title: Skew-invariant curves and the algebraic independence of Mahler functions

abstract:
  For $p \in \mathbb{Q}_+ \smallsetminus \{ 1 \}$ a positive rational number
different from one, we say that the Puisseux series $f \in
\mathbb{C}((t))^\text{alg}$ is $p$-Mahler of non-exceptional polynomial type if
there is a polynomial $P \in \mathbb{C}(t)^\text{alg}[X]$ of degree at least
two which is not conjugate to either a monomial or to plus or minus a Chebyshev
polynomial for which the equation $f(t^p) = P(f(t))$ holds. We show that if $p$
and $q$ are multiplicatively independent and $f$ and $g$ are $p$-Mahler and
$q$-Mahler, respectively, of non-exceptional polynomial type, then $f$ and $g$
are algebraically independent over $\mathbb{C}(t)$. This theorem is proven as a
consequence of a more general theorem that if $f$ is $p$-Mahler of
non-exceptional polynomial type, and $g_1, \ldots, g_n$ each satisfy some
difference equation with respect to the substitution $t \mapsto t^q$, then $f$
is algebraically independent from $g_1, \ldots, g_n$. These theorems are
themselves consequences of a refined classification of skew-invariant curves
for split polynomial dynamical systems on $\mathbb{A}^2$.


title:
IncShrink: Architecting Efficient Outsourced Databases using Incremental
  MPC and Differential Privacy

abstract:
  In this paper, we consider secure outsourced growing databases that support
view-based query answering. These databases allow untrusted servers to
privately maintain a materialized view, such that they can use only the
materialized view to process query requests instead of accessing the original
data from which the view was derived. To tackle this, we devise a novel
view-based secure outsourced growing database framework, Incshrink. The key
features of this solution are: (i) Incshrink maintains the view using
incremental MPC operators which eliminates the need for a trusted third party
upfront, and (ii) to ensure high performance, Incshrink guarantees that the
leakage satisfies DP in the presence of updates. To the best of our knowledge,
there are no existing systems that have these properties. We demonstrate
Incshrink's practical feasibility in terms of efficiency and accuracy with
extensive empirical evaluations on real-world datasets and the TPC-ds
benchmark. The evaluation results show that Incshrink provides a 3-way
trade-off in terms of privacy, accuracy, and efficiency guarantees, and offers
at least a 7,800 times performance advantage over standard secure outsourced
databases that do not support the view-based query paradigm.


title: Census TopDown: The Impacts of Differential Privacy on Redistricting

abstract:
  The 2020 Decennial Census will be released with a new disclosure avoidance
system in place, putting differential privacy in the spotlight for a wide range
of data users. We consider several key applications of Census data in
redistricting, developing tools and demonstrations for practitioners who are
concerned about the impacts of this new noising algorithm called TopDown. Based
on a close look at reconstructed Texas data, we find reassuring evidence that
TopDown will not threaten the ability to produce districts with tolerable
population balance or to detect signals of racial polarization for Voting
Rights Act enforcement.


title:
Detecting and Diagnosing Terrestrial Gravitational-Wave Mimics Through
  Feature Learning

abstract:
  As engineered systems grow in complexity, there is an increasing need for
automatic methods that can detect, diagnose, and even correct transient
anomalies that inevitably arise and can be difficult or impossible to diagnose
and fix manually. Among the most sensitive and complex systems of our
civilization are the detectors that search for incredibly small variations in
distance caused by gravitational waves -- phenomena originally predicted by
Albert Einstein to emerge and propagate through the universe as the result of
collisions between black holes and other massive objects in deep space. The
extreme complexity and precision of such detectors causes them to be subject to
transient noise issues that can significantly limit their sensitivity and
effectiveness.
  In this work, we present a demonstration of a method that can detect and
characterize emergent transient anomalies of such massively complex systems. We
illustrate the performance, precision, and adaptability of the automated
solution via one of the prevalent issues limiting gravitational-wave
discoveries: noise artifacts of terrestrial origin that contaminate
gravitational wave observatories' highly sensitive measurements and can obscure
or even mimic the faint astrophysical signals for which they are listening.
Specifically, we demonstrate how a highly interpretable convolutional
classifier can automatically learn to detect transient anomalies from auxiliary
detector data without needing to observe the anomalies themselves. We also
illustrate several other useful features of the model, including how it
performs automatic variable selection to reduce tens of thousands of auxiliary
data channels to only a few relevant ones; how it identifies behavioral
signatures predictive of anomalies in those channels; and how it can be used to
investigate individual anomalies and the channels associated with them.


title:
False Data Injection Attack on Electric Vehicle-Assisted Voltage
  Regulation

abstract:
  With the large scale penetration of electric vehicles (EVs) and the advent of
bidirectional chargers, EV aggregators will become a major player in the
voltage regulation market. This paper proposes a novel false data injection
attack (FDIA) against the voltage regulation capacity estimation of EV charging
stations, the process that underpins voltage regulation in distribution system.
The proposed FDIA takes into account the uncertainty in EV mobility and network
conditions. The attack vector with the largest expected adverse impact is the
solution of a stochastic optimization problem subject to a constraint that
ensures it can bypass bad data detection. We show that this attack vector can
be determined by solving a sequence of convex quadratically constrained linear
programs. The case studies examined in a co-simulation platform, based on two
standard test feeders, reveal the vulnerability of the voltage regulation
capacity estimation.


title:
Momentum-dependent oscillator strength crossover of excitons and
  plasmons in two-dimensional PtSe2

abstract:
  The 1T-phase layered PtX2 chalcogenides has attracted widespread interest due
to its thickness dependent metal-semiconductor transition driven by strong
interlayer coupling. While the ground state properties of this paradigmatic
material system have been widely explored, its fundamental excitation spectrum
remains poorly understood. Here we combine first principles calculations with
momentum (q) resolved electron energy loss spectroscopy (q-EELS) to study the
collective excitations in 1T-PtSe2 from the monolayer limit to the bulk. At
finite momentum transfer all the spectra are dominated by two distinct
interband plasmons that disperse to higher energy with increasing q.
Interestingly, the absence of long-range screening in the two-dimensional (2D)
limit, inhibits the formation of long wavelength plasmons. Consequently, in the
small-q limit, excitations in monolayer PtSe2 are exclusively of excitonic
nature, and the loss spectrum coincides with the optical spectrum. Our work
unravels the excited state spectrum of layered 1T-PtSe2 and establishes the
qualitatively different momentum dependence of excitons and plasmons in 2D
materials.


title: gcimpute: A Package for Missing Data Imputation

abstract:
  This article introduces the Python package gcimpute for missing data
imputation. gcimpute can impute missing data with many different variable
types, including continuous, binary, ordinal, count, and truncated values, by
modeling data as samples from a Gaussian copula model. This semiparametric
model learns the marginal distribution of each variable to match the empirical
distribution, yet describes the interactions between variables with a joint
Gaussian that enables fast inference, imputation with confidence intervals, and
multiple imputation. The package also provides specialized extensions to handle
large datasets (with complexity linear in the number of observations) and
streaming datasets (with online imputation). This article describes the
underlying methodology and demonstrates how to use the software package.


title: The Forward Physics Facility at the High-Luminosity LHC

abstract:
  High energy collisions at the High-Luminosity Large Hadron Collider (LHC)
produce a large number of particles along the beam collision axis, outside of
the acceptance of existing LHC experiments. The proposed Forward Physics
Facility (FPF), to be located several hundred meters from the ATLAS interaction
point and shielded by concrete and rock, will host a suite of experiments to
probe Standard Model (SM) processes and search for physics beyond the Standard
Model (BSM). In this report, we review the status of the civil engineering
plans and the experiments to explore the diverse physics signals that can be
uniquely probed in the forward region. FPF experiments will be sensitive to a
broad range of BSM physics through searches for new particle scattering or
decay signatures and deviations from SM expectations in high statistics
analyses with TeV neutrinos in this low-background environment. High statistics
neutrino detection will also provide valuable data for fundamental topics in
perturbative and non-perturbative QCD and in weak interactions. Experiments at
the FPF will enable synergies between forward particle production at the LHC
and astroparticle physics to be exploited. We report here on these physics
topics, on infrastructure, detector, and simulation studies, and on future
directions to realize the FPF's physics potential.

categories: hep-ex astro-ph.CO astro-ph.HE hep-ph physics.ins-det


title: On the identity of Tsesevich's "YY Dra"

abstract:
  Hill et al. (2022, arXiv:2203.00221) recently published analysis of the
intermediate polar DO Dra (YY Dra) using TESS, ASAS-SN and ZTF data. They also
attempted a search for the "lost" variable YY Dra using modern catalogs of
variable stars and found no corresponding one. This search drew our renewed
attention and we studied the original discovery paper of YY Dra by Tsesevich
(Zessewitch). We found that two out of four variables reported by him were
either lost or improperly studied. The coordinate offset from the correct
position in another object was much larger than the expected error. Using the
information of the published period and epoch of YY Dra, we suspect that
Tsesevich used a couple of plates on which the object was invisible to derive
the period rather than from a completely phased light curve. DO Dra sometimes
spends high states around 14 mag for a month to several months and it would not
be surprising if Tsesevich observed DO Dra in such a state and suspected it to
be an Algol, urging him to examine the plate archive to obtain the moments when
the variable was undetected. We suspect that Tsesevich indeed observed DO Dra,
rather than a different, lost eclipsing variable. The final conclusion of this
matter is almost impossible to reach due to the consequence of the World War
II, destructing the large part of plate collections. Until the humanity becomes
wise enough to restore this destruction and wise enough to be able to avoid
further destruction of human achievements, the designation YY Dra would better
be conserved as a record of our negative history and unambiguous DO Dra would
better be used to designate the intermediate polar 3A 1148+719.


title: A Tree-Structured Multi-Task Model Recommender

abstract:
  Tree-structured multi-task architectures have been employed to jointly tackle
multiple vision tasks in the context of multi-task learning (MTL). The major
challenge is to determine where to branch out for each task given a backbone
model to optimize for both task accuracy and computation efficiency. To address
the challenge, this paper proposes a recommender that, given a set of tasks and
a convolutional neural network-based backbone model, automatically suggests
tree-structured multi-task architectures that could achieve a high task
performance while meeting a user-specified computation budget without
performing model training. Extensive evaluations on popular MTL benchmarks show
that the recommended architectures could achieve competitive task accuracy and
computation efficiency compared with state-of-the-art MTL methods.


title:
Sampling from the Sherrington-Kirkpatrick Gibbs measure via algorithmic
  stochastic localization

abstract:
  We consider the Sherrington-Kirkpatrick model of spin glasses at
high-temperature and no external field, and study the problem of sampling from
the Gibbs distribution $\mu$ in polynomial time. We prove that, for any inverse
temperature $\beta<1/2$, there exists an algorithm with complexity $O(n^2)$
that samples from a distribution $\mu^{alg}$ which is close in normalized
Wasserstein distance to $\mu$. Namely, there exists a coupling of $\mu$ and
$\mu^{alg}$ such that if $(x,x^{alg})\in\{-1,+1\}^n\times \{-1,+1\}^n$ is a
pair drawn from this coupling, then $n^{-1}\mathbb
E\{||x-x^{alg}||_2^2\}=o_n(1)$. The best previous results, by Bauerschmidt and
Bodineau and by Eldan, Koehler, and Zeitouni, implied efficient algorithms to
approximately sample (under a stronger metric) for $\beta<1/4$.
  We complement this result with a negative one, by introducing a suitable
"stability" property for sampling algorithms, which is verified by many
standard techniques. We prove that no stable algorithm can approximately sample
for $\beta>1$, even under the normalized Wasserstein metric.
  Our sampling method is based on an algorithmic implementation of stochastic
localization, which progressively tilts the measure $\mu$ towards a single
configuration, together with an approximate message passing algorithm that is
used to approximate the mean of the tilted measure.


title:
Mathematical modelling of transport phenomena in compressible
  multicomponent flows

abstract:
  The present article proposes a diffuse interface model for compressible
multicomponent flows with transport phenomena of mass, momentum and energy
(i.e., mass diffusion, viscous dissipation and heat conduction). The model is
reduced from the seven-equation Baer-Nuziato type model with asymptotic
analysis in the limit of instantaneous mechanical relaxations. The main
difference between the present model and the Kapila's five-equation model
consists in that different time scales for pressure and velocity relaxations
are assumed, the former being much smaller than the latter. Thanks to this
assumption, the velocity disequilibrium is retained to model the mass diffusion
process. Aided by the diffusion laws, the final model still formally consists
of five equations. The proposed model satisfy two desirable properties : (1) it
respects the laws of thermodynamics, (2) it is free of the spurious oscillation
problem in the vicinity of the diffused interface zone. The mass diffusion,
viscous dissipation and heat conduction processes contribute parabolic partial
differential equations that are solved with the Chebyshev method of local
iterations. Numerical results show that the proposed model maintains pressure,
velocity and temperature equilibrium near the diffused interface. Convergence
tests demonstrate that the numerical methods achieve second order in space and
time. The proposed model and numerical methods are applied to simulate the
laser-driven RM instability problem in inertial confinement fusion, good
agreement with experimental results are observed.


title:
Model-Architecture Co-Design for High Performance Temporal GNN Inference
  on FPGA

abstract:
  Temporal Graph Neural Networks (TGNNs) are powerful models to capture
temporal, structural, and contextual information on temporal graphs. The
generated temporal node embeddings outperform other methods in many downstream
tasks. Real-world applications require high performance inference on real-time
streaming dynamic graphs. However, these models usually rely on complex
attention mechanisms to capture relationships between temporal neighbors. In
addition, maintaining vertex memory suffers from intrinsic temporal data
dependency that hinders task-level parallelism, making it inefficient on
general-purpose processors. In this work, we present a novel model-architecture
co-design for inference in memory-based TGNNs on FPGAs. The key modeling
optimizations we propose include a light-weight method to compute attention
scores and a related temporal neighbor pruning strategy to further reduce
computation and memory accesses. These are holistically coupled with key
hardware optimizations that leverage FPGA hardware. We replace the temporal
sampler with an on-chip FIFO based hardware sampler and the time encoder with a
look-up-table. We train our simplified models using knowledge distillation to
ensure similar accuracy vis-\'a-vis the original model. Taking advantage of the
model optimizations, we propose a principled hardware architecture using
batching, pipelining, and prefetching techniques to further improve the
performance. We also propose a hardware mechanism to ensure the chronological
vertex updating without sacrificing the computation parallelism. We evaluate
the performance of the proposed hardware accelerator on three real-world
datasets.


title:
Heterogeneous Sparse Matrix-Vector Multiplication via Compressed Sparse
  Row Format

abstract:
  Due to ill performance on many devices, sparse matrix-vector multiplication
(SpMV) normally requires special care to store and tune for a given device.
However, SpMV is one of the most important kernels in high-performance
computing (HPC), and therefore, a storage format and tuning are required that
allows for efficient SpMV operations with low memory and tuning overheads
across heterogeneous devices. Additionally, the primary users of SpMV
operations in HPC are normally application scientists that already have
numerous other libraries they depend on the use of some standard sparse matrix
storage format. As such, the ideal heterogeneous format would also be something
that could easily be understood and requires no major changes to be translated
into a standard sparse matrix format, such as compressed sparse row (CSR). This
paper presents a heterogeneous format based on CSR, named CSR-k, that can be
tuned quickly, requires minimal memory overheads, outperforms the average
performance of NVIDIA's cuSPARSE on our test suite, and does not need any
conversion to be used by standard library calls that require a CSR format
input. In particular, CSR-k achieves this by grouping rows into a hierarchical
structure of super-rows and super-super-rows that are represented by just a few
extra arrays of pointers (i.e., <2% memory overhead to keep arrays for both GPU
and CPU execution). Due to its simplicity, a model can be tuned for a device,
and this model can be used to select super-row and super-super-rows sizes in
constant time. We observe in this paper that CSR-k can achieve about 15%
improvement on a V100 and about 17.4% improvement on an A100 over NVIDIA's
cuSPARSE while still providing about a 13x speedup on an AMD Epyc 7713.


title:
A Framework for the Interoperability of Cloud Platforms: Towards FAIR
  Data in SAFE Environments

abstract:
  As the number of cloud platforms supporting biomedical research grows, there
is an increasing need to support interoperability between two or more cloud
platforms. A well accepted core concept is to make data in cloud platforms
findable, accessible, interoperable and reusable (FAIR). We introduce a
companion concept that applies to cloud-based computing environments that we
call a Secure and Authorized FAIR Environment (SAFE). SAFE environments require
data and platform governance structures. A SAFE environment is a cloud platform
that has been approved through a defined data and platform governance process
as authorized to hold data from another cloud platform and exposes appropriate
APIs for the two platforms to interoperate.


title: The first study of four doubly eclipsing systems

abstract:
  We present the discovery and the very first analysis of four stellar systems
showing two periods of eclipses, that are the objects classified as doubly
eclipsing systems. Some of them were proved to orbit each other thanks to their
eclipse-timing-variations (ETVs) of both pairs, hence they really constitute
rare quadruples with two eclipsing pairs. Some of them do not, as we are still
waiting for more data to detect their mutual movement. Their light curves and
period changes were analysed. All of them are detached and near-contact, but
none of them contact; moreover, to our knowledge none of these stars can be
considered as blend of two spatially unresolved close components on the sky.
These systems are CzeV2647 (0.5723296 + 0.9637074 days), proved to orbit with
4.5-year periodicity; CzeV1645 (1.0944877 + 1.6594641 days), with a rather
questionable detection of ETV; CzeV3436 (0.6836870 + 0.3833930 days); and,
finally, OGLE SMC-ECL-1758 (0.9291925 + 3.7350826 days), proved to move on its
30-year orbit. Even more surprising is the fact that most of these systems show
the ratio of their two orbital periods close to coupling near some resonant
values of small integers, namely CzeV2647, with only 1% from 3:5 resonance,
CzeV1645 1% from 2:3 resonance, and OGLE SMC-ECL-1758 with only 0.49% from 1:4
resonance. The nature of these near-resonant states still remains a mystery.


title: The $L_p$-Minkowski problem with super-critical exponents

abstract:
  The $L_p$-Minkowski problem deals with the existence of closed convex
hypersurfaces in $\mathbb{R}^{n+1}$ with prescribed $p$-area measures. It
extends the classical Minkowski problem and embraces several important
geometric and physical applications. The Existence of solutions has been
obtained in the sub-critical case $p>-n-1$, but the problem remains widely open
in the super-critical case $p<-n-1$. In this paper, we introduce new ideas to
solve the problem for all the super-critical exponents. A crucial ingredient in
our proof is a topological method based on the calculation of the homology of a
topological space of ellipsoids.


title: Unwrapped two-point functions on high-dimensional tori

abstract:
  We study unwrapped two-point functions for the Ising model, the self-avoiding
walk and a random-length loop-erased random walk on high-dimensional lattices
with periodic boundary conditions. While the standard two-point functions of
these models have been observed to display an anomalous plateau behaviour, the
unwrapped two-point functions are shown to display standard mean-field
behaviour. Moreover, we argue that the asymptotic behaviour of these unwrapped
two-point functions on the torus can be understood in terms of the standard
two-point function of a random-length random walk model on Zd. A precise
description is derived for the asymptotic behaviour of the latter. Finally, we
consider a natural notion of the Ising walk length, and show numerically that
the Ising and SAW walk lengths on high-dimensional tori show the same universal
behaviour known for the SAW walk length on the complete graph.


title: Geometry over algebras

abstract:
  We study geometric structures arising from Hermitian forms on linear spaces
over real algebras beyond the division ones. Our focus is on the dual numbers,
the split-complex numbers, and the split-quaternions. The corresponding
geometric structures are employed to describe the spaces of oriented geodesics
in the hyperbolic plane, the Euclidean plane, and the round $2$-sphere. We also
introduce a simple and natural geometric transition between these spaces.
Finally, we present a projective model for the hyperbolic bidisc, that is, the
Riemannian product of two hyperbolic discs.


title:
Adaptive relaying for streaming erasure codes in a three node relay
  network

abstract:
  This paper investigates adaptive streaming codes over a three-node relayed
network. In this setting, a source node transmits a sequence of message packets
to a destination through a relay. The source-to-relay and relay-to-destination
links are unreliable and introduce at most $N_1$ and $N_2$ packet erasures,
respectively. The destination node must recover each message packet within a
strict delay constraint $T$. The paper presents achievable streaming codes for
all feasible parameters $\{N_1, N_2, T\}$ that exploit the fact that the relay
naturally observes the erasure pattern occurring in the link from source to
relay, thus it can adapt its relaying strategy based on these observations. In
a recent work, Fong et al. provide streaming codes featuring
channel-state-independent relaying strategies. The codes proposed in this paper
achieve rates higher than the ones proposed by Fong et al. whenever $N_2 >
N_1$, and achieve the same rate when $N_2 = N_1$. The paper also presents an
upper bound on the achievable rate that takes into account erasures in both
links in order to bound the rate in the second link. The upper bound is shown
to be tighter than a trivial bound that considers only the erasures in the
second link.


title: Improving Neural ODEs via Knowledge Distillation

abstract:
  Neural Ordinary Differential Equations (Neural ODEs) construct the continuous
dynamics of hidden units using ordinary differential equations specified by a
neural network, demonstrating promising results on many tasks. However, Neural
ODEs still do not perform well on image recognition tasks. The possible reason
is that the one-hot encoding vector commonly used in Neural ODEs can not
provide enough supervised information. We propose a new training based on
knowledge distillation to construct more powerful and robust Neural ODEs
fitting image recognition tasks. Specially, we model the training of Neural
ODEs into a teacher-student learning process, in which we propose ResNets as
the teacher model to provide richer supervised information. The experimental
results show that the new training manner can improve the classification
accuracy of Neural ODEs by 24% on CIFAR10 and 5% on SVHN. In addition, we also
quantitatively discuss the effect of both knowledge distillation and time
horizon in Neural ODEs on robustness against adversarial examples. The
experimental analysis concludes that introducing the knowledge distillation and
increasing the time horizon can improve the robustness of Neural ODEs against
adversarial examples.


title:
Transition to Linearity of Wide Neural Networks is an Emerging Property
  of Assembling Weak Models

abstract:
  Wide neural networks with linear output layer have been shown to be
near-linear, and to have near-constant neural tangent kernel (NTK), in a region
containing the optimization path of gradient descent. These findings seem
counter-intuitive since in general neural networks are highly complex models.
Why does a linear structure emerge when the networks become wide? In this work,
we provide a new perspective on this "transition to linearity" by considering a
neural network as an assembly model recursively built from a set of sub-models
corresponding to individual neurons. In this view, we show that the linearity
of wide neural networks is, in fact, an emerging property of assembling a large
number of diverse "weak" sub-models, none of which dominate the assembly.


title: An expanding curvature flow and Christoffel-Minkowski type problems

abstract:
  In this paper, we investigate an expanding flow of a convex hypersurface in
Euclidean space Rn, with speed depending on the k-th symmetric polynomial of
the principal curvature radii, support function and radial function. We prove
that the flow has smooth and uniformly convex solution for all time and
converges smoothly to a homothetic self-similar solution satisfies a nonlinear
PDE of Hessian type. As an application of this expanding flow, we further
confirm the existence of non-even smooth solution to the Christoffel-Minkowski
type problem with respect to M(p,q),k(K,{\omega}), which can be regarded as a
natural generalization of the celebrated works by Huang and Zhao, Guan and Xia,
Chen and Ma in [4, 11, 15, 16].


title:
Parity as the foundation of the non-relativistic spin-statistics
  connection

abstract:
  It is shown that the symmetry under parity of the wavefunctions of two
identical particles with an arbitrary spin $s$ in three spatial dimensions
accounts for the appropriate wavefunction exchange statistics under the
permutations of particles. The standard properties of the angular momentum in
non-relativistic quantum mechanics account for the sign factor $(-1)^{2s}$ that
the wavefunctions acquire under the permutation of coordinates of the two
particles, without any additional requirements, directly relating spin and the
particle exchange statistics in the non-relativistic context.

categories: quant-ph cond-mat.mes-hall math-ph math.MP


title: Ricci Flow and Gromov Almost Flat Manifolds

abstract:
  We employ the Ricci flow to derive a new theorem about Gromov almost flat
manifolds, which generalizes and strengthens the celebrated Gromov--Ruh
Theorem. In our theorem, the condition $diam^2 |K| \leq \epsilon_n$ in the
Gromov--Ruh Theorem is replaced by the substantially weaker condition
$\|Rm\|_{n/2}$ $ C_S^2 \leq \varepsilon_n$.


title: A Tighter Approximation Guarantee for Greedy Minimum Entropy Coupling

abstract:
  We examine the minimum entropy coupling problem, where one must find the
minimum entropy variable that has a given set of distributions $S = \{p_1,
\dots, p_m \}$ as its marginals. Although this problem is NP-Hard, previous
works have proposed algorithms with varying approximation guarantees. In this
paper, we show that the greedy coupling algorithm of [Kocaoglu et al., AAAI'17]
is always within $\log_2(e)$ ($\approx 1.44$) bits of the minimum entropy
coupling. In doing so, we show that the entropy of the greedy coupling is
upper-bounded by $H\left(\bigwedge S \right) + \log_2(e)$. This improves the
previously best known approximation guarantee of $2$ bits within the optimal
[Li, IEEE Trans. Inf. Theory '21]. Moreover, we show our analysis is tight by
proving there is no algorithm whose entropy is upper-bounded by
$H\left(\bigwedge S \right) + c$ for any constant $c<\log_2(e)$. Additionally,
we examine a special class of instances where the greedy coupling algorithm is
exactly optimal.


title:
The Pattern is in the Details: An Evaluation of Interaction Techniques
  for Locating, Searching, and Contextualizing Details in Multivariate Matrix
  Visualizations

abstract:
  Matrix visualizations are widely used to display large-scale network,
tabular, set, or sequential data. They typically only encode a single value per
cell, e.g., through color. However, this can greatly limit the visualizations'
utility when exploring multivariate data, where each cell represents a data
point with multiple values (referred to as details). Three well-established
interaction approaches can be applicable in multivariate matrix visualizations
(or MMV): focus+context, pan&zoom, and overview+detail. However, there is
little empirical knowledge of how these approaches compare in exploring MMV. We
report on two studies comparing them for locating, searching, and
contextualizing details in MMV. We first compared four focus+context techniques
and found that the fisheye lens overall outperformed the others. We then
compared the fisheye lens, to pan&zoom and overview+detail. We found that
pan&zoom was faster in locating and searching details, and as good as
overview+detail in contextualizing details.


title:
$(\omega,{\rho})$-Periodic solutions of abstract integro-differential
  impulsive equations on Banach space

abstract:
  In this paper, we investigate the existence and uniqueness of
$(\omega,{\rho})$-periodic solutions for a class of the abstract impulsive
integro-differential equations on Banach space.


title: Usefulness of the Age-Structured SIR Dynamics in Modelling COVID-19

abstract:
  We examine the age-structured SIR model, a variant of the classical
Susceptible-Infected-Recovered (SIR) model of epidemic propagation, in the
context of COVID-19. In doing so, we provide a theoretical basis for the model,
perform an empirical validation, and discover the limitations of the model in
approximating arbitrary epidemics. We first establish the differential
equations defining the age-structured SIR model as the mean-field limits of a
continuous-time Markov process that models epidemic spreading on a social
network involving random, asynchronous interactions. We then show that, as the
population size grows, the infection rate for any pair of age groups converges
to its mean-field limit if and only if the edge update rate of the network
approaches infinity, and we show how the rate of mean-field convergence depends
on the edge update rate. We then propose a system identification method for
parameter estimation of the bilinear ODEs of our model, and we test the model
performance on a Japanese COVID-19 dataset by generating the trajectories of
the age-wise numbers of infected individuals in the prefecture of Tokyo for a
period of over 365 days. In the process, we also develop an algorithm to
identify the different \textit{phases} of the pandemic, each phase being
associated with a unique set of contact rates. Our results show a good
agreement between the generated trajectories and the observed ones.


title:
Librarian-in-the-Loop: A Natural Language Processing Paradigm for
  Detecting Informal Mentions of Research Data in Academic Literature

abstract:
  Data citations provide a foundation for studying research data impact.
Collecting and managing data citations is a new frontier in archival science
and scholarly communication. However, the discovery and curation of research
data citations is labor intensive. Data citations that reference unique
identifiers (i.e. DOIs) are readily findable; however, informal mentions made
to research data are more challenging to infer. We propose a natural language
processing (NLP) paradigm to support the human task of identifying informal
mentions made to research datasets. The work of discovering informal data
mentions is currently performed by librarians and their staff in the
Inter-university Consortium for Political and Social Research (ICPSR), a large
social science data archive that maintains a large bibliography of data-related
literature. The NLP model is bootstrapped from data citations actively
collected by librarians at ICPSR. The model combines pattern matching with
multiple iterations of human annotations to learn additional rules for
detecting informal data mentions. These examples are then used to train an NLP
pipeline. The librarian-in-the-loop paradigm is centered in the data work
performed by ICPSR librarians, supporting broader efforts to build a more
comprehensive bibliography of data-related literature that reflects the
scholarly communities of research data users.


title:
Enhancement of heat and mass transfer by herringbone microstructures in
  a simple shear flow

abstract:
  The heat and mass transfer characteristics in a simple shear flow over
staggered herringbone structures are numerically investigated with the lattice
Boltzmann method. Two flow motions are identified. The first is a spiral flow
oscillation above the herringbone structures that advects heat and mass from
the top plane to herringbone structures. The second is a flow recirculation in
the grooves between herringbone ridges that advects heat and mass from the area
around herringbone tips to the side walls of herringbone ridges and the bottom
surfaces. These two basic flow motions couple together to form complex
transport mechanisms. The results show that when advective heat and mass
transfer takes effect at relatively larger Reynolds and Schmidt numbers, the
dependence of the total transfer rate on the Schmidt number follows a power
law, with the power being the same as that in the Dittus-Boelter equation for
turbulent heat transfer. As Reynolds number increases, the dependence of the
total transfer rate on Reynolds number also approaches a power law, and the
power is close to that in the Dittus-Boelter equation.


title: OpenTAL: Towards Open Set Temporal Action Localization

abstract:
  Temporal Action Localization (TAL) has experienced remarkable success under
the supervised learning paradigm. However, existing TAL methods are rooted in
the closed set assumption, which cannot handle the inevitable unknown actions
in open-world scenarios. In this paper, we, for the first time, step toward the
Open Set TAL (OSTAL) problem and propose a general framework OpenTAL based on
Evidential Deep Learning (EDL). Specifically, the OpenTAL consists of
uncertainty-aware action classification, actionness prediction, and temporal
location regression. With the proposed importance-balanced EDL method,
classification uncertainty is learned by collecting categorical evidence
majorly from important samples. To distinguish the unknown actions from
background video frames, the actionness is learned by the positive-unlabeled
learning. The classification uncertainty is further calibrated by leveraging
the guidance from the temporal localization quality. The OpenTAL is general to
enable existing TAL models for open set scenarios, and experimental results on
THUMOS14 and ActivityNet1.3 benchmarks show the effectiveness of our method.
The code and pre-trained models are released at
https://www.rit.edu/actionlab/opental.


title:
Internet-augmented language models through few-shot prompting for
  open-domain question answering

abstract:
  In this work, we aim to capitalize on the unique few-shot capabilities
offered by large-scale language models to overcome some of their challenges
with respect to grounding to factual and up-to-date information. Motivated by
semi-parametric language models, which ground their decisions in external
retrieved evidence, we use few-shot prompting to learn to condition language
models on information returned from the web using Google Search, a broad and
constantly updated knowledge source. Our approach does not involve fine-tuning
or learning additional parameters, thus making it applicable to any language
model, offering like this a strong baseline. Indeed, we find that language
models conditioned on the web surpass performance of closed-book models of
similar, or even larger, model sizes in open-domain question answering.
Finally, we find that increasing the inference-time compute of models, achieved
via using multiple retrieved evidences to generate multiple answers followed by
a reranking stage, alleviates generally decreased performance of smaller
few-shot language models. All in all, our findings suggest that it might be
beneficial to slow down the race towards the biggest model and instead shift
the attention towards finding more effective ways to use models, including but
not limited to better prompting or increasing inference-time compute.


title: Bulk photospin effect

abstract:
  We compute the electric spin susceptibility of Bloch electrons with
spin-orbit coupling to second order. We find that it is possible to generate a
nonequilibrium spin polarization in the bulk of non-magnetic
inversion-symmetric materials using linearly polarized electric fields, but the
process depends on interband coherence and produces heating. It may be possible
to avoid heating with circular polarization in certain scenarios. The standard
Edelstein effect and spin orientation effects are recovered in appropriate
limits within the formalism. Finally, the electric spin susceptibility of
metals has contributions proportional to spin multipole moments of the Fermi
sea that dominate the low frequency spin response.


title: Optimal Methods for Risk Averse Distributed Optimization

abstract:
  This paper studies the communication complexity of risk averse optimization
over a network. The problem generalizes the well-studied risk-neutral
finite-sum distributed optimization problem and its importance stems from the
need to handle risk in an uncertain environment. For algorithms in the
literature, there exists a gap in communication complexities for solving
risk-averse and risk-neutral problems. We propose two distributed algorithms,
namely the distributed risk averse optimization (DRAO) method and the
distributed risk averse optimization with sliding (DRAO-S) method, to close the
gap. Specifically, the DRAO method achieves the optimal communication
complexity by assuming a certain saddle point subproblem can be easily solved
in the server node. The DRAO-S method removes the strong assumption by
introducing a novel saddle point sliding subroutine which only requires the
projection over the ambiguity set $P$. We observe that the number of
$P$-projections performed by DRAO-S is optimal. Moreover, we develop matching
lower complexity bounds to show that communication complexities of both DRAO
and DRAO-S are not improvable. Numerical experiments are conducted to
demonstrate the encouraging empirical performance of the DRAO-S method.


title:
Semi-supervision semantic segmentation with uncertainty-guided self
  cross supervision

abstract:
  As a powerful way of realizing semi-supervised segmentation, the cross
supervision method learns cross consistency based on independent ensemble
models using abundant unlabeled images. However, the wrong pseudo labeling
information generated by cross supervision would confuse the training process
and negatively affect the effectiveness of the segmentation model. Besides, the
training process of ensemble models in such methods also multiplies the cost of
computation resources and decreases the training efficiency. To solve these
problems, we propose a novel cross supervision method, namely
uncertainty-guided self cross supervision (USCS). In addition to ensemble
models, we first design a multi-input multi-output (MIMO) segmentation model
which can generate multiple outputs with shared model and consequently impose
consistency over the outputs, saving the cost on parameters and calculations.
On the other hand, we employ uncertainty as guided information to encourage the
model to focus on the high confident regions of pseudo labels and mitigate the
effects of wrong pseudo labeling in self cross supervision, improving the
performance of the segmentation model. Extensive experiments show that our
method achieves state-of-the-art performance while saving 40.5% and 49.1% cost
on parameters and calculations.


title: MetAug: Contrastive Learning via Meta Feature Augmentation

abstract:
  What matters for contrastive learning? We argue that contrastive learning
heavily relies on informative features, or "hard" (positive or negative)
features. Early works include more informative features by applying complex
data augmentations and large batch size or memory bank, and recent works design
elaborate sampling approaches to explore informative features. The key
challenge toward exploring such features is that the source multi-view data is
generated by applying random data augmentations, making it infeasible to always
add useful information in the augmented data. Consequently, the informativeness
of features learned from such augmented data is limited. In response, we
propose to directly augment the features in latent space, thereby learning
discriminative representations without a large amount of input data. We perform
a meta learning technique to build the augmentation generator that updates its
network parameters by considering the performance of the encoder. However,
insufficient input data may lead the encoder to learn collapsed features and
therefore malfunction the augmentation generator. A new margin-injected
regularization is further added in the objective function to avoid the encoder
learning a degenerate mapping. To contrast all features in one gradient
back-propagation step, we adopt the proposed optimization-driven unified
contrastive loss instead of the conventional contrastive loss. Empirically, our
method achieves state-of-the-art results on several benchmark datasets.


title: A low-rank ensemble Kalman filter for elliptic observations

abstract:
  We propose a regularization method for ensemble Kalman filtering (EnKF) with
elliptic observation operators. Commonly used EnKF regularization methods
suppress state correlations at long distances. For observations described by
elliptic partial differential equations, such as the pressure Poisson equation
(PPE) in incompressible fluid flows, distance localization cannot be applied,
as we cannot disentangle slowly decaying physical interactions from spurious
long-range correlations. This is particularly true for the PPE, in which
distant vortex elements couple nonlinearly to induce pressure. Instead, these
inverse problems have a low effective dimension: low-dimensional projections of
the observations strongly inform a low-dimensional subspace of the state space.
We derive a low-rank factorization of the Kalman gain based on the spectrum of
the Jacobian of the observation operator. The identified eigenvectors
generalize the source and target modes of the multipole expansion,
independently of the underlying spatial distribution of the problem. Given
rapid spectral decay, inference can be performed in the low-dimensional
subspace spanned by the dominant eigenvectors. This low-rank EnKF is assessed
on dynamical systems with Poisson observation operators, where we seek to
estimate the positions and strengths of point singularities over time from
potential or pressure observations. We also comment on the broader
applicability of this approach to elliptic inverse problems outside the context
of filtering.

categories: physics.flu-dyn physics.data-an stat.CO stat.ML


title: Collusion Detection in Team-Based Multiplayer Games

abstract:
  In the context of competitive multiplayer games, collusion happens when two
or more teams decide to collaborate towards a common goal, with the intention
of gaining an unfair advantage from this cooperation. The task of identifying
colluders from the player population is however infeasible to game designers
due to the sheer size of the player population. In this paper, we propose a
system that detects colluding behaviors in team-based multiplayer games and
highlights the players that most likely exhibit colluding behaviors. The game
designers then proceed to analyze a smaller subset of players and decide what
action to take. For this reason, it is important and necessary to be extremely
careful with false positives when automating the detection. The proposed method
analyzes the players' social relationships paired with their in-game behavioral
patterns and, using tools from graph theory, infers a feature set that allows
us to detect and measure the degree of collusion exhibited by each pair of
players from opposing teams. We then automate the detection using Isolation
Forest, an unsupervised learning technique specialized in highlighting
outliers, and show the performance and efficiency of our approach on two real
datasets, each with over 170,000 unique players and over 100,000 different
matches.


title: DEER: Detection-agnostic End-to-End Recognizer for Scene Text Spotting

abstract:
  Recent end-to-end scene text spotters have achieved great improvement in
recognizing arbitrary-shaped text instances. Common approaches for text
spotting use region of interest pooling or segmentation masks to restrict
features to single text instances. However, this makes it hard for the
recognizer to decode correct sequences when the detection is not accurate i.e.
one or more characters are cropped out. Considering that it is hard to
accurately decide word boundaries with only the detector, we propose a novel
Detection-agnostic End-to-End Recognizer, DEER, framework. The proposed method
reduces the tight dependency between detection and recognition modules by
bridging them with a single reference point for each text instance, instead of
using detected regions. The proposed method allows the decoder to recognize the
texts that are indicated by the reference point, with features from the whole
image. Since only a single point is required to recognize the text, the
proposed method enables text spotting without an arbitrarily-shaped detector or
bounding polygon annotations. Experimental results present that the proposed
method achieves competitive results on regular and arbitrarily-shaped text
spotting benchmarks. Further analysis shows that DEER is robust to the
detection errors. The code and dataset will be publicly available.


title:
Multi-Task Adversarial Learning for Treatment Effect Estimation in
  Basket Trials

abstract:
  Estimating treatment effects from observational data provides insights about
causality guiding many real-world applications such as different clinical study
designs, which are the formulations of trials, experiments, and observational
studies in medical, clinical, and other types of research. In this paper, we
describe causal inference for application in a novel clinical design called
basket trial that tests how well a new drug works in patients who have
different types of cancer that all have the same mutation. We propose a
multi-task adversarial learning (MTAL) method, which incorporates feature
selection multi-task representation learning and adversarial learning to
estimate potential outcomes across different tumor types for patients sharing
the same genetic mutation but having different tumor types. In our paper, the
basket trial is employed as an intuitive example to present this new causal
inference setting. This new causal inference setting includes, but is not
limited to basket trials. This setting has the same challenges as the
traditional causal inference problem, i.e., missing counterfactual outcomes
under different subgroups and treatment selection bias due to confounders. We
present the practical advantages of our MTAL method for the analysis of
synthetic basket trial data and evaluate the proposed estimator on two
benchmarks, IHDP and News. The results demonstrate the superiority of our MTAL
method over the competing state-of-the-art methods.


title:
Ultrafast optically induced magnetic state transition in 2D
  antiferromagnets

abstract:
  Manipulating spin in antiferromagnetic (AFM) materials has great potential in
AFM opto-spintronics. Laser pulses can induce a transient ferromagnetic (FM)
state in AFM metallic systems, but have never been proven in two-dimensional
(2D) AFM semiconductors and related van der Waals (vdW) heterostructures. Here,
using 2D vdW heterostructures of FM MnS2 and AFM MXenes as prototypes, we
investigated optically induced interlayer spin transfer dynamics based on the
real-time time-dependent density functional theory (rt-TDDFT). We observed that
laser pulses induce significant spin injection and the interfacial
atom-mediated spin transfer from MnS2 to Cr2CCl2. In particular, we first
demonstrated the transient FM state in semiconducting AFM/FM heterostructures
during photoexcited processes. Because the proximity magnetism breaks the
magnetic symmetry of Cr2CCl2 in heterostructures. Our results provide the
microscopic understanding for optically controlled interlayer spin dynamics in
2D magnetic heterostructures and open a new way to manipulate magnetic orders
in ultrafast opto-spintronics.


title: A Lifted $\ell_1 $ Framework for Sparse Recovery

abstract:
  Motivated by re-weighted $\ell_1$ approaches for sparse recovery, we propose
a lifted $\ell_1$ (LL1) regularization which is a generalized form of several
popular regularizations in the literature. By exploring such connections, we
discover there are two types of lifting functions which can guarantee that the
proposed approach is equivalent to the $\ell_0$ minimization. Computationally,
we design an efficient algorithm via the alternating direction method of
multiplier (ADMM) and establish the convergence for an unconstrained
formulation. Experimental results are presented to demonstrate how this
generalization improves sparse recovery over the state-of-the-art.


title:
PACTran: PAC-Bayesian Metrics for Estimating the Transferability of
  Pretrained Models to Classification Tasks

abstract:
  With the increasing abundance of pretrained models in recent years, the
problem of selecting the best pretrained checkpoint for a particular downstream
classification task has been gaining increased attention. Although several
methods have recently been proposed to tackle the selection problem (e.g. LEEP,
H-score), these methods resort to applying heuristics that are not well
motivated by learning theory. In this paper we present PACTran, a theoretically
grounded family of metrics for pretrained model selection and transferability
measurement. We first show how to derive PACTran metrics from the optimal
PAC-Bayesian bound under the transfer learning setting. We then empirically
evaluate three metric instantiations of PACTran on a number of vision tasks
(VTAB) as well as a language-and-vision (OKVQA) task. An analysis of the
results shows PACTran is a more consistent and effective transferability
measure compared to existing selection methods.


title:
Action-Constrained Reinforcement Learning for Frame-Level Bit Allocation
  in HEVC/H.265 through Frank-Wolfe Policy Optimization

abstract:
  This paper presents a reinforcement learning (RL) framework that leverages
Frank-Wolfe policy optimization to address frame-level bit allocation for
HEVC/H.265. Most previous RL-based approaches adopt the single-critic design,
which weights the rewards for distortion minimization and rate regularization
by an empirically chosen hyper-parameter. More recently, the dual-critic design
is proposed to update the actor network by alternating the rate and distortion
critics. However, the convergence of training is not guaranteed. To address
this issue, we introduce Neural Frank-Wolfe Policy Optimization (NFWPO) in
formulating the frame-level bit allocation as an action-constrained RL problem.
In this new framework, the rate critic serves to specify a feasible action set,
and the distortion critic updates the actor network towards maximizing the
reconstruction quality while conforming to the action constraint. Experimental
results show that when trained to optimize the video multi-method assessment
fusion (VMAF) metric, our NFWPO-based model outperforms both the single-critic
and the dual-critic methods. It also demonstrates comparable rate-distortion
performance to the 2-pass average bit rate control of x265.


title: LlamaTune: Sample-Efficient DBMS Configuration Tuning

abstract:
  Tuning a database system to achieve optimal performance on a given workload
is a long-standing problem in the database community. A number of recent papers
have leveraged ML-based approaches to guide the sampling of large parameter
spaces (hundreds of tuning knobs) in search for high performance
configurations. Looking at Microsoft production services operating millions of
databases, sample efficiency emerged as a crucial requirement to use tuners on
diverse workloads. This motivates our investigation in LlamaTune, a system that
leverages two key insights: 1) an automated dimensionality reduction technique
based on randomized embeddings, and 2) a biased sampling approach to handle
special values for certain tuning knobs. LlamaTune compares favorably with the
state-of-the-art optimizers across a diverse set of workloads achieving the
best performing configurations with up to $11\times$ fewer workload runs, and
reaching up to $21\%$ higher throughput. We also show that benefits from
LlamaTune generalizes across random-forest and Gaussian Process-based Bayesian
optimizers. While the journey to perform database tuning at cloud-scale remains
long, LlamaTune goes a long way in making automatic DB tuning practical at
scale.


title:
Extended Ces\`aro composition operators on weak Bloch-type spaces on the
  unit ball of a Hilbert space

abstract:
  Denote by $ B_X $ the unit ball of an infinite-dimensional complex Hilbert
space $ X. $ Let $\psi \in H(B_X),$ the space of all holomorphic functions on
the unit ball $B_X,$ $\varphi \in S(B_X)$ the set of holomorphic self-maps of
$B_X. $ Let $C_{\psi, \varphi}: \mathcal B_{\nu}(B_X)$ (and $ \mathcal
B_{\nu,0}(B_X)$) $\to \mathcal B_{\mu}(B_X) $ (and $ \mathcal B_{\mu,0}(B_X)$)
be weighted extended Ces\`aro operators induced by products of the extended
Ces\`aro operator $ C_\varphi $ and integral operator $T_\psi.$ In this paper,
we characterize the boundedness and compactness of $ C_{\psi,\varphi} $ via the
estimates for the restrictions of $ \psi $ and $ \varphi $ to a $
m$-dimensional subspace of $ X $ for some $ m\ge2. $ Based on these we give
necessary as well as sufficient conditions for the boundednees, the (weak)
compactness of $ \widetilde{C}_{\psi, \varphi} $ between spaces of
Banach-valued holomorphic functions weak-associated to $ \mathcal B_{\nu}(B_X)
$ and $ \mathcal B_{\mu}(B_X). $


title:
The Waiting-Time Distribution for Network Partitions in Cascading
  Failures in Power Networks

abstract:
  Network redundancy is one of the spatial network structural properties
critical to robustness against cascading failures in power networks. The
waiting-time distributions for network partitions in cascading failures explain
how the spatial network structures affect the cascading behaviors. Two waiting
time events associated with the first and largest network partitions are
studied for cascading failures under different network redundancies. With the
synthetic power networks, the waiting-time distributions of network partitions
can be systematically analyzed for various network redundancies. Waiting-time
distributions shift to the right accordingly when network redundancies
increase. Meanwhile, the sizes of the largest partitions decrease while the
numbers of them increase statistically. The realistic power networks of France,
Texas, and Poland also show the same trend for waiting-time distributions.


title:
SPLASH: The Southern Parkes Large-Area Survey in Hydroxyl -- Data
  Description & Release

abstract:
  We present the full data release for the Southern Parkes Large-Area Survey in
Hydroxyl (SPLASH), a sensitive, unbiased single-dish survey of the Southern
Galactic Plane in all four ground-state transitions of the OH radical at 1612,
1665, 1667 and 1720 MHz. The survey covers the inner Galactic Plane, Central
Molecular Zone and Galactic Centre over the range $|b|<$ 2$^{\circ}$,
332$^{\circ}$ $< l <$ 10$^{\circ}$, with a small extension between 2$^{\circ}$
$< b <$ 6$^{\circ}$, 358$^{\circ}$ $< l <$ 4$^{\circ}$. SPLASH is the most
sensitive large-scale survey of OH to-date, reaching a characteristic
root-mean-square sensitivity of $\sim15$ mK for an effective velocity
resolution of $\sim0.9$ km/s. The spectral line datacubes are optimised for the
analysis of extended, quasi-thermal OH, but also contain numerous maser
sources, which have been confirmed interferometrically and published elsewhere.
We also present radio continuum images at 1612, 1666 and 1720 MHz. Based on
initial comparisons with $^{12}$CO(J=1-0), we find that OH rarely extends
outside CO cloud boundaries in our data, but suggest that large variations in
CO-to-OH brightness temperature ratios may reflect differences in the total gas
column density traced by each. Column density estimation in the complex,
continuum-bright Inner Galaxy is a challenge, and we demonstrate how failure to
appropriately model sub-beam structure and the line-of-sight source
distribution can lead to order-of-magnitude errors. Anomalous excitation of the
1612 and 1720 MHz satellite lines is ubiquitous in the inner Galaxy, but is
disabled by line overlap in and around the Central Molecular Zone.


title: Compilable Neural Code Generation with Compiler Feedback

abstract:
  Automatically generating compilable programs with (or without) natural
language descriptions has always been a touchstone problem for computational
linguistics and automated software engineering. Existing deep-learning
approaches model code generation as text generation, either constrained by
grammar structures in decoder, or driven by pre-trained language models on
large-scale code corpus (e.g., CodeGPT, PLBART, and CodeT5). However, few of
them account for compilability of the generated programs. To improve
compilability of the generated programs, this paper proposes COMPCODER, a
three-stage pipeline utilizing compiler feedback for compilable code
generation, including language model fine-tuning, compilability reinforcement,
and compilability discrimination. Comprehensive experiments on two code
generation tasks demonstrate the effectiveness of our proposed approach,
improving the success rate of compilation from 44.18 to 89.18 in code
completion on average and from 70.3 to 96.2 in text-to-code generation,
respectively, when comparing with the state-of-the-art CodeGPT.


title:
Bayesian Copula Directional Dependence for causal inference on gene
  expression data

abstract:
  Modelling and understanding directional gene networks is a major challenge in
biology as they play an important role in the architecture and function of
genetic systems. Copula Directional Dependence (CDD) can measure the directed
connectivity among variables without any strict requirements of distributional
and linearity assumptions. Furthermore, copulas can achieve that by isolating
the dependence structure of a joint distribution. In this work, a novel
extension of the frequentist CDD in the Bayesian setting is introduced. The new
method is compared against the frequentist CDD and validated on six gene
interactions, three coming from a mouse scRNA-seq dataset and three coming from
a bulk epigenome dataset. The results illustrate that the novel proposed
Bayesian CDD was able to identify four out of six true interactions with
increased robustness compared to the frequentist method. Therefore, the
Bayesian CDD can be considered as an alternative way for modeling the
information flow in gene networks.


title:
Manifold Modeling in Quotient Space: Learning An Invariant Mapping with
  Decodability of Image Patches

abstract:
  This study proposes a framework for manifold learning of image patches using
the concept of equivalence classes: manifold modeling in quotient space (MMQS).
In MMQS, we do not consider a set of local patches of the image as it is, but
rather the set of their canonical patches obtained by introducing the concept
of equivalence classes and performing manifold learning on their canonical
patches. Canonical patches represent equivalence classes, and their
auto-encoder constructs a manifold in the quotient space. Based on this
framework, we produce a novel manifold-based image model by introducing
rotation-flip-equivalence relations. In addition, we formulate an image
reconstruction problem by fitting the proposed image model to a corrupted
observed image and derive an algorithm to solve it. Our experiments show that
the proposed image model is effective for various self-supervised image
reconstruction tasks, such as image inpainting, deblurring, super-resolution,
and denoising.


title: Rotation equivalence and cocycle superrigidity

abstract:
  We analyze Euclidean spheres in higher dimensions and the corresponding orbit
equivalence relations induced by the group of rational rotations from the
viewpoint of descriptive set theory. It turns out that such equivalence
relations are not treeable in dimension greater than $2$. Then we show that the
rotation equivalence relation in dimension $n \geq 5$ is not Borel reducible to
the one in any lower dimension. Our methods combine a cocycle superrigidity
result from the works of Furman and Ioana with the superrigidity theorem for
$S$-arithmetic groups of Margulis. We also apply our techniques to give a
geometric proof of the existence of uncountably many pairwise incomparable
equivalence relations up to Borel reducibility.


title:
Strong influence of the Pd-Si ratio on the valence transition in
  EuPd$_2$Si$_2$ single crystals

abstract:
  Single crystals of intermediate valent EuPd$_2$Si$_2$ were grown from an
Eu-rich melt by the Bridgman as well as the Czochralski technique. The chemical
and structural characterization of an extracted single crystalline
Czochralski-grown specimen yielded a slight variation of the Si-Pd ratio along
the growth direction and confirms the existence of a finite
Eu(Pd$_{1-m}$Si$_m$)$_2$ homogeneity range. The thorough physical
characterization carried out on the same crystal showed that this tiny
variation in the composition strongly affects the temperature T$_v$ at which
the valence transition occurs. These experiments demonstrate a strong coupling
between structural and physical properties in the prototypical
valence-fluctuating system EuPd$_2$Si$_2$ and explain the different reported
values of T$_v$.


title: Cross-modal Map Learning for Vision and Language Navigation

abstract:
  We consider the problem of Vision-and-Language Navigation (VLN). The majority
of current methods for VLN are trained end-to-end using either unstructured
memory such as LSTM, or using cross-modal attention over the egocentric
observations of the agent. In contrast to other works, our key insight is that
the association between language and vision is stronger when it occurs in
explicit spatial representations. In this work, we propose a cross-modal map
learning model for vision-and-language navigation that first learns to predict
the top-down semantics on an egocentric map for both observed and unobserved
regions, and then predicts a path towards the goal as a set of waypoints. In
both cases, the prediction is informed by the language through cross-modal
attention mechanisms. We experimentally test the basic hypothesis that
language-driven navigation can be solved given a map, and then show competitive
results on the full VLN-CE benchmark.


title:
Anomalous magneto-optical response and chiral interface of dipolar
  excitons at twisted valleys

abstract:
  An anomalous magneto-optical spectrum is discovered for dipolar valley
excitons in twisted double layer transition metal dichalcogenides (TMD), where
in-plane magnetic field induces a sizable multiplet splitting of exciton states
inside the light cone. Chiral dispersions of the split branches make possible
efficient optical injection of unidirectional exciton current. We also find an
analog effect with a modest heterostrain replacing the magnetic field for
introducing large splitting and chiral dispersions in the light cone. Angular
orientation of photo-injected exciton flow can be controlled by strain, with
left-right unidirectionality selected by circular polarisation.


title:
On the surplus management of funds with assets and liabilities in
  presence of solvency requirements

abstract:
  In this paper we consider a company whose assets and liabilities evolve
according to a correlated bivariate geometric Brownian motion, such as in
Gerber and Shiu (2003). We determine what dividend strategy maximises the
expected present value of dividends until ruin in two cases: (i) when
shareholders won't cover surplus shortfalls and a solvency constraint (as in
Paulsen, 2003) is consequently imposed, and (ii) when shareholders are always
to fund any capital deficiency with capital (asset) injections. In the latter
case, ruin will never occur and the objective is to maximise the difference
between dividends and capital injections.
  Developing and using appropriate verification lemmas, we show that the
optimal dividend strategy is, in both cases, of barrier type. Both value
functions are derived in closed form. Furthermore, the barrier is defined on
the ratio of assets to liabilities, which mimics some of the dividend
strategies that can be observed in practice by insurance companies. Existence
and uniqueness of the optimal strategies are shown. Results are illustrated.


title:
Speciesist Language and Nonhuman Animal Bias in English Masked Language
  Models

abstract:
  Various existing studies have analyzed what social biases are inherited by
NLP models. These biases may directly or indirectly harm people, therefore
previous studies have focused only on human attributes. If the social biases in
NLP models can be indirectly harmful to humans involved, then the models can
also indirectly harm nonhuman animals. However, until recently no research on
social biases in NLP regarding nonhumans existed. In this paper, we analyze
biases to nonhuman animals, i.e. speciesist bias, inherent in English Masked
Language Models. We analyze this bias using template-based and corpus-extracted
sentences which contain speciesist (or non-speciesist) language, to show that
these models tend to associate harmful words with nonhuman animals. Our code
for reproducing the experiments will be made available on GitHub.


title:
Detectability of hadron-quark phase transition in neutrino signals of
  failing core-collapse supernova

abstract:
  We introduce a systematic and quantitative methodology for establishing the
presence of neutrino oscillatory signals due to the hadron-quark phase
transition (PT) in failing core-collapse supernovae from the observed neutrino
event rate in water- or ice-based neutrino detectors. The methodology uses a
likelihood ratio in the frequency domain as a test-statistic; it is also
employed to estimate the frequency, amplitude, starting time, and duration of
the PT-induced oscillatory signal present in the neutrino events. Assuming a
core-collapse simulation of a 17 solar-mass star by Zha \emph{et al.} (2021)
can be used as representative of a future PT-induced oscillatory signal in the
two detectors, we find that the presence of a PT can be identified with high
confidence for a core-collapse supernovae out to a distance of $\sim 10$ kpc
for IceCube and to $\sim 5$ kpc for a 0.4 Mt mass water Cherenkov detector.
This methodology will aid the investigation of a future galactic supernova and
the study of hadron-quark matter phase in the core of massive stars.


title: IAE-Net: Integral Autoencoders for Discretization-Invariant Learning

abstract:
  Discretization invariant learning aims at learning in the
infinite-dimensional function spaces with the capacity to process heterogeneous
discrete representations of functions as inputs and/or outputs of a learning
model. This paper proposes a novel deep learning framework based on integral
autoencoders (IAE-Net) for discretization invariant learning. The basic
building block of IAE-Net consists of an encoder and a decoder as integral
transforms with data-driven kernels, and a fully connected neural network
between the encoder and decoder. This basic building block is applied in
parallel in a wide multi-channel structure, which are repeatedly composed to
form a deep and densely connected neural network with skip connections as
IAE-Net. IAE-Net is trained with randomized data augmentation that generates
training data with heterogeneous structures to facilitate the performance of
discretization invariant learning. The proposed IAE-Net is tested with various
applications in predictive data science, solving forward and inverse problems
in scientific computing, and signal/image processing. Compared with
alternatives in the literature, IAE-Net achieves state-of-the-art performance
in existing applications and creates a wide range of new applications.


title: Long-lasting standstill and fading episode in the IW And star V507 Cyg

abstract:
  We studied the IW And star V507 Cyg using ASAS-SN data, ZTF data and our
snapshot photometry. The star has been found to be in a long standstill in 2020
May-2022 March (and it still continues now). Such long-lasting standstills have
been found in a few other IW And stars in the past literature. We found that
this system was systematically fainter by 0.3 mag between 2018 March and 2019
January when it showed dwarf nova-type variations, including IW And-type
states. This is one of the clearest pieces of evidence that the dwarf nova-type
state in Z Cam stars (in a broader sense) is associated with a decrease in the
mass-transfer rate (~25% for a duration of 300 d in this case). The object also
showed IW And-type phenomena in 2019, when the system was as bright as in the
current long standstill. This finding suggests that the condition whether the
IW And-type phenomenon occurs or not is subtle under the same mass-transfer
rate.


title: Earthquake Location and Magnitude Estimation with Graph Neural Networks

abstract:
  We solve the traditional problems of earthquake location and magnitude
estimation through a supervised learning approach, where we train a Graph
Neural Network to predict estimates directly from input pick data, and each
input allows a distinct seismic network with variable number of stations and
positions. We train the model using synthetic simulations from assumed
travel-time and amplitude-distance attenuation models. The architecture uses
one graph to represent the station set, and another to represent the model
space. The input includes theoretical predictions of data, given model
parameters, and the adjacency matrices of the graphs defined link spatially
local elements. As we show, graph convolutions on this combined representation
are highly effective at inference, data fusion, and outlier suppression. We
compare our results with traditional methods and observe favorable performance.


title: Intention-aware Feature Propagation Network for Interactive Segmentation

abstract:
  We aim to tackle the problem of point-based interactive segmentation, in
which two key challenges are to infer user's intention correctly and to
propagate the user-provided annotations to unlabeled regions efficiently. To
address those challenges, we propose a novel intention-aware feature
propagation strategy that performs explicit user intention estimation and
learns an efficient click-augmented feature representation for high-resolution
foreground segmentation. Specifically, we develop a coarse-to-fine sparse
propagation network for each interactive segmentation step, which consists of a
coarse-level network for more effective tracking of user's interest, and a
fine-level network for zooming to the target object and performing fine-level
segmentation. Moreover, we design a new sparse graph network module for both
levels to enable efficient long-range propagation of click information.
Extensive experiments show that our method surpasses the previous
state-of-the-art methods on all popular benchmarks, demonstrating its efficacy.


title: A class of semilinear elliptic equations on lattice graphs

abstract:
  In this paper, we study the semilinear elliptic equation of the form
\begin{eqnarray*} -\Delta u+a(x)|u|^{p-2}u-b(x)|u|^{q-2}u=0 \end{eqnarray*} on
lattice graphs $\mathbb{Z}^{N}$, where $N\geq 2$ and $2\leq p<q<+\infty$. By
the Br\'{e}zis-Lieb lemma and concentration compactness principle, we prove the
existence of positive solutions to the above equation with constant
coefficients $\bar{a},\bar{b}$ and the decomposition of bounded Palais-Smale
sequences for the functional with variable coefficients, which tend to some
constants $\bar{a},\bar{b}$ at infinity, respectively.


title:
An Accurate Unsupervised Method for Joint Entity Alignment and Dangling
  Entity Detection

abstract:
  Knowledge graph integration typically suffers from the widely existing
dangling entities that cannot find alignment cross knowledge graphs (KGs). The
dangling entity set is unavailable in most real-world scenarios, and manually
mining the entity pairs that consist of entities with the same meaning is
labor-consuming. In this paper, we propose a novel accurate Unsupervised method
for joint Entity alignment (EA) and Dangling entity detection (DED), called
UED. The UED mines the literal semantic information to generate pseudo entity
pairs and globally guided alignment information for EA and then utilizes the EA
results to assist the DED. We construct a medical cross-lingual knowledge graph
dataset, MedED, providing data for both the EA and DED tasks. Extensive
experiments demonstrate that in the EA task, UED achieves EA results comparable
to those of state-of-the-art supervised EA baselines and outperforms the
current state-of-the-art EA methods by combining supervised EA data. For the
DED task, UED obtains high-quality results without supervision.


title:
Efficient Topology Assessment for Integrated Transmission and
  Distribution Network with 10,000+ Inverter-based Resources

abstract:
  The renewable energy proliferation calls upon the grid operators and planners
to systematically evaluate the potential impacts of distributed energy
resources (DERs). Considering the significant differences between various
inverter-based resources (IBRs), especially the different capabilities between
grid-forming inverters and grid-following inverters, it is crucial to develop
an efficient and effective assessment procedure besides available co-simulation
framework with high computation burdens. This paper presents a streamlined
graph-based topology assessment for the integrated power system transmission
and distribution networks. Graph analyses were performed based on the
integrated graph of modified miniWECC grid model and IEEE 8500-node test feeder
model, high performance computing platform with 40 nodes and total 2400 CPUs
has been utilized to process this integrated graph, which has 100,000+ nodes
and 10,000+ IBRs. The node ranking results not only verified the applicability
of the proposed method, but also revealed the potential of distributed grid
forming (GFM) and grid following (GFL) inverters interacting with the
centralized power plants.


title:
Stable numerical simulation of Einstein equations in gravitational
  collapse space--time

abstract:
  We perform simulations in a gravitational collapsing model using the Einstein
equations. In this paper, we review the equations for constructing the initial
values and the evolution form of the Einstein equations called the BSSN
formulation. In addition, since we treat a nonvacuum case, we review the
evolution equations of the matter fields of a perfect fluid. To make the
simulations stable, we propose a modified system, which decreases numerical
errors in analysis, and we actually perform stable simulations with decreased
numerical errors.


title: Non-inertial torques and the Euler equation

abstract:
  The fundamental equation describing the rotational dynamics of a rigid body
is ${\vec \tau}=d{\vec L} / dt$ which is a straightforward consequence of the
Newton's second law of motion and is only valid in an inertial coordinate
system. While this equation is written down by an inertial observer, for
practical purposes, it is worked out within a non-inertial ancillary coordinate
system which is typically fixed in the rigid body. This results in the famous
Euler equation for rotation of the rigid bodies. We show that it is also
possible to describe the rotational dynamics of a rigid body from the point of
view of a non-inertial observer (rotating with the ancillary coordinate system
fixed in the rigid body), provided that the non-inertial torques are taken into
account. We explicitly calculate the non-inertial torques and express them in
terms of physical characteristics of the rigid body. We show that the resulting
dynamical equations exactly recover the Euler equation.


title: Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity

abstract:
  Current adversarial attack research reveals the vulnerability of
learning-based classifiers against carefully crafted perturbations. However,
most existing attack methods have inherent limitations in cross-dataset
generalization as they rely on a classification layer with a closed set of
categories. Furthermore, the perturbations generated by these methods may
appear in regions easily perceptible to the human visual system (HVS). To
circumvent the former problem, we propose a novel algorithm that attacks
semantic similarity on feature representations. In this way, we are able to
fool classifiers without limiting attacks to a specific dataset. For
imperceptibility, we introduce the low-frequency constraint to limit
perturbations within high-frequency components, ensuring perceptual similarity
between adversarial examples and originals. Extensive experiments on three
datasets (CIFAR-10, CIFAR-100, and ImageNet-1K) and three public online
platforms indicate that our attack can yield misleading and transferable
adversarial examples across architectures and datasets. Additionally,
visualization results and quantitative performance (in terms of four different
metrics) show that the proposed algorithm generates more imperceptible
perturbations than the state-of-the-art methods. Code is made available at.


title:
NOMA-enabled Backscatter Communications for Green Transportation in
  Automotive-Industry 5.0

abstract:
  Automotive-Industry 5.0 will use emerging 6G communications to provide
robust, computationally intelligent, and energy-efficient data sharing among
various onboard sensors, vehicles, and other Intelligent Transportation System
(ITS) entities. Non-Orthogonal Multiple Access (NOMA) and backscatter
communications are two key techniques of 6G communications for enhanced
spectrum and energy efficiency. In this paper, we provide an introduction to
green transportation and also discuss the advantages of using backscatter
communications and NOMA in Automotive Industry 5.0. We also briefly review the
recent work in the area of NOMA empowered backscatter communications. We
discuss different use cases of backscatter communications in NOMA-enabled 6G
vehicular networks. We also propose a multi-cell optimization framework to
maximize the energy efficiency of the backscatter-enabled NOMA vehicular
network. In particular, we jointly optimize the transmit power of the roadside
unit and the reflection coefficient of the backscatter device in each cell,
where several practical constraints are also taken into account. The problem of
energy efficiency is formulated as nonconvex which is hard to solve directly.
Thus, first, we adopt the Dinkelbach method to transform the objective function
into a subtractive one, then we decouple the problem into two subproblems.
Second, we employ dual theory and KKT conditions to obtain efficient solutions.
Finally, we highlight some open issues and future research opportunities
related to NOMA-enabled backscatter communications in 6G vehicular networks.


title:
Determining Existence of Logical Obstructions to the Distributed Task
  Solvability

abstract:
  To study the distributed task solvability, Goubault, Ledent, and Rajsbaum
devised a model of dynamic epistemic logic that is equivalent to the
topological model for distributed computing. In the logical model, the
unsolvability of a particular distributed task can be proven by finding a
formula, called logical obstruction. This logical method is very appealing
because the concrete formulas that prevent to solve task would have
implications of intuitive factors for the unsolvability. However, it has not
been well studied when a logical obstruction exists and how to systematically
construct a concrete logical obstruction formula, if any. In addition, it is
proved that there are some tasks that are solvable but do not admit logical
obstructions.
  In this paper, we propose a method to prove the non-existence of logical
obstructions to the solvability of distributed tasks, based on the technique of
simulation. Moreover, we give a method to determine whether a logical
obstruction exists or not for a finite protocol and a finite task, and if it
exists, construct a concrete obstruction. Using this method, we demonstrate
that the language of the standard epistemic logic, without distributed
knowledge, does not admit logical obstruction to the solvability of $k$-set
agreement tasks. We also show that there is no logical obstruction for
multi-round immediate snapshot even in the language of epistemic logic with
distributed knowledge. In addition, for the know-all model, we provide a
concrete obstruction formula that shows the unsolvability of the $k$-set
agreement task.


title: Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack

abstract:
  Defense models against adversarial attacks have grown significantly, but the
lack of practical evaluation methods has hindered progress. Evaluation can be
defined as looking for defense models' lower bound of robustness given a budget
number of iterations and a test dataset. A practical evaluation method should
be convenient (i.e., parameter-free), efficient (i.e., fewer iterations) and
reliable (i.e., approaching the lower bound of robustness). Towards this
target, we propose a parameter-free Adaptive Auto Attack (A$^3$) evaluation
method which addresses the efficiency and reliability in a test-time-training
fashion. Specifically, by observing that adversarial examples to a specific
defense model follow some regularities in their starting points, we design an
Adaptive Direction Initialization strategy to speed up the evaluation.
Furthermore, to approach the lower bound of robustness under the budget number
of iterations, we propose an online statistics-based discarding strategy that
automatically identifies and abandons hard-to-attack images. Extensive
experiments demonstrate the effectiveness of our A$^3$. Particularly, we apply
A$^3$ to nearly 50 widely-used defense models. By consuming much fewer
iterations than existing methods, i.e., $1/10$ on average (10$\times$ speed
up), we achieve lower robust accuracy in all cases. Notably, we won
$\textbf{first place}$ out of 1681 teams in CVPR 2021 White-box Adversarial
Attacks on Defense Models competitions with this method. Code is available at:
$\href{https://github.com/liuye6666/adaptive_auto_attack}{https://github.com/liuye6666/adaptive\_auto\_attack}$


title: Nanoscale Fabrication of Graphene by Hydrogen-Plasma Etching

abstract:
  Graphene is attracting vast interest due to its superior electronic and
mechanical properties. However, structure and electronic properties of its edge
are often neglected, although they are important for nanoscale devices because
the edge ratio becomes larger by decreasing the device size. In this study, we
suggest a way to fabricate a graphene with atomically aligned zigzag edges by
applying hydrogen-plasma etching (HPE) technique. By patterning a graphene
prior to HPE, it is succeeded to shape a graphene in desired structure. Both
atomic force microscopy and Raman spectroscopy confirm that the graphene shaped
by this technique preserves its honeycomb structure even on the edge, which is
aligned with zigzag structure. Although the mechanism of the anisotropic
etching by hydrogen-plasma have not been clarified yet, the sample position
dependence of the etching rate suggests that the hydrogen-radicals are
responsible for the anisotropic etching.


title:
Zero-Shot Action Recognition with Transformer-based Video Semantic
  Embedding

abstract:
  While video action recognition has been an active area of research for
several years, zero-shot action recognition has only recently started gaining
traction. However, there is a lack of a formal definition for the zero-shot
learning paradigm leading to uncertainty about classes that can be considered
as previously unseen. In this work, we take a new comprehensive look at the
inductive zero-shot action recognition problem from a realistic standpoint.
Specifically, we advocate for a concrete formulation for zero-shot action
recognition that avoids an exact overlap between the training and testing
classes and also limits the intra-class variance; and propose a novel
end-to-end trained transformer model which is capable of capturing long range
spatiotemporal dependencies efficiently, contrary to existing approaches which
use 3D-CNNs. The proposed approach outperforms the existing state-of-the-art
algorithms in many settings on all benchmark datasets by a wide margin.


title: Shadow and weak deflection angle of a black hole in nonlocal gravity

abstract:
  Black hole shadow and gravitational lensing play important roles in testing
gravitational theories in the strong field regime. As the first-order
modifications from quantum gravity, the nonlocality can be manifested by black
hole shadow and gravitational lensing. For example, the cut-off parameter
introduced by nonlocality will affect the shape and size of the black hole
shadow, and also affect the deflection angle of light rays. In this paper, we
mainly investigate the effects of the nonlocality on the black hole shadow and
the gravitational lensing for two types of rotating black holes in nonlocal
gravity. It is found that the size of the black hole shadow decreases with the
cut-off parameter since the nonlocality weakens the gravitational constant, and
the shape of the shadow gets more deformed with the increase of the cut-off
parameter. However, if the rotation parameter is small, the shape of the shadow
is almost a circle even though the cut-off parameter approaches its maximum.
The energy emission rate in both models is also studied. The results show that
there is a peak for each curve and the peak decreases and shifts to the low
frequency with the increase of the cut-off parameter. Besides, we also explore
the shadow of both types of black holes surrounded by a nonmagnetized
pressureless plasma which satisfies the separability condition. It is found
that the plasma has a frequency-dependent dispersive effect on the size and
shape of the black hole shadow. For the gravitational lensing, we find that the
cut-off parameter of model A makes a positive contribution to the deflection
angle, which can be compared with the contribution of the rotation parameter,
while the cut-off parameter of model B makes a negative contribution which can
be ignored. These results may be helpful for probing nonlocal gravity in future
observations.


title: Devouring the Leader Bottleneck in BFT Consensus

abstract:
  There is a resurgence of interest in Byzantine fault-tolerant (BFT) systems
due to blockchains. However, leader-based BFT consensus protocols used by
permissioned blockchains have limited scalability and robustness. To alleviate
the leader bottleneck in BFT consensus, we introduce Stratus, a robust shared
mempool protocol that decouples transaction distribution from consensus. Our
idea is to have replicas disseminate transactions in a distributed manner and
have the leader only propose transaction ids. Stratus uses a provably available
broadcast (PAB) protocol to ensure the availability of the referenced
transactions.
  We implemented and evaluated Stratus by integrating it with state-of-the-art
BFT-based blockchain protocols and evaluated these protocols in both LAN and
WAN settings. Our results show that Stratus-based protocols achieve up to
$5\sim20\times$ more throughput than their native counterparts in a network
with hundreds of replicas. In addition, the performance of Stratus degrades
gracefully in the presence of network asynchrony, Byzantine attackers, and
unbalanced workloads. Our design provides easy-to-use APIs so that other BFT
systems suffering from leader bottlenecks can use Stratus.


title: Precision calculation of hyperfine structure of $^{7,9}$Be$^{2+}$ ions

abstract:
  The hyperfine structures of the $2\,^3\!S_1$ and $2\,^3\!P_J$ states of the
$^7$Be$^{2+}$ and $^9$Be$^{2+}$ ions are investigated within the framework of
the nonrelativistic quantum electrodynamics (NRQED). The uncertainties of
present hyperfine splitting results of $^9$Be$^{2+}$ are in the order of
several tens of ppm, where two orders of magnitude improvement over the
previous theory and experiment values has been achieved. The contribution of
nuclear electric quadrupole moment to hyperfine splitting of $^7$Be$^{2+}$ has
been studied. A scheme for determining the properties of Be nuclei in terms of
Zemach radius or the electric quadrupole moment based on precise spectra is
proposed, and it opens a new window for the study of Be nuclei.


title: Deterministic Rendezvous in Infinite Trees

abstract:
  The rendezvous task calls for two mobile agents, starting from different
nodes of a network modeled as a graph to meet at the same node. Agents have
different labels which are integers from a set $\{1,\dots,L\}$. They wake up at
possibly different times and move in synchronous rounds. In each round, an
agent can either stay idle or move to an adjacent node. We consider
deterministic rendezvous algorithms. The time of such an algorithm is the
number of rounds since the wakeup of the earlier agent till the meeting. In
this paper we consider rendezvous in infinite trees. Our main goal is to study
the impact of orientation of a tree on the time of rendezvous.
  We first design a rendezvous algorithm working for unoriented regular trees,
whose time is in $O(z(D) \log L)$, where $z(D)$ is the size of the ball of
radius $D$, i.e, the number of nodes at distance at most $D$ from a given node.
The algorithm works for arbitrary delay between waking times of agents and does
not require any initial information about parameters $L$ or $D$. Its
disadvantage is its complexity: $z(D)$ is exponential in $D$ for any degree
$d>2$ of the tree. We prove that this high complexity is inevitable:
$\Omega(z(D))$ turns out to be a lower bound on rendezvous time in unoriented
regular trees, even for simultaneous start and even when agents know $L$ and
$D$. Then we turn attention to oriented trees. While for arbitrary delay
between waking times of agents the lower bound $\Omega(z(D))$ still holds, for
simultaneous start the time of rendezvous can be dramatically shortened. We
show that if agents know either a polynomial upper bound on $L$ or a linear
upper bound on $D$, then rendezvous can be accomplished in oriented trees in
time $O(D\log L)$, which is optimal. When no such extra knowledge is available,
we design an algorithm working in time $O(D^2+\log ^2L)$.


title:
Container Orchestration in Edge and Fog Computing Environments for
  Real-Time IoT Applications

abstract:
  Resource management is the principal factor to fully utilize the potential of
Edge/Fog computing to execute real-time and critical IoT applications. Although
some resource management frameworks exist, the majority are not designed based
on distributed containerized components. Hence, they are not suitable for
highly distributed and heterogeneous computing environments. Containerized
resource management frameworks such as FogBus2 enable efficient distribution of
framework's components alongside IoT applications' components. However, the
management, deployment, health-check, and scalability of a large number of
containers are challenging issues. To orchestrate a multitude of containers,
several orchestration tools are developed. But, many of these orchestration
tools are heavy-weight and have a high overhead, especially for
resource-limited Edge/Fog nodes. Thus, for hybrid computing environments,
consisting of heterogeneous Edge/Fog and/or Cloud nodes, lightweight container
orchestration tools are required to support both resource-limited resources at
the Edge/Fog and resource-rich resources at the Cloud. Thus, in this paper, we
propose a feasible approach to build a hybrid and lightweight cluster based on
K3s, for the FogBus2 framework that offers containerized resource management
framework. This work addresses the challenge of creating lightweight computing
clusters in hybrid computing environments. It also proposes three design
patterns for the deployment of the FogBus2 framework in hybrid environments,
including 1) Host Network, 2) Proxy Server, and 3) Environment Variable. The
performance evaluation shows that the proposed approach improves the response
time of real-time IoT applications up to 29% with acceptable and low overhead.


title: Categorical entropy, (co-)t-structures and ST-triples

abstract:
  In this paper, we study a dynamical property of an exact endofunctor $\Phi :
\mathcal{D} \to \mathcal{D}$ of a triangulated category $\mathcal{D}$. In
particular, we are interested in the following question: Given full
triangulated subcategories $\mathcal{A},\mathcal{B} \subset \mathcal{D}$ such
that $\Phi(\mathcal{A}) \subset \mathcal{A}$ and $\Phi(\mathcal{B}) \subset
\mathcal{B}$, how the categorical entropies of $\Phi|_\mathcal{A}$ and
$\Phi|_\mathcal{B}$ are related? To answer this question, we introduce new
entropy-type invariants using bounded (co-)t-structures with finite (co-)hearts
and prove their basic properties. We then apply these results to answer our
question for the situation where $\mathcal{A}$ has a bounded t-structure and
$\mathcal{B}$ has a bounded co-t-structure which are, in some sense, dual to
each other.


title: Thermal quantum correlations and teleportation under PT-symmetric system

abstract:
  In this work, we explore the different measures of quantum correlations and
quantum teleportation in the Heisenberg XY model for two different cases,
namely without PT-symmetric operation and with PT-symmetric operation.
Initially, we inspect the quantum correlation measures of thermally entangled
states without PT-symmetric operation. Among the different measures, violation
of Bell's inequality and concurrence failed to manifest the non-local
correlations of the entangled states. Interestingly, the entangled states which
show no correlation in terms of Bell's inequality and concurrence are also
useful for the teleportation process. Secondly, we incorporate the role of
non-unitary PT-symmetric time evolution to the model and study the measures
which show the considered states have correlations. The results reveal that the
considered model can act as an efficient resource for teleportation with
maximum fidelity under non-unitary time evolution.