstract:
  One of the main challenges in Environmental sciences is the identification
and chemical evolution of polluting traces (e.g, cadmium or antimony) in soil,
which requires long acquistion times for accurate measurements at synchrotron
facilities. In this context, the potential of a new generation multi-element
germanium detectors to identify traces at 0.1-1~ppm in a reasonable time has
been studied using Allpix Squared framework. This code has been customized to
include the three dimensional electric and weighting field maps generated by
COMSOL Multiphysics software, and several features to model the sample
environment at SOLEIL synchrotron and the signal response of a germanium
detector equipped with a Digital Pulse Processor (DPP). The full simulation
chain has been validated by experimental data from SAMBA beamline of SOLEIL
synchrotron. This work presents a first estimation of the detection limit to
cadmium traces in a soil sample for a future multi-element germanium detector,
using this simulation chain.