title: HiTPR: Hierarchical Transformer for Place Recognition in Point Cloud

abstract:
  Place recognition or loop closure detection is one of the core components in
a full SLAM system. In this paper, aiming at strengthening the relevancy of
local neighboring points and the contextual dependency among global points
simultaneously, we investigate the exploitation of transformer-based network
for feature extraction, and propose a Hierarchical Transformer for Place
Recognition (HiTPR). The HiTPR consists of four major parts: point cell
generation, short-range transformer (SRT), long-range transformer (LRT) and
global descriptor aggregation. Specifically, the point cloud is initially
divided into a sequence of small cells by downsampling and nearest neighbors
searching. In the SRT, we extract the local feature for each point cell. While
in the LRT, we build the global dependency among all of the point cells in the
whole point cloud. Experiments on several standard benchmarks demonstrate the
superiority of the HiTPR in terms of average recall rate, achieving 93.71% at
top 1% and 86.63% at top 1 on the Oxford RobotCar dataset for example.


title:
Spin Peltier effect and its length scale in Pt/YIG system at high
  temperatures

abstract:
  The temperature and yttrium-iron-garnet (YIG) thickness dependences of the
spin Peltier effect (SPE) have been investigated using a Pt/YIG junction system
at temperatures ranging from room temperature to the Curie temperature of YIG
by the lock-in thermography method. By analyzing the YIG thickness dependence
using an exponential decay model, the characteristic length of SPE in YIG is
estimated to be 0.9 $\mu$m near room temperature and almost constant even near
the Curie temperature. The high-temperature behavior of SPE is clearly
different from that of the spin Seebeck effect, providing a clue for
microscopically understanding the reciprocal relation between them.


title:
Redwood: Using Collision Detection to Grow a Large-Scale Intent
  Classification Dataset

abstract:
  Dialog systems must be capable of incorporating new skills via updates over
time in order to reflect new use cases or deployment scenarios. Similarly,
developers of such ML-driven systems need to be able to add new training data
to an already-existing dataset to support these new skills. In intent
classification systems, problems can arise if training data for a new skill's
intent overlaps semantically with an already-existing intent. We call such
cases collisions. This paper introduces the task of intent collision detection
between multiple datasets for the purposes of growing a system's skillset. We
introduce several methods for detecting collisions, and evaluate our methods on
real datasets that exhibit collisions. To highlight the need for intent
collision detection, we show that model performance suffers if new data is
added in such a way that does not arbitrate colliding intents. Finally, we use
collision detection to construct and benchmark a new dataset, Redwood, which is
composed of 451 ntent categories from 13 original intent classification
datasets, making it the largest publicly available intent classification
benchmark.


title: Hamiltonicity in generalized quasi-dihedral groups

abstract:
  Witte Morris showed in [21] that every connected Cayley graph of a finite
(generalized) dihedral group has a Hamiltonian path. The infinite dihedral
group is defined as the free product with amalgamation $\mathbb Z_2 \ast
\mathbb Z_2$. We show that every connected Cayley graph of the infinite
dihedral group has both a Hamiltonian double ray, and extend this result to all
two-ended generalized quasi-dihedral groups.


title: Very Special Linear Gravity: A Gauge-Invariant Graviton Mass

abstract:
  Linearized gravity in the Very Special Relativity (VSR) framework is
considered. We prove that this theory allows for a non-zero graviton mass $m_g$
without breaking gauge invariance nor modifying the relativistic dispersion
relation. We find the analytic solution for the new equations of motion in our
gauge choice, verifying as expected the existence of only two physical degrees
of freedom. Finally, through the geodesic deviation equation, we confront some
results for classic gravitational waves (GW) with the VSR ones: we see that the
ratios between VSR effects and classical ones are proportional to $(m_g/E)^2$,
$E$ being the energy of a graviton in the GW. For GW detectable by the
interferometers LIGO and VIRGO this ratio is at most $10^{-20}$. However, for
GW in the lower frequency range of future detectors, like LISA, the ratio
increases significantly to $ 10^{-10}$, that combined with the anisotropic
nature of VSR phenomena may lead to observable effects.


title:
Neural Graph Matching for Modification Similarity Applied to Electronic
  Document Comparison

abstract:
  In this paper, we present a novel neural graph matching approach applied to
document comparison. Document comparison is a common task in the legal and
financial industries. In some cases, the most important differences may be the
addition or omission of words, sentences, clauses, or paragraphs. However, it
is a challenging task without recording or tracing whole edited process. Under
many temporal uncertainties, we explore the potentiality of our approach to
proximate the accurate comparison to make sure which element blocks have a
relation of edition with others. In beginning, we apply a document layout
analysis that combining traditional and modern technics to segment layout in
blocks of various types appropriately. Then we transform this issue to a
problem of layout graph matching with textual awareness. About graph matching,
it is a long-studied problem with a broad range of applications. However,
different from previous works focusing on visual images or structural layout,
we also bring textual features into our model for adapting this domain.
Specifically, based on the electronic document, we introduce an encoder to deal
with the visual presentation decoding from PDF. Additionally, because the
modifications can cause the inconsistency of document layout analysis between
modified documents and the blocks can be merged and split, Sinkhorn divergence
is adopted in our graph neural approach, which tries to overcome both these
issues with many-to-many block matching. We demonstrate this on two categories
of layouts, as follows., legal agreement and scientific articles, collected
from our real-case datasets.


title:
Light from the Darkness: Detecting Ultra-Diffuse Galaxies in the Perseus
  Cluster through Over-densities of Globular Clusters with a Log-Gaussian Cox
  Process

abstract:
  We introduce a new method for detecting ultra-diffuse galaxies by searching
for over-densities in intergalactic globular cluster populations. Our approach
is based on an application of the log-Gaussian Cox process, which is a commonly
used model in the spatial statistics literature but rarely used in astronomy.
This method is applied to the globular cluster data obtained from the PIPER
survey, a \textit{Hubble Space Telescope} imaging program targeting the Perseus
cluster. We successfully detect all confirmed ultra-diffuse galaxies with known
globular cluster populations in the survey. We also identify a potential galaxy
that has no detected diffuse stellar content. Preliminary analysis shows that
it is unlikely to be merely an accidental clump of globular clusters or other
objects. If confirmed, this system would be the first of its kind. Simulations
are used to assess how the physical parameters of the globular cluster systems
within ultra-diffuse galaxies affect their detectability using our method. We
quantify the correlation of the detection probability with the total number of
globular clusters in the galaxy and the anti-correlation with increasing
half-number radius of the globular cluster system. The S\'{e}rsic index of the
globular cluster distribution has little impact on detectability.


title:
Overlapping Word Removal is All You Need: Revisiting Data Imbalance in
  Hope Speech Detection

abstract:
  Hope Speech Detection, a task of recognizing positive expressions, has made
significant strides recently. However, much of the current works focus on model
development without considering the issue of inherent imbalance in the data.
Our work revisits this issue in hope-speech detection by introducing focal
loss, data augmentation, and pre-processing strategies. Accordingly, we find
that introducing focal loss as part of Multilingual-BERT's (M-BERT) training
process mitigates the effect of class imbalance and improves overall F1-Macro
by 0.11. At the same time, contextual and back-translation-based word
augmentation with M-BERT improves results by 0.10 over baseline despite
imbalance. Finally, we show that overlapping word removal based on
pre-processing, though simple, improves F1-Macro by 0.28. In due process, we
present detailed studies depicting various behaviors of each of these
strategies and summarize key findings from our empirical results for those
interested in getting the most out of M-BERT for hope speech detection under
real-world conditions of data imbalance.


title: Symbolic defects of edge ideals of unicyclic graphs

abstract:
  We introduce the concept of minimum edge cover for an induced subgraph in a
graph. Let $G$ be a unicyclic graph with a unique odd cycle and $I=I(G)$ be its
edge ideal. We compute the exact values of all symbolic defects of $I$ using
the concept of minimum edge cover for an induced subgraph in a graph. We
describe one method to find the quasi-polynomial associated with the symbolic
defects of edge ideal $I$. We classify the class of unicyclic graphs when some
power of maximal ideal annihilates $ I^{(s)}/I^s $ for any fixed $ s $. Also
for those class of graphs, we compute the Hilbert function of the module
$I^{(s)}/I^s$ for all $s.$


title:
Modelling Evolutionary and Stationary User Preferences for Temporal Sets
  Prediction

abstract:
  Given a sequence of sets, where each set is associated with a timestamp and
contains an arbitrary number of elements, the task of temporal sets prediction
aims to predict the elements in the subsequent set. Previous studies for
temporal sets prediction mainly capture each user's evolutionary preference by
learning from his/her own sequence. Although insightful, we argue that: 1) the
collaborative signals latent in different users' sequences are essential but
have not been exploited; 2) users also tend to show stationary preferences
while existing methods fail to consider. To this end, we propose an integrated
learning framework to model both the evolutionary and the stationary
preferences of users for temporal sets prediction, which first constructs a
universal sequence by chronologically arranging all the user-set interactions,
and then learns on each user-set interaction. In particular, for each user-set
interaction, we first design an evolutionary user preference modelling
component to track the user's time-evolving preference and exploit the latent
collaborative signals among different users. This component maintains a memory
bank to store memories of the related user and elements, and continuously
updates their memories based on the currently encoded messages and the past
memories. Then, we devise a stationary user preference modelling module to
discover each user's personalized characteristics according to the historical
sequence, which adaptively aggregates the previously interacted elements from
dual perspectives with the guidance of the user's and elements' embeddings.
Finally, we develop a set-batch algorithm to improve the model efficiency,
which can create time-consistent batches in advance and achieve 3.5x training
speedups on average. Experiments on real-world datasets demonstrate the
effectiveness and good interpretability of our approach.


title: Positive mass theorem with arbitrary ends and its application

abstract:
  In this article, we give a proof for positive mass theorem of asymptotically
flat manifolds with arbitrary ends when the dimension is no greater than seven.
As an application, we also show a positive mass theorem for asymptotically
locally Euclidean manifolds with necessary incompressible conditions.


title: The performance of quadratic models for complex phase retrieval

abstract:
  The aim of this paper is to study the performance of the amplitude-based
model $\widehat{x} \in {\rm argmin}_{x\in
\mathbb{C}^d}\sum_{j=1}^m\left(|\langle a_j,x\rangle |-b_j\right)^2$, where
$b_j=|\langle a_j,x_0\rangle|+\eta_j$ and $x_0\in {\mathbb C}^d$ is a target
signal. The model is raised in phase retrieval and one has developed many
efficient algorithms to solve it. However, there are very few results about the
estimation performance in complex case. We show that
$\min_{\theta\in[0,2\pi)}\|\widehat{x}-\exp(i\theta)\cdot x_0\|_2 \lesssim
\frac{\|\eta\|_2}{\sqrt{m}}$ holds with high probability provided the
measurement vectors $a_j\in {\mathbb C}^d,$ $j=1,\ldots,m,$ are complex
Gaussian random vectors and $m\gtrsim d$. Here $\eta=(\eta_1,\ldots,\eta_m)\in
\mathbb{R}^m$ is the noise vector without any assumption on the distribution.
Furthermore, we prove that the reconstruction error is sharp. For the case
where the target signal $x_0\in \mathbb{C}^{d}$ is sparse, we establish a
similar result for the nonlinear constrained LASSO. This paper presents the
first theoretical guarantee on quadratic models for complex phase retrieval. To
accomplish this, we leverage a strong version of restricted isometry property
for low-rank matrices.


title:
Physics-informed graph neural networks enhance scalability of
  variational nonequilibrium optimal control

abstract:
  When a physical system is driven away from equilibrium, the statistical
distribution of its dynamical trajectories informs many of its physical
properties. Characterizing the nature of the distribution of dynamical
observables, such as a current or entropy production rate, has become a central
problem in nonequilibrium statistical mechanics. Asymptotically, for a broad
class of observables, the distribution of a given observable satisfies a large
deviation principle when the dynamics is Markovian, meaning that fluctuations
can be characterized in the long-time limit by computing a scaled cumulant
generating function. Calculating this function is not tractable analytically
(nor often numerically) for complex, interacting systems, so the development of
robust numerical techniques to carry out this computation is needed to probe
the properties of nonequilibrium materials. Here, we describe an algorithm that
recasts this task as an optimal control problem that can be solved
variationally. We solve for optimal control forces using neural network
ans\"atze that are tailored to the physical systems to which the forces are
applied. We demonstrate that this approach leads to transferable and accurate
solutions in two systems featuring large numbers of interacting particles.


title: Few-shot Learning with Noisy Labels

abstract:
  Few-shot learning (FSL) methods typically assume clean support sets with
accurately labeled samples when training on novel classes. This assumption can
often be unrealistic: support sets, no matter how small, can still include
mislabeled samples. Robustness to label noise is therefore essential for FSL
methods to be practical, but this problem surprisingly remains largely
unexplored. To address mislabeled samples in FSL settings, we make several
technical contributions. (1) We offer simple, yet effective, feature
aggregation methods, improving the prototypes used by ProtoNet, a popular FSL
technique. (2) We describe a novel Transformer model for Noisy Few-Shot
Learning (TraNFS). TraNFS leverages a transformer's attention mechanism to
weigh mislabeled versus correct samples. (3) Finally, we extensively test these
methods on noisy versions of MiniImageNet and TieredImageNet. Our results show
that TraNFS is on-par with leading FSL methods on clean support sets, yet
outperforms them, by far, in the presence of label noise.


title:
Research on FAST Active Reflector Adjustment Algorithm Based on Computer
  Simulation

abstract:
  Based on the background of the 2021 Higher Education Club Cup National
College Students Mathematical Contest in Modeling A, according to the relevant
data of the China Sky Eye (FAST) radio telescope, the main cable nodes and
actuators are adjusted and controlled by mathematical modeling and computer
simulation methods to realize the active reflector. The adjustment of the shape
enables it to better receive the signal of the external celestial body and
improve the utilization rate of the reflector, so as to achieve a higher
receiving ratio of the feed cabin. In this paper, a point set mapping algorithm
based on the rotation matrix of the spatial coordinate axis is proposed, that
is, the mapping matrix is obtained by mathematical derivation, and the linear
interpolation algorithm based on the original spherical surface and the ideal
paraboloid to solve the working paraboloid is obtained. When the interpolation
ratio is adjusted to 89% to satisfy the optimal solution under realistic
constraints. Then, a three-dimensional spatial signal reflection model based on
spatial linear invariance is proposed. Each reflective panel is used as an
evaluation index. For each reflective surface, the 0-1 variable of signal
accessibility on the feeder is defined. The signal is mapped to the plane where
the feeder is located, which reduces a lot of computational difficulty.
Finally, it is found that the panel of the feed cabin that can receive the
reflected signal accounts for 19.3%. Compared with the original spherical
reflection model, the working paraboloid model established in this paper has a
The signal ratio has increased by 224%.


title:
Scalable privacy-preserving cancer type prediction with homomorphic
  encryption

abstract:
  Machine Learning (ML) alleviates the challenges of high-dimensional data
analysis and improves decision making in critical applications like healthcare.
Effective cancer type from high-dimensional genetic mutation data can be useful
for cancer diagnosis and treatment, if the distinguishable patterns between
cancer types are identified. At the same time, analysis of high-dimensional
data is computationally expensive and is often outsourced to cloud services.
Privacy concerns in outsourced ML, especially in the field of genetics,
motivate the use of encrypted computation, like Homomorphic Encryption (HE).
But restrictive overheads of encrypted computation deter its usage. In this
work, we explore the challenges of privacy preserving cancer detection using a
real-world dataset consisting of more than 2 million genetic information for
several cancer types. Since the data is inherently high-dimensional, we explore
smaller ML models for cancer prediction to enable fast inference in the privacy
preserving domain. We develop a solution for privacy preserving cancer
inference which first leverages the domain knowledge on somatic mutations to
efficiently encode genetic mutations and then uses statistical tests for
feature selection. Our logistic regression model, built using our novel
encoding scheme, achieves 0.98 micro-average area under curve with 13% higher
test accuracy than similar studies. We exhaustively test our model's predictive
capabilities by analyzing the genes used by the model. Furthermore, we propose
a fast matrix multiplication algorithm that can efficiently handle
high-dimensional data. Experimental results show that, even with 40,000
features, our proposed matrix multiplication algorithm can speed up concurrent
inference of multiple individuals by approximately 10x and inference of a
single individual by approximately 550x, in comparison to standard matrix
multiplication.


title: A note on $d$-maximal $p$-groups I

abstract:
  A finite $p$-group $G$ is said to be $d$-maximal if $d(H)<d(G)$ for every
subgroup $H<G$, where $d(G)$ denotes the minimal number of generators of $G$. A
similar definition can be formulated when $G$ is acted on by some group $A$. We
generalize results of B. Kahn and T. Laffey to the latter case, and give them
in particular alternative short proofs. We answer moreover a question of Y.
Berkovich about the minimal non-metacyclic $p$-groups.


title:
Lie Symmetries, Painlev\'{e} analysis and global dynamics for the
  temporal equation of radiating stars

abstract:
  We study the temporal equation of radiating stars by using three powerful
methods for the analysis of nonlinear differential equations. Specifically, we
investigate the global dynamics for the given master ordinary differential
equation to understand the evolution of solutions for various initial
conditions as also to investigate the existence of asymptotic solutions.
Moreover, with the application of Lie's theory, we can reduce the order of the
master differential equation, while an exact similarity solution is determined.
Finally, the master equation possesses the Painlev\'{e} property, which means
that the analytic solution can be expressed in terms of a Laurent expansion.


title: Position-aware Location Regression Network for Temporal Video Grounding

abstract:
  The key to successful grounding for video surveillance is to understand a
semantic phrase corresponding to important actors and objects. Conventional
methods ignore comprehensive contexts for the phrase or require heavy
computation for multiple phrases. To understand comprehensive contexts with
only one semantic phrase, we propose Position-aware Location Regression Network
(PLRN) which exploits position-aware features of a query and a video.
Specifically, PLRN first encodes both the video and query using positional
information of words and video segments. Then, a semantic phrase feature is
extracted from an encoded query with attention. The semantic phrase feature and
encoded video are merged and made into a context-aware feature by reflecting
local and global contexts. Finally, PLRN predicts start, end, center, and width
values of a grounding boundary. Our experiments show that PLRN achieves
competitive performance over existing methods with less computation time and
memory.


title: Impact of an Employment Policy on Companies' Expectations Fulfilment

abstract:
  We study the effect of Chile's Employment Protection Law (Ley de Protecci\'on
del Empleo, EPL), a law which allowed temporal suspensions of job contracts in
exceptional circumstances during the COVID-19 pandemic, on the fulfillment of
firms' expectations regarding layoffs. We use monthly surveys directed at a
representative group of firms in the national territory. This panel data allows
to follow firms through time and analyze the match between their expectations
and the actual realization to model their expectation fulfilment. We model the
probability of expectation fulfilment through a logit model that allows for
moderation effects. Results suggest that for those firms that expected to fire
workers, for the firms that used the EPL, the odds they finally ended up with a
job separation are 50% of the odds for those that did not used the EPL. Small
firms increase their probability of expectation fulfilment in 11.9% when using
the EPL compared to large firms if they declared they were expecting to fire
workers.


title: Skew graded $(A_\infty)$ hypersurface singularities

abstract:
  For a skew version of a graded $(A_\infty)$ hypersurface singularity $A$, we
study the stable category of graded maximal Cohen-Macaulay modules over $A$. As
a consequence, we see that $A$ has countably infinite Cohen-Macaulay
representation type and is not a noncommutative graded isolated singularity.


title: CoupleFace: Relation Matters for Face Recognition Distillation

abstract:
  Knowledge distillation is an effective method to improve the performance of a
lightweight neural network (i.e., student model) by transferring the knowledge
of a well-performed neural network (i.e., teacher model), which has been widely
applied in many computer vision tasks, including face recognition.
Nevertheless, the current face recognition distillation methods usually utilize
the Feature Consistency Distillation (FCD) (e.g., L2 distance) on the learned
embeddings extracted by the teacher and student models for each sample, which
is not able to fully transfer the knowledge from the teacher to the student for
face recognition. In this work, we observe that mutual relation knowledge
between samples is also important to improve the discriminative ability of the
learned representation of the student model, and propose an effective face
recognition distillation method called CoupleFace by additionally introducing
the Mutual Relation Distillation (MRD) into existing distillation framework.
Specifically, in MRD, we first propose to mine the informative mutual
relations, and then introduce the Relation-Aware Distillation (RAD) loss to
transfer the mutual relation knowledge of the teacher model to the student
model. Extensive experimental results on multiple benchmark datasets
demonstrate the effectiveness of our proposed CoupleFace for face recognition.
Moreover, based on our proposed CoupleFace, we have won the first place in the
ICCV21 Masked Face Recognition Challenge (MS1M track).


title:
FSOINet: Feature-Space Optimization-Inspired Network for Image
  Compressive Sensing

abstract:
  In recent years, deep learning-based image compressive sensing (ICS) methods
have achieved brilliant success. Many optimization-inspired networks have been
proposed to bring the insights of optimization algorithms into the network
structure design and have achieved excellent reconstruction quality with low
computational complexity. But they keep the information flow in pixel space as
traditional algorithms by updating and transferring the image in pixel space,
which does not fully use the information in the image features. In this paper,
we propose the idea of achieving information flow phase by phase in feature
space and design a Feature-Space Optimization-Inspired Network (dubbed FSOINet)
to implement it by mapping both steps of proximal gradient descent algorithm
from pixel space to feature space. Moreover, the sampling matrix is learned
end-to-end with other network parameters. Experiments show that the proposed
FSOINet outperforms the existing state-of-the-art methods by a large margin
both quantitatively and qualitatively. The source code is available on
https://github.com/cwjjun/FSOINet.


title: Mottness, Phase String, and High-$T_c$ Superconductivity

abstract:
  It is a great discovery in physics of the twentieth century that the
elementary particles in Nature are dictated by gauge forces, characterized by a
nonintegrable phase factor that an elementary particle of charge $q$ acquires
from A to B points: $$P \exp\left(i\frac{q}{\hbar c} \int_A^B A_\mu
dx^\mu\right)$$ where $A_\mu$ is the gauge potential and $P$ stands for path
ordering. In a many-body system of strongly correlated electrons, if the
so-called Mott gap is opened up by interaction, the corresponding Hilbert space
will be fundamentally changed. A novel nonintegrable phase factor known as
phase-string will appear and replace the conventional Fermi statistics to
dictate the low-lying physics. Protected by the Mott gap, which is clearly
identified in the high-$T_c$ cuprate with a magnitude > 1.5 eV, such a singular
phase factor can enforce a fractionalization of the electrons, leading to a
dual world of exotic elementary particles with a topological gauge structure. A
non-Fermi-liquid "parent" state will emerge, in which the gapless Landau
quasiparticle is only partially robust around the so-called Fermi arc regions,
while the main dynamics are dominated by two types of gapped spinons.
Antiferromagnetism, superconductivity, and a Fermi liquid with full Fermi
surface can be regarded as the low-temperature instabilities of this new parent
state. Both numerics and experiments provide direct evidence for such an
emergent physics of the Mottness, which lies in the core of a high-$T_c$
superconducting mechanism.


title:
Lattice paths and negatively indexed weight-dependent binomial
  coefficients

abstract:
  In 1992, Loeb considered a natural extension of the binomial coefficients to
negative entries and gave a combinatorial interpretation in terms of hybrid
sets. He showed that many of the fundamental properties of binomial
coefficients continue to hold in this extended setting. Recently, Formichella
and Straub showed that these results can be extended to the $q$-binomial
coefficients with arbitrary integer values and extended the work of Loeb
further by examining arithmetic properties of the $q$-binomial coefficients. In
this paper, we give an alternative combinatorial interpretation in terms of
lattice paths and consider an extension of the more general weight-dependent
binomial coefficients, first defined by the second author, to arbitrary integer
values. Remarkably, many of the results of Loeb, Formichella and Straub
continue to hold in the general weighted setting. We also examine important
special cases of the weight-dependent binomial coefficients, including
ordinary, $q$- and elliptic binomial coefficients as well as elementary and
complete homogeneous symmetric functions.


title: Imaging based feedback cooling of a levitated nanoparticle

abstract:
  Imaging-based detection of the motion of the levitated nanoparticles
complements a widely-used interferometric detection method, providing a precise
and robust way to estimate the position of the particle. Here, we show the
camera-based feedback cooling of a charged nanoparticle levitated in a linear
Paul trap. A charged nanoparticle levitated in a vacuum was observed by CMOS
camera systems. The nanoparticle images were processed in realtime with a
microcontroller integrated with a CMOS image sensor. The phase-delayed position
signal was fed-back to one of the trap electrodes resulting in the velocity
damping cooling. Our study provides a simple and versatile approach applicable
for control of low-frequency mechanical oscillators.


title: Inducing Social Optimality in Games via Adaptive Incentive Design

abstract:
  How can a social planner adaptively incentivize selfish agents who are
learning in a strategic environment to induce a socially optimal outcome in the
long run? We propose a two-timescale learning dynamics to answer this question
in both atomic and non-atomic games. In our learning dynamics, players adopt a
class of learning rules to update their strategies at a faster timescale, while
a social planner updates the incentive mechanism at a slower timescale. In
particular, the update of the incentive mechanism is based on each player's
externality, which is evaluated as the difference between the player's marginal
cost and the society's marginal cost in each time step. We show that any fixed
point of our learning dynamics corresponds to the optimal incentive mechanism
such that the corresponding Nash equilibrium also achieves social optimality.
We also provide sufficient conditions for the learning dynamics to converge to
a fixed point so that the adaptive incentive mechanism eventually induces a
socially optimal outcome. Finally, we demonstrate that the sufficient
conditions for convergence are satisfied in a variety of games, including (i)
atomic networked quadratic aggregative games, (ii) atomic Cournot competition,
and (iii) non-atomic network routing games.

categories: cs.GT cs.SY econ.GN econ.TH eess.SY q-fin.EC


title:
Fast Selective Flushing to Mitigate Contention-based Cache Timing
  Attacks

abstract:
  Caches are widely used to improve performance in modern processors. By
carefully evicting cache lines and identifying cache hit/miss time,
contention-based cache timing channel attacks can be orchestrated to leak
information from the victim process. Existing hardware countermeasures explored
cache partitioning and randomization, are either costly, not applicable for the
L1 data cache, or are vulnerable to sophisticated attacks. Countermeasures
using cache flush exist but are slow since all cache lines have to be evacuated
during a cache flush. In this paper, we propose for the first time a
hardware/software flush-based countermeasure, called fast selective flushing
(FaSe). By utilizing an ISA extension (one flush instruction) and cache
modification (additional state bits and control logic), FaSe selectively
flushes cache lines and provides a mitigation method with a similar effect to
existing methods using naive flushing methods. FaSe is implemented on RISC-V
Rocket Core/Chip and evaluated on Xilinx FPGA running user programs and the
Linux operating system. Our experimental results show that FaSe reduces time
overhead significantly by 36% for user programs and 42% for the operating
system compared to the methods with naive flushing, with less than 1% hardware
overhead. Our security test shows FaSe is capable of mitigating target cache
timing attacks.


title:
Learning Design and Construction with Varying-Sized Materials via
  Prioritized Memory Resets

abstract:
  Can a robot autonomously learn to design and construct a bridge from
varying-sized blocks without a blueprint? It is a challenging task with long
horizon and sparse reward -- the robot has to figure out physically stable
design schemes and feasible actions to manipulate and transport blocks. Due to
diverse block sizes, the state space and action trajectories are vast to
explore. In this paper, we propose a hierarchical approach for this problem. It
consists of a reinforcement-learning designer to propose high-level building
instructions and a motion-planning-based action generator to manipulate blocks
at the low level. For high-level learning, we develop a novel technique,
prioritized memory resetting (PMR) to improve exploration. PMR adaptively
resets the state to those most critical configurations from a replay buffer so
that the robot can resume training on partial architectures instead of from
scratch. Furthermore, we augment PMR with auxiliary training objectives and
fine-tune the designer with the locomotion generator. Our experiments in
simulation and on a real deployed robotic system demonstrate that it is able to
effectively construct bridges with blocks of varying sizes at a high success
rate. Demos can be found at https://sites.google.com/view/bridge-pmr.


title: Inverse scattering problem with a bare state

abstract:
  In hadron physics, molecular-like multi-hadron states can interact with
compact multi-quark states. The latter are modelled as bare states in the
Hilbert space of a potential model. In this work, we study several potential
models relevant to the bare state, and solve their inverse scattering problems.
The first model labelled as `cc' is a separable potential model. We show that
it can approximate S-wave near-threshold physics with an error of
$\mathcal{O}(\beta^3/M_V^3)$, where $\beta$ sets the maximum momentum of the
near-threshold region and $M_V$ is the typical scale of the potential. The
second model labelled as `bc' serves as the bare-state-dominance approximation,
where interaction between continuum states is ignored. Under this model, we
show that a shallow bound state naturally tends to have a small bare-state
proportion (which is one minus the compositeness), even if the bare state is
crucial for the bound state's generation. Therefore the bare-state proportion,
or the compositeness, cannot figure out the importance of the bare state. The
last model labelled as `bcc' is a combination of the former two models. This
model not only serves as a correction to the bare-state-dominance
approximation, but also can be used to understand the interplay between quark
and hadron degrees of freedom. This model naturally leads to the presence of a
Castillejo-Dalitz-Dyson (CDD) zero. We consider the energy decomposition of a
bound state. The potential ratio of the bare-continuum interaction to the
continuum self-interaction is proposed to understand how the bound state is
generated. Model-independently, an inequality for the potential ratio is
derived. Based on the model `bcc', the CDD zero can be used to estimate the
potential ratio. At last, we apply these studies to the deuteron, $\rho$ meson,
and $D_{s0}^*(2317)$, and analyze the properties of them.


title: GERE: Generative Evidence Retrieval for Fact Verification

abstract:
  Fact verification (FV) is a challenging task which aims to verify a claim
using multiple evidential sentences from trustworthy corpora, e.g., Wikipedia.
Most existing approaches follow a three-step pipeline framework, including
document retrieval, sentence retrieval and claim verification. High-quality
evidences provided by the first two steps are the foundation of the effective
reasoning in the last step. Despite being important, high-quality evidences are
rarely studied by existing works for FV, which often adopt the off-the-shelf
models to retrieve relevant documents and sentences in an
"index-retrieve-then-rank" fashion. This classical approach has clear drawbacks
as follows: i) a large document index as well as a complicated search process
is required, leading to considerable memory and computational overhead; ii)
independent scoring paradigms fail to capture the interactions among documents
and sentences in ranking; iii) a fixed number of sentences are selected to form
the final evidence set. In this work, we propose GERE, the first system that
retrieves evidences in a generative fashion, i.e., generating the document
titles as well as evidence sentence identifiers. This enables us to mitigate
the aforementioned technical issues since: i) the memory and computational cost
is greatly reduced because the document index is eliminated and the heavy
ranking process is replaced by a light generative process; ii) the dependency
between documents and that between sentences could be captured via sequential
generation process; iii) the generative formulation allows us to dynamically
select a precise set of relevant evidences for each claim. The experimental
results on the FEVER dataset show that GERE achieves significant improvements
over the state-of-the-art baselines, with both time-efficiency and
memory-efficiency.


title:
Make The Most of Prior Data: A Solution for Interactive Text
  Summarization with Preference Feedback

abstract:
  For summarization, human preference is critical to tame outputs of the
summarizer in favor of human interests, as ground-truth summaries are scarce
and ambiguous. Practical settings require dynamic exchanges between human and
AI agent wherein feedback is provided in an online manner, a few at a time. In
this paper, we introduce a new framework to train summarization models with
preference feedback interactively. By properly leveraging offline data and a
novel reward model, we improve the performance regarding ROUGE scores and
sample-efficiency. Our experiments on three various datasets confirm the
benefit of the proposed framework in active, few-shot and online settings of
preference learning.


title:
Fully End-to-end Autonomous Driving with Semantic Depth Cloud Mapping
  and Multi-Agent

abstract:
  Focusing on the task of point-to-point navigation for an autonomous driving
vehicle, we propose a novel deep learning model trained with end-to-end and
multi-task learning manners to perform both perception and control tasks
simultaneously. The model is used to drive the ego vehicle safely by following
a sequence of routes defined by the global planner. The perception part of the
model is used to encode high-dimensional observation data provided by an RGBD
camera while performing semantic segmentation, semantic depth cloud (SDC)
mapping, and traffic light state and stop sign prediction. Then, the control
part decodes the encoded features along with additional information provided by
GPS and speedometer to predict waypoints that come with a latent feature space.
Furthermore, two agents are employed to process these outputs and make a
control policy that determines the level of steering, throttle, and brake as
the final action. The model is evaluated on CARLA simulator with various
scenarios made of normal-adversarial situations and different weathers to mimic
real-world conditions. In addition, we do a comparative study with some recent
models to justify the performance in multiple aspects of driving. Moreover, we
also conduct an ablation study on SDC mapping and multi-agent to understand
their roles and behavior. As a result, our model achieves the highest driving
score even with fewer parameters and computation load. To support future
studies, we share our codes at
https://github.com/oskarnatan/end-to-end-driving.


title:
A Comparative Study of Faithfulness Metrics for Model Interpretability
  Methods

abstract:
  Interpretation methods to reveal the internal reasoning processes behind
machine learning models have attracted increasing attention in recent years. To
quantify the extent to which the identified interpretations truly reflect the
intrinsic decision-making mechanisms, various faithfulness evaluation metrics
have been proposed. However, we find that different faithfulness metrics show
conflicting preferences when comparing different interpretations. Motivated by
this observation, we aim to conduct a comprehensive and comparative study of
the widely adopted faithfulness metrics. In particular, we introduce two
assessment dimensions, namely diagnosticity and time complexity. Diagnosticity
refers to the degree to which the faithfulness metric favours relatively
faithful interpretations over randomly generated ones, and time complexity is
measured by the average number of model forward passes. According to the
experimental results, we find that sufficiency and comprehensiveness metrics
have higher diagnosticity and lower time complexity than the other faithfulness
metric


title:
CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for
  Multimodal Sentiment Detection

abstract:
  Compared with unimodal data, multimodal data can provide more features to
help the model analyze the sentiment of data. Previous research works rarely
consider token-level feature fusion, and few works explore learning the common
features related to sentiment in multimodal data to help the model fuse
multimodal features. In this paper, we propose a Contrastive Learning and
Multi-Layer Fusion (CLMLF) method for multimodal sentiment detection.
Specifically, we first encode text and image to obtain hidden representations,
and then use a multi-layer fusion module to align and fuse the token-level
features of text and image. In addition to the sentiment analysis task, we also
designed two contrastive learning tasks, label based contrastive learning and
data based contrastive learning tasks, which will help the model learn common
features related to sentiment in multimodal data. Extensive experiments
conducted on three publicly available multimodal datasets demonstrate the
effectiveness of our approach for multimodal sentiment detection compared with
existing methods. The codes are available for use at
https://github.com/Link-Li/CLMLF


title: Contracting differential equations in weighted Banach spaces

abstract:
  Geodesic contraction in vector-valued differential equations is readily
verified by linearized operators which are uniformly negative-definite in the
Riemannian metric. In the infinite-dimensional setting, however, such analysis
is generally restricted to norm-contracting systems. We develop a
generalization of geodesic contraction rates to Banach spaces using a
smoothly-weighted semi-inner product structure on tangent spaces. We show that
negative contraction rates in bijectively weighted spaces imply asymptotic
norm-contraction, and apply recent results on asymptotic contractions in Banach
spaces to establish the existence of fixed points. We then show that
contraction in surjectively weighted spaces verify non-equilibrium asymptotic
properties, such as convergence to finite- and infinite-dimensional subspaces,
submanifolds, limit cycles, and phase-locking phenomena. We use contraction
rates in weighted Sobolev spaces to establish existence and continuous data
dependence in nonlinear PDEs, and pose a method for constructing weak solutions
using vanishing dissipative approximations. We discuss applications to control
and order reduction of distributed systems.


title: A Finite-State Fixed-Corridor Model for UAS Traffic Management

abstract:
  This paper proposes a physics-inspired solution for low altitude Unmanned
Aircraft System (UAS) Traffic Management (UTM) in urban areas. We decompose UTM
into spatial and temporal planning problems. For the spatial planning problem,
we use the principles of Eulerian continuum mechanics to safely and optimally
allocate finite airspace to a UAS. To this end, the finite airspace is
partitioned into planned and unplanned subspaces with unplanned subspace(s) or
zone(s) enclosing buildings and restricted no-fly regions. The planned subspace
is divided into navigable channels that safely wrap unplanned zone(s). We model
the airspace planning problem as a Markov Decision Process (MDP) with states
defined based on spatial and temporal airspace features and actions authorizing
transitions between safe navigable channels. We apply the proposed traffic
management solution to plan safe coordination of small UAS in the airspace
above downtown Tucson, Arizona.


title:
Trigger-GNN: A Trigger-Based Graph Neural Network for Nested Named
  Entity Recognition

abstract:
  Nested named entity recognition (NER) aims to identify the entity boundaries
and recognize categories of the named entities in a complex hierarchical
sentence. Some works have been done using character-level, word-level, or
lexicon-level based models. However, such researches ignore the role of the
complementary annotations. In this paper, we propose a trigger-based graph
neural network (Trigger-GNN) to leverage the nested NER. It obtains the
complementary annotation embeddings through entity trigger encoding and
semantic matching, and tackle nested entity utilizing an efficient graph
message passing architecture, aggregation-update mode. We posit that using
entity triggers as external annotations can add in complementary supervision
signals on the whole sentences. It helps the model to learn and generalize more
efficiently and cost-effectively. Experiments show that the Trigger-GNN
consistently outperforms the baselines on four public NER datasets, and it can
effectively alleviate the nested NER.


title:
Investigating the effects of electron bounce-cyclotron resonance on
  plasma dynamics in capacitive discharges operated in the presence of a weak
  transverse magnetic field

abstract:
  Recently, S Patil et al. have reported the existence of an enhanced operating
regime when a low-pressure (5 mTorr) capacitively coupled discharge (CCP) is
driven by a very high radio-frequency (60 MHz) source in the presence of a weak
external magnetic field applied parallel to its electrodes. Their
Particle-in-Cell (PIC) simulations show, that a significantly higher bulk
plasma density and ion flux can be achieved at the electrode when the electron
cyclotron frequency equals half of the applied RF frequency for a given fixed
voltage. In the present work we take a detailed look at this phenomenon and
further delineate the effect of this "electron bounce cyclotron resonance
(EBCR)" on the electron and ion dynamics of the system. We find that the
ionization collision rate and stochastic heating is maximum under resonance
condition. The electron energy distribution function also indicates that the
population of tail end electrons is highest for the case where EBCR is maximum.
Formation of electric field transients in the bulk plasma region are also seen
at lower values of applied magnetic field. Finally, we demonstrate that the
EBCR induced effect is a low pressure phenomenon and weakens as the neutral gas
pressure increases. The potential utility of this effect to advance the
operational performance of CCP devices for industrial purposes is discussed.


title:
OptimizedDP: An Efficient, User-friendly Library For Optimal Control and
  Dynamic Programming

abstract:
  This paper introduces OptimizedDP, a high-performance software library that
solves time-dependent Hamilton-Jacobi partial differential equation (PDE),
computes backward reachable sets with application in robotics, and contains
value iterations algorithm implementation for continuous action-state space
Markov Decision Process (MDP) while leveraging user-friendliness of Python for
different problem specifications without sacrificing efficiency of the core
computation. These algorithms are all based on dynamic programming, and hence
can both be challenging to implement and have bad execution runtime due to the
large high-dimensional tabular arrays. Although there are existing toolboxes
for level set methods that are used to solve the HJ PDE, our toolbox makes
solving the PDE at higher dimensions possible as well as having an order of
magnitude improvement in execution times compared to other toolboxes while
keeping the interface easy to specify different dynamical systems description.
Our toolbox is available online at https://github.com/SFU-MARS/optimized_dp.


title: Quantum transduction is enhanced by single mode squeezing operators

abstract:
  Quantum transduction is an essential ingredient in scaling up distributed
quantum architecture and is actively pursued based on various physical
platforms. However, demonstrating a transducer with positive quantum capacity
is still practically challenging. In this work, we discuss a new approach to
relax the impedance matching condition to half impedance matching condition,
which is achieved by introducing two-photon drive in the electro-optic
transducer. We show the quantum transduction capacity can be enhanced and can
be understood in a simple interference picture with the help of Bloch-Messiah
decomposition. The parameter regimes with positive quantum capacity is
identified and compared with and without the drive, indicating that the
parametric drive-induced enhancement is really promising in demonstrating
quantum state conversion, and is expected to boost the performance of
transduction with various physical platforms.


title: Deep Annotation of Therapeutic Working Alliance in Psychotherapy

abstract:
  The therapeutic working alliance is an important predictor of the outcome of
the psychotherapy treatment. In practice, the working alliance is estimated
from a set of scoring questionnaires in an inventory that both the patient and
the therapists fill out. In this work, we propose an analytical framework of
directly inferring the therapeutic working alliance from the natural language
within the psychotherapy sessions in a turn-level resolution with deep
embeddings such as the Doc2Vec and SentenceBERT models. The transcript of each
psychotherapy session can be transcribed and generated in real-time from the
session speech recordings, and these embedded dialogues are compared with the
distributed representations of the statements in the working alliance
inventory. We demonstrate, in a real-world dataset with over 950 sessions of
psychotherapy treatments in anxiety, depression, schizophrenia and suicidal
patients, the effectiveness of this method in mapping out trajectories of
patient-therapist alignment and the interpretability that can offer insights in
clinical psychiatry. We believe such a framework can be provide timely feedback
to the therapist regarding the quality of the conversation in interview
sessions.


title:
Ferrofluid drop impacts and Rosensweig peak formation in a non-uniform
  magnetic field

abstract:
  Vertical drop impacts of ferrofluids onto glass slides in a non-uniform
magnetic field have been studied using high-speed photography. Outcomes have
been classified based on the motion of the fluid-surface contact lines, and
formation of peaks (Rosensweig instabilities) which affect the height of the
spreading drop. The largest peaks are nucleated at the edge of a spreading
drop, similarly to crown-rim instabilities in drop impacts with conventional
fluids, and remain there for an extended time. Impact Weber numbers ranged from
18.0 to 489, and the vertical component of the B-field was varied between 0 and
0.37 T at the surface by changing the vertical position of a simple disc magnet
placed below the surface. The falling drop was aligned with the vertical
cylindrical axis of the 25 mm diameter magnet, and the impacts produced
Rosensweig instabilities without splashing. At high magnetic field strengths a
stationary ring of ferrofluid forms approximately above the outer edge of the
magnet.


title: The integral Chow rings of moduli of Weierstrass fibrations

abstract:
  We compute the Chow rings with integral coefficients of moduli stacks of
minimal Weierstrass fibrations over the projective line. For each integer
$N\geq 1$, there is a moduli stack $\mathcal{W}^{\mathrm{min}}_N$ parametrizing
minimal Weierstrass fibrations with fundamental invariant $N$. Following work
of Miranda and Park--Schmitt, we give a quotient stack presentation for each
$\mathcal{W}^{\mathrm{min}}_N$. Using these presentations and equivariant
intersection theory, we determine a complete set of generators and relations
for each of the Chow rings. For the cases $N=1$ (respectively, $N=2$),
parametrizing rational (respectively, K3) elliptic surfaces, we give a more
explicit computation of the relations.


title: TopFormer: Token Pyramid Transformer for Mobile Semantic Segmentation

abstract:
  Although vision transformers (ViTs) have achieved great success in computer
vision, the heavy computational cost hampers their applications to dense
prediction tasks such as semantic segmentation on mobile devices. In this
paper, we present a mobile-friendly architecture named \textbf{To}ken
\textbf{P}yramid Vision Trans\textbf{former} (\textbf{TopFormer}). The proposed
\textbf{TopFormer} takes Tokens from various scales as input to produce
scale-aware semantic features, which are then injected into the corresponding
tokens to augment the representation. Experimental results demonstrate that our
method significantly outperforms CNN- and ViT-based networks across several
semantic segmentation datasets and achieves a good trade-off between accuracy
and latency. On the ADE20K dataset, TopFormer achieves 5\% higher accuracy in
mIoU than MobileNetV3 with lower latency on an ARM-based mobile device.
Furthermore, the tiny version of TopFormer achieves real-time inference on an
ARM-based mobile device with competitive results. The code and models are
available at: https://github.com/hustvl/TopFormer


title: Tubular neighborhoods of local models

abstract:
  We show that the v-sheaf local models of moduli spaces of $p$-adic shtukas
are unibranch. In particular, this proves that the scheme-theoretic local
models defined in our joint work with Ansch\"{u}tz and Richarz are always
normal with reduced special fiber, thereby establishing the remaining cases of
the geometric part of the Scholze$-$Weinstein conjecture when $p \leq 3$. Our
methods are general, topological, and simplify Zhu's proof of the coherence
conjecture. As a technical input, we generalize a comparison theorem of nearby
cycles of Huber to the v-sheaf setup.


title: Minimax policies for best arm identification with two arms

abstract:
  This note describes the optimal policy rule, according to the local
asymptotic minimax regret criterion, for best arm identification when there are
only two treatments. It is shown that the optimal sampling rule allocates a
constant fraction of units to one of the treatments. The fraction is
proportional to the standard deviation of the treatment outcomes. When the
variances are equal, the optimal ratio is one-half. This policy is independent
of the data, so there is no adaptation to previous outcomes. At the end of the
experiment, the policy maker adopts the treatment with higher average average
outcomes.


title:
Organic near-infrared epsilon-near-zero materials with large optical
  nonlinearity

abstract:
  Epsilon-near-zero (ENZ) materials have shown great potential for nonlinear
optical applications, such as photon generation, all-optical switch, and time
refraction because of the ultrafast hot carriers and hot-carrier-induced
nonlinearity enhancement. Here, modified poly(3,4-ethylenedioxythiophene)
(PEDOT) films exhibit vanishing real permittivity near 1550nm, namely in the
conventional band (C band) for optical communications. For nonlinear refractive
index n_2 and absorption coefficient \alpha_2 without ENZ reinforcement, PEDOT
is 100 times larger than indium tin oxide (ITO), a kind of typical inorganic
ENZ material. The n_2 and {\alpha}_2 of modified PEDOT films at ENZ wavelengths
obtain a 22-fold enhancement due to ENZ-induced hot carrier redistribution
inside materials, compared to those at non-ENZ wavelengths. Pump-probe reveals
that hot holes in modified PEDOT (p-type) films exhibit a longer relaxation
time and a lower enhancement multiple of the carrier temperature than hot
electrons in n-type inorganic ENZ materials. Our findings firstly offer new
promising tunable organic ENZ materials with ENZ wavelengths in optical
communication bands and show unique properties of organic ENZ materials as
distinct from inorganic ENZ materials, which provide more possibilities for
on-chip nanophotonic devices, nonlinear optics, and plasmonics.


title: Forecasting SQL Query Cost at Twitter

abstract:
  With the advent of the Big Data era, it is usually computationally expensive
to calculate the resource usages of a SQL query with traditional DBMS
approaches. Can we estimate the cost of each query more efficiently without any
computation in a SQL engine kernel? Can machine learning techniques help to
estimate SQL query resource utilization? The answers are yes. We propose a SQL
query cost predictor service, which employs machine learning techniques to
train models from historical query request logs and rapidly forecasts the CPU
and memory resource usages of online queries without any computation in a SQL
engine. At Twitter, infrastructure engineers are maintaining a large-scale SQL
federation system across on-premises and cloud data centers for serving ad-hoc
queries. The proposed service can help to improve query scheduling by relieving
the issue of imbalanced online analytical processing (OLAP) workloads in the
SQL engine clusters. It can also assist in enabling preemptive scaling.
Additionally, the proposed approach uses plain SQL statements for the model
training and online prediction, indicating it is both hardware and
software-agnostic. The method can be generalized to broader SQL systems and
heterogeneous environments. The models can achieve 97.9\% accuracy for CPU
usage prediction and 97\% accuracy for memory usage prediction.


title: Computational Statistics and Data Science in the Twenty-first Century

abstract:
  Data science has arrived, and computational statistics is its engine. As the
scale and complexity of scientific and industrial data grow, the discipline of
computational statistics assumes an increasingly central role among the
statistical sciences. An explosion in the range of real-world applications
means the development of more and more specialized computational methods, but
five Core Challenges remain. We provide a high-level introduction to
computational statistics by focusing on its central challenges, present recent
model-specific advances and preach the ever-increasing role of non-sequential
computational paradigms such as multi-core, many-core and quantum computing.
Data science is bringing major changes to computational statistics, and these
changes will shape the trajectory of the discipline in the 21st century.


title: Assessment of sub-sampling schemes for compressive nano-FTIR imaging

abstract:
  Nano-FTIR imaging is a powerful scanning-based technique at nanometer spatial
resolution which combines Fourier transform infrared spectroscopy (FTIR) and
scattering-type scanning near-field optical microscopy (s-SNOM). However,
recording large spatial areas with nano-FTIR is limited by long measurement
times due to its sequential data acquisition. Several mathematical approaches
have been proposed to tackle this problem. All of them have in common that only
a small fraction of randomly chosen measurements is required.
  However, choosing the fraction of measurements in a random fashion poses
practical challenges for scanning procedures and does not lead to time savings
as large as desired. We consider different, practically relevant sub-sampling
schemes assuring a faster acquisition. It is demonstrated that results for
almost all considered sub-sampling schemes, namely original Lissajous, triangle
Lissajous, and random reflection sub-sampling, at a sub-sampling rate of 10%,
are comparable to results when using a random sub-sampling of 10%. This implies
that random sub-sampling is not required for efficient data acquisition.


title:
Unidirectional Video Denoising by Mimicking Backward Recurrent Modules
  with Look-ahead Forward Ones

abstract:
  While significant progress has been made in deep video denoising, it remains
very challenging for exploiting historical and future frames. Bidirectional
recurrent networks (BiRNN) have exhibited appealing performance in several
video restoration tasks. However, BiRNN is intrinsically offline because it
uses backward recurrent modules to propagate from the last to current frames,
which causes high latency and large memory consumption. To address the offline
issue of BiRNN, we present a novel recurrent network consisting of forward and
look-ahead recurrent modules for unidirectional video denoising. Particularly,
look-ahead module is an elaborate forward module for leveraging information
from near-future frames. When denoising the current frame, the hidden features
by forward and look-ahead recurrent modules are combined, thereby making it
feasible to exploit both historical and near-future frames. Due to the scene
motion between non-neighboring frames, border pixels missing may occur when
warping look-ahead feature from near-future frame to current frame, which can
be largely alleviated by incorporating forward warping and border enlargement.
Experiments show that our method achieves state-of-the-art performance with
constant latency and memory consumption. The source code and pre-trained models
will be publicly available.


title:
How does fake news use a thumbnail? CLIP-based Multimodal Detection on
  the Unrepresentative News Image

abstract:
  This study investigates how fake news uses a thumbnail for a news article
with a focus on whether a news article's thumbnail represents the news content
correctly. A news article shared with an irrelevant thumbnail can mislead
readers into having a wrong impression of the issue, especially in social media
environments where users are less likely to click the link and consume the
entire content. We propose to capture the degree of semantic incongruity in the
multimodal relation by using the pretrained CLIP representation. From a
source-level analysis, we found that fake news employs a more incongruous image
to the main content than general news. Going further, we attempted to detect
news articles with image-text incongruity. Evaluation experiments suggest that
CLIP-based methods can successfully detect news articles in which the thumbnail
is semantically irrelevant to news text. This study contributes to the research
by providing a novel view on tackling online fake news and misinformation. Code
and datasets are available at
https://github.com/ssu-humane/fake-news-thumbnail.


title:
Improvement in light collection of a photomultiplier tube using a
  wavelength-shifting plate

abstract:
  Large-volume water-Cherenkov neutrino detectors are a light-starved
environment, as each interaction produces only $\sim 50-100$ photons per MeV.
As such, maximizing the light collection efficiency of the detector is vital to
performance. Since Cherenkov emission is heavily weighted towards the near UV,
one method to maximize overall detector light collection without increasing the
number of photomultiplier tubes is to couple each tube to a wavelength-shifting
plastic plate, thus shifting photon wavelengths to a regime better suited to
maximize photomultiplier efficiency and potentially detecting photons that miss
the photocathode. To better understand the behavior of such plates, a scan of a
rectangular wavelength-shifting plate was performed, and the results were used
to calculate the overall percentage improvement in light collection that could
be expected for individual PMTs in a large water-Cherenkov detector.
Measurements of a 15.1 in. by 11.5 in. wavelength-shifting plate using a 365 nm
LED were found to increase overall light collection at the photomultiplier tube
by $7.4\pm0.7\%$. A simulation tuned to reproduce these results was used to
predict the behavior of a wavelength shifting plate exposed to Cherenkov
spectrum light and found increases in light collection that were linear with
edge length, assuming square geometries. These results demonstrate the
potential of wavelength-shifting plates to increase the overall light
collection efficiency in a large detector.


title: Open-set Text Recognition via Character-Context Decoupling

abstract:
  The open-set text recognition task is an emerging challenge that requires an
extra capability to cognize novel characters during evaluation. We argue that a
major cause of the limited performance for current methods is the confounding
effect of contextual information over the visual information of individual
characters. Under open-set scenarios, the intractable bias in contextual
information can be passed down to visual information, consequently impairing
the classification performance. In this paper, a Character-Context Decoupling
framework is proposed to alleviate this problem by separating contextual
information and character-visual information. Contextual information can be
decomposed into temporal information and linguistic information. Here, temporal
information that models character order and word length is isolated with a
detached temporal attention module. Linguistic information that models n-gram
and other linguistic statistics is separated with a decoupled context anchor
mechanism. A variety of quantitative and qualitative experiments show that our
method achieves promising performance on open-set, zero-shot, and close-set
text recognition datasets.


title: HODLR2D: A new class of Hierarchical matrices

abstract:
  This article introduces HODLR2D, a new hierarchical low-rank representation
for a class of dense matrices arising out of $N$ body problems in two
dimensions. Using this new hierarchical framework, we propose a new fast
matrix-vector product that scales almost linearly. We apply this fast
matrix-vector product to accelerate the iterative solution of large dense
linear systems arising out of radial basis function interpolation and
discretized integral equation. The space and computational complexity of
HODLR2D matrix-vector products scales as $\mathcal{O}(pN \log(N))$, where $p$
is the maximum rank of the compressed matrix subblocks. We also prove that $p
\in \mathcal{O}(\log(N)\log(\log(N)))$, which ensures that the storage and
computational complexity of HODLR2D matrix-vector products remain tractable for
large $N$. Additionally, we also present the parallel scalability of HODLR2D as
part of this article.


title: Nonclassical temporal correlation powers quantum random access codes

abstract:
  We explore the fundamental origin of the quantum advantage behind random
access code. We propose new temporal inequalities compatible with
noninvasive-realist models and show that any non-zero quantum advantage of n
bits encoded to 1-bit random access code in presence of shared randomness is
equivalent to the violation of the corresponding temporal inequality. As an
immediate consequence of this connection we also prove that maximal success
probability of n bits encoded to 1-bit random access code can be obtained when
the maximal violation of the corresponding inequality is achieved. We then show
that any non-zero quantum advantage of n bits encoded to 1-bit random access
code, or in other words, any non-zero violation of the corresponding temporal
inequality can certify genuine randomness


title:
NightLab: A Dual-level Architecture with Hardness Detection for
  Segmentation at Night

abstract:
  The semantic segmentation of nighttime scenes is a challenging problem that
is key to impactful applications like self-driving cars. Yet, it has received
little attention compared to its daytime counterpart. In this paper, we propose
NightLab, a novel nighttime segmentation framework that leverages multiple deep
learning models imbued with night-aware features to yield State-of-The-Art
(SoTA) performance on multiple night segmentation benchmarks. Notably, NightLab
contains models at two levels of granularity, i.e. image and regional, and each
level is composed of light adaptation and segmentation modules. Given a
nighttime image, the image level model provides an initial segmentation
estimate while, in parallel, a hardness detection module identifies regions and
their surrounding context that need further analysis. A regional level model
focuses on these difficult regions to provide a significantly improved
segmentation. All the models in NightLab are trained end-to-end using a set of
proposed night-aware losses without handcrafted heuristics. Extensive
experiments on the NightCity and BDD100K datasets show NightLab achieves SoTA
performance compared to concurrent methods.


title:
Content and Style Aware Generation of Text-line Images for Handwriting
  Recognition

abstract:
  Handwritten Text Recognition has achieved an impressive performance in public
benchmarks. However, due to the high inter- and intra-class variability between
handwriting styles, such recognizers need to be trained using huge volumes of
manually labeled training data. To alleviate this labor-consuming problem,
synthetic data produced with TrueType fonts has been often used in the training
loop to gain volume and augment the handwriting style variability. However,
there is a significant style bias between synthetic and real data which hinders
the improvement of recognition performance. To deal with such limitations, we
propose a generative method for handwritten text-line images, which is
conditioned on both visual appearance and textual content. Our method is able
to produce long text-line samples with diverse handwriting styles. Once
properly trained, our method can also be adapted to new target data by only
accessing unlabeled text-line images to mimic handwritten styles and produce
images with any textual content. Extensive experiments have been done on making
use of the generated samples to boost Handwritten Text Recognition performance.
Both qualitative and quantitative results demonstrate that the proposed
approach outperforms the current state of the art.


title: A q-analog of the adjacency matrix of the n-cube

abstract:
  We define a q-analog of the adjacency matrix of the n-cube, determine its
eigenvalues and write down a canonical eigenbasis. We give a weighted count of
the number of rooted spanning trees in the q-analog of the n-cube.
  Remarks on the previous version: The q-analog of the Kac matrix appears in
Terwilliger's classification of Leonard pairs and as such its eigenvalues and
eigenvectors were known. Reference added to Terwilliger's papers and also to a
paper of Johnson. Title changed to reflect this.


title:
Not always about you: Prioritizing community needs when developing
  endangered language technology

abstract:
  Languages are classified as low-resource when they lack the quantity of data
necessary for training statistical and machine learning tools and models.
Causes of resource scarcity vary but can include poor access to technology for
developing these resources, a relatively small population of speakers, or a
lack of urgency for collecting such resources in bilingual populations where
the second language is high-resource. As a result, the languages described as
low-resource in the literature are as different as Finnish on the one hand,
with millions of speakers using it in every imaginable domain, and Seneca, with
only a small-handful of fluent speakers using the language primarily in a
restricted domain. While issues stemming from the lack of resources necessary
to train models unite this disparate group of languages, many other issues cut
across the divide between widely-spoken low resource languages and endangered
languages. In this position paper, we discuss the unique technological,
cultural, practical, and ethical challenges that researchers and indigenous
speech community members face when working together to develop language
technology to support endangered language documentation and revitalization. We
report the perspectives of language teachers, Master Speakers and elders from
indigenous communities, as well as the point of view of academics. We describe
an ongoing fruitful collaboration and make recommendations for future
partnerships between academic researchers and language community stakeholders.


title: Noise-induced dynamical localization and delocalization

abstract:
  We investigate the effect of a two-level jump process or random telegraph
noise on a square wave driven tight-binding lattice. In the absence of the
noise, the system is known to exhibit dynamical localization for specific
ratios of the amplitude and the frequency of the drive. We obtain an exact
expression for the probability propagator to study the stability of dynamical
localization against telegraph noise. Our analysis shows that in the presence
of noise, a proper tuning of the noise parameters destroys dynamical
localization of the clean limit in one case, while it induces dynamical
localization in an otherwise delocalized phase of the clean model. Numerical
results help verify the analytical findings. A study of the dynamics of
entanglement entropy from an initially half-filled state offers complementary
perspective.


title:
Towards Reliable Image Outpainting: Learning Structure-Aware Multimodal
  Fusion with Depth Guidance

abstract:
  Image outpainting technology generates visually reasonable content regardless
of authenticity, making it unreliable to serve for practical applications even
though introducing additional modalities eg. the sketch. Since sparse depth
maps are widely captured in robotics and autonomous systems, together with RGB
images, we combine the sparse depth in the image outpainting task to provide
more reliable performance. Concretely, we propose a Depth-Guided Outpainting
Network (DGONet) to model the feature representations of different modalities
differentially and learn the structure-aware cross-modal fusion. To this end,
two components are designed to implement: 1) The Multimodal Learning Module
produces unique depth and RGB feature representations from the perspectives of
different modal characteristics. 2) The Depth Guidance Fusion Module leverages
the complete depth modality to guide the establishment of RGB contents by
progressive multimodal feature fusion. Furthermore, we specially design an
additional constraint strategy consisting of Cross-modal Loss and Edge Loss to
enhance ambiguous contours and expedite reliable content generation. Extensive
experiments on KITTI demonstrate our superiority over the state-of-the-art
methods with more reliable content generation.


title:
Delving Deep into Regularity: A Simple but Effective Method for Chinese
  Named Entity Recognition

abstract:
  Recent years have witnessed the improving performance of Chinese Named Entity
Recognition (NER) from proposing new frameworks or incorporating word lexicons.
However, the inner composition of entity mentions in character-level Chinese
NER has been rarely studied. Actually, most mentions of regular types have
strong name regularity. For example, entities end with indicator words such as
"company" or "bank" usually belong to organization. In this paper, we propose a
simple but effective method for investigating the regularity of entity spans in
Chinese NER, dubbed as Regularity-Inspired reCOgnition Network (RICON).
Specifically, the proposed model consists of two branches: a regularity-aware
module and a regularityagnostic module. The regularity-aware module captures
the internal regularity of each span for better entity type prediction, while
the regularity-agnostic module is employed to locate the boundary of entities
and relieve the excessive attention to span regularity. An orthogonality space
is further constructed to encourage two modules to extract different aspects of
regularity features. To verify the effectiveness of our method, we conduct
extensive experiments on three benchmark datasets and a practical medical
dataset. The experimental results show that our RICON significantly outperforms
previous state-of-the-art methods, including various lexicon-based methods.


title:
A Reinforcement Learning Approach for Electric Vehicle Routing Problem
  with Vehicle-to-Grid Supply

abstract:
  The use of electric vehicles (EV) in the last mile is appealing from both
sustainability and operational cost perspectives. In addition to the inherent
cost efficiency of EVs, selling energy back to the grid during peak grid
demand, is a potential source of additional revenue to a fleet operator. To
achieve this, EVs have to be at specific locations (discharge points) during
specific points in time (peak period), even while meeting their core purpose of
delivering goods to customers. In this work, we consider the problem of EV
routing with constraints on loading capacity; time window; vehicle-to-grid
energy supply (CEVRPTW-D); which not only satisfy multiple system objectives,
but also scale efficiently to large problem sizes involving hundreds of
customers and discharge stations. We present QuikRouteFinder that uses
reinforcement learning (RL) for EV routing to overcome these challenges. Using
Solomon datasets, results from RL are compared against exact formulations based
on mixed-integer linear program (MILP) and genetic algorithm (GA)
metaheuristics. On an average, the results show that RL is 24 times faster than
MILP and GA, while being close in quality (within 20%) to the optimal.


title: Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation

abstract:
  Existing works typically treat cross-domain semantic segmentation (CDSS) as a
data distribution mismatch problem and focus on aligning the marginal
distribution or conditional distribution. However, the label shift issue is
unfortunately overlooked, which actually commonly exists in the CDSS task, and
often causes a classifier bias in the learnt model. In this paper, we give an
in-depth analysis and show that the damage of label shift can be overcome by
aligning the data conditional distribution and correcting the posterior
probability. To this end, we propose a novel approach to undo the damage of the
label shift problem in CDSS. In implementation, we adopt class-level feature
alignment for conditional distribution alignment, as well as two simple yet
effective methods to rectify the classifier bias from source to target by
remolding the classifier predictions. We conduct extensive experiments on the
benchmark datasets of urban scenes, including GTA5 to Cityscapes and SYNTHIA to
Cityscapes, where our proposed approach outperforms previous methods by a large
margin. For instance, our model equipped with a self-training strategy reaches
59.3% mIoU on GTA5 to Cityscapes, pushing to a new state-of-the-art. The code
will be available at https://github.com/manmanjun/Undoing UDA.


title:
DistPro: Searching A Fast Knowledge Distillation Process via Meta
  Optimization

abstract:
  Recent Knowledge distillation (KD) studies show that different manually
designed schemes impact the learned results significantly. Yet, in KD,
automatically searching an optimal distillation scheme has not yet been well
explored. In this paper, we propose DistPro, a novel framework which searches
for an optimal KD process via differentiable meta-learning. Specifically, given
a pair of student and teacher networks, DistPro first sets up a rich set of KD
connection from the transmitting layers of the teacher to the receiving layers
of the student, and in the meanwhile, various transforms are also proposed for
comparing feature maps along its pathway for the distillation. Then, each
combination of a connection and a transform choice (pathway) is associated with
a stochastic weighting process which indicates its importance at every step
during the distillation. In the searching stage, the process can be effectively
learned through our proposed bi-level meta-optimization strategy. In the
distillation stage, DistPro adopts the learned processes for knowledge
distillation, which significantly improves the student accuracy especially when
faster training is required. Lastly, we find the learned processes can be
generalized between similar tasks and networks. In our experiments, DistPro
produces state-of-the-art (SoTA) accuracy under varying number of learning
epochs on popular datasets, i.e. CIFAR100 and ImageNet, which demonstrate the
effectiveness of our framework.


title:
Dust Extinction Law in Nearby Star-Resolved Galaxies. II. M33 Traced by
  Supergiants

abstract:
  The dust extinction curves toward individual sight lines in M33 are derived
for the first time with a sample of reddened O-type and B-type supergiants
obtained from the LGGS. The observed photometric data are obtained from the
LGGS, PS1 Survey, UKIRT, PHATTER Survey, GALEX, Swift/UVOT and XMM-SUSS. We
combine the intrinsic spectral energy distributions (SEDs) obtained from the
ATLAS9 and Tlusty stellar model atmosphere extinguished by the model extinction
curves from the silicate-graphite dust model to construct model SEDs. The
extinction traces are distributed along the arms in M33, and the derived
extinction curves cover a wide range of shapes ($R_V \approx 2-6$), indicating
the complexity of the interstellar environment and the inhomogeneous
distribution of interstellar dust in M33. The average extinction curve with
$R_V \approx 3.39$ and dust size distribution $dn/da \sim a^{-3.45}{\rm
exp}(-a/0.25)$ is similar to that of the MW but with a weaker 2175 Ang bump and
a slightly steeper rise in the far-UV band. The extinction in the $V$ band of
M33 is up to 2 mag, with a median value of $ A_V \approx 0.43$ mag. The
multiband extinction values from the UV to IR bands are also predicted for M33,
which will provide extinction corrections for future works. The method adopted
in this work is also applied to other star-resolved galaxies (NGC 6822 and
WLM), but only a few extinction curves can be derived because of the limited
observations.


title: Galactic Token Sliding

abstract:
  Given a graph $G$ and two independent sets $I_s$ and $I_t$ of size $k$, the
independent set reconfiguration problem asks whether there exists a sequence of
$k$-sized independent sets $I_s = I_0, I_1, I_2, \ldots, I_\ell = I_t$ such
that each independent set is obtained from the previous one using a so-called
reconfiguration step. Viewing each independent set as a collection of $k$
tokens placed on the vertices of a graph $G$, the two most studied
reconfiguration steps are token jumping and token sliding. In the token jumping
variant of the problem, a single step allows a token to jump from one vertex to
any other vertex in the graph. In the token sliding variant, a token is only
allowed to slide from a vertex to one of its neighbors. Like the independent
set problem, both of the aforementioned problems are known to be W[1]-hard on
general graphs. A very fruitful line of research has showed that the
independent set problem becomes fixed-parameter tractable when restricted to
sparse graph classes, such as planar, bounded treewidth, nowhere-dense, and all
the way to biclique-free graphs. Over a series of papers, the same was shown to
hold for the token jumping problem. As for the token sliding problem, which is
mentioned in most of these papers, almost nothing is known beyond the fact that
the problem is polynomial-time solvable on trees and interval graphs. We remedy
this situation by introducing a new model for the reconfiguration of
independent sets, which we call galactic reconfiguration. Using this new model,
we show that (standard) token sliding is fixed-parameter tractable on graphs of
bounded degree, planar graphs, and chordal graphs of bounded clique number. We
believe that the galactic reconfiguration model is of independent interest and
could potentially help in resolving the remaining open questions concerning the
(parameterized) complexity of token sliding.


title:
Perception of Mechanical Properties via Wrist Haptics: Effects of
  Feedback Congruence

abstract:
  Despite non-co-location, haptic stimulation at the wrist can potentially
provide feedback regarding interactions at the fingertips without encumbering
the user's hand. Here we investigate how two types of skin deformation at the
wrist (normal and shear) relate to the perception of the mechanical properties
of virtual objects. We hypothesized that a congruent mapping between force at
the fingertips and deformation at the wrist would be better, i.e. mapping
finger normal force to skin indentation at the wrist, and shear force to skin
shear at the wrist, would result in better perception than other mappings that
either mixed or merged the two direction into a single type of feedback. We
performed an experiment where haptic devices at the wrist rendered either
normal or shear feedback during manipulation of virtual objects with varying
stiffness, mass, or friction properties. Perception of mechanical properties
was more accurate with congruent skin stimulation than noncongruent. In
addition, discrimination performance and subjective reports were positively
influenced by congruence. This study demonstrates that users can perceive
mechanical properties via haptic feedback provided at the wrist with a
consistent mapping between haptic feedback and interaction forces at the
fingertips, regardless of congruence.


title:
Near-Optimal Distributed Linear-Quadratic Regulator for Networked
  Systems

abstract:
  This paper studies the trade-off between the degree of decentralization and
the performance of a distributed controller in a linear-quadratic control
setting. We study a system of interconnected agents over a graph and a
distributed controller, called $\kappa$-distributed control, which lets the
agents make control decisions based on the state information within distance
$\kappa$ on the underlying graph. This controller can tune its degree of
decentralization using the parameter $\kappa$ and thus allows a
characterization of the relationship between decentralization and performance.
We show that under mild assumptions, including stabilizability, detectability,
and a polynomially growing graph condition, the performance difference between
$\kappa$-distributed control and centralized optimal control becomes
exponentially small in $\kappa$. This result reveals that distributed control
can achieve near-optimal performance with a moderate degree of
decentralization, and thus it is an effective controller architecture for
large-scale networked systems.


title:
The Effects of Dynamic Learning and the Forgetting Process on an
  Optimizing Modelling for Full-Service Repair Pricing Contracts for Medical
  Devices

abstract:
  In order to improve the profitability and customer service management of
original equipment manufacturers (OEMs) in a market where full-service (FS) and
on-call service (OS) co-exist, this article extends the optimizing modelling
for pricing FS repair contracts with the effects of dynamic learning and
forgetting. Along with considering autonomous learning in maintenance practice,
this study also analyses how induced learning and forgetting process in a
workplace put impact on the pricing optimizing model of FS contracts in the
portfolio of FS and OS. A numerical analysis based on real data from a medical
industry proves that the enhanced FS pricing model discussed here has two main
advantages: (1) It could prominently improve repair efficiency, and (2) It help
OEMs gain better profits compared to the original FS model and the sole OS
maintenance. Sensitivity analysis shows that if internal failure rate
increases, the optimized FS price rises gradually until reaching the maximum
value, and profitability to the OEM increases overall; if frequency of induced
learning goes up, the optimal FS price rises after a short-term downward trend,
with a stable profitability to the OEM.


title: The average dust attenuation curve at z~1.3 based on HST grism surveys

abstract:
  We present the first characterisation of the average dust attenuation curve
at $z\sim1.3$ by combining rest-frame ultraviolet through near-IR photometry
with Balmer decrement ($\mathrm{H}\alpha$/$\mathrm{H}\beta$) constraints for
$\sim$900 galaxies with $8\lesssim\log (M_\star /M_\odot)<10.2$ at $0.75<z<1.5$
in the HST WFC3 IR Spectroscopic Parallel (WISP) and 3D-HST grism surveys.
Using galaxies in SDSS, we establish that the ($\mathrm{H}\alpha$+[NII])/[OIII]
line ratio and stellar mass are good proxies for the Balmer decrement in
low-spectral resolution grism data when only upper-limits on $\mathrm{H}\beta$
are available and/or $\mathrm{H}\alpha$ is blended with [NII]. The slope of the
$z\sim1.3$ attenuation curve ($A(0.15\mu m)/A(V)=3.15$) and its normalization
($R_V=3.26$) lie in-between the values found for $z=0$ and $z\sim2$ dust
attenuation curves derived with similar methods. These provide supporting
evidence that the average dust attenuation curve of star forming galaxies
evolves continuously with redshift. The $z\sim1.3$ curve has a mild 2175\r{A}
feature (bump amplitude, $E_b=0.83$; $\sim$25% that of the MW extinction
curve), which is comparable to several other studies at $0<z\lesssim3$, and
suggests that the average strength of this feature may not evolve significantly
with redshift. The methods we develop to constrain dust attenuation from HST
grism data can be applied to future grism surveys with JWST, Euclid, and RST.
These new facilities will detect millions of emission line galaxies and offer
the opportunity to significantly improve our understanding of how and why dust
attenuation curves evolve.


title:
Towards Optimal Kron-based Reduction Of Networks (Opti-KRON) for the
  Electric Power Grid

abstract:
  For fast timescales or long prediction horizons, the AC optimal power flow
(OPF) problem becomes a computational challenge for large-scale, realistic AC
networks. To overcome this challenge, this paper presents a novel network
reduction methodology that leverages an efficient mixed-integer linear
programming (MILP) formulation of a Kron-based reduction that is optimal in the
sense that it balances the degree of the reduction with resulting modeling
errors in the reduced network. The method takes as inputs the full AC network
and a pre-computed library of AC load flow data and uses the graph Laplacian to
constraint nodal reductions to only be feasible for neighbors of non-reduced
nodes. This results in a highly effective MILP formulation which is embedded
within an iterative scheme to successively improve the Kron-based network
reduction until convergence. The resulting optimal network reduction is, thus,
grounded in the physics of the full network. The accuracy of the network
reduction methodology is then explored for a 100+ node medium-voltage
distribution feeder example across a wide range of operating conditions. It is
finally shown that a network reduction of 25-85% can be achieved within seconds
and with worst-case voltage magnitude deviation errors within any super node
cluster of less than 0.01pu. These results illustrate that the proposed
optimization-based approach to Kron reduction of networks is viable for larger
networks and suitable for use within various power system applications.


title:
Solving Price Per Unit Problem Around the World: Formulating Fact
  Extraction as Question Answering

abstract:
  Price Per Unit (PPU) is an essential information for consumers shopping on
e-commerce websites when comparing products. Finding total quantity in a
product is required for computing PPU, which is not always provided by the
sellers. To predict total quantity, all relevant quantities given in a product
attributes such as title, description and image need to be inferred correctly.
We formulate this problem as a question-answering (QA) task rather than named
entity recognition (NER) task for fact extraction. In our QA approach, we first
predict the unit of measure (UoM) type (e.g., volume, weight or count), that
formulates the desired question (e.g., "What is the total volume?") and then
use this question to find all the relevant answers. Our model architecture
consists of two subnetworks for the two subtasks: a classifier to predict UoM
type (or the question) and an extractor to extract the relevant quantities. We
use a deep character-level CNN architecture for both subtasks, which enables
(1) easy expansion to new stores with similar alphabets, (2) multi-span
answering due to its span-image architecture and (3) easy deployment by keeping
model-inference latency low. Our QA approach outperforms rule-based methods by
34.4% in precision and also BERT-based fact extraction approach in all stores
globally, with largest precision lift of 10.6% in the US store.


title:
The Role of Phase Stabilization and Surface Orientation in
  4,4'-Biphenyl-Dicarboxylic Acid Self-Assembly and Transformation on Silver
  Substrates

abstract:
  Molecular functionalization of nanoparticles and metallic substrates can be
used to tune their properties for specific applications. However,
polycrystalline substrates and nanoparticles exhibit surface planes with
distinct crystallographic orientations. Therefore, the development of reliable
strategies for molecular functionalization requires knowledge of the role of
the surface plane orientation in the growth kinetics, structure, and properties
of the molecular layer. Here, we apply a multiscale analysis to investigate the
self-assembly of 4,4'-biphenyl-dicarboxylic acid (BDA) on Ag(111) and
critically discuss the difference to Ag(100). Whereas the structural motifs for
intact and fully deprotonated BDA are similar on both surfaces, the
intermediate phases comprising partially deprotonated BDA differ in the
structure and chemical composition. A real-time view of the phase
transformations enables us to present a generalized picture of the phase
transformations between the self-assembled molecular phases on the surfaces and
underline important features such as the phase stabilization of the chemical
composition and the mechanism of the related burst transformation. The
influence of the substrate orientation on the structure of molecular layers and
phase transformations provides the necessary background for developing
functionalization strategies of the substrates displaying multiple surface
planes and kinetic models for the growth near thermodynamic equilibrium.


title:
Janus van der Waals equations for real molecules with two-sided phase
  transitions

abstract:
  We obtain families of generalised van der Waals equations characterised by an
even number $n=2,4,6$ and a continuous free parameter which is tunable for a
critical compressibility factor. Each equation features two adjacent critical
points which have a common critical temperature $T_{c}$ and arbitrarily close
two critical densities. The critical phase transitions are naturally two-sided:
the critical exponents are
$\alpha_{\scriptscriptstyle{P}}=\gamma_{\scriptscriptstyle{P}}=\frac{2}{3}$,
$\beta_{\scriptscriptstyle{P}}=\delta^{-1}=\frac{1}{3}$ for $T>T_{c}$ and
$\alpha_{\scriptscriptstyle{P}}=\gamma_{\scriptscriptstyle{P}}=\frac{n}{n+1}$,
$\beta_{\scriptscriptstyle{P}}=\delta^{-1}=\frac{1}{n+1}$ for $T<T_{c}$. In
contrast with the original van der Waals equation, our novel equations all
reduce consistently to the classical ideal gas law in low density limit. We
test our formulas against NIST data for eleven major molecules and show
agreements better than the original van der Waals equation, not only near to
the critical points but also in low density regions.


title:
Global Surfaces of Section and Periodic Orbits in The Spatial Isosceles
  Three Body Problem

abstract:
  We study the spatial isosceles three body problem, which is a system with two
degrees of freedom after modulo the rotation symmetry. For certain choices of
energy and angular momentum, we find some disk-like global surfaces of section
with the Euler orbit as their common boundary, and a brake orbit passing
through them. By considering the Poincar\'e maps of these global surfaces of
section, we prove the existence of all kinds of different periodic orbits under
certain assumption. Moreover, we are able to prove, for generic choices of
masses, the system always has infinitely many periodic orbits.
  One of the key is to estimate the rotation numbers of the Euler orbit and the
brake orbit with respect to the Poincar\'e map. For this, we establish formulas
connected these numbers with the mean indices of the corresponding orbits using
the Maslov-type index.


title: Injectivity in second-gradient Nonlinear Elasticity

abstract:
  We study injectivity for models of Nonlinear Elasticity that involve the
second gradient. We assume that $\Omega\subset\mathbb{R}^n$ is a domain, $f\in
W^{2,q}(\Omega,\mathbb{R}^n)$ satisfies $|J_f|^{-a}\in L^1$ and that $f$ equals
a given homeomorphism on $\partial \Omega$. Under suitable conditions on $q$
and $a$ we show that $f$ must be a homeomorphism. As a main new tool we find an
optimal condition for $a$ and $q$ that imply that
$\mathcal{H}^{n-1}(\{J_f=0\})=0$ and hence $J_f$ cannot change sign. We further
specify in dependence of $q$ and $a$ the maximal Hausdorff dimension $d$ of the
critical set $\{J_f=0\}$. The sharpness of our conditions for $d$ is
demonstrated by constructing respective counterexamples.