title: Learning latent causal relationships in multiple time series

abstract:
  Identifying the causal structure of systems with multiple dynamic elements is
critical to several scientific disciplines. The conventional approach is to
conduct statistical tests of causality, for example with Granger Causality,
between observed signals that are selected a priori. Here it is posited that,
in many systems, the causal relations are embedded in a latent space that is
expressed in the observed data as a linear mixture. A technique for blindly
identifying the latent sources is presented: the observations are projected
into pairs of components -- driving and driven -- to maximize the strength of
causality between the pairs. This leads to an optimization problem with closed
form expressions for the objective function and gradient that can be solved
with off-the-shelf techniques. After demonstrating proof-of-concept on
synthetic data with known latent structure, the technique is applied to
recordings from the human brain and historical cryptocurrency prices. In both
cases, the approach recovers multiple strong causal relationships that are not
evident in the observed data. The proposed technique is unsupervised and can be
readily applied to any multiple time series to shed light on the causal
relationships underlying the data.


title: The Gerber-Shiu discounted penalty function: From practical perspectives

abstract:
  The Gerber-Shiu function provides a unified framework for the evaluation of a
variety of risk quantities. Ever since its establishment, it has attracted
constantly increasing interests in actuarial science, whereas the conventional
research has been focused on finding analytical or semi-analytical solutions,
either of which is rarely available, except for limited classes of penalty
functions on rather simple risk models. In contrast to its great generality,
the Gerber-Shiu function does not seem sufficiently prevalent in practice,
largely due to a variety of difficulties in numerical approximation and
statistical inference. To enhance research activities on such implementation
aspects, we provide a full review of existing formulations and underlying
surplus processes, as well as an extensive survey of analytical,
semi-analytical and asymptotic methods for the Gerber-Shiu function, which
altogether shed fresh light on its numerical methods and statistical inference
for further developments. On the basis of an exhaustive collection of 207
references, the present survey can serve as an insightful guidebook to model
and method selection from practical perspectives as well.


title: Online Continual Learning for Embedded Devices

abstract:
  Real-time on-device continual learning is needed for new applications such as
home robots, user personalization on smartphones, and augmented/virtual reality
headsets. However, this setting poses unique challenges: embedded devices have
limited memory and compute capacity and conventional machine learning models
suffer from catastrophic forgetting when updated on non-stationary data
streams. While several online continual learning models have been developed,
their effectiveness for embedded applications has not been rigorously studied.
In this paper, we first identify criteria that online continual learners must
meet to effectively perform real-time, on-device learning. We then study the
efficacy of several online continual learning methods when used with mobile
neural networks. We measure their performance, memory usage, compute
requirements, and ability to generalize to out-of-domain inputs.


title:
Mean-Field Control Approach to Partially Observable Stochastic Control
  for a Non-linear and Memory-limited System

abstract:
  Partially observable stochastic control (POSC) is a problem to find the
optimal control for a partially observable stochastic system. However, POSC is
not solvable even numerically in a non-linear and memory-limited system because
its compression of the observation history is infinite-dimensional. In this
paper, we propose Memory-limited POSC (ML-POSC) by employing a mathematical
technique of Mean-Field Control. ML-POSC is solvable even in a non-linear and
memory-limited system because its compression of the observation history is
finite-dimensional. Moreover, we demonstrate ML-POSC for LQG problem. In
ML-POSC, Riccati equation is modified, which improves the performance. Our
numerical experiments show that ML-POSC is superior to the previous approaches.


title: Indirect Inference for Nonlinear Panel Models with Fixed Effects

abstract:
  Fixed effect estimators of nonlinear panel data models suffer from the
incidental parameter problem. This leads to two undesirable consequences in
applied research: (1) point estimates are subject to large biases, and (2)
confidence intervals have incorrect coverages. This paper proposes a
simulation-based method for bias reduction. The method simulates data using the
model with estimated individual effects, and finds values of parameters by
equating fixed effect estimates obtained from observed and simulated data. The
asymptotic framework provides consistency, bias correction, and asymptotic
normality results. An application and simulations to female labor force
participation illustrates the finite-sample performance of the method.


title: On $ \mu$-Zariski pairs of links

abstract:
  The notion of Zariski pairs for projective curves in $\mathbb P^2$ is known
since the pioneer paper of Zariski \cite{Zariski} and for further development,
we refer the reference in \cite{Bartolo}.In this paper, we introduce a notion
of Zariski pair of links in the class of isolated hypersurface singularities.
Such a pair is canonically produced from a Zariski (or a weak Zariski ) pair of
curves $C=\{f(x,y,z)=0\}$ and $C'=\{g(x,y,z)=0\}$ of degree $d$ by simply
adding a monomial $z^{d+m}$ to $f$ and $g$ so that the corresponding affine
hypersurfaces have isolated singularities at the origin. They have a same zeta
function and a same Milnor number (\cite{Almost}). We give new examples of
Zariski pairs which have the same $\mu^*$ sequence and a same zeta function but
two functions belong to different connected components of $\mu$-constant strata
(Theorem \ref{mu-zariski}). Two link 3-folds are not diffeomorphic and they are
distinguished by the first homology which implies the Jordan form of their
monodromies are different (Theorem \ref{main2}). We start from weak Zariski
pairs of projective curves to construct new Zariski pairs of surfaces which
have non-diffeomorphic link 3-folds. We also prove that hypersurface pair
constructed from a Zariski pair give a diffeomorphic links (Theorem
\ref{main3}).


title:
Tactile Pose Estimation and Policy Learning for Unknown Object
  Manipulation

abstract:
  Object pose estimation methods allow finding locations of objects in
unstructured environments. This is a highly desired skill for autonomous robot
manipulation as robots need to estimate the precise poses of the objects in
order to manipulate them. In this paper, we investigate the problems of tactile
pose estimation and manipulation for category-level objects. Our proposed
method uses a Bayes filter with a learned tactile observation model and a
deterministic motion model. Later, we train policies using deep reinforcement
learning where the agents use the belief estimation from the Bayes filter. Our
models are trained in simulation and transferred to the real world. We analyze
the reliability and the performance of our framework through a series of
simulated and real-world experiments and compare our method to the baseline
work. Our results show that the learned tactile observation model can localize
the pose of novel objects at 2-mm and 1-degree resolution for position and
orientation, respectively. Furthermore, we experiment on a bottle opening task
where the gripper needs to reach the desired grasp state.


title:
Gate-tunable exchange bias effect in FePS3-Fe5GeTe2 van der Waals
  heterostructures

abstract:
  Electrical gate-manipulated exchange bias (EB) effect is a long-term goal for
spintronics applications. Meanwhile, the emergence of van der Waals (vdW)
magnetic heterostructures provides ideal platforms for the study of interlayer
magnetic coupling. However, to date, the electrical gate-controlled EB effect
has yet to be realized in vdW heterostructures. Here, for the first time, we
realized electrically-controllable EB effects in a vdW antiferromagnetic
(AFM)-ferromagnetic (FM) heterostructure, FePS3-Fe5GeTe2. For pristine
FePS3-Fe5GeTe2 heterostructures, sizable EB effects can be generated due to the
strong interface coupling, which also depend on the thickness of the
ferromagnetic layers. By applying a solid protonic gate, the EB effects can be
electrically tuned largely by proton intercalations and deintercalations. The
EB field reaches up to 23% of the coercive field and the blocking temperature
exceeds 50 K at Vg= -3.15 V. The proton intercalations not only tune the
average magnetic exchange coupling, but also change the AFM configurations and
transform the heterointerface between an uncompensated AFM-FM interface and a
compensated AFM-FM interface. These alterations result in a dramatic modulation
of the total interface exchange coupling and the resultant EB effects. The
study is a significant step towards vdW heterostructure-based magnetic logic
for future low-energy electronics.


title: A Constructive Brownian Limit Theorem

abstract:
  In this paper, we present and prove a boundary limit theorem for Brownian
motions for the Hardy space $\mathbf{h}^{p}$ of harmonic functions on the unit
ball in $R^m$, where $p\geq1$ and $m\geq2$ are arbitrary. Our proof is
constructive in the sense of [Bishop and Bridges 1985, Chan 2021, Chan 2022].
Roughly speaking, a mathematical proof is constructive if it can be compiled
into some computer code with the guarantee of exit in a finite number of steps
on execution. A constructive proof of said boundary limit theorem is contained
in [Durret 1984] for the case of $p>1$. In this article, we give a constructive
proof for $p=1$, which then implies, via the Lyapunov's inequality, a
constructive proof for the general case $p\geq1$. We conjecture that the result
can be used to give a constructive proof of the nontangential limit theorem for
Hardy spaces $\mathbf{h}^{p}$ with $p\geq1$.
  We note that, ca 1970, R. Getoor gave a talk on the Brownian limit theorem at
the University of Washington. We believe that the proof he presented is
constructive only for the case $p>1$ and not for the case $p=1$. We are however
unable to find a reference for his proof.


title:
The numerical search for the internal dynamics of NHIMs and their
  pictorial representation

abstract:
  The topic of this article is the numerical search of codimension 2 Normally
Hyperbolic Invariant Manifolds (NHIM) in Hamiltonian systems with 3-degrees of
freedom and their internal dynamics. We point out relations between different
strategies to find such surfaces numerically. We can start from index-1 saddles
of the effective potential or from a partially integrable case and follow the
NHIM along some curve in the parameter space of the system. Or, we can look for
the stable and unstable manifolds of such surfaces by an appropriate indicator
method. We show numerical examples for an electron moving in a perturbed
magnetic dipole field.


title: Low noise phase-locked laser system for atom interferometry

abstract:
  A low noise laser system for atom interferometry is realized with
phase-locked fiber lasers, where the performance of the OPLL is greatly
enhanced by the FEOM feedback loop and the narrow linewidths. The laser system
demonstrated contribute 2.2 mrad per shot to the interferometer noise and
permit continuous long-term operation for more than 115 hours without relocking
in the field test. Also, the mobile gravimeter equipped with this phase-locked
laser system reaches a sensitivity as good as 29 uGal/\sqrt{Hz} and a
resolution of 1.1 uGal within 1500 s, demonstrating performances comparable to
the state of the art.


title:
CNN Attention Guidance for Improved Orthopedics Radiographic Fracture
  Classification

abstract:
  Convolutional neural networks (CNNs) have gained significant popularity in
orthopedic imaging in recent years due to their ability to solve fracture
classification problems. A common criticism of CNNs is their opaque learning
and reasoning process, making it difficult to trust machine diagnosis and the
subsequent adoption of such algorithms in clinical setting. This is especially
true when the CNN is trained with limited amount of medical data, which is a
common issue as curating sufficiently large amount of annotated medical imaging
data is a long and costly process. While interest has been devoted to
explaining CNN learnt knowledge by visualizing network attention, the
utilization of the visualized attention to improve network learning has been
rarely investigated. This paper explores the effectiveness of regularizing CNN
network with human-provided attention guidance on where in the image the
network should look for answering clues. On two orthopedics radiographic
fracture classification datasets, through extensive experiments we demonstrate
that explicit human-guided attention indeed can direct correct network
attention and consequently significantly improve classification performance.
The development code for the proposed attention guidance is publicly available
on GitHub.


title: A note on weighted Calder\'on-Hardy spaces

abstract:
  Let $0 < p \leq 1 < q < \infty$ and $\gamma >0$. In this note we discuss the
weighted Calder\'on-Hardy spaces on $\mathbb{R}^{n}$, $\mathcal{H}^{p}_{q,
\gamma}(\mathbb{R}^{n}, w)$. For $\gamma = 2m$, $m \in \mathbb{N}$, and $n (2m
+ n/q)^{-1} < p \leq 1$, we show that for certain weights $w$ the iterated
Laplace operator $\Delta^{m}$ is a surjective mapping from $\mathcal{H}^{p}_{q,
2m}(\mathbb{R}^{n},w)$ onto $H^{p}(\mathbb{R}^{n}, w)$.


title: Better Language Model with Hypernym Class Prediction

abstract:
  Class-based language models (LMs) have been long devised to address context
sparsity in $n$-gram LMs. In this study, we revisit this approach in the
context of neural LMs. We hypothesize that class-based prediction leads to an
implicit context aggregation for similar words and thus can improve
generalization for rare words. We map words that have a common WordNet hypernym
to the same class and train large neural LMs by gradually annealing from
predicting the class to token prediction during training. Empirically, this
curriculum learning strategy consistently improves perplexity over various
large, highly-performant state-of-the-art Transformer-based models on two
datasets, WikiText-103 and Arxiv. Our analysis shows that the performance
improvement is achieved without sacrificing performance on rare words. Finally,
we document other attempts that failed to yield empirical gains, and discuss
future directions for the adoption of class-based LMs on a larger scale.


title:
Leveraging Expert Guided Adversarial Augmentation For Improving
  Generalization in Named Entity Recognition

abstract:
  Named Entity Recognition (NER) systems often demonstrate great performance on
in-distribution data, but perform poorly on examples drawn from a shifted
distribution. One way to evaluate the generalization ability of NER models is
to use adversarial examples, on which the specific variations associated with
named entities are rarely considered. To this end, we propose leveraging
expert-guided heuristics to change the entity tokens and their surrounding
contexts thereby altering their entity types as adversarial attacks. Using
expert-guided heuristics, we augmented the CoNLL 2003 test set and manually
annotated it to construct a high-quality challenging set. We found that
state-of-the-art NER systems trained on CoNLL 2003 training data drop
performance dramatically on our challenging set. By training on adversarial
augmented training examples and using mixup for regularization, we were able to
significantly improve the performance on the challenging set as well as improve
out-of-domain generalization which we evaluated by using OntoNotes data. We
have publicly released our dataset and code at
https://github.com/GT-SALT/Guided-Adversarial-Augmentation.


title: Fourier Disentangled Space-Time Attention for Aerial Video Recognition

abstract:
  We present an algorithm, Fourier Activity Recognition (FAR), for UAV video
activity recognition. Our formulation uses a novel Fourier object
disentanglement method to innately separate out the human agent (which is
typically small) from the background. Our disentanglement technique operates in
the frequency domain to characterize the extent of temporal change of spatial
pixels, and exploits convolution-multiplication properties of Fourier transform
to map this representation to the corresponding object-background entangled
features obtained from the network. To encapsulate contextual information and
long-range space-time dependencies, we present a novel Fourier Attention
algorithm, which emulates the benefits of self-attention by modeling the
weighted outer product in the frequency domain. Our Fourier attention
formulation uses much fewer computations than self-attention. We have evaluated
our approach on multiple UAV datasets including UAV Human RGB, UAV Human Night,
Drone Action, and NEC Drone. We demonstrate a relative improvement of 8.02% -
38.69% in top-1 accuracy and up to 3 times faster over prior works.


title: Mean hitting time formula for positive maps

abstract:
  In the classical theory of Markov chains, one may study the mean time to
reach some chosen state, and it is well-known that in the irreducible, finite
case, such quantity can be calculated in terms of the fundamental matrix of the
walk, as stated by the mean hitting time formula. In this work, we present an
analogous construction for the setting of irreducible, positive, trace
preserving maps. The reasoning on positive maps generalizes recent results
given for quantum Markov chains, a class of completely positive maps acting on
graphs, presented by S. Gudder. The tools employed in this work are based on a
proper choice of block matrices of operators, inspired in part by recent work
on Schur functions for closed operators on Banach spaces, due to F.A.Gr\"unbaum
and one of the authors. The problem at hand is motivated by questions on
quantum information theory, most particularly the study of quantum walks, and
provides a basic context on which statistical aspects of dissipative and closed
quantum evolutions on finite graphs can be expressed in terms of the
fundamental matrix, which turns out to be an useful generalized inverse
associated with the dynamics.


title: A portable atom gravimeter operating in noisy urban environments

abstract:
  The gravimeter based on atom interferometry has potentially wide applications
on building the gravity networks, geophysics as well as gravity assisted
navigation. Here, we demonstrate experimentally a portable atom gravimeter
operating in the noisy urban environment. Despite the influence of noisy
external vibrations, our portable atom gravimeter reaches a sensitivity as good
as 65 uGal/\sqrt{Hz} and a resolution of 1.1 uGal after 4000 s integration
time, being comparable to state-of-the-art atom gravimeters. Our achievement
paves the way for bring the portable atom gravimeter to field applications,
such as gravity survey on a moving platform.


title: Replicas, averaging and factorization in the IIB matrix model

abstract:
  We study the partition functions of multiple replicas (copies) of D-brane
configurations in the type IIB (IKKT) matrix model. We consider the quenched
regime, where small fluctuations of the matrices are superimposed onto the slow
(quenched) dynamics of the background, so the partition function is an ensemble
average over the background. Interacting D-branes always factorize in a simple
way. On the other hand, the non-interacting BPS configurations may or may not
factorize depending on the number of replicas, and their factorization
mechanism is more involved as the corresponding saddle-point solutions
(half-wormholes) break the replica symmetry. We argue that the simple
factorization mechanism of interacting branes is actually more interesting as
it carries the specific signatures of quantum gravity, which are absent from
disordered field theories like the SYK model.


title: A Policy Driven AI-Assisted PoW Framework

abstract:
  Proof of Work (PoW) based cyberdefense systems require incoming network
requests to expend effort solving an arbitrary mathematical puzzle. Current
state of the art is unable to differentiate between trustworthy and
untrustworthy connections, requiring all to solve complex puzzles. In this
paper, we introduce an Artificial Intelligence (AI)-assisted PoW framework that
utilizes IP traffic based features to inform an adaptive issuer which can then
generate puzzles with varying hardness. The modular framework uses these
capabilities to ensure that untrustworthy clients solve harder puzzles thereby
incurring longer latency than authentic requests to receive a response from the
server. Our preliminary findings reveal our approach effectively throttles
untrustworthy traffic.


title:
HP-Capsule: Unsupervised Face Part Discovery by Hierarchical Parsing
  Capsule Network

abstract:
  Capsule networks are designed to present the objects by a set of parts and
their relationships, which provide an insight into the procedure of visual
perception. Although recent works have shown the success of capsule networks on
simple objects like digits, the human faces with homologous structures, which
are suitable for capsules to describe, have not been explored. In this paper,
we propose a Hierarchical Parsing Capsule Network (HP-Capsule) for unsupervised
face subpart-part discovery. When browsing large-scale face images without
labels, the network first encodes the frequently observed patterns with a set
of explainable subpart capsules. Then, the subpart capsules are assembled into
part-level capsules through a Transformer-based Parsing Module (TPM) to learn
the compositional relations between them. During training, as the face
hierarchy is progressively built and refined, the part capsules adaptively
encode the face parts with semantic consistency. HP-Capsule extends the
application of capsule networks from digits to human faces and takes a step
forward to show how the neural networks understand homologous objects without
human intervention. Besides, HP-Capsule gives unsupervised face segmentation
results by the covered regions of part capsules, enabling qualitative and
quantitative evaluation. Experiments on BP4D and Multi-PIE datasets show the
effectiveness of our method.


title:
Temporal decay of strong solutions for generalized Newtonian fluids with
  variable power-law index

abstract:
  We consider the motion of a power-law-like generalized Newtonian fluid in
R^3, where the power-law index is a variable function. This system of nonlinear
partial differential equations arises in mathematical models of
electrorheological fluids. The aim of this paper is to investigate the decay
properties of strong solutions for the model, based on the Fourier splitting
method. We first prove that the L^2-norm of the solution has the decay rate (1
+ t)^{-3/4}. If the H^1-norm of the initial data is sufficiently small, we
further show that the derivative of the solution decays in L^2-norm at the rate
(1 + t)^{-5/4}.


title: Choosing good subsamples for regression modelling

abstract:
  A common problem in health research is that we have a large database with
many variables measured on a large number of individuals. We are interested in
measuring additional variables on a subsample; these measurements may be newly
available, or expensive, or simply not considered when the data were first
collected. The intended use for the new measurements is to fit a regression
model generalisable to the whole cohort (and to its source population). This is
a two-phase sampling problem; it differs from some other two-phase sampling
problems in the richness of the phase I data and in the goal of regression
modelling. In particular, an important special case is measurement-error
models, where a variable strongly correlated with the phase II measurements is
available at phase I. We will explain how influence functions have been useful
as a unifying concept for extending classical results to this setting, and
describe the steps from designing for a simple weighted estimator at known
parameter values through adaptive multiwave designs and the use of prior
information. We will conclude with some comments on the information gap between
design-based and model-based estimators in this setting.


title: Unveiling the higher-order organization of multivariate time series

abstract:
  Time series analysis has proven to be a powerful method to characterize
several phenomena in biological, neural and socio-economic systems, and to
understand their underlying dynamical features. Despite a plethora of methods
having been proposed for the analysis of multivariate time series, most of them
do not investigate whether signals result from independent, pairwise, or group
interactions. Here, we propose a novel framework to characterize the temporal
evolution of higher-order dependencies within multivariate time series. Using
network analysis and topology, we show that, unlike traditional tools, our
framework robustly differentiates various spatiotemporal regimes of coupled
chaotic maps, including chaotic dynamical phases and various types of
synchronization. By analysing fMRI signals, we find that, during rest, the
human brain mainly oscillates between chaotic and few partially intermittent
states, with higher-order structures reflecting sensorimotor areas. Similarly,
in financial and epidemic time series, instead, higher-order information
efficiently discriminates between radically different coordination and
spreading regimes. Overall, our approach sheds new light on the higher-order
organization of multivariate time series, allowing for a better
characterization of dynamical group dependencies inherent to real-world
systems.


title:
Risk Analysis of Unmanned Aerial System Operations in Urban Airspace
  Considering Spatiotemporal Population Dynamics

abstract:
  This study aims to estimate the fatality risk of Unmanned Aerial System (UAS)
operations from a population perspective using high-resolution de facto
population data. In doing so, it provides more practical risk values compared
to the risk values derived from the residential population data. We then set
restricted airspace using the risk values and the acceptable level of safety.
We regard the restricted airspace as airspace being blocked by a dynamic
obstacle. Scenario analysis on the study area in Seoul, South Korea presents a
richer set of results for time-dependent differences in restricted airspace.
Especially during the daytime, the restricted airspace is clustered around
commercial and business areas. The difference between restricting airspace
based on residential population-derived risk and restricting airspace based on
de facto population-derived risk is also observed. The findings confirm the
importance of accurately taking into account population density when assessing
and mitigating the risk of UAS operations. Sensitivity analysis also reveals
the importance of an accurate estimate of population density when estimating
the risk.


title:
The Interface Usage Skills Test: An Open Source Tool for Real-Time
  Quantitative Evaluation for Clinicians and Researchers

abstract:
  Assistive machines endow people with limited mobility the opportunity to live
more independently. However, operating these machines poses risks to the safety
of the human operator as well as the surrounding environment. Thus, proper user
training is an essential step towards independent control and use of
functionally assistive machines. A variety of control interfaces can be
employed by the human to issue control signals to the device, dependent on the
residual mobility and level of injury of the human operator. Proficiency in
operating the interface of choice precedes the skill in operating the assistive
machine. In this systems paper, we present an open source tool for quantifying
user skill in operating various interfaces devices.


title: Compression of Generative Pre-trained Language Models via Quantization

abstract:
  The increasing size of generative Pre-trained Language Models (PLMs) has
greatly increased the demand for model compression. Despite various methods to
compress BERT or its variants, there are few attempts to compress generative
PLMs, and the underlying difficulty remains unclear. In this paper, we compress
generative PLMs by quantization. We find that previous quantization methods
fail on generative tasks due to the \textit{homogeneous word embeddings} caused
by reduced capacity, and \textit{varied distribution of weights}.
Correspondingly, we propose a token-level contrastive distillation to learn
distinguishable word embeddings, and a module-wise dynamic scaling to make
quantizers adaptive to different modules. Empirical results on various tasks
show that our proposed method outperforms the state-of-the-art compression
methods on generative PLMs by a clear margin. With comparable performance with
the full-precision models, we achieve 14.4x and 13.4x compression rates on
GPT-2 and BART, respectively.


title: Predicting Cricket Outcomes using Bayesian Priors

abstract:
  This research has developed a statistical modeling procedure to predict
outcomes of future cricket tournaments. Proposed model provides an insight into
the application of stratified survey sampling to the team selection pattern by
incorporating individual players' performance history coupled with Bayesian
priors not only against a particular opposition but also against any cricket
playing nation - full member of International Cricket Council (ICC). A case
study for the next ICC cricket world cup 2023 in India is provided, predictions
are obtained for all participating teams against one another, and simulation
results are discussed. The proposed statistical model is tested on 2020 Indian
Premier League (IPL) season. The model predicted the top three finishers of IPL
2020 correctly, including the winners of the tournament, Mumbai Indians, and
other positions with reasonable accuracy. The method can predict probabilities
of winning for each participating team. This method can be extended to other
cricket tournaments as well.


title: Monocular Vision-based Prediction of Cut-in Maneuvers with LSTM Networks

abstract:
  Advanced driver assistance and automated driving systems should be capable of
predicting and avoiding dangerous situations. This study proposes a method to
predict potentially dangerous cut-in maneuvers happening in the ego lane. We
follow a computer vision-based approach that only employs a single in-vehicle
RGB camera, and we classify the target vehicle's maneuver based on the recent
video frames. Our algorithm consists of a CNN-based vehicle detection and
tracking step and an LSTM-based maneuver classification step. It is more
computationally efficient than other vision-based methods since it exploits a
small number of features for the classification step rather than feeding CNNs
with RGB frames. We evaluated our approach on a publicly available driving
dataset and a lane change detection dataset. We obtained 0.9585 accuracy with
side-aware two-class (cut-in vs. lane-pass) classification models. Experiment
results also reveal that our approach outperforms state-of-the-art approaches
when used for lane change detection.


title:
A Comprehensive Review on Blockchains for Internet of Vehicles:
  Challenges and Directions

abstract:
  Internet of Vehicles (IoVs) consist of smart vehicles, Autonomous Vehicles
(AVs) as well as roadside units (RSUs) that communicate wirelessly to provide
enhanced transportation services such as improved traffic efficiency and
reduced traffic congestion and accidents. IoVs, however, suffer from issues of
security, privacy and trust. Blockchain technology has been emerged as a
decentralized approach for enhanced security without depending on trusted third
parties to run services. Blockchain offers the benefits of trustworthiness,
immutability, and mitigates the problem of single point of failure and other
attacks. In this work, we present the state-of-the-art of Blockchain-enabled
IoVs (BIoV) with a particular focus on their applications such as
crowdsourcing-based applications, energy trading, traffic congestion reduction,
collision and accident avoidance and infotainment and content cashing. We also
present in-depth applications federated learning (FL) applications for BIoVs.
The key challenges resulted from the integration of Blockchain with IoV is
investigated in several domains such as edge computing, ML, and FL. Lastly, a
number of open issues and challenges as well as future opportunities in the
area of AI-enabled BIoV, hardware-assisted security for BIoV and quantum
computing attacks on BIoV.


title:
Hesitant Adaptive Search with Estimation and Quantile Adaptive Search
  for Global Optimization with Noise

abstract:
  Adaptive random search approaches have been shown to be effective for global
optimization problems, where under certain conditions, the expected performance
time increases only linearly with dimension. However, previous analyses assume
that the objective function can be observed directly. We consider the case
where the objective function must be estimated, often using a noisy function,
as in simulation. We present a finite-time analysis of algorithm performance
that combines estimation with a sampling distribution. We present a framework
called Hesitant Adaptive Search with Estimation, and derive an upper bound on
function evaluations that is cubic in dimension, under certain conditions. We
extend the framework to Quantile Adaptive Search with Estimation, which focuses
sampling points from a series of nested quantile level sets. The analyses
suggest that computational effort is better expended on sampling improving
points than refining estimates of objective function values during the progress
of an adaptive search algorithm.


title: FAIR standards for astronomical data

abstract:
  We present an overview of the "FAIR Guiding Principles for scientific data
management and stewardship", first published in 2016, and how they relate to
astronomical data management. In particular, we discuss the connection between
the FAIR principles and IVOA standards, and how data management systems with
these standards implemented are close to compliance. We then look at what extra
components are required to make astronomical data FAIR. Finally, we give a case
study of the All-Sky Virtual Observatory (Australia's node of the VO) and their
implementation of the FAIR principles.


title: Limits, Colimits, and Spectra of Modelled Spaces

abstract:
  It is well-known that the construction of Zariski spectra of (commutative)
rings yields a dual adjunction between the category of rings and the category
of locally ringed spaces. There are many constructions of spectra of algebras
in various contexts giving such adjunctions. Michel Coste unified them in the
language of categorical logic by showing that, for an appropriate triple $
(T_0,T,\Lambda) $ (which we call a spatial Coste context), each $ T_0 $-model
can be associated with a $ T $-modelled space and that this yields a dual
adjunction between the category of $ T_0 $-models and the category of $ T
$-modelled spaces and "admissible" morphisms. However, most of his proofs
remain unpublished. In this paper, we introduce an alternative construction of
spectra of $ T_0 $-models and give another proof of Coste adjunction. Moreover,
we also extend spectra of $ T_0 $-models to relative spectra of $ T_0
$-modelled spaces and prove the existence of limits and colimits in the
involved categories of modelled spaces. We can deduce, for instance, that the
category of ringed spaces whose stalks are fields is complete and cocomplete.


title: What Makes RAFT Better Than PWC-Net?

abstract:
  How important are training details and datasets to recent optical flow models
like RAFT? And do they generalize? To explore these questions, rather than
develop a new model, we revisit three prominent models, PWC-Net, IRR-PWC and
RAFT, with a common set of modern training techniques and datasets, and observe
significant performance gains, demonstrating the importance and generality of
these training details. Our newly trained PWC-Net and IRR-PWC models show
surprisingly large improvements, up to 30% versus original published results on
Sintel and KITTI 2015 benchmarks. They outperform the more recent Flow1D on
KITTI 2015 while being 3x faster during inference. Our newly trained RAFT
achieves an Fl-all score of 4.31% on KITTI 2015, more accurate than all
published optical flow methods at the time of writing. Our results demonstrate
the benefits of separating the contributions of models, training techniques and
datasets when analyzing performance gains of optical flow methods. Our source
code will be publicly available.


title: Record-quality GaAs two-dimensional hole systems

abstract:
  The complex band structure, large spin-orbit induced band splitting, and
heavy effective mass of two-dimensional (2D) hole systems hosted in GaAs
quantum wells render them rich platforms to study many-body physics and
ballistic transport phenomena. Here we report ultra-high-quality (001) GaAs 2D
hole systems, fabricated using molecular beam epitaxy and modulation doping,
with mobility values as high as $5.8\times10^6$ cm$^2$/Vs at a hole density of
$p=1.3\times10^{11}$ /cm$^2$, implying a mean-free path of $\simeq27$ $\mu$m.
In the low-temperature magnetoresistance trace of this sample, we observe
high-order fractional quantum Hall states up to the Landau level filling
$\nu=12/25$ near $\nu=1/2$. Furthermore, we see a deep minimum develop at
$\nu=1/5$ in the magnetoresistance of a sample with a much lower hole density
of $p=4.0\times10^{10}$ /cm$^2$ where we measure a mobility of $3.6\times10^6$
cm$^2$/Vs. These improvements in sample quality were achieved by reduction of
residual impurities both in the GaAs channel and the AlGaAs barrier material,
as well as optimization in design of the sample structure.


title:
A Prompting-based Approach for Adversarial Example Generation and
  Robustness Enhancement

abstract:
  Recent years have seen the wide application of NLP models in crucial areas
such as finance, medical treatment, and news media, raising concerns of the
model robustness and vulnerabilities. In this paper, we propose a novel
prompt-based adversarial attack to compromise NLP models and robustness
enhancement technique. We first construct malicious prompts for each instance
and generate adversarial examples via mask-and-filling under the effect of a
malicious purpose. Our attack technique targets the inherent vulnerabilities of
NLP models, allowing us to generate samples even without interacting with the
victim NLP model, as long as it is based on pre-trained language models (PLMs).
Furthermore, we design a prompt-based adversarial training method to improve
the robustness of PLMs. As our training method does not actually generate
adversarial samples, it can be applied to large-scale training sets
efficiently. The experimental results show that our attack method can achieve a
high attack success rate with more diverse, fluent and natural adversarial
examples. In addition, our robustness enhancement method can significantly
improve the robustness of models to resist adversarial attacks. Our work
indicates that prompting paradigm has great potential in probing some
fundamental flaws of PLMs and fine-tuning them for downstream tasks.


title:
Correlated states of 2D electrons near the Landau level filling
  $\nu=1/7$

abstract:
  The ground state of two-dimensional electron systems (2DESs) at low Landau
level filling factors ($\nu\lesssim1/6$) has long been a topic of interest and
controversy in condensed matter. Following the recent breakthrough in the
quality of ultra-high-mobility GaAs 2DESs, we revisit this problem
experimentally and investigate the impact of reduced disorder. In a GaAs 2DES
sample with density $n=6.1\times10^{10}$ /cm$^2$ and mobility
$\mu=25\times10^6$ cm$^2$/Vs, we find a deep minimum in the longitudinal
magnetoresistance ($R_{xx}$) at $\nu=1/7$ when $T\simeq104$ mK. There is also a
clear sign of a developing minimum in the $R_{xx}$ at $\nu=2/13$. While
insulating phases are still predominant when $\nu\lesssim1/6$, these minima
strongly suggest the existence of fractional quantum Hall states at filling
factors that comply with the Jain sequence $\nu=p/(2mp\pm1)$ even in the very
low Landau level filling limit. The magnetic field dependent activation
energies deduced from the relation $R_{xx}\propto e^{E_A/2kT}$ corroborate this
view, and imply the presence of pinned Wigner solid states when $\nu\neq
p/(2mp\pm1)$. Similar results are seen in another sample with a lower density,
further generalizing our observations.


title:
Forecast Evaluation for Data Scientists: Common Pitfalls and Best
  Practices

abstract:
  Machine Learning (ML) and Deep Learning (DL) methods are increasingly
replacing traditional methods in many domains involved with important decision
making activities. DL techniques tailor-made for specific tasks such as image
recognition, signal processing, or speech analysis are being introduced at a
fast pace with many improvements. However, for the domain of forecasting, the
current state in the ML community is perhaps where other domains such as
Natural Language Processing and Computer Vision were at several years ago. The
field of forecasting has mainly been fostered by statisticians/econometricians;
consequently the related concepts are not the mainstream knowledge among
general ML practitioners. The different non-stationarities associated with time
series challenge the data-driven ML models. Nevertheless, recent trends in the
domain have shown that with the availability of massive amounts of time series,
ML techniques are quite competent in forecasting, when related pitfalls are
properly handled. Therefore, in this work we provide a tutorial-like
compilation of the details of one of the most important steps in the overall
forecasting process, namely the evaluation. This way, we intend to impart the
information of forecast evaluation to fit the context of ML, as means of
bridging the knowledge gap between traditional methods of forecasting and
state-of-the-art ML techniques. We elaborate on the different problematic
characteristics of time series such as non-normalities and non-stationarities
and how they are associated with common pitfalls in forecast evaluation. Best
practices in forecast evaluation are outlined with respect to the different
steps such as data partitioning, error calculation, statistical testing, and
others. Further guidelines are also provided along selecting valid and suitable
error measures depending on the specific characteristics of the dataset at
hand.


title:
An Intellectual Property Entity Recognition Method Based on Transformer
  and Technological Word Information

abstract:
  Patent texts contain a large amount of entity information. Through named
entity recognition, intellectual property entity information containing key
information can be extracted from it, helping researchers to understand the
patent content faster. Therefore, it is difficult for existing named entity
extraction methods to make full use of the semantic information at the word
level brought about by professional vocabulary changes. This paper proposes a
method for extracting intellectual property entities based on Transformer and
technical word information , and provides accurate word vector representation
in combination with the BERT language method. In the process of word vector
generation, the technical word information extracted by IDCNN is added to
improve the understanding of intellectual property entities Representation
ability. Finally, the Transformer encoder that introduces relative position
encoding is used to learn the deep semantic information of the text from the
sequence of word vectors, and realize entity label prediction. Experimental
results on public datasets and annotated patent datasets show that the method
improves the accuracy of entity recognition.


title:
Prediction Algorithm for Heat Demand of Science and Technology Topics
  Based on Time Convolution Network

abstract:
  Thanks to the rapid development of deep learning, big data analysis
technology is not only widely used in the field of natural language processing,
but also more mature in the field of numerical prediction. It is of great
significance for the subject heat prediction and analysis of science and
technology demand data. How to apply theme features to accurately predict the
theme heat of science and technology demand is the core to solve this problem.
In this paper, a prediction method of subject heat of science and technology
demand based on time convolution network (TCN) is proposed to obtain the
subject feature representation of science and technology demand. Time series
prediction is carried out based on TCN network and self attention mechanism,
which increases the accuracy of subject heat prediction of science and
technology demand data Experiments show that the prediction accuracy of this
algorithm is better than other time series prediction methods on the real
science and technology demand datasets.


title: LocATe: End-to-end Localization of Actions in 3D with Transformers

abstract:
  Understanding a person's behavior from their 3D motion is a fundamental
problem in computer vision with many applications. An important component of
this problem is 3D Temporal Action Localization (3D-TAL), which involves
recognizing what actions a person is performing, and when. State-of-the-art
3D-TAL methods employ a two-stage approach in which the action span detection
task and the action recognition task are implemented as a cascade. This
approach, however, limits the possibility of error-correction. In contrast, we
propose LocATe, an end-to-end approach that jointly localizes and recognizes
actions in a 3D sequence. Further, unlike existing autoregressive models that
focus on modeling the local context in a sequence, LocATe's transformer model
is capable of capturing long-term correlations between actions in a sequence.
Unlike transformer-based object-detection and classification models which
consider image or patch features as input, the input in 3D-TAL is a long
sequence of highly correlated frames. To handle the high-dimensional input, we
implement an effective input representation, and overcome the diffuse attention
across long time horizons by introducing sparse attention in the model. LocATe
outperforms previous approaches on the existing PKU-MMD 3D-TAL benchmark
(mAP=93.2%). Finally, we argue that benchmark datasets are most useful where
there is clear room for performance improvement. To that end, we introduce a
new, challenging, and more realistic benchmark dataset, BABEL-TAL-20 (BT20),
where the performance of state-of-the-art methods is significantly worse. The
dataset and code for the method will be available for research purposes.


title:
Linear Convergence of Generalized Proximal Point Algorithms for Monotone
  Inclusion Problems

abstract:
  We focus on the linear convergence of generalized proximal point algorithms
for solving monotone inclusion problems. Under the assumption that the
associated monotone operator is metrically subregular or that the inverse of
the monotone operator is Lipschitz continuous, we provide Q-linear and R-linear
convergence results on generalized proximal point algorithms. Comparisons
between our results and related ones in the literature are presented in remarks
of this work.


title:
Advances and Challenges in Observations and Modeling of the Global-Sun
  Dynamics and Dynamo

abstract:
  Computational heliophysics has shed light on the fundamental physical
processes inside the Sun, such as the differential rotation, meridional
circulation, and dynamo-generation of magnetic fields. However, despite the
substantial advances, the current results of 3D MHD simulations are still far
from reproducing helioseismic inferences and surface observations. The reason
is the multi-scale nature of the solar dynamics, covering a vast range of
scales, which cannot be solved with the current computational resources. In
such a situation, significant progress has been achieved by the mean-field
approach, based on the separation of small-scale turbulence and large-scale
dynamics. The mean-field simulations can reproduce solar observations,
qualitatively and quantitatively, and uncover new phenomena. However, they do
not reveal the complex physics of large-scale convection, solar magnetic
cycles, and the magnetic self-organization that causes sunspots and solar
eruptions. Thus, developing a synergy of these approaches seems to be a
necessary but very challenging task.


title: Discovery of a maximally charged Weyl point

abstract:
  The hypothetical Weyl particles in high-energy physics have been discovered
in three-dimensional crystals as collective quasiparticle excitations near
two-fold degenerate Weyl points. Such momentum-space Weyl particles carry
quantized chiral charges, which can be measured by counting the number of Fermi
arcs emanating from the corresponding Weyl points. It is known that merging
unit-charged Weyl particles can create new ones with more charges. However,
only very recently has it been realised that there is an upper limit - the
maximal charge number that a two-fold Weyl point can host is four - achievable
only in crystals without spin-orbit coupling. Here, we report the experimental
realisation of such a maximally charged Weyl point in a three-dimensional
photonic crystal. The four charges support quadruple-helicoid Fermi arcs,
forming an unprecedented topology of two non-contractible loops in the surface
Brillouin zone. The helicoid Fermi arcs also exhibit the long-pursued type-II
van Hove singularities that can reside at arbitrary momenta. This discovery
reveals a type of maximally charged Weyl particles beyond conventional
topological particles in crystals.


title: An Intermediate-level Attack Framework on The Basis of Linear Regression

abstract:
  This paper substantially extends our work published at ECCV, in which an
intermediate-level attack was proposed to improve the transferability of some
baseline adversarial examples. We advocate to establish a direct linear mapping
from the intermediate-level discrepancies (between adversarial features and
benign features) to classification prediction loss of the adversarial example.
In this paper, we delve deep into the core components of such a framework by
performing comprehensive studies and extensive experiments. We show that 1) a
variety of linear regression models can all be considered in order to establish
the mapping, 2) the magnitude of the finally obtained intermediate-level
discrepancy is linearly correlated with adversarial transferability, 3) further
boost of the performance can be achieved by performing multiple runs of the
baseline attack with random initialization. By leveraging these findings, we
achieve new state-of-the-arts on transfer-based $\ell_\infty$ and $\ell_2$
attacks.


title: Some properties of Pre-topological groups

abstract:
  In this paper, we pose the concepts of pre-topological groups and some
generalizations of pre-topological groups. First, we systematically investigate
some basic properties of pre-topological groups; in particular, we prove that
each $T_{0}$ pre-topological group is regular and every almost topological
group is completely regular which extends A.A. Markov's theorem to the class of
almost topological groups. Moreover, it is shown that an almost topological
group is $\tau$-narrow if and only if it can be embedded as a subgroup of a
pre-topological product of almost topological groups of weight less than or
equal to $\tau$. Finally, the cardinal invariant, the precompactness and the
resolvability are investigated in the class of pre-topological groups.


title: Some properties of Pre-uniform spaces

abstract:
  In this paper, we introduce the notions of pre-uniform spaces and
pre-proximities and investigate some basic properties about them. First, we
prove that each pre-uniform pre-topology is regular, and give an example to
show that there exists a pre-uniform structure on a finite set such that the
pre-uniform pre-topology is not discrete. Moreover, we give three methods of
generating (strongly) pre-uniformities, that is, the definition of a pre-base,
a family of strongly pre-uniform covers, or a family of strongly pre-uniform
pseudometrics. As an application, we show that each strongly pre-topological
group is completely regular. Finally, we pose the concept of the pre-proximity
on a set and discuss some properties of the pre-proximity.


title:
TransFusion: Multi-view Divergent Fusion for Medical Image Segmentation
  with Transformers

abstract:
  Combining information from multi-view images is crucial to improve the
performance and robustness of automated methods for disease diagnosis. However,
due to the non-alignment characteristics of multi-view images, building
correlation and data fusion across views largely remain an open problem. In
this study, we present TransFusion, a Transformer-based architecture to merge
divergent multi-view imaging information using convolutional layers and
powerful attention mechanisms. In particular, the Divergent Fusion Attention
(DiFA) module is proposed for rich cross-view context modeling and semantic
dependency mining, addressing the critical issue of capturing long-range
correlations between unaligned data from different image views. We further
propose the Multi-Scale Attention (MSA) to collect global correspondence of
multi-scale feature representations. We evaluate TransFusion on the
Multi-Disease, Multi-View \& Multi-Center Right Ventricular Segmentation in
Cardiac MRI (M\&Ms-2) challenge cohort. TransFusion demonstrates leading
performance against the state-of-the-art methods and opens up new perspectives
for multi-view imaging integration towards robust medical image segmentation.


title: Structures and stability of the Cu$_{38}$ cluster at finite temperature

abstract:
  The UV-visible and IR properties of the Cu$_{38}$ nanocluster depend to a
great extent on the temperature. Density functional theory and
nanothermodynamics can be combined to compute the geometrical optimization of
isomers and their spectroscopic properties in an approximate manner. In this
article, we investigate entropy-driven isomer distributions of Cu$_{38}$
clusters and the effect of temperature on their UV-visible and IR spectra. An
extensive, systematic global search is performed on the potential and free
energy surfaces of Cu38 using a two-stage strategy to identify the
lowest-energy structure and its low-energy neighbors. The effects of
temperature on the UV and IR spectra are considered via Boltzmann probability.
The computed UV-visible and IR spectrum of each isomer is multiplied by its
corresponding Boltzmann weight at finite temperature. Then, they are summed
together to produce a final temperature-dependent, Boltzmann-weighted
UV-visible and IR spectrum. Additionally, Molecular Dynamics simulation of the
Cu$_{38}$ nanocluster was performed to gain insight into the system dynamics
and make a three-dimensional movie of the system with atomistic resolution. Our
results show the thermal populations at the absolute temperature of Cu38
cluster, and the disordered structure that dominates at high temperatures.


title: Parametric Euler Sums of Harmonic Numbers

abstract:
  We define a parametric variant of generalized Euler sums and construct
contour integration to give some explicit evaluations of these parametric Euler
sums. In particular, we establish several explicit formulas of (Hurwitz) zeta
functions, linear and quadratic parametric Euler sums. Furthermore, we also
give an explicit evaluation of alternating double zeta values
$\ze(\overline{2j},2m+1)$ in terms of a combination of alternating Riemann zeta
values by using the parametric Euler sums.


title:
DSRRTracker: Dynamic Search Region Refinement for Attention-based
  Siamese Multi-Object Tracking

abstract:
  Many multi-object tracking (MOT) methods follow the framework of "tracking by
detection", which associates the target objects-of-interest based on the
detection results. However, due to the separate models for detection and
association, the tracking results are not optimal.Moreover, the speed is
limited by some cumbersome association methods to achieve high tracking
performance. In this work, we propose an end-to-end MOT method, with a Gaussian
filter-inspired dynamic search region refinement module to dynamically filter
and refine the search region by considering both the template information from
the past frames and the detection results from the current frame with little
computational burden, and a lightweight attention-based tracking head to
achieve the effective fine-grained instance association. Extensive experiments
and ablation study on MOT17 and MOT20 datasets demonstrate that our method can
achieve the state-of-the-art performance with reasonable speed.


title: Semantic Segmentation with Active Semi-Supervised Learning

abstract:
  Using deep learning, we now have the ability to create exceptionally good
semantic segmentation systems; however, collecting the prerequisite pixel-wise
annotations for training images remains expensive and time-consuming.
Therefore, it would be ideal to minimize the number of human annotations needed
when creating a new dataset. Here, we address this problem by proposing a novel
algorithm that combines active learning and semi-supervised learning. Active
learning is an approach for identifying the best unlabeled samples to annotate.
While there has been work on active learning for segmentation, most methods
require annotating all pixel objects in each image, rather than only the most
informative regions. We argue that this is inefficient. Instead, our active
learning approach aims to minimize the number of annotations per-image. Our
method is enriched with semi-supervised learning, where we use pseudo labels
generated with a teacher-student framework to identify image regions that help
disambiguate confused classes. We also integrate mechanisms that enable better
performance on imbalanced label distributions, which have not been studied
previously for active learning in semantic segmentation. In experiments on the
CamVid and CityScapes datasets, our method obtains over 95% of the network's
performance on the full-training set using less than 19% of the training data,
whereas the previous state of the art required 40% of the training data.


title: Research Scholar Interest Mining Method based on Load Centrality

abstract:
  In the era of big data, it is possible to carry out cooperative research on
the research results of researchers through papers, patents and other data, so
as to study the role of researchers, and produce results in the analysis of
results. For the important problems found in the research and application of
reality, this paper also proposes a research scholar interest mining algorithm
based on load centrality (LCBIM), which can accurately solve the problem
according to the researcher's research papers and patent data. Graphs of
creative algorithms in various fields of the study aggregated ideas, generated
topic graphs by aggregating neighborhoods, used the generated topic information
to construct with similar or similar topic spaces, and utilize keywords to
construct one or more topics. The regional structure of each topic can be used
to closely calculate the weight of the centrality research model of the node,
which can analyze the field in the complete coverage principle. The scientific
research cooperation based on the load rate center proposed in this paper can
effectively extract the interests of scientific research scholars from papers
and corpus.


title:
Least-squares formulations for Stokes equations with non-standard
  boundary conditions -- A unified approach

abstract:
  In this paper, we propose a unified non-conforming least-squares spectral
element approach for solving Stokes equations with various non-standard
boundary conditions. Existing least-squares formulations mostly deal with
Dirichlet boundary conditions are formulated using ADN theory-based regularity
estimates. However, changing boundary conditions lead to a search for
parameters satisfying supplementing and complimenting conditions [4] which is
not easy always. Here we have avoided ADN theory-based regularity estimates and
proposed a unified approach for dealing with various boundary conditions.
Stability estimates and error estimates have been discussed. Numerical results
displaying exponential accuracy have been presented for both two and
three-dimensional cases with various boundary conditions.


title: Constraints on cosmic birefringence using $E$-mode polarisation

abstract:
  A birefringent universe could show itself through a rotation of the plane of
polarisation of the cosmic microwave background photons. This is usually
investigated using polarisation $B$ modes, which is degenerate with
miscalibration of the orientation of the polarimeters. Here we point out an
independent method for extracting the birefringence angle using only
temperature and $E$-mode signals. We forecast that, with an ideal
cosmic-variance-limited experiment, we could constrain a birefringence angle of
$0.3^\circ$ with $3\,\sigma$ statistical significance, which is close to the
current constraints using $B$ modes. We explore how this method is affected by
the systematic errors introduced by the polarisation efficiency. In the future,
this could provide an additional way of checking any claimed $B$-mode derived
birefringence signature.


title:
Defending against Co-residence Attack in Energy-Efficient Cloud: An
  Optimization based Real-time Secure VM Allocation Strategy

abstract:
  Resource sharing among users serves as the foundation of cloud computing,
which, however, may also cause vulnerabilities to diverse co-residence attacks
launched by malicious virtual machines (VM) residing in the same physical
server with the victim VMs. In this paper, we aim to defend against such
co-residence attacks through a secure, workload-balanced, and energy-efficient
VM allocation strategy. Specifically, we model the problem as an optimization
problem by quantifying and minimizing three key factors: (1) the security
risks, (2) the power consumption and (3) the unbalanced workloads among
different physical servers. Furthermore, this work considers a realistic
environmental setting by assuming a random number of VMs from different users
arriving at random timings, which requires the optimization solution to be
continuously evolving. As the optimization problem is NP-hard, we propose to
first cluster VMs in time windows, and further adopt the Ant Colony
Optimization (ACO) algorithm to identify the optimal allocation strategy for
each time window. Comprehensive experimental results based on real world cloud
traces validates the effectiveness of the proposed scheme.


title: Modeling the arc and ring structures in the HD 143006 disk

abstract:
  Rings and asymmetries in protoplanetary disks are considered as signposts of
ongoing planet formation. In this work, we conduct three-dimensional radiative
transfer simulations to model the intriguing disk around HD 143006 that has
three dust rings and a bright arc. A complex geometric configuration, with a
misaligned inner disk, is assumed to account for the asymmetric structures. The
two-dimensional surface density is constructed by iteratively fitting the ALMA
data. We find that the dust temperature displays a notable discontinuity at the
boundary of the misalignment. The ring masses range from 0.6 to 16Mearth that
are systematically lower than those inferred in the younger HL Tau disk. The
arc occupies nearly 20% of the total dust mass. Such a high mass fraction of
dust grains concentrated in a local region is consistent with the mechanism of
dust trapping into vortices. Assuming a gas-to-dust mass ratio of 30 that is
constant throughout the disk, the dense and cold arc is close to the threshold
of being gravitationally unstable, with the Toomre parameter Q~1.3.
Nevertheless, our estimate of Q relies on the assumption for the unknown
gas-to-dust mass ratio. Adopting a lower gas-to-dust mass ratio would increase
the inferred Q value. Follow-up high resolution observations of dust and gas
lines are needed to clarify the origin of the substructures.


title:
The activity-weight duality in feed forward neural networks: The
  geometric determinants of generalization

abstract:
  One of the fundamental problems in machine learning is generalization. In
neural network models with a large number of weights (parameters), many
solutions can be found to fit the training data equally well. The key question
is which solution can describe testing data not in the training set. Here, we
report the discovery of an exact duality (equivalence) between changes in
activities in a given layer of neurons and changes in weights that connect to
the next layer of neurons in a densely connected layer in any feed forward
neural network. The activity-weight (A-W) duality allows us to map variations
in inputs (data) to variations of the corresponding dual weights. By using this
mapping, we show that the generalization loss can be decomposed into a sum of
contributions from different eigen-directions of the Hessian matrix of the loss
function at the solution in weight space. The contribution from a given
eigen-direction is the product of two geometric factors (determinants): the
sharpness of the loss landscape and the standard deviation of the dual weights,
which is found to scale with the weight norm of the solution. Our results
provide an unified framework, which we used to reveal how different
regularization schemes (weight decay, stochastic gradient descent with
different batch sizes and learning rates, dropout), training data size, and
labeling noise affect generalization performance by controlling either one or
both of these two geometric determinants for generalization. These insights can
be used to guide development of algorithms for finding more generalizable
solutions in overparametrized neural networks.


title:
Sm-Co-based amorphous alloy films for zero-field operation of transverse
  thermoelectric generation

abstract:
  Transverse thermoelectric generation using magnetic materials is essential to
develop active thermal engineering technologies, for which the improvements of
not only the thermoelectric output but also applicability and versatility are
required. In this study, using combinatorial material science and lock-in
thermography technique, we have systematically investigated the transverse
thermoelectric performance of Sm-Co-based alloy films. The high-throughput
material investigation revealed the best Sm-Co-based alloys with the large
anomalous Nernst effect (ANE) as well as the anomalous Ettingshausen effect
(AEE). In addition to ANE/AEE, we discovered unique and superior material
properties in these alloys: the amorphous structure, low thermal conductivity,
and large in-plane coercivity and remanent magnetization. These properties make
it advantageous over conventional materials to realize heat flux sensing
applications based on ANE, as our Sm-Co-based films can generate thermoelectric
output without an external magnetic field. Importantly, the amorphous nature
enables the fabrication of these films on various substrates including flexible
sheets, making the large-scale and low-cost manufacturing easier. Our
demonstration will provide a pathway to develop flexible transverse
thermoelectric devices for smart thermal management.


title: Exciton interaction with acoustic phonons in PbS nanocrystals

abstract:
  The interaction between exciton and acoustic phonons via deformation
potential in PbS nanocrystals is calculated in the ${\bf k} \cdot {\bf p}$
model. The size-dependence of the Huang-Rhys factors for the fundamental
breathing mode and its overtones as well as for spheroidal vibrations with the
total angular momentum $j=2$ are evaluated.


title: Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentation

abstract:
  Sparsely annotated semantic segmentation (SASS) aims to train a segmentation
network with coarse-grained (i.e., point-, scribble-, and block-wise)
supervisions, where only a small proportion of pixels are labeled in each
image. In this paper, we propose a novel tree energy loss for SASS by providing
semantic guidance for unlabeled pixels. The tree energy loss represents images
as minimum spanning trees to model both low-level and high-level pair-wise
affinities. By sequentially applying these affinities to the network
prediction, soft pseudo labels for unlabeled pixels are generated in a
coarse-to-fine manner, achieving dynamic online self-training. The tree energy
loss is effective and easy to be incorporated into existing frameworks by
combining it with a traditional segmentation loss. Compared with previous SASS
methods, our method requires no multistage training strategies, alternating
optimization procedures, additional supervised data, or time-consuming
post-processing while outperforming them in all SASS settings. Code is
available at https://github.com/megvii-research/TreeEnergyLoss.


title:
Gravothermal catastrophe and critical dimension in a $D$-dimensional
  asymptotically AdS spacetime

abstract:
  We investigate the structure and stability of the thermal equilibrium states
of a spherically symmetric self-gravitating system in a $D$-dimensional
asymptotically Anti-de Sitter(AdS) spacetime. The system satisfies the
Einstein-Vlasov equations with a negative cosmological constant. Due to the
confined structure of the AdS potential, we can construct thermal equilibrium
states without any artificial wall in the asymptotically AdS spacetime.
Accordingly, the AdS radius can be regarded as the typical size of the system.
Then the system can be characterized by the gravothermal energy and AdS radius
normalized by the total particle number. We investigate the catastrophic
instability of the system in a $D$-dimensional spacetime by using the turning
point method. As a result, we find that the curve has a double spiral structure
for $4\le D\le 10$ while it does not have any spiral structures for $D\ge11$ as
in the asymptotically flat case confined by an adiabatic wall. Irrespective of
the existence of the spiral structure, there exist upper and lower bounds for
the value of the gravothermal energy. This fact indicates that there is no
thermal equilibrium solution outside the allowed region of the gravothermal
energy. This property is also similar to the asymptotically flat case.


title:
HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long
  Document Summarization

abstract:
  Document structure is critical for efficient information consumption.
However, it is challenging to encode it efficiently into the modern Transformer
architecture. In this work, we present HIBRIDS, which injects Hierarchical
Biases foR Incorporating Document Structure into the calculation of attention
scores. We further present a new task, hierarchical question-summary
generation, for summarizing salient content in the source document into a
hierarchy of questions and summaries, where each follow-up question inquires
about the content of its parent question-summary pair. We also annotate a new
dataset with 6,153 question-summary hierarchies labeled on long government
reports. Experiment results show that our model produces better
question-summary hierarchies than comparisons on both hierarchy quality and
content coverage, a finding also echoed by human judges. Additionally, our
model improves the generation of long-form summaries from lengthy government
reports and Wikipedia articles, as measured by ROUGE scores.


title:
Particle Ratios at Large $p_{\rm T}$ in Xe + Xe collisions at
  $\sqrt{s_\mathrm{NN}}$ = 5.44 TeV

abstract:
  In this work we calculate $\pi^0$, $\eta$ and $\phi$ yields in Xe + Xe
collisions at $\sqrt{s_\mathrm{NN}}$ = 5.44 TeV in the framework of
perturbative QCD improved parton model at the accuracy of next-to-leading order
(NLO). The jet quenching effect is effectively incorporated by the
medium-modified fragmentation functions via the higher-twist approach. We
predict the nuclear modification factors of $\pi^0$, $\eta$ and $\phi$ as
functions of the transverse momentum $p_{\rm T}$, with the jet transport
parameter $\hat{q}_0$ extracted from the available experimental data of the
charged hadron provided by ALICE and CMS. We also present the particle ratios
$\eta/\pi^0,\ \phi/\pi^0$ as functions of $p_{\rm T}$ in Xe + Xe collisions at
$\sqrt{s_\mathrm{NN}}$ = 5.44 TeV, as well as those in Pb + Pb collisions at
$\sqrt{s_\mathrm{NN}}$ = 5.02 TeV. Our numerical simulations of the scaled
ratios of the charged hadron productions in Xe + Xe 5.44 TeV system over that
in Pb + Pb 5.02 TeV give a nice description of the CMS data, and we show that
the calculated scaled ratios of the $\pi^0$, $\eta$ and $\phi$ productions
coincide with the curve of charged hadron.


title:
Academic Resource Text Level Multi-label Classification based on
  Attention

abstract:
  Hierarchical multi-label academic text classification (HMTC) is to assign
academic texts into a hierarchically structured labeling system. We propose an
attention-based hierarchical multi-label classification algorithm of academic
texts (AHMCA) by integrating features such as text, keywords, and hierarchical
structure, the academic documents are classified into the most relevant
categories. We utilize word2vec and BiLSTM to obtain embedding and latent
vector representations of text, keywords, and hierarchies. We use hierarchical
attention mechanism to capture the associations between keywords, label
hierarchies, and text word vectors to generate hierarchical-specific document
embedding vectors to replace the original text embeddings in HMCN-F. The
experimental results on the academic text dataset demonstrate the effectiveness
of the AHMCA algorithm.


title:
Programming Language Agnostic Mining of Code and Language Pairs with
  Sequence Labeling Based Question Answering

abstract:
  Mining aligned natural language (NL) and programming language (PL) pairs is a
critical task to NL-PL understanding. Existing methods applied specialized
hand-crafted features or separately-trained models for each PL. However, they
usually suffered from low transferability across multiple PLs, especially for
niche PLs with less annotated data. Fortunately, a Stack Overflow answer post
is essentially a sequence of text and code blocks and its global textual
context can provide PL-agnostic supplementary information. In this paper, we
propose a Sequence Labeling based Question Answering (SLQA) method to mine
NL-PL pairs in a PL-agnostic manner. In particular, we propose to apply the BIO
tagging scheme instead of the conventional binary scheme to mine the code
solutions which are often composed of multiple blocks of a post. Experiments on
current single-PL single-block benchmarks and a manually-labeled cross-PL
multi-block benchmark prove the effectiveness and transferability of SLQA. We
further present a parallel NL-PL corpus named Lang2Code automatically mined
with SLQA, which contains about 1.4M pairs on 6 PLs. Under statistical analysis
and downstream evaluation, we demonstrate that Lang2Code is a large-scale
high-quality data resource for further NL-PL research.


title:
Projective representations of Hecke groups from Topological quantum
  field theory

abstract:
  We construct projective (unitary) representations of Hecke groups from the
vector spaces associated with Witten-Reshetikhin-Turaev topological quantum
field theory of higher genus surfaces. In particular, we generalize the modular
data of Temperley-Lieb-Jones modular categories. We also study some properties
of the representation. We show the image group of the representation is
infinite at low levels in genus $2$ by explicit computations. We also show the
representation is reducible with at least three irreducible summands when the
level equals $4l+2$ for $l\geq 1$.


title:
Topological Expansion of Oscillatory BGW and HCIZ Integrals at Strong
  Coupling

abstract:
  We prove that the BGW and HCIZ integrals admit large N topological expansions
for complex coupling and complex external fields, provided the coupling is
sufficiently strong. The expansion coefficients are holomorphic functions which
are genus-specific generating functions for the monotone single and double
Hurwitz numbers, respectively.


title: EAutoDet: Efficient Architecture Search for Object Detection

abstract:
  Training CNN for detection is time-consuming due to the large dataset and
complex network modules, making it hard to search architectures on detection
datasets directly, which usually requires vast search costs (usually tens and
even hundreds of GPU-days). In contrast, this paper introduces an efficient
framework, named EAutoDet, that can discover practical backbone and FPN
architectures for object detection in 1.4 GPU-days. Specifically, we construct
a supernet for both backbone and FPN modules and adopt the differentiable
method. To reduce the GPU memory requirement and computational cost, we propose
a kernel reusing technique by sharing the weights of candidate operations on
one edge and consolidating them into one convolution. A dynamic channel
refinement strategy is also introduced to search channel numbers. Extensive
experiments show significant efficacy and efficiency of our method. In
particular, the discovered architectures surpass state-of-the-art object
detection NAS methods and achieve 40.1 mAP with 120 FPS and 49.2 mAP with 41.3
FPS on COCO test-dev set. We also transfer the discovered architectures to
rotation detection task, which achieve 77.05 mAP$_{\text{50}}$ on DOTA-v1.0
test set with 21.1M parameters.


title: Whose AI Dream? In search of the aspiration in data annotation

abstract:
  This paper present the practice of data annotation from the perspective of
the annotators. Data is fundamental to ML models. This paper investigates the
work practices concerning data annotation as performed in the industry, in
India. Previous investigations have largely focused on annotator subjectivity,
bias and efficiency. We present a wider perspective of the data annotation,
following a grounded approach, we conducted three sets of interviews with 25
annotators, 10 industry experts and 12 ML practitioners. Our results show that
the work of annotators is dictated by the interests, priorities and values of
others above their station. More than technical, we contend that data
annotation is a systematic exercise of power through organizational structure
and practice. We propose a set of implications for how we can cultivate and
encourage better practice to balance the tension between the need for high
quality data at low cost and the annotator aspiration for well being, career
perspective, and active participation in building the AI dream.


title:
STCGAT: Spatial-temporal causal networks for complex urban road traffic
  flow prediction

abstract:
  Traffic forecasting is an essential component of intelligent transportation
systems. However, traffic data are highly nonlinear and have complex spatial
correlations between road nodes. Therefore, it is incredibly challenging to dig
deeper into the underlying Spatial-temporal relationships from the complex
traffic data. Existing approaches usually use fixed traffic road network
topology maps and independent time series modules to capture Spatial-temporal
correlations, ignoring the dynamic changes of traffic road networks and the
inherent temporal causal relationships between traffic events. Therefore, a new
prediction model is proposed in this study. The model dynamically captures the
spatial dependence of the traffic network through a Graph Attention
Network(GAT) and then analyzes the causal relationship of the traffic data
using our proposed Causal Temporal Convolutional Network(CTCN) to obtain the
overall temporal dependence. We conducted extensive comparison experiments with
other traffic prediction methods on two real traffic datasets to evaluate the
model's prediction performance. Compared with the best experimental results of
different prediction methods, the prediction performance of our approach is
improved by more than 50%. You can get our source code and data through
https://github.com/zhangshqii/STCGAT.


title: WeSinger: Data-augmented Singing Voice Synthesis with Auxiliary Losses

abstract:
  In this paper, we develop a new multi-singer Chinese neural singing voice
synthesis (SVS) system named WeSinger. To improve the accuracy and naturalness
of synthesized singing voice, we design several specifical modules and
techniques: 1) A deep bi-directional LSTM based duration model with multi-scale
rhythm loss and post-processing step; 2) A Transformer-alike acoustic model
with progressive pitch-weighted decoder loss; 3) a 24 kHz pitch-aware LPCNet
neural vocoder to produce high-quality singing waveforms; 4) A novel data
augmentation method with multi-singer pre-training for stronger robustness and
naturalness. To our knowledge, WeSinger is the first SVS system to adopt 24 kHz
LPCNet and multi-singer pre-training simultaneously. Both quantitative and
qualitative evaluation results demonstrate the effectiveness of WeSinger in
terms of accuracy and naturalness, and WeSinger achieves state-of-the-art
performance on the recently public Chinese singing corpus Opencpop. Some
synthesized singing samples are available online
(https://zzw922cn.github.io/wesinger/).


title:
On Rangasamy's outsourcing algorithm for solving quadratic congruence
  equations

abstract:
  Outsourcing computation is a desired approach for IoT (Internet of Things)
devices to transfer their burdens of heavy computations to those nearby,
resource-abundant cloud servers. Recently, Rangasamy presented a passive attack
against two outsourcing algorithms proposed by Zhang et al. for solving
quadratic congruence equations, which is widely used in IoT applications.
Furthermore, he also proposed a modified algorithm to fix these schemes and
claimed that his algorithm was correct and enabled secure and verifiable
delegation of solving quadratic congruence equations in IoTs. However, we show
that Rangasamy's modified algorithm has a flaw which makes it incorrect and
also propose some further attacks to break the security claim, even when the
flaw has been corrected.


title: XTREME-S: Evaluating Cross-lingual Speech Representations

abstract:
  We introduce XTREME-S, a new benchmark to evaluate universal cross-lingual
speech representations in many languages. XTREME-S covers four task families:
speech recognition, classification, speech-to-text translation and retrieval.
Covering 102 languages from 10+ language families, 3 different domains and 4
task families, XTREME-S aims to simplify multilingual speech representation
evaluation, as well as catalyze research in "universal" speech representation
learning. This paper describes the new benchmark and establishes the first
speech-only and speech-text baselines using XLS-R and mSLAM on all downstream
tasks. We motivate the design choices and detail how to use the benchmark.
Datasets and fine-tuning scripts are made easily accessible at
https://hf.co/datasets/google/xtreme_s.


title:
Match the Script, Adapt if Multilingual: Analyzing the Effect of
  Multilingual Pretraining on Cross-lingual Transferability

abstract:
  Pretrained multilingual models enable zero-shot learning even for unseen
languages, and that performance can be further improved via adaptation prior to
finetuning. However, it is unclear how the number of pretraining languages
influences a model's zero-shot learning for languages unseen during
pretraining. To fill this gap, we ask the following research questions: (1) How
does the number of pretraining languages influence zero-shot performance on
unseen target languages? (2) Does the answer to that question change with model
adaptation? (3) Do the findings for our first question change if the languages
used for pretraining are all related? Our experiments on pretraining with
related languages indicate that choosing a diverse set of languages is crucial.
Without model adaptation, surprisingly, increasing the number of pretraining
languages yields better results up to adding related languages, after which
performance plateaus. In contrast, with model adaptation via continued
pretraining, pretraining on a larger number of languages often gives further
improvement, suggesting that model adaptation is crucial to exploit additional
pretraining languages.


title: Strong posterior contraction rates via Wasserstein dynamics

abstract:
  In this paper, we develop a novel approach to posterior contractions rates
(PCRs), for both finite-dimensional (parametric) and infinite-dimensional
(nonparametric) Bayesian models. Critical to our approach is the combination of
an assumption of local Lipschitz-continuity for the posterior distribution with
a dynamic formulation of the Wasserstein distance, here referred to as
Wasserstein dynamics, which allows to set forth a connection between the
problem of establishing PCRs and some classical problems in mathematical
analysis, probability theory and mathematical statistics: the Laplace method
for approximating integrals, Sanov's large deviation principles in the
Wasserstein distance, rates of convergence of the mean Glivenko-Cantelli
theorem, and estimates of weighted Poincar\'e-Wirtinger constants. Under
dominated Bayesian models, we present two main results: i) a theorem on PCRs
for the regular infinite-dimensional exponential family of statistical models;
ii) a theorem on PCRs for a general dominated statistical model. Some
applications of our results are presented for the regular parametric model, the
multinomial model, the finite-dimensional and the infinite-dimensional
logistic-Gaussian model and the infinite-dimensional linear regression. In
general, our results lead to optimal PCRs in finite dimension, whereas in
infinite dimension it is shown how the prior distribution may affect PCRs. With
regards to infinite-dimensional Bayesian models for density estimation, our
approach to PCRs is the first to consider strong norm distances on parameter
spaces of functions, such as Sobolev-like norms, as most of the approaches in
the classical (frequentist) and Bayesian literature deal with spaces of density
functions endowed with $\mathrm{L}^p$ norms or the Hellinger distance.


title:
The Dirichlet Problem for mixed Hessian type equations on Riemannian
  manifolds

abstract:
  In this paper, we derive $C^2$ estimates for a class of mixed Hessian
equations with Dirichlet boundary condition, and obtain the existence theorem
of admissible solutions for the classical Dirichlet problem of these mixed
Hessian equations.


title:
On the Cauchy problem for the cutoff Boltzmann equation with small
  initial data

abstract:
  We prove the global existence of the non-negative unique mild solution for
the Cauchy problem of the cutoff Boltzmann equation for soft potential model
$-1\leq \gamma< 0$ with the small initial data in three dimensional space. Thus
our result fixes the gap for the case $\gamma=-1$ in three dimensional space in
the authors' previous work where the estimate for the loss term was improperly
used. The other gap there for the case $\gamma=0$ in two dimensional space is
recently fixed by Chen, Denlinger and Pavlovi\'{c}. The initial data $f_{0}$ is
non-negative, small in weighted $L^{3}_{x,v}$ and finite in weighted
$L^{15/8}_{x,v}$. We also show that the solution scatters with respect to the
kinetic transport operator. The novel contribution of this work lies in the
exploration of the symmetric property of the gain term in terms of weighted
estimate. It is the key ingredient for solving the model $-1<\gamma<0$ when
applying the Strichartz estimates.


title: Unconventional Quantum Electrodynamics with Hofstadter-Ladder Waveguide

abstract:
  We propose a novel quantum electrodynamics (QED) platform where quantum
emitters interact with a Hofstadter-ladder waveguide. We demonstrate several
intriguing phenomena stemming from the exotic dispersion relation and vacuum
mode properties led by the effective spin-orbit coupling, which have no analog
in other QED setups. First, by assuming emitter's frequency to be resonant with
the lower band, we find that the spontaneous emission is chiral with most
photonic field decaying unidirectionally. Both numerical and analytical results
indicate that the Hofstadter-ladder waveguide can be engineered as a
well-performed chiral quantum bus. Second, the dynamics of emitters of giant
atom form is explored by considering their frequencies below the lower band.
Due to quantum interference, we find that both the emitter-waveguide
interaction and the amplitudes of bound states are periodically modulated by
giant emitter's size. The periodical length depends on the positions of energy
minima points induced by the spin-orbit coupling. Last, we consider the
interaction between two giant emitters mediated by bound states, and find that
their dipole-dipole interaction vanishes (is enhanced) when maximum destructive
(constructive) interference happens.


title:
Small-time global approximate controllability for incompressible MHD
  with coupled Navier slip boundary conditions

abstract:
  We study the small-time global approximate controllability for incompressible
magnetohydrodynamic (MHD) flows in smoothly bounded two- or three-dimensional
domains. The controls act on arbitrary open portions of each connected boundary
component, while linearly coupled Navier slip-with-friction conditions are
imposed along the uncontrolled parts of the boundary. Certain choices for the
friction operators at the boundary give rise to interacting velocity and
magnetic field boundary layers. We achieve sufficient dissipation properties
for these layers by a detailed analysis of the corresponding asymptotic
expansions. It is important that the boundary controls are compatible with the
equation for the magnetic field, otherwise a bulk pressure gradient appears in
the induction equation. Our results avoid such a gradient term for all
uncoupled and many coupled Navier slip-with-friction conditions.


title:
A Slot Is Not Built in One Utterance: Spoken Language Dialogs with
  Sub-Slots

abstract:
  A slot value might be provided segment by segment over multiple-turn
interactions in a dialog, especially for some important information such as
phone numbers and names. It is a common phenomenon in daily life, but little
attention has been paid to it in previous work. To fill the gap, this paper
defines a new task named Sub-Slot based Task-Oriented Dialog (SSTOD) and builds
a Chinese dialog dataset SSD for boosting research on SSTOD. The dataset
includes a total of 40K dialogs and 500K utterances from four different
domains: Chinese names, phone numbers, ID numbers and license plate numbers.
The data is well annotated with sub-slot values, slot values, dialog states and
actions. We find some new linguistic phenomena and interactive manners in SSTOD
which raise critical challenges of building dialog agents for the task. We test
three state-of-the-art dialog models on SSTOD and find they cannot handle the
task well on any of the four domains. We also investigate an improved model by
involving slot knowledge in a plug-in manner. More work should be done to meet
the new challenges raised from SSTOD which widely exists in real-life
applications. The dataset and code are publicly available via
https://github.com/shunjiu/SSTOD.


title: Scaling Up Maximal k-plex Enumeration

abstract:
  Finding all maximal $k$-plexes on networks is a fundamental research problem
in graph analysis due to many important applications, such as community
detection, biological graph analysis, and so on. A $k$-plex is a subgraph in
which every vertex is adjacent to all but at most $k$ vertices within the
subgraph. In this paper, we study the problem of enumerating all large maximal
$k$-plexes of a graph and develop several new and efficient techniques to solve
the problem. Specifically, we first propose several novel upper-bounding
techniques to prune unnecessary computations during the enumeration procedure.
We show that the proposed upper bounds can be computed in linear time. Then, we
develop a new branch-and-bound algorithm with a carefully-designed pivot
re-selection strategy to enumerate all $k$-plexes, which outputs all $k$-plexes
in $O(n^2\gamma_k^n)$ time theoretically, where $n$ is the number of vertices
of the graph and $\gamma_k$ is strictly smaller than 2. In addition, a parallel
version of the proposed algorithm is further developed to scale up to process
large real-world graphs. Finally, extensive experimental results show that the
proposed sequential algorithm can achieve up to $2\times$ to $100\times$
speedup over the state-of-the-art sequential algorithms on most benchmark
graphs. The results also demonstrate the high scalability of the proposed
parallel algorithm. For example, on a large real-world graph with more than 200
million edges, our parallel algorithm can finish the computation within two
minutes, while the state-of-the-art parallel algorithm cannot terminate within
24 hours.


title: Decoupled Mixup for Data-efficient Learning

abstract:
  Mixup is an efficient data augmentation approach that improves the
generalization of neural networks by smoothing the decision boundary with mixed
data. Recently, dynamic mixup methods improve previous static policies (e.g.,
linear interpolation) by maximizing discriminative regions or maintaining the
salient objects in mixed samples. We notice that The mixed samples from dynamic
policies are more separable than the static ones while preventing models from
overfitting. Inspired by this finding, we first argue that there exists an
over-smoothing issue in the mixup objective, which focuses on regression the
mixing ratio instead of identifying discriminative features. We are therefore
prompted to propose a decoupled mixup (DM) loss that can adaptively mine
discriminative features without losing smoothness. DM enables static mixup
methods to achieve comparable performance with dynamic methods while avoiding
heavy computational overhead. This also leads to an interesting objective
design problem for mixup training that we need to focus not only on smoothing
the decision boundaries but also on identifying discriminative features.
Extensive experiments on supervised and semi-supervised learning benchmarks
across seven classification datasets validate the effectiveness of DM by
equipping with various mixup methods.


title: Preference Robust Modified Optimized Certainty Equivalent

abstract:
  Ben-Tal and Teboulle \cite{BTT86} introduce the concept of optimized
certainty equivalent (OCE) of an uncertain outcome as the maximum present value
of a combination of the cash to be taken out from the uncertain income at
present and the expected utility value of the remaining uncertain income. In
this paper, we consider two variations of the OCE. First, we introduce a
modified OCE by maximizing the combination of the utility of the cash and the
expected utility of the remaining uncertain income so that the combined
quantity is in a unified utility value. Second, we consider a situation where
the true utility function is unknown but it is possible to use partially
available information to construct a set of plausible utility functions. To
mitigate the risk arising from the ambiguity, we introduce a robust model where
the modified OCE is based on the worst-case utility function from the ambiguity
set. In the case when the ambiguity set of utility functions is constructed by
a Kantorovich ball centered at a nominal utility function, we show how the
modified OCE and the corresponding worst case utility function can be
identified by solving two linear programs alternatively. We also show the
robust modified OCE is statistically robust in a data-driven environment where
the underlying data are potentially contaminated. Some preliminary numerical
results are reported to demonstrate the performance of the modified OCE and the
robust modified OCE model.


title:
Performance-Robustness Tradeoffs in Adversarially Robust
  Linear-Quadratic Control

abstract:
  While $\mathcal{H}_\infty$ methods can introduce robustness against
worst-case perturbations, their nominal performance under conventional
stochastic disturbances is often drastically reduced. Though this fundamental
tradeoff between nominal performance and robustness is known to exist, it is
not well-characterized in quantitative terms. Toward addressing this issue, we
borrow from the increasingly ubiquitous notion of adversarial training from
machine learning to construct a class of controllers which are optimized for
disturbances consisting of mixed stochastic and worst-case components. We find
that this problem admits a stationary optimal controller that has a simple
analytic form closely related to suboptimal $\mathcal{H}_\infty$ solutions. We
then provide a quantitative performance-robustness tradeoff analysis, in which
system-theoretic properties such as controllability and stability explicitly
manifest in an interpretable manner. This provides practitioners with general
guidance for determining how much robustness to incorporate based on a priori
system knowledge. We empirically validate our results by comparing the
performance of our controller against standard baselines, and plotting tradeoff
curves.


title: Support theory for the small quantum group and the Springer resolution

abstract:
  We consider the small quantum group u_q(G), for an almost-simple algebraic
group G over the complex numbers and a root of unity q of sufficiently large
order. We show that the Balmer spectrum for the small quantum group in type A
admits a continuous surjection P(\~N) \to Spec(stab u_q(G)) from the
(projectivized) Springer resolution. This surjection is shown to be a
homeomorphism over a dense open subset in the spectrum. In type A_1 we
calculate the Balmer spectrum precisely, where it is shown to be the
projectivized nilpotent cone. Our results extend to arbitrary Dynkin type
provided certain conjectures hold for the small quantum Borel. At the
conclusion of the paper we touch on relations with geometric representation
theory and logarithmic TQFTs, as represented in works of
Arkhipov-Bezrukavnikov-Ginzburg and Schweigert-Woike respectively.