title:
A new safety-guided design methodology to complement model-based safety
  analysis for safety assurance

abstract:
  With the rapid advancement of Formal Methods, Model-based Safety Analysis
(MBSA) has been gaining tremendous attention for its ability to rigorously
verify whether the safety-critical scenarios are adequately addressed by the
design solution of a cyber-physical human system. However, there is a gap. If
specific safety-critical scenarios are not included in the given design
solution (i.e., the model) in the first place, the results of MBSA cannot be
trusted for safety assurance. To tackle this problem, we propose a new
safety-guided design methodology (called STPA+) to complement MBSA. Inspired by
STPA, STPA+ treats a system as a control structure, which is particularly fit
for systems with complex interactions between human, machine, and automation.
Three methods are developed in STPA+ to tackle the possible omissions of
safety-critical scenarios caused by incorrectly defined safety constraints,
improperly constrained process model, and inadequately designed controller. In
this way, STPA+ directly derives an adequately defined design solution as the
input to an MBSA verification program and bridges the gap between current MBSA
approaches and safety assurance.


title: Computation of Large-Genus Solutions of the Korteweg-de Vries Equation

abstract:
  We consider the numerical computation of finite-genus solutions of the
Korteweg-de Vries equation when the genus is large. Our method applies both to
the initial-value problem when spectral data can be computed and to dressing
scenarios when spectral data is specified arbitrarily. In order to compute
large genus solutions, we employ a weighted Chebyshev basis to solve an
associated singular integral equation. We also extend previous work to compute
period matrices and the Abel map when the genus is large, maintaining numerical
stability. We demonstrate our method on four different classes of solutions.
Specifically, we demonstrate dispersive quantization for "box" initial data and
demonstrate how a large genus limit can be taken to produce a new class of
potentials.


title: Universality in Anomaly Flow

abstract:
  Universality in anomaly flow by an Aharonov-Bohm (AB) phase $\theta_H$ is
shown in the flat $M^4 \times (S^1/Z_2)$ spacetime and in the Randall-Sundrum
(RS) warped space. We analyze $SU(2)$ gauge theory with doublet fermions. With
orbifold boundary conditions the $U(1)$ part of gauge symmetry remains unbroken
at $\theta_H = 0$ and $\pi$. Chiral anomalies smoothly vary with $\theta_H$ in
the RS space. It is shown that anomaly coefficients associated with this
anomaly flow are expressed in terms of the values of the wave functions of
gauge fields at the UV and IR branes in the RS space. The anomaly coefficients
depend on $\theta_H$, the warp factor of the RS space, and the orbifold
boundary conditions for fermions, but not on the bulk mass parameters of
fermions.


title:
Real-Time Phase and Task Estimation for Controlling a Powered Ankle
  Exoskeleton on Extremely Uneven Terrain

abstract:
  Positive biomechanical outcomes have been reported with lower-limb
exoskeletons in laboratory settings, but these devices have difficulty
delivering appropriate assistance in synchrony with human gait as the task or
rate of phase progression change in real-world environments. This paper
presents a torque controller for an ankle exoskeleton that uses state
estimation with a data-driven kinematic model to continuously estimate the
phase, phase rate, stride length, and ramp parameters during locomotion. The
controller applies torque assistance based on the estimated phase and adapts
the torque profile based on the estimated task variables to match human torques
observed in a multi-activity database of 10 able-bodied subjects. We
demonstrate in silico that the controller yields phase estimates that are more
accurate than state of the art, while also estimating task variables with
comparable accuracy to recent machine learning approaches. The controller
implemented in an ankle exoskeleton successfully adapts its assistance in
response to changing phase and task variables, both during controlled treadmill
trials (6 able-bodied subjects) and a real-world stress test with extremely
uneven terrain.


title: A Sequential MPC Approach to Reactive Planning for Bipedal Robots

abstract:
  This paper presents a sequential Model Predictive Control (MPC) approach to
reactive motion planning for bipedal robots in dynamic environments. The
approach relies on a sequential polytopic decomposition of the free space,
which provides an ordered collection of mutually intersecting obstacle free
polytopes and waypoints. These are subsequently used to define a corresponding
sequence of MPC programs that drive the system to a goal location avoiding
static and moving obstacles. This way, the planner focuses on the free space in
the vicinity of the robot, thus alleviating the need to consider all the
obstacles simultaneously and reducing computational time. We verify the
efficacy of our approach in high-fidelity simulations with the bipedal robot
Digit, demonstrating robust reactive planning in the presence of static and
moving obstacles.


title:
Edge states and anomalous SUSY in (2+1)-dimensional Maxwell Chern-Simon
  theory

abstract:
  In a $(2+1)$-dimensional Maxwell-Chern-Simon theory coupled with a fermion
and a scalar, which has $\mathcal{N}=2$ SUSY in absence of the boundary, the
insertion of a spatial boundary breaks the supersymmetry. We show that only a
subset of the boundary conditions allowed by the self-adjointness of the
Hamiltonian can preserve partial $\mathcal{N}=1$ supersymmetry, while for the
remaining boundary conditions SUSY is completely broken. In the latter case, we
demonstrate two distinct SUSY-breaking mechanisms. For some of the
SUSY-breaking boundary conditions, the SUSY variation of the action does not
vanish which explicitly breaks SUSY. While for certain other boundary
conditions, despite the invariance of action under SUSY transformations,
unpaired fermionic edge states in the domain of the Hamiltonian leads to an
anomalous breaking of the supersymmetry.

categories: hep-th cond-mat.mtrl-sci cond-mat.supr-con math-ph math.MP


title:
AnimalTrack: A Large-scale Benchmark for Multi-Animal Tracking in the
  Wild

abstract:
  Multi-animal tracking (MAT), a multi-object tracking (MOT) problem, is
crucial for animal motion and behavior analysis and has many crucial
applications such as biology, ecology, animal conservation and so forth.
Despite its importance, MAT is largely under-explored compared to other MOT
problems such as multi-human tracking due to the scarcity of large-scale
benchmark. To address this problem, we introduce AnimalTrack, a large-scale
benchmark for multi-animal tracking in the wild. Specifically, AnimalTrack
consists of 58 sequences from a diverse selection of 10 common animal
categories. On average, each sequence comprises of 33 target objects for
tracking. In order to ensure high quality, every frame in AnimalTrack is
manually labeled with careful inspection and refinement. To our best knowledge,
AnimalTrack is the first benchmark dedicated to multi-animal tracking. In
addition, to understand how existing MOT algorithms perform on AnimalTrack and
provide baselines for future comparison, we extensively evaluate 14
state-of-the-art representative trackers. The evaluation results demonstrate
that, not surprisingly, most of these trackers become degenerated due to the
differences between pedestrians and animals in various aspects (e.g., pose,
motion, appearance, etc), and more efforts are desired to improve multi-animal
tracking. We hope that AnimalTrack together with evaluation and analysis will
foster further progress on multi-animal tracking. The dataset and evaluation as
well as our analysis will be made available upon the acceptance.


title: SVTR: Scene Text Recognition with a Single Visual Model

abstract:
  Dominant scene text recognition models commonly contain two building blocks,
a visual model for feature extraction and a sequence model for text
transcription. This hybrid architecture, although accurate, is complex and less
efficient. In this study, we propose a Single Visual model for Scene Text
recognition within the patch-wise image tokenization framework, which dispenses
with the sequential modeling entirely. The method, termed SVTR, firstly
decomposes an image text into small patches named character components.
Afterward, hierarchical stages are recurrently carried out by component-level
mixing, merging and/or combining. Global and local mixing blocks are devised to
perceive the inter-character and intra-character patterns, leading to a
multi-grained character component perception. Thus, characters are recognized
by a simple linear prediction. Experimental results on both English and Chinese
scene text recognition tasks demonstrate the effectiveness of SVTR. SVTR-L
(Large) achieves highly competitive accuracy in English and outperforms
existing methods by a large margin in Chinese, while running faster. In
addition, SVTR-T (Tiny) is an effective and much smaller model, which shows
appealing speed at inference. The code is publicly available at
https://github.com/PaddlePaddle/PaddleOCR.


title:
Elucidating Meta-Structures of Noisy Labels in Semantic Segmentation by
  Deep Neural Networks

abstract:
  The supervised training of deep neural networks (DNNs) by noisy labels has
been studied extensively in image classification but much less in image
segmentation. So far, our understanding of the learning behavior of DNNs
trained by noisy segmentation labels remains limited. In this study, we address
this deficiency in both binary segmentation of biological microscopy images and
multi-class segmentation of natural images. We classify segmentation labels
according to their noise transition matrices (NTM) and compare performance of
DNNs trained by different types of labels. When we randomly sample a small
fraction (e.g., 10%) or flipping a large fraction (e.g., 90%) of the
ground-truth labels to train DNNs, their segmentation performance remains
largely the same. This indicates that DNNs learn structures hidden in labels
rather than pixel-level labels per se in their supervised training for semantic
segmentation. We call these hidden structures "meta-structures". When we use
labels with different perturbations to the meta-structures to train DNNs, their
performance in feature extraction and segmentation degrades consistently. In
contrast, addition of meta-structure information substantially improves
performance of an unsupervised model in binary semantic segmentation. We
formulate meta-structures mathematically as spatial density distributions and
quantify semantic information of different types of labels, which we find to
correlate strongly with ranks of their NTM. We show theoretically and
experimentally how this formulation explains key observed learning behavior of
DNNs.


title: On the vacuum structure of gauge-Higgs unification models

abstract:
  In this paper, we discuss the vacuum structure of the gauge-Higgs unification
theory, which is one of the attractive candidates of physics beyond the
standard model. This scenario has a remarkable feature, namely it has
infinitely degenerate vacua due to the characteristic periodic potential of the
Higgs filed, to be identified with the extra space component of the higher
dimensional gauge field. We address a question, whether to form the
superposition of such degenerate vacua, like the $\theta$-vacuum in QCD, is
necessary or not, in order to realize the true vacuum state of the theory. We
derive gauge field configuration which describes the transition between
neighboring vacua, like the instanton (or anti-instanton) solution in QCD, and
the corresponding Euclidean action in two models. In a simplified 2-dimensional
U(1) model, the derived configuration to describe the transition is shown to
have finite Euclidean action, and accordingly the "$\theta$-vacuum" and the
resultant "$\theta$-term" are formulated. In a realistic 5D U(1) model,
however, the gauge field configuration to describe the transition is shown to
have infinite Euclidean action and therefore the tunneling probability between
the degenerate vacua vanishes. Thus the superposition of the degenerate vacua
is not necessary.


title:
A Faster Algorithm for Betweenness Centrality Based on Adjacency
  Matrices

abstract:
  Betweenness centrality is essential in complex network analysis; it
characterizes the importance of nodes and edges in networks. It is a crucial
problem that exactly computes the betweenness centrality in large networks
faster, which urgently needs to be solved. We propose a novel algorithm for
betweenness centrality based on the parallel computing of adjacency matrices,
which is faster than the existing algorithms for large networks. The time
complexity of the algorithm is only related to the number of nodes in the
network, not the number of edges. Experimental evidence shows that the
algorithm is effective and efficient. This novel algorithm is faster than
Brandes' algorithm on small and dense networks and offers excellent solutions
for betweenness centrality index computing on large-scale complex networks.


title: Deep Ensemble as a Gaussian Process Approximate Posterior

abstract:
  Deep Ensemble (DE) is an effective alternative to Bayesian neural networks
for uncertainty quantification in deep learning. The uncertainty of DE is
usually conveyed by the functional inconsistency among the ensemble members,
say, the disagreement among their predictions. Yet, the functional
inconsistency stems from unmanageable randomness and may easily collapse in
specific cases. To render the uncertainty of DE reliable, we propose a
refinement of DE where the functional inconsistency is explicitly
characterized, and further tuned w.r.t. the training data and certain priori
beliefs. Specifically, we describe the functional inconsistency with the
empirical covariance of the functions dictated by ensemble members, which,
along with the mean, define a Gaussian process (GP). Then, with specific priori
uncertainty imposed, we maximize functional evidence lower bound to make the GP
specified by DE approximate the Bayesian posterior. In this way, we relate DE
to Bayesian inference to enjoy reliable Bayesian uncertainty. Moreover, we
provide strategies to make the training efficient. Our approach consumes only
marginally added training cost than the standard DE, but achieves better
uncertainty quantification than DE and its variants across diverse scenarios.


title: Indefinite temporal order without gravity

abstract:
  According to the general theory of relativity, time can flow at different
rates depending on the configuration of massive objects, affecting the temporal
order of events. Recent research has shown that, combined with quantum theory,
this gravitational effect can result in events with an indefinite temporal
order, which can be tested through the violation of Bell-type inequalities.
According to Einstein, we shall assume physical equivalence of a uniform
gravitational field and a corresponding acceleration of a reference system.
Here we construct a non-gravitational scenario where accelerating particles
interacting with optical cavities result in a violation of the temporal Bell
inequalities analogous to the gravitational case. However, we find that the
inequalities can also be violated by time-like events, exposing an ambiguity in
their use as a theory-independent test of indefinite temporal order.


title: NeuralEF: Deconstructing Kernels by Deep Neural Networks

abstract:
  Learning the principal eigenfunctions of an integral operator defined by a
kernel and a data distribution is at the core of many machine learning
problems. Traditional nonparametric solutions based on the Nystr{\"o}m formula
suffer from scalability issues. Recent work has resorted to a parametric
approach, i.e., training neural networks to approximate the eigenfunctions.
However, the existing method relies on an expensive orthogonalization step and
is difficult to implement. We show that these problems can be fixed by using a
new series of objective functions that generalizes the
EigenGame~\citep{gemp2020eigengame} to function space. We test our method on a
variety of supervised and unsupervised learning problems and show it provides
accurate approximations to the eigenfunctions of polynomial, radial basis,
neural network Gaussian process, and neural tangent kernels. Finally, we
demonstrate our method can scale up linearised Laplace approximation of deep
neural networks to modern image classification datasets through approximating
the Gauss-Newton matrix.


title: Geodesic deviation on symmetry axis in Taub-NUT metric

abstract:
  An important aspect of General Relativity is to study properties of
geodesics. A useful tool for describing geodesic behavior is the geodesic
deviation equation. It allows to describe the tidal properties of gravitating
objects through the curvature of spacetime. This article focuses on the study
of the axially symmetric Taub-NUT metric. We study tidal effects in this metric
using the geodesic deviation equation. Radial geodesics along the symmetry axis
of spacetime are considered. We show that all spatial components of tidal
forces always change sign under the event horizon. We find a solution of the
geodesic deviation equation for all geodesic deviation vector components. It
allows us to quantify the effect of the NUT-charge on the tidal properties of
Taub-NUT metric. And another important feature that we found is the regular
behavior of all tidal force components at all points of spacetime.


title: An Initial Look at Self-Reprogramming Artificial Intelligence

abstract:
  Rapid progress in deep learning research has greatly extended the
capabilities of artificial intelligence technology. Conventional AI models are
constrained to explicit human-designed algorithms, although a growing body of
work in meta-learning, neural architecture search, and related approaches have
explored algorithms that self-modify to some extent. In this paper, we develop
and experimentally validate the first fully self-reprogramming AI system.
Applying AI-based computer code generation to AI itself, we implement an
algorithm with the ability to continuously modify and rewrite its own neural
network source code.


title:
Ultracool Dwarfs Observed with the Spitzer Infrared Spectrograph. II.
  Emergence and Sedimentation of Silicate Clouds in L Dwarfs, and Analysis of
  the Full M5--T9 Field Dwarf Spectroscopic Sample

abstract:
  We present a uniform analysis of all mid-infrared $R\approx90$ spectra of
field M5--T9 dwarfs obtained with the Spitzer Infrared Spectrograph (IRS). The
sample contains 113 spectra out of which 12 belong to late-M dwarfs, 69 to L
dwarfs, and 32 to T dwarfs. Sixty-eight of these spectra are presented for the
first time. We measure strengths of the main absorption bands in the IRS
spectra, namely H$_2$O at 6.25 $\mu$m, CH$_4$ at 7.65 $\mu$m, NH$_3$ at 10.5
$\mu$m, and silicates over 8--11 $\mu$m. Water absorption is present in all
spectra and strengthens with spectral type. The onset of methane and ammonia
occurs at the L8 and T2.5 types, respectively, although ammonia can be
detectable as early as T1.5. Silicate absorption sets in at spectral type L2,
is on average the strongest in L4--L6 dwarfs, and disappears past L8. However,
silicate absorption can also be absent from the spectra at any L subtype. We
find a positive correlation between the silicate absorption strength and the
excess (deviation from median) near-infrared colour at a given L subtype, which
supports the idea that variations of silicate cloud thickness produce the
observed colour scatter in L dwarfs. We also find that variable L3--L7 dwarfs
are twice more likely to have above-average silicate absorption than
non-variables. The ensemble of results solidifies the evidence for silicate
condensate clouds in the atmospheres of L dwarfs, and for the first time
observationally establishes their emergence and sedimentation between effective
temperatures of $\approx$2000 K and $\approx$1300 K, respectively.


title: Variational principles for topological pressures on subsets

abstract:
  In this paper, we investigate the relations between various types of
topological pressures and different versions of measure-theoretical pressures.
We extend Feng- Huang's variational principle for packing entropy to packing
pressure and obtain two new variational principles for Pesin-Pitskel and
packing pressures respectively. We show that various types of Katok pressures
for an ergodic measure with respect to a potential function are equal to the
sum of measure-theoretic entropy of this measure and the integral of the
potential function. Moreover, we obtain Billingsley type theorem for packing
pressure, which indicates that packing pressure can be determined by
measure-theoretic upper local pressure of measures, and a variational principle
for packing pressure of the set of generic points for any invariant ergodic
Borel probability measure.


title: Quantum Tomography and Schwinger's Picture of Quantum Mechanics

abstract:
  In this paper the problem of tomographic reconstruction of states is
investigated within the so-called Schwinger's picture of Quantum Mechanics in
which a groupoid is associated with every quantum system. The attention is
focused on spin tomography: In this context the groupoid of interest is the
groupoid of pairs over a finite set. In a nutshell, this groupoid is made up of
transitions between all possible pairs of outcomes belonging to a finite set.
In addition, these transitions possess a partial composition rule, generalizing
the notion of groups. The main goal of the paper consists in providing a
reconstruction formula for states on the groupoid-algebra associated with the
observables of the system. Using the group of bisections of this groupoid,
which are special subsets in one-to-one correspondence with the outcomes, a
frame is defined and it is used to prove the validity of the tomographic
reconstruction. The special case of the set of outcomes being the set of
integers modulo n, with n odd prime, is considered in detail. In this case the
subgroup of discrete affine linear transformations, whose graphs are linear
subspaces of the groupoid, provides a \textit{quorum} in close analogy with the
continuos case.


title:
Testing Overidentifying Restrictions with High-Dimensional Data and
  Heteroskedasticity

abstract:
  This paper proposes a new test of overidentifying restrictions (called the Q
test) with high-dimensional data. This test is based on estimation and
inference for a quadratic form of high-dimensional parameters. It is shown to
have the desired asymptotic size and power properties under heteroskedasticity,
even if the number of instruments and covariates is larger than the sample
size. Simulation results show that the new test performs favorably compared to
existing alternative tests (Chao et al., 2014; Kolesar, 2018; Carrasco and
Doukali, 2021) under the scenarios when those tests are feasible or not. An
empirical example of the trade and economic growth nexus manifests the
usefulness of the proposed test.


title:
FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated
  Distillation

abstract:
  Federated learning provides a privacy guarantee for generating good deep
learning models on distributed clients with different kinds of data.
Nevertheless, dealing with non-IID data is one of the most challenging problems
for federated learning. Researchers have proposed a variety of methods to
eliminate the negative influence of non-IIDness. However, they only focus on
the non-IID data provided that the universal class distribution is balanced. In
many real-world applications, the universal class distribution is long-tailed,
which causes the model seriously biased. Therefore, this paper studies the
joint problem of non-IID and long-tailed data in federated learning and
proposes a corresponding solution called Federated Ensemble Distillation with
Imbalance Calibration (FEDIC). To deal with non-IID data, FEDIC uses model
ensemble to take advantage of the diversity of models trained on non-IID data.
Then, a new distillation method with logit adjustment and calibration gating
network is proposed to solve the long-tail problem effectively. We evaluate
FEDIC on CIFAR-10-LT, CIFAR-100-LT, and ImageNet-LT with a highly non-IID
experimental setting, in comparison with the state-of-the-art methods of
federated learning and long-tail learning. Our code is available at
https://github.com/shangxinyi/FEDIC.


title:
Investigating non-minimal flavor-violating CMSSM using information
  theory

abstract:
  Flavor-violating interactions of the stop-quarks are expected to provide
additional few GeV contribution to the Higgs-Boson mass particularly when it
mixes with scharm-quarks, thereby allowing reduced supersymmetry (SUSY)
breaking scale compared to flavor-conserving constrained minimal supersymmetric
Standard Model (CMSSM). Inspired by this, we analyze the aforementioned
interactions in the context of CMSSM using the information entropy of the
Higgs-Boson for a wide region of flavor-violating CMSSM parameter space $(m_0,
m_{1/2}, A_0, \tan\beta,$ $sgn(\mu), \delta_{ct}^{ij})$ with $i, j$
representing left and right chiralities of squarks. Our information-theoretic
analysis of the aforementioned model reveals the most favorable values of
$(m_0, m_{1/2},$ $A_0, \tan\beta,\delta_{ct}^{ij})$ to be $(4.30 {\rm~TeV},
2.32 {\rm~TeV}, -4.96{\rm~ TeV}, 22.8, 0.037)$ and $(4.16 {\rm ~TeV}, 3.89$
$~{\rm TeV},-4.10~{\rm TeV}, 19.4, 0.039)$ for $(i, j) = (L, R)$ and $(R, L)$,
respectively, corresponding to the maximum entropy which suggest the SUSY
breaking scale to be about $5$ TeV thereby allowing considerably sparticles
masses than the flavor-conserving CMSSM.


title:
Single-photon scattering on a qubit. Space-time structure of the
  scattered field

abstract:
  We study the space-time structure of the scattered field induced by the
scattering of a single photon pulse on a qubit embedded in 1D open waveguide.
We show that for large distance $x$ from qubit and at times $t$ long after the
scattering event our theory for the reflected and transmitted amplitudes
provides the result which is well known from the stationary scattering
theories. However, in general, the structure of the scattered field is
different from the stationary limit. This limit is obtained when both the
distance from the qubit and the time that proceed after the scattering event
tend to infinity. The approach to the stationary limit is very slow. The
correction field decreases as the inverse powers of $x$ and $t$ as the distance
from the qubit and the time after the interaction increase. Moreover, the
scattered field contains the terms which depend only on the distance from the
qubit, but not on the time. It means that as the distance is fixed the field
corrections to the stationary limit remain unaltered no matter how long has
passed since the beginning of the scattering event.


title:
Spacetime evolution during moduli stabilization in radiation dominated
  era beyond 4D effective theory

abstract:
  We investigate the time evolution of the background spacetime during the
moduli stabilization process, which is assumed to occur in the radiation
dominated era. The setup is basically the Salam-Sezgin model, but we add a
potential term for the dilaton in order to stabilize the moduli completely. We
numerically solve the higher-dimensional background field equations, including
a case that the stabilization process cannot be described within the 4D
effective theory. In contrast to the conventional 4D effective theory analysis,
we find that when the mass scale of the stabilization is larger than the
compactification scale, the radiation contribution to the total energy density
remains to be non-negligible for a much longer time than the stabilization time
scale. As a result, the non-compact 3D space expands slower than the matter
dominated universe. We also find the equation of state for the
radiation~$w_{\rm rad}$ remains to be smaller than 1/3 for a long time, which
indicates that the radiation still feels the extra dimensions for a while even
after the moduli are stabilized.


title:
Building a Role Specified Open-Domain Dialogue System Leveraging
  Large-Scale Language Models

abstract:
  Recent open-domain dialogue models have brought numerous breakthroughs.
However, building a chat system is not scalable since it often requires a
considerable volume of human-human dialogue data, especially when enforcing
features such as persona, style, or safety. In this work, we study the
challenge of imposing roles on open-domain dialogue systems, with the goal of
making the systems maintain consistent roles while conversing naturally with
humans. To accomplish this, the system must satisfy a role specification that
includes certain conditions on the stated features as well as a system policy
on whether or not certain types of utterances are allowed. For this, we propose
an efficient data collection framework leveraging in-context few-shot learning
of large-scale language models for building role-satisfying dialogue dataset
from scratch. We then compare various architectures for open-domain dialogue
systems in terms of meeting role specifications while maintaining
conversational abilities. Automatic and human evaluations show that our models
return few out-of-bounds utterances, keeping competitive performance on general
metrics. We release a Korean dialogue dataset we built for further research.


title:
Practice Makes a Solver Perfect: Data Augmentation for Math Word Problem
  Solvers

abstract:
  Existing Math Word Problem (MWP) solvers have achieved high accuracy on
benchmark datasets. However, prior works have shown that such solvers do not
generalize well and rely on superficial cues to achieve high performance. In
this paper, we first conduct experiments to showcase that this behaviour is
mainly associated with the limited size and diversity present in existing MWP
datasets. Next, we propose several data augmentation techniques broadly
categorized into Substitution and Paraphrasing based methods. By deploying
these methods we increase the size of existing datasets by five folds.
Extensive experiments on two benchmark datasets across three state-of-the-art
MWP solvers show that proposed methods increase the generalization and
robustness of existing solvers. On average, proposed methods significantly
increase the state-of-the-art results by over five percentage points on
benchmark datasets. Further, the solvers trained on the augmented dataset
perform comparatively better on the challenge test set. We also show the
effectiveness of proposed techniques through ablation studies and verify the
quality of augmented samples through human evaluation.


title: Research on Sparsity Measures for Rotating Machinery Health Monitoring

abstract:
  Machine health management is one of the main research contents of PHM
technology, which aims to monitor the health states of machines online and
evaluate degradation stages through real-time sensor data. In recent years,
classic sparsity measures such as kurtosis, Lp/Lq norm, pq-mean, smoothness
index, negative entropy, and Gini index have been widely used to characterize
the impulsivity of repetitive transients. Since smoothness index and negative
entropy were proposed, the sparse properties have not been fully analyzed. The
first work of this paper is to analyze six properties of smoothness index and
negative entropy. In addition, this paper conducts a thorough investigation on
multivariate power average function and finds that existing classical sparsity
measures can be respectively reformulated as the ratio of multivariate power
mean functions (MPMFs). Finally, a general paradigm of index design are
proposed for the expansion of sparsity measures family, and several newly
designed dimensionless health indexes are given as examples. Two different
run-to-failure bearing datasets were used to analyze and validate the
capabilities and advantages of the newly designed health indexes. Experimental
results prove that the newly designed health indexes show good performance in
terms of monotonic degradation description, first fault occurrence time
determination and degradation state assessment.


title:
ClusterQ: Semantic Feature Distribution Alignment for Data-Free
  Quantization

abstract:
  Network quantization has emerged as a promising method for model compression
and inference acceleration. However, tradtional quantization methods (such as
quantization aware training and post training quantization) require original
data for the fine-tuning or calibration of quantized model, which makes them
inapplicable to the cases that original data are not accessed due to privacy or
security. This gives birth to the data-free quantization with synthetic data
generation. While current DFQ methods still suffer from severe performance
degradation when quantizing a model into lower bit, caused by the low
inter-class separability of semantic features. To this end, we propose a new
and effective data-free quantization method termed ClusterQ, which utilizes the
semantic feature distribution alignment for synthetic data generation. To
obtain high inter-class separability of semantic features, we cluster and align
the feature distribution statistics to imitate the distribution of real data,
so that the performance degradation is alleviated. Moreover, we incorporate the
intra-class variance to solve class-wise mode collapse. We also employ the
exponential moving average to update the centroid of each cluster for further
feature distribution improvement. Extensive experiments across various deep
models (e.g., ResNet-18 and MobileNet-V2) over the ImageNet dataset demonstrate
that our ClusterQ obtains state-of-the-art performance.


title: Katana: Dual Slicing-Based Context for Learning Bug Fixes

abstract:
  Contextual information plays a vital role for software developers when
understanding and fixing a bug. Context can also be important in deep
learning-based program repair to provide extra information about the bug and
its fix. Existing techniques, however, treat context in an arbitrary manner, by
extracting code in close proximity of the buggy statement within the enclosing
file, class, or method, without any analysis to find actual relations with the
bug. To reduce noise, they use a predefined maximum limit on the number of
tokens to be used as context. We present a program slicing-based approach, in
which instead of arbitrarily including code as context, we analyze statements
that have a control or data dependency on the buggy statement. We propose a
novel concept called dual slicing, which leverages the context of both buggy
and fixed versions of the code to capture relevant repair ingredients. We
present our technique and tool called Katana, the first to apply slicing-based
context for a program repair task. The results show Katana effectively
preserves sufficient information for a model to choose contextual information
while reducing noise. We compare against four recent state-of-the-art
context-aware program repair techniques. Our results show Katana fixes between
1.5 to 3.7 times more bugs than existing techniques.


title: W-core inverses in rings with involution

abstract:
  Let $R$ be a unital $*$-ring and let $a,w,v\in R$. The initial goal of this
work is to introduce two new classes of generalized inverses, called the
$w$-core inverse and the dual $v$-core inverse. It is shown that the core
inverse and the pseudo core inverse belongs to the defined $w$-core inverse,
and the dual core inverse and the dual pseudo core inverse are instances of the
dual $v$-core inverse. An element $a\in R$ is $w$-core invertible if there
exists some $x\in R$ such that $awx^2=x$, $xawa=a$ and $(awx)^*=awx$. Such an
$x$ is called a $w$-core inverse of $a$. The dual $v$-core inverse of $a$ is
defined by the existence of $y\in R$ satisfying $y^2va=y$, $avay=a$ and
$(yva)^*=yva$. Several characterizations of them are given, and the expression
of the $w$-core inverse (resp. the dual $v$-core inverse) of $a$ is given by
the inverse of $w$ along $a$ and $\{1,3\}$-inverses of $a$ (resp. the inverse
of $v$ along $a$ and $\{1,4\}$-inverses of $a$). Also, the characterization for
both $w$-core invertible and dual $v$-core invertible elements is given by
units. Finally, the relationships among the $w$-core inverse, the dual $v$-core
inverse and other generalized inverses are given.


title: Gender neutrality in robots: An open living review framework

abstract:
  Gender is a primary characteristic by which people organize themselves.
Previous research has shown that people tend to unknowingly ascribe gender to
robots based on features of their embodiment. Yet, robots are not necessarily
ascribed the same, or any, gender by different people. Indeed, robots may be
ascribed non-human genders or used as "genderless" alternatives. This underlies
the notion of gender neutrality in robots: neither masculine nor feminine but
somewhere in between or even beyond gender. Responding to calls for gender as a
locus of study within robotics, we offer a framework for conducting an open
living review to be updated periodically as work emerges. Significantly, we
provide an open, formalized submission process and open access dataset of
research on gender neutrality in robots. This novel and timely approach to
consensus-building is expected to pave the way for similar endeavours on other
key topics within human-robot interaction research.


title: Neither "Hear" Nor "Their": Interrogating Gender Neutrality in Robots

abstract:
  Gender is a social framework through which people organize themselves-and
non-human subjects, including robots. Research stretching back decades has
found evidence that people tend to gender artificial agents unwittingly, even
with the slightest cue of humanlike features in voice, body, role, and other
social features. This has led to the notion of gender neutrality in robots:
ways in which we can avoid gendering robots in line with human models, as well
explorations of extra-human genders. This rapid review critically surveyed the
literature to capture the state of art on gender neutrality in robots that
interact with people. We present findings on theory, methods, results, and
reflexivity. We interrogate the very idea that robot gender/ing can be neutral
and explore alternate ways of approaching gender/ing through the design and
study of robots interacting with people.


title:
A spectral element solution of the 2D linearized potential flow
  radiation problem

abstract:
  We present a scalable 2D Galerkin spectral element method solution to the
linearized potential flow radiation problem for wave induced forcing of a
floating offshore structure. The pseudo-impulsive formulation of the problem is
solved in the time-domain using a Gaussian displacement signal tailored to the
discrete resolution. The added mass and damping coefficients are then obtained
via Fourier transformation. The spectral element method is used to discretize
the spatial fluid domain, whereas the classical explicit 4-stage 4th order
Runge-Kutta scheme is employed for the temporal integration. Spectral
convergence of the proposed model is established for both affine and
curvilinear elements, and the computational effort is shown to scale with
$\mathcal{O}(N^p)$, with $N$ begin the total number of grid points and $p
\approx 1$. Temporal stability properties, caused by the spatial resolution,
are considered to ensure a stable model. The solver is used to compute the
hydrodynamic coefficients for several floating bodies and compare against known
public benchmark results. The results are showing excellent agreement,
ultimately validating the solver and emphasizing the geometrical flexibility
and high accuracy and efficiency of the proposed solver strategy. Lastly, an
extensive investigation of non-resolved energy from the pseudo-impulse is
carried out to characterise the induced spurious oscillations of the free
surface quantities leading to a verification of a proposal on how to
efficiently and accurately calculate added mass and damping coefficients in
pseudo-impulsive solvers.


title:
Protecting the Integrity of IoT Sensor Data and Firmware With A
  Feather-Light Blockchain Infrastructure

abstract:
  Smart cities deploy large numbers of sensors and collect a tremendous amount
of data from them. For example, Advanced Metering Infrastructures (AMIs), which
consist of physical meters that collect usage data about public utilities such
as power and water, are an important building block in a smart city. In a
typical sensor network, the measurement devices are connected through a
computer network, which exposes them to cyber attacks. Furthermore, the data is
centrally managed at the operator's servers, making it vulnerable to insider
threats.
  Our goal is to protect the integrity of data collected by large-scale sensor
networks and the firmware in measurement devices from cyber attacks and insider
threats. To this end, we first develop a comprehensive threat model for attacks
against data and firmware integrity, which can target any of the stakeholders
in the operation of the sensor network. Next, we use our threat model to
analyze existing defense mechanisms, including signature checks, remote
firmware attestation, anomaly detection, and blockchain-based secure logs.
However, the large size of the Trusted Computing Base and a lack of scalability
limit the applicability of these existing mechanisms. We propose the
Feather-Light Blockchain Infrastructure (FLBI) framework to address these
limitations. Our framework leverages a two-layer architecture and cryptographic
threshold signature chains to support large networks of low-capacity devices
such as meters and data aggregators. We have fully implemented the FLBI's
end-to-end functionality on the Hyperledger Fabric and private Ethereum
blockchain platforms. Our experiments show that the FLBI is able to support
millions of end devices.


title:
Reliable Label Correction is a Good Booster When Learning with Extremely
  Noisy Labels

abstract:
  Learning with noisy labels has aroused much research interest since data
annotations, especially for large-scale datasets, may be inevitably imperfect.
Recent approaches resort to a semi-supervised learning problem by dividing
training samples into clean and noisy sets. This paradigm, however, is prone to
significant degeneration under heavy label noise, as the number of clean
samples is too small for conventional methods to behave well. In this paper, we
introduce a novel framework, termed as LC-Booster, to explicitly tackle
learning under extreme noise. The core idea of LC-Booster is to incorporate
label correction into the sample selection, so that more purified samples,
through the reliable label correction, can be utilized for training, thereby
alleviating the confirmation bias. Experiments show that LC-Booster advances
state-of-the-art results on several noisy-label benchmarks, including CIFAR-10,
CIFAR-100, Clothing1M and WebVision. Remarkably, under the extreme 90\% noise
ratio, LC-Booster achieves 93.5\% and 48.4\% accuracy on CIFAR-10 and
CIFAR-100, surpassing the state-of-the-art by 1.6\% and 7.2\% respectively.


title: Examples of H\"older-stable Phase Retrieval

abstract:
  Examples are constructed of infinite-dimensional subspaces $V\subset
L^2(\mu)$ with the property that for any $f,g\in V$, if $|f|$ is approximately
equal to $|g|$ with respect to the $L^2$ norm, then there exists a unimodular
scalar $z$ such that $f$ is approximately equal to $zg$.


title:
Finding Strategies Against Misinformation in Social Media: A Qualitative
  Study

abstract:
  Misinformation spread through social media has become a fundamental challenge
in modern society. Recent studies have evaluated various strategies for
addressing this problem, such as by modifying social media platforms or
educating people about misinformation, to varying degrees of success. Our goal
is to develop a new strategy for countering misinformation: intelligent tools
that encourage social media users to foster metacognitive skills "in the wild."
As a first step, we conducted focus groups with social media users to discover
how they can be best supported in combating misinformation. Qualitative
analyses of the discussions revealed that people find it difficult to detect
misinformation. Findings also indicated a need for but lack of resources to
support cross-validation of information. Moreover, misinformation had a nuanced
emotional impact on people. Suggestions for the design of intelligent tools
that support social media users in information selection, information
engagement, and emotional response management are presented.


title: Trust in Human-AI Interaction: Scoping Out Models, Measures, and Methods

abstract:
  Trust has emerged as a key factor in people's interactions with AI-infused
systems. Yet, little is known about what models of trust have been used and for
what systems: robots, virtual characters, smart vehicles, decision aids, or
others. Moreover, there is yet no known standard approach to measuring trust in
AI. This scoping review maps out the state of affairs on trust in human-AI
interaction (HAII) from the perspectives of models, measures, and methods.
Findings suggest that trust is an important and multi-faceted topic of study
within HAII contexts. However, most work is under-theorized and under-reported,
generally not using established trust models and missing details about methods,
especially Wizard of Oz. We offer several targets for systematic review work as
well as a research agenda for combining the strengths and addressing the
weaknesses of the current literature.


title:
Robust nuclear spin entanglement via dipolar interactions in polar
  molecules

abstract:
  We propose a general protocol for on-demand generation of robust entangled
states of nuclear and/or electron spins of ultracold $^1\Sigma$ and $^2\Sigma$
polar molecules using electric dipolar interactions. By encoding a spin-1/2
degree of freedom in a combined set of spin and rotational molecular levels, we
theoretically demonstrate the emergence of effective spin-spin interactions of
the Ising and XXZ forms, enabled by efficient magnetic control over electric
dipolar interactions. We show how to use these interactions to create
long-lived cluster and squeezed spin states.


title: Exploring Gender-Expansive Categorization Options for Robots

abstract:
  Gender is increasingly being explored as a social characteristic ascribed to
robots by people. Yet, research involving social robots that may be gendered
tends not to address gender perceptions, such as through pilot studies or
manipulation checks. Moreover, research that does address gender perceptions
has been limited by a reliance on the human gender binary model of feminine and
masculine, prescriptive response options, and/or researcher assumptions and/or
ascriptions of participant gendering. In response, we conducted an online pilot
categorization study (n=55) wherein we provided gender-expansive response
options for rating four robots ranging across four levels of anthropomorphism.
Findings indicate that people gender robots in diverse ways, and not
necessarily in relation to the gender binary. Additionally, less
anthropomorphic robots and the childlike humanoid robot were deemed masculine,
while the iconic robot was deemed gender neutral, fluid, and/or ambiguous. We
discuss implications for future work on all humanoid robots.


title: Optimal Anonymous Independent Reward Scheme Design

abstract:
  We consider designing reward schemes that incentivize agents to create
high-quality content (e.g., videos, images, text, ideas). The problem is at the
center of a real-world application where the goal is to optimize the overall
quality of generated content on user-generated content platforms. We focus on
anonymous independent reward schemes (AIRS) that only take the quality of an
agent's content as input. We prove the general problem is NP-hard. If the cost
function is convex, we show the optimal AIRS can be formulated as a convex
optimization problem and propose an efficient algorithm to solve it. Next, we
explore the optimal linear reward scheme and prove it has a 1/2-approximation
ratio, and the ratio is tight. Lastly, we show the proportional scheme can be
arbitrarily bad compared to AIRS.


title: Ferroelectric nematic liquid crystalline phases

abstract:
  Recent experimental realization of ferroelectric nematic liquid crystalline
phases stimulated material development and numerous experimental studies of
these new phases, guided by their fundamental and applicative interest. In this
Perspective, we give an overview of this emerging field by linking history and
theoretical predictions to a general outlook of the development and properties
of the materials exhibiting ferroelectric nematic phases. We will highlight the
most relevant observations up-to-date, e.g., giant dielectric permittivity
values, polarization values an order of magnitude larger than in classical
ferroelectric liquid crystals, and NLO coefficients comparable to several
ferroelectric solid materials. Key observations of anchoring and electro-optic
behavior will also be examined. The collected contributions lead to a final
discussion on open challenges in materials development, theoretical
description, experimental explorations, and possible applications of the
ferroelectric phases.


title:
Are there larger stellar velocity dispersions in low redshift Type-1 AGN
  than in Type-2 AGN?

abstract:
  The main objective is to check the Unified Model (UM) expected similar
stellar velocity dispersions between Type-1 AGN and Type-2 AGN, then to provide
further clues on BH mass properties. Not similar as previous comparisons of BH
masses estimated by \msig relations to Type-2 AGN but Virial BH masses in
Type-1 AGN, reliable stellar velocity dispersions $\sigma$ measured through
absorption features around 4000\AA~ are directly compared between so-far the
largest samples of 6260 low redshift ($z~<~0.3$) Type-1 AGN and almost all the
Type-2 AGN in SDSS DR12. Although half of Type-1 AGN do not have measured
$\sigma$ due to unapparent absorption features overwhelmed by AGN activities,
both properties of mean spectra of Type-1 AGN with and without measured
$\sigma$ and positive dependence of $\sigma$ on [O~{\sc iii}] luminosity can
lead to statistically larger $\sigma$ of all the Type-1 AGN than the 6260
Type-1 AGN with measured stellar velocity dispersions. Then, direct $\sigma$
comparisons can lead to statistically larger $\sigma$ in Type-1 AGN, with
confidence level higher than 10sigma, after considering necessary effects of
different redshift and different central AGN activities. Although Type-1 AGN
have $\sigma$ only about $(9\pm3)$\% larger than Type-2 AGN, the difference
cannot be well explained at current stage. Unless there was strong evidence to
support different \msig relations or to support quite different evolution
histories between Type-1 AGN and Type-2 AGN, the statistically larger $\sigma$
in Type-1 AGN provides a strong challenge to the Unified model of AGN.


title: Holographic Schwinger-Keldysh field theory of SU(2) diffusion

abstract:
  We construct effective field theory for SU(2) isospin charge diffusion, based
on holographic Schwinger-Keldysh contour arXiv:2008.01269. The holographic
model consists of a probe SU(2) gauge field in a doubled Schwarzschild-AdS$_5$
geometry. Accurate to first order in derivative expansion, we analytically
compute the effective action up to quartic order in hydrodynamical fields. The
effective theory contains both non-Gaussianity for noises and nonlinear
interactions between noises and dynamical variables. Moreover, the effective
theory captures both thermal and quantum fluctuations, which perfectly satisfy
dynamical Kubo-Martin-Schwinger (KMS) symmetry at quantum level. Interestingly,
the dynamical KMS symmetry, which is crucial in formulating non-equilibrium
effective field theory for a quantum many-body system, is found to have a nice
holographic interpretation.


title: Effects of the acceleration on holographic complexity

abstract:
  In this work we consider a spacial kind of spacetime called AdS accelerating
black holes. This is a kind of black holes which contain a stringlike
singularity along polar axises attached to the black hole and it accelerates
the black hole. In these kind of spacetimes the growth of Einstein-Hilbert
action is independent of the acceleration as found in the previous works.
  By using a string as a probe, we found the effect of the acceleration is
captured by the string prove in our previous work. Here in this work we
consider the case of rotating black holes. By the prove string we clearly
describe the effect of the acceleration and its relation to the rotation of the
black holes.


title:
Solar observations with single-dish INAF radio telescopes: continuum
  imaging in the 18-26 GHz range

abstract:
  We present a new solar radio imaging system implemented through the upgrade
of the large single-dish telescopes of the Italian National Institute for
Astrophysics (INAF), not originally conceived for solar observations.
  During the development and early science phase of the project (2018-2020), we
obtained about 170 maps of the entire solar disk in the 18-26 GHz band, filling
the observational gap in the field of solar imaging at these frequencies. These
solar images have typical resolutions in the 0.7-2 arcmin range and a
brightness temperature sensitivity <10 K. Accurate calibration adopting the
Supernova Remnant Cas A as a flux reference, provided typical errors <3% for
the estimation of the quiet-Sun level components and for active regions flux
measurements.
  As a first early science result of the project, we present a catalog of radio
continuum solar imaging observations with Medicina 32-m and SRT 64-m radio
telescopes including the multi-wavelength identification of active regions,
their brightness and spectral characterization. The interpretation of the
observed emission as thermal bremsstrahlung components combined with
gyro-magnetic variable emission pave the way to the use of our system for
long-term monitoring of the Sun. We also discuss useful outcomes both for solar
physics (e.g. study of the chromospheric network dynamics) and space weather
applications (e.g. flare precursors studies).


title: Temporal witnesses of non-classicality and conservation laws

abstract:
  A general entanglement-based witness of non-classicality has recently been
proposed, which can be applied to testing quantum effects in gravity. This
witness is based on generating entanglement between two quantum probes via a
mediator. In this paper we provide a "temporal" variant of this witness, using
a single quantum probe to assess the non-classicality of the mediator. Within
the formalism of quantum theory, we show that if a system $M$ is capable of
inducing a coherent dynamical evolution of a quantum system $Q$, in the
presence of a conservation law, then $M$ must be non-classical. This argument
supports witnesses of non-classicality relying on a single quantum probe, which
can be applied to a number of open issues, notably in quantum gravity or
quantum biology.


title:
"And Then There Were None": Cracking White-box DNN Watermarks via
  Invariant Neuron Transforms

abstract:
  Recently, how to protect the Intellectual Property (IP) of deep neural
networks (DNN) becomes a major concern for the AI industry. To combat potential
model piracy, recent works explore various watermarking strategies to embed
secret identity messages into the prediction behaviors or the internals (e.g.,
weights and neuron activation) of the target model. Sacrificing less
functionality and involving more knowledge about the target model, the latter
branch of watermarking schemes (i.e., white-box model watermarking) is claimed
to be accurate, credible and secure against most known watermark removal
attacks, with emerging research efforts and applications in the industry.
  In this paper, we present the first effective removal attack which cracks
almost all the existing white-box watermarking schemes with provably no
performance overhead and no required prior knowledge. By analyzing these IP
protection mechanisms at the granularity of neurons, we for the first time
discover their common dependence on a set of fragile features of a local neuron
group, all of which can be arbitrarily tampered by our proposed chain of
invariant neuron transforms. On $9$ state-of-the-art white-box watermarking
schemes and a broad set of industry-level DNN architectures, our attack for the
first time reduces the embedded identity message in the protected models to be
almost random. Meanwhile, unlike known removal attacks, our attack requires no
prior knowledge on the training data distribution or the adopted watermark
algorithms, and leaves model functionality intact.


title: New approaches to particle induced prompt gamma imaging

abstract:
  The distribution of the prompt gamma emissions induced by proton interactions
in a target material carries information concerning the proton range and target
composition. We propose multiple approaches for prompt gamma imaging depending
on the quantity of interest and the detection scenario. The first approach is a
practical design based on knife-edge collimator utilized in tandem with a
compact PET type detector to verify range by measuring depth distribution of
prompt gamma in an efficient manner. A second approach aims at unambiguously
identifying the presence of protons in a region of interest by introducing a
fiducial marker and measuring the characteristic prompt gamma of the marker
against background material. A third approach illustrates a novel design for
position sensitive gamma imaging with a millimeter level adjustable position
resolution with a high detection efficiency which is a step towards 3D prompt
gamma imaging, useful if the goal is to better understand the target
composition. Simulations of the designs were performed using GATE/Geant4 Monte
Carlo framework. Experimental tests for some cases were conducted at the proton
facilities in INER and Chang Gung Memorial hospital. Using the knife-edge PET
approach a range verification of $\pm$ 0.7 mm can be achieved for shifts within
a 1 cm near the tumor region and up to $\pm$ 4 mm for shifts within a 4 cm
window. For the fiducial marker approach we identify 984 keV as the dominant
prompt gamma and show that for the 50% relative PG intensity, the R80 position
falls within a 3 mm thick $^{48}$Ti marker. Using the position sensitive gamma
imaging approach we present a feasible design to achieve spatial resolution
values of 2.6 mm with a detection efficiency of 5.4E-6 and up to eight depth
positions in a single run. The relative advantage of these methods and the
challenges in the implementation are discussed.


title:
Late-time critical behavior of local string-like observables under
  quantum quenches

abstract:
  In recent times it has been observed that signatures of equilibrium quantum
criticality surprisingly show up in many-body systems which are manifestly far
from equilibrium. We explore such scenarios in interacting spin systems subject
to a quench and develop a robust method to systematically probe ground state
critical physics through nonequilibrium post-quench dynamics. Analyzing
late-time behavior of finite string-like observables, we find emerging sharp
signatures of equilibrium criticality. Specifically, these observables
accurately detect equilibrium critical points and universal critical exponents
after long times following a quench. This happens despite the fact that the
analyzed systems are chaotic/ergodic and is interestingly due to a strong
memory of the initial conditions retained by these observables after quench. We
find that our results can also be used to explain critical signatures in
post-quench domain formation, seen in a recent experiment with trapped ion
quantum simulators.


title:
Explainable Artificial Intelligence for Bayesian Neural Networks:
  Towards trustworthy predictions of ocean dynamics

abstract:
  The trustworthiness of neural networks is often challenged because they lack
the ability to express uncertainty and explain their skill. This can be
problematic given the increasing use of neural networks in high stakes
decision-making such as in climate change applications. We address both issues
by successfully implementing a Bayesian Neural Network (BNN), where parameters
are distributions rather than deterministic, and applying novel implementations
of explainable AI (XAI) techniques. The uncertainty analysis from the BNN
provides a comprehensive overview of the prediction more suited to
practitioners' needs than predictions from a classical neural network. Using a
BNN means we can calculate the entropy (i.e. uncertainty) of the predictions
and determine if the probability of an outcome is statistically significant. To
enhance trustworthiness, we also spatially apply the two XAI techniques of
Layer-wise Relevance Propagation (LRP) and SHapley Additive exPlanation (SHAP)
values. These XAI methods reveal the extent to which the BNN is suitable and/or
trustworthy. Using two techniques gives a more holistic view of BNN skill and
its uncertainty, as LRP considers neural network parameters, whereas SHAP
considers changes to outputs. We verify these techniques using comparison with
intuition from physical theory. The differences in explanation identify
potential areas where new physical theory guided studies are needed.


title:
A universal robust limit theorem for nonlinear L\'evy processes under
  sublinear expectation

abstract:
  This article establishes a universal robust limit theorem under a sublinear
expectation framework. Under moment and consistency conditions, we show that,
for $\alpha \in (1,2)$, the i.i.d. sequence \begin{equation*} \left\{ \left(
\frac{1}{\sqrt{n}}\sum_{i=1}^{n}X_{i},\frac{1}{n}
\sum_{i=1}^{n}Y_{i},\frac{1}{\sqrt[\alpha ]{n}}\sum_{i=1}^{n}Z_{i}\right)
\right\} _{n=1}^{\infty } \end{equation*} converges in distribution to
$\tilde{L}_{1}$, where
$\tilde{L}_{t}=(\tilde{\xi}_{t},\tilde{\eta}_{t},\tilde{\zeta}_{t})$, $t\in
\lbrack 0,1]$, is a multidimensional nonlinear L\'{e}vy process with an
uncertainty set $\Theta$ as a set of L\'{e}vy triplets. This nonlinear L\'{e}vy
process is characterized by a fully nonlinear degenerate partial
integro-differential equation (PIDE) \begin{equation*} \left\{ \begin{array}{l}
\displaystyle\partial _{t}u(t,x,y,z)-\sup\limits_{(F_{\mu },q,Q)\in \Theta
}\left\{ \int_{\mathbb{R}^{d}}\delta _{\lambda }u(t,x,y,z)F_{\mu }(d\lambda
)\right. \\ \displaystyle\text{\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ }\left. +\langle
D_{y}u(t,x,y,z),q\rangle +\frac{1}{2}tr[D_{x}^{2}u(t,x,y,z)Q]\right\} =0, \\
\displaystyle u(0,x,y,z)=\phi (x,y,z),\ \ \forall (t,x,y,z)\in \lbrack
0,1]\times \mathbb{R}^{3d}, \end{array} \right. \end{equation*} with $\delta
_{\lambda }u(t,x,y,z):=u(t,x,y,z+\lambda )-u(t,x,y,z)-\langle
D_{z}u(t,x,y,z),\lambda \rangle $. To construct the limit process
$(\tilde{L}_{t})_{t\in \lbrack 0,1]}$, we develop a novel weak convergence
approach based on the notion of tightness and weak compactness on a sublinear
expectation space. We further prove a new type of L\'{e}vy-Khintchine
representation formula to characterize $(\tilde{L}_{t})_{t\in \lbrack 0,1]}$.
As a byproduct, we also provide a probabilistic approach to prove the existence
of the above fully nonlinear degenerate PIDE.


title:
Minimization of Secrecy Outage Probability in Reconfigurable Intelligent
  Surface-Assisted MIMOME System

abstract:
  This article investigates physical layer security (PLS) in reconfigurable
intelligent surface (RIS)-assisted multiple-input multiple-output
multiple-antenna-eavesdropper (MIMOME) channels. Existing researches ignore the
problem that secrecy rate can not be calculated if the eavesdropper's
instantaneous channel state information (CSI) is unknown. Furthermore, without
the secrecy rate expression, beamforming and phase shifter optimization with
the purpose of PLS enhancement is not available. To address these problems, we
first give the expression of secrecy outage probability for any beamforming
vector and phase shifter matrix as the RIS-assisted PLS metric, which is
measured based on the eavesdropper's statistical CSI. Then, with the aid of the
expression, we formulate the minimization problem of secrecy outage probability
that is solved via alternately optimizing beamforming vectors and phase shift
matrices. In the case of single-antenna transmitter or single-antenna
legitimate receiver, the proposed alternating optimization (AO) scheme can be
simplified to reduce computational complexity. Finally, it is demonstrated that
the secrecy outage probability is significantly reduced with the proposed
methods compared to current RIS-assisted PLS systems.


title:
Fluorinated graphene films with graphene quantum dots for electronic
  applications

abstract:
  This work analyzes carrier transport, the relaxation of non-equilibrium
charge, and the electronic structure of fluorinated graphene (FG) films with
graphene quantum dots (GQDs). The FG films with GQDs were fabricated by means
of chemical functionalization in an aqueous solution of hydrofluoric acid. High
fluctuations of potential relief inside the FG barriers have been detected in
the range of up to 200 mV. A phenomenological expression that describes the
dependence of the time of non-equilibrium charge emission from GQDs on quantum
confinement levels and film thickness (potential barrier parameters between
GQDs) is suggested. An increase in the degree of functionalization leads to a
decrease in GQD size, the removal of the GQD effect on carrier transport, and
the relaxation of non-equilibrium charge. The study of the electronic
properties of FG films with GQDs has revealed a unipolar resistive switching
effect in the films with a relatively high degree of fluorination and a high
current modulation in transistorlike structures with a lower degree of
fluorination. 2D films with GQDs are believed to have considerable potential
for various electronic applications (nonvolatile memory, 2D connections with
optical control and logic elements).


title:
Taylor, Can You Hear Me Now? A Taylor-Unfolding Framework for Monaural
  Speech Enhancement

abstract:
  While the deep learning techniques promote the rapid development of the
speech enhancement (SE) community, most schemes only pursue the performance in
a black-box manner and lack adequate model interpretability. Inspired by
Taylor's approximation theory, we propose an interpretable decoupling-style SE
framework, which disentangles the complex spectrum recovery into two separate
optimization problems \emph{i.e.}, magnitude and complex residual estimation.
Specifically, serving as the 0th-order term in Taylor's series, a filter
network is delicately devised to suppress the noise component only in the
magnitude domain and obtain a coarse spectrum. To refine the phase
distribution, we estimate the sparse complex residual, which is defined as the
difference between target and coarse spectra, and measures the phase gap. In
this study, we formulate the residual component as the combination of various
high-order Taylor terms and propose a lightweight trainable module to replace
the complicated derivative operator between adjacent terms. Finally, following
Taylor's formula, we can reconstruct the target spectrum by the superimposition
between 0th-order and high-order terms. Experimental results on two benchmark
datasets show that our framework achieves state-of-the-art performance over
previous competing baselines in various evaluation metrics. The source code is
available at github.com/Andong-Lispeech/TaylorSENet.


title: Increasing performance of planar PIV

abstract:
  To date, the iterative image deformation method of PIV for two-pulse
measurements is widely used in experimental fluid dynamics due to its
robustness in many scientific and industrial applications. However, it has a
known limitation associated with the difficulty of image deformation due to
errors that occur when calculating the necessary velocity derivatives using
higher-order schemes. In this work, we propose a simple method that helps to
noticeably improve the accuracy of the measured velocities and their
derivatives, and thereby increase the spatial resolution. The method does not
require the recovery of erroneous velocity vectors, avoids the numerical
calculation of derivatives necessary for the interrogation window deformation
using finite difference schemes, and can be easily applied in practice.

categories: physics.flu-dyn physics.comp-ph physics.data-an physics.ins-det


title: Security and Privacy in Virtual Reality -- A Literature Survey

abstract:
  Virtual Reality (VR) is a multibillionaire market that keeps growing, year
after year. As VR is becoming prevalent in households and small businesses, it
is critical to address the effects that this technology might have on the
privacy and security of its users. In this paper, we explore the
state-of-the-art in VR privacy and security, we categorise potential issues and
threats, and we analyse causes and effects of the identified threats. Besides,
we focus on the research previously conducted in the field of authentication in
VR, as it stands as the most investigated area in the topic. We also provide an
overview of other interesting uses of VR in the field of cybersecurity, such as
the use of VR to teach cybersecurity or evaluate the usability of security
solutions.


title: Morphological stability and identity in self-organizing systems

abstract:
  In this essay I aim to investigate and discuss the process through which
bundles of things "self-organize" into other things. In particular, I engage in
such investigation by trying to apply a framework of analysis of structural
stability of morphological forms to the context of a continuous cellular
automata known as $\textit{Lenia}$, which displays great diversity of
distinguishable, individuated complex subsystems, who behave autonomously and
in lifelike manner (such subsystems are often known as $\textit{solitons}$). I
do so in order to suggest a path to the development of methods to formally
distinguish said subsystems from their environment.


title: Software Testing for Machine Learning

abstract:
  Machine learning has become prevalent across a wide variety of applications.
Unfortunately, machine learning has also shown to be susceptible to deception,
leading to errors, and even fatal failures. This circumstance calls into
question the widespread use of machine learning, especially in safety-critical
applications, unless we are able to assure its correctness and trustworthiness
properties. Software verification and testing are established technique for
assuring such properties, for example by detecting errors. However, software
testing challenges for machine learning are vast and profuse - yet critical to
address. This summary talk discusses the current state-of-the-art of software
testing for machine learning. More specifically, it discusses six key challenge
areas for software testing of machine learning systems, examines current
approaches to these challenges and highlights their limitations. The paper
provides a research agenda with elaborated directions for making progress
toward advancing the state-of-the-art on testing of machine learning.


title: DefakeHop++: An Enhanced Lightweight Deepfake Detector

abstract:
  On the basis of DefakeHop, an enhanced lightweight Deepfake detector called
DefakeHop++ is proposed in this work. The improvements lie in two areas. First,
DefakeHop examines three facial regions (i.e., two eyes and mouth) while
DefakeHop++ includes eight more landmarks for broader coverage. Second, for
discriminant features selection, DefakeHop uses an unsupervised approach while
DefakeHop++ adopts a more effective approach with supervision, called the
Discriminant Feature Test (DFT). In DefakeHop++, rich spatial and spectral
features are first derived from facial regions and landmarks automatically.
Then, DFT is used to select a subset of discriminant features for classifier
training. As compared with MobileNet v3 (a lightweight CNN model of 1.5M
parameters targeting at mobile applications), DefakeHop++ has a model of 238K
parameters, which is 16% of MobileNet v3. Furthermore, DefakeHop++ outperforms
MobileNet v3 in Deepfake image detection performance in a weakly-supervised
setting.


title: Aggregation of Stack Trace Similarities for Crash Report Deduplication

abstract:
  The automatic collection of stack traces in bug tracking systems is an
integral part of many software projects and their maintenance. However, such
reports often contain a lot of duplicates, and the problem of de-duplicating
them into groups arises. In this paper, we propose a new approach to solve the
deduplication task and report on its use on the real-world data from JetBrains,
a leading developer of IDEs and other software. Unlike most of the existing
methods, which assign the incoming stack trace to a particular group in which a
single most similar stack trace is located, we use the information about all
the calculated similarities to the group, as well as the information about the
timestamp of the stack traces. This approach to aggregating all available
information shows significantly better results compared to existing solutions.
The aggregation improved the results over the state-of-the-art solutions by 15
percentage points in the Recall Rate Top-1 metric on the existing NetBeans
dataset and by 8 percentage points on the JetBrains data. Additionally, we
evaluated a simpler k-Nearest Neighbors approach to aggregation and showed that
it cannot reach the same levels of improvement. Finally, we studied what
features from the aggregation contributed the most towards better quality to
understand which of them to develop further. We publish the implementation of
the suggested approach, and will release the newly collected industrial dataset
upon acceptance to facilitate further research in the area.


title:
Radial distribution of the carbonaceous nano-grains in the
  protoplanetary disk around HD 169142

abstract:
  HD 169142 is part of the class of (pre-)transitional protoplanetary disks
showing multiple carbon nanodust spectroscopic signatures (aromatic, aliphatic)
dominating the infrared spectrum. Precise constraints on the spatial
distribution and properties of carbonaceous dust particles are essential to
understanding the physics of the disk. The HD 169142 disk is seen almost
face-on and thus offers a unique opportunity to study the dust radial
evolution. We investigate the spatial distribution and properties of the
carriers of several dust aromatic emission features in the disk across a broad
spatial range (10-200 AU). We analysed imaging and spectroscopic observations
in the 8-12 microns range from VLT/VISIR, as well as adaptive optics
spectroscopic observations in the 3-4 microns range from VLT/NACO. The data
probes the spatial evolution of the 3.3, 8.6, and 11.3 microns aromatic bands.
To constrain the radial distribution of carbonaceous nano-grains, the
observations were compared to models using The Heterogeneous dust Evolution
Model for Interstellar Solids (THEMIS), integrated into the POLARIS radiative
transfer code by calculating the thermal and stochastic heating of
sub-micrometer dust grains. Our data show predominant nano-particle emission at
all radii (resolution of about 0.1", 12 AU at 3 microns and 0.3", 35 AU at 10
microns) in the HD 169142 disk. This unambiguously shows that carbonaceous
nano-grains dominate radiatively the infrared spectrum in most of the disk, as
suggested by previous studies. In order to account for both VISIR and NACO
emission maps, we show the need for aromatic particles distributed within the
disk from the outermost regions to a radius of 20 AU, corresponding to the
outer limit of the inner cavity derived from previous observations. In the
inner cavity, these aromatic particles might be present but their abundance
would then be significantly decreased.


title:
Coarse-to-Fine Video Denoising with Dual-Stage Spatial-Channel
  Transformer

abstract:
  Video denoising aims to recover high-quality frames from the noisy video.
While most existing approaches adopt convolutional neural networks(CNNs) to
separate the noise from the original visual content, however, CNNs focus on
local information and ignore the interactions between long-range regions.
Furthermore, most related works directly take the output after spatio-temporal
denoising as the final result, neglecting the fine-grained denoising process.
In this paper, we propose a Dual-stage Spatial-Channel Transformer (DSCT) for
coarse-to-fine video denoising, which inherits the advantages of both
Transformer and CNNs. Specifically, DSCT is proposed based on a progressive
dual-stage architecture, namely a coarse-level and a fine-level to extract
dynamic feature and static feature, respectively. At both stages, a
Spatial-Channel Encoding Module(SCEM) is designed to model the long-range
contextual dependencies at spatial and channel levels. Meanwhile, we design a
Multi-scale Residual Structure to preserve multiple aspects of information at
different stages, which contains a Temporal Features Aggregation Module(TFAM)
to summarize the dynamic representation. Extensive experiments on four publicly
available datasets demonstrate our proposed DSCT achieves significant
improvements compared to the state-of-the-art methods.


title:
An attention model for the formation of collectives in real-world
  domains

abstract:
  We consider the problem of forming collectives of agents for real-world
applications aligned with Sustainable Development Goals (e.g., shared mobility,
cooperative learning). We propose a general approach for the formation of
collectives based on a novel combination of an attention model and an integer
linear program (ILP). In more detail, we propose an attention encoder-decoder
model that transforms a collective formation instance to a weighted set packing
problem, which is then solved by an ILP. Results on two real-world domains
(i.e., ridesharing and team formation for cooperative learning) show that our
approach provides solutions that are comparable (in terms of quality) to the
ones produced by state-of-the-art approaches specific to each domain. Moreover,
our solution outperforms the most recent general approach for forming
collectives based on Monte Carlo tree search.


title: Twisted geometry for submanifolds of $\mathbb{R}^n$

abstract:
  This is a friendly introduction to our recent general procedure for
constructing noncommutative deformations of an embedded submanifold $M$ of
$\mathbb{R}^n$ determined by a set of smooth equations $f^a(x)=0$. We use the
framework of Drinfel'd twist deformation of differential geometry pioneered in
[Aschieri et al., Class. Quantum Gravity 23 (2006), 1883]; the commutative
pointwise product is replaced by a (generally noncommutative) $\star$-product
induced by a Drinfel'd twist.


title:
A New Evaluation Method: Evaluation Data and Metrics for Chinese Grammar
  Error Correction

abstract:
  As a fundamental task in natural language processing, Chinese Grammatical
Error Correction (CGEC) has gradually received widespread attention and become
a research hotspot. However, one obvious deficiency for the existing CGEC
evaluation system is that the evaluation values are significantly influenced by
the Chinese word segmentation results or different language models. The
evaluation values of the same error correction model can vary considerably
under different word segmentation systems or different language models.
However, it is expected that these metrics should be independent of the word
segmentation results and language models, as they may lead to a lack of
uniqueness and comparability in the evaluation of different methods. To this
end, we propose three novel evaluation metrics for CGEC in two dimensions:
reference-based and reference-less. In terms of the reference-based metric, we
introduce sentence-level accuracy and char-level BLEU to evaluate the corrected
sentences. Besides, in terms of the reference-less metric, we adopt char-level
meaning preservation to measure the semantic preservation degree of the
corrected sentences. We deeply evaluate and analyze the reasonableness and
validity of the three proposed metrics, and we expect them to become a new
standard for CGEC.


title:
Distributed Exponential State Estimation of Linear Systems over Jointly
  Connected Switching Networks

abstract:
  Recently, an asymptotic convergence result on the distributed state
estimation problem for linear systems over jointly connected switching networks
was given. This paper further presents an exponential convergence result on the
distributed state estimation problem for linear systems over jointly connected
switching networks. In comparison with the existing result, the strengthened
result offers two advantages. First, the exponential convergence result leads
to the guaranteed convergence rate, which is much desired in practice. Second,
since exponential stability is equivalent to uniform asymptotic stability, the
strengthened result implies that the error system is totally stable and hence
being able to withstand small disturbances. These two advantages are achieved
by establishing the exponential stability of two classes of linear time-varying
systems, which may have other applications.


title: Non-parametric power-law surrogates

abstract:
  Power-law distributions are essential in computational and statistical
investigations of extreme events and complex systems. The usual technique to
generate power-law distributed data is to first infer the scale exponent
$\alpha$ using the observed data of interest and then sample from the
associated distribution. This approach has important limitations because it
relies on a fixed $\alpha$ (e.g., it has limited applicability in testing the
{\it family} of power-law distributions) and on the hypothesis of independent
observations (e.g., it ignores temporal correlations and other constraints
typically present in complex systems data). Here we propose a constrained
surrogate method that overcomes these limitations by choosing uniformly at
random from a set of sequences exactly as likely to be observed under a
discrete power-law as the original sequence (i.e., regardless of $\alpha$) and
by showing how additional constraints can be imposed in the sequence (e.g., the
Markov transition probability between states). This non-parametric approach
involves redistributing observed prime factors to randomize values in
accordance with a power-law model but without restricting ourselves to
independent observations or to a particular $\alpha$. We test our results in
simulated and real data, ranging from the intensity of earthquakes to the
number of fatalities in disasters.


title:
Channel Measurement, Characterization and Modeling for Terahertz Indoor
  Communications Above 200 GHz

abstract:
  Terahertz (THz) communications are envisioned as a promising technology for
sixth-generation (6G) and beyond systems, owing to its unprecedented
multi-gigahertz (GHz) bandwidth. In this paper, channel measurement campaigns
in indoor scenarios at 201-209~GHz are reported. Four different communication
scenarios including 90 transmitter-receiver pairs are measured in two channel
measurement campaigns of a meeting room and an office room, respectively. The
two measurement campaigns contains four scenarios, namely, a meeting room,
cubicle area, hallway and non-line-of-sight (NLoS) case. The propagation of
multi-path components (MPCs) in the four scenarios is characterized by the
power-delay-angular profiles. Based on them, the temporal and spatial
consistency for varying receiver locations in the complex hallway and NLoS
scenarios are verified. To characterize, the large-scale best-direction and
omni-directional path losses in indoor scenarios are separately analyzed and
modeled by the close-in (CI) model. Furthermore, the small-scale channel
parameters, e.g., the number of clusters, delay spread, angular spread, and
cluster time-of-arrival are analyzed and modeled by proper distributions. As a
general framework, a ray-tracing-statistical hybrid model is proposed for
wireless propagation at 201-209~GHz, although, admittedly, the measurement
results and analysis reveal that the channel characteristics in various indoor
scenarios exhibit noticeable differences that need tailored parameter settings.


title: What Petri Net Obliges Us to Say

abstract:
  We identify and demonstrate a weakness of Petri Nets (PN) in specifying
complex behavior. Specifically, we show how, when specifying multiple
requirements in one Petri net model, modelers are obliged to specify mechanisms
for combining these requirements. This yields, in many cases,
over-specification and incorrect models. We demonstrate how some execution
paths are missed, and some are generated unintentionally. To support this
claim, we analyze Petri net models from the literature, identify the
combination mechanisms, and demonstrate their effect on the correctness of the
model. To address this problem, we propose to model the system behavior using
behavioral programming (BP), a software development and modeling paradigm
designed for seamless integration of independent requirements. Specifically, we
demonstrate how the semantics of BP, which define how to interweave scenarios
into a single model, allow avoiding the over-specification. Additionally, while
BP maintains the same mathematical properties as PN, it provides means for
changing the model dynamically, thus increasing the agility of the
specification. We compare BP and PN in quantitative and qualitative measures by
analyzing the models, their generated execution paths, and the specification
process. Finally, while BP is supported by tools that allow for applying formal
methods and reasoning techniques to the model, it lacks the legacy of PN tools
and algorithms. To address this issue, we propose semantics and a tool for
translating BP models to PN and vice versa.


title: StorSeismic: A new paradigm in deep learning for seismic processing

abstract:
  Machine learned tasks on seismic data are often trained sequentially and
separately, even though they utilize the same features (i.e. geometrical) of
the data. We present StorSeismic, as a framework for seismic data processing,
which consists of neural network pre-training and fine-tuning procedures. We,
specifically, utilize a neural network as a preprocessing model to store
seismic data features of a particular dataset for any downstream tasks. After
pre-training, the resulting model can be utilized later, through a fine-tuning
procedure, to perform tasks using limited additional training. Used often in
Natural Language Processing (NLP) and lately in vision tasks, BERT
(Bidirectional Encoder Representations from Transformer), a form of a
Transformer model, provides an optimal platform for this framework. The
attention mechanism of BERT, applied here on a sequence of traces within the
shot gather, is able to capture and store key geometrical features of the
seismic data. We pre-train StorSeismic on field data, along with synthetically
generated ones, in the self-supervised step. Then, we use the labeled synthetic
data to fine-tune the pre-trained network in a supervised fashion to perform
various seismic processing tasks, like denoising, velocity estimation, first
arrival picking, and NMO. Finally, the fine-tuned model is used to obtain
satisfactory inference results on the field data.

categories: cs.LG eess.SP physics.comp-ph physics.geo-ph


title:
A Method of the Quasidifferential Descent in a Problem of Bringing a
  Nonsmooth System from One Point to Another

abstract:
  The paper considers the problem of constructing a program control for an
object described by a system with nonsmooth (but only quasidifferentiable)
right-hand side. The goal of control is to bring such a system from a given
initial position to a given final state in a certain finite time. The
admissible controls are piecewise continuous and bounded vector-functions with
values from some parallelepiped. The original problem is reduced to an
unconditional minimization of some penalty functional, which takes into account
constraints in the form of differential equations, constraints on the initial
and the final positions of the object, as well as constraints on controls.
Moreover, it is known that this functional vanishes on the solution of the
original problem and only on it. The quasidifferentiability of this functional
is proved, necessary and sufficient conditions for its minimum are written out
in terms of quasidifferential. Further, in order to solve the obtained
minimization problem in the functional space, the method of the
quasidifferential descent is applied. The algorithm developed is demonstrated
by examples.


title: Loss Function Entropy Regularization for Diverse Decision Boundaries

abstract:
  Is it possible to train several classifiers to perform meaningful
crowd-sourcing to produce a better prediction label set without any
ground-truth annotation? In this paper, we will attempt to modify the
contrastive learning objectives to automatically train a self-complementing
ensemble to produce a state-of-the-art prediction on the CIFAR10 and
CIFAR100-20 task. This paper will present a remarkably simple method to modify
a single unsupervised classification pipeline to automatically generate an
ensemble of neural networks with varied decision boundaries to learn a larger
feature set of classes. Loss Function Entropy Regularization (LFER), are
regularization terms to be added upon the pre-training and contrastive learning
objective functions, gives us a gear to modify the entropy state of the output
space of unsupervised learning, thereby diversifying the latent representation
of decision boundaries of neural networks. Ensemble trained with LFER have
higher successful prediction accuracy for samples near decision boundaries.
LFER is a effective gear to perturb decision boundaries, and has proven to be
able to produce classifiers that beat state-of-the-art at contrastive learning
stage. Experiments show that LFER can produce an ensemble where each have
accuracy comparable to the state-of-the-art, yet have each have varied latent
decision boundaries. It allows us to essence perform meaningful verification
for samples near decision boundaries, encouraging correct classification of
near-boundary samples. By compounding the probability of correct prediction of
a single sample amongst an ensemble of neural network trained, our method is
able to improve upon a single classifier by denoising and affirming correct
feature mappings.


title:
Recognising Known Configurations of Garments For Dual-Arm Robotic
  Flattening

abstract:
  Robotic deformable-object manipulation is a challenge in the robotic industry
because deformable objects have complicated and various object states.
Predicting those object states and updating manipulation planning are
time-consuming and computationally expensive. In this paper, we propose an
effective robotic manipulation approach for recognising 'known configurations'
of garments with a 'Known Configuration neural Network' (KCNet) and choosing
pre-designed manipulation plans based on the recognised known configurations.
Our robotic manipulation plan features a four-action strategy: finding two
critical grasping points, stretching the garments, and lifting down the
garments. We demonstrate that our approach only needs 98 seconds on average to
flatten garments of five categories.


title: Chaotic dynamics of string around the conformal black hole

abstract:
  In this paper, we make a systematical and in-depth study on the chaotic
dynamics of the string around the conformal black hole. Depending on the
characteristic parameter of the conformal black hole and the initial position
of the string, there are three kinds of dynamical behaviors: ordered, chaotic
and being captured, chaotic but not being captured. A particular interesting
observation is that there is a sharp transition in chaotic dynamics when the
black hole horizon disappears, which is indepent of the initial position of the
string. It provides a possible way to probe the horizon structure of the
massive body. We also examine the generalized MSS (Maldacena, Shenker and
Stanford) inequality, which is proposed in holographic dual field theory, and
find that the generalized MSS inequality holds even in the asymptotically flat
black hole background. Especially, as the initial position of the string
approaches the black hole horizon, the Lyapunov exponent also approaches the
upper bound of the generalized MSS inequality.


title:
Transport properties of a $3$-dimensional holographic effective theory
  with gauge-axion coupling

abstract:
  In this paper, we implement a $3$-dimensional holographic effective theory
with gauge-axion coupling. The analytical black hole solution is worked out. We
investigate the Direct current (DC) thermoelectric conductivities. A novel
property is that DC electric conductivity for vanishing gauge-axion coupling is
temperature dependent. It is different from that of $4$-dimensional axion model
whose DC electric conductivity is temperature independent. In addition, the
gauge-axion coupling induces a metal insulator transition (MIT) at zero
temperature. The properties of other DC thermoelectric conductivities are also
discussed. Moreover we find that the Wiedemann-Franz (WF) law is violated in
our model.


title:
Realizing a robust, reconfigurable active quenching design for multiple
  architectures of single-photon avalanche detectors

abstract:
  Most active quench circuits used for single-photon avalanche detectors are
designed either with discrete components which lack the flexibility of
dynamically changing the control parameters, or with custom ASICs which require
a long development time and high cost. As an alternative, we present a
reconfigurable and robust hybrid design implemented using a System-on-Chip
(SoC), which integrates both an FPGA and a microcontroller. We take advantage
of the FPGA's speed and configuration capabilities to vary the quench and reset
parameters dynamically over a large range, thus allowing our circuit to operate
with a wide variety of APDs without having to re-design the system. The
microcontroller enables the remote adjustment of control parameters and
re-calibration of APDs in the field. The ruggedized design uses components with
space heritage, thus making it suitable for space-based applications in the
fields of telecommunications and quantum key distribution (QKD). We
characterize our circuit with a commercial APD cooled to 253K, and obtain a
deadtime of 35ns while maintaining the after-pulsing probability at close to
3%. We also demonstrate versatility of the circuit by directly testing custom
fabricated chip-scale APDs, which paves the way for automated wafer-scale
testing and characterization.


title:
Electron dynamics in small magnetospheres: insights from global
  fully-kinetic plasma simulations of planet Mercury

abstract:
  The planet Mercury possesses a small but highly dynamic magnetosphere in
which the role and dynamics of electrons are still largely unknown. We aim at
modeling the global dynamics of solar wind electrons impinging on Mercury's
magnetosphere. Particular relevance is given to local acceleration processes
and the global circulation patterns. The goals of this work are pursued by
means of three-dimensional, fully kinetic particle-in-cell simulations modeling
the interaction of the solar wind with the Hermean magnetosphere. This method
allows a self-consistent representation of the plasma dynamics from the large
planetary scale down to the electron kinetic scale. Numerical simulations are
carried out using two different solar wind conditions: purely northward or
purely southward interplanetary magnetic field direction. We find a high plasma
current (of the order of few $\mu$A/m2) flowing at the magnetospheric
boundaries (bow shock and magnetopause) dominated by electrons. This current is
driven by the small-scale electron physics resolved in our model. Furthermore,
we observe strong electron acceleration up to tens of keV as a consequence of
magnetic reconnection when the interplanetary magnetic field is directed
southward. Such energetic electrons are partially trapped in the dipolar
magnetic field of the planet mainly at nightside. Finally, by studying the
distribution of electrons in our simulations along Mariner10 and BepiColombo
first-Mercury-flyby trajectories, we propose that both spacecraft observed this
energetic quasi-trapped electron population around closest approach.


title:
A semilinear partial differential equation induced by Hermitian
  Yang-Mills metrics

abstract:
  This paper will discuss a class of semilinear partial differential equations
induced by studying the limiting behaviour of Hermitian Yang-Mills metrics. We
will study the radial symmetry of the $C^{2}$ global solution of this equation
in $\mathbb{R}^{2}$ and the existence of $C^{2,\alpha}$ solution of the
Dirichlet boundary value problem in any bounded domain.


title:
Theoretical analysis on the stability of 1-pyrenebutanoic acid,
  succinimidyl ester adsorbed on graphene

abstract:
  In this study, the adsorbed structure of 1-pyrenebutanoic acid, succinimidyl
ester (PASE) on graphene was theoretically investigated based on density
functional theory (DFT). We found two locally stable structures: a straight
structure with the chain-like part of butanoic acid succinimidyl ester (BSE)
lying down and a bent structure with the BSE part directing away from the
graphene surface keeping the pyrene (Py) part adsorbed on graphene. We
estimated the contribution of each part, i.e., the Py and BSE parts, to the
entire PASE adsorption to elucidate the adsorption mechanism. The BSE
contribution to the entire adsorption was found to be nonnegligible compared
with that of Py; however, the adsorption effect of the BSE part was secondary.
Kinematics analysis demonstrates that the direction of the BSE part can easily
change at the room temperature. Considering the hydration effect on the PASE
molecule, we applied a three dimensional reference interaction site model. The
solvent environment was found to contribute to the stabilization of the bent
PASE structure relative to the straight PASE structure. In a DFT simulation
including glycines, the contribution to stabilizing the bent PASE structure was
also found. Therefore, the effect of external environment around PASE is of
importance when the standing up process of the BSE part from the graphene
surface is considered.