title:
Discrepancy Modeling Framework: Learning missing physics, modeling
  systematic residuals, and disambiguating between deterministic and random
  effects

abstract:
  Physics-based and first-principles models pervade the engineering and
physical sciences, allowing for the ability to model the dynamics of complex
systems with a prescribed accuracy. The approximations used in deriving
governing equations often result in discrepancies between the model and
sensor-based measurements of the system, revealing the approximate nature of
the equations and/or the signal-to-noise ratio of the sensor itself. In modern
dynamical systems, such discrepancies between model and measurement can lead to
poor quantification, often undermining the ability to produce accurate and
precise control algorithms. We introduce a discrepancy modeling framework to
resolve deterministic model-measurement mismatch with two distinct approaches:
(i) by learning a model for the evolution of systematic state-space residual,
and (ii) by discovering a model for the missing deterministic physics.
Regardless of approach, a common suite of data-driven model discovery methods
can be used. Specifically, we use four fundamentally different methods to
demonstrate the mathematical implementations of discrepancy modeling: (i) the
sparse identification of nonlinear dynamics (SINDy), (ii) dynamic mode
decomposition (DMD), (iii) Gaussian process regression (GPR), and (iv) neural
networks (NN). The choice of method depends on one's intent for discrepancy
modeling, as well as quantity and quality of the sensor measurements. We
demonstrate the utility and suitability for both discrepancy modeling
approaches using the suite of data-driven modeling methods on three dynamical
systems under varying signal-to-noise ratios. We compare reconstruction and
forecasting accuracies and provide detailed comparatives, allowing one to
select the appropriate approach and method in practice.


title:
Maximum Principle for State-Constrained Optimal Control Problems of
  Volterra Integral Equations having Singular and Nonsingular Kernels

abstract:
  In this paper, we study the optimal control problem with terminal and
inequality state constraints for state equations described by Volterra integral
equations having singular and nonsingular kernels. The singular kernel
introduces abnormal behavior of the state trajectory with respect to the
parameter of $\alpha \in (0,1)$. Our state equation is able to cover various
state dynamics such as any types of Volterra integral equations with
nonsingular kernels only, fractional differential equations (in the sense of
Riemann-Liouville or Caputo), and ordinary differential state equations. We
obtain the well-posedness (in $L^p$ and $C$ spaces) and precise estimates of
the state equation using the generalized Gronwall's inequality and the proper
regularities of integrals having singular and nonsingular integrands. We then
prove the maximum principle for the corresponding state-constrained optimal
control problem. In the derivation of the maximum principle, due the presence
of the state constraints and the control space being only a separable metric
space, we have to employ the Ekeland variational principle and the spike
variation technique, together with the intrinsic properties of distance
functions and the generalized Gronwall's inequality, to obtain the desired
necessary conditions for optimality. In fact, as the state equation has both
singular and nonsingular kernels, the maximum principle of this paper is new,
where its proof is more involved than that for the problems of Volterra
integral equations studied in the existing literature. Examples are provided to
illustrate the theoretical results of this paper.


title: Program Repair: Automated vs. Manual

abstract:
  Various automated program repair (APR) techniques have been proposed to fix
bugs automatically in the last decade. Although recent researches have made
significant progress on the effectiveness and efficiency, it is still unclear
how APR techniques perform with human intervention in a real debugging
scenario. To bridge this gap, we conduct an extensive study to compare three
state-of-the-art APR tools with manual program repair, and further investigate
whether the assistance of APR tools (i.e., repair reports) can improve manual
program repair. To that end, we recruit 20 participants for a controlled
experiment, resulting in a total of 160 manual repair tasks and a questionnaire
survey. The experiment reveals several notable observations that (1) manual
program repair may be influenced by the frequency of repair actions sometimes;
(2) APR tools are more efficient in terms of debugging time, while manual
program repair tends to generate a correct patch with fewer attempts; (3) APR
tools can further improve manual program repair regarding the number of
correctly-fixed bugs, while there exists a negative impact on the patch
correctness; (4) participants are used to consuming more time to identify
incorrect patches, while they are still misguided easily; (5) participants are
positive about the tools' repair performance, while they generally lack
confidence about the usability in practice. Besides, we provide some guidelines
for improving the usability of APR tools (e.g., the misleading information in
reports and the observation of feedback).


title: TiSAT: Time Series Anomaly Transformer

abstract:
  While anomaly detection in time series has been an active area of research
for several years, most recent approaches employ an inadequate evaluation
criterion leading to an inflated F1 score. We show that a rudimentary Random
Guess method can outperform state-of-the-art detectors in terms of this popular
but faulty evaluation criterion. In this work, we propose a proper evaluation
metric that measures the timeliness and precision of detecting sequential
anomalies. Moreover, most existing approaches are unable to capture temporal
features from long sequences. Self-attention based approaches, such as
transformers, have been demonstrated to be particularly efficient in capturing
long-range dependencies while being computationally efficient during training
and inference. We also propose an efficient transformer approach for anomaly
detection in time series and extensively evaluate our proposed approach on
several popular benchmark datasets.


title: Topological hinge modes in Dirac semimetals

abstract:
  Dirac semimetals (DSMs) are an important class of topological states of
matter. Here, focusing on DSMs of band inversion type, we investigate their
boundary modes from the effective model perspective. We show that in order to
properly capture the boundary modes, $k$-cubic terms must be included in the
effective model, which would drive an evolution of surface degeneracy manifold
from a nodal line to a nodal point. Using first-principles calculations, we
demonstrate that this feature and the topological hinge modes can be clearly
exhibited in $\beta$-CuI. We further extend the discussion to magnetic DSMs and
show that the time-reversal symmetry breaking can gap out the surface bands and
hence help to expose the hinge modes in the spectrum, which could be beneficial
for the experimental detection of hinge modes.


title:
Unpacking Invisible Work Practices, Constraints, and Latent Power
  Relationships in Child Welfare through Casenote Analysis

abstract:
  Caseworkers are trained to write detailed narratives about families in
Child-Welfare (CW) which informs collaborative high-stakes decision-making.
Unlike other administrative data, these narratives offer a more credible source
of information with respect to workers' interactions with families as well as
underscore the role of systemic factors in decision-making. SIGCHI researchers
have emphasized the need to understand human discretion at the street-level to
be able to design human-centered algorithms for the public sector. In this
study, we conducted computational text analysis of casenotes at a child-welfare
agency in the midwestern United States and highlight patterns of invisible
street-level discretionary work and latent power structures that have direct
implications for algorithm design. Casenotes offer a unique lens for
policymakers and CW leadership towards understanding the experiences of
on-the-ground caseworkers. As a result of this study, we highlight how
street-level discretionary work needs to be supported by sociotechnical systems
developed through worker-centered design. This study offers the first
computational inspection of casenotes and introduces them to the SIGCHI
community as a critical data source for studying complex sociotechnical
systems.


title:
LensingETC: a tool to optimize multi-filter imaging campaigns of
  galaxy-scale strong lensing systems

abstract:
  Imaging data is the principal observable required to use galaxy-scale strong
lensing in a multitude of applications in extragalactic astrophysics and
cosmology. In this paper, we develop Lensing Exposure Time Calculator
(LensingETC) to optimize the efficiency of telescope time usage when planning
multi-filter imaging campaigns for galaxy-scale strong lenses. This tool
simulates realistic data tailored to specified instrument characteristics and
then automatically models them to assess the power of the data in constraining
lens model parameters. We demonstrate a use case of this tool by optimizing a
two-filter observing strategy (in IR and UVIS) within the limited exposure time
per system allowed by a Hubble Space Telescope (HST) Snapshot program. We find
that higher resolution is more advantageous to gain constraining power on the
lensing observables, when there is a trade-off between signal-to-noise ratio
and resolution; e.g., between the UVIS and IR filters of the HST. We also find
that, whereas a point spread function (PSF) with sub-Nyquist sampling allows
the sample mean for a model parameter to be robustly recovered for both
galaxy-galaxy and point-source lensing systems, a sub-Nyquist sampled PSF
introduces a larger scatter than a Nyquist sampled one in the deviation from
the ground truth for point-source lens systems.


title:
Multi-Band Superconductivity in Strongly Hybridized 1T'-WTe$_2$/NbSe$_2$
  Heterostructures

abstract:
  The interplay of topology and superconductivity has become a subject of
intense research in condensed matter physics for the pursuit of topologically
non-trivial forms of superconducting pairing. An intrinsically
normal-conducting material can inherit superconductivity via electrical contact
to a parent superconductor via the proximity effect, usually understood as
Andreev reflection at the interface between the distinct electronic structures
of two separate conductors. However, at high interface transparency, strong
coupling inevitably leads to changes in the band structure, locally, owing to
hybridization of electronic states. Here, we investigate such strongly
proximity-coupled heterostructures of monolayer 1T'-WTe$_2$, grown on NbSe$_2$
by van-der-Waals epitaxy. The superconducting local density of states (LDOS),
resolved in scanning tunneling spectroscopy down to 500~mK, reflects a hybrid
electronic structure, well-described by a multi-band framework based on the
McMillan equations which captures the multi-band superconductivity inherent to
the NbSe$_2$ substrate and that induced by proximity in WTe$_2$,
self-consistently. Our material-specific tight-binding model captures the
hybridized heterostructure quantitatively, and confirms that strong inter-layer
hopping gives rise to a semi-metallic density of states in the 2D WTe$_2$ bulk,
even for nominally band-insulating crystals. The model further accurately
predicts the measured order parameter $\Delta \simeq 0.6$~meV induced in the
WTe$_2$ monolayer bulk, stable beyond a 2~T magnetic field. We believe that our
detailed multi-band analysis of the hybrid electronic structure provides a
useful tool for sensitive spatial mapping of induced order parameters in
proximitized atomically thin topological materials.


title: Interplay of charge and spin fluctuations in a Hund's coupled impurity

abstract:
  In Hund's metals, the local ferromagnetic interaction between orbitals leads
to an emergence of complex electronic states with large and slowly fluctuating
magnetic moments. Introducing the Hund's coupled mixed valence quantum
impurity, we gain analytic insight into recent numerical renormalization group
studies. We show that valence fluctuations drastically impede the development
of a large fluctuating moment over a wide range of temperatures and energy,
characterized by quenched orbital degrees of freedom and a singular logarithmic
behavior of the spin susceptibility $\chi_{\rm sp}''(\omega) \propto [\omega
\ln(\omega/T_K^{\rm eff})^2]^{-1}$, closely resembling power-law scaling
$\chi_{\rm sp}''(\omega) \sim \omega^{-\gamma}$. Such singular spin
fluctuations are suspected to play an important role in future models of Hund's
driven Cooper pairing.


title:
TextConvoNet:A Convolutional Neural Network based Architecture for Text
  Classification

abstract:
  In recent years, deep learning-based models have significantly improved the
Natural Language Processing (NLP) tasks. Specifically, the Convolutional Neural
Network (CNN), initially used for computer vision, has shown remarkable
performance for text data in various NLP problems. Most of the existing
CNN-based models use 1-dimensional convolving filters n-gram detectors), where
each filter specialises in extracting n-grams features of a particular input
word embedding. The input word embeddings, also called sentence matrix, is
treated as a matrix where each row is a word vector. Thus, it allows the model
to apply one-dimensional convolution and only extract n-gram based features
from a sentence matrix. These features can be termed as intra-sentence n-gram
features. To the extent of our knowledge, all the existing CNN models are based
on the aforementioned concept. In this paper, we present a CNN-based
architecture TextConvoNet that not only extracts the intra-sentence n-gram
features but also captures the inter-sentence n-gram features in input text
data. It uses an alternative approach for input matrix representation and
applies a two-dimensional multi-scale convolutional operation on the input. To
evaluate the performance of TextConvoNet, we perform an experimental study on
five text classification datasets. The results are evaluated by using various
performance metrics. The experimental results show that the presented
TextConvoNet outperforms state-of-the-art machine learning and deep learning
models for text classification purposes.


title: Assessing Phenotype Definitions for Algorithmic Fairness

abstract:
  Disease identification is a core, routine activity in observational health
research. Cohorts impact downstream analyses, such as how a condition is
characterized, how patient risk is defined, and what treatments are studied. It
is thus critical to ensure that selected cohorts are representative of all
patients, independently of their demographics or social determinants of health.
While there are multiple potential sources of bias when constructing phenotype
definitions which may affect their fairness, it is not standard in the field of
phenotyping to consider the impact of different definitions across subgroups of
patients. In this paper, we propose a set of best practices to assess the
fairness of phenotype definitions. We leverage established fairness metrics
commonly used in predictive models and relate them to commonly used
epidemiological cohort description metrics. We describe an empirical study for
Crohn's disease and diabetes type 2, each with multiple phenotype definitions
taken from the literature across two sets of patient subgroups (gender and
race). We show that the different phenotype definitions exhibit widely varying
and disparate performance according to the different fairness metrics and
subgroups. We hope that the proposed best practices can help in constructing
fair and inclusive phenotype definitions.


title: MVP: Multimodality-guided Visual Pre-training

abstract:
  Recently, masked image modeling (MIM) has become a promising direction for
visual pre-training. In the context of vision transformers, MIM learns
effective visual representation by aligning the token-level features with a
pre-defined space (e.g., BEIT used a d-VAE trained on a large image corpus as
the tokenizer). In this paper, we go one step further by introducing guidance
from other modalities and validating that such additional knowledge leads to
impressive gains for visual pre-training. The proposed approach is named
Multimodality-guided Visual Pre-training (MVP), in which we replace the
tokenizer with the vision branch of CLIP, a vision-language model pre-trained
on 400 million image-text pairs. We demonstrate the effectiveness of MVP by
performing standard experiments, i.e., pre-training the ViT models on ImageNet
and fine-tuning them on a series of downstream visual recognition tasks. In
particular, pre-training ViT-Base/16 for 300 epochs, MVP reports a 52.4% mIoU
on ADE20K, surpassing BEIT (the baseline and previous state-of-the-art) with an
impressive margin of 6.8%.


title: Stability of structure-aware Taylor methods for tents

abstract:
  Structure-aware Taylor (SAT) methods are a class of timestepping schemes
designed for propagating linear hyperbolic solutions within a tent-shaped
spacetime region. Tents are useful to design explicit time marching schemes on
unstructured advancing fronts with built-in locally variable timestepping for
arbitrary spatial and temporal discretization orders. The main result of this
paper is that an $s$-stage SAT timestepping within a tent is weakly stable
under the time step constraint $\Delta t \leq Ch^{1+1/s}$, where $\Delta t$ is
the time step size and $h$ is the spatial mesh size. Improved stability
properties are also presented for high order SAT time discretizations coupled
with low order spatial polynomials. A numerical verification of the sharpness
of proven estimates is also included.


title:
New Open Cluster candidates Found in Galactic Disk Using Gaia DR2/EDR3
  Data

abstract:
  We report 541 new open cluster candidates in Gaia EDR3 through revisiting the
cluster results from an earlier analysis of the Gaia DR2, which revealed nearly
a thousand open cluster candidates in the solar neighborhood (mostly d < 3 kpc)
resideing at Galactic latitudes |b| < 20 degrees. A subsequent comparison with
lists of known clusters shows a large increases of the cluster samples within 2
kpc from the Sun. We assign membership probabilities to the stars through the
open source pyUPMASK algorithm, and also estimate the physical parameters
through isochrone fitting for each candidate. Most of the new candidates show
small total proper motion dispersions and clear features in the color-magnitude
diagrams. Besides, the metallicity gradient of the new candidates is consistent
with those found in the literature. The cluster parameters and member stars are
available at CDS via anonymous ftp to cdsarc.u-strasbg.fr(130.79.128.5) or via
https://cdsarc.unistra.fr/viz-bin/cat/J/ApJS. The discovery of these new
objects shows that the open cluster samples in Gaia data is still not complete,
and more discoveries are expected in the future researches.


title:
An Audio-Visual Attention Based Multimodal Network for Fake Talking Face
  Videos Detection

abstract:
  DeepFake based digital facial forgery is threatening the public media
security, especially when lip manipulation has been used in talking face
generation, the difficulty of fake video detection is further improved. By only
changing lip shape to match the given speech, the facial features of identity
is hard to be discriminated in such fake talking face videos. Together with the
lack of attention on audio stream as the prior knowledge, the detection failure
of fake talking face generation also becomes inevitable. Inspired by the
decision-making mechanism of human multisensory perception system, which
enables the auditory information to enhance post-sensory visual evidence for
informed decisions output, in this study, a fake talking face detection
framework FTFDNet is proposed by incorporating audio and visual representation
to achieve more accurate fake talking face videos detection. Furthermore, an
audio-visual attention mechanism (AVAM) is proposed to discover more
informative features, which can be seamlessly integrated into any audio-visual
CNN architectures by modularization. With the additional AVAM, the proposed
FTFDNet is able to achieve a better detection performance on the established
dataset (FTFDD). The evaluation of the proposed work has shown an excellent
performance on the detection of fake talking face videos, which is able to
arrive at a detection rate above 97%.


title: Towards Open-Set Text Recognition via Label-to-Prototype Learning

abstract:
  Scene text recognition is a popular topic and extensively used in the
industry. Although many methods have achieved satisfactory performance for the
close-set text recognition challenges, these methods lose feasibility in
open-set scenarios, where collecting data or retraining models for novel
characters is too expensive. E.g., annotating samples for foreign languages can
be expensive, whereas retraining the model each time a "novel" character is
discovered from historical documents also costs time and resources. In this
paper, we introduce and formulate a new task, i.e., the open-set text
recognition task, which demands the capability to spot and cognize novel
characters without retraining. Here, we propose a label-to-prototype learning
framework that fulfills the new requirements in the proposed task.
Specifically, novel characters are mapped to their corresponding prototypes
with a Label-to-Prototype Learning module. The module is trained on seen labels
and holds generalization capability for generating class centers for novel
characters without retraining. The framework also implements rejection
capability over out-of-set characters, which allows spotting unknown characters
during the evaluation process. Extensive experiments show that our method
achieves promising performance on a variety of zero-shot, close-set, and
open-set text recognition datasets.


title:
Knowledge Distillation as Efficient Pre-training: Faster Convergence,
  Higher Data-efficiency, and Better Transferability

abstract:
  Large-scale pre-training has been proven to be crucial for various computer
vision tasks. However, with the increase of pre-training data amount, model
architecture amount, and the private/inaccessible data, it is not very
efficient or possible to pre-train all the model architectures on large-scale
datasets. In this work, we investigate an alternative strategy for
pre-training, namely Knowledge Distillation as Efficient Pre-training (KDEP),
aiming to efficiently transfer the learned feature representation from existing
pre-trained models to new student models for future downstream tasks. We
observe that existing Knowledge Distillation (KD) methods are unsuitable
towards pre-training since they normally distill the logits that are going to
be discarded when transferred to downstream tasks. To resolve this problem, we
propose a feature-based KD method with non-parametric feature dimension
aligning. Notably, our method performs comparably with supervised pre-training
counterparts in 3 downstream tasks and 9 downstream datasets requiring 10x less
data and 5x less pre-training time. Code is available at
https://github.com/CVMI-Lab/KDEP.


title:
LineVD: Statement-level Vulnerability Detection using Graph Neural
  Networks

abstract:
  Current machine-learning based software vulnerability detection methods are
primarily conducted at the function-level. However, a key limitation of these
methods is that they do not indicate the specific lines of code contributing to
vulnerabilities. This limits the ability of developers to efficiently inspect
and interpret the predictions from a learnt model, which is crucial for
integrating machine-learning based tools into the software development
workflow. Graph-based models have shown promising performance in function-level
vulnerability detection, but their capability for statement-level vulnerability
detection has not been extensively explored. While interpreting function-level
predictions through explainable AI is one promising direction, we herein
consider the statement-level software vulnerability detection task from a fully
supervised learning perspective. We propose a novel deep learning framework,
LineVD, which formulates statement-level vulnerability detection as a node
classification task. LineVD leverages control and data dependencies between
statements using graph neural networks, and a transformer-based model to encode
the raw source code tokens. In particular, by addressing the conflicting
outputs between function-level and statement-level information, LineVD
significantly improve the prediction performance without vulnerability status
for function code. We have conducted extensive experiments against a
large-scale collection of real-world C/C++ vulnerabilities obtained from
multiple real-world projects, and demonstrate an increase of 105\% in F1-score
over the current state-of-the-art.


title:
Prolongations, invariants, and fundamental identities of geometric
  structures

abstract:
  Working in the framework of nilpotent geometry, we give a unified scheme for
the equivalence problem of geometric structures which extends and integrates
the earlier works by Cartan, Singer-Sternberg, Tanaka, and Morimoto. By giving
a new formulation of the higher order geometric structures and the universal
frame bundles, we reconstruct the step prolongation of Singer-Sternberg and
Tanaka. We then investigate the structure function $\gamma$ of the complete
step prolongation of a normal geometric structure by expanding it into
components $\gamma = \kappa + \tau + \sigma$ and establish the fundamental
identities for $\kappa$, $\tau$, $\sigma$. This then enables us to study the
equivalence problem of geometric structures in full generality and to extend
applications largely to the geometric structures which have not necessarily
Cartan connections.
  Among all we give an algorithm to construct a complete system of invariants
for any higher order normal geometric structure of constant symbol by making
use of generalized Spencer cohomology group associated to the symbol of the
geometric structure. We then discuss thoroughly the equivalence problem for
geometric structure in both cases of infinite and finite type. We also give a
characterization of the Cartan connections by means of the structure function
$\tau$ and make clear where the Cartan connections are placed in the
perspective of the step prolongations.


title:
Viscosity Enhancement by Electron-Hole Collisions in Dirac Electron
  Fluid

abstract:
  Rejuvenation of hydrodynamic transport in solids provides a new window to
study collective motion of electrons, where electrons behave like a viscous
fluid akin to classical liquids. Experimental observations of such exotic
states have not been realized until recent years, and an on-going quest is to
amplify the hydrodynamic effect in electron fluids. Here we investigate the
hydrodynamic properties of Dirac electron fluid in graphene from a microscopic
viewpoint, and elucidate a novel way to enhance electron hydrodynamics. In
particular, we present strong evidence that the shear viscosity of Dirac
electrons can be enhanced by frequent electron-hole collisions, through three
distinct aspects: promoting electrons and holes around the Dirac point by
disorder, creating electron-hole shared zeroth Landau level by external
magnetic field, and inducing electron-hole excitations by dynamic deformation.
We also study Hall viscosity, which is closely related to the geometric
topology and exhibits quantum behavior analogous to Hall conductivity.
Therefore, our work demonstrates the exotic landscape of hydrodynamic
electronics in graphene, and presents experimentally relevant responses to
quantify the effects of electronic viscosity.


title:
Nontrivial solutions for a class of gradient-type quasilinear elliptic
  systems

abstract:
  The aim of this paper is investigating the existence of weak bounded
solutions of the gradient-type quasilinear elliptic system $$(P)\qquad \left\{
\begin{array}{ll} - {\rm div} ( a_i(x, u_i, \nabla u_i) ) + A_{i, t} (x, u_i,
\nabla u_i) = G_i(x, \mathbf{u}) &\hbox{ in $\Omega$}\\
\quad\qquad\qquad\qquad\qquad \mbox{ for }\; i\in\{1,\dots,m\},\\ \mathbf{u} =
0 &\hbox{ on $\partial\Omega$,} \end{array} \right.$$ with $m\geq 2$ and
$\mathbf{u}=(u_1,\dots, u_{m})$, where $\Omega\subset\mathbb{R}^N$ is an open
bounded domain and some functions $A_i:\Omega\times\mathbb{R} \times
\mathbb{R}^N\rightarrow\mathbb{R}$, $i\in\{1,\dots,m\}$, and
$G:\Omega\times\mathbb{R}^m\rightarrow\mathbb{R}$ exist such that $a_i(x,t,\xi)
= \nabla_{\xi} A_i(x,t,\xi)$, $A_{i, t} (x,t,\xi) = \frac{\partial
A_i}{\partial t} (x,t,\xi)$ and $G_{i}(x,\mathbf{u}) = \frac{\partial
G}{\partial u_i}(x,\mathbf{u})$. We prove that, under suitable hypotheses, the
functional $\mathcal{J}$ related to problem $(P)$ is $\mathcal{C}^1$ on a
"good" Banach space $X$ and satisfies the weak Cerami-Palais-Smale condition.
Then, generalized versions of the Mountain Pass Theorems allow us to prove the
existence of at least one critical point and, if $\mathcal{J}$ is even, of
infinitely many ones, too.


title:
Optimal Entanglement of Polymers Promotes Formation of Highly Oriented
  Fibers

abstract:
  Polymer fibers consist of macromolecules oriented along the fiber axis.
Better alignment of chains leads to an increased strength of the fiber. It is
believed that the key factor preventing formation of a perfectly oriented fiber
is entanglement of polymers. We performed large-scale computer simulations of
uniaxial stretching of semicrystalline ultrahigh molecular weight polyethylene.
We discovered that there is an optimal number of entanglements per
macromolecule necessary to maximize chain orientation in a fiber. Polymers that
were entangled too strongly formed less oriented fibers. On the other hand,
when polymers had too few entanglements per chain, they disentangled during
stretching, and the strong fiber was not formed. We constructed a microscopic
analytical theory describing both the fiber formation and disentanglement
processes. Our work presents a novel view on the role of entanglements during
fiber production and predicts the existence of a single universal optimal
number of entanglements per chain maximizing the fiber quality: approximately
$10^2$ entanglements.


title:
Suspected Object Matters: Rethinking Model's Prediction for One-stage
  Visual Grounding

abstract:
  Recently, one-stage visual grounders attract high attention due to the
comparable accuracy but significantly higher efficiency than two-stage
grounders. However, inter-object relation modeling has not been well studied
for one-stage grounders. Inter-object relationship modeling, though important,
is not necessarily performed among all the objects within the image, as only a
part of them are related to the text query and may confuse the model. We call
these objects "suspected objects". However, exploring relationships among these
suspected objects in the one-stage visual grounding paradigm is non-trivial due
to two core problems: (1) no object proposals are available as the basis on
which to select suspected objects and perform relationship modeling; (2)
compared with those irrelevant to the text query, suspected objects are more
confusing, as they may share similar semantics, be entangled with certain
relationships, etc, and thereby more easily mislead the model's prediction. To
address the above issues, this paper proposes a Suspected Object Graph (SOG)
approach to encourage the correct referred object selection among the suspected
ones in the one-stage visual grounding. Suspected objects are dynamically
selected from a learned activation map as nodes to adapt to the current
discrimination ability of the model during training. Afterward, on top of the
suspected objects, a Keyword-aware Node Representation module (KNR) and an
Exploration by Random Connection strategy (ERC) are concurrently proposed
within the SOG to help the model rethink its initial prediction. Extensive
ablation studies and comparison with state-of-the-art approaches on prevalent
visual grounding benchmarks demonstrate the effectiveness of our proposed
method.


title:
Cluttered Food Grasping with Adaptive Fingers and Synthetic-Data Trained
  Object Detection

abstract:
  The food packaging industry handles an immense variety of food products with
wide-ranging shapes and sizes, even within one kind of food. Menus are also
diverse and change frequently, making automation of pick-and-place difficult. A
popular approach to bin-picking is to first identify each piece of food in the
tray by using an instance segmentation method. However, human annotations to
train these methods are unreliable and error-prone since foods are packed close
together with unclear boundaries and visual similarity making separation of
pieces difficult. To address this problem, we propose a method that trains
purely on synthetic data and successfully transfers to the real world using
sim2real methods by creating datasets of filled food trays using high-quality
3d models of real pieces of food for the training instance segmentation models.
Another concern is that foods are easily damaged during grasping. We address
this by introducing two additional methods -- a novel adaptive finger mechanism
to passively retract when a collision occurs, and a method to filter grasps
that are likely to cause damage to neighbouring pieces of food during a grasp.
We demonstrate the effectiveness of the proposed method on several kinds of
real foods.


title:
Transfer and evolution of structured polarization in a double-V atomic
  system

abstract:
  We numerically investigate the transfer of optical information from a
vector-vortex control beam to an unstructured probe beam, as mediated by an
atomic vapour. The right and left circular components of these beams drive the
atomic transitions of a double-$V$ system, with the atoms acting as a spatially
varying circular birefringent medium. Modelling the propagation of the light
fields, we find that, for short distances, the vectorial light structure is
transferred from the control field to the probe. However, for larger
propagation lengths, diffraction causes the circular components of the probe
field to spatially separate. We model this system for the D1 line of cold
rubidium atoms. Our investigation is a first step to investigating the coupled
dynamics of internal and external degrees of freedom of atoms in four wave
mixing.


title: NeRFocus: Neural Radiance Field for 3D Synthetic Defocus

abstract:
  Neural radiance fields (NeRF) bring a new wave for 3D interactive
experiences. However, as an important part of the immersive experiences, the
defocus effects have not been fully explored within NeRF. Some recent
NeRF-based methods generate 3D defocus effects in a post-process fashion by
utilizing multiplane technology. Still, they are either time-consuming or
memory-consuming. This paper proposes a novel thin-lens-imaging-based NeRF
framework that can directly render various 3D defocus effects, dubbed NeRFocus.
Unlike the pinhole, the thin lens refracts rays of a scene point, so its
imaging on the sensor plane is scattered as a circle of confusion (CoC). A
direct solution sampling enough rays to approximate this process is
computationally expensive. Instead, we propose to inverse the thin lens imaging
to explicitly model the beam path for each point on the sensor plane and
generalize this paradigm to the beam path of each pixel, then use the
frustum-based volume rendering to render each pixel's beam path. We further
design an efficient probabilistic training (p-training) strategy to simplify
the training process vastly. Extensive experiments demonstrate that our
NeRFocus can achieve various 3D defocus effects with adjustable camera pose,
focus distance, and aperture size. Existing NeRF can be regarded as our special
case by setting aperture size as zero to render large depth-of-field images.
Despite such merits, NeRFocus does not sacrifice NeRF's original performance
(e.g., training and inference time, parameter consumption, rendering quality),
which implies its great potential for broader application and further
improvement.


title: Analysis of accretion disk around a black hole in dRGT massive gravity

abstract:
  We show the analysis of a thin accretion disk around a static and spherically
symmetric black hole in dRGT massive gravity. We present the accretion disk
analysis in a gravitational theory with a nonzero graviton mass. Also, we study
the event horizons of the black hole and we calculate the equations of motion
and effective potential. In the following, we obtain the specific energy,
specific angular momentum, and angular velocity of the particles which move in
circular orbits. In addition, we plot the effective potentials for two cases
and we show the locations of stable circular orbits. At last, we show the
possibility of constraining the parameter space of dRGT massive gravity by the
astrophysical gamma-ray bursts.


title:
Evaluation of Performance for Human In-Vivo Conductivity Estimation from
  EEG and sEEG Recorded in Simultaneous with Intracerebral Electrical
  Stimulation

abstract:
  Assigning accurate conductivity values in human head models is an essential
factor for performing precise electroencephalographic (EEG) source localization
and targeting of transcranial electrical stimulation (TES). Unfortunately, the
literature reports diverging conductivity values of the different tissues in
the human head. The current study analyzes first the performance of in-vivo
conductivity estimation for different configurations concerning the
localization of the electrical source and measurement. Then, it presents
conductivity estimates for three epileptic patients using scalp EEG and
intracerebral stereotactic EEG (sEEG) acquired in simultaneous with
intracerebral electrical stimulation. The estimates of the conductivities were
based on finite-element models of the human head with five tissue compartments
of homogeneous and isotropic conductivities. The results of this study show
that in-vivo conductivity estimation can lead to different estimated
conductivities for the same patient when considering different stimulation
positions, different measurement positions or different measurement modalities
(sEEG or EEG). This work provides important guidelines to in-vivo conductivity
estimation and explains the variability among the conductivity values which
have been reported in previous studies.


title:
A Systematic Literature Review on Blockchain Enabled Federated Learning
  Framework for Internet of Vehicles

abstract:
  While the convergence of Artificial Intelligence (AI) techniques with
improved information technology systems ensured enormous benefits to the
Internet of Vehicles (IoVs) systems, it also introduced an increased amount of
security and privacy threats. To ensure the security of IoVs data, privacy
preservation methodologies have gained significant attention in the literature.
However, these strategies also need specific adjustments and modifications to
cope with the advances in IoVs design. In the interim, Federated Learning (FL)
has been proven as an emerging idea to protect IoVs data privacy and security.
On the other hand, Blockchain technology is showing prominent possibilities
with secured, dispersed, and auditable data recording and sharing schemes. In
this paper, we present a comprehensive survey on the application and
implementation of Blockchain-Enabled Federated Learning frameworks for IoVs.
Besides, probable issues, challenges, solutions, and future research directions
for BC-Enabled FL frameworks for IoVs are also presented. This survey can
further be used as the basis for developing modern BC-Enabled FL solutions to
resolve different data privacy issues and scenarios of IoVs.


title: Adaptive Background Matting Using Background Matching

abstract:
  Due to the difficulty of solving the matting problem, lots of methods use
some kinds of assistance to acquire high quality alpha matte. Green screen
matting methods rely on physical equipment. Trimap-based methods take manual
interactions as external input. Background-based methods require a
pre-captured, static background. The methods are not flexible and convenient
enough to use widely. Trimap-free methods are flexible but not stable in
complicated video applications. To be stable and flexible in real applications,
we propose an adaptive background matting method. The user first captures their
videos freely, moving the cameras. Then the user captures the background video
afterwards, roughly covering the previous captured regions. We use dynamic
background video instead of static background for accurate matting. The
proposed method is convenient to use in any scenes as the static camera and
background is no more the limitation. To achieve this goal, we use background
matching network to find the best-matched background frame by frame from
dynamic backgrounds. Then, robust semantic estimation network is used to
estimate the coarse alpha matte. Finally, we crop and zoom the target region
according to the coarse alpha matte, and estimate the final accurate alpha
matte. In experiments, the proposed method is able to perform comparably
against the state-of-the-art matting methods.


title: Learning Torque Control for Quadrupedal Locomotion

abstract:
  Reinforcement learning (RL) is a promising tool for developing controllers
for quadrupedal locomotion. The design of most learning-based locomotion
controllers adopts the joint position-based paradigm, wherein a low-frequency
RL policy outputs target joint positions that are then tracked by a
high-frequency proportional-derivative (PD) controller that outputs joint
torques. However, the low frequency of such a policy hinders the advancement of
highly dynamic locomotion behaviors. Moreover, determining the PD gains for
optimal tracking performance is laborious and dependent on the task at hand. In
this paper, we introduce a learning torque control framework for quadrupedal
locomotion, which trains an RL policy that directly predicts joint torques at a
high frequency, thus circumventing the use of PD controllers. We validate the
proposed framework with extensive experiments where the robot is able to both
traverse various terrains and resist external pushes, given user-specified
commands. To our knowledge, this is the first attempt of learning torque
control for quadrupedal locomotion with an end-to-end single neural network
that has led to successful real-world experiments among recent research on
learning-based quadrupedal locomotion which is mostly position-based.


title: A Review of Open Source Software Tools for Time Series Analysis

abstract:
  Time series data is used in a wide range of real world applications. In a
variety of domains , detailed analysis of time series data (via Forecasting and
Anomaly Detection) leads to a better understanding of how events associated
with a specific time instance behave. Time Series Analysis (TSA) is commonly
performed with plots and traditional models. Machine Learning (ML) approaches ,
on the other hand , have seen an increase in the state of the art for
Forecasting and Anomaly Detection because they provide comparable results when
time and data constraints are met. A number of time series toolboxes are
available that offer rich interfaces to specific model classes (ARIMA/filters ,
neural networks) or framework interfaces to isolated time series modelling
tasks (forecasting , feature extraction , annotation , classification).
Nonetheless , open source machine learning capabilities for time series remain
limited , and existing libraries are frequently incompatible with one another.
The goal of this paper is to provide a concise and user friendly overview of
the most important open source tools for time series analysis. This article
examines two related toolboxes (1) forecasting and (2) anomaly detection. This
paper describes a typical Time Series Analysis (TSA) framework with an
architecture and lists the main features of TSA framework. The tools are
categorized based on the criteria of analysis tasks completed , data
preparation methods employed , and evaluation methods for results generated.
This paper presents quantitative analysis and discusses the current state of
actively developed open source Time Series Analysis frameworks. Overall , this
article considered 60 time series analysis tools , and 32 of which provided
forecasting modules , and 21 packages included anomaly detection.


title: Individual qubit addressing of rotating ion crystals in a Penning trap

abstract:
  Trapped ions boast long coherence times and excellent gate fidelities, making
them a useful platform for quantum information processing. Scaling to larger
numbers of ion qubits in RF Paul traps demands great effort. Another technique
for trapping ions is via a Penning trap where a 2D crystal of hundreds of ions
is formed by controlling the rotation of the ions in the presence of a strong
magnetic field. However, the rotation of the ion crystal makes single ion
addressability a significant challenge. We propose a protocol that takes
advantage of a deformable mirror to introduce AC Stark shift patterns that are
static in the rotating frame of the crystal. Through numerical simulations we
validate the potential of this protocol to perform high-fidelity single-ion
gates in crystalline arrays of hundreds of ions.


title:
Spatially-Varying Bayesian Predictive Synthesis for Flexible and
  Interpretable Spatial Prediction

abstract:
  Spatial data are characterized by their spatial dependence, which is often
complex, non-linear, and difficult to capture with a single model. Significant
levels of model uncertainty -- arising from these characteristics -- cannot be
resolved by model selection or simple ensemble methods, as performances are not
homogeneous. We address this issue by proposing a novel methodology that
captures spatially-varying model uncertainty, which we call spatial Bayesian
predictive synthesis. Our proposal is defined by specifying a latent factor
spatially-varying coefficient model as the synthesis function, which enables
model coefficients to vary over the region to achieve flexible spatial model
ensembling. Two MCMC strategies are implemented for full uncertainty
quantification, as well as a variational inference strategy for fast point
inference. We also extend the estimations strategy for general responses. A
finite sample theoretical guarantee is given for the predictive performance of
our methodology, showing that the predictions are exact minimax. Through
simulation examples and two real data applications, we demonstrate that our
proposed spatial Bayesian predictive synthesis outperforms standard spatial
models and advanced machine learning methods, in terms of predictive accuracy,
while maintaining interpretability of the prediction mechanism.


title:
A Screen-Shooting Resilient Document Image Watermarking Scheme using
  Deep Neural Network

abstract:
  With the advent of the screen-reading era, the confidential documents
displayed on the screen can be easily captured by a camera without leaving any
traces. Thus, this paper proposes a novel screen-shooting resilient
watermarking scheme for document image using deep neural network. By applying
this scheme, when the watermarked image is displayed on the screen and captured
by a camera, the watermark can be still extracted from the captured
photographs. Specifically, our scheme is an end-to-end neural network with an
encoder to embed watermark and a decoder to extract watermark. During the
training process, a distortion layer between encoder and decoder is added to
simulate the distortions introduced by screen-shooting process in real scenes,
such as camera distortion, shooting distortion, light source distortion.
Besides, an embedding strength adjustment strategy is designed to improve the
visual quality of the watermarked image with little loss of extraction
accuracy. The experimental results show that the scheme has higher robustness
and visual quality than other three recent state-of-the-arts. Specially, even
if the shooting distances and angles are in extreme, our scheme can also obtain
high extraction accuracy.


title: Hyperspectral Imaging for cherry tomato

abstract:
  Cherry tomato (Solanum Lycopersicum) is popular with consumers over the world
due to its special flavor. Soluble solids content (SSC) and firmness are two
key metrics for evaluating the product qualities. In this work, we develop
non-destructive testing techniques for SSC and fruit firmness based on
hyperspectral images and a corresponding deep learning regression model.
Hyperspectral reflectance images of over 200 tomato fruits are derived with
spectrum ranging from 400 to 1000 nm. The acquired hyperspectral images are
corrected and the spectral information is extracted. A novel
one-dimensional(1D) convolutional ResNet (Con1dResNet) based regression model
is prosed and compared with the state of art techniques. Experimental results
show that, with a relatively large number of samples our technique is 26.4\%
better than state of art technique for SSC and 33.7\% for firmness. The results
of this study indicate the application potential of hyperspectral imaging
technique in the SSC and firmness detection, which provides a new option for
non-destructive testing of cherry tomato fruit quality in the future.


title:
Exploring the intrinsic energy resolution of liquid scintillator to
  approximately 1 MeV electrons

abstract:
  We proposed a novel method for exploring the intrinsic energy resolution of a
liquid scintillator (LAB + 2.5 g/L PPO + 3 mg/L bis-MSB) for approximately 1
MeV electrons. With the help of coincidence detection technology, single-energy
electrons of Bi 207 were effectively selected. With careful measurement and
analysis of the energy resolution of a small liquid scintillator detector, the
intrinsic energy resolution to 976 keV electrons was extracted to be 1.83%. We
used the wide-angle Compton coincidence (WACC) method to measure the
luminescent nonlinearity of the liquid scintillator and found that it
contributes only weakly to the intrinsic energy resolution of electrons. Such
an unexpected large intrinsic energy resolution may come from fluctuations in
energy transfer processes.


title: Online Deep Metric Learning via Mutual Distillation

abstract:
  Deep metric learning aims to transform input data into an embedding space,
where similar samples are close while dissimilar samples are far apart from
each other. In practice, samples of new categories arrive incrementally, which
requires the periodical augmentation of the learned model. The fine-tuning on
the new categories usually leads to poor performance on the old, which is known
as "catastrophic forgetting". Existing solutions either retrain the model from
scratch or require the replay of old samples during the training. In this
paper, a complete online deep metric learning framework is proposed based on
mutual distillation for both one-task and multi-task scenarios. Different from
the teacher-student framework, the proposed approach treats the old and new
learning tasks with equal importance. No preference over the old or new
knowledge is caused. In addition, a novel virtual feature estimation approach
is proposed to recover the features assumed to be extracted by the old models.
It allows the distillation between the new and the old models without the
replay of old training samples or the holding of old models during the
training. A comprehensive study shows the superior performance of our approach
with the support of different backbones.


title: Schmidt-type theorems for partitions with uncounted parts

abstract:
  Schmidt's theorem is significantly generalized, to partitions in which
periodic but otherwise arbitrary subsets of parts are counted or uncounted. The
identification of such sets of partitions with colored partitions satisfying
certain specifications may be a generally useful tool for establishing
sum-product $q$-series identities, examples of which are given.


title: MORE: Multi-Order RElation Mining for Dense Captioning in 3D Scenes

abstract:
  3D dense captioning is a recently-proposed novel task, where point clouds
contain more geometric information than the 2D counterpart. However, it is also
more challenging due to the higher complexity and wider variety of inter-object
relations. Existing methods only treat such relations as by-products of object
feature learning in graphs without specifically encoding them, which leads to
sub-optimal results. In this paper, aiming at improving 3D dense captioning via
capturing and utilizing the complex relations in the 3D scene, we propose MORE,
a Multi-Order RElation mining model, to support generating more descriptive and
comprehensive captions. Technically, our MORE encodes object relations in a
progressive manner since complex relations can be deduced from a limited number
of basic ones. We first devise a novel Spatial Layout Graph Convolution (SLGC),
which semantically encodes several first-order relations as edges of a graph
constructed over 3D object proposals. Next, from the resulting graph, we
further extract multiple triplets which encapsulate basic first-order relations
as the basic unit and construct several Object-centric Triplet Attention Graphs
(OTAG) to infer multi-order relations for every target object. The updated node
features from OTAG are aggregated and fed into the caption decoder to provide
abundant relational cues so that captions including diverse relations with
context objects can be generated. Extensive experiments on the Scan2Cap dataset
prove the effectiveness of our proposed MORE and its components, and we also
outperform the current state-of-the-art method.


title:
When Self-Generated Gradients interact with Expansion by Cell Division
  and Diffusion. Analysis of a Minimal Model

abstract:
  We investigate a minimal model for cell propagation involving migration along
self-generated signaling gradients and cell division, which has been proposed
in an earlier study. The model consists in a system of two coupled parabolic
diffusion-advection-reaction equations. Because of a discontinuous advection
term, the Cauchy problem should be handled with care. We first establish
existence and uniqueness locally in time through the reduction of the problem
to the well-posedness of an ODE, under a monotonicity condition on the
signaling gradient. Then, we carry out an asymptotic analysis of the system.
All positive and bounded traveling waves of the system are computed and an
explicit formula for the minimal wave speed is deduced. An analysis on the
inside dynamics of the wave establishes a dichotomy between pushed and pulled
waves depending on the strength of the advection. We identified the minimal
wave speed as the biologically relevant speed, in a weak sense, that is, the
solution propagates slower, respectively faster, than the minimal wave speed,
up to time extraction. Finally, we extend the study to a hyperbolic
two-velocity model with persistence.


title: Crowd Source Scene Change Detection and Local Map Update

abstract:
  As scene changes with time map descriptors become outdated, affecting VPS
localization accuracy. In this work, we propose an approach to detect
structural and texture scene changes to be followed by map update. In our
method - map includes 3D points with descriptors generated either via LiDAR or
SFM. Common approaches suffer from shortcomings: 1) Direct comparison of the
two point-clouds for change detection is slow due to the need to build new
point-cloud every time we want to compare; 2) Image based comparison requires
to keep the map images adding substantial storage overhead. To circumvent this
problems, we propose an approach based on point-clouds descriptors comparison:
1) Based on VPS poses select close query and map images pairs, 2) Registration
of query images to map image descriptors, 3) Use segmentation to filter out
dynamic or short term temporal changes, 4) Compare the descriptors between
corresponding segments.


title: ReF -- Rotation Equivariant Features for Local Feature Matching

abstract:
  Sparse local feature matching is pivotal for many computer vision and
robotics tasks. To improve their invariance to challenging appearance
conditions and viewing angles, and hence their usefulness, existing
learning-based methods have primarily focused on data augmentation-based
training. In this work, we propose an alternative, complementary approach that
centers on inducing bias in the model architecture itself to generate
`rotation-specific' features using Steerable E2-CNNs, that are then
group-pooled to achieve rotation-invariant local features. We demonstrate that
this high performance, rotation-specific coverage from the steerable CNNs can
be expanded to all rotation angles by combining it with augmentation-trained
standard CNNs which have broader coverage but are often inaccurate, thus
creating a state-of-the-art rotation-robust local feature matcher. We benchmark
our proposed methods against existing techniques on HPatches and a newly
proposed UrbanScenes3D-Air dataset for visual place recognition. Furthermore,
we present a detailed analysis of the performance effects of ensembling, robust
estimation, network architecture variations, and the use of rotation priors.


title: Computing Whittle (and Gittins) Index in Subcubic Time

abstract:
  Whittle index is a generalization of Gittins index that provides very
efficient allocation rules for restless multiarmed bandits. In this paper, we
develop an algorithm to test the indexability and compute the Whittle indices
of any finite-state Markovian bandit problem. This algorithm works in the
discounted and non-discounted cases. As a byproduct, it can also be used to
compute Gittins index. Our algorithm builds on three tools: (1) a careful
characterization of Whittle index that allows one to compute recursively the th
smallest index from the (-- 1)th smallest, and to test indexability, (2) the
use of Sherman-Morrison formula to make this recursive computation efficient,
and (3) a sporadic use of fast matrix inversion and multiplication to obtain a
subcubic complexity. We show that an efficient use of the Sherman-Morrison
formula leads to an algorithm that computes Whittle index in (2$\Uparrow$3) 3 +
(3) arithmetic operations, where is the number of states of the arm. The
careful use of fast matrix multiplication leads to the first subcubic algorithm
to compute Whittle (or Gittins) index. By using the current fastest matrix
multiplications, our algorithm runs in (2.5286). We also conduct a series of
experiments that demonstrate that our algorithm is very efficient in practice
and can compute indices of Markov chains with several thousands of states in a
few seconds.


title:
Transferring Dual Stochastic Graph Convolutional Network for Facial
  Micro-expression Recognition

abstract:
  Micro-expression recognition has drawn increasing attention due to its wide
application in lie detection, criminal detection and psychological
consultation. To improve the recognition performance of the small
micro-expression data, this paper presents a transferring dual stochastic Graph
Convolutional Network (TDSGCN) model. We propose a stochastic graph
construction method and dual graph convolutional network to extract more
discriminative features from the micro-expression images. We use transfer
learning to pre-train SGCNs from macro expression data. Optical flow algorithm
is also integrated to extract their temporal features. We fuse both spatial and
temporal features to improve the recognition performance. To the best of our
knowledge, this is the first attempt to utilize the transferring learning and
graph convolutional network in micro-expression recognition task. In addition,
to handle the class imbalance problem of dataset, we focus on the design of
focal loss function. Through extensive evaluation, our proposed method achieves
state-of-the-art performance on SAMM and recently released MMEW benchmarks. Our
code will be publicly available accompanying this paper.


title: Classical Notions and Problems in Thurston Geometries

abstract:
  Of the Thurston geometries, those with constant curvature geometries
(Euclidean $\EUC$, hyperbolic $\HYP$, spherical $\SPH$) have been extensively
studied, but the other five geometries, $\HXR$, $\SXR$, $\NIL$, $\SLR$, $\SOL$
have been thoroughly studied only from a differential geometry and topological
point of view. However, classical concepts highlighting the beauty and
underlying structure of these -- such as geodesic curves and spheres, the
lattices, the geodesic triangles and their surfaces, their interior sum of
angles and similar statements to those known in constant curvature geometries
can be formulated. These have not been the focus of attention. In this survey,
we summarize our results on this topic and pose additional open questions.


title:
On the resolvability of the dynamics of one fluid flow via a testing
  fluid in a two-fluids flow model

abstract:
  In this work, by considering an isentropic fluid-fluid interaction model with
a large symmetric drag force, a commonly used simplified two-fluids flow model
is justified as the asymptotic limit. Equations for each fluid component with
an interaction term are identified in addition to the simplified two-fluids
flow model, which can be used to resolve the density of one fluid specie based
on information on density and the velocity of the other fluid specie, i.e., the
testing flow.


title:
Enhancement and tuning of the defect-induced electroluminescence of ZnO
  mesoporous layers in the visible range

abstract:
  We show a way to pattern the visible electroluminescence of
solution-processed mesoporous ZnO layers. Our approach consists in locally
changing the nanoscale morphology of the coated ZnO layers by patterning the
underlying surface with thin metallic patches. Above the metal, the ZnO film is
organized in clusters that enhance its defect-induced electroluminescence. The
resulting emission occurs over a large continuum of wavelengths in the visible
and near-infrared range. This broad emission continuum is filtered by thin film
interferences that develop within the device, making it possible to fabricate
LEDs with different colours by adjusting the thickness of their transparent
electrode. When the metallic patterns used to change the morphology of the ZnO
layer reach sub-micron dimensions, additional plasmonic effects arise,
providing extra degrees of freedom to tune the colour and polarization of the
emitted photons.


title:
Membership Privacy Protection for Image Translation Models via
  Adversarial Knowledge Distillation

abstract:
  Image-to-image translation models are shown to be vulnerable to the
Membership Inference Attack (MIA), in which the adversary's goal is to identify
whether a sample is used to train the model or not. With daily increasing
applications based on image-to-image translation models, it is crucial to
protect the privacy of these models against MIAs.
  We propose adversarial knowledge distillation (AKD) as a defense method
against MIAs for image-to-image translation models. The proposed method
protects the privacy of the training samples by improving the generalizability
of the model. We conduct experiments on the image-to-image translation models
and show that AKD achieves the state-of-the-art utility-privacy tradeoff by
reducing the attack performance up to 38.9% compared with the regular training
model at the cost of a slight drop in the quality of the generated output
images. The experimental results also indicate that the models trained by AKD
generalize better than the regular training models. Furthermore, compared with
existing defense methods, the results show that at the same privacy protection
level, image translation models trained by AKD generate outputs with higher
quality; while at the same quality of outputs, AKD enhances the privacy
protection over 30%.


title:
Dynamical steady-states of active colloids interacting via chemical
  fields

abstract:
  We study the dynamical steady-states of a monolayer of chemically active
self-phoretic colloids as a function of packing fraction and self-propulsion
speed by means of Brownian dynamics simulations. We focus on the case that a
chemical field induces competing attractive positional and repulsive
orientational interactions. Analyzing the distribution of cluster size and
local density as well as the hexatic order parameter, we distinguish four
distinct dynamical states which include collapsed, active gas, dynamical
clustering, and motility-induced phase-separated states. The long-range
chemical field-induced interactions shift the onset of motility-induced phase
separation (MIPS) to very low packing fractions at intermediate self-propulsion
speeds. We also find that the fraction of particles in the largest clusters is
a suitable order parameter characterizing the dynamical phase transitions from
an active gas or dynamical clustering steady-state to a phase-separated state
upon increase of the packing fraction. The order parameter changes
discontinuously when going from an active gas to a MIPS-like state at
intermediate self-propulsion speeds, whereas it changes continuously at larger
activities where the system undergoes a transition from a dynamical clustering
state to MIPS-like state.


title:
Ultra-high frequency vortex-based tweezers for microparticles
  manipulation with high spatial selectivity and nanoNewton forces

abstract:
  Acoustical tweezers based on focused acoustical vortices open some tremendous
perspectives for the in vitro and in vivo remote manipulation of millimetric
down to micrometric objects, with combined selectivity and applied forces out
of reach with any other contactless manipulation technique. Yet, the synthesis
of ultra-high frequency acoustical vortices to manipulate precisely micrometric
objects remains a major challenge. In this paper, the synthesis of a 250 MHz
acoustical vortex is achieved with an active holographic source based on
spiraling interdigitated transducers. It is shown that this ultra-high
frequency vortex enables to trap and position individual particles in a
standard microscopy environment with high spatial selectivity and nanoNewton
forces. This work opens perspectives to explore acoustic force spectroscopy in
some force ranges that were not accessible before.


title: A Benchmark for Active Learning of Variability-Intensive Systems

abstract:
  Behavioral models are the key enablers for behavioral analysis of Software
Product Lines (SPL), including testing and model checking. Active model
learning comes to the rescue when family behavioral models are non-existent or
outdated. A key challenge on active model learning is to detect commonalities
and variability efficiently and combine them into concise family models.
Benchmarks and their associated metrics will play a key role in shaping the
research agenda in this promising field and provide an effective means for
comparing and identifying relative strengths and weaknesses in the forthcoming
techniques. In this challenge, we seek benchmarks to evaluate the efficiency
(e.g., learning time and memory footprint) and effectiveness (e.g., conciseness
and accuracy of family models) of active model learning methods in the software
product line context. These benchmark sets must contain the structural and
behavioral variability models of at least one SPL. Each SPL in a benchmark must
contain products that requires more than one round of model learning with
respect to the basic active learning $L^{*}$ algorithm. Alternatively, tools
supporting the synthesis of artificial benchmark models are also welcome.


title:
Mass$\unicode{x2013}$spin Re-Parameterization for Rapid Parameter
  Estimation of Inspiral Gravitational-Wave Signals

abstract:
  Estimating the source parameters of gravitational waves from compact binary
coalescence(CBC) is a key analysis task in gravitational-wave astronomy. To
deal with the increasing detection rate of CBC signals, optimizing the
parameter estimation analysis is crucial. The analysis typically employs a
stochastic sampling technique such as Markov Chain Monte Carlo(MCMC), where the
source parameter space is explored and regions of high Bayesian posterior
probability density are found. One of the bottlenecks slowing down the analysis
is the non-trivial correlation between masses and spins of colliding objects,
which makes the exploration of mass$\unicode{x2013}$spin space extremely
inefficient. We introduce a new set of mass$\unicode{x2013}$spin sampling
parameters which makes the posterior distribution to be simple in the new
parameter space, regardless of the true values of the parameters. The new
parameter combinations are obtained as the principal components of the Fisher
matrix for the restricted 1.5 post-Newtonian waveform. Our re-parameterization
improves the efficiency of MCMC by a factor of $\sim10$ for binary neutron star
with narrow-spin prior ($|\vec{\chi}|<0.05$) and $\sim100$ with broad-spin
prior ($|\vec{\chi}|<0.99$), under the assumption that the binary has spins
aligned with its orbital angular momentum.


title: Full $K_{\ell 3}$ radiative corrections and implications on $|V_{us}|$

abstract:
  We fill up the missing piece in our own re-analysis of the long-distance
electromagnetic radiative corrections to semileptonic kaon decays by performing
a rigorous study in the $K\rightarrow\pi\mu^+\nu_\mu(\gamma)$ channels. With
appropriate experimental and lattice inputs, we achieve a precision level of
$10^{-4}$ in these channels. This is comparable to our previous analysis in the
$K\rightarrow\pi e^+\nu_e(\gamma)$ channels. With this new result, we present
an updated global analysis to extract the Standard Model parameter $|V_{us}|$
from semileptonic kaon decays. We obtain $|V_{us}|=0.22308(55)$ and
$0.22356(73)$, using the lattice average of the $K^0\rightarrow\pi^-$
transition form factor at $N_f=2+1+1$ and $N_f=2+1$, respectively.


title:
Understanding Electrolyte Filling of Lithium-Ion Battery Electrodes on
  the Pore Scale Using the Lattice Boltzmann Method

abstract:
  Electrolyte filling is a time-critical step during battery manufacturing that
also affects the battery performance. The underlying physical phenomena during
filling mainly occur on the pore scale and are hard to study experimentally. In
this paper, a computational approach, i.e.\ the lattice Boltzmann method, is
used to study the filling process and corresponding pore-scale phenomena in 3D
lithium-ion battery cathodes. The electrolyte flow through the nanoporous
binder is simulated using a homogenization approach. Besides the process time,
the influence of structural and physico-chemical properties is investigated.
Those are the particle size, the binder distribution, and the volume fraction
and wetting behavior of active material and binder. Optimized filling
conditions are discussed by capillary pressure-saturation relationships. It is
shown how the aforementioned influencing factors affect the electrolyte
saturation. Moreover, the amount of the entrapped residual gas phase and the
corresponding size distribution of the gas agglomerates are analyzed in detail.
Both factors are shown to have a strong impact on mechanisms that can adversely
affect the battery performance. The results obtained here indicate how the
filling process, the final electrolyte saturation, and potentially also the
battery performance can be optimized by adapting process parameters and the
electrode and electrolyte design.


title:
Organisations (de-)centralised to a greater or lesser degree for
  allocating cities in two Multiple Travelling Salesmen Problems

abstract:
  Decisions in organisations may be made either by a Central Authority (CA),
e.g., in a hierarchy, or by the agents in a decentralised way, e.g., in a
heterarchy. Since both kinds of organisations have their advantages (e.g.,
optimality for centralised organisations and reactivity for decentralised
ones), our goal is ultimately to understand when and how to use each of them.
Our previous work proposed a variant of the Multiple Travelling Salesmen
Problem, which we now call MTSPs . We use the subscript "s" to refer to
salesmen's selfishness when they minimise their individual route length. If, on
the contrary, they are assumed to be benevolent, we add subscript "b" and thus
the term MTSPb to refer to the traditional MTSP in which the salesmen minimise
the total route length. This article shows how to obtain such benevolent agents
by slightly modifying selfish agents. We can then compare organisations which
are (de-)centralised to a greater or lesser degree, which enables us to carry
out the allocation of cities in the MTSPb . The first experiment shows that the
relative efficiency (ranking) of the organisations differs between MTSPb and
MTSPs . Since reactivity fosters decentralisation, the second experiment
gradually reduces the time taken for it to impact this ranking. Both
experiments show that pure centralisation is either the best or the worst
option, and that the zone between the two situations is very narrow.


title:
Quantifying the Dzyaloshinskii-Moriya Interaction Induced by the Bulk
  Magnetic Asymmetry

abstract:
  A broken interfacial inversion symmetry in ultrathin ferromagnet/heavy metal
(FM/HM) bilayers is generally believed to be a prerequisite for accommodating
the Dzyaloshinskii-Moriya interaction (DMI) and for stabilizing chiral spin
textures. In these bilayers, the strength of the DMI decays as the thickness of
the FM layer increases and vanishes around a few nanometers. In the present
study, through synthesizing relatively thick films of compositions CoPt or
FePt, CoCu or FeCu, FeGd and FeNi, contributions to DMI from the composition
gradient induced bulk magnetic asymmetry (BMA) and spin-orbit coupling (SOC)
are systematically examined. Using Brillouin light scattering spectroscopy,
both the sign and amplitude of DMI in films with controllable direction and
strength of BMA, in the presence and absence of SOC are experimentally studied.
In particular, we show that a sizable amplitude of DMI (0.15 mJ/m^2) can be
realized in CoPt or FePt films with BMA and strong SOC, whereas negligible DMI
strengths are observed in other thick films with BMA but without significant
SOC. The pivotal roles of BMA and SOC are further examined based on the
three-site Fert-Levy model and first-principles calculations. It is expected
that our findings may help to further understand the origin of chiral magnetism
and to design novel non-collinear spin textures.


title: Realizing Implicit Computational Complexity

abstract:
  This abstract aims at presenting an ongoing effort to apply a novel typing
mechanism stemming from Implicit Computational Complexity (ICC), that tracks
dependencies between variables in three different ways, at different stages of
maturation.The first and third projects bend the original typing discipline to
gain finer-grained view on statements independence, to optimize loops by
hoisting invariant and by splitting loops "horizontally" to parallelize them
more efficiently.The second project refines and implements the original
analysis to obtain a fast, modular static analyzer.All three projects aims at
pushing the original type system, inspired from ICC, to its limits, to assess
how ICC can in practice leads to original, sometimes orthogonal, approaches.


title: Clustering Label Inference Attack against Practical Split Learning

abstract:
  Split learning is deemed as a promising paradigm for privacy-preserving
distributed learning, where the learning model can be cut into multiple
portions to be trained at the participants collaboratively. The participants
only exchange the intermediate learning results at the cut layer, including
smashed data via forward-pass (i.e., features extracted from the raw data) and
gradients during backward-propagation.Understanding the security performance of
split learning is critical for various privacy-sensitive applications.With the
emphasis on private labels, this paper proposes a passive clustering label
inference attack for practical split learning. The adversary (either clients or
servers) can accurately retrieve the private labels by collecting the exchanged
gradients and smashed data.We mathematically analyse potential label leakages
in split learning and propose the cosine and Euclidean similarity measurements
for clustering attack. Experimental results validate that the proposed approach
is scalable and robust under different settings (e.g., cut layer positions,
epochs, and batch sizes) for practical split learning.The adversary can still
achieve accurate predictions, even when differential privacy and gradient
compression are adopted for label protections.


title:
Weak localization on moir\'e superlattice in twisted double bilayer
  graphene

abstract:
  Moir\'e superlattice created by twist stacking has novel physical properties.
These physical properties depend on the twist angle, hence investigation of the
twist angle dependency is important for the deep understanding of physical
phenomena in moir\'e superlattice. In this work, negative magnetoresistance
owing to weak localization (WL) was investigated in twisted double bilayer
graphene (TDBG) as a function of the twist angle. The ratio of the intervalley
scattering time to the intravalley scattering time, estimated using the WL
formula for bilayer graphene, tended to decrease as the twist angle increased.
This feature is qualitatively explained by the enhancement of intervalley
scattering due to the reduction of the intervalley distance in the moir\'e
Brillouin zone (BZ) of the TDBG. This indicates that WL in the TDBG occurs for
the moir\'e superlattice with the reconstructed BZ.


title: Efficient MIP Techniques for Computing the Relaxation Complexity

abstract:
  The relaxation complexity rc(X) of the set of integer points X contained in a
polyhedron is the minimal number of inequalities needed to formulate a linear
optimization problem over X without using auxiliary variables. Besides its
relevance in integer programming, this concept has interpretations in aspects
of social choice, symmetric cryptanalysis, and machine learning.
  We employ efficient mixed-integer programming techniques to compute a robust
and numerically more practical variant of the relaxation complexity. Our
proposed models require row or column generation techniques and can be enhanced
by symmetry handling and suitable propagation algorithms. Theoretically, we
compare the quality of our models in terms of their LP relaxation values. The
performance of those models is investigated on a broad test set and is
underlined by their ability to solve challenging instances that could not be
solved previously.


title:
Correlation-driven threefold topological phase transition in monolayer
  $\mathrm{OsBr_2}$

abstract:
  Spin-orbit coupling (SOC) combined with electronic correlation can induce
topological phase transition, producing novel electronic states. Here, we
investigate the impact of SOC combined with correlation effects on physical
properties of monolayer $\mathrm{OsBr_2}$, based on first-principles
calculations with generalized gradient approximation plus $U$ (GGA+$U$)
approach. With intrinsic out-of-plane magnetic anisotropy, $\mathrm{OsBr_2}$
undergoes threefold topological phase transition with increasing $U$, and
valley-polarized quantum anomalous Hall insulator (VQAHI) to half-valley-metal
(HVM) to ferrovalley insulator (FVI) to HVM to VQAHI to HVM to FVI transitions
can be induced. These topological phase transitions are connected with
sign-reversible Berry curvature and band inversion between
$d_{xy}$/$d_{x^2-y^2}$ and $d_{z^2}$ orbitals. Due to $\bar{6}m2$ symmetry,
piezoelectric polarization of $\mathrm{OsBr_2}$ is confined along the in-plane
armchair direction, and only one $d_{11}$ is independent. For a given material,
the correlation strength should be fixed, and $\mathrm{OsBr_2}$ may be a
piezoelectric VQAHI (PVQAHI), piezoelectric HVM (PHVM) or piezoelectric FVI
(PFVI). The valley polarization can be flipped by reversing the magnetization
of Os atoms, and the ferrovalley (FV) and nontrivial topological properties
will be suppressed by manipulating out-of-plane magnetization to in-plane one.
In considered reasonable $U$ range, the estimated Curie temperatures all are
higher than room temperature. Our findings provide a comprehensive
understanding on possible electronic states of $\mathrm{OsBr_2}$, and confirm
that strong SOC combined with electronic correlation can induce multiple
quantum phase transition.


title:
Analytical non-Hermitian description of Photonic Crystals with arbitrary
  Lateral and Transverse symmetry

abstract:
  In this work we propose a general theoretical approach to the modelling of
complex dispersion characteristics of leaky optical modes operating in photonic
crystal slab composed of two high-index contrast gratings, beyond the
protection of the light cone. Opening access of wave-guided resonances to free
space continuum provides large amount of extra degrees of freedom for mode
coupling engineering. Not only can the two gratings communicate via near field
coupling, but they are also allowed to couple via the propagating radiated
field. Our analytical model, based on a non-Hermitian Hamiltonian, including
both coupling schemes, allows for a unified description of the wide family of
optical modes which may be generated within uni-dimensional photonic crystal.
Through a variety of illustrative examples, we show that our theoretical
approach provide a simplified categorization of these modes, but it is also a
powerful enabler for the discovery of novel photonic species. Finally, as
proof-of-concept, we demonstrate experimentally the formation of a Dirac point
at the merging of three bound states in the continuum that is the most achieved
photonic specie discussed in this work.


title:
Faithfulness in Natural Language Generation: A Systematic Survey of
  Analysis, Evaluation and Optimization Methods

abstract:
  Natural Language Generation (NLG) has made great progress in recent years due
to the development of deep learning techniques such as pre-trained language
models. This advancement has resulted in more fluent, coherent and even
properties controllable (e.g. stylistic, sentiment, length etc.) generation,
naturally leading to development in downstream tasks such as abstractive
summarization, dialogue generation, machine translation, and data-to-text
generation. However, the faithfulness problem that the generated text usually
contains unfaithful or non-factual information has become the biggest
challenge, which makes the performance of text generation unsatisfactory for
practical applications in many real-world scenarios. Many studies on analysis,
evaluation, and optimization methods for faithfulness problems have been
proposed for various tasks, but have not been organized, compared and discussed
in a combined manner. In this survey, we provide a systematic overview of the
research progress on the faithfulness problem of NLG, including problem
analysis, evaluation metrics and optimization methods. We organize the
evaluation and optimization methods for different tasks into a unified taxonomy
to facilitate comparison and learning across tasks. Several research trends are
discussed further.


title: Critical structure and emergent symmetry of Dirac fermion systems

abstract:
  Emergent symmetry in Dirac system means that the system acquires an
enlargement of two basic symmetries at some special critical point. The
continuous quantum criticality between the two symmetry broken phases can be
described within the framework of Gross-Neveu-Yukawa (GNY) model. Using the
first-order $\epsilon$ expansion in $4-\epsilon$ dimensions, we study the
critical structure and emergent symmetry of the chiral GNY model with $N_f$
flavors of four-component Dirac fermions coupled strongly to an $O(N)$ scalar
field under a small $O(N)$-symmetry breaking perturbation. After determining
the stable fixed point, we calculate the inverse correlation length exponent
and the anomalous dimensions (bosonic and fermionic) for general $N$ and $N_f$.
Further, we discuss the emergent-symmetry and the emergent supersymmetric
critical point for $N\geq4$ on the basis of $O(N)$-GNY model. It turns out that
the chiral emergent-$O(N)$ universality class is physically meaningful if and
only if $N<2N_f+4$. On this premise, the small $O(N)$-symmetry breaking
perturbation is always irrelevant in the chiral emergent-$O(N)$ universality
class. Our studies show that the emergent symmetry in Dirac systems has an
upper boundary $O(2N_f+3)$, depending on the flavor numbers $N_f$. As a result,
the emergent-$O(4)$ and $O(5)$ symmetries are possible to be found in the
systems with fermion flavor $N_f=1$, and the emergent-$O(4)$, $O(5)$, $O(6)$
and $O(7)$ symmetries are expected to be found in the systems with fermion
flavor $N_f=2$. Our result also suggests some rich transitions with
emergent-$Z_2\times O(2)\times O(3)$ symmetry and so on. Interestingly, in the
emergent-$O(4)$ universality class, there is a supersymmetric critical point
which is expected to be found in the systems with fermion flavor $N_f=1$.


title: Sensitivity kernels for transmission fiber optics

abstract:
  Fiber-optic sensing technologies based on transmission offer an alternative
to scattering-based Distributed Acoustic Sensing (DAS). Being able to
interrogate fibers that are thousands of kilometers long, opens opportunities
for seismological studies of remote regions, including ocean basins. However,
by averaging deformation along the fiber, transmission systems only produce
integrated and not distributed measurements. Here we develop a formalism to
calculate sensitivity kernels with respect to (Earth) structure, using optical
phase delay measurements. With this, we demonstrate that transmission-based
sensing can effectively provide distributed measurements when the phase delay
time series is dissected into different windows. The extent to which a
potentially useful sensitivity coverage can be achieved, depends on the fiber
geometry, and specifically on its local curvature. This work establishes a
theoretical foundation for both tomographic inversions and experimental design,
using transmission-based optical sensing.


title: An accurate lattice Boltzmann method for interface capturing

abstract:
  Accurately solving phase interface plays a great role in modeling immiscible
multiphase flow system. In this letter, we propose an accurate
interface-capturing lattice Boltzmann method from the perspective of the
modified Allen-Cahn equation (ACE). The modified ACE is built based on the
commonly used conservative formulation via the relation between the
signed-distance function and the order parameter also maintaining the
mass-conserved characteristic. A suitable forcing term is carefully
incorporated into the lattice Boltzmann equation for correctly recovering the
target equation. We then test the proposed method by simulating a typical
interface-tracking problem of single vortex and demonstrate that the present
model can be more numerically accurate than the existing lattice Boltzmann
models for the conservative ACE, especially at a small interface-thickness
scale.


title:
A magnetic cloud prediction model for forecasting space weather relevant
  properties of Earth-directed coronal mass ejections

abstract:
  Coronal Mass Ejections (CMEs) are energetic storms in the Sun that result in
the ejection of large-scale magnetic clouds (MCs) in interplanetary space that
contain enhanced magnetic fields with coherently changing field direction. The
severity of geomagnetic perturbations depends on the direction and strength of
the interplanetary magnetic field (IMF), as well as the speed and duration of
passage of the storm. The coupling between the heliospheric environment and
Earth's magnetosphere is the strongest when the IMF direction is persistently
southward for a prolonged period. Predicting the magnetic profile of such
Earth-directed CMEs is crucial for estimating their geomagnetic impact. We aim
to build upon and integrate diverse techniques towards development of a
comprehensive magnetic cloud prediction (MCP) model that can forecast the
magnetic field vectors, Earth-impact time, speed and duration of passage of
solar storms. A novelty of our scheme is the ability to predict the passage
duration of the storm without recourse to computationally intensive,
time-dependent dynamical equations. Our methodology is validated by comparing
the MCP model output with observations of ten MCs at 1 AU. In our sample, we
find that eight MCs show a root mean square deviation of less than 0.1 between
predicted and observed magnetic profiles and the passage duration of seven MCs
fall within the predicted range. Based on the success of this approach, we
conclude that predicting the near-Earth properties of MCs based on analysis and
modelling of near-Sun CME observations is a viable endeavor with potential
benefits for space weather assessment.


title:
Evaluation of Machine Learning Algorithms in Network-Based Intrusion
  Detection System

abstract:
  Cybersecurity has become one of the focuses of organisations. The number of
cyberattacks keeps increasing as Internet usage continues to grow. An intrusion
detection system (IDS) is an alarm system that helps to detect cyberattacks. As
new types of cyberattacks continue to emerge, researchers focus on developing
machine learning (ML) based IDS to detect zero-day attacks. Researchers usually
remove some or all attack samples from the training dataset and only include
them in the testing dataset when evaluating the performance of an IDS on
detecting zero-day attacks. Although this method may show the ability of an IDs
to detect unknown attacks; however, it does not reflect the long-term
performance of the IDS as it only shows the changes in the type of attacks. In
this paper, we focus on evaluating the long-term performance of ML based IDS.
To achieve this goal, we propose evaluating the ML-based IDS using a dataset
that is created later than the training dataset. The proposed method can better
assess the long-term performance of an ML-based IDS, as the testing dataset
reflects the changes in the type of attack and the changes in network
infrastructure over time. We have implemented six of the most popular ML models
that are used for IDS, including decision tree (DT), random forest (RF),
support vector machine (SVM), na\"ive Bayes (NB), artificial neural network
(ANN) and deep neural network (DNN). Our experiments using the CIC-IDS2017 and
the CSE-CIC-IDS2018 datasets show that SVM and ANN are most resistant to
overfitting. Besides that, our experiment results also show that DT and RF
suffer the most from overfitting, although they perform well on the training
dataset. On the other hand, our experiments using the LUFlow dataset have shown
that all models can perform well when the difference between the training and
testing datasets is small.


title:
HADOKEN: An Open-Source Software Package for Predicting Electron
  Confinement Effects in Various Nanowire Geometries and Configurations

abstract:
  We present an open-source software package, HADOKEN (High-level Algorithms to
Design, Optimize, and Keep Electrons in Nanowires), for predicting electron
confinement/localization effects in nanowires with various geometries,
{arbitrary number of concentric shell layers,} doping densities, and external
boundary conditions. The HADOKEN code is written in the MATLAB programming
environments to aid in its readability and general accessibility to both users
and practitioners. We provide several examples and outputs on a variety of
different nanowire geometries, boundary conditions, and doping densities to
demonstrate the capabilities of the HADOKEN software package. As such, the use
of this predictive and versatile tool by both experimentalists and theorists
could lead to further advances in both understanding and tailoring electron
confinement effects in these nanosystems.

categories: cond-mat.mes-hall cond-mat.mtrl-sci cond-mat.quant-gas physics.comp-ph quant-ph


title:
Pathwise least-squares estimator for linear SPDEs with additive
  fractional noise

abstract:
  This paper deals with the drift estimation in linear stochastic evolution
equations (with emphasis on linear SPDEs) with additive fractional noise (with
Hurst index ranging from 0 to 1) via least-squares procedure. Since the
least-squares estimator contains stochastic integrals of divergence type, we
address the problem of its pathwise (and robust to observation errors)
evaluation by comparison with the pathwise integral of Stratonovich type and
using its chain-rule property. The resulting pathwise LSE is then defined
implicitly as a solution to a non-linear equation. We study its numerical
properties (existence and uniqueness of the solution) as well as statistical
properties (strong consistency and the speed of its convergence). The
asymptotic properties are obtained assuming fixed time horizon and increasing
number of the observed Fourier modes (space asymptotics). We also conjecture
the asymptotic normality of the pathwise LSE.


title:
Data-Folding and Hyperspace Coding for Multi-Dimensonal Time-Series Data
  Imaging

abstract:
  Time-series classification and prediction are widely used in many
applications. However, traditional machine learning algorithms, due to their
limitations, have difficulty improving the performance of time-series
classification and prediction. Inspired by the recent successes of the deep
learning technology in computer vision, we develop a new time-series image
encoding method for data reconstruction. Featuring data-folding and hyperspace
coding this method breaks the barriers between time-series signals and images
and establishes a close relationship between them, allowing effective
application of the deep learning technology for time-series data. Besides a raw
data coding method, we also present other four extended coding methods for
other potential applications. For comparison purposes, we present the results
of the five different types of image coding methods with our previous keystroke
recognition datasets. The results show that our method can achieve an
impressive accuracy of 96.27% when RGB coding images are used, and an accuracy
of up to 97.33% when using radon coding way. We can expect that this method can
also be used and perform well in other classification and prediction
applications.


title:
Cellular automata that generate symmetrical patterns give singular
  functions

abstract:
  In this paper, we mainly study linear one-dimensional and two-dimensional
elementary cellular automata that generate symmetrical spatio-temporal
patterns. For spatio-temporal patterns of cellular automata from the single
site seed, we normalize the number of nonzero states of the patterns, take the
limits, and give one-variable functions for the limit sets. We can obtain a
one-variable function for each limit set and show that the resulting functions
are singular functions, which are non-constant, are continuous everywhere, and
have a zero derivative almost everywhere. We show that for Rule 90, a
one-dimensional elementary cellular automaton (CA), and a two-dimensional
elementary CA, the resulting functions are Salem's singular functions. We also
discuss two nonlinear elementary CAs, Rule 22, and Rule 126. Although their
spatio-temporal patterns are different from that of Rule 90, their resulting
functions from the number of nonzero states equal the function of Rule 90.


title: Entropy Rate Bounds via Second-Order Statistics

abstract:
  This work contains two single-letter upper bounds on the entropy rate of a
discrete-valued stationary stochastic process, which only depend on
second-order statistics, and are primarily suitable for models which consist of
relatively large alphabets. The first bound stems from Gaussian maximum-entropy
considerations and depends on the power spectral density (PSD) function of the
process. While the PSD function cannot always be calculated in a closed-form,
we also propose a second bound, which merely relies on some finite collection
of auto-covariance values of the process. Both of the bounds consist of a
one-dimensional integral, while the second bound also consists of a
minimization problem over a bounded region, hence they can be efficiently
calculated numerically. Examples are also provided to show that the new bounds
outperform the standard conditional entropy bound.


title:
Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided
  Label Enhancement

abstract:
  In this paper, we propose a weakly-supervised approach for 3D object
detection, which makes it possible to train a strong 3D detector with
position-level annotations (i.e. annotations of object centers). In order to
remedy the information loss from box annotations to centers, our method, namely
Back to Reality (BR), makes use of synthetic 3D shapes to convert the weak
labels into fully-annotated virtual scenes as stronger supervision, and in turn
utilizes the perfect virtual labels to complement and refine the real labels.
Specifically, we first assemble 3D shapes into physically reasonable virtual
scenes according to the coarse scene layout extracted from position-level
annotations. Then we go back to reality by applying a virtual-to-real domain
adaptation method, which refine the weak labels and additionally supervise the
training of detector with the virtual scenes. Furthermore, we propose a more
challenging benckmark for indoor 3D object detection with more diversity in
object sizes to better show the potential of BR. With less than 5% of the
labeling labor, we achieve comparable detection performance with some popular
fully-supervised approaches on the widely used ScanNet dataset. Code is
available at: https://github.com/wyf-ACCEPT/BackToReality