title: Conditional Injective Flows for Bayesian Imaging

abstract:
  Most deep learning models for computational imaging regress a single
reconstructed image. In practice, however, ill-posedness, nonlinearity, model
mismatch, and noise often conspire to make such point estimates misleading or
insufficient. The Bayesian approach models images and (noisy) measurements as
jointly distributed random vectors and aims to approximate the posterior
distribution of unknowns. Recent variational inference methods based on
conditional normalizing flows are a promising alternative to traditional MCMC
methods, but they come with drawbacks: excessive memory and compute demands for
moderate to high resolution images and underwhelming performance on hard
nonlinear problems. In this work, we propose C-Trumpets -- conditional
injective flows specifically designed for imaging problems, which greatly
diminish these challenges. Injectivity reduces memory footprint and training
time while low-dimensional latent space together with architectural innovations
like fixed-volume-change layers and skip-connection revnet layers, C-Trumpets
outperform regular conditional flow models on a variety of imaging and image
restoration tasks, including limited-view CT and nonlinear inverse scattering,
with a lower compute and memory budget. C-Trumpets enable fast approximation of
point estimates like MMSE or MAP as well as physically-meaningful uncertainty
quantification.


title:
Higher-Order SGFEM for One-Dimensional Interface Elliptic Problems with
  Discontinuous Solutions

abstract:
  We study a class of enriched unfitted finite element or generalized finite
element methods (GFEM) to solve a larger class of interface problems, that is,
1D elliptic interface problems with discontinuous solutions, including those
having implicit or Robin-type interface jump conditions. The major challenge of
GFEM development is to construct enrichment functions that capture the imposed
discontinuity of the solution while keeping the condition number from fast
growth. The linear stable generalized finite element method (SGFEM) was
recently developed using one enrichment function. We generalized it to an
arbitrary degree using two simple discontinuous one-sided enrichment functions.
Optimal order convergence in the $L^2$ and broken $H^1$-norms are established.
So is the optimal order convergence at all nodes. To prove the efficiency of
the SGFEM, the enriched linear, quadratic, and cubic elements are applied to a
multi-layer wall model for drug-eluting stents in which zero-flux jump
conditions and implicit concentration interface conditions are both present.


title:
Designing Creative AI Partners with COFI: A Framework for Modeling
  Interaction in Human-AI Co-Creative Systems

abstract:
  Human-AI co-creativity involves both humans and AI collaborating on a shared
creative product as partners. In a creative collaboration, interaction
dynamics, such as turn-taking, contribution type, and communication, are the
driving forces of the co-creative process. Therefore the interaction model is a
critical and essential component for effective co-creative systems. There is
relatively little research about interaction design in the co-creativity field,
which is reflected in a lack of focus on interaction design in many existing
co-creative systems. The primary focus of co-creativity research has been on
the abilities of the AI. This paper focuses on the importance of interaction
design in co-creative systems with the development of the Co-Creative Framework
for Interaction design (COFI) that describes the broad scope of possibilities
for interaction design in co-creative systems. Researchers can use COFI for
modeling interaction in co-creative systems by exploring alternatives in this
design space of interaction. COFI can also be beneficial while investigating
and interpreting the interaction design of existing co-creative systems. We
coded a dataset of existing 92 co-creative systems using COFI and analyzed the
data to show how COFI provides a basis to categorize the interaction models of
existing co-creative systems. We identify opportunities to shift the focus of
interaction models in co-creativity to enable more communication between the
user and AI leading to human-AI partnerships.


title:
Just Fine-tune Twice: Selective Differential Privacy for Large Language
  Models

abstract:
  With the increasing adoption of NLP models in real-world products, it becomes
more and more important to protect these models from privacy leakage. Because
private information in language data is sparse, previous research formalized a
Selective-Differential-Privacy (SDP) notion to provide protection for sensitive
tokens detected by policy functions, and prove its effectiveness on RNN-based
models. But the previous mechanism requires separating the private and public
model parameters and thus cannot be applied on large attention-based models. In
this paper, we propose a simple yet effective just-fine-tune-twice privacy
mechanism to first fine-tune on in-domain redacted data and then on in-domain
private data, to achieve SDP for large Transformer-based language models. We
also design explicit and contextual policy functions to provide protections at
different levels. Experiments show that our models achieve strong performance
while staying robust to the canary insertion attack. We further show that even
under low-resource settings with a small amount of in-domain data, SDP can
still improve the model utility. We will release the code, data and models to
facilitate future research.


title:
Induced Gravitational Waves from Multi-Sound Speed Resonances during
  Cosmological Inflation

abstract:
  We explore the possibility of multi-parametric resonances from time varying
sound speed during cosmological inflation. In particular, we fix our set-up to
the simpler case beyond a single oscillation model already explored in
literature: two sinusoidal harmonics around a constant sound speed equal to
one. We find that, within the perturbative regime, except for some certain
extreme corners of the parameter space, the primordial density spectrum is
characterized by two groups of amplified peaks centered around two critical
oscillatory frequencies of the sound speed. As a general result, we show that
the energy spectrum of the secondary induced GWs from the inflationary era has
a single major broad peak, whereas the one from the radiation dominated phase
consists of one/two principle peak-like configuration(s) for relatively
small/large ratio of two oscillatory frequencies. The GW relic stochastic
backgrounds carry a gravitational memory of the parametric resonances during
inflation. GW signals from double sound speed resonances can be tested in
complementary channels from Pulsar-timing radio-astronomy, space and
terrestrial GW interferometers.


title: Investigating the Impact of Forgetting in Software Development

abstract:
  Context: Forgetting is defined as a gradual process of losing information.
Even though there are many studies demonstrating the effect of forgetting in
software development, to the best of our knowledge, no study explores the
impact of forgetting in software development using a controlled experiment
approach. Objective: We would like to provide insights on the impact of
forgetting in software development projects. We want to examine whether the
recency & frequency of interaction impact forgetting in software development.
Methods: We will conduct an experiment that examines the impact of forgetting
in software development. Participants will first do an initial task. According
to their initial task performance, they will be assigned to either the
experiment or the control group. The experiment group will then do two
additional tasks to enhance their exposure to the code. Both groups will then
do a final task to see if additional exposure to the code benefits the
experiment group's performance in the final task. Finally, we will conduct a
survey and a recall task with the same participants to collect data about their
perceptions of forgetting and quantify their memory performance, respectively.


title: Twin-width can be exponential in treewidth

abstract:
  For any small positive real $\varepsilon$ and integer $t >
\frac{1}{\varepsilon}$, we build a graph with a vertex deletion set of size $t$
to a tree, and twin-width greater than $2^{(1-\varepsilon) t}$. In particular,
this shows that the twin-width is sometimes exponential in the treewidth, in
the so-called oriented twin-width and grid number, and that adding an apex may
multiply the twin-width by at least $2-\varepsilon$. Except for the one in
oriented twin-width, these lower bounds are essentially tight.


title:
Minimal induced subgraphs of the class of 2-connected non-Hamiltonian
  wheel-free graphs

abstract:
  Given a graph $G$ and a graph property $P$ we say that $G$ is minimal with
respect to $P$ if no proper induced subgraph of $G$ has the property $P$. An
HC-obstruction is a minimal 2-connected non-Hamiltonian graph. Given a graph
$H$, a graph $G$ is $H$-free if $G$ has no induced subgraph isomorphic to $H$.
The main motivation for this paper originates from a theorem of Duffus, Gould,
and Jacobson (1981), which characterizes all the minimal connected graphs with
no Hamiltonian path. In 1998, Brousek characterized all the claw-free
HC-obstructions. On a similar note, Chiba and Furuya (2021), characterized all
(not only the minimal) 2-connected non-Hamiltonian $\{K_{1,3},
N_{3,1,1}\}$-free graphs. Recently, Cheriyan, Hajebi, and two of us (2022),
characterized all triangle-free HC-obstructions and all the HC-obstructions
which are split graphs. A wheel is a graph obtained from a cycle by adding a
new vertex with at least three neighbors in the cycle. In this paper we
characterize all the HC-obstructions which are wheel-free graphs.


title:
Abadie's Kappa and Weighting Estimators of the Local Average Treatment
  Effect

abstract:
  In this paper we study the finite sample and asymptotic properties of various
weighting estimators of the local average treatment effect (LATE), several of
which are based on Abadie (2003)'s kappa theorem. Our framework presumes a
binary endogenous explanatory variable ("treatment") and a binary instrumental
variable, which may only be valid after conditioning on additional covariates.
We argue that one of the Abadie estimators, which we show is weight normalized,
is likely to dominate the others in many contexts. A notable exception is in
settings with one-sided noncompliance, where certain unnormalized estimators
have the advantage of being based on a denominator that is bounded away from
zero. We use a simulation study and three empirical applications to illustrate
our findings. In applications to causal effects of college education using the
college proximity instrument (Card, 1995) and causal effects of childbearing
using the sibling sex composition instrument (Angrist and Evans, 1998), the
unnormalized estimates are clearly unreasonable, with "incorrect" signs,
magnitudes, or both. Overall, our results suggest that (i) the relative
performance of different kappa weighting estimators varies with features of the
data-generating process; and that (ii) the normalized version of Tan (2006)'s
estimator may be an attractive alternative in many contexts. Applied
researchers with access to a binary instrumental variable should also consider
covariate balancing or doubly robust estimators of the LATE.


title:
Self-Similarity Priors: Neural Collages as Differentiable Fractal
  Representations

abstract:
  Many patterns in nature exhibit self-similarity: they can be compactly
described via self-referential transformations. Said patterns commonly appear
in natural and artificial objects, such as molecules, shorelines, galaxies and
even images. In this work, we investigate the role of learning in the automated
discovery of self-similarity and in its utilization for downstream tasks. To
this end, we design a novel class of implicit operators, Neural Collages, which
(1) represent data as the parameters of a self-referential, structured
transformation, and (2) employ hypernetworks to amortize the cost of finding
these parameters to a single forward pass. We investigate how to leverage the
representations produced by Neural Collages in various tasks, including data
compression and generation. Neural Collages image compressors are orders of
magnitude faster than other self-similarity-based algorithms during encoding
and offer compression rates competitive with implicit methods. Finally, we
showcase applications of Neural Collages for fractal art and as deep generative
models.


title:
CILDA: Contrastive Data Augmentation using Intermediate Layer Knowledge
  Distillation

abstract:
  Knowledge distillation (KD) is an efficient framework for compressing
large-scale pre-trained language models. Recent years have seen a surge of
research aiming to improve KD by leveraging Contrastive Learning, Intermediate
Layer Distillation, Data Augmentation, and Adversarial Training. In this work,
we propose a learning based data augmentation technique tailored for knowledge
distillation, called CILDA. To the best of our knowledge, this is the first
time that intermediate layer representations of the main task are used in
improving the quality of augmented samples. More precisely, we introduce an
augmentation technique for KD based on intermediate layer matching using
contrastive loss to improve masked adversarial data augmentation. CILDA
outperforms existing state-of-the-art KD approaches on the GLUE benchmark, as
well as in an out-of-domain evaluation.


title:
MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided
  Adaptation

abstract:
  Pre-trained language models have demonstrated superior performance in various
natural language processing tasks. However, these models usually contain
hundreds of millions of parameters, which limits their practicality because of
latency requirements in real-world applications. Existing methods train small
compressed models via knowledge distillation. However, performance of these
small models drops significantly compared with the pre-trained models due to
their reduced model capacity. We propose MoEBERT, which uses a
Mixture-of-Experts structure to increase model capacity and inference speed. We
initialize MoEBERT by adapting the feed-forward neural networks in a
pre-trained model into multiple experts. As such, representation power of the
pre-trained model is largely retained. During inference, only one of the
experts is activated, such that speed can be improved. We also propose a
layer-wise distillation method to train MoEBERT. We validate the efficiency and
effectiveness of MoEBERT on natural language understanding and question
answering tasks. Results show that the proposed method outperforms existing
task-specific distillation algorithms. For example, our method outperforms
previous approaches by over 2% on the MNLI (mismatched) dataset. Our code is
publicly available at https://github.com/SimiaoZuo/MoEBERT.


title: Limit Theorems for Patterns in Ranked Tree-Child Networks

abstract:
  We prove limit laws for the number of occurrences of a pattern on the fringe
of a ranked tree-child network which is picked uniformly at random. Our results
extend the limit law for cherries proved by Bienvenu et al. (2022). For
patterns of height $1$ and $2$, we show that they either occur frequently (mean
is asymptotically linear and limit law is normal) or sporadically (mean is
asymptotically constant and limit law is Poisson) or not all (mean tends to $0$
and limit law is degenerate). We expect that these are the only possible limit
laws for any fringe pattern.


title: Astrometric Gravitational-Wave Detection via Stellar Interferometry

abstract:
  We evaluate the potential for gravitational-wave (GW) detection in the
frequency band from 10 nHz to 1 $\mu$Hz using extremely high-precision
astrometry of a small number of stars. In particular, we argue that
non-magnetic, photometrically stable hot white dwarfs (WD) located at $\sim$
kpc distances may be optimal targets for this approach. Previous studies of
astrometric GW detection have focused on the potential for less precise surveys
of large numbers of stars; our work provides an alternative optimization
approach to this problem. Interesting GW sources in this band are expected at
characteristic strains around $h_c \sim 10^{-17} \times
\left(\mu\text{Hz}/f_{\text{GW}}\right)$. The astrometric angular precision
required to see these sources is $\Delta \theta \sim h_c$ after integrating for
a time $T \sim 1/f_{\text{GW}}$. We show that jitter in the photometric center
of WD of this type due to starspots is bounded to be small enough to permit
this high-precision, small-$N$ approach. We discuss possible noise arising from
stellar reflex motion induced by orbiting objects and show how it can be
mitigated. The only plausible technology able to achieve the requisite
astrometric precision is a space-based stellar interferometer. Such a future
mission with few-meter-scale collecting dishes and baselines of
$\mathcal{O}(100\text{ km})$ is sufficient to achieve the target precision.
This collector size is broadly in line with the collectors proposed for some
formation-flown, space-based astrometer or optical synthetic-aperature
imaging-array concepts proposed for other science reasons. The proposed
baseline is however somewhat larger than the km-scale baselines discussed for
those concepts, but we see no fundamental technical obstacle to utilizing such
baselines. A mission of this type thus also holds the promise of being one of
the few ways to access interesting GW sources in this band.


title:
Host movement, transmission hot spots, and vector-borne disease dynamics
  on spatial networks

abstract:
  We examine how spatial heterogeneity combines with mobility network structure
to influence vector-borne disease dynamics. Specifically, we consider a
Ross-Macdonald-type disease model on $n$ spatial locations that are coupled by
host movement on a strongly connected, weighted, directed graph. We derive a
closed form approximation to the domain reproduction number using a Laurent
series expansion, and use this approximation to compute sensitivities of the
basic reproduction number to model parameters. To illustrate how these results
can be used to help inform mitigation strategies, as a case study we apply
these results to malaria dynamics in Namibia, using published cell phone data
and estimates for local disease transmission. Our analytical results are
particularly useful for understanding drivers of transmission when mobility
sinks and transmission hot spots do not coincide.


title:
DialAug: Mixing up Dialogue Contexts in Contrastive Learning for Robust
  Conversational Modeling

abstract:
  Retrieval-based conversational systems learn to rank response candidates for
a given dialogue context by computing the similarity between their vector
representations. However, training on a single textual form of the multi-turn
context limits the ability of a model to learn representations that generalize
to natural perturbations seen during inference. In this paper we propose a
framework that incorporates augmented versions of a dialogue context into the
learning objective. We utilize contrastive learning as an auxiliary objective
to learn robust dialogue context representations that are invariant to
perturbations injected through the augmentation method. We experiment with four
benchmark dialogue datasets and demonstrate that our framework combines well
with existing augmentation methods and can significantly improve over baseline
BERT-based ranking architectures. Furthermore, we propose a novel data
augmentation method, ConMix, that adds token level perturbations through
stochastic mixing of tokens from other contexts in the batch. We show that our
proposed augmentation method outperforms previous data augmentation approaches,
and provides dialogue representations that are more robust to common
perturbations seen during inference.


title:
Space-sequential particle filters for high-dimensional dynamical systems
  described by stochastic differential equations

abstract:
  We introduce a novel methodology for particle filtering in dynamical systems
where the evolution of the signal of interest is described by a SDE and
observations are collected instantaneously at prescribed time instants. The new
approach includes the discretisation of the SDE and the design of efficient
particle filters for the resulting discrete-time state-space model. The
discretisation scheme converges with weak order 1 and it is devised to create a
sequential dependence structure along the coordinates of the discrete-time
state vector. We introduce a class of space-sequential particle filters that
exploits this structure to improve performance when the system dimension is
large. This is numerically illustrated by a set of computer simulations for a
stochastic Lorenz 96 system with additive noise. The new space-sequential
particle filters attain approximately constant estimation errors as the
dimension of the Lorenz 96 system is increased, with a computational cost that
increases polynomially, rather than exponentially, with the system dimension.
  Besides the new numerical scheme and particle filters, we provide in this
paper a general framework for discrete-time filtering in continuous-time
dynamical systems described by a SDE and instantaneous observations. Provided
that the SDE is discretised using a weakly-convergent scheme, we prove that the
marginal posterior laws of the resulting discrete-time state-space model
converge to the posterior marginal posterior laws of the original
continuous-time state-space model under a suitably defined metric. This result
is general and not restricted to the numerical scheme or particle filters
specifically studied in this manuscript.


title: Dust dynamics in RAMSES -- I. Methods and turbulent acceleration

abstract:
  Supernova ejecta and stellar winds are believed to produce interstellar dust
grains with relatively large sizes. One way to produce smaller grains is via
the shattering of large grains that have been stochastically accelerated to
high velocities. To understand this stochastic acceleration, we have
implemented novel magnetohydrodynamic(MHD)-particle-in-cell(PIC) methods into
the astrophysical fluid code RAMSES. We treat dust grains as a set of massive
"superparticles" that experience aerodynamic drag and Lorentz force. We subject
our code to a range of numerical tests designed to validate our method in
different physical conditions, as well as to illustrate possible mechanisms by
which grains can be accelerated. As a final test as well as a foundation for
future work, we present the results of decaying dusty MHD turbulence
simulations with grain parameters chosen to resemble 1-2 $\mu$m grains in
typical cold neutral medium conditions. We find that in these conditions, these
grains can be effectively accelerated to well beyond their shattering
velocities. This is true for both electrically charged and neutral grains.
While the peak of the gas-grain relative drift velocity distribution is higher
for neutral grains, the drift velocity distribution of charged grains exhibits
an extended exponential tail out to much greater velocities. Even so, the
shapes of the distributions are such that the extra gas-grain coupling provided
by the Lorentz force offers grains relative protection from shattering.


title:
Data-Centric Distrust Quantification for Responsible AI: When
  Data-driven Outcomes Are Not Reliable

abstract:
  At the same time that AI and machine learning are becoming central to human
life, their potential harms become more vivid. In the presence of such
drawbacks, a critical question one needs to address before using these
data-driven technologies to make a decision is whether to trust their outcomes.
Aligned with recent efforts on data-centric AI, this paper proposes a novel
approach to address the trust question through the lens of data, by associating
data sets with distrust quantification that specify their scope of use for
predicting future query points. The distrust values raise warning signals when
a prediction based on a dataset is questionable and are valuable alongside
other techniques for trustworthy AI. We propose novel algorithms for computing
the distrust values in the neighborhood of a query point efficiently and
effectively. Learning the necessary components of the measures from the data
itself, our sub-linear algorithms scale to very large and multi-dimensional
settings. Besides demonstrating the efficiency of our algorithms, our extensive
experiments reflect a consistent correlation between distrust values and model
performance. This underscores the message that when the distrust value of a
query point is high, the prediction outcome should be discarded or at least not
considered for critical decisions.


title: Safe Self-Refinement for Transformer-based Domain Adaptation

abstract:
  Unsupervised Domain Adaptation (UDA) aims to leverage a label-rich source
domain to solve tasks on a related unlabeled target domain. It is a challenging
problem especially when a large domain gap lies between the source and target
domains. In this paper we propose a novel solution named SSRT (Safe
Self-Refinement for Transformer-based domain adaptation), which brings
improvement from two aspects. First, encouraged by the success of vision
transformers in various vision tasks, we arm SSRT with a transformer backbone.
We find that the combination of vision transformer with simple adversarial
adaptation surpasses best reported Convolutional Neural Network (CNN)-based
results on the challenging DomainNet benchmark, showing its strong transferable
feature representation. Second, to reduce the risk of model collapse and
improve the effectiveness of knowledge transfer between domains with large
gaps, we propose a Safe Self-Refinement strategy. Specifically, SSRT utilizes
predictions of perturbed target domain data to refine the model. Since the
model capacity of vision transformer is large and predictions in such
challenging tasks can be noisy, a safe training mechanism is designed to
adaptively adjust learning configuration. Extensive evaluations are conducted
on several widely tested UDA benchmarks and SSRT achieves consistently the best
performances, including 85.43% on Office-Home, 88.76% on VisDA-2017 and 45.2%
on DomainNet.


title: Circuit-theoretic Line Outage Distribution Factor

abstract:
  This work presents the design of AC line outage distribution factor created
from the circuit-theoretic power flow models. Experiment results are shown to
demonstrate its efficacy in quantifying the impact of line outages on the grid,
and its resulting potential for fast contingency screening.


title: Stable submanifolds in the product of projective spaces II

abstract:
  We prove that there do not exist odd-dimensional stable compact minimal
immersions in the product of two complex projective spaces. We also prove that
the only stable compact minimal immersions in the product of a quaternionic
projective space with any other Riemannian manifold are the products of
quaternionic projective subspaces with compact stable minimal immersions of the
second manifold in the Riemmanian product. These generalize similar results of
the second-named author of immersions with low dimensions or codimensions to
immersions with arbitrary dimensions. In addition, we prove that the only
stable compact minimal immersions in the product of a octonionic projective
plane with any other Riemannian manifold are the products of octonionic
projective subspaces with compact stable minimal immersions of the second
manifold in the Riemmanian product.


title: Homonuclear J-Coupling Spectroscopy using J-Synchronized Echo Detection

abstract:
  In the strong coupling regime with J-coupling much larger than chemical shift
differences, J-coupling spectroscopy enables spectral identification of
molecules even when conventional NMR fails. While this classically required the
presence of a heteronucleus, we recently showed that J-coupling spectra can be
acquired in many homonuclear systems using spin-lock induced crossing (SLIC).
Here, we present an alternative method using a spin echo train in lieu of a
spin-locking SLIC pulse, which has a number of advantages. In particular, spin
echo acquisition within the pulse train enables simultaneous collection of time
and frequency data. The resulting 2D spectrum can be used to study dynamic spin
evolution, and the time domain data can be averaged to create a 1D J-coupling
spectrum with increased signal-to-noise ratio.


title:
LO-mode phonon of KCl and NaCl at 300 K by inelastic X ray scattering
  measurements and first principles calculations

abstract:
  Longitudinal-optical (LO) mode phonon branches of KCl and NaCl were measured
using inelastic X-ray scattering (IXS) at 300 K and calculated by the
first-principles phonon calculation with the stochastic self-consistent
harmonic approximation. Spectral shapes of the IXS measurements and calculated
spectral functions agreed well. We analyzed the calculated spectral functions
that provide higher resolutions of the spectra than the IXS measurements. Due
to strong anharmonicity, the spectral functions of these phonon branches have
several peaks and the LO modes along $\Gamma$--L paths are disconnected.


title:
Relative accelerations characterize the hydrodynamic interaction of
  cloud droplets

abstract:
  Water droplets coalesce into larger ones in atmospheric clouds to form rain.
But droplets on collision courses do not always coalesce due to the cushioning
effects of the air between them. The extent to which these so-called
hydrodynamic interactions reduce coalescence rates is embodied in the collision
efficiency, which is often small and is not generally known. In order to
characterize the mechanisms that determine the collision efficiency, we
exploited new time-resolved three-dimensional droplet tracking techniques to
measure the positions of cloud droplet pairs settling through quiescent air. We
did so with an unprecedented precision that enabled us to calculate the
relative positions, velocities, and accelerations of the droplets at droplet
surface-to-surface separations as small as about one-tenth of a droplet
diameter. We show that relative accelerations clearly distinguish coalescing
from non-coalescing droplet trajectories, the former being associated with
relative accelerations that exceeded a threshold value. We outline how relative
accelerations relate to hydrodynamic interactions, and present scaling
arguments that predict the threshold relative acceleration. We speculate that
the relative acceleration distribution of droplets in turbulent clouds can
parameterize the collision efficiency, and that this distribution together with
the well-known relative position and velocity distributions can generate a
physical description of both the collision and coalescence rates of cloud
droplets.


title: Sparsely Activated Mixture-of-Experts are Robust Multi-Task Learners

abstract:
  Traditional multi-task learning (MTL) methods use dense networks that use the
same set of shared weights across several different tasks. This often creates
interference where two or more tasks compete to pull model parameters in
different directions. In this work, we study whether sparsely activated
Mixture-of-Experts (MoE) improve multi-task learning by specializing some
weights for learning shared representations and using the others for learning
task-specific information. To this end, we devise task-aware gating functions
to route examples from different tasks to specialized experts which share
subsets of network weights conditioned on the task. This results in a sparsely
activated multi-task model with a large number of parameters, but with the same
computational cost as that of a dense model. We demonstrate such sparse
networks to improve multi-task learning along three key dimensions: (i)
transfer to low-resource tasks from related tasks in the training mixture; (ii)
sample-efficient generalization to tasks not seen during training by making use
of task-aware routing from seen related tasks; (iii) robustness to the addition
of unrelated tasks by avoiding catastrophic forgetting of existing tasks.


title:
Optimal H\"older Regularity of Solution Operator to the
  $\bar\partial$-equation on Product Domains

abstract:
  This note seeks to prove the existence of a canonical solution operator to
the $\bar\partial$-equation that preserves H\"older regularity on product
domains. It is a well known fact that such solution operators do not in general
gain H\"older regularity, and as such, our solution operator is optimal in this
regard.


title:
Unification of Mixed Hilbert-Space Representations in Condensed Matter
  Physics and Quantum Field Theory

abstract:
  We present a unification of mixed-space quantum representations in Condensed
Matter Physics (CMP) and Quantum Field Theory (QFT). The unifying formalism is
based on being able to expand any quantum operator, for bosons, fermions, and
spin systems, using a universal basis operator involving mixed Hilbert spaces
of P and Q, respectively, where P and Q are momentum and position operators in
CMP (which can be considered as a bozonization of free Bloch electrons which
incorporates the Pauli exclusion and Fermi-Dirac distribution), whereas these
are related to the creation and annihilation operators in QFT, where
{\psi}^{{\dag}}=-iP and {\psi}=Q. The expansion coefficient is the Fourier
transform of the Wigner quantum distribution function (lattice Weyl transform)
otherwise known as the characteristic distribution function. Thus, in
principle, bosonization, fermionization and Jordan-Wigner fermionization for
spin systems, as well as the Holstein--Primakoff transformation from boson
operators to the spin operators can be performed depending on the physical
situations and ease in the calculations. In quantum physics, unitary
transformation on the creation and annihilation operators themselves is also
employed, as exemplified by the Bogoliubov transformation. Moreover, whenever
Y(u,v) is already expressed in matrix form, M_{ij}, the Jordan--Schwinger
transformation is a map from matrices to bilinear expressions of creation and
annihilation operators which expedites computation of representations. We show
that the well-known coherent states formulation of quantum physics is a special
case of the present unification. The case of nonequilibrium quantum transport
physics, which not only involves non-Hermitian operators but also time-reversal
symmetry breaking, is discussed in the Appendix.


title: FedVQCS: Federated Learning via Vector Quantized Compressed Sensing

abstract:
  In this paper, a new communication-efficient federated learning (FL)
framework is proposed, inspired by vector quantized compressed sensing. The
basic strategy of the proposed framework is to compress the local model update
at each device by applying dimensionality reduction followed by vector
quantization. Subsequently, the global model update is reconstructed at a
parameter server (PS) by applying a sparse signal recovery algorithm to the
aggregation of the compressed local model updates. By harnessing the benefits
of both dimensionality reduction and vector quantization, the proposed
framework effectively reduces the communication overhead of local update
transmissions. Both the design of the vector quantizer and the key parameters
for the compression are optimized so as to minimize the reconstruction error of
the global model update under the constraint of wireless link capacity. By
considering the reconstruction error, the convergence rate of the proposed
framework is also analyzed for a smooth loss function. Simulation results on
the MNIST and CIFAR-10 datasets demonstrate that the proposed framework
provides more than a 2.5% increase in classification accuracy compared to
state-of-art FL frameworks when the communication overhead of the local model
update transmission is less than 0.1 bit per local model entry.


title:
Calibrating Trust of Multi-Hop Question Answering Systems with
  Decompositional Probes

abstract:
  Multi-hop Question Answering (QA) is a challenging task since it requires an
accurate aggregation of information from multiple context paragraphs and a
thorough understanding of the underlying reasoning chains. Recent work in
multi-hop QA has shown that performance can be boosted by first decomposing the
questions into simpler, single-hop questions. In this paper, we explore one
additional utility of the multi-hop decomposition from the perspective of
explainable NLP: to create explanation by probing a neural QA model with them.
We hypothesize that in doing so, users will be better able to construct a
mental model of when the underlying QA system will give the correct answer.
Through human participant studies, we verify that exposing the decomposition
probes and answers to the probes to users can increase their ability to predict
system performance on a question instance basis. We show that decomposition is
an effective form of probing QA systems as well as a promising approach to
explanation generation. In-depth analyses show the need for improvements in
decomposition systems.


title: Sandwiched Renyi Relative Entropy in AdS/CFT

abstract:
  We explore the role of sandwiched Renyi relative entropy in AdS/CFT and in
finite-dimensional models of holographic quantum error correction. In
particular, in the context of operator algebra quantum error correction, we
discuss a suitable generalization of sandwiched Renyi relative entropy over
finite-dimensional von Neumann algebras. It is then shown that the equality of
bulk and boundary sandwiched relative Renyi entropies is equivalent to
algebraic encoding of bulk and boundary states, the Ryu-Takayanagi formula, the
equality of bulk and boundary relative entropy, and subregion duality. This
adds another item to an equivalence theorem between the last four items
established in arxiv:1607.03901. We then discuss the sandwiched Renyi relative
entropy defined in terms of modular operators, and show that this becomes the
definition naturally suited to the finite-dimensional models of holographic
quantum error correction. Finally, we explore some numerical calculations of
sandwiched Renyi relative entropies for a simple holographic random tensor
network in order to obtain a better understanding of corrections to the exact
equality of bulk and boundary sandwiched relative Renyi entropy.


title: Time dependent correlators of holographic EPR pairs

abstract:
  We study the field correlators between the quark and anti-quark EPR pair in
the super Yang-Mills theory using the holographic description, which is a
string in AdS space with its two ends anchored on the boundary. We consider the
cases that the end points of the string are static, and that end points are
uniformly accelerated in opposite direction where the exact solutions for the
string's profiles are available. In both cases, the two-points correlators of
the boundary fields which are described by the linearized perturbations in the
worldsheet can also be derived exactly and the all-time evolution of the
correlators are obtained. In the case of the accelerating string, the induced
geometry on the string worldsheet has the causal structure of a two-sided AdS
black hole with a wormhole connects two causally disconnected boundaries, which
is a realization of the ER=EPR conjecture. We find that the causality plays an
crucial role in determining the nature of the dispersion relation of the
particles anchoring at the end points of the string, and the feature of the
induced mutual interaction between two particles. In the case of the causally
disconnected two ends, the induced effect from the field gives the dissipative
dynamics of each particles with no dependence of the distance between two ends,
and the induced mutual coupling between two particles vanishes in the late
times, following a power law. When two ends are causally connected, the induced
dispersion relation becomes non-dissipative at the late times. Here we will
also comment on the implication of our findings in the entangled particle
dynamics and the ER=EPR conjecture.


title:
Efficient Reinforcement Learning for Unsupervised Controlled Text
  Generation

abstract:
  Controlled text generation tasks such as unsupervised text style transfer
have increasingly adopted the use of Reinforcement Learning (RL). A major
challenge in applying RL to such tasks is the sparse reward, which is available
only after the full text is generated. Sparse rewards, combined with a large
action space make RL training sample-inefficient and difficult to converge.
Recently proposed reward-shaping strategies to address this issue have shown
only negligible gains. In contrast, this work proposes a novel approach that
provides dense rewards to each generated token. We evaluate our approach by its
usage in unsupervised text style transfer. Averaged across datasets, our style
transfer system improves upon current state-of-art systems by 21\% on human
evaluation and 12\% on automatic evaluation. Upon ablated comparison with the
current reward shaping approach (the `roll-out strategy'), using dense rewards
improves the overall style transfer quality by 22\% based on human evaluation.
Further the RL training is 2.5 times as sample efficient, and 7 times faster.


title: Theory of Graph Neural Networks: Representation and Learning

abstract:
  Graph Neural Networks (GNNs), neural network architectures targeted to
learning representations of graphs, have become a popular learning model for
prediction tasks on nodes, graphs and configurations of points, with wide
success in practice. This article summarizes a selection of the emerging
theoretical results on approximation and learning properties of widely used
message passing GNNs and higher-order GNNs, focusing on representation,
generalization and extrapolation. Along the way, it summarizes mathematical
connections.


title: Galoisian Galois Theory

abstract:
  These notes are an exposition of Galois Theory from the original Lagrangian
and Galoisian point of view. A particular effort was made here to better
understand the connection between Lagrange's purely combinatorial approach and
Galois algebraic extensions of the latter. Moreover, stimulated by the
necessities of present day computer explorations, the algorithmic approach has
been given priority here over every other aspect of presentation. In
particular, you may not find here the clean simplistic look characteristic of
the classical exposition of E. Artin. In contrast these notes should provide a
good starting point in attempting constructions in this most difficult
computational arena.


title: Quantum Rabi interferometry of motion and radiation

abstract:
  The precise determination of a displacement of a mechanical oscillator or a
microwave field in a predetermined direction in phase space can be carried out
with trapped ions or superconducting circuits, respectively, by coupling the
oscillator with ancilla qubits. Through that coupling, the displacement
information is transferred to the qubits which are then subsequently read out.
However, unambiguous estimation of displacement in an unknown direction in the
phase space has not been attempted in such oscillator-qubit systems. Here, we
propose a hybrid oscillator-qubit interferometric setup for the unambiguous
estimation of phase space displacements in an arbitrary direction, based on
feasible Rabi interactions beyond the rotating-wave approximation. Using such a
hybrid Rabi interferometer for quantum sensing, we show that the performance is
superior to the ones attained by single-mode estimation schemes and a
conventional interferometer based on Jaynes-Cummings interactions. Moreover, we
find that the sensitivity of the Rabi interferometer is independent of the
thermal occupation of the oscillator mode, and thus cooling it to the ground
state before sensing is not required. We also perform a thorough investigation
of the effect of qubit dephasing and oscillator thermalization. We find the
interferometer to be fairly robust, outperforming different benchmark
estimation schemes even for large dephasing and thermalization.


title:
Two-dimensional plasma density evolution local to the inversion layer
  during sawtooth crash events using Beam Emission Spectroscopy

abstract:
  We present methods for analyzing Beam Emission Spectroscopy (BES) data to
obtain the plasma density evolution associated with rapid sawtooth crash events
at the DIII-D tokamak. BES allows coverage over a 2-D spatial plane, inherently
local measurements, with fast time responses, and therefore provides a valuable
new channel for data during sawtooth events. A method is developed to remove
sawtooth-induced edge-light pulses contained in the BES data. The edge light
pulses appear to be from the $\rm{D}_{\alpha}$ emission produced by edge
recycling during sawtooth events, and are large enough that traditional
spectroscopic filtering and data analysis techniques are insufficient to deduce
physically meaningful quantities. A cross-calibration of 64 BES channels is
performed using a novel method to ensure accurate measurements. For the
large-amplitude density oscillations observed, we discuss and use the
non-linear relationship between BES signal $\delta I/I_{0}$ and plasma density
variation $\delta n_{e}/n_{e0}$. 2-D BES images cover a 8~cm~$\times$~20~cm
region around the sawtooth inversion layer and show large-amplitude density
oscillations, with additional significant spatial variations across the
inversion layer, which grows and peaks near the time of the temperature crash.
The edge light removal technique and method of converting large-amplitude
$\delta I/I_{0}$ to $\delta n_{e}/n_{e0}$ presented here may help analyze other
impulsive MHD phenomena in tokamaks.


title:
BLCU-ICALL at SemEval-2022 Task 1: Cross-Attention Multitasking
  Framework for Definition Modeling

abstract:
  This paper describes the BLCU-ICALL system used in the SemEval-2022 Task 1
Comparing Dictionaries and Word Embeddings, the Definition Modeling subtrack,
achieving 1st on Italian, 2nd on Spanish and Russian, and 3rd on English and
French. We propose a transformer-based multitasking framework to explore the
task. The framework integrates multiple embedding architectures through the
cross-attention mechanism, and captures the structure of glosses through a
masking language model objective. Additionally, we also investigate a simple
but effective model ensembling strategy to further improve the robustness. The
evaluation results show the effectiveness of our solution. We release our code
at: https://github.com/blcuicall/SemEval2022-Task1-DM.


title:
On Acceleration of Gradient-Based Empirical Risk Minimization using
  Local Polynomial Regression

abstract:
  We study the acceleration of the Local Polynomial Interpolation-based
Gradient Descent method (LPI-GD) recently proposed for the approximate solution
of empirical risk minimization problems (ERM). We focus on loss functions that
are strongly convex and smooth with condition number $\sigma$. We additionally
assume the loss function is $\eta$-H\"older continuous with respect to the
data. The oracle complexity of LPI-GD is $\tilde{O}\left(\sigma m^d
\log(1/\varepsilon)\right)$ for a desired accuracy $\varepsilon$, where $d$ is
the dimension of the parameter space, and $m$ is the cardinality of an
approximation grid. The factor $m^d$ can be shown to scale as
$O((1/\varepsilon)^{d/2\eta})$. LPI-GD has been shown to have better oracle
complexity than gradient descent (GD) and stochastic gradient descent (SGD) for
certain parameter regimes. We propose two accelerated methods for the ERM
problem based on LPI-GD and show an oracle complexity of
$\tilde{O}\left(\sqrt{\sigma} m^d \log(1/\varepsilon)\right)$. Moreover, we
provide the first empirical study on local polynomial interpolation-based
gradient methods and corroborate that LPI-GD has better performance than GD and
SGD in some scenarios, and the proposed methods achieve acceleration.


title: TeleGraph: A Benchmark Dataset for Hierarchical Link Prediction

abstract:
  Link prediction is a key problem for network-structured data, attracting
considerable research efforts owing to its diverse applications. The current
link prediction methods focus on general networks and are overly dependent on
either the closed triangular structure of networks or node attributes. Their
performance on sparse or highly hierarchical networks has not been well
studied. On the other hand, the available tree-like benchmark datasets are
either simulated, with limited node information, or small in scale. To bridge
this gap, we present a new benchmark dataset TeleGraph, a highly sparse and
hierarchical telecommunication network associated with rich node attributes,
for assessing and fostering the link inference techniques. Our empirical
results suggest that most of the algorithms fail to produce a satisfactory
performance on a nearly tree-like dataset, which calls for special attention
when designing or deploying the link prediction algorithm in practice.


title: Technical Report: Hybrid Autonomous Intersection Management

abstract:
  This document provides technical details regarding the Hybrid-AIM simulator
that was used in Sharon and Stone (2017).


title:
Benchmarking Generalization via In-Context Instructions on 1,600+
  Language Tasks

abstract:
  How can we measure the generalization of models to a variety of unseen tasks
when provided with their language instructions? To facilitate progress in this
goal, we introduce Natural-Instructions v2, a benchmark of 1,600+ diverse
language tasks and their expert-written instructions. It covers 70+ distinct
task types, such as tagging, in-filling, and rewriting. These tasks are
collected with contributions of NLP practitioners in the community and through
an iterative peer review process to ensure their quality. With this large and
diverse collection of tasks, we are able to rigorously benchmark cross-task
generalization of models -- training on a subset of tasks and evaluating on the
remaining unseen ones. For instance, we quantify generalization as a function
of various scaling parameters, such as the number of observed tasks, the number
of instances, and model sizes. Based on these insights, we introduce
Tk-Instruct, an encoder-decoder Transformer that is trained to follow a variety
of in-context instructions (plain language task definitions or k-shot examples)
which outperforms existing larger models on our benchmark. We hope this
benchmark facilitates future progress toward more general-purpose language
understanding models.


title:
On the existence of cut points of connected generalized Sierpinski
  carpets

abstract:
  In a previous work joint with Dai and Luo, we show that a connected
generalized Sierpinski carpet (or shortly GSCs) has cut points if and only if
the associated level-$k$ Hata graph has a long tail for all $k\geq 2$. In this
paper, we extend the above result by showing that it suffices to check a finite
number of level-$k$ Hata graphs to reach a conclusion. This criterion provides
a truly "algorithmic" solution to the cut point problem of connected GSCs. Some
interesting examples such as connected GSCs with exactly $n\geq 1$ cut points
are also included in addition.


title: Privacy-Preserving Image Classification Using Isotropic Network

abstract:
  In this paper, we propose a privacy-preserving image classification method
that uses encrypted images and an isotropic network such as the vision
transformer. The proposed method allows us not only to apply images without
visual information to deep neural networks (DNNs) for both training and testing
but also to maintain a high classification accuracy. In addition, compressible
encrypted images, called encryption-then-compression (EtC) images, can be used
for both training and testing without any adaptation network. Previously, to
classify EtC images, an adaptation network was required before a classification
network, so methods with an adaptation network have been only tested on small
images. To the best of our knowledge, previous privacy-preserving image
classification methods have never considered image compressibility and patch
embedding-based isotropic networks. In an experiment, the proposed
privacy-preserving image classification was demonstrated to outperform
state-of-the-art methods even when EtC images were used in terms of
classification accuracy and robustness against various attacks under the use of
two isotropic networks: vision transformer and ConvMixer.


title:
Entanglement and quantum correlations in the XX spin-$1/2$ honeycomb
  lattice

abstract:
  The ground state phase diagram of the dimerized spin-1/2 XX honeycomb model
in presence of a transverse magnetic field (TF) is known. With the absence of
the magnetic field, two quantum phases, namely, the N\'eel and the dimerized
phases have been identified. Moreover, canted N\'eel and the paramagnetic (PM)
phases also emerge by applying the magnetic field. In this paper, using two
complementary numerical exact techniques, Lanczos exact diagonalization, and
Density matrix renormalization group (DMRG) methods, we study this model by
focusing on the quantum correlations, the concurrence, and the quantum discord
(QD) among nearest-neighbor spins. We show that the quantum correlations can
capture the position of the quantum critical points in the whole range of the
ground state phase diagram consistent with previous results. Although the
concurrence and the QD are short-range, informative about long-ranged critical
correlations. In addition, we address a "magnetic-entanglement" behavior that
starts from an entangled field around the saturation field.


title:
Easy-Sec: PUF-Based Rapid and Robust Authentication Framework for the
  Internet of Vehicles

abstract:
  With the rapid growth of new technological paradigms such as the Internet of
Things (IoT), it opens new doors for many applications in the modern era for
the betterment of human life. One of the recent applications of the IoT is the
Internet of Vehicles (IoV) which helps to see unprecedented growth of connected
vehicles on the roads. The IoV is gaining attention due to enhancing traffic
safety and providing low route information. One of the most important and major
requirements of the IoV is preserving security and privacy under strict
latency. Moreover, vehicles are required to be authenticated frequently and
fast considering limited bandwidth, high mobility, and density of the vehicles.
To address the security vulnerabilities and data integrity, an ultralight
authentication scheme has been proposed in this article. Physical Unclonable
Function (PUF) and XOR function are used to authenticate both server and
vehicle in two message flow which makes the proposed scheme ultralight, and
less computation is required. The proposed Easy-Sec can authenticate vehicles
maintaining low latency and resisting known security threats. Furthermore, the
proposed Easy-Sec needs low overhead so that it does not increase the burden of
the IoV network. Computational ( around 4 ms) and Communication (32 bytes)
overhead shows the feasibility, efficiency, and also security features are
depicted using formal analysis, Burrows, Abadi, and Needham (BAN) logic, and
informal analysis to show the robustness of the proposed mechanisms against
security threats.


title: Accelerated Magnonic Motional Cooling with Deep Reinforcement Learning

abstract:
  Achieving fast cooling of motional modes is a prerequisite for leveraging
such bosonic quanta for high-speed quantum information processing. In this
work, we address the aspect of reducing the time limit for cooling below that
constrained by the conventional sideband cooling techniques; and propose a
scheme to apply deep reinforcement learning (DRL) to achieve this. In
particular, we have shown how the scheme can be used effectively to accelerate
the dynamic motional cooling of a macroscopic magnonic sphere, and how it can
be uniformly extended for more complex systems, for example, a tripartite
opto-magno-mechanical system to obtain cooling of the motional mode below the
time bound of coherent cooling. While conventional sideband cooling methods do
not work beyond the well-known rotating wave approximation (RWA) regimes, our
proposed DRL scheme can be applied uniformly to regimes operating within and
beyond the RWA, and thus this offers a new and complete toolkit for rapid
control and generation of macroscopic quantum states for application in quantum
technologies.


title:
Disturbance-free single-pixel imaging camera based on P=0.5 Bernoulli
  modulation and complementary detection

abstract:
  We present a technique called single-pixel imaging camera based on
complementary detection and optimized Bernoulli modulation (CSPI camera), which
can significantly reduce the influence of the disturbance light to single-pixel
imaging (SPI). The numerical simulation demonstrates that when the probability
of the value "1" is P=0.5 for each coded pattern, CSPI camera is
disturbance-free is still disturbance-free even if the intensity fluctuation of
the disturbance light is much larger than the signal's intensity. The
reconstruction results of both SPI and differential SPI are also compared. This
technique of CSPI camera can dramatically promote real application of
single-pixel imaging Lidar.


title:
Quantum critical behavior of entanglement in lattice bosons with
  cavity-mediated long-range interactions

abstract:
  We analyze the ground-state entanglement entropy of the extended Bose-Hubbard
model with infinite-range interactions. This model describes the low-energy
dynamics of ultracold bosons tightly bound to an optical lattice and
dispersively coupled to a cavity mode. The competition between onsite repulsion
and global cavity-induced interactions leads to a rich phase diagram, which
exhibits superfluid, supersolid, and insulating (Mott and checkerboard) phases.
We use a slave-boson treatment of harmonic quantum fluctuations around the
mean-field solution and calculate the entanglement entropy across the phase
transitions. At commensurate filling, the insulator-superfluid transition is
signalled by a singularity in the area-law scaling coefficient of the
entanglement entropy, that is similar to the one reported for the standard
Bose-Hubbard model. Remarkably, at the continuous $\mathbb{Z}_2$
superfluid-to-supersolid transition we find a critical logarithmic term,
regardless of the filling. This behavior originates from the appearance of a
roton mode in the excitation and entanglement spectrum, becoming gapless at the
critical point, and it is characteristic of collective models.


title:
GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing
  with Spatial Smoothness

abstract:
  In recent hyperspectral unmixing (HU) literature, the application of deep
learning (DL) has become more prominent, especially with the autoencoder (AE)
architecture. We propose a split architecture and use a pseudo-ground truth for
abundances to guide the `unmixing network' (UN) optimization. Preceding the UN,
an `approximation network' (AN) is proposed, which will improve the association
between the centre pixel and its neighbourhood. Hence, it will accentuate
spatial correlation in the abundances as its output is the input to the UN and
the reference for the `mixing network' (MN). In the Guided Encoder-Decoder
Architecture for Hyperspectral Unmixing with Spatial Smoothness (GAUSS), we
proposed using one-hot encoded abundances as the pseudo-ground truth to guide
the UN; computed using the k-means algorithm to exclude the use of prior HU
methods. Furthermore, we release the single-layer constraint on MN by
introducing the UN generated abundances in contrast to the standard AE for HU.
Secondly, we experimented with two modifications on the pre-trained network
using the GAUSS method. In GAUSS$_\textit{blind}$, we have concatenated the UN
and the MN to back-propagate the reconstruction error gradients to the encoder.
Then, in the GAUSS$_\textit{prime}$, abundance results of a signal processing
(SP) method with reliable abundance results were used as the pseudo-ground
truth with the GAUSS architecture. According to quantitative and graphical
results for four experimental datasets, the three architectures either
transcended or equated the performance of existing HU algorithms from both DL
and SP domains.


title:
Pushing the Performance Limit of Scene Text Recognizer without Human
  Annotation

abstract:
  Scene text recognition (STR) attracts much attention over the years because
of its wide application. Most methods train STR model in a fully supervised
manner which requires large amounts of labeled data. Although synthetic data
contributes a lot to STR, it suffers from the real-tosynthetic domain gap that
restricts model performance. In this work, we aim to boost STR models by
leveraging both synthetic data and the numerous real unlabeled images,
exempting human annotation cost thoroughly. A robust consistency regularization
based semi-supervised framework is proposed for STR, which can effectively
solve the instability issue due to domain inconsistency between synthetic and
real images. A character-level consistency regularization is designed to
mitigate the misalignment between characters in sequence recognition. Extensive
experiments on standard text recognition benchmarks demonstrate the
effectiveness of the proposed method. It can steadily improve existing STR
models, and boost an STR model to achieve new state-of-the-art results. To our
best knowledge, this is the first consistency regularization based framework
that applies successfully to STR.


title: On sums of $k$-th powers with almost equal primes

abstract:
  For "almost all" sufficiently large $N,$ satisfying necessary congruence
conditions and $k\geq 2$, we show that
  there is an {\bf asymptotic formula} for the number of solutions of the
equation \begin{align*} \begin{split} &N=p_{1}^{k}+p_{2}^{k}+\cdots+p_{s}^{k},
\\ &\left|p_{i}-( N/s)^{1/k}\right|\leq (N/s)^{\theta/k},\ (1\leq i\leq s)
\end{split} \end{align*} with \begin{align*} s\geq \frac{k(k+1)}{2}+1\ \
\textup{and}\ \ \theta\geq
  {\bf 2/3}+\varepsilon. \end{align*}
  This enlarges the effective range of $s$ for which can be obtained by the
method of M\"{a}tomaki and Xuancheng Shao \cite{MS}. [Discorrelation between
primes in short intervals and polynomial phase, Int. Math. Res. Not. IMRN 2021,
no. 16, 12330-12355.]
  The idea is to avoid using the exponential sums (1.2) and Vinogradov mean
value theorems in Lemma 2.4 simultaneously. And the main new ingredient is from
Kumchev and Liu \cite{KL} (see Lemma 2.2).


title: FKreg: A MATLAB toolbox for fast Multivariate Kernel Regression

abstract:
  Kernel smooth is the most fundamental technique for data density and
regression estimation. However, time-consuming is the biggest obstacle for the
application that the direct evaluation of kernel smooth for $N$ samples needs
${O}\left( {{N}^{2}} \right)$ operations. People have developed fast smooth
algorithms using the idea of binning with FFT. Unfortunately, the accuracy is
not controllable, and the implementation for multivariable and its bandwidth
selection for the fast method is not available. Hence, we introduce a new
MATLAB toolbox for fast multivariate kernel regression with the idea of
non-uniform FFT (NUFFT), which implemented the algorithm for $M$ gridding
points with ${O}\left( N+M\log M \right)$ complexity and accuracy
controllability. The bandwidth selection problem utilizes the Fast Monte-Carlo
algorithm to estimate the degree of freedom (DF), saving enormous
cross-validation time even better when data share the same grid space for
multiple regression. Up to now, this is the first toolbox for fast-binning
high-dimensional kernel regression. Moreover, the estimation for local
polynomial regression, the conditional variance for the heteroscedastic model,
and the complex-valued datasets are also implemented in this toolbox. The
performance is demonstrated with simulations and an application on the
quantitive EEG.


title:
Emergence of Self-dual Patterns in Active Colloids with Periodical
  Feedback to Local Density

abstract:
  The central task in the study of self-organization is to explore the general
mechanism of emergences. However, this is inhibited by the missing of a full
knowledge of the microscopic dynamics of emergence. Here, in this study, the
microscopic dynamics of self-organization for patterns is investigated and
quantified in a periodically propelled Quincke system. The periodical coupling
between propulsion and repulsion at the particle level leads to local directed
oscillating particle flows and promises a loop of positive feedback to density
fluctuations. Nevertheless, the global evolution of the resulting cluster phase
is dominated by a global dual transformation. As stable attractors of the dual
transformation, self-dual patterns including stripe patterns and square
lattices can be achieved by tuning the strength and the frequency of
propelling. However, stripes are possible only at strong propelling where
boundary particle flows can form. The findings in this study show that the
dynamics of emergence on different length scales are controlled by different
mechanisms. The competition and the interplay between different microscopic
dynamic processes play the central role in determining the product of
emergence. Moreover, the periodically oscillating self-dual patterns
demonstrate a classical approach to time crystals.


title: Interactiveness Field in Human-Object Interactions

abstract:
  Human-Object Interaction (HOI) detection plays a core role in activity
understanding. Though recent two/one-stage methods have achieved impressive
results, as an essential step, discovering interactive human-object pairs
remains challenging. Both one/two-stage methods fail to effectively extract
interactive pairs instead of generating redundant negative pairs. In this work,
we introduce a previously overlooked interactiveness bimodal prior: given an
object in an image, after pairing it with the humans, the generated pairs are
either mostly non-interactive, or mostly interactive, with the former more
frequent than the latter. Based on this interactiveness bimodal prior we
propose the "interactiveness field". To make the learned field compatible with
real HOI image considerations, we propose new energy constraints based on the
cardinality and difference in the inherent "interactiveness field" underlying
interactive versus non-interactive pairs. Consequently, our method can detect
more precise pairs and thus significantly boost HOI detection performance,
which is validated on widely-used benchmarks where we achieve decent
improvements over state-of-the-arts. Our code is available at
https://github.com/Foruck/Interactiveness-Field.


title: Stress-Testing LiDAR Registration

abstract:
  Point cloud registration (PCR) is an important task in many fields including
autonomous driving with LiDAR sensors. PCR algorithms have improved
significantly in recent years, by combining deep-learned features with robust
estimation methods. These algorithms succeed in scenarios such as indoor scenes
and object models registration. However, testing in the automotive LiDAR
setting, which presents its own challenges, has been limited. The standard
benchmark for this setting, KITTI-10m, has essentially been saturated by recent
algorithms: many of them achieve near-perfect recall.
  In this work, we stress-test recent PCR techniques with LiDAR data. We
propose a method for selecting balanced registration sets, which are
challenging sets of frame-pairs from LiDAR datasets. They contain a balanced
representation of the different relative motions that appear in a dataset, i.e.
small and large rotations, small and large offsets in space and time, and
various combinations of these.
  We perform a thorough comparison of accuracy and run-time on these
benchmarks. Perhaps unexpectedly, we find that the fastest and simultaneously
most accurate approach is a version of advanced RANSAC. We further improve
results with a novel pre-filtering method.


title: DMCS : Density Modularity based Community Search

abstract:
  Community Search, or finding a connected subgraph (known as a community)
containing the given query nodes in a social network, is a fundamental problem.
Most of the existing community search models only focus on the internal
cohesiveness of a community. However, a high-quality community often has high
modularity, which means dense connections inside communities and sparse
connections to the nodes outside the community. In this paper, we conduct a
pioneer study on searching a community with high modularity. We point out that
while modularity has been popularly used in community detection (without query
nodes), it has not been adopted for community search, surprisingly, and its
application in community search (related to query nodes) brings in new
challenges. We address these challenges by designing a new graph modularity
function named Density Modularity. To the best of our knowledge, this is the
first work on the community search problem using graph modularity. The
community search based on the density modularity, termed as DMCS, is to find a
community in a social network that contains all the query nodes and has high
density-modularity. We prove that the DMCS problem is NP-hard. To efficiently
address DMCS, we present new algorithms that run in log-linear time to the
graph size. We conduct extensive experimental studies in real-world and
synthetic networks, which offer insights into the efficiency and effectiveness
of our algorithms. In particular, our algorithm achieves up to 8.5 times higher
accuracy in terms of NMI than baseline algorithms.


title: TVShowGuess: Character Comprehension in Stories as Speaker Guessing

abstract:
  We propose a new task for assessing machines' skills of understanding
fictional characters in narrative stories. The task, TVShowGuess, builds on the
scripts of TV series and takes the form of guessing the anonymous main
characters based on the backgrounds of the scenes and the dialogues. Our human
study supports that this form of task covers comprehension of multiple types of
character persona, including understanding characters' personalities, facts and
memories of personal experience, which are well aligned with the psychological
and literary theories about the theory of mind (ToM) of human beings on
understanding fictional characters during reading. We further propose new model
architectures to support the contextualized encoding of long scene texts.
Experiments show that our proposed approaches significantly outperform
baselines, yet still largely lag behind the (nearly perfect) human performance.
Our work serves as a first step toward the goal of narrative character
comprehension.


title: Searching Intrinsic Dimensions of Vision Transformers

abstract:
  It has been shown by many researchers that transformers perform as well as
convolutional neural networks in many computer vision tasks. Meanwhile, the
large computational costs of its attention module hinder further studies and
applications on edge devices. Some pruning methods have been developed to
construct efficient vision transformers, but most of them have considered image
classification tasks only. Inspired by these results, we propose SiDT, a method
for pruning vision transformer backbones on more complicated vision tasks like
object detection, based on the search of transformer dimensions. Experiments on
CIFAR-100 and COCO datasets show that the backbones with 20\% or 40\%
dimensions/parameters pruned can have similar or even better performance than
the unpruned models. Moreover, we have also provided the complexity analysis
and comparisons with the previous pruning methods.


title:
Local multiscale model reduction using discontinuous Galerkin coupling
  for elasticity problems

abstract:
  In this paper, we consider the constrained energy minimizing generalized
multiscale finite element method (CEM-GMsFEM) with discontinuous Galerkin (DG)
coupling for the linear elasticity equations in highly heterogeneous and high
contrast media. We will introduce the construction of a DG version of the
CEM-GMsFEM, such as auxiliary basis functions and offline basis functions. The
DG version of the method offers some advantages such as flexibility in coarse
grid construction and sparsity of resulting discrete systems. Moreover, to our
best knowledge, this is the first time where the proof of the convergence of
the CEM-GMsFEM in the DG form is given. Some numerical examples will be
presented to illustrate the performance of the method.


title:
Semantic interpretation for convolutional neural networks: What makes a
  cat a cat?

abstract:
  The interpretability of deep neural networks has attracted increasing
attention in recent years, and several methods have been created to interpret
the "black box" model. Fundamental limitations remain, however, that impede the
pace of understanding the networks, especially the extraction of understandable
semantic space. In this work, we introduce the framework of semantic
explainable AI (S-XAI), which utilizes row-centered principal component
analysis to obtain the common traits from the best combination of superpixels
discovered by a genetic algorithm, and extracts understandable semantic spaces
on the basis of discovered semantically sensitive neurons and visualization
techniques. Statistical interpretation of the semantic space is also provided,
and the concept of semantic probability is proposed for the first time. Our
experimental results demonstrate that S-XAI is effective in providing a
semantic interpretation for the CNN, and offers broad usage, including
trustworthiness assessment and semantic sample searching.


title:
Is Blockchain for Internet of Medical Things a Panacea for COVID-19
  Pandemic?

abstract:
  The outbreak of the COVID-19 pandemic has deeply influenced the lifestyle of
the general public and the healthcare system of the society. As a promising
approach to address the emerging challenges caused by the epidemic of
infectious diseases like COVID-19, Internet of Medical Things (IoMT) deployed
in hospitals, clinics, and healthcare centers can save the diagnosis time and
improve the efficiency of medical resources though privacy and security
concerns of IoMT stall the wide adoption. In order to tackle the privacy,
security, and interoperability issues of IoMT, we propose a framework of
blockchain-enabled IoMT by introducing blockchain to incumbent IoMT systems. In
this paper, we review the benefits of this architecture and illustrate the
opportunities brought by blockchain-enabled IoMT. We also provide use cases of
blockchain-enabled IoMT on fighting against the COVID-19 pandemic, including
the prevention of infectious diseases, location sharing and contact tracing,
and the supply chain of injectable medicines. We also outline future work in
this area.


title:
A Hierarchical Terminal Recognition Approach based on Network Traffic
  Analysis

abstract:
  Recognizing the type of connected devices to a network helps to perform
security policies. In smart grids, identifying massive number of grid metering
terminals based on network traffic analysis is almost blank and existing
research has not proposed a targeted end-to-end model to solve the flow
classification problem. Therefore, we proposed a hierarchical terminal
recognition approach that applies the details of grid data. We have formed a
two-level model structure by segmenting the grid data, which uses the
statistical characteristics of network traffic and the specific behavior
characteristics of grid metering terminals. Moreover, through the selection and
reconstruction of features, we combine three algorithms to achieve accurate
identification of terminal types that transmit network traffic. We conduct
extensive experiments on a real dataset containing three types of grid metering
terminals, and the results show that our research has improved performance
compared to common recognition models. The combination of an autoencoder,
K-Means and GradientBoost algorithm achieved the best recognition rate with F1
value of 98.3%.


title: The Tree Loss: Improving Generalization with Many Classes

abstract:
  Multi-class classification problems often have many semantically similar
classes. For example, 90 of ImageNet's 1000 classes are for different breeds of
dog. We should expect that these semantically similar classes will have similar
parameter vectors, but the standard cross entropy loss does not enforce this
constraint.
  We introduce the tree loss as a drop-in replacement for the cross entropy
loss. The tree loss re-parameterizes the parameter matrix in order to guarantee
that semantically similar classes will have similar parameter vectors. Using
simple properties of stochastic gradient descent, we show that the tree loss's
generalization error is asymptotically better than the cross entropy loss's. We
then validate these theoretical results on synthetic data, image data
(CIFAR100, ImageNet), and text data (Twitter).


title: Fault-Tolerant Multiparty Session Types (Technical Report)

abstract:
  Multiparty session types are designed to abstractly capture the structure of
communication protocols and verify behavioural properties. One important such
property is progress, i.e., the absence of deadlock. Distributed algorithms
often resemble multiparty communication protocols. But proving their
properties, in particular termination that is closely related to progress, can
be elaborate. Since distributed algorithms are often designed to cope with
faults, a first step towards using session types to verify distributed
algorithms is to integrate fault-tolerance.
  We extend multiparty session types to cope with system failures such as
unreliable communication and process crashes. Moreover, we augment the
semantics of processes by failure patterns that can be used to represent system
requirements (as, e.g., failure detectors). To illustrate our approach we
analyse a variant of the well-known rotating coordinator algorithm by Chandra
and Toueg. This technical report presents the proofs and some additional
material to extend [30].


title:
Efficient Bayesian Policy Reuse with a Scalable Observation Model in
  Deep Reinforcement Learning

abstract:
  Bayesian policy reuse (BPR) is a general policy transfer framework for
selecting a source policy from an offline library by inferring the task belief
based on some observation signals and a trained observation model. In this
paper, we propose an improved BPR method to achieve more efficient policy
transfer in deep reinforcement learning (DRL). First, most BPR algorithms use
the episodic return as the observation signal that contains limited information
and cannot be obtained until the end of an episode. Instead, we employ the
state transition sample, which is informative and instantaneous, as the
observation signal for faster and more accurate task inference. Second, BPR
algorithms usually require numerous samples to estimate the probability
distribution of the tabular-based observation model, which may be expensive and
even infeasible to learn and maintain, especially when using the state
transition sample as the signal. Hence, we propose a scalable observation model
based on fitting state transition functions of source tasks from only a small
number of samples, which can generalize to any signals observed in the target
task. Moreover, we extend the offline-mode BPR to the continual learning
setting by expanding the scalable observation model in a plug-and-play fashion,
which can avoid negative transfer when faced with new unknown tasks.
Experimental results show that our method can consistently facilitate faster
and more efficient policy transfer.


title:
Bidirectional Self-Training with Multiple Anisotropic Prototypes for
  Domain Adaptive Semantic Segmentation

abstract:
  A thriving trend for domain adaptive segmentation endeavors to generate the
high-quality pseudo labels for target domain and retrain the segmentor on them.
Under this self-training paradigm, some competitive methods have sought to the
latent-space information, which establishes the feature centroids (a.k.a
prototypes) of the semantic classes and determines the pseudo label candidates
by their distances from these centroids. In this paper, we argue that the
latent space contains more information to be exploited thus taking one step
further to capitalize on it. Firstly, instead of merely using the source-domain
prototypes to determine the target pseudo labels as most of the traditional
methods do, we bidirectionally produce the target-domain prototypes to degrade
those source features which might be too hard or disturbed for the adaptation.
Secondly, existing attempts simply model each category as a single and
isotropic prototype while ignoring the variance of the feature distribution,
which could lead to the confusion of similar categories. To cope with this
issue, we propose to represent each category with multiple and anisotropic
prototypes via Gaussian Mixture Model, in order to fit the de facto
distribution of source domain and estimate the likelihood of target samples
based on the probability density. We apply our method on GTA5->Cityscapes and
Synthia->Cityscapes tasks and achieve 61.2 and 62.8 respectively in terms of
mean IoU, substantially outperforming other competitive self-training methods.
Noticeably, in some categories which severely suffer from the categorical
confusion such as "truck" and "bus", our method achieves 56.4 and 68.8
respectively, which further demonstrates the effectiveness of our design.


title: Efficient Linear Attention for Fast and Accurate Keypoint Matching

abstract:
  Recently Transformers have provided state-of-the-art performance in sparse
matching, crucial to realize high-performance 3D vision applications. Yet,
these Transformers lack efficiency due to the quadratic computational
complexity of their attention mechanism. To solve this problem, we employ an
efficient linear attention for the linear computational complexity. Then, we
propose a new attentional aggregation that achieves high accuracy by
aggregating both the global and local information from sparse keypoints. To
further improve the efficiency, we propose the joint learning of feature
matching and description. Our learning enables simpler and faster matching than
Sinkhorn, often used in matching the learned descriptors from Transformers. Our
method achieves competitive performance with only 0.84M learnable parameters
against the bigger SOTAs, SuperGlue (12M parameters) and SGMNet (30M
parameters), on three benchmarks, HPatch, ETH, and Aachen Day-Night.


title: The Solar Ultraviolet Imaging Telescope onboard Aditya-L1

abstract:
  The Solar Ultraviolet Imaging Telescope (SUIT) is an instrument onboard the
Aditya-L1 mission of ISRO that will measure and monitor the solar radiation
emitted in the near-ultraviolet wavelength range (200-400 nm). SUIT will
simultaneously map the photosphere and the chromosphere of the Sun using 11
filters sensitive to different wavelengths and covering different heights in
the solar atmosphere and help us understand the processes involved in the
transfer of mass and energy from one layer to the other. SUIT will also allow
us to measure and monitor spatially resolved solar spectral irradiance that
governs the chemistry of oxygen and ozone in the stratosphere of Earth's
atmosphere. This is central to our understanding of the Sun climate
relationship.


title:
GitNet: Geometric Prior-based Transformation for Birds-Eye-View
  Segmentation

abstract:
  Birds-eye-view (BEV) semantic segmentation is critical for autonomous driving
for its powerful spatial representation ability. It is challenging to estimate
the BEV semantic maps from monocular images due to the spatial gap, since it is
implicitly required to realize both the perspective-to-BEV transformation and
segmentation. We present a novel two-stage Geometry Prior-based Transformation
framework named GitNet, consisting of (i) the geometry-guided pre-alignment and
(ii) ray-based transformer. In the first stage, we decouple the BEV
segmentation into the perspective image segmentation and geometric prior-based
mapping, with explicit supervision by projecting the BEV semantic labels onto
the image plane to learn visibility-aware features and learnable geometry to
translate into BEV space. Second, the pre-aligned coarse BEV features are
further deformed by ray-based transformers to take visibility knowledge into
account. GitNet achieves the leading performance on the challenging nuScenes
and Argoverse Datasets. The code will be publicly available.


title:
The Franke-Gorini-Kossakowski-Lindblad-Sudarshan (FGKLS) Equation for
  Two-Dimensional Systems

abstract:
  Open quantum systems are, in general, described by a density matrix that is
evolving under transformations belonging to a dynamical semigroup. They can
obey the Franke-Gorini-Kossakowski-Lindblad-Sudarshan (FGKLS) equation. We
exhaustively study the case of a Hilbert space of dimension $2$. First, we find
final fixed states (called pointers) of an evolution of an open system, and we
then obtain a general solution to the FGKLS equation and confirm that it
converges to a pointer. After this, we check that the solution has physical
meaning, i.e., it is Hermitian, positive and has trace equal to $1$, and find a
moment of time starting from which the FGKLS equation can be used - the range
of applicability of the semigroup symmetry. Next, we study the behavior of a
solution for a weak interaction with an environment and make a distinction
between interacting and non-interacting cases. Finally, we prove that there
cannot exist oscillating solutions to the FGKLS equation, which would resemble
the behavior of a closed quantum system.


title: Experimental Verification of the Acoustic Geometric Phase

abstract:
  The optical geometric phase encoded by the in-plane spatial orientation of
microstructures has promoted the rapid development of numerous new-type optical
meta-devices. However, pushing the concept of the geometric phase toward the
acoustic community still faces challenges. In this work, we take advantage of
two acoustic nonlocal metagratings that could support the direct conversion
between plane wave and designated vortex mode, of which the orbital angular
momentum conversion process plays a vital role in obtaining the acoustic
geometric phase. We obtain the acoustic geometric phases of different orders by
merely varying the orientation angle of one of the acoustic nonlocal
metagratings. Intriguingly, according to our developed theory, we reveal that
the reflective acoustic geometric phase, which is twice of the transmissive
one, can be readily realized by transferring the transmitted configuration to a
reflected one. Both the theoretical model and experimental measurements
successfully verify the announced transmissive and reflective acoustic
geometric phases. Moreover, the characteristics of reconfigurability and
continuous phase modulation that covers the 2{\pi} range shown by the acoustic
geometric phases provide us with new possibilities in advanced acoustic
wavefront control.


title:
$BV$-structure on Hochschild cohomology for exceptional local algebras
  of quaternion type. Case of the small parameter

abstract:
  This is the second paper in the cycle of articles about $BV$-structure on
Hochshild cohomology of exceptional algebras of quaternion type. We give
$BV$-structure's full description in the case of quaternion algebras
$R(k,0,d)$, defined by parameter $k = 2$ according to Erdmann classification.


title:
Trivial and topological bound states in bilayer graphene quantum dots
  and rings

abstract:
  We discuss and compare two different types of confinement in bilayer graphene
by top and bottom gating with symmetrical microelectrodes. Trivial confinement
corresponds to the same polarity of all top gates, which is opposed to that of
all bottom ones. Topological confinement requires the polarity of part of the
top-bottom pairs of gates to be reversed. We show that the main qualitative
difference between trivial and topological bound states manifests itself in the
magnetic field dependence. We illustrate our finding with an explicit
calculation of the energy spectrum for quantum dots and rings. Trivial
confinement shows bunching of levels into degenerate Landau bands, with a
non-centered gap, while topological confinement shows no field-induced gap and
a sequence of state branches always crossing zero-energy.


title:
MMV-Based Sequential AoA and AoD Estimation for Millimeter Wave MIMO
  Channels

abstract:
  The fact that the millimeter-wave (mmWave) multiple-input multiple-output
(MIMO) channel has sparse support in the spatial domain has motivated recent
compressed sensing (CS)-based mmWave channel estimation methods, where the
angles of arrivals (AoAs) and angles of departures (AoDs) are quantized using
angle dictionary matrices. However, the existing CS-based methods usually
obtain the estimation result through one-stage channel sounding that have two
limitations: (i) the requirement of large-dimensional dictionary and (ii)
unresolvable quantization error. These two drawbacks are irreconcilable;
improvement of the one implies deterioration of the other. To address these
challenges, we propose, in this paper, a two-stage method to estimate the AoAs
and AoDs of mmWave channels. In the proposed method, the channel estimation
task is divided into two stages, Stage I and Stage II. Specifically, in Stage
I, the AoAs are estimated by solving a multiple measurement vectors (MMV)
problem. In Stage II, based on the estimated AoAs, the receive sounders are
designed to estimate AoDs. The dimension of the angle dictionary in each stage
can be reduced, which in turn reduces the computational complexity
substantially. We then analyze the successful recovery probability (SRP) of the
proposed method, revealing the superiority of the proposed framework over the
existing one-stage CS-based methods. We further enhance the reconstruction
performance by performing resource allocation between the two stages. We also
overcome the unresolvable quantization error issue present in the prior
techniques by applying the atomic norm minimization method to each stage of the
proposed two-stage approach. The simulation results illustrate the
substantially improved performance with low complexity of the proposed
two-stage method.


title:
Quantum phase diagram for two species hardcore bosons in one dimensional
  optical lattice with resonantly driven Rabi frequency

abstract:
  We propose an experimental realization of using time-periodically modulated
Rabi oscillation and consider two species of hard-core bosons with density
dependent hoppings in a one-dimensional optical lattice. Different from the
previous work [Phys. Rev. Research $\mathbf 2$, 013275 (2020)], in the first
resonance region, the intra-species hopping in the effective Hamiltonian occurs
only if the density discrepancy of the other species on these sites is zero,
while the inter-species hopping process is allowed once the relevant density
discrepancy becomes nonzero. The quantum phase diagram for integer-$1$ filling
is determined by combining analytic calculations and various numerical methods.
We find that in the limit of dominant $J_{1}$, the system becomes a
double-degenerate dimerized state, with breaking translation symmetry
spontaneously. Interplay of $J_{0}$, $J_{1}$ and the fixed ${\bar U}=1$ leads
to three BKT transition lines and a tricritical BKT point. Exact transition
lines are obtained by extrapolation following the level spectroscopic
technique. Besides, general physical properties, including the charge gap,
neutral gap, superfluidity density and dimerization strength, are investigated
as well.


title:
Conventional type-II superconductivity in locally non-centrosymmetric
  LaRh$_2$As$_2$ single crystals

abstract:
  We report on the observation of superconductivity in LaRh$_2$As$_2$, which is
the analogue without $f$-electrons of the heavy-fermion system with two
superconducting phases CeRh$_2$As$_2$. A zero-resistivity transition, a
specific-heat jump and a drop in magnetic ac susceptibility consistently point
to a superconducting transition at a transition temperature of $T_c = 0.28$\,K.
The magnetic field-temperature superconducting phase diagrams determined from
field-dependent ac-susceptibility measurements reveal small upper critical
fields $\mu_{\mathrm{0}}H_{c2} \approx 12$\,mT for $H\parallel ab$ and
$\mu_{\mathrm{0}}H_{c2} \approx 9$\,mT for $H\parallel c$. The observed
$H_{c2}$ is larger than the estimated thermodynamic critical field $H_c$
derived from the heat-capacity data, suggesting that LaRh$_2$A$s_2$ is a
type-II superconductor with Ginzburg-Landau parameters $\kappa^{ab}_{GL}
\approx 1.9$ and $\kappa^{c}_{GL}\approx 2.7$. The microscopic Eliashberg
theory indicates superconductivity to be in the weak-coupling regime with an
electron-phonon coupling constant $\lambda_{e-ph} \approx 0.4$. Despite a
similar $T_c$ and the same crystal structure as the Ce compound, LaRh$_2$As$_2$
displays conventional superconductivity, corroborating the substantial role of
the 4$f$ electrons for the extraordinary superconducting state in
CeRh$_2$As$_2$.


title:
Persua: A Visual Interactive System to Enhance the Persuasiveness of
  Arguments in Online Discussion

abstract:
  Persuading people to change their opinions is a common practice in online
discussion forums on topics ranging from political campaigns to relationship
consultation. Enhancing people's ability to write persuasive arguments could
not only practice their critical thinking and reasoning but also contribute to
the effectiveness and civility in online communication. It is, however, not an
easy task in online discussion settings where written words are the primary
communication channel. In this paper, we derived four design goals for a tool
that helps users improve the persuasiveness of arguments in online discussions
through a survey with 123 online forum users and interviews with five debating
experts. To satisfy these design goals, we analyzed and built a labeled dataset
of fine-grained persuasive strategies (i.e., logos, pathos, ethos, and
evidence) in 164 arguments with high ratings on persuasiveness from
ChangeMyView, a popular online discussion forum. We then designed an
interactive visual system, Persua, which provides example-based guidance on
persuasive strategies to enhance the persuasiveness of arguments. In
particular, the system constructs portfolios of arguments based on different
persuasive strategies applied to a given discussion topic. It then presents
concrete examples based on the difference between the portfolios of user input
and high-quality arguments in the dataset. A between-subjects study shows
suggestive evidence that Persua encourages users to submit more times for
feedback and helps users improve more on the persuasiveness of their arguments
than a baseline system. Finally, a set of design considerations was summarized
to guide future intelligent systems that improve the persuasiveness in text.


title:
DRFLM: Distributionally Robust Federated Learning with Inter-client
  Noise via Local Mixup

abstract:
  Recently, federated learning has emerged as a promising approach for training
a global model using data from multiple organizations without leaking their raw
data. Nevertheless, directly applying federated learning to real-world tasks
faces two challenges: (1) heterogeneity in the data among different
organizations; and (2) data noises inside individual organizations.
  In this paper, we propose a general framework to solve the above two
challenges simultaneously. Specifically, we propose using distributionally
robust optimization to mitigate the negative effects caused by data
heterogeneity paradigm to sample clients based on a learnable distribution at
each iteration. Additionally, we observe that this optimization paradigm is
easily affected by data noises inside local clients, which has a significant
performance degradation in terms of global model prediction accuracy. To solve
this problem, we propose to incorporate mixup techniques into the local
training process of federated learning. We further provide comprehensive
theoretical analysis including robustness analysis, convergence analysis, and
generalization ability. Furthermore, we conduct empirical studies across
different drug discovery tasks, such as ADMET property prediction and
drug-target affinity prediction.


title:
Tensor-networks for High-order Polynomial Approximation: A Many-body
  Physics Perspective

abstract:
  We analyze the problem of high-order polynomial approximation from a
many-body physics perspective, and demonstrate the descriptive power of
entanglement entropy in capturing model capacity and task complexity.
Instantiated with a high-order nonlinear dynamics modeling problem,
tensor-network models are investigated and exhibit promising modeling
advantages. This novel perspective establish a connection between quantum
information and functional approximation, which worth further exploration in
future research.

categories: quant-ph cond-mat.dis-nn cs.LG cs.NA math.NA


title:
An explicit example for the high temperature convolution: crossover
  between the binomial law $B(2,1/2)$ and the arcsine law

abstract:
  In this note, we study the high-temperature convolution introduced in Ref.\
\cite{mergny_cconv}, between two symmetric Bernoulli distributions. We give an
analytical expression for both the Stieltjes transform and the density. This
result provides the first non-trivial expression for the high-temperature
convolution of two distributions and gives a new family of densities,
interpolating between the centered binomial distribution with number of trials
$n{=}2$ and probability of success $p{=}1/2$, and the centered and re-scaled
arcsine law.