title:
On The Design of a Light-weight FPGA Programming Framework for Graph
  Applications

abstract:
  FPGA accelerators designed for graph processing are gaining popularity.
Domain Specific Language (DSL) frameworks for graph processing can reduce the
programming complexity and development cost of algorithm design. However,
accelerator-specific development requires certain technical expertise and
significant effort to devise, implement, and validate the system. For most
algorithm designers, the expensive cost for hardware programming experience
makes FPGA accelerators either unavailable or uneconomic. Although
general-purpose High-Level Synthesis (HLS) tools help to map high-level
language to Hardware Description Languages (HDLs), the generated code is often
inefficient and lengthy compared with the highly-optimized graph accelerators.
One cannot make full use of an FPGA accelerator's capacity with low development
cost.
  To easily program graph algorithms while keeping performance degradation
acceptable, we propose a graph programming system named JGraph, which contains
two main parts: 1) a DSL for graph atomic operations with a graph library for
high-level abstractions including user-defined functions with parameters, 2) a
light-weight HLS translator to generate high-performance HDL code, cooperating
with a communication manager and a runtime scheduler. To the best of our
knowledge, our work is the first graph programming system with DSL and
translator on the FPGA platform. Our system can generate up to 300 MTEPS BFS
traversal within tens of seconds.


title:
Revisiting the secondary climate attributes for transportation
  infrastructure management: A Redux and Update for 2020

abstract:
  Environmental conditions in various regions can have a severely negative
impact on the longevity and durability of the civil engineering
infrastructures. In 2018, a published paper used 1971 to 2010 NOAA data from
the contiguous United States to examine the temporal changes in secondary
climate attributes (freeze-thaw cycles and freeze index) using the climate
normals from two time windows, 1971-2000 and 1981-2010. That paper investigated
whether there have been statistically significant changes in climate attribute
levels across the two time windows, and used GIS-based interpolation methods to
develop isarithmic maps of the climate attributes to facilitate their
interpretation and application. This paper updates that study. In this study,
we use NOAA climatic data from 1991 to 2020 to construct continuous surface
maps showing the values of the primary and secondary climate attributes at the
48 continental states. The new maps provide an updated picture of the freeze
index values and freeze-thaw cycles for the most recent climate condition.
These new values provide a better picture of the freezing season
characteristics of the United States, and will provide information necessary
for better winter maintenance procedures and infrastructure design to
accommodate regional climate differences.


title:
Multi-View Graph Representation for Programming Language Processing: An
  Investigation into Algorithm Detection

abstract:
  Program representation, which aims at converting program source code into
vectors with automatically extracted features, is a fundamental problem in
programming language processing (PLP). Recent work tries to represent programs
with neural networks based on source code structures. However, such methods
often focus on the syntax and consider only one single perspective of programs,
limiting the representation power of models. This paper proposes a multi-view
graph (MVG) program representation method. MVG pays more attention to code
semantics and simultaneously includes both data flow and control flow as
multiple views. These views are then combined and processed by a graph neural
network (GNN) to obtain a comprehensive program representation that covers
various aspects. We thoroughly evaluate our proposed MVG approach in the
context of algorithm detection, an important and challenging subfield of PLP.
Specifically, we use a public dataset POJ-104 and also construct a new
challenging dataset ALG-109 to test our method. In experiments, MVG outperforms
previous methods significantly, demonstrating our model's strong capability of
representing source code.


title:
Sparse Neural Additive Model: Interpretable Deep Learning with Feature
  Selection via Group Sparsity

abstract:
  Interpretable machine learning has demonstrated impressive performance while
preserving explainability. In particular, neural additive models (NAM) offer
the interpretability to the black-box deep learning and achieve
state-of-the-art accuracy among the large family of generalized additive
models. In order to empower NAM with feature selection and improve the
generalization, we propose the sparse neural additive models (SNAM) that employ
the group sparsity regularization (e.g. Group LASSO), where each feature is
learned by a sub-network whose trainable parameters are clustered as a group.
We study the theoretical properties for SNAM with novel techniques to tackle
the non-parametric truth, thus extending from classical sparse linear models
such as the LASSO, which only works on the parametric truth.
  Specifically, we show that SNAM with subgradient and proximal gradient
descents provably converges to zero training loss as $t\to\infty$, and that the
estimation error of SNAM vanishes asymptotically as $n\to\infty$. We also prove
that SNAM, similar to LASSO, can have exact support recovery, i.e. perfect
feature selection, with appropriate regularization. Moreover, we show that the
SNAM can generalize well and preserve the `identifiability', recovering each
feature's effect. We validate our theories via extensive experiments and
further testify to the good accuracy and efficiency of SNAM.


title: Negative Energetic Elasticity of Lattice Polymer Chain in Solvent

abstract:
  Negative internal energetic contribution to elastic modulus (negative
energetic elasticity) has recently been observed in polymer gels. This finding
challenges the conventional notion that the elastic moduli of rubberlike
materials are determined mainly by entropic elasticity. However, the
microscopic origin of negative energetic elasticity has not yet been clarified.
Here, we consider the $n$-step interacting self-avoiding walk on a cubic
lattice as a model of a single polymer chain (a subchain of a network in a
polymer gel) in a solvent. We show the occurrence of negative energetic
elasticity based on an exact enumeration up to $n=20$ and analytic expressions
for arbitrary $n$ in three cases where the chain is highly stretched.
Furthermore, we demonstrate that the negative energetic elasticity of this
model originates from the attractive polymer-solvent interaction, which locally
stiffens the chain and conversely softens the stiffness of the entire chain.
This model qualitatively reproduces the temperature dependence of negative
energetic elasticity observed in the polymer-gel experiments, indicating that
the analysis of a single chain can explain the properties of negative energetic
elasticity in polymer gels.


title: Switching and amplifying three-body Casimir effects

abstract:
  The dynamics of three interacting objects has been investigated extensively
in Newtonian gravitational physics (often termed the three-body problem), and
is important for many quantum systems, including nuclei, Efimov states, and
frustrated spin systems. However, the dynamics of three macroscopic objects
interacting through quantum vacuum fluctuations (virtual photons) is still an
unexplored frontier. Here, we report the first observation of Casimir
interactions between three isolated macroscopic objects. We propose and
demonstrate a three terminal switchable architecture exploiting opto-mechanical
Casimir interactions that can lay the foundations of a Casimir transistor.
Beyond the paradigm of Casimir forces between two objects in different
geometries, our Casimir transistor represents an important development for
control of three-body virtual photon interactions and will have potential
applications in sensing and information processing with the Casimir effect.


title:
Stochastic Galerkin methods for linear stability analysis of systems
  with parametric uncertainty

abstract:
  We present a method for linear stability analysis of systems with parametric
uncertainty formulated in the stochastic Galerkin framework. Specifically, we
assume that for a model partial differential equation, the parameter is given
in the form of generalized polynomial chaos expansion. The stability analysis
leads to the solution of a stochastic eigenvalue problem, and we wish to
characterize the rightmost eigenvalue. We focus, in particular, on problems
with nonsymmetric matrix operators, for which the eigenvalue of interest may be
a complex conjugate pair, and we develop methods for their efficient solution.
These methods are based on inexact, line-search Newton iteration, which entails
use of preconditioned GMRES. The method is applied to linear stability analysis
of Navier-Stokes equation with stochastic viscosity, its accuracy is compared
to that of Monte Carlo and stochastic collocation, and the efficiency is
illustrated by numerical experiments.


title:
Magnetic-Field-Dependent Thermodynamic Properties of Square and
  Quadrupolar Artificial Spin Ice

abstract:
  Applied magnetic fields are an important tuning parameter for artificial spin
ice (ASI) systems, as they can drive phase transitions between different
magnetic ground states, or tune through regimes with high populations of
emergent magnetic excitations (e.g., monopole-like quasiparticles). Here, using
simulations supported by experiments, we investigate the thermodynamic
properties and magnetic phases of square and quadrupolar ASI as a function of
applied in-plane magnetic fields. Monte Carlo simulations are used to generate
field-dependent maps of the magnetization, the magnetic specific heat, the
thermodynamic magnetization fluctuations, and the magnetic order parameters,
all under equilibrium conditions. These maps reveal the diversity of magnetic
orderings and the phase transitions that occur in different regions of the
phase diagrams of these ASIs, and are experimentally supported by
magneto-optical measurements of the equilibrium "magnetization noise" in
thermally-active ASIs.


title: Lacunarity Transition

abstract:
  Experiments investigating particles floating on a randomly stirred fluid show
regions of very low density, which are not well understood. We introduce a
simplified model for understanding sparsely occupied regions of the phase space
of non-autonomous, chaotic dynamical systems, based upon an extension of the
skinny bakers' map. We show how the distribution of the sizes of voids in the
phase space can be mapped to the statistics of the running maximum of a Wiener
process. We find that the model exhibits a lacunarity transition, which is
characterised by regions of the phase space remaining empty as the number of
trajectories is increased.


title: Learn From the Past: Experience Ensemble Knowledge Distillation

abstract:
  Traditional knowledge distillation transfers "dark knowledge" of a
pre-trained teacher network to a student network, and ignores the knowledge in
the training process of the teacher, which we call teacher's experience.
However, in realistic educational scenarios, learning experience is often more
important than learning results. In this work, we propose a novel knowledge
distillation method by integrating the teacher's experience for knowledge
transfer, named experience ensemble knowledge distillation (EEKD). We save a
moderate number of intermediate models from the training process of the teacher
model uniformly, and then integrate the knowledge of these intermediate models
by ensemble technique. A self-attention module is used to adaptively assign
weights to different intermediate models in the process of knowledge transfer.
Three principles of constructing EEKD on the quality, weights and number of
intermediate models are explored. A surprising conclusion is found that strong
ensemble teachers do not necessarily produce strong students. The experimental
results on CIFAR-100 and ImageNet show that EEKD outperforms the mainstream
knowledge distillation methods and achieves the state-of-the-art. In
particular, EEKD even surpasses the standard ensemble distillation on the
premise of saving training cost.


title: Twistings and the Alexander polynomial

abstract:
  We give an explicit formula of the Alexander polynomial of the link obtained
by adding an arbitrary number of full twists to positively oriented parallel
n-strands in terms of the Alexander polynomials of the links obtained by adding
0,1,...,n-1 full twists. From this, we see that the Alexander polynomials
stabilize after adding sufficiently many full twists. The main tool used in the
computation is expressing the Alexander polynomial using the vector space
representation of $U_{q}(gl(1|1))$.


title:
Quantum Radiation and Dissipation in Relation to Classical Radiation and
  Radiation Reaction

abstract:
  This work continues the investigation of radiation phenomena from atom-field
interactions, extending our earlier study of quantum radiation from a
stationary atom's internal degree of freedom, modeled by a harmonic oscillator,
to the emittance of classical radiation. By assuming that the atom interacts
with a quantum scalar field initially in a coherent state, we show how a
stochastic component of the internal dynamics of the atom arises from the
vacuum fluctuations of the field, resulting in the emittance of quantum
radiation, whose reaction induces quantum dissipation in the internal dynamics.
We also show how the deterministic mean field drives the internal classical
mean component to emit classical radiation and receive classical radiation
reaction. Both components are statistically distinct and fully decoupled. It is
clearly seen that the effects of the vacuum fluctuations of the field are
matched with those of quantum radiation reaction, not with classical radiation
reaction, as the folklore goes. In contrast to the quantum component of the
atom's internal dynamics, which always equilibrates, the relaxation dynamics of
the classical component largely depends on the late-time behavior of the mean
field. For the values of the parameters defining the coherent state of the
field much greater than unity, if the mean field remains periodic, then the
internal dynamics of the atom will appear classical and periodic. If the mean
field diminishes with time, then the classical component of the atom's internal
dynamics subsides but the quantum component will abide and dynamically
equilibrate. This also explains why quantum radiation from a stationary atom is
not observed, and a probe located far away only sees classical radiation. Our
analysis therefore paints a continuum landscape starting from vacuum
fluctuations in the quantum field to classical radiation and radiation
reaction.


title: Monogenic Wavelet Scattering Network for Texture Image Classification

abstract:
  The scattering transform network (STN), which has a similar structure as that
of a popular convolutional neural network except its use of predefined
convolution filters and a small number of layers, can generates a robust
representation of an input signal relative to small deformations. We propose a
novel Monogenic Wavelet Scattering Network (MWSN) for 2D texture image
classification through a cascade of monogenic wavelet filtering with nonlinear
modulus and averaging operators by replacing the 2D Morlet wavelet filtering in
the standard STN. Our MWSN can extract useful hierarchical and directional
features with interpretable coefficients, which can be further compressed by
PCA and fed into a classifier. Using the CUReT texture image database, we
demonstrate the superior performance of our MWSN over the standard STN. This
performance improvement can be explained by the natural extension of 1D
analyticity to 2D monogenicity.


title:
Cluster Formation in GGD12-15: Infall Motion with Rotation of the Natal
  Clump

abstract:
  We report results of observations of the GGD12-15 region, where cluster
formation is actively taking place, using various molecular emission lines. The
C18O (J= 1-0) emission line reveals a massive clump in the region with a mass
of ~2800 Mo distributed over ~2 pc. The distribution of the C18O(J= 3-2)
emission is similar to that of a star cluster forming therein, with an
elliptical shape of ~1 pc in size. A bipolar molecular outflow driven by IRS
9Mc, a constituent star of the cluster, is blowing in a direction perpendicular
to the elongated C18O (J= 3-2) distribution, covering the entire clump. There
is a massive core with a radius of 0.3 pc and a mass of 530 Mo in the center of
the clump. There are two velocity components around the core, which are
prominent in a position-velocity (PV) diagram along the major axis of the
clump. In addition, a PV diagram along the minor axis of the clump, which is
parallel to the outflow, shows a velocity gradient opposite to that of the
outflow. The velocity structure strongly indicates the infalling motion of the
clump. Comparison of the observational data with a simple model of infalling
oblate clumps indicates that the clump is undergoing gravitational contraction
with rotation.


title:
Combined significance of spatial coincidence of high energy neutrinos
  from PSR B1509-58 by Super-Kamiokande and MACRO

abstract:
  In their searches for astrophysical point sources of high energy neutrinos,
both the Super-Kamiokande and MACRO neutrino detectors saw the largest angular
excess from the same source, viz. PSR B1509-58. We estimate the probability for
the observed number of events by {\it both} Super-Kamiokande and MACRO to be a
chance coincidence due to atmospheric neutrino background. We find that this
probability is about 0.4\%, corresponding to 2.6$\sigma$ significance. We also
propose some additional tests to ascertain if this excess corresponds to an
astrophysical signal or is only a background event.


title:
Configuration spaces on a wedge of spheres and Hochschild-Pirashvili
  homology

abstract:
  We study the compactly supported rational cohomology of configuration spaces
of points on wedges of spheres, equipped with natural actions of the symmetric
group and the group $Out(F_g)$ of outer automorphism of the free group. These
representations are closely related to Hochschild-Pirashvili homology with
coefficients in square-zero algebras, and they show up in seemingly unrelated
parts of mathematics, from cohomology of moduli spaces of curves to polynomial
functors on free groups.
  We show that these cohomology representations form a polynomial functor, and
use various geometric models to compute a substantial part of its composition
factors. We further compute the composition factors completely for all
configurations of $n\leq 10$ particles. An application of this analysis is a
new super-exponential lower bound on the symmetric group action on the weight
$0$ component of $H^*_c(M_{2,n})$.


title: Fast variational inference for multinomial probit models

abstract:
  The multinomial probit model is often used to analyze choice behaviour.
However, estimation with existing Markov Chain Monte Carlo (MCMC) methods is
computationally costly, which limits its applicability to large choice data
sets. This paper proposes a variational inference method that is fast, even
when a large number of choice alternatives and observations are considered.
Variational methods usually require an analytical expression for the
unnormalized posterior density and an adequate choice of variational family.
Both are challenging to specify in a multinomial probit, which has a posterior
that requires identifying restrictions and is augmented with a large set of
latent utilities. We employ a spherical transformation on the covariance matrix
of the latent utilities to construct an unnormalized augmented posterior that
identifies the parameters, and use the conditional posterior of the latent
utilities as part of the variational family. The proposed method is faster than
MCMC, and can be made scalable to both a large numbers of choice alternatives
and a large number of observations. The accuracy and scalability of our method
is illustrated in numerical experiments and real purchase data with one million
observations.


title: Parametrized constant-depth quantum neuron

abstract:
  Quantum computing has been revolutionizing the development of algorithms.
However, only noisy intermediate-scale quantum devices are available currently,
which imposes several restrictions on the circuit implementation of quantum
algorithms. In this paper, we propose a framework that builds quantum neurons
based on kernel machines, including on actual quantum devices. Previous schemes
are particular cases of our generalized framework, where quantum neurons differ
from each other by their feature space mappings. Under that framework, we
present a neuron that applies a tensor-product feature mapping to an
exponentially larger space. The proposed neuron is implemented by a circuit of
constant depth with a linear number of elementary single-qubit gates. The
existing neuron applies a phase-based feature mapping with an exponentially
expensive circuit implementation, even using multi-qubit gates. Additionally,
the proposed neuron has parameters that can change its activation function
shape. Here, we show the activation function shape of each quantum neuron. It
turns out that parametrization allows the proposed neuron to fit underlying
patterns that the existing neuron cannot fit. As a result, the proposed neuron
produces optimal solutions for the six classification problems addressed here,
while the existing neuron only solves two of them. Finally, we demonstrate the
feasibility of those quantum neuron solutions on a quantum simulator.


title:
High sensitivity ultraviolet graphene-metamaterial integrated
  electro-optic modulator enhanced by superlubricity

abstract:
  Ultraviolet (UV) electro-optic modulation system based on graphene-plasmonic
metamaterials nanomechanical system (NEMS) with superlubricity is investigated.
Due to the strong optical absorption intensity of graphene in the UV region and
the combination of metamaterial structure based on surface plasmons, the
modulation depth of the UV NEMS electro-optic modulator approaches as high as
8.5 times compared to the counterpart modulator in visible light region.
Meanwhile, the superlubricity significantly reduces the power consumption of
the UV electro-optic modulation system due to its extremely low friction
coefficient. It also significantly improves the response speed of the
modulator, which can reach the order of a nanosecond. The modulation voltage
can be lower than 200 mV. The proposed electro-optic modulation system has a
simple structure and high sensitivity, which is supposed to have important
applications in UV optoelectronic devices and systems.


title: Diffeomorphic Image Registration with Neural Velocity Field

abstract:
  Diffeomorphic image registration is a crucial task in medical image analysis.
Recent learning-based image registration methods utilize convolutional neural
networks (CNN) to learn the spatial transformation between image pairs and
achieve a fast inference speed. However, these methods often require a large
number of training data to improve their generalization abilities. During the
test time, learning-based methods might fail to provide a good registration
result, which is likely because of the model overfitting on the training
dataset. In this paper, we propose a neural representation of continuous
velocity field (NeVF) to describe the deformations across two images.
Specifically, this neural velocity field assigns a velocity vector to each
point in the space, which has higher flexibility in modeling the complex
deformation field. Furthermore, we propose a simple sparse-sampling strategy to
reduce the memory consumption for the diffeomorphic registration. The proposed
NeVF can also incorporate with a pre-trained learning-based model whose
predicted deformation is taken as an initial state for optimization. Extensive
experiments conducted on two large-scale 3D MR brain scan datasets demonstrate
that our proposed method outperforms the state-of-the-art registration methods
by a large margin.


title: PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks

abstract:
  This paper focuses on the Data Augmentation for low-resource Natural Language
Understanding (NLU) tasks. We propose Prompt-based D}ata Augmentation model
(PromDA) which only trains small-scale Soft Prompt (i.e., a set of trainable
vectors) in the frozen Pre-trained Language Models (PLMs). This avoids human
effort in collecting unlabeled in-domain data and maintains the quality of
generated synthetic data. In addition, PromDA generates synthetic data via two
different views and filters out the low-quality data using NLU models.
Experiments on four benchmarks show that synthetic data produced by PromDA
successfully boost up the performance of NLU models which consistently
outperform several competitive baseline models, including a state-of-the-art
semi-supervised model using unlabeled in-domain data. The synthetic data from
PromDA are also complementary with unlabeled in-domain data. The NLU models can
be further improved when they are combined for training.


title: Involutive knot Floer homology and bordered modules

abstract:
  We prove that, up to local equivalences, a suitable truncation of the
involutive knot Floer homology of a knot in $S^3$ and the involutive bordered
Heegaard Floer theory of its complement determine each other. In particular,
given two knots $K_1$ and $K_2$, we prove that the
$\mathbb{F}_2[U,V]/(UV)$-coefficient involutive knot Floer homology of $K_1
\sharp -K_2$ is $\iota_K$-locally trivial if $\widehat{CFD}(S^3 \backslash
K_1)$ and $\widehat{CFD}(S^2 \backslash K_2)$ satisfy a certain condition which
can be seen as the bordered counterpart of $\iota_K$-local equivalence. We
further establish an explicit algebraic formula that computes the hat-flavored
truncation of the involutive knot Floer homology of a knot from the involutive
bordered Floer homology of its complement. It follows that there exists an
algebraic satellite operator defined on the local equivalence group of knot
Floer chain complexes, which can be computed explicitly up to a suitable
truncation.


title: Security Attacks and Solutions for Digital Twins

abstract:
  Digital twins, being the virtual replicas of their physical counterparts,
share valuable knowledge of the underlying system. Therefore, they might become
a potential source of data breaches and a playground for attackers to launch
covert attacks. It is imperative to investigate necessary countermeasures to
mitigate such attacks.


title: Renormalized Magic Angles in Asymmetric Twisted Graphene Multilayers

abstract:
  Stacked graphene multilayers with a small relative twist angle between each
of the layers have been found to host flat bands at a series of magic angles.
We consider the effect that Dirac point asymmetry between the layers, and in
particular different Fermi velocities in each layer, may have on this
phenomenon. Such asymmetry may be introduced by unequal Fermi velocity
renormalizations through Coulomb interactions with a dielectric substrate. It
also arises in an approximate way in tetralayer systems, in which the outer
twist angles are large enough that there is a dominant moire periodicity from
the stacking of the inner two layers. We find in such models that the flat band
phenomenon persists in spite of this asymmetry, and that the magic angles
acquire a degree of tunability through either controlling the screening in the
bilayer system or the twist angles of the outer layers in the tetralayer
system. Notably, we find in our models that the quantitative values of the
magic angles are increased.


title: Pinsker $\sigma$-algebra Character and mean Li-Yorke chaos

abstract:
  Let $G$ be an infinite countable discrete amenable group. For any $G$-action
on a compact metric space $X$, it is proved that for any sequence $(G_n)_{n\ge
1}$ consisting of finite subsets of $G$ with $\lim_{n\to \infty}|G_n|=\infty$,
Pinsker $\sigma$-algebra is a character for $(G_n)_{n\ge 1}$. As a consequence,
for a $\mathbb{Z}$-topological dynamical system, positive topological entropy
implies mean Li-Yorke chaos along any sequence with pairwise distinct elements
in $\mathbb{N}$.


title: Consolidated Adaptive T-soft Update for Deep Reinforcement Learning

abstract:
  Demand for deep reinforcement learning (DRL) is gradually increased to enable
robots to perform complex tasks, while DRL is known to be unstable. As a
technique to stabilize its learning, a target network that slowly and
asymptotically matches a main network is widely employed to generate stable
pseudo-supervised signals. Recently, T-soft update has been proposed as a
noise-robust update rule for the target network and has contributed to
improving the DRL performance. However, the noise robustness of T-soft update
is specified by a hyperparameter, which should be tuned for each task, and is
deteriorated by a simplified implementation. This study develops adaptive
T-soft (AT-soft) update by utilizing the update rule in AdaTerm, which has been
developed recently. In addition, the concern that the target network does not
asymptotically match the main network is mitigated by a new consolidation for
bringing the main network back to the target network. This so-called
consolidated AT-soft (CAT-soft) update is verified through numerical
simulations.


title:
A Deep Learning Approach for Network-wide Dynamic Traffic Prediction
  during Hurricane Evacuation

abstract:
  Proactive evacuation traffic management largely depends on real-time
monitoring and prediction of traffic flow at a high spatiotemporal resolution.
However, evacuation traffic prediction is challenging due to the uncertainties
caused by sudden changes in projected hurricane paths and consequently
household evacuation behavior. Moreover, modeling spatiotemporal traffic flow
patterns requires extensive data over a longer time period, whereas evacuations
typically last for 2 to 5 days. In this paper, we present a novel data-driven
approach for predicting evacuation traffic at a network scale. We develop a
dynamic graph convolution LSTM (DGCN-LSTM) model to learn the network dynamics
of hurricane evacuation. We first train the model for non-evacuation period
traffic data showing that the model outperforms existing deep learning models
for predicting non-evacuation period traffic with an RMSE value of 226.84.
However, when we apply the model for evacuation period, the RMSE value
increased to 1440.99. We overcome this issue by adopting a transfer learning
approach with additional features related to evacuation traffic demand such as
distance from the evacuation zone, time to landfall, and other zonal level
features to control the transfer of information (network dynamics) from
non-evacuation periods to evacuation periods. The final transfer learned
DGCN-LSTM model performs well to predict evacuation traffic flow (RMSE=399.69).
The implemented model can be applied to predict evacuation traffic over a
longer forecasting horizon (6 hour). It will assist transportation agencies to
activate appropriate traffic management strategies to reduce delays for
evacuating traffic.


title: On the Effectiveness of Dataset Watermarking in Adversarial Settings

abstract:
  In a data-driven world, datasets constitute a significant economic value.
Dataset owners who spend time and money to collect and curate the data are
incentivized to ensure that their datasets are not used in ways that they did
not authorize. When such misuse occurs, dataset owners need technical
mechanisms for demonstrating their ownership of the dataset in question.
Dataset watermarking provides one approach for ownership demonstration which
can, in turn, deter unauthorized use. In this paper, we investigate a recently
proposed data provenance method, radioactive data, to assess if it can be used
to demonstrate ownership of (image) datasets used to train machine learning
(ML) models. The original paper reported that radioactive data is effective in
white-box settings. We show that while this is true for large datasets with
many classes, it is not as effective for datasets where the number of classes
is low $(\leq 30)$ or the number of samples per class is low $(\leq 500)$. We
also show that, counter-intuitively, the black-box verification technique is
effective for all datasets used in this paper, even when white-box verification
is not. Given this observation, we show that the confidence in white-box
verification can be improved by using watermarked samples directly during the
verification process. We also highlight the need to assess the robustness of
radioactive data if it were to be used for ownership demonstration since it is
an adversarial setting unlike provenance identification.
  Compared to dataset watermarking, ML model watermarking has been explored
more extensively in recent literature. However, most of the model watermarking
techniques can be defeated via model extraction. We show that radioactive data
can effectively survive model extraction attacks, which raises the possibility
that it can be used for ML model ownership verification robust against model
extraction.


title:
FAEP: Fast Autonomous Exploration Planner for UAV Equipped with Limited
  FOV Sensor

abstract:
  Autonomous exploration is one of the important parts to achieve the
autonomous operation of Unmanned Aerial Vehicles (UAVs). To improve the
efficiency of the exploration process, a fast and autonomous exploration
planner (FAEP) is proposed in this paper. We firstly design a novel frontiers
exploration sequence generation method to obtain a more reasonable exploration
path, which considers not only the flight-level but frontier-level factors into
TSP. According to the exploration sequence and the distribution of frontiers, a
two-stage heading planning strategy is proposed to cover more frontiers by
heading change during an exploration journey. To improve the stability of path
searching, a guided kinodynamic path searching based on a guiding path is
devised. In addition, a dynamic start point selection method for replanning is
also adopted to increase the fluency of flight. We present sufficient benchmark
and real-world experiments. Experimental results show the superiority of the
proposed exploration planner compared with typical and state-of-the-art
methods.


title: Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision

abstract:
  Learning useful node and graph representations with graph neural networks
(GNNs) is a challenging task. It is known that deep GNNs suffer from
over-smoothing where, as the number of layers increases, node representations
become nearly indistinguishable and model performance on the downstream task
degrades significantly. To address this problem, we propose deeply-supervised
GNNs (DSGNNs), i.e., GNNs enhanced with deep supervision where representations
learned at all layers are used for training. We show empirically that DSGNNs
are resilient to over-smoothing and can outperform competitive benchmarks on
node and graph property prediction problems.


title: RRL:Regional Rotation Layer in Convolutional Neural Networks

abstract:
  Convolutional Neural Networks (CNNs) perform very well in image
classification and object detection in recent years, but even the most advanced
models have limited rotation invariance. Known solutions include the
enhancement of training data and the increase of rotation invariance by
globally merging the rotation equivariant features. These methods either
increase the workload of training or increase the number of model parameters.
To address this problem, this paper proposes a module that can be inserted into
the existing networks, and directly incorporates the rotation invariance into
the feature extraction layers of the CNNs. This module does not have learnable
parameters and will not increase the complexity of the model. At the same time,
only by training the upright data, it can perform well on the rotated testing
set. These advantages will be suitable for fields such as biomedicine and
astronomy where it is difficult to obtain upright samples or the target has no
directionality. Evaluate our module with LeNet-5, ResNet-18 and tiny-yolov3, we
get impressive results.


title:
Asyncval: A Toolkit for Asynchronously Validating Dense Retriever
  Checkpoints during Training

abstract:
  The process of model checkpoint validation refers to the evaluation of the
performance of a model checkpoint executed on a held-out portion of the
training data while learning the hyperparameters of the model, and is used to
avoid over-fitting and determine when the model has converged so as to stop
training. A simple and efficient strategy to validate deep learning checkpoints
is the addition of validation loops to execute during training. However, the
validation of dense retrievers (DR) checkpoints is not as trivial -- and the
addition of validation loops is not efficient. This is because, in order to
accurately evaluate the performance of a DR checkpoint, the whole document
corpus needs to be encoded into vectors using the current checkpoint before any
actual retrieval operation for checkpoint validation can be performed. This
corpus encoding process can be very time-consuming if the document corpus
contains millions of documents (e.g., 8.8m for MS MARCO and 21m for Natural
Questions). Thus, a naive use of validation loops during training will
significantly increase training time. To address this issue, in this demo
paper, we propose Asyncval: a Python-based toolkit for efficiently validating
DR checkpoints during training. Instead of pausing the training loop for
validating DR checkpoints, Asyncval decouples the validation loop from the
training loop, uses another GPU to automatically validate new DR checkpoints
and thus permits to perform validation asynchronously from training. Asyncval
also implements a range of different corpus subset sampling strategies for
validating DR checkpoints; these strategies allow to further speed up the
validation process. We provide an investigation of these methods in terms of
their impact on validation time and validation fidelity. Asyncval is made
available as an open-source project at https://github.com/ielab/asyncval.


title: A general characterization of optimal tie-breaker designs

abstract:
  In a regression discontinuity design, subjects with a running variable $x$
exceeding a threshold $t$ receive a binary treatment while those with $x\le t$
do not. When the investigator can randomize the treatment, a tie-breaker design
allows for greater statistical efficiency. Our setting has random $x\sim F$, a
working model where the response satisfies a two line regression model, and two
economic constraints. One constraint is on the expected proportion of treated
subjects and the other is on how treatment correlates with $x$, to express the
strength of a preference for treating subjects with higher $x$. Under these
conditions we show that there always exists an optimal design with treatment
probabilities piecewise constant in $x$. It is natural to require these
treatment probabilities to be non-decreasing in $x$; under this constraint, we
find an optimal design requires just two probability levels, when $F$ is
continuous. By contrast, a typical tie-breaker design as in Owen and Varian
(2020) uses a three level design with fixed treatment probabilities $0$, $0.5$
and $1$. We find large efficiency gains for our optimal designs compared to
using those three levels when fewer than half of the subjects are to be
treated, or $F$ is not symmetric. Our methods easily extend to the fixed $x$
design problem and can optimize for any efficiency metric that is a continuous
functional of the information matrix in the two-line regression. We illustrate
the optimal designs with a data example based on Head Start, a U.S. government
early-childhood intervention program.


title:
MUC-driven Feature Importance Measurement and Adversarial Analysis for
  Random Forest

abstract:
  The broad adoption of Machine Learning (ML) in security-critical fields
demands the explainability of the approach. However, the research on
understanding ML models, such as Random Forest (RF), is still in its infant
stage. In this work, we leverage formal methods and logical reasoning to
develop a novel model-specific method for explaining the prediction of RF. Our
approach is centered around Minimal Unsatisfiable Cores (MUC) and provides a
comprehensive solution for feature importance, covering local and global
aspects, and adversarial sample analysis. Experimental results on several
datasets illustrate the high quality of our feature importance measurement. We
also demonstrate that our adversarial analysis outperforms the state-of-the-art
method. Moreover, our method can produce a user-centered report, which helps
provide recommendations in real-life applications.


title: TeachAugment: Data Augmentation Optimization Using Teacher Knowledge

abstract:
  Optimization of image transformation functions for the purpose of data
augmentation has been intensively studied. In particular, adversarial data
augmentation strategies, which search augmentation maximizing task loss, show
significant improvement in the model generalization for many tasks. However,
the existing methods require careful parameter tuning to avoid excessively
strong deformations that take away image features critical for acquiring
generalization. In this paper, we propose a data augmentation optimization
method based on the adversarial strategy called TeachAugment, which can produce
informative transformed images to the model without requiring careful tuning by
leveraging a teacher model. Specifically, the augmentation is searched so that
augmented images are adversarial for the target model and recognizable for the
teacher model. We also propose data augmentation using neural networks, which
simplifies the search space design and allows for updating of the data
augmentation using the gradient method. We show that TeachAugment outperforms
existing methods in experiments of image classification, semantic segmentation,
and unsupervised representation learning tasks.


title:
NOMA Channel Estimation and Signal Detection using Rotational Invariant
  Codes and Machine Learning

abstract:
  This paper studies the joint channel estimation and signal detection for the
uplink power domain non-orthogonal multiple access. The proposed technique
performs both detection and estimation without the need of pilot symbols by
using a clustering technique. To remove the effect of channel fading, we apply
rotational invariant coding to assist signal detection at receiver without
sending pilots. We utilize Gaussian mixture model (GMM) to automatically
cluster the received signals without supervision and optimize decision
boundaries to improve the bit error rate (BER) performance.


title: Faithful learning with sure data for lung nodule diagnosis

abstract:
  Recent evolution in deep learning has proven its value for CT-based lung
nodule classification. Most current techniques are intrinsically black-box
systems, suffering from two generalizability issues in clinical practice.
First, benign-malignant discrimination is often assessed by human observers
without pathologic diagnoses at the nodule level. We termed these data as
"unsure data". Second, a classifier does not necessarily acquire reliable
nodule features for stable learning and robust prediction with patch-level
labels during learning. In this study, we construct a sure dataset with
pathologically-confirmed labels and propose a collaborative learning framework
to facilitate sure nodule classification by integrating unsure data knowledge
through nodule segmentation and malignancy score regression. A loss function is
designed to learn reliable features by introducing interpretability constraints
regulated with nodule segmentation maps. Furthermore, based on model inference
results that reflect the understanding from both machine and experts, we
explore a new nodule analysis method for similar historical nodule retrieval
and interpretable diagnosis. Detailed experimental results demonstrate that our
approach is beneficial for achieving improved performance coupled with faithful
model reasoning for lung cancer prediction. Extensive cross-evaluation results
further illustrate the effect of unsure data for deep-learning-based methods in
lung nodule classification.


title:
Estimation of Gr\"uneisen parameter of high-entropy-alloy-type
  functional materials

abstract:
  In functional materials like thermoelectric materials and superconductors,
the interplay between functionality, electronic structure, and phonon
characteristics is one of the key factors to improve functionality and to
understand the mechanisms. In the first part of this article, we briefly review
investigations on lattice anharmonicity in functional materials by Gr\"uneisen
parameter ({\gamma}G). One can find that the {\gamma}G can be a good scale for
large lattice anharmonicity and for detecting a change in anharmonicity
amplitude in functional materials. Then, we show original results on estimation
of {\gamma}G for recently-developed high-entropy-alloy-type (HEA-type)
functional materials with a layered structure and a NaCl-type structure. As a
common trend between those two systems with two- and three-dimensional
structures, we find that {\gamma}G increases by a slight increase in
configurational entropy of mixing ({\Delta}Smix), and then {\gamma}G decreases
with increasing {\Delta}Smix in high-entropy region.


title: Quantifying chemical and kinematical properties of Galactic disks

abstract:
  We aim to quantify the chemical and kinematical properties of the Galactic
disks with a sample of 119,558 giant stars having abundances and 3D velocities
taken or derived from the APOGEE DR17 and Gaia EDR3 catalogs. The Gaussian
Mixture Model is employed to distinguish the high-$\alpha$ and low-$\alpha$
sequences along the metallicity by simutaneously using the chemical and
kinematical data. Four disk components are identified and quantified that named
as h$\alpha$mp, h$\alpha$mr, l$\alpha$mp, and l$\alpha$mr disks, which
correspond to the features of high-$\alpha$ or low-$\alpha$, and metal-poor or
metal-rich. Combined with the spatial and stellar age information, we confirm
that they are well interpreted in the two-infall formation model. The first
infall of turbulent gas quickly forms the hot and thick h$\alpha$mp disk with
consequent thinner h$\alpha$mr and l$\alpha$mr disks. Then the second gas
accretion forms a thinner and outermost l$\alpha$mp disk. We find that the
inside-out and upside-down scenario does not only satisfy the overall Galactic
disk formation of these two major episodes, but also presents in the formation
sequence of three inner disks. Importantly, we reveal the inverse Age-[M/H]
trend of the l$\alpha$mr disk, which means its younger stars are more
metal-poor, indicating that the rejuvenate gas from the second accretion
gradually dominates the later star formation. Meanwhile, the recently formed
stars convergence to [M/H]$\sim$-0.1 dex, demonstrating a sufficiently mixture
of gas from two infalls.


title:
Transition graph decomposition for complex balanced reaction networks
  with non-mass-action kinetics

abstract:
  Reaction networks are widely used models to describe biochemical processes.
Stochastic fluctuations in the counts of biological macromolecules have
amplified consequences due to their small population sizes. This makes it
necessary to favor stochastic, discrete population, continuous time models. The
stationary distributions provide snapshots of the model behavior at the
stationary regime, and as such finding their expression in terms of the model
parameters is of great interest. The aim of the present paper is to describe
when the stationary distributions of the original model, whose state space is
potentially infinite, coincide exactly with the stationary distributions of the
process truncated to finite subsets of states, up to a normalizing constant.
The finite subsets of states we identify are called copies and are inspired by
the modular topology of reaction network models. With such a choice we prove a
novel graphical characterization of the concept of complex balancing for
stochastic models of reaction networks. The results of the paper hold for the
commonly used mass-action kinetics but are not restricted to it, and are in
fact stated for more general setting.


title:
A Novel Hand Gesture Detection and Recognition system based on
  ensemble-based Convolutional Neural Network

abstract:
  Nowadays, hand gesture recognition has become an alternative for
human-machine interaction. It has covered a large area of applications like 3D
game technology, sign language interpreting, VR (virtual reality) environment,
and robotics. But detection of the hand portion has become a challenging task
in computer vision and pattern recognition communities. Deep learning algorithm
like convolutional neural network (CNN) architecture has become a very popular
choice for classification tasks, but CNN architectures suffer from some
problems like high variance during prediction, overfitting problem and also
prediction errors. To overcome these problems, an ensemble of CNN-based
approaches is presented in this paper. Firstly, the gesture portion is detected
by using the background separation method based on binary thresholding. After
that, the contour portion is extracted, and the hand region is segmented. Then,
the images have been resized and fed into three individual CNN models to train
them in parallel. In the last part, the output scores of CNN models are
averaged to construct an optimal ensemble model for the final prediction. Two
publicly available datasets (labeled as Dataset-1 and Dataset-2) containing
infrared images and one self-constructed dataset have been used to validate the
proposed system. Experimental results are compared with the existing
state-of-the-art approaches, and it is observed that our proposed ensemble
model outperforms other existing proposed methods.


title:
Cathodoluminescence excitation spectroscopy: nanoscale imaging of
  excitation pathways

abstract:
  By recording the lifespan of optical excitations from their creation to their
decay in the far-field, photoluminescence excitation spectroscopy (PLE) is
crucial in the understanding the optical properties in materials, and therefore
indispensable in devices design and optimization. The investigation of
materials in general would benefit from an analogous technique, but with
nanoscale spatial resolution. By coincidentally measuring the energy loss of a
relativistic electron of optical materials (plasmonic nanoparticles,
luminescent 2D materials) and their subsequent light emission, we demonstrate
the possibility of making cathodoluminescence excitation spectroscopy, a
natural counterpart of PLE with a resolution better than 40 nm. The information
retrieved should prove invaluable for understanding material optics at the
nanometer scale.


title:
How to reduce the search space of Entity Resolution: with Blocking or
  Nearest Neighbor search?

abstract:
  Entity Resolution suffers from quadratic time complexity. To increase its
time efficiency, three kinds of filtering techniques are typically used for
restricting its search space: (i) blocking workflows, which group together
entity profiles with identical or similar signatures, (ii) string similarity
join algorithms, which quickly detect entities more similar than a threshold,
and (iii) nearest-neighbor methods, which convert every entity profile into a
vector and quickly detect the closest entities according to the specified
distance function. Numerous methods have been proposed for each type, but the
literature lacks a comparative analysis of their relative performance. As we
show in this work, this is a non-trivial task, due to the significant impact of
configuration parameters on the performance of each filtering technique. We
perform the first systematic experimental study that investigates the relative
performance of the main methods per type over 10 real-world datasets. For each
method, we consider a plethora of parameter configurations, optimizing it with
respect to recall and precision. For each dataset, we consider both
schema-agnostic and schema-based settings. The experimental results provide
novel insights into the effectiveness and time efficiency of the considered
techniques, demonstrating the superiority of blocking workflows and string
similarity joins.


title:
On periodic and compactly supported least energy solutions to semilinear
  elliptic equations with non-Lipschitz nonlinearity

abstract:
  We discuss the existence and non-existence of periodic in one variable and
compactly supported in the other variables least energy solutions for equations
with non-Lipschitz nonlinearity of the form: $-\Delta u=\lambda u^p - u^q$ in
$\mathbb{R}^{N+1}$, where $ 0< q < p \leq 1$, $\lambda \in \mathbb{R}$. The
approach is based on the Nehari manifold method supplemented by a one-sided
constraint given through the functional of the suitable Pohozaev identity. The
limit value of the parameter $\lambda$, where the approach is applicable,
corresponds to the existence of periodic in one variable and compactly
supported in the other variables least energy solutions. This value is found
through the extrem values of nonlinear generalized Rayleigh quotients and the
so-called curve of the critical exponents of $p,q$. Important properties of the
solutions are derived, such as that they are not trivial with respect to the
periodic variable and do not coincide with compactly supported solutions on the
entire space $\mathbb{R}^{N+1}$.


title: Special triple covers of algebraic surfaces

abstract:
  We study special triple covers $f\colon T \to S$ of algebraic surfaces, where
the Tschirnhausen bundle $\mathcal E = \left(f_*\mathcal O_T/\mathcal
O_S\right)^\vee$ is a quotient of a split rank three vector bundle, and we
provide several necessary and sufficient criteria for the existence.
  As an application, we give a complete classification of special triple
planes, finding among others two nice families of K3 surfaces.


title: MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation

abstract:
  Large-scale e-commercial platforms in the real-world usually contain various
recommendation scenarios (domains) to meet demands of diverse customer groups.
Multi-Domain Recommendation (MDR), which aims to jointly improve
recommendations on all domains, has attracted increasing attention from
practitioners and researchers. Existing MDR methods often employ a shared
structure to leverage reusable features for all domains and several specific
parts to capture domain-specific information. However, data from different
domains may conflict with each other and cause shared parameters to stay at a
compromised position on the optimization landscape. This could deteriorate the
overall performance. Despite the specific parameters are separately learned for
each domain, they can easily overfit on data sparsity domains. Furthermore,
data distribution differs across domains, making it challenging to develop a
general model that can be applied to all circumstances. To address these
problems, we propose a novel model agnostic learning method, namely MAMDR, for
the multi-domain recommendation. Specifically, we first propose a Domain
Negotiation (DN) strategy to alleviate the conflict between domains and learn
better shared parameters. Then, we develop a Domain Regularization (DR) scheme
to improve the generalization ability of specific parameters by learning from
other domains. Finally, we integrate these components into a unified framework
and present MAMDR which can be applied to any model structure to perform
multi-domain recommendation. Extensive experiments on various real-world
datasets and online applications demonstrate both the effectiveness and
generalizability of MAMDR.


title:
Embedded Soft Sensing in Soft Ring Actuator for Aiding with
  theSelf-Organisation of the In-Hand Rotational Manipulation

abstract:
  This paper proposes a soft sensor embedded in a soft ring actuator with five
fingers as a soft hand to identify the bifurcation of manipulated objects
during the in-hand manipulation process. The manipulation is performed by
breaking the symmetry method with an underactuated control system by
bifurcating the object to clockwise or counter-clockwise rotations. Two soft
sensors are embedded in parallel over a single soft finger, and the difference
in the resistance measurements is compared when the finger is displaced or bent
in a particular direction, which can identify the bifurcation direction and aid
in the break of symmetry approach without the need of external tracking
devices. The sensors performance is also characterised by extending and bending
the finger without an object interaction. During an experiment that performs a
break of symmetry, manipulated objects turn clockwise and counter-clockwise
depending on the perturbation and actuation frequency, sensors can track the
direction of rotation. The embedded sensors provide a self-sensing capability
for implementing a closed-loop control in future work. The soft ring actuator
performance presents a self-organisation behaviour with soft fingers rotating
an object without a required control for rotating the object. Therefore, the
soft fingers are an underactuated system with complex behaviour when
interacting with objects that serve in-hand manipulation field.


title:
On singular values of large dimensional lag-tau sample autocorrelation
  matrices

abstract:
  We study the limiting behavior of singular values of a lag-$\tau$ sample
auto-correlation matrix $\bf{R}_{\tau}^{\epsilon}$ of error term $\epsilon$ in
the high-dimensional factor model. We establish the limiting spectral
distribution (LSD) which characterizes the global spectrum of
$\bf{R}_{\tau}^{\epsilon}$, and derive the limit of its largest singular value.
All the asymptotic results are derived under the high-dimensional asymptotic
regime where the data dimension and sample size go to infinity proportionally.
Under mild assumptions, we show that the LSD of $\bf{R}_{\tau}^{\epsilon}$ is
the same as that of the lag-$\tau$ sample auto-covariance matrix. Based on this
asymptotic equivalence, we additionally show that the largest singular value of
$\bf{R}_{\tau}^{\epsilon}$ converges almost surely to the right end point of
the support of its LSD. Our results take the first step to identify the number
of factors in factor analysis using lag-$\tau$ sample auto-correlation
matrices. Our theoretical results are fully supported by numerical experiments
as well.


title: The concavity of generalized entropy powers

abstract:
  In this note we introduce a new family of entropy powers which are related to
generalized entropies, called Sharma-Mittal entropies, and we prove their
concavity along diffusion processes generated by $L^2$-Wasserstein gradient
flows of corresponding entropy functionals. This result extends the result of
Savar\'e and Toscani on the concavity of R\'enyi entropy powers (IEEE Trans.
Inf. Theory, 2014) and reveals a connection to R\'enyi entropy power
inequalities by Bobkov and Marsiglietti (IEEE Trans. Inf. Theory, 2017).


title:
Observing 0D subwavelength-localized modes at ~100 THz protected by weak
  topology

abstract:
  Topological photonic crystals (TPhCs) provide robust manipulation of light
with built-in immunity to fabrication tolerances and disorder. Recently, it was
shown that TPhCs based on weak topology with a dislocation inherit this
robustness and further host topologically protected lower-dimensional localized
modes. However, TPhCs with weak topology at optical frequencies have not been
demonstrated so far. Here, we use scattering-type scanning near field optical
microscopy to verify mid-bandgap zero-dimensional light localization close to
100 THz in a TPhC with nontrivial Zak phase and an edge dislocation. We show
that due to the weak topology, differently extended dislocation centers induce
similarly strong light localization. The experimental results are supported by
full-field simulations. Along with the underlying fundamental physics, our
results lay a foundation for the application of TPhCs based on weak topology in
active topological nanophotonics, and nonlinear and quantum optic integrated
devices due to their strong and robust light localization.


title: Random Features for High-Dimensional Nonlocal Mean-Field Games

abstract:
  We propose an efficient solution approach for high-dimensional nonlocal
mean-field game (MFG) systems based on the Monte Carlo approximation of
interaction kernels via random features. We avoid costly space-discretizations
of interaction terms in the state-space by passing to the feature-space. This
approach allows for a seamless mean-field extension of virtually any
single-agent trajectory optimization algorithm. Here, we extend the direct
transcription approach in optimal control to the mean-field setting. We
demonstrate the efficiency of our method by solving MFG problems in
high-dimensional spaces which were previously out of reach for conventional
non-deep-learning techniques.


title: Banyan: A Scoped Dataflow Engine for Graph Query Service

abstract:
  Graph query services (GQS) are widely used today to interactively answer
graph traversal queries on large-scale graph data. Existing graph query engines
focus largely on optimizing the latency of a single query. This ignores
significant challenges posed by GQS, including fine-grained control and
scheduling during query execution, as well as performance isolation and load
balancing in various levels from across user to intra-query. To tackle these
control and scheduling challenges, we propose a novel scoped dataflow for
modeling graph traversal queries, which explicitly exposes concurrent execution
and control of any subquery to the finest granularity. We implemented Banyan,
an engine based on the scoped dataflow model for GQS. Banyan focuses on scaling
up the performance on a single machine, and provides the ability to easily
scale out. Extensive experiments on multiple benchmarks show that Banyan
improves performance by up to three orders of magnitude over state-of-the-art
graph query engines, while providing performance isolation and load balancing.


title: Phase diagram of the dipolar Ising ferromagnet on a kagome lattice

abstract:
  We study the field--temperature phase diagram of the two-dimensional dipolar
Ising ferromagnet on a kagome lattice with a specific ratio between the
exchange and dipolar constants, $\delta = 1$. Using the stochastic cutoff (SCO)
$O(N)$ Monte Carlo method, we calculated order parameters for stripe and bubble
phases and other thermodynamical quantities. We find two kinds of stripe phases
at low fields, where the arrangement of the branch spins neighboring the stripe
frame varies, and two bubble phases at high fields, in which three-spin domains
(bubbles) form a regular triangular lattice but the triangular array of bubbles
changes on a kagome lattice. We also find that with increasing the field, there
exist a disordered phase between the stripe and bubble phases and between the
two bubble phases. We discuss the details of the features of these phases and
phase transitions.


title: Nuclear force with LapH smearing

abstract:
  The nuclear forces are determined by combining the HAL QCD method and a new
type of source smearing technique. The new smearing is a projection to a space
spanned by the lowest-lying eigenvectors of the free Laplacian operator on a
lattice, which enables efficient calculation of hadron correlators at an
affordable cost by utilizing the hadron-level momentum conservations. We find
that this new approach reduces the statistical and systematic errors in the
resultant nuclear forces.


abstract:
  Deep graph clustering, which aims to reveal the underlying graph structure
and divide the nodes into different clusters without human annotations, is a
fundamental yet challenging task. However, we observed that the existing
methods suffer from the representation collapse problem and easily tend to
encode samples with different classes into the same latent embedding.
Consequently, the discriminative capability of nodes is limited, resulting in
sub-optimal clustering performance. To address this problem, we propose a novel
deep graph clustering algorithm termed Improved Dual Correlation Reduction
Network (IDCRN) through improving the discriminative capability of samples.
Specifically, by approximating the cross-view feature correlation matrix to an
identity matrix, we reduce the redundancy between different dimensions of
features, thus improving the discriminative capability of the latent space
explicitly. Meanwhile, the cross-view sample correlation matrix is forced to
approximate the designed clustering-refined adjacency matrix to guide the
learned latent representation to recover the affinity matrix even across views,
thus enhancing the discriminative capability of features implicitly. Moreover,
we avoid the collapsed representation caused by the over-smoothing issue in
Graph Convolutional Networks (GCNs) through an introduced propagation
regularization term, enabling IDCRN to capture the long-range information with
the shallow network structure. Extensive experimental results on six benchmarks
have demonstrated the effectiveness and the efficiency of IDCRN compared to the
existing state-of-the-art deep graph clustering algorithms.


title: Self-Stabilizing Self-Assembly

abstract:
  The emerging field of passive macro-scale tile-based self-assembly (TBSA)
holds promise for enabling effective manufacturing processes by harnessing
TBSA's intrinsic parallelism. However, the current TBSA methodology still does
not fulfill its potential, largely because such assemblies are typically
error-prone and the size of an individual assembly is limited by a lack of
mechanical stability. Moreover, the instability issue becomes worse as
assemblies become larger. Here, using a novel type of tile that is carried by a
bristle-bot drive, we propose a framework that reverts this tendency; i.e., as
an assembly grows, it becomes more stable. Using physics-based computational
experiments, we show that a system of such tiles indeed possesses
self-stabilizing characteristics and enables building assemblies containing
hundreds of tiles. These results indicate that one of the main current
limitations of mechanical, agitation-based TBSA approaches might be overcome by
employing a swarm of free-running sensorless mobile robots, herein represented
by passive tiles at the macroscopic scale.


title:
Break Up the Pipeline Structure to Reach a Nearly Optimal End-to-End
  Latency

abstract:
  Query optimization is still problematic in the commercial database system
because database optimizers sometimes choose a bad execution plan with
several-fold latency differences. In this paper, we de-sign a new dynamic
optimization strategy called query split, which takes advantage of run-time
statistics. Integrating query split into PostgreSQL, we have a 2x speedup in
total end-to-end latency on Join Order Benchmark and achieve near-optimal
latency by comparing with the optimal execution plan. Our finding reveals that
breaking up the static pipeline between database optimizer and executor can
benefit the query processing and greatly reduce end-to-end latency.


title: Twin-width and Transductions of Proper k-Mixed-Thin Graphs

abstract:
  The new graph parameter twin-width, recently introduced by Bonnet, Kim,
Thomass\'e and Watrigant, admits an FPT algorithm for testing all FO properties
of graphs. This makes classes of efficiently bounded twin-width attractive from
the algorithmic point of view. In particular, such classes (of small
twin-width) include proper interval graphs, and (as digraphs) posets of width
k. Inspired by an existing generalization of interval graphs into so-called
k-thin graphs, we define a new class of proper k-mixed-thin graphs which
largely generalizes proper interval graphs. We prove that proper k-mixed-thin
graphs have twin-width linear in k, and that a certain subclass of k-mixed-thin
graphs is transduction-equivalent to posets of width k' such that there is a
quadratic relation between k and k'.


title:
An Ensemble Approach for Patient Prognosis of Head and Neck Tumor Using
  Multimodal Data

abstract:
  Accurate prognosis of a tumor can help doctors provide a proper course of
treatment and, therefore, save the lives of many. Traditional machine learning
algorithms have been eminently useful in crafting prognostic models in the last
few decades. Recently, deep learning algorithms have shown significant
improvement when developing diagnosis and prognosis solutions to different
healthcare problems. However, most of these solutions rely solely on either
imaging or clinical data. Utilizing patient tabular data such as demographics
and patient medical history alongside imaging data in a multimodal approach to
solve a prognosis task has started to gain more interest recently and has the
potential to create more accurate solutions. The main issue when using clinical
and imaging data to train a deep learning model is to decide on how to combine
the information from these sources. We propose a multimodal network that
ensembles deep multi-task logistic regression (MTLR), Cox proportional hazard
(CoxPH) and CNN models to predict prognostic outcomes for patients with head
and neck tumors using patients' clinical and imaging (CT and PET) data.
Features from CT and PET scans are fused and then combined with patients'
electronic health records for the prediction. The proposed model is trained and
tested on 224 and 101 patient records respectively. Experimental results show
that our proposed ensemble solution achieves a C-index of 0.72 on The HECKTOR
test set that saved us the first place in prognosis task of the HECKTOR
challenge. The full implementation based on PyTorch is available on
\url{https://github.com/numanai/BioMedIA-Hecktor2021}.


title:
Summarizing empirical information on between-study heterogeneity for
  Bayesian random-effects meta-analysis

abstract:
  In Bayesian meta-analysis, the specification of prior probabilities for the
between-study heterogeneity is commonly required, and is of particular benefit
in situations where only few studies are included. Among the considerations in
the set-up of such prior distributions, the consultation of available empirical
data on a set of relevant past analyses sometimes plays a role. How exactly to
summarize historical data sensibly is not immediately obvious; in particular,
the investigation of an empirical collection of heterogeneity estimates will
not target the actual problem and will usually only be of limited use. The
commonly used normal-normal hierarchical model for random-effects meta-analysis
is extended to infer a heterogeneity prior. Using an example data set, we
demonstrate how to fit a distribution to empirically observed heterogeneity
data from a set of meta-analyses. Considerations also include the choice of a
parametric distribution family. Here, we focus on simple and readily applicable
approaches to then translate these into (prior) probability distributions.


title:
Bounds and long term convergence for the voltage-conductance kinetic
  system arising in neuroscience

abstract:
  The voltage-conductance equation determines the probability distribution of a
stochastic process describing a fluctuation-driven neuronal network arising in
the visual cortex. Its structure and degeneracy share many common features with
the kinetic Fokker-Planck equation, which has attracted much attention
recently. We prove an L $\infty$ bound on the steady state solution and the
long term convergence of the evolution problem towards this stationary state.
Despite the hypoellipticity property, the difficulty is to treat the boundary
conditions that change type along the boundary. This leads us to use specific
weights in the Alikakos iterations and adapt the relative entropy method.


title: Familial Inference

abstract:
  Statistical hypotheses are translations of scientific hypotheses into
statements about one or more distributions, often concerning their center.
Tests that assess statistical hypotheses of center implicitly assume a specific
center, e.g., the mean or median. Yet, scientific hypotheses do not always
specify a particular center. This ambiguity leaves the possibility for a gap
between scientific theory and statistical practice that can lead to rejection
of a true null. In the face of replicability crises in many scientific
disciplines, "significant results" of this kind are concerning. Rather than
testing a single center, this paper proposes testing a family of plausible
centers, such as that induced by the Huber loss function (the "Huber family").
Each center in the family generates a testing problem, and the resulting family
of hypotheses constitutes a familial hypothesis. A Bayesian nonparametric
procedure is devised to test familial hypotheses, enabled by a pathwise
optimization routine to fit the Huber family. The favorable properties of the
new test are verified through numerical simulation in one- and two-sample
settings. Two experiments from psychology serve as real-world case studies.


title:
Oscillatory Neural Network as Hetero-Associative Memory for Image Edge
  Detection

abstract:
  The increasing amount of data to be processed on edge devices, such as
cameras, has motivated Artificial Intelligence (AI) integration at the edge.
Typical image processing methods performed at the edge, such as feature
extraction or edge detection, use convolutional filters that are energy,
computation, and memory hungry algorithms. But edge devices and cameras have
scarce computational resources, bandwidth, and power and are limited due to
privacy constraints to send data over to the cloud. Thus, there is a need to
process image data at the edge. Over the years, this need has incited a lot of
interest in implementing neuromorphic computing at the edge. Neuromorphic
systems aim to emulate the biological neural functions to achieve
energy-efficient computing. Recently, Oscillatory Neural Networks (ONN) present
a novel brain-inspired computing approach by emulating brain oscillations to
perform autoassociative memory types of applications. To speed up image edge
detection and reduce its power consumption, we perform an in-depth
investigation with ONNs. We propose a novel image processing method by using
ONNs as a hetero-associative memory (HAM) for image edge detection. We simulate
our ONN-HAM solution using first, a Matlab emulator, and then a fully digital
ONN design. We show results on gray scale square evaluation maps, also on black
and white and gray scale 28x28 MNIST images and finally on black and white
512x512 standard test images. We compare our solution with standard edge
detection filters such as Sobel and Canny. Finally, using the fully digital
design simulation results, we report on timing and resource characteristics,
and evaluate its feasibility for real-time image processing applications. Our
digital ONN-HAM solution can process images with up to 120x120 pixels (166 MHz
system frequency) respecting real-time camera constraints. This work is the
first to explore ONNs as hetero-associative memory for image processing
applications.


title: Sur les donn{\'e}es endoscopiques dans le cas de l'endoscopie tordue

abstract:
  We give a simple combinatorial description of the elliptic endoscopic data of
a twisted space under a group $G$, assuming that $G$ is semi-simple and simply
connected. Assuming the same hypothesis and that the base field is a number
field, we prove that, if two elliptic endoscopic data are equivalent almost
everywhere, then they are equivalent.


title:
Emergent O(4) symmetry at an one-dimensional deconfined quantum
  tricritical point

abstract:
  We show an $\rm O(4)$ symmetry emerges at a deconfined quantum tricritical
point of a valence bond solid and two ferromagnetic phases in an $S = 1/2$
frustrated spin chain by combining analytical analysis and numerical
calculations with the time evolution of infinite matrix product states. With
this symmetry, the valence-bond solid and the three magnetic order parameters
form an $\rm O(4)$ pseudovector in the infrared limit, and can continuously
rotate into each other. We numerically determine the location of the quantum
tricritical point and study the scaling of the correlation functions of the
$\rm O(4)$ vector components and associated conserved currents. The critical
behaviors of these correlation functions are all in accord with field
theoretical results. The emergent $\rm O(4)$ symmetry at the tricritical point
is justified by the integer value of the scaling dimension of the emergent
Noether conserved currents. Our findings not only give direct evidence of such
a high emergent symmetry at an one-dimensional valence bond solid to magnetic
transition but also shed light on exploring emergent symmetries in higher
dimensions.


title: Quantum cost of dense coding and teleportation

abstract:
  The quantum cost is a key ingredient to evaluate the quality of quantum
protocols from a practical viewpoint. We show that the quantum cost of
d-dimensional dense coding protocol is equal to d+3 when transmitting the
classical message (0,0), and that is equal to d+4 when transmitting other
classical message. It appears linear growth with the dimension and thus makes
sense for implementation. In contrast, the quantum cost of high-dimensional
teleportation protocols is equal to 13, which is the maximum value of the cost
for the two-dimensional case. As an application, we establish the relation
between the quantum cost and fidelity of dense coding protocols in terms of
four typical noise scenario.


title:
Equivalent formulations of optimal control problems with maximum cost
  and applications

abstract:
  We revisit the optimal control problem with maximum cost with the objective
to provide different equivalent reformulations suitable to numerical methods.
We propose two reformulations in terms of extended Mayer problems with
constraint, and another one in terms of a differential inclusion with
upper-semi continuous right member but without constraint. For this last one we
also propose an approximation scheme of the optimal value from below. These
approaches are illustrated and discussed on several examples.


title:
Reachability analysis in stochastic directed graphs by reinforcement
  learning

abstract:
  We characterize the reachability probabilities in stochastic directed graphs
by means of reinforcement learning methods. In particular, we show that the
dynamics of the transition probabilities in a stochastic digraph can be modeled
via a difference inclusion, which, in turn, can be interpreted as a Markov
decision process. Using the latter framework, we offer a methodology to design
reward functions to provide upper and lower bounds on the reachability
probabilities of a set of nodes for stochastic digraphs. The effectiveness of
the proposed technique is demonstrated by application to the diffusion of
epidemic diseases over time-varying contact networks generated by the proximity
patterns of mobile agents.


title:
Pion Partonic Distributions in the Statistical Model from Pion-induced
  Drell-Yan and $J/\Psi$ Production Data

abstract:
  We present a new analysis to extract pion parton distribution functions
(PDFs) within the framework of the statistical model. Starting from the
statistical model first developed for the spin-1/2 nucleon, we extend this
model to describe the spin-0 pion. Based on a combined fit to both the
pion-induced Drell-Yan data and the pion-induced $J/\Psi$ production data, a
new set of pion PDFs has been obtained. The inclusion of the $J/\Psi$
production data in the combined fit has provided additional constraints for
better determining the gluon distribution in the pion. We also compare the pion
PDFs obtained in the statistical model with other existing pion PDFs.


title: Policy choices and outcomes for offshore wind auctions globally

abstract:
  Offshore wind energy is rapidly expanding, facilitated largely through
auctions run by governments. We provide a detailed quantified overview of
applied auction schemes, including geographical spread, volumes, results, and
design specifications. Our comprehensive global dataset reveals heterogeneous
designs. Although most remuneration designs provide some form of revenue
stabilisation, their specific instrument choices vary and include feed-in
tariffs, one-sided and two-sided contracts for difference, mandated power
purchase agreements, and mandated renewable energy certificates. We review the
schemes used in all eight major offshore wind jurisdictions across Europe,
Asia, and North America and evaluate bids in their jurisdictional context. We
analyse cost competitiveness, likelihood of timely construction, occurrence of
strategic bidding, and identify jurisdictional aspects that might have
influenced auction results. We find that auctions are embedded within their
respective regulatory and market design context, and are remarkably diverse,
though with regional similarities. Auctions in each jurisdiction have evolved
and tend to become more exposed to market price risks over time. Less mature
markets are more prone to make use of lower-risk designs. Still, some form of
revenue stabilisation is employed for all auctioned offshore wind energy farms
analysed here, regardless of the specific policy choices. Our data confirm a
coincidence of declining costs and growing diffusion of auction regimes.


title: Raman Spectrum Matching with Contrastive Representation Learning

abstract:
  Raman spectroscopy is an effective, low-cost, non-intrusive technique often
used for chemical identification. Typical approaches are based on matching
observations to a reference database, which requires careful preprocessing, or
supervised machine learning, which requires a fairly large number of training
observations from each class. We propose a new machine learning technique for
Raman spectrum matching, based on contrastive representation learning, that
requires no preprocessing and works with as little as a single reference
spectrum from each class. On three datasets we demonstrate that our approach
significantly improves or is on par with the state of the art in prediction
accuracy, and we show how to compute conformal prediction sets with specified
frequentist coverage. Based on our findings, we believe contrastive
representation learning is a promising alternative to existing methods for
Raman spectrum matching.


title: Modulated Collective Behaviors and Condensation of Bacteria

abstract:
  Bacteria can spontaneously develop collective motions by aligning their
motions in dense systems. Here, we show that bacteria can also respond
collectively to an alternating electrical field and form active clusters. The
active clusters can diffuse and merge. Most intriguingly, as the active
clusters go beyond a critical size, they split into smaller ones spontaneously.
The critical size for splitting depends on the frequency of electric field and
the concentration of bacteria. As the formation of active clusters can be
explained by the electric-field-induced long-range hydrodynamic attraction, we
suggest that the critical size and the spontaneous splitting result from the
instability of the core-shell structure due to the inhomogeneous stress inside
the active clusters. The findings in this study illustrate that bio-systems can
respond collectively to an external field, promising an effective way to
control and modulate the behavior of organisms. Moreover, the controlled
aggregation and condensation of bacteria offer a robust approach to improve the
local concentration of bacteria for early and rapid detection, which has wide
applications in clinical applications.


title:
Alloying Ratio Versus Cluster Size for Reversible Hydrogen Storage in Ni
  Doped Small Mg Clusters: Dispersion Corrected DFT Study

abstract:
  Dispersion corrected density functional theory ($\omega$B97X-D DFT) method is
used to study the molecular hydrogen adsorption in $Ni_nMg_m$ $(1\geq n\geq
3,1\geq m\geq9)$ clusters. All these clusters can effectively adsorb multiple
$H_2$ in the preferred binding energy (BE) range between physisorption and
chemisorption, i.e., $0.1 eV\geq BE \geq0.8 $eV. $H_2$ adsorption on $Ni_kMg_k$
(Ni:Mg=1:1), $Ni_kMg_{2k}$ (Ni:Mg=1:2) and $Ni_kMg_{3k}$ (Ni:Mg=1:3) (k=1-3)
clusters shows fascinating behaviours in terms of Ni:Mg alloying ratio and
cluster size. In each Ni:Mg ratio, the number of adsorbed $H_2$ in the heavier
clusters (k=2, 3) becomes integral multiples of that in the lightest
configuration (k=1). As a consequence, the gravimetric density of molecular
hydrogen remains fixed at each Ni:Mg ratio irrespective of the cluster size.
The corresponding values are $17.94$ $wt\%$ $(1:1)$, $14.46$ $wt\%$ $(1:2)$ and
$13.28$ $wt\%$ $(1:3)$, which are significantly higher than the ultimate target
of $6.5$ $wt\%$ set by DOE, US. Molecular dynamics simulations further reveal
that room temperature desorption of almost all $H_2$ molecules are possible for
all the clusters.


title:
A piecewise deterministic Markov process approach modeling a dry
  friction problem with noise

abstract:
  Understanding and predicting the dynamical properties of systems involving
dry friction is a major concern in physics and engineering. It abounds in many
mechanical processes, from the sound produced by a violin to the screeching of
chalk on a blackboard to human infant crawling dynamics and friction-based
locomotion of a multitude of living organisms (snakes, bacteria, scallops..) to
the displacement of mechanical structures (building, bridges, nuclear plants,
massive industrial infrastructures) under earthquakes and beyond. Surprisingly,
even for low-dimensional systems, the modeling of dry friction in the presence
of random forcing has not been elucidated. In this paper, we propose a
piecewise deterministic Markov process approach modeling a system with dry
friction including different coefficients for the static and dynamic forces. In
this mathematical framework, we derive the corresponding Kolmogorov equations
and related tools to compute statistical quantities of interest related to the
distributions of the static (sticked) and dynamic phases. We show ergodicity
and provide a representation formula of the stationary measure using
independent identically distributed portions of the trajectory (excursions). We
also obtain deterministic characterizations of the Laplace transforms of the
probability density functions of the durations of the static and dynamic
phases.


title:
A quasi-stationary approach to the long-term asymptotics of the
  growth-fragmentation equation

abstract:
  In a growth-fragmentation system, cells grow in size slowly and split apart
at random. Typically, the number of cells in the system grows exponentially and
the distribution of the sizes of cells settles into an equilibrium 'asymptotic
profile'. In this work we introduce a new method to prove this asymptotic
behaviour for the growth-fragmentation equation, and show that the convergence
to the asymptotic profile occurs at exponential rate. We do this by identifying
an associated sub-Markov process and studying its quasi-stationary behaviour
via a Lyapunov function condition. By doing so, we are able to simplify and
generalise results in a number of common cases and offer a unified framework
for their study. In the course of this work we are also able to prove the
existence and uniqueness of solutions to the growth-fragmentation equation in a
wide range of situations.


title:
Global well-posedness of shock front solutions to one-dimensional piston
  problem for combustion Euler flows

abstract:
  This paper is devoted to the well-posedness theory of piston problem for
compressible {combustion} Euler flows with physical ignition condition. A
significant combustion phenomena called detonation will occur provided the
reactant is compressed and ignited by a leading shock. Mathematically, the
problem can be formulated as an initial-boundary value problem for hyperbolic
balance laws with a large shock front as free boundary. In present paper, we
establish the global well-posedness of entropy solutions via wave front
tracking scheme within the framework of $BV\cap L^1$ space. The main
difficulties here stem from the discontinuous source term without uniform
dissipation structure, and from the characteristic-boundary associated with
degenerate characteristic field. In dealing with the obstacles caused by
ignition temperature, we develop a modified Glimm-type functional to control
the oscillation growth of combustion waves, even if the exothermic source fails
to uniformly decay. As to the characteristic boundary, the degeneracy of
contact discontinuity is fully employed to get elegant stability estimates near
the piston boundary. Meanwhile, we devise a weighted Lyapunov functional to
balance the nonlinear effects arising from large shock, characteristic boundary
and exothermic reaction, then obtain the $L^1-$stability of combustion wave
solutions. Our results reveal that one dimensional \emph{ZND} detonation waves
{supported} by a forward piston are indeed nonlinearly stable under small
perturbation in $BV$ sense. This is the first work on well-posedness of
inviscid reacting Euler fluids dominated by ignition temperature.


title: 6D Rotation Representation For Unconstrained Head Pose Estimation

abstract:
  In this paper, we present a method for unconstrained end-to-end head pose
estimation. We address the problem of ambiguous rotation labels by introducing
the rotation matrix formalism for our ground truth data and propose a
continuous 6D rotation matrix representation for efficient and robust direct
regression. This way, our method can learn the full rotation appearance which
is contrary to previous approaches that restrict the pose prediction to a
narrow-angle for satisfactory results. In addition, we propose a geodesic
distance-based loss to penalize our network with respect to the SO(3) manifold
geometry. Experiments on the public AFLW2000 and BIWI datasets demonstrate that
our proposed method significantly outperforms other state-of-the-art methods by
up to 20\%. We open-source our training and testing code along with our
pre-trained models: https://github.com/thohemp/6DRepNet.


title: Basis-set correction for coupled-cluster estimation of dipole moments

abstract:
  The present work proposes an approach to obtain a basis-set correction based
on density-functional theory (DFT) for the computation of molecular properties
in wave-function theory (WFT). This approach allows one to accelerate the
basis-set convergence of any energy derivative of a non-variational WFT method,
generalizing previous works on the DFT-based basis-set correction where either
only ground-state energies could be computed with non-variational wave
functions [J. Phys. Chem. Lett. 10, 2931 (2019)] or properties where computed
as expectation values over variational wave functions [J. Chem. Phys. 155,
044109 (2021)]. This work focuses on the basis-set correction of dipole moments
in coupled-cluster with single, double, and perturbative triple excitations
(CCSD(T)), which is numerically tested on a set of fourteen molecules with
dipole moments covering two orders of magnitude. As the basis-set correction
relies only on Hartree-Fock densities, its computational cost is marginal with
respect to the one of the CCSD(T) calculations. Statistical analysis of the
numerical results shows a clear improvement of the basis convergence of the
dipole moment with respect to the usual CCSD(T) calculations.


title: Visibility Maximization Controller for Robotic Manipulation

abstract:
  Occlusions caused by a robot's own body is a common problem for closed-loop
control methods employed in eye-to-hand camera setups. We propose an
optimization-based reactive controller that minimizes self-occlusions while
achieving a desired goal pose. The approach allows coordinated control between
the robot's base, arm and head by encoding the line-of-sight visibility to the
target as a soft constraint along with other task-related constraints, and
solving for feasible joint and base velocities. The generalizability of the
approach is demonstrated in simulated and real-world experiments, on robots
with fixed or mobile bases, with moving or fixed objects, and multiple objects.
The experiments revealed a trade-off between occlusion rates and other task
metrics. While a planning-based baseline achieved lower occlusion rates than
the proposed controller, it came at the expense of highly inefficient paths and
a significant drop in the task success. On the other hand, the proposed
controller is shown to improve visibility to the line target object(s) without
sacrificing too much from the task success and efficiency. Videos and code can
be found at: rhys-newbury.github.io/projects/vmc/.


title:
Structure and morphology evolution of concave-shaped SiC {0001} surfaces
  in liquid silicon

abstract:
  Concave-shaped 4H-SiC {0001} surfaces have been prepared and reconstructed in
pure liquid silicon for investigating the structure and morphology evolution of
the SiC surface as a function of both azimuthal and off-axis angles. Different
surface characteristics are revealed on two polar surfaces where only the Si
face reflects the six-fold symmetry of 4H-SiC crystal. On the Si face, the step
bunching along the <1100> direction is stronger than the <1120> direction,
which is related to the bonding state at the step edge. More significant step
bunching is observed on the C face whereas it is not sensitive to azimuthal
orientation. The extent of step faceting is stronger on the Si face. The step
faceting is independent of the off angle on both polarities of SiC {0001}
surfaces.


title:
A new chaotic image encryption algorithm based on transversals in a
  Latin square

abstract:
  There are some good combinatorial structures suitable for image encryption.
In this study, a new chaotic image encryption algorithm based on transversals
in a Latin square is proposed. By means of an n-transversal of a Latin square,
we can permutate an image data group by group for the first time, then use two
Latin squares for auxiliary diffusion on the basis of a chaotic sequence,
finally make use of a pair of orthogonal Latin squares to do the second
scrambling. As a whole, the encryption process of
"scrambling-diffusion-scrambling" is formed. The experimental results indicate
that this algorithm achieves a secure and fast encryption effect. The final
information entropy is very close to 8, and the correlation coefficient is
approximate to 0. All these tests verify the robustness and practicability of
this proposed algorithm.


title: Kron Reduction and Effective Resistance of Directed Graphs

abstract:
  In network theory, the concept of the effective resistance is a distance
measure on graphs which relates global network properties to the individual
connections between nodes, and the Kron reduction method is a standard tool to
reduce or eliminate the desired nodes, which preserves the interconnection
structure and the effective resistance of the original graph. While these two
graph-theoretic concepts stem from the electric network on an undirected graph,
they have a number of applications over not only that but also a wide variety
of fields. In this paper, we propose a generalization of Kron reduction to
directed graphs. Furthermore, we prove this reduction method preserves the
structure of the original graphs such as strong connectivity or weight
balancedness. Also, we generalize the effective resistance to directed graphs
via Markov chain theory, which is invariant under Kron reduction. Although the
effective resistance of our proposal is asymmetric, we prove that it induces
the novel graph metric on strongly connected directed graphs. Finally, we
compare our method with existing studies and prove that the hitting probability
metric is the logarithm of the effective resistance in the stochastic case, and
the effective resistance in the doubly stochastic case is the same with the
resistance distance on ergodic Markov chain.


title:
Investigations of strong cosmic censorship in 3-dimensinonal black
  strings

abstract:
  We investigate the strong cosmic censorship (SCC) in the 3-dimensional black
string (3dBS) by examining the smoothness of the quasinormal mode of a massive
scalar field at the Cauchy horizon. We find that according to the global
properties of the 3dBS that the time coordinate is changed from the radial
coordinate $r$ to the longitudinal coordinate $x$ at the Cauchy horizon, the
spectral gap of the quasinormal mode is determined by the `$m$-frequency mode',
not by the `$\omega$-frequency mode'. This property of the 3dBS results in the
fact that the near extremal 3dBS together with an appropriate condition for the
mass of the scalar field does not satisfy the SCC conjecture. Such a result for
the SCC is more similar to the BTZ black hole in dual relationship with the
3dBS than the asymptotically flat black holes like the 3dBS: asymptotically
flat black holes have been known to satisfy the SCC conjecture. This means that
the asymptotic flatness is not a sufficient condition for the SCC.


title:
Measuring Space-Time Curvature using Maximally Path-Entangled Quantum
  States

abstract:
  Experiments at the interface of quantum field theory and general relativity
would greatly benefit theoretical research towards their unification. The
gravitational aspects of quantum experiments performed so far can be explained
either within Newtonian gravity or by Einstein's equivalence principle. Here,
we describe a way to measure components of the Riemann curvature tensor with
maximally path-entangled quantum states of light. We show that the
entanglement-induced increase in sensitivity also holds for
gravitationally-induced phases in Mach-Zehnder interferometers. As a result,
the height difference between the two interferometer arms necessary to measure
gravity gradients can be reduced significantly, thus ruling out flat
space-time.