title:
The pioneering scientific endeavor of the first Colombian modern
  astronomer Jos\'e Mar\'ia Gonz\'alez Benito (1843-1903)

abstract:
  Astronomical interest within the current Colombian territory has its roots in
the Botanical Expedition of the New Kingdom of Granada, which stimulated the
creation of an astronomical observatory in 1803, the first one established in
the New World to pursue systematic observations and meteorological studies.
After the death in 1816 of its first director, Francisco Jos\'e de Caldas,
during the convulsive independence period, no major astronomical observations
were made for decades, with few exceptions. In this work we delve into the
contributions of the astronomer Jos\'e Mar\'ia Gonz\'alez Benito, the main
reactivator of the National Astronomical Observatory of Colombia in the second
half of the 19th century, pointing out his pioneering efforts that put
worldwide attention to it, and to his own private observatory making him one of
the most committed figures to the development of astronomical sciences in the
country and the most renowned Colombian in the international astronomical
research scene of his time.


title:
Extensible circuit-QED architecture via amplitude- and
  frequency-variable microwaves

abstract:
  We introduce a circuit-QED architecture combining fixed-frequency qubits and
microwave-driven couplers. In the appropriate frame, the drive parameters
appear as tunable knobs enabling selective two-qubit coupling and
coherent-error suppression. We moreover introduce a set of controlled-phase
gates based on drive-amplitude and drive-frequency modulation. We develop a
theoretical framework based on Floquet theory to model microwave-activated
interactions with time-dependent drive parameters, which we also use for pulse
shaping. We perform numerical simulations of the gate fidelity for realistic
circuit parameters, and discuss the impact of drive-induced decoherence. We
estimate average gate fidelities beyond $99.9\%$ for all-microwave
controlled-phase operations with gate times in the range $50-120\,\mathrm{ns}$.
These two-qubit gates can operate over a large drive-frequency bandwidth and in
a broad range of circuit parameters, thereby improving extensibility. We
address the frequency allocation problem for this architecture using
perturbation theory, demonstrating that qubit, coupler and drive frequencies
can be chosen such that undesired static and driven interactions remain bounded
in a multi-qubit device. Our numerical methods are useful for describing the
time-evolution of driven systems in the adiabatic limit, and are applicable to
a wide variety of circuit-QED setups.


title: Baryon Cycles in the Biggest Galaxies

abstract:
  The universe's biggest galaxies have both vast atmospheres and supermassive
central black holes. This article reviews how those two components of a large
galaxy couple and regulate the galaxy's star formation rate. Models of
interactions between a supermassive black hole and the large-scale atmosphere
suggest that the energy released as cold gas clouds accrete onto the black hole
suspends the atmosphere in a state that is marginally stable to formation of
cold clouds. A growing body of observational evidence indicates that many
massive galaxies, ranging from the huge central galaxies of galaxy clusters
down to our own Milky Way, are close to that marginal state. The gas supply for
star formation within a galaxy in such a marginal state is closely tied to the
central velocity dispersion (sigma_v) of its stars. We therefore explore the
consequences of a model in which energy released during blackhole accretion
shuts down star formation when sigma_v exceeds a critical value determined by
the galaxy's supernova heating rate.


title: Multi-Model Ensemble Optimization

abstract:
  Methodology and optimization algorithms for sparse regression are extended to
multi-model regression ensembles. In particular, we adapt optimization
algorithms for l0-penalized problems to learn ensembles of sparse and diverse
models. To generate an initial solution for our algorithm, we generalize
forward stepwise regression to multi-model regression ensembles. The sparse and
diverse models are learned jointly from the data and constitute alternative
explanations for the relationship between the predictors and the response
variable. Beyond the advantage of interpretability, in prediction tasks the
ensembles are shown to outperform state-of-the-art competitors on both
simulated and gene expression data. We study the effect of the number of models
and show how the ensembles achieve excellent prediction accuracy by exploiting
the accuracy-diversity tradeoff of ensembles. The optimization algorithms are
implemented in publicly available R/C++ software packages.


title: Ultrafast disinfection of SARS-CoV-2 viruses

abstract:
  The wide use of surgical masks has been proven effective for mitigating the
spread of respiration diseases, such as COVID-19, alongside social distance
control, vaccines, and other efforts. With the newly reported variants, such as
Delta and Omicron, a higher spread rate had been found compared to the initial
strains. People might get infected even by inhaling fewer loading of viruses.
More frequent sterilization of surgical masks is needed to protect the wearers.
However, it is challenging to sterilize the commodity surgical masks with a
fast and effective method. Herein, we reported the sterilization of the
SARS-CoV-2 viruses within an ultra-short time, while retaining the mask
performance. Silver thin film is coated on commercial polyimide film by
physical vapor deposition and patterned by laser scribing to form a Joule
heating electrode. Another layer of the gold thin film was coated onto the
opposite side of the device to promote the uniformity of the Joule heating
through nano-heat transfer regulation. As a result, the surgical mask can be
heated to inactivation temperature within a short time and with high
uniformity. By Joule-heating the surgical mask with the temperature at 90
{\deg}C for 3 minutes, the inactivation of the SARS-CoV-2 showed an efficacy of
99.89%. Normal commodity surgical masks can be sterilized faster, more
frequently, and efficiently against SARS-CoV-2 viruses and the new invariants.


title:
kpfriends at SemEval-2022 Task 2: NEAMER -- Named Entity Augmented
  Multi-word Expression Recognizer

abstract:
  We present NEAMER -- Named Entity Augmented Multi-word Expression Recognizer.
This system is inspired by non-compositionality characteristics shared between
Named Entity and Idiomatic Expressions. We utilize transfer learning and
locality features to enhance idiom classification task. This system is our
submission for SemEval Task 2: Multilingual Idiomaticity Detection and Sentence
Embedding Subtask A OneShot shared task. We achieve SOTA with F1 0.9395 during
post-evaluation phase. We also observe improvement in training stability.
Lastly, we experiment with non-compositionality knowledge transfer,
cross-lingual fine-tuning and locality features, which we also introduce in
this paper.


title:
Should Young Computer Scientists Stop Collaborating with their Doctoral
  Advisors?

abstract:
  One of the first steps in an academic career, and perhaps the pillar thereof,
is completing a PhD under the supervision of a doctoral advisor. While prior
work has examined the advisor-advisee relationship and its potential effects on
the prospective academic success of the advisee, very little is known on the
possibly continued relationship after the advisee has graduated. We harnessed
three genealogical and scientometric datasets to identify 3 distinct groups of
computer scientists: Highly independent, who cease collaborating with their
advisors (almost) instantly upon graduation; Moderately independent, who
(quickly) reduce the collaboration rate over ~5 years; and Weakly independent
who continue collaborating with their advisors for at least 10 years
post-graduation. We find that highly independent researchers are more
academically successful than their peers in terms of H-index, i10-index and
total number of citations throughout their careers. Moderately independent
researchers perform, on average, better than weakly independent researchers,
yet the differences are not found to be statistically significant. In addition,
both highly and moderately independent researchers are found to have longer
academic careers. Interestingly, weakly independent researchers tend to be
supervised by more academically successful advisors.


title: Investigation of the bottom analog of the Zcs(3985) state

abstract:
  Motivated by the recent discovery of the hidden charm exotic state with
strangeness by the BESIII and LHCb Collaborations, we study the $S$ wave
strange hidden bottom tetraquark in two kinds of quark models. Both meson-meson
and diquark-antidiquark configurations are taken into account. The numerical
results indicate that there is no bound state in both quark models. However,
several resonance states have been predicted. Three resonance states with
$I(J^{P})=\frac{1}{2}(0^{+})$ are found, the energy ranges of which are
$10479\sim 10550$, $10528\sim 10632$, and $10597\sim 10681$ MeV, respectively.
Three resonance states with $I(J^{P})=\frac{1}{2}(1^{+})$ are predicted to be
located in $10491\sim 10675$, $10502\sim 10679$, and $10522\sim 10723$ MeV,
respectively. Moreover, there also exist a resonance with
$I(J^{P})=\frac{1}{2}(2^{+})$ and the mass is estimated to be $10531\sim 10680$
MeV. All these predicted states in the present work should be accessible for
the further experiments in LHCb


title: Monte Carlo Tree Search for Interpreting Stress in Natural Language

abstract:
  Natural language processing can facilitate the analysis of a person's mental
state from text they have written. Previous studies have developed models that
can predict whether a person is experiencing a mental health condition from
social media posts with high accuracy. Yet, these models cannot explain why the
person is experiencing a particular mental state. In this work, we present a
new method for explaining a person's mental state from text using Monte Carlo
tree search (MCTS). Our MCTS algorithm employs trained classification models to
guide the search for key phrases that explain the writer's mental state in a
concise, interpretable manner. Furthermore, our algorithm can find both
explanations that depend on the particular context of the text (e.g., a recent
breakup) and those that are context-independent. Using a dataset of Reddit
posts that exhibit stress, we demonstrate the ability of our MCTS algorithm to
identify interpretable explanations for a person's feeling of stress in both a
context-dependent and context-independent manner.


title: A New Dynamic Algorithm for Densest Subhypergraphs

abstract:
  Computing a dense subgraph is a fundamental problem in graph mining, with a
diverse set of applications ranging from electronic commerce to community
detection in social networks. In many of these applications, the underlying
context is better modelled as a weighted hypergraph that keeps evolving with
time.
  This motivates the problem of maintaining the densest subhypergraph of a
weighted hypergraph in a {\em dynamic setting}, where the input keeps changing
via a sequence of updates (hyperedge insertions/deletions). Previously, the
only known algorithm for this problem was due to Hu et al. [HWC17]. This
algorithm worked only on unweighted hypergraphs, and had an approximation ratio
of $(1+\epsilon)r^2$ and an update time of $O(\text{poly} (r, \log n))$, where
$r$ denotes the maximum rank of the input across all the updates.
  We obtain a new algorithm for this problem, which works even when the input
hypergraph is weighted. Our algorithm has a significantly improved
(near-optimal) approximation ratio of $(1+\epsilon)$ that is independent of
$r$, and a similar update time of $O(\text{poly} (r, \log n))$. It is the first
$(1+\epsilon)$-approximation algorithm even for the special case of weighted
simple graphs.
  To complement our theoretical analysis, we perform experiments with our
dynamic algorithm on large-scale, real-world data-sets. Our algorithm
significantly outperforms the state of the art [HWC17] both in terms of
accuracy and efficiency.


title:
Exploiting Embodied Simulation to Detect Novel Object Classes Through
  Interaction

abstract:
  In this paper we present a novel method for a naive agent to detect novel
objects it encounters in an interaction. We train a reinforcement learning
policy on a stacking task given a known object type, and then observe the
results of the agent attempting to stack various other objects based on the
same trained policy. By extracting embedding vectors from a convolutional
neural net trained over the results of the aforementioned stacking play, we can
determine the similarity of a given object to known object types, and determine
if the given object is likely dissimilar enough to the known types to be
considered a novel class of object. We present the results of this method on
two datasets gathered using two different policies and demonstrate what
information the agent needs to extract from its environment to make these
novelty judgments.


title:
How are Software Repositories Mined? A Systematic Literature Review of
  Workflows, Methodologies, Reproducibility, and Tools

abstract:
  With the advent of open source software, a veritable treasure trove of
previously proprietary software development data was made available. This
opened the field of empirical software engineering research to anyone in
academia. Data that is mined from software projects, however, requires
extensive processing and needs to be handled with utmost care to ensure valid
conclusions. Since the software development practices and tools have changed
over two decades, we aim to understand the state-of-the-art research workflows
and to highlight potential challenges. We employ a systematic literature review
by sampling over one thousand papers from leading conferences and by analyzing
the 286 most relevant papers from the perspective of data workflows,
methodologies, reproducibility, and tools. We found that an important part of
the research workflow involving dataset selection was particularly problematic,
which raises questions about the generality of the results in existing
literature. Furthermore, we found a considerable number of papers provide
little or no reproducibility instructions -- a substantial deficiency for a
data-intensive field. In fact, 33% of papers provide no information on how
their data was retrieved. Based on these findings, we propose ways to address
these shortcomings via existing tools and also provide recommendations to
improve research workflows and the reproducibility of research.


title:
ArcaneQA: Dynamic Program Induction and Contextualized Encoding for
  Knowledge Base Question Answering

abstract:
  Question answering on knowledge bases (KBQA) poses a unique challenge for
semantic parsing research due to two intertwined factors: large search space
and ambiguities in schema linking. The predominant ranking-based KBQA models,
which rely on a candidate enumeration step to reduce the search space, struggle
with flexibility and have impractical online running time. In this paper, we
present ArcaneQA, a novel generation-based model that addresses both the large
search space and schema linking in a unified framework with two mutually
boosting ingredients: we use dynamic program induction to tackle the large
search space and dynamic contextualized encoding to enhance schema linking.
Experiment results on multiple popular KBQA datasets demonstrate the highly
competitive performance of ArcaneQA in both effectiveness and efficiency.


title:
Language Contamination Explains the Cross-lingual Capabilities of
  English Pretrained Models

abstract:
  English pretrained language models, which make up the backbone of many modern
NLP systems, require huge amounts of unlabeled training data. These models are
generally presented as being trained only on English text but have been found
to transfer surprisingly well to other languages. We investigate this
phenomenon and find that common English pretraining corpora actually contain
significant amounts of non-English text: even when less than 1% of data is not
English (well within the error rate of strong language classifiers), this leads
to hundreds of millions of foreign language tokens in large-scale datasets. We
then demonstrate that even these small percentages of non-English data
facilitate cross-lingual transfer for models trained on them, with target
language performance strongly correlated to the amount of in-language data seen
during pretraining. In light of these findings, we argue that no model is truly
monolingual when pretrained at scale, which should be considered when
evaluating cross-lingual transfer.


title:
Material properties of a low contraction and resistivity
  silicon-aluminum composite for cryogenic detectors

abstract:
  We report on the cryogenic properties of a low-contraction silicon-aluminum
composite, namely Japan Fine Ceramics SA001, to use as a packaging structure
for cryogenic silicon devices. SA001 is a silicon--aluminum composite material
(75% silicon by volume) and has a low thermal expansion coefficient ($\sim$1/3
that of aluminum). The superconducting transition temperature of SA001 is
measured to be 1.18 K, which is in agreement with that of pure aluminum, and is
thus available as a superconducting magnetic shield material. The residual
resistivity of SA001 is 0.065 $\mathrm{\mu \Omega m}$, which is considerably
lower than an equivalent silicon--aluminum composite material. The measured
thermal contraction of SA001 immersed in liquid nitrogen is
$\frac{L_{293\mathrm{K}}-L_{77\mathrm{K}}}{L_{293\mathrm{K}}}=0.12$%, which is
consistent with the expected rate obtained from the volume-weighted mean of the
contractions of silicon and aluminum. The machinability of SA001 is also
confirmed with a demonstrated fabrication of a conical feedhorn array, with a
wall thickness of 100 $\mathrm{\mu m}$. These properties are suitable for
packaging applications for large-format superconducting detector devices.


title:
Testing synchrotron models and frequency resolution in BINGO 21 cm
  simulated maps using GNILC

abstract:
  To recover the 21 cm hydrogen line, it is essential to separate the
cosmological signal from the much stronger foreground contributions at radio
frequencies. The BINGO radio telescope is designed to measure the 21 cm line
and detect BAOs using the intensity mapping technique. This work analyses the
performance of the GNILC method, combined with a power spectrum debiasing
procedure. The method was applied to a simulated BINGO mission, building upon
previous work from the collaboration. It compares two different synchrotron
emission models and different instrumental configurations, in addition to the
combination with ancillary data to optimize both the foreground removal and
recovery of the 21 cm signal across the full BINGO frequency band, as well as
to determine an optimal number of frequency bands for the signal recovery. We
have produced foreground emissions maps using the Planck Sky Model, the
cosmological Hi emission maps are generated using the FLASK package and thermal
noise maps are created according to the instrumental setup. We apply the GNILC
method to the simulated sky maps to separate the Hi plus thermal noise
contribution and, through a debiasing procedure, recover an estimate of the
noiseless 21 cm power spectrum. We found a near optimal reconstruction of the
Hi signal using a 80 bins configuration, which resulted in a power spectrum
reconstruction average error over all frequencies of 3%. Furthermore, our tests
showed that GNILC is robust against different synchrotron emission models.
Finally, adding an extra channel with CBASS foregrounds information, we reduced
the estimation error of the 21 cm signal. The optimisation of our previous
work, producing a configuration with an optimal number of channels for binning
the data, impacts greatly the decisions regarding BINGO hardware configuration
before commissioning.


title:
Rotating regular black holes and rotating stars with a Tolman type
  potential as a regular interior for the Kerr metric

abstract:
  We obtain a new class of singularity-free axisymmetric spacetimes by applying
a slightly modified version of the Newman-Janis algorithm to a spherically
symmetric seed in order to generate a rotating core that may be smoothly
matched to the Kerr metric. The starting spherically symmetric configuration
represents a nonisotropic de-Sitter type fluid whose radial pressure $p_r$
satisfies an state equation of the form $p_r=-\rho$, where the energy density
$\rho$ is chosen to be the Tolman type-VII energy density [R. C. Tolman, Phys.
Rev. 55, 364 (1939)]. The resulting rotating metric is then smoothly matched to
the exterior Kerr metric, and the main properties of the obtained geometries
are investigated. Depending on the relative values of the total mass $m$ and
rotation parameter $a$, the resulting rotating spacetimes represent different
kinds of rotating compact objects such as regular stars, regular black holes,
and extreme regular black holes.


title:
A Distributed control framework for the optimal operation of DC
  microgrids

abstract:
  In this paper we propose an original distributed control framework for DC
mcirogrids. We first formulate the (optimal) control objectives as an
aggregative game suitable for the energy trading market. Then, based on the
dual theory, we analyze the equivalent distributed optimal condition for the
proposed aggregative game and design a distributed control scheme to solve it.
By interconnecting the DC mcirogrid and the designed distributed control system
in a power preserving way, we steer the DC microgrid's state to the desired
optimal equilibrium, satisfying a predefined set of local and coupling
constraints. Finally, based on the singular perturbation system theory, we
analyze the convergence of the closed-loop system. The simulation results show
excellent performance of the proposed control framework.


title: HFT-ONLSTM: Hierarchical and Fine-Tuning Multi-label Text Classification

abstract:
  Many important classification problems in the real-world consist of a large
number of closely related categories in a hierarchical structure or taxonomy.
Hierarchical multi-label text classification (HMTC) with higher accuracy over
large sets of closely related categories organized in a hierarchy or taxonomy
has become a challenging problem. In this paper, we present a hierarchical and
fine-tuning approach based on the Ordered Neural LSTM neural network,
abbreviated as HFT-ONLSTM, for more accurate level-by-level HMTC. First, we
present a novel approach to learning the joint embeddings based on parent
category labels and textual data for accurately capturing the joint features of
both category labels and texts. Second, a fine tuning technique is adopted for
training parameters such that the text classification results in the upper
level should contribute to the classification in the lower one. At last, the
comprehensive analysis is made based on extensive experiments in comparison
with the state-of-the-art hierarchical and flat multi-label text classification
approaches over two benchmark datasets, and the experimental results show that
our HFT-ONLSTM approach outperforms these approaches, in particular reducing
computational costs while achieving superior performance.


title:
Degree of constantly curved holomorphic 2-spheres in the complex
  Grassmannians G(2,n+2;C)

abstract:
  We show that the degree of the linearly full constantly curved holomorphic
2-spheres in the complex Grassmannians G(2,n+2;C) is greater than or equal to n
for general n and less than or equal to 2n for n=2,3.


title:
Fully-Decentralized Alternating Projected Gradient Descent for Low Rank
  column-wise Compressive Sensing

abstract:
  This work develops a provably accurate fully-decentralized fast and
communication-efficient alternating projected gradient descent (Dec-AltProjGD)
algorithm for solving the following low-rank (LR) matrix recovery problem:
recover an LR matrix from independent columnwise linear projections (LR
column-wise Compressive Sensing). To our best knowledge, this work is the first
attempt to develop a provably correct decentralized algorithm for any problem
involving use of an alternating projected GD algorithm and one in which the
constraint set to be projected to is a non-convex set.


title: On the Differential Properties of the Power Mapping $x^{p^m+2}$

abstract:
  Let $m$ be a positive integer and $p$ a prime. In this paper, we investigate
the differential properties of the power mapping $x^{p^m+2}$ over
$\mathbb{F}_{p^n}$, where $n=2m$ or $n=2m-1$. For the case $n=2m$, by
transforming the derivative equation of $x^{p^m+2}$ and studying some related
equations, we completely determine the differential spectrum of this power
mapping. For the case $n=2m-1$, the derivative equation can be transformed to a
polynomial of degree $p+3$. The problem is more difficult and we obtain partial
results about the differential spectrum of $x^{p^m+2}$.


title:
Split Learning over Wireless Networks: Parallel Design and Resource
  Management

abstract:
  Split learning (SL) is a collaborative learning framework, which can train an
artificial intelligence (AI) model between a device and an edge server by
splitting the AI model into a device-side model and a server-side model at a
cut layer. The existing SL approach conducts the training process sequentially
across devices, which incurs significant training latency especially when the
number of devices is large. In this paper, we design a novel SL scheme to
reduce the training latency, named Cluster-based Parallel SL (CPSL) which
conducts model training in a "first-parallel-then-sequential" manner.
Specifically, the CPSL is to partition devices into several clusters,
parallelly train device-side models in each cluster and aggregate them, and
then sequentially train the whole AI model across clusters, thereby
parallelizing the training process and reducing training latency. Furthermore,
we propose a resource management algorithm to minimize the training latency of
CPSL considering device heterogeneity and network dynamics in wireless
networks. This is achieved by stochastically optimizing the cut layer
selection, real-time device clustering, and radio spectrum allocation. The
proposed two-timescale algorithm can jointly make the cut layer selection
decision in a large timescale and device clustering and radio spectrum
allocation decisions in a small timescale. Extensive simulation results on
non-independent and identically distributed data demonstrate that the proposed
solutions can greatly reduce the training latency as compared with the existing
SL benchmarks, while adapting to network dynamics.


title:
Neural Gaits: Learning Bipedal Locomotion via Control Barrier Functions
  and Zero Dynamics Policies

abstract:
  This work presents Neural Gaits, a method for learning dynamic walking gaits
through the enforcement of set invariance that can be refined episodically
using experimental data from the robot. We frame walking as a set invariance
problem enforceable via control barrier functions (CBFs) defined on the
reduced-order dynamics quantifying the underactuated component of the robot:
the zero dynamics. Our approach contains two learning modules: one for learning
a policy that satisfies the CBF condition, and another for learning a residual
dynamics model to refine imperfections of the nominal model. Importantly,
learning only over the zero dynamics significantly reduces the dimensionality
of the learning problem while using CBFs allows us to still make guarantees for
the full-order system. The method is demonstrated experimentally on an
underactuated bipedal robot, where we are able to show agile and dynamic
locomotion, even with partially unknown dynamics.


title: End-to-end Dense Video Captioning as Sequence Generation

abstract:
  Dense video captioning aims to identify the events of interest in an input
video, and generate descriptive captions for each event. Previous approaches
usually follow a two-stage generative process, which first proposes a segment
for each event, then renders a caption for each identified segment. Recent
advances in large-scale sequence generation pretraining have seen great success
in unifying task formulation for a great variety of tasks, but so far, more
complex tasks such as dense video captioning are not able to fully utilize this
powerful paradigm. In this work, we show how to model the two subtasks of dense
video captioning jointly as one sequence generation task, and simultaneously
predict the events and the corresponding descriptions. Experiments on YouCook2
and ViTT show encouraging results and indicate the feasibility of training
complex tasks such as end-to-end dense video captioning integrated into
large-scale pre-trained models.


title: Optimal Conformal Prediction for Small Areas

abstract:
  Existing inferential methods for small area data involve a trade-off between
maintaining area-level frequentist coverage rates and improving inferential
precision via the incorporation of indirect information. In this article, we
propose a method to obtain an area-level prediction region for a future
observation which mitigates this trade-off. The proposed method takes a
conformal prediction approach in which the conformity measure is the posterior
predictive density of a working model that incorporates indirect information.
The resulting prediction region has guaranteed frequentist coverage regardless
of the working model, and, if the working model assumptions are accurate, the
region has minimum expected volume compared to other regions with the same
coverage rate. When constructed under a normal working model, we prove such a
prediction region is an interval and construct an efficient algorithm to obtain
the exact interval. We illustrate the performance of our method through
simulation studies and an application to EPA radon survey data.


title: Non-Parallel Text Style Transfer with Self-Parallel Supervision

abstract:
  The performance of existing text style transfer models is severely limited by
the non-parallel datasets on which the models are trained. In non-parallel
datasets, no direct mapping exists between sentences of the source and target
style; the style transfer models thus only receive weak supervision of the
target sentences during training, which often leads the model to discard too
much style-independent information, or utterly fail to transfer the style. In
this work, we propose LaMer, a novel text style transfer framework based on
large-scale language models. LaMer first mines the roughly parallel expressions
in the non-parallel datasets with scene graphs, and then employs MLE training,
followed by imitation learning refinement, to leverage the intrinsic
parallelism within the data. On two benchmark tasks (sentiment & formality
transfer) and a newly proposed challenging task (political stance transfer),
our model achieves qualitative advances in transfer accuracy, content
preservation, and fluency. Further empirical and human evaluations demonstrate
that our model not only makes training more efficient, but also generates more
readable and diverse expressions than previous models.


title: A repeating fast radio burst residing in a magnetar/Be star binary

abstract:
  Fast radio bursts (FRBs) are mysterious cosmic sources emitting
millisecond-duration radio bursts\cite{Lorimer2007}. Although several hundreds
FRBs have been discovered\cite{TheCHIME/FRBCollaboration2021}, their physical
nature and central engine are still
unclear\cite{Cordes2019,Petroff2019,Zhang2020,Xiao2021}. The variations of
Faraday rotation measure and dispersion measure, due to local environment, are
crucial clues to understand their physical
nature\cite{Michilli2018,Anna-Thomas2022}. The recent observations on the
rotation measure of FRB 20201124A show a significant variation on a daytime
scale\cite{FAST21}. The variable rotation measure demonstrates that FRB
20201124A is in a dynamic magneto-ionic environment\cite{FAST21}. Intriguingly,
the oscillation of rotation measure supports that the local contribution can
change sign, which indicates the direction changes of the magnetic field along
the line of sight. Here we propose that this FRB resides in a binary system
containing a magnetar and a Be star with a decretion disk. When the magnetar
approaches the periastron, the propagation of radio waves through the disk of
the Be star naturally leads to the observed varying rotation measure,
depolarization, large scattering timescale, and Faraday conversion. Searching
for FRB signals from Be/X-ray binaries is encouraging.


title:
FedKL: Tackling Data Heterogeneity in Federated Reinforcement Learning
  by Penalizing KL Divergence

abstract:
  As a distributed learning paradigm, Federated Learning (FL) faces the
communication bottleneck issue due to many rounds of model synchronization and
aggregation. Heterogeneous data further deteriorates the situation by causing
slow convergence. Although the impact of data heterogeneity on supervised FL
has been widely studied, the related investigation for Federated Reinforcement
Learning (FRL) is still in its infancy. In this paper, we first define the type
and level of data heterogeneity for policy gradient based FRL systems. By
inspecting the connection between the global and local objective functions, we
prove that local training can benefit the global objective, if the local update
is properly penalized by the total variation (TV) distance between the local
and global policies. A necessary condition for the global policy to be
learn-able from the local policy is also derived, which is directly related to
the heterogeneity level. Based on the theoretical result, a Kullback-Leibler
(KL) divergence based penalty is proposed, which, different from the
conventional method that penalizes the model divergence in the parameter space,
directly constrains the model outputs in the distribution space. By jointly
penalizing the divergence of the local policy from the global policy with a
global penalty and constraining each iteration of the local training with a
local penalty, the proposed method achieves a better trade-off between training
speed (step size) and convergence. Experiment results on two popular RL
experiment platforms demonstrate the advantage of the proposed algorithm over
existing methods in accelerating and stabilizing the training process with
heterogeneous data.


title:
Optimal Power Flow in Four-Wire Distribution Networks: Formulation and
  Benchmarking

abstract:
  In recent years, several applications have been proposed in the context of
distribution networks. Many of these can be formulated as an optimal power flow
problem, a mathematical optimization program which includes a model of the
steady-state physics of the electricity network. If the network loading is
balanced and the lines are transposed, the network model can be simplified to a
single-phase equivalent model. However, these assumptions do not apply to
low-voltage distribution networks, so the network model should model the
effects of phase unbalance correctly. In many parts of the world, the
low-voltage distribution network has four conductors, i.e. three phases and a
neutral. This paper develops OPF formulations for such networks, including
transformers, shunts and voltage-dependent loads, in two variable spaces, i.e.
current-voltage and power-voltage, and compares them for robustness and
scalability. A case study across 128 low-voltage networks also quantifies the
modelling error introduced by Kron reductions and its impact on the solve time.
This work highlights the advantages of formulations in current-voltage
variables over power-voltage, for four-wire networks.


title:
Parallel Network with Channel Attention and Post-Processing for Carotid
  Arteries Vulnerable Plaque Segmentation in Ultrasound Images

abstract:
  Carotid arteries vulnerable plaques are a crucial factor in the screening of
atherosclerosis by ultrasound technique. However, the plaques are contaminated
by various noises such as artifact, speckle noise, and manual segmentation may
be time-consuming. This paper proposes an automatic convolutional neural
network (CNN) method for plaque segmentation in carotid ultrasound images using
a small dataset. First, a parallel network with three independent scale
decoders is utilized as our base segmentation network, pyramid dilation
convolutions are used to enlarge receptive fields in the three segmentation
sub-networks. Subsequently, the three decoders are merged to be rectified in
channels by SENet. Thirdly, in test stage, the initially segmented plaque is
refined by the max contour morphology post-processing to obtain the final
plaque. Moreover, three loss function Dice loss, SSIM loss and cross-entropy
loss are compared to segment plaques. Test results show that the proposed
method with dice loss function yields a Dice value of 0.820, an IoU of 0.701,
Acc of 0.969, and modified Hausdorff distance (MHD) of 1.43 for 30 vulnerable
cases of plaques, it outperforms some of the conventional CNN-based methods on
these metrics. Additionally, we apply an ablation experiment to show the
validity of each proposed module. Our study provides some reference for similar
researches and may be useful in actual applications for plaque segmentation of
ultrasound carotid arteries.


title:
Less is More: Learning to Refine Dialogue History for Personalized
  Dialogue Generation

abstract:
  Personalized dialogue systems explore the problem of generating responses
that are consistent with the user's personality, which has raised much
attention in recent years. Existing personalized dialogue systems have tried to
extract user profiles from dialogue history to guide personalized response
generation. Since the dialogue history is usually long and noisy, most existing
methods truncate the dialogue history to model the user's personality. Such
methods can generate some personalized responses, but a large part of dialogue
history is wasted, leading to sub-optimal performance of personalized response
generation. In this work, we propose to refine the user dialogue history on a
large scale, based on which we can handle more dialogue history and obtain more
abundant and accurate persona information. Specifically, we design an MSP model
which consists of three personal information refiners and a personalized
response generator. With these multi-level refiners, we can sparsely extract
the most valuable information (tokens) from the dialogue history and leverage
other similar users' data to enhance personalization. Experimental results on
two real-world datasets demonstrate the superiority of our model in generating
more informative and personalized responses.


title:
Animal Kingdom: A Large and Diverse Dataset for Animal Behavior
  Understanding

abstract:
  Understanding animals' behaviors is significant for a wide range of
applications. However, existing animal behavior datasets have limitations in
multiple aspects, including limited numbers of animal classes, data samples and
provided tasks, and also limited variations in environmental conditions and
viewpoints. To address these limitations, we create a large and diverse
dataset, Animal Kingdom, that provides multiple annotated tasks to enable a
more thorough understanding of natural animal behaviors. The wild animal
footages used in our dataset record different times of the day in extensive
range of environments containing variations in backgrounds, viewpoints,
illumination and weather conditions. More specifically, our dataset contains 50
hours of annotated videos to localize relevant animal behavior segments in long
videos for the video grounding task, 30K video sequences for the fine-grained
multi-label action recognition task, and 33K frames for the pose estimation
task, which correspond to a diverse range of animals with 850 species across 6
major animal classes. Such a challenging and comprehensive dataset shall be
able to facilitate the community to develop, adapt, and evaluate various types
of advanced methods for animal behavior analysis. Moreover, we propose a
Collaborative Action Recognition (CARe) model that learns general and specific
features for action recognition with unseen new animals. This method achieves
promising performance in our experiments. Our dataset can be found at
https://sutdcv.github.io/Animal-Kingdom.


title:
Almost global smooth solutions of the 3D quasilinear Klein-Gordon
  equations on the product space $\mathbb{R}^{2}\times \mathbb{T}$

abstract:
  In the paper, for the 3D quasilinear Klein-Gordon equation with the small
initial data posed on the product space $\mathbb{R}^{2}\times \mathbb{T}$, we
focus on the lower bound of the lifespan of the smooth solution. When the size
of initial data is bounded by $\varepsilon_0>0$, by the space-time resonance
method, it is shown that smooth solution exists up to the time
$e^{c_{0}/\varepsilon_{0}^2}$ with $\varepsilon_0$ being sufficiently small and
$c_0>0$ being some suitable constant.


title:
Positioning Using Visible Light Communications: A Perspective Arcs
  Approach

abstract:
  Visible light positioning (VLP) is an accurate indoor positioning technology
that uses luminaires as transmitters. In particular, circular luminaires are a
common source type for VLP, that are typically treated only as point sources
for positioning, while ignoring their geometry characteristics. In this paper,
the arc feature of the circular luminaire and the coordinate information
obtained via visible light communication (VLC) are jointly used for VLC-enabled
indoor positioning, and a novel perspective arcs approach is proposed. The
proposed approach does not rely on any inertial measurement unit, and has no
tilted angle limitations at the user. First, a VLC assisted perspective circle
and arc algorithm (V-PCA) is proposed for a scenario in which a complete
luminaire and an incomplete one can be captured by the user. Considering the
cases in which parts of VLC links are blocked, an anti-occlusion VLC assisted
perspective arcs algorithm (OA-V-PA) is proposed. Simulation results show that
the proposed indoor positioning algorithm can achieve a 95th percentile
positioning accuracy of around 10 cm. Moreover, an experimental prototype based
on mobile phone is implemented, in which, a fused image processing method is
proposed. Experimental results show that the average positioning accuracy is
less than 5 cm.


title:
Intermittency of turbulent velocity and scalar fields using 3D local
  averaging

abstract:
  An efficient approach for extracting 3D local averages in spherical
subdomains is proposed and applied to study the intermittency of small-scale
velocity and scalar fields in direct numerical simulations of isotropic
turbulence. We focus on the inertial-range scaling exponents of locally
averaged energy dissipation rate, enstrophy and scalar dissipation rate
corresponding to the mixing of a passive scalar $\theta$ in the presence of a
uniform mean gradient. The Taylor-scale Reynolds number $R_\lambda$ goes up to
$1300$, and the Schmidt number $Sc$ up to $512$ (albeit at smaller
$R_\lambda$). The intermittency exponent of the energy dissipation rate is $\mu
\approx 0.23$, whereas that of enstrophy is slightly larger; trends with
$R_\lambda$ suggest that this will be the case even at extremely large
$R_\lambda$. The intermittency exponent of the scalar dissipation rate is
$\mu_\theta \approx 0.35$ for $Sc=1$. These findings are in essential agreement
with previously reported results in the literature. We further show that
$\mu_\theta$ decreases monotonically with increasing $Sc$, either as $1/\log
Sc$ or a weak power law, suggesting that $\mu_\theta \to 0$ as $Sc \to \infty$,
reaffirming recent results on the breakdown of scalar dissipation anomaly in
this limit.


title: Deconfined quantum criticality and emergent symmetry in SrCu2(BO3)2

abstract:
  The deconfined quantum critical point (DQCP) represents a paradigm shift in
theories of quantum matter, presenting a "beyond Landau" scenario for
order-order transitions. Its experimental realization, however, has remained
elusive. Here we demonstrate by high-pressure 11B NMR measurements on the
quantum magnet SrCu2(BO3)2 that the magnetic field induced plaquette-singlet to
antiferromagnetic transition above 1.8 GPa is proximate to a DQCP. We find a
weak first-order transition between the two phases at a remarkably low
temperature, Tc~0.07 K. Above Tc we observe quantum critical scaling at the
highest pressure, 2.4 GPa. We explain the low first-order Tc values by a
DQCP-induced emergent O(3) symmetry that is broken in the coexistence state.
Our findings take the DQCP from a theoretical concept to a concrete
experimental platform.


title: A Practical Cross-Device Federated Learning Framework over 5G Networks

abstract:
  The concept of federated learning (FL) was first proposed by Google in 2016.
Thereafter, FL has been widely studied for the feasibility of application in
various fields due to its potential to make full use of data without
compromising the privacy. However, limited by the capacity of wireless data
transmission, the employment of federated learning on mobile devices has been
making slow progress in practical. The development and commercialization of the
5th generation (5G) mobile networks has shed some light on this. In this paper,
we analyze the challenges of existing federated learning schemes for mobile
devices and propose a novel cross-device federated learning framework, which
utilizes the anonymous communication technology and ring signature to protect
the privacy of participants while reducing the computation overhead of mobile
devices participating in FL. In addition, our scheme implements a
contribution-based incentive mechanism to encourage mobile users to participate
in FL. We also give a case study of autonomous driving. Finally, we present the
performance evaluation of the proposed scheme and discuss some open issues in
federated learning.


title: On Secure NOMA-Aided Semi-Grant-Free Systems

abstract:
  Semi-grant-free (SGF) transmission scheme enables grant-free (GF) users to
utilize resource blocks allocated for grant-based (GB) users while maintaining
the quality of service of GB users. This work investigates the secrecy
performance of non-orthogonal multiple access (NOMA)-aided SGF systems. First,
analytical expressions for the exact and asymptotic secrecy outage probability
(SOP) of NOMA-aided SGF systems with a single GF user are derived. Then, the
SGF systems with multiple GF users and a best-user scheduling scheme is
considered. By utilizing order statistics theory, closed-form expressions for
the exact and asymptotic SOP are derived. Monte Carlo simulation results
demonstrate the effects of system parameters on the SOP of the considered
system and verify the accuracy of the developed analytical results. The results
indicate that both the outage target rate for GB and the secure target rate for
GF are the main factors of the secrecy performance of SGF systems.


title: Trinary Tools for Continuously Valued Binary Classifiers

abstract:
  Classification methods for binary (yes/no) tasks often produce a continuously
valued score. Machine learning practitioners must perform model selection,
calibration, discretization, performance assessment, tuning, and fairness
assessment. Such tasks involve examining classifier results, typically using
summary statistics and manual examination of details. In this paper, we provide
an interactive visualization approach to support such continuously-valued
classifier examination tasks. Our approach addresses the three phases of these
tasks: calibration, operating point selection, and examination. We enhance
standard views and introduce task-specific views so that they can be integrated
into a multi-view coordination (MVC) system. We build on an existing
comparison-based approach, extending it to continuous classifiers by treating
the continuous values as trinary (positive, unsure, negative) even if the
classifier will not ultimately use the 3-way classification. We provide use
cases that demonstrate how our approach enables machine learning practitioners
to accomplish key tasks.


title: Ingredient Extraction from Text in the Recipe Domain

abstract:
  In recent years, there has been an increase in the number of devices with
virtual assistants (e.g: Siri, Google Home, Alexa) in our living rooms and
kitchens. As a result of this, these devices receive several queries about
recipes. All these queries will contain terms relating to a "recipe-domain"
i.e: they will contain dish-names, ingredients, cooking times, dietary
preferences etc. Extracting these recipe-relevant aspects from the query thus
becomes important when it comes to addressing the user's information need. Our
project focuses on extracting ingredients from such plain-text user utterances.
Our best performing model was a fine-tuned BERT which achieved an F1-score of
$95.01$. We have released all our code in a GitHub repository.


title: Walking to Infinity on the Fibonacci Sequence

abstract:
  An interesting open problem in number theory asks whether it is possible to
walk to infinity on primes, where each term in the sequence has one more digit
than the previous. In this paper, we study its variation where we walk on the
Fibonacci sequence. We prove that all walks starting with a Fibonacci number
and the following terms are Fibonacci numbers obtained by appending exactly one
digit at a time to the right have a length of at most two. In the more general
case where we append at most a bounded number of digits each time, we give a
formula for the length of the longest walk.


title: A Deep Learning Galerkin Method for the Closed-Loop Geothermal System

abstract:
  There has been an arising trend of adopting deep learning methods to study
partial differential equations (PDEs). This article is to propose a Deep
Learning Galerkin Method (DGM) for the closed-loop geothermal system, which is
a new coupled multi-physics PDEs and mainly consists of a framework of
underground heat exchange pipelines to extract the geothermal heat from the
geothermal reservoir. This method is a natural combination of Galerkin Method
and machine learning with the solution approximated by a neural network instead
of a linear combination of basis functions. We train the neural network by
randomly sampling the spatiotemporal points and minimize loss function to
satisfy the differential operators, initial condition, boundary and interface
conditions. Moreover, the approximate ability of the neural network is proved
by the convergence of the loss function and the convergence of the neural
network to the exact solution in L^2 norm under certain conditions. Finally,
some numerical examples are carried out to demonstrate the approximation
ability of the neural networks intuitively.


title: Pricing Real-time Stochastic Storage Operations

abstract:
  Pricing storage operation in the real-time market under demand and generation
stochasticities is considered. A scenario-based stochastic rolling-window
dispatch model is formulated for the real-time market, consisting of
conventional generators, utility-scale storage, and distributed energy resource
aggregators. We show that uniform pricing mechanisms require discriminative
out-of-the-market uplifts, making settlements under locational marginal pricing
(LMP) discriminative. It is shown that the temporal locational marginal pricing
(TLMP) that adds nonuniform shadow prices of ramping and state-of-charge to LMP
removes the need for out-of-the-market uplifts. Truthful bidding incentives are
also established for price-taking participants under TLMP. Revenue adequacy and
uplifts are evaluated in numerical simulations.


title:
On Riedtmann's Lie algebra of the gentle one-cycle algebra
  $\Lambda(n-1,1,1)$

abstract:
  An extended version of Riedtmann's Lie algebra of the gentle one-cycle
algebra $\Lambda(n-1,1,1)$ is computed and is shown to be like the Borel
subalgebra of an Lie algebra of type $\mathbf{BC}$.


title: Dynamic Position Encoding for Transformers

abstract:
  Recurrent models have been dominating the field of neural machine translation
(NMT) for the past few years. Transformers \citep{vaswani2017attention}, have
radically changed it by proposing a novel architecture that relies on a
feed-forward backbone and self-attention mechanism. Although Transformers are
powerful, they could fail to properly encode sequential/positional information
due to their non-recurrent nature. To solve this problem, position embeddings
are defined exclusively for each time step to enrich word information. However,
such embeddings are fixed after training regardless of the task and the word
ordering system of the source or target language.
  In this paper, we propose a novel architecture with new position embeddings
depending on the input text to address this shortcoming by taking the order of
target words into consideration. Instead of using predefined position
embeddings, our solution \textit{generates} new embeddings to refine each
word's position information. Since we do not dictate the position of source
tokens and learn them in an end-to-end fashion, we refer to our method as
\textit{dynamic} position encoding (DPE). We evaluated the impact of our model
on multiple datasets to translate from English into German, French, and Italian
and observed meaningful improvements in comparison to the original Transformer.


title:
Detect Rumors in Microblog Posts for Low-Resource Domains via
  Adversarial Contrastive Learning

abstract:
  Massive false rumors emerging along with breaking news or trending topics
severely hinder the truth. Existing rumor detection approaches achieve
promising performance on the yesterday's news, since there is enough corpus
collected from the same domain for model training. However, they are poor at
detecting rumors about unforeseen events especially those propagated in
different languages due to the lack of training data and prior knowledge (i.e.,
low-resource regimes). In this paper, we propose an adversarial contrastive
learning framework to detect rumors by adapting the features learned from
well-resourced rumor data to that of the low-resourced. Our model explicitly
overcomes the restriction of domain and/or language usage via language
alignment and a novel supervised contrastive training paradigm. Moreover, we
develop an adversarial augmentation mechanism to further enhance the robustness
of low-resource rumor representation. Extensive experiments conducted on two
low-resource datasets collected from real-world microblog platforms demonstrate
that our framework achieves much better performance than state-of-the-art
methods and exhibits a superior capacity for detecting rumors at early stages.


title:
Hawking Temperature for 4D-Einstein-Gauss-Bonnet Black Holes from
  uncertainty principle

abstract:
  Inspired by string theory, Heisenberg's uncertainty principle can be
generalized to include the photon-electron gravitational interaction, which
leads to the Generalized Uncertainty Principle (GUP). Although GUP considers
gravitational uncertainty at the minimum fundamental length scale in physics,
it does not consider the effects of spacetime curvature on quantum mechanical
uncertainty relations. The Extended Uncertainty Principle (EUP) is a
generalization of Heisenberg's Uncertainty Principle that, unlike the GUP,
applies to large length scales. GEUP is also a linear combination of EUP and
GUP that creates minimal uncertainty on large length scales. The
Einstein-Gauss-Bonnet theory (EGB) can be considered as one of the most
promising candidates for modified gravity. In this paper, by using GUP, EUP,
and GEUP, we intend to obtain the Hawking temperature of a four-dimensional EGB
black hole in the asymptotically flat and (Anti)-de Sitter spacetime. We show
that coupling constant, cosmological constant, mass, and radius significantly
affect Hawking temperature and decrease or increase Hawking temperature
depending on the chosen horizons.


title: The Convergence of Circle Packings on Tori

abstract:
  The notion of circle packings has been explored intensively since Thurston
proposed it as an approximation to conformal maps. One of the fundamental
questions about circle packings is whether it converges to the Riemann mapping
if the radius of the circles tends to zero. In this paper, we prove the
convergence of circle packings to the maps induced by the uniformization
theorem in a different sense proposed by Gu-Luo-Wu for surfaces of genus one.
This is the first result of the convergence of circle packings on surfaces with
nontrivial topology.


title:
PrivateRec: Differentially Private Training and Serving for Federated
  News Recommendation

abstract:
  Privacy protection is an essential issue in personalized news recommendation,
and federated learning can potentially mitigate the privacy concern by training
personalized news recommendation models over decentralized user data.For a
theoretical privacy guarantee, differential privacy is necessary. However,
applying differential privacy to federated recommendation training and serving
conventionally suffers from the unsatisfactory trade-off between privacy and
utility due to the high-dimensional characteristics of model gradients and
hidden representations. In addition, there is no formal privacy guarantee for
both training and serving in federated recommendation. In this paper, we
propose a unified federated news recommendation method for effective and
privacy-preserving model training and online serving with differential privacy
guarantees. We first clarify the notion of differential privacy over users'
behavior data for both model training and online serving in the federated
recommendation scenario. Next, we propose a privacy-preserving online serving
mechanism under this definition with differentially private user interest
decomposition. More specifically, it decomposes the high-dimensional and
privacy-sensitive user embedding into a combination of public basic vectors and
adds noise to the combination coefficients. In this way, it can avoid the
dimension curse and improve the utility by reducing the required noise
intensity for differential privacy. Besides, we design a federated
recommendation model training method with differential privacy, which can avoid
the dimension-dependent noise for large models via label permutation and
differentially private attention modules. Experiments on real-world news
recommendation datasets validate the effectiveness of our method in achieving a
good trade-off between privacy protection and utility for federated news
recommendations.


title:
Numerical computation of the equilibrium-reduced density matrix for
  strongly coupled open quantum systems

abstract:
  We describe a numerical algorithm for approximating the equilibrium-reduced
density matrix and the effective (mean force) Hamiltonian for a set of system
spins coupled strongly to a set of bath spins when the total system
(system+bath) is held in canonical thermal equilibrium by weak coupling with a
"super-bath". Our approach is a generalization of now standard typicality
algorithms for computing the quantum expectation value of observables of bare
quantum systems via trace estimators and Krylov subspace methods. In
particular, our algorithm makes use of the fact that the reduced system
density, when the bath is measured in a given random state, tends to
concentrate about the corresponding thermodynamic averaged reduced system
density. Theoretical error analysis and numerical experiments are given to
validate the accuracy of our algorithm. Further numerical experiments
demonstrate the potential of our approach for applications including the study
of quantum phase transitions and entanglement entropy for long-range
interaction systems.


title: Frustrated magnetic interactions in a Wigner-Mott insulator

abstract:
  Two-dimensional semiconductor moir\'e materials have emerged as a highly
controllable platform to simulate and explore quantum condensed matter.
Compared to real solids, electrons in semiconductor moir\'e materials are less
strongly attracted to the moir\'e lattice sites, making the nonlocal
contributions to the magnetic interactions as important as the Anderson
super-exchange. It provides a unique platform to study the effects of competing
magnetic interactions. Here, we report the observation of strongly frustrated
magnetic interactions in a Wigner-Mott insulating state at 2/3 filling of the
moir\'e lattice in angle-aligned WSe2/WS2 heterobilayers. Magneto-optical
measurements show that the net exchange interaction is antiferromagnetic for
filling factors below 1 with a strong suppression at 2/3 filling. The
suppression is lifted upon screening of the long-range Coulomb interactions and
melting of the Wigner-Mott insulator by a nearby metallic gate. The results can
be qualitatively captured by a honeycomb-lattice spin model with an
antiferromagnetic nearest-neighbor coupling and a ferromagnetic second-neighbor
coupling. Our study establishes semiconductor moir\'e materials as a model
system for the lattice-spin physics and frustrated magnetism.


title: Phase covariant channel: Quantum speed limit of evolution

abstract:
  The quantum speed of evolution for the phase covariant map is investigated.
In this context, we consider various combinations of (non)-Markovian,
CP-(in)divisible, quantum channels and analyze how they affect the quantum
speed limit time. The role of coherence-mixedness balance on the speed limit
time is checked in the presence of both vacuum and finite temperature effects.
We also investigate how the combination of (non)-Markovian channels decides the
rate at which Holevo's information changes under different purity conditions.


title: Characterizing and Understanding Distributed GNN Training on GPUs

abstract:
  Graph neural network (GNN) has been demonstrated to be a powerful model in
many domains for its effectiveness in learning over graphs. To scale GNN
training for large graphs, a widely adopted approach is distributed training
which accelerates training using multiple computing nodes. Maximizing the
performance is essential, but the execution of distributed GNN training remains
preliminarily understood. In this work, we provide an in-depth analysis of
distributed GNN training on GPUs, revealing several significant observations
and providing useful guidelines for both software optimization and hardware
optimization.


title:
Machine-learning rationalization and prediction of solid-state synthesis
  conditions

abstract:
  There currently exist no quantitative methods to determine the appropriate
conditions for solid-state synthesis. This not only hinders the experimental
realization of novel materials but also complicates the interpretation and
understanding of solid-state reaction mechanisms. Here, we demonstrate a
machine-learning approach that predicts synthesis conditions using large
solid-state synthesis datasets text-mined from scientific journal articles.
Using feature importance ranking analysis, we discovered that optimal heating
temperatures have strong correlations with the stability of precursor materials
quantified using melting points and formation energies ($\Delta G_f$, $\Delta
H_f$). In contrast, features derived from the thermodynamics of
synthesis-related reactions did not directly correlate to the chosen heating
temperatures. This correlation between optimal solid-state heating temperature
and precursor stability extends Tamman's rule from intermetallics to oxide
systems, suggesting the importance of reaction kinetics in determining
synthesis conditions. Heating times are shown to be strongly correlated with
the chosen experimental procedures and instrument setups, which may be
indicative of human bias in the dataset. Using these predictive features, we
constructed machine-learning models with good performance and general
applicability to predict the conditions required to synthesize diverse chemical
systems. Codes and data used in this work can be found at:
https://github.com/CederGroupHub/s4.


title:
Back to the Future: Bidirectional Information Decoupling Network for
  Multi-turn Dialogue Modeling

abstract:
  Multi-turn dialogue modeling as a challenging branch of natural language
understanding (NLU), aims to build representations for machines to understand
human dialogues, which provides a solid foundation for multiple downstream
tasks. Recent studies of dialogue modeling commonly employ pre-trained language
models (PrLMs) to encode the dialogue history as successive tokens, which is
insufficient in capturing the temporal characteristics of dialogues. Therefore,
we propose Bidirectional Information Decoupling Network (BiDeN) as a universal
dialogue encoder, which explicitly incorporates both the past and future
contexts and can be generalized to a wide range of dialogue-related tasks.
Experimental results on datasets of different downstream tasks demonstrate the
universality and effectiveness of our BiDeN.


title: Distributed Projections onto a Simplex

abstract:
  Projecting a vector onto a simplex is a well-studied problem that arises in a
wide range of optimization problems. Numerous algorithms have been proposed for
determining the projection; however, all but one of these algorithms are
serial. We address this gap by developing a method that preprocesses the input
vector by decomposing and distributing it across multiple processors for local
projection. Our method is especially effective when the projection is highly
sparse; which is the case, for instance, in large-scale problems with i.i.d.
entries. Moreover, the method can be adapted to work with a broad range of
serial algorithms from the literature. We fill in theoretical gaps in serial
algorithm analysis, and develop similar results for our parallel analogues.
Numerical experiments conducted on a wide range of large-scale instances
demonstrate the practical effectiveness of the method.


title:
End-to-end Weakly-supervised Multiple 3D Hand Mesh Reconstruction from
  Single Image

abstract:
  In this paper, we consider the challenging task of simultaneously locating
and recovering multiple hands from single 2D image. Previous studies either
focus on single hand reconstruction or solve this problem in a multi-stage way.
Moreover, the conventional two-stage pipeline firstly detects hand areas, and
then estimates 3D hand pose from each cropped patch. To reduce the
computational redundancy in preprocessing and feature extraction, we propose a
concise but efficient single-stage pipeline. Specifically, we design a
multi-head auto-encoder structure for multi-hand reconstruction, where each
head network shares the same feature map and outputs the hand center, pose and
texture, respectively. Besides, we adopt a weakly-supervised scheme to
alleviate the burden of expensive 3D real-world data annotations. To this end,
we propose a series of losses optimized by a stage-wise training scheme, where
a multi-hand dataset with 2D annotations is generated based on the publicly
available single hand datasets. In order to further improve the accuracy of the
weakly supervised model, we adopt several feature consistency constraints in
both single and multiple hand settings. Specifically, the keypoints of each
hand estimated from local features should be consistent with the re-projected
points predicted from global features. Extensive experiments on public
benchmarks including FreiHAND, HO3D, InterHand2.6M and RHD demonstrate that our
method outperforms the state-of-the-art model-based methods in both
weakly-supervised and fully-supervised manners.


title: A dynamical systems based framework for dimension reduction

abstract:
  We propose a novel framework for learning a low-dimensional representation of
data based on nonlinear dynamical systems, which we call dynamical dimension
reduction (DDR). In the DDR model, each point is evolved via a nonlinear flow
towards a lower-dimensional subspace; the projection onto the subspace gives
the low-dimensional embedding. Training the model involves identifying the
nonlinear flow and the subspace. Following the equation discovery method, we
represent the vector field that defines the flow using a linear combination of
dictionary elements, where each element is a pre-specified linear/nonlinear
candidate function. A regularization term for the average total kinetic energy
is also introduced and motivated by optimal transport theory. We prove that the
resulting optimization problem is well-posed and establish several properties
of the DDR method. We also show how the DDR method can be trained using a
gradient-based optimization method, where the gradients are computed using the
adjoint method from optimal control theory. The DDR method is implemented and
compared on synthetic and example datasets to other dimension reductions
methods, including PCA, t-SNE, and Umap.


title:
Interaction Design of Dwell Selection Toward Gaze-based AR/VR
  Interaction

abstract:
  In this paper, we first position the current dwell selection among gaze-based
interactions and its advantages against head-gaze selection, which is the
mainstream interface for HMDs. Next, we show how dwell selection and head-gaze
selection are used in an actual interaction situation. By comparing these two
selection methods, we describe the potential of dwell selection as an essential
AR/VR interaction.


title: Deep Learning Coherent Diffractive Imaging

abstract:
  We report the development of deep learning coherent electron diffractive
imaging at sub-angstrom resolution using convolutional neural networks (CNNs)
trained with only simulated data. We experimentally demonstrate this method by
applying the trained CNNs to directly recover the phase images from electron
diffraction patterns of twisted hexagonal boron nitride, monolayer graphene and
a Au nanoparticle with comparable quality to those reconstructed by a
conventional ptychographic method. Fourier ring correlation between the CNN and
ptychographic images indicates the achievement of a spatial resolution in the
range of 0.70 and 0.55 angstrom (depending on different resolution criteria).
The ability to replace iterative algorithms with CNNs and perform real-time
imaging from coherent diffraction patterns is expected to find broad
applications in the physical and biological sciences.


title: Forecasts on CMB lensing observations with AliCPT-1

abstract:
  AliCPT-1 is the first Chinese CMB experiment aiming for high precision
measurement of Cosmic Microwave Background B-mode polarization. The telescope,
currently under deployment in Tibet, will observe in two frequency bands
centered at 90 and 150 GHz. We forecast the CMB lensing reconstruction,
lensing-galaxy as well as lensing-CIB (Cosmic Infrared Background) cross
correlation signal-to-noise ratio (SNR) for AliCPT-1. We consider two stages
with different integrated observation time, namely "4 module*yr" (first stage)
and "48 module*yr" (final stage). For lensing reconstruction, we use three
different quadratic estimators, namely temperature-only, polarization-only and
minimum-variance estimators, using curved sky geometry. We take into account
the impact of inhomogeneous hit counts as well as of the mean-field bias due to
incomplete sky coverage. In the first stage, our results show that the 150 GHz
channel is able to measure the lensing signal at $15\sigma$ significance with
the minimum-variance estimator. In the final stage, the measurement
significance will increase to $31\sigma$. We also combine the two frequency
data in the harmonic domain to optimize the SNR. Our result show that the
coadding procedure can significantly reduce the reconstruction bias in the
multiple range l>800. Thanks to the high quality of the polarization data in
the final stage of AliCPT-1, the EB estimator will dominate the lensing
reconstruction in this stage. We also estimate the SNR of cross-correlations
between AliCPT-1 CMB lensing and other tracers of the large scale structure of
the universe. For its cross-correlation with DESI galaxies/quasars, we report
the cross-correlation SNR = 10-20 for the 4 redshift bins at 0.05<z<2.1. In the
first stage, the total SNR is about $32$. In the final stage, the
lensing-galaxy cross-correlation can reach SNR=52.


title: Multi-scale Anomaly Detection for Big Time Series of Industrial Sensors

abstract:
  Given a multivariate big time series, can we detect anomalies as soon as they
occur? Many existing works detect anomalies by learning how much a time series
deviates away from what it should be in the reconstruction framework. However,
most models have to cut the big time series into small pieces empirically since
optimization algorithms cannot afford such a long series. The question is
raised: do such cuts pollute the inherent semantic segments, like incorrect
punctuation in sentences? Therefore, we propose a reconstruction-based anomaly
detection method, MissGAN, iteratively learning to decode and encode naturally
smooth time series in coarse segments, and finding out a finer segment from
low-dimensional representations based on HMM. As a result, learning from
multi-scale segments, MissGAN can reconstruct a meaningful and robust time
series, with the help of adversarial regularization and extra conditional
states. MissGAN does not need labels or only needs labels of normal instances,
making it widely applicable. Experiments on industrial datasets of real water
network sensors show our MissGAN outperforms the baselines with scalability.
Besides, we use a case study on the CMU Motion dataset to demonstrate that our
model can well distinguish unexpected gestures from a given conditional motion.


title:
On Arbitrary Compression for Decentralized Consensus and Stochastic
  Optimization over Directed Networks

abstract:
  We study the decentralized consensus and stochastic optimization problems
with compressed communications over static directed graphs. We propose an
iterative gradient-based algorithm that compresses messages according to a
desired compression ratio. The proposed method provably reduces the
communication overhead on the network at every communication round. Contrary to
existing literature, we allow for arbitrary compression ratios in the
communicated messages. We show a linear convergence rate for the proposed
method on the consensus problem. Moreover, we provide explicit convergence
rates for decentralized stochastic optimization problems on smooth functions
that are either (i) strongly convex, (ii) convex, or (iii) non-convex. Finally,
we provide numerical experiments to illustrate convergence under arbitrary
compression ratios and the communication efficiency of our algorithm.


title:
Decompositions of graphs of nonnegative characteristic with some
  forbidden subgraphs

abstract:
  A {\em $(d,h)$-decomposition} of a graph $G$ is an order pair $(D,H)$ such
that $H$ is a subgraph of $G$ where $H$ has the maximum degree at most $h$ and
$D$ is an acyclic orientation of $G-E(H)$ of maximum out-degree at most $d$. A
graph $G$ is {\em $(d, h)$-decomposable} if $G$ has a $(d,h)$-decomposition.
Let $G$ be a graph embeddable in a surface of nonnegative characteristic. In
this paper, we prove the following results. (1) If $G$ has no chord $5$-cycles
or no chord $6$-cycles or no chord $7$-cycles and no adjacent $4$-cycles, then
$G$ is $(3,1)$-decomposable, which generalizes the results of Chen, Zhu and
Wang [Comput. Math. Appl, 56 (2008) 2073--2078] and the results of Zhang
[Comment. Math. Univ. Carolin, 54(3) (2013) 339--344]. (2) If $G$ has no
$i$-cycles nor $j$-cycles for any subset $\{i,j\}\subseteq \{3,4,6\}$ is
$(2,1)$-decomposable, which generalizes the results of Dong and Xu [Discrete
Math. Alg. and Appl., 1(2) (2009), 291--297].


title:
Gaussian quantum information over general quantum kinematical systems I:
  Gaussian states

abstract:
  We develop a theory of Gaussian states over general quantum kinematical
systems with finitely many degrees of freedom. The underlying phase space is
described by a locally compact abelian (LCA) group $G$ with a symplectic
structure determined by a 2-cocycle on $G$. We use the concept of Gaussian
distributions on LCA groups in the sense of Bernstein to define Gaussian states
and completely characterize Gaussian states over 2-regular LCA groups of the
form $G= F\times\hat{F}$ endowed with a canonical normalized 2-cocycle. This
covers, in particular, the case of $n$-bosonic modes, $n$-qudit systems with
odd $d\ge 3$, and $p$-adic quantum systems. Our characterization reveals a
topological obstruction to Gaussian state entanglement when we decompose the
quantum kinematical system into the Euclidean part and the remaining part
(whose phase space admits a compact open subgroup). We then generalize the
discrete Hudson theorem \cite{Gro} to the case of totally disconnected
2-regular LCA groups. We also examine angle-number systems with phase space
$\mathbb{T}^n\times\mathbb{Z}^n$ and fermionic/hard-core bosonic systems with
phase space $\mathbb{Z}^{2n}_2$ (which are not 2-regular), and completely
characterize their Gaussian states.


title:
Mapping While Following: 2D LiDAR SLAM in Indoor Dynamic Environments
  with a Person Tracker

abstract:
  2D LiDAR SLAM (Simultaneous Localization and Mapping) is widely used in
indoor environments due to its stability and flexibility. However, its mapping
procedure is usually operated by a joystick in static environments, while
indoor environments often are dynamic with moving objects such as people. The
generated map with noisy points due to the dynamic objects is usually
incomplete and distorted. To address this problem, we propose a framework of
2D-LiDAR-based SLAM without manual control that effectively excludes dynamic
objects (people) and simplify the process for a robot to map an environment.
The framework, which includes three parts: people tracking, filtering and
following. We verify our proposed framework in experiments with two classic
2D-LiDAR-based SLAM algorithms in indoor environments. The results show that
this framework is effective in handling dynamic objects and reducing the
mapping error.


title: Robust End-to-end Speaker Diarization with Generic Neural Clustering

abstract:
  End-to-end speaker diarization approaches have shown exceptional performance
over the traditional modular approaches. To further improve the performance of
the end-to-end speaker diarization for real speech recordings, recently works
have been proposed which integrate unsupervised clustering algorithms with the
end-to-end neural diarization models. However, these methods have a number of
drawbacks: 1) The unsupervised clustering algorithms cannot leverage the
supervision from the available datasets; 2) The K-means-based unsupervised
algorithms that are explored often suffer from the constraint violation
problem; 3) There is unavoidable mismatch between the supervised training and
the unsupervised inference. In this paper, a robust generic neural clustering
approach is proposed that can be integrated with any chunk-level predictor to
accomplish a fully supervised end-to-end speaker diarization model. Also, by
leveraging the sequence modelling ability of a recurrent neural network, the
proposed neural clustering approach can dynamically estimate the number of
speakers during inference. Experimental show that when integrating an
attractor-based chunk-level predictor, the proposed neural clustering approach
can yield better Diarization Error Rate (DER) than the constrained
K-means-based clustering approaches under the mismatched conditions.


title: Effect of anharmonicity on the thermal conductivity of amorphous silica

abstract:
  Proper consideration of anharmonicity is important for the calculation of the
thermal conductivity. However, how the anharmonicity influences the thermal
conduction in amorphous materials is still an open question. In this work, we
uncover the role of anharmonicity on the thermal conductivity of amorphous
silica (a-SiO2) by comparing the thermal conductivity predicted from the
harmonic theory and the anharmonic theory. Moreover, we explore the effect of
anharmonicity-induced frequency shift on the prediction of the thermal
conductivity. It is found that the thermal conductivity calculated by the
recently developed anharmonic theory (quasi-harmonic Green-Kubo approximation,
QHGK) is higher than that by the harmonic theory developed by Allen and
Feldman. The use of anharmonic vibrational frequencies also leads to a higher
thermal conductivity compared with that calculated using harmonic vibrational
frequencies. The anharmonicity induced frequency shifts is a mechanism for the
positive temperature dependence of the thermal conductivity of a-SiO2 at higher
temperatures. Further investigation on mode diffusivity suggests that although
anharmonicity has larger influence on locons than diffusons, the increase of
the thermal conductivity due to the anharmonicity is mainly contributed by the
anharmonicity induced increase of the diffusivity of diffusons. Finally, it is
found that the cross-correlations between diffusons and diffusons contribute
most to the thermal conductivity of a-SiO2, and the locons contribute to the
thermal conductivity mainly through collaboration with diffusons. These results
offer new insights into the nature of the thermal conduction in a-SiO2.


title:
TOD-CNN: An Effective Convolutional Neural Network for Tiny Object
  Detection in Sperm Videos

abstract:
  The detection of tiny objects in microscopic videos is a problematic point,
especially in large-scale experiments. For tiny objects (such as sperms) in
microscopic videos, current detection methods face challenges in fuzzy,
irregular, and precise positioning of objects. In contrast, we present a
convolutional neural network for tiny object detection (TOD-CNN) with an
underlying data set of high-quality sperm microscopic videos (111 videos, $>$
278,000 annotated objects), and a graphical user interface (GUI) is designed to
employ and test the proposed model effectively. TOD-CNN is highly accurate,
achieving $85.60\%$ AP$_{50}$ in the task of real-time sperm detection in
microscopic videos. To demonstrate the importance of sperm detection technology
in sperm quality analysis, we carry out relevant sperm quality evaluation
metrics and compare them with the diagnosis results from medical doctors.


title:
A Study on Prompt-based Few-Shot Learning Methods for Belief State
  Tracking in Task-oriented Dialog Systems

abstract:
  We tackle the Dialogue Belief State Tracking(DST) problem of task-oriented
conversational systems. Recent approaches to this problem leveraging
Transformer-based models have yielded great results. However, training these
models is expensive, both in terms of computational resources and time.
Additionally, collecting high quality annotated dialogue datasets remains a
challenge for researchers because of the extensive annotation required for
training these models. Driven by the recent success of pre-trained language
models and prompt-based learning, we explore prompt-based few-shot learning for
Dialogue Belief State Tracking. We formulate the DST problem as a 2-stage
prompt-based language modelling task and train language models for both tasks
and present a comprehensive empirical analysis of their separate and joint
performance. We demonstrate the potential of prompt-based methods in few-shot
learning for DST and provide directions for future improvement.


title:
Nonlinear and Nonseparable Structural Functions in Fuzzy Regression
  Discontinuity Designs

abstract:
  This paper examines the identification and estimation of the structural
function in fuzzy regression discontinuity (RD) designs with a continuous
treatment variable. Under a dual monotonicity condition, we show that the
nonlinear and nonseparable structural function can be nonparametrically
identified at the RD cutoff. The dual monotonicity condition requires that the
structural function and the treatment choice be strictly increasing in the
unobserved causal factor. This condition is satisfied by standard parametric
models used in practice. The identification result contrasts with the local
average treatment effect literature, where only a certain weighted average of
the structural function is identified. We propose a three-step semiparametric
estimation procedure and derive the asymptotic distribution of the estimator.
The semiparametric estimator achieves the same convergence rate as in the case
of a binary treatment variable. As an application of the method, we estimate
the causal effect of sleep time on health status by the discontinuity in
natural light timing at time-zone boundaries.


title:
Actions at the Edge: Jointly Optimizing the Resources in Multi-access
  Edge Computing

abstract:
  Multi-access edge computing (MEC) is an emerging paradigm that pushes
resources for sensing, communications, computing, storage and intelligence
(SCCSI) to the premises closer to the end users, i.e., the edge, so that they
could leverage the nearby rich resources to improve their quality of experience
(QoE). Due to the growing emerging applications targeting at intelligentizing
life-sustaining cyber-physical systems, this paradigm has become a hot research
topic, particularly when MEC is utilized to provide edge intelligence and
real-time processing and control. This article is to elaborate the research
issues along this line, including basic concepts and performance metrics,
killer applications, architectural design, modeling approaches and solutions,
and future research directions. It is hoped that this article provides a quick
introduction to this fruitful research area particularly for beginning
researchers.


title: Compact Gauduchon-flat Hermitian manifolds

abstract:
  We complete the classification of compact Hermitian manifolds with a flat
Gauduchon connection. In particular, we confirm a conjecture of Yang and Zheng,
by proving that except for the cases of a flat Chern or Bismut connection, such
manifolds are K\"ahler. We also treat the non-compact case.


title:
Distributed Neural Precoding for Hybrid mmWave MIMO Communications with
  Limited Feedback

abstract:
  Hybrid precoding is a cost-efficient technique for millimeter wave (mmWave)
massive multiple-input multiple-output (MIMO) communications. This paper
proposes a deep learning approach by using a distributed neural network for
hybrid analog-and-digital precoding design with limited feedback. The proposed
distributed neural precoding network, called DNet, is committed to achieving
two objectives. First, the DNet realizes channel state information (CSI)
compression with a distributed architecture of neural networks, which enables
practical deployment on multiple users. Specifically, this neural network is
composed of multiple independent sub-networks with the same structure and
parameters, which reduces both the number of training parameters and network
complexity. Secondly, DNet learns the calculation of hybrid precoding from
reconstructed CSI from limited feedback. Different from existing black-box
neural network design, the DNet is specifically designed according to the data
form of the matrix calculation of hybrid precoding. Simulation results show
that the proposed DNet significantly improves the performance up to nearly 50%
compared to traditional limited feedback precoding methods under the tests with
various CSI compression ratios.


title: A new proof for global rigidity of vertex scaling on polyhedral surfaces

abstract:
  The vertex scaling for piecewise linear metrics on polyhedral surfaces was
introduced by Luo, who proved the local rigidity by establishing a variational
principle and conjectured the global rigidity. Luo's conjecture was solved by
Bobenko-Pinkall-Springborn, who also introduced the vertex scaling for
piecewise hyperbolic metrics and proved its global rigidity.
Bobenko-Pinkall-Spingborn's proof is based on their observation of the
connection of vertex scaling and the geometry of polyhedra in $3$-dimensional
hyperbolic space and the concavity of the volume of ideal and hyper-ideal
tetrahedra. In this paper, we give an elementary and short variational proof of
the global rigidity of vertex scaling without involving $3$-dimensional
hyperbolic geometry. The method is based on continuity of eigenvalues of
matrices and the extension of convex functions.


title: TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval

abstract:
  Entity retrieval--retrieving information about entity mentions in a query--is
a key step in open-domain tasks, such as question answering or fact checking.
However, state-of-the-art entity retrievers struggle to retrieve rare entities
for ambiguous mentions due to biases towards popular entities. Incorporating
knowledge graph types during training could help overcome popularity biases,
but there are several challenges: (1) existing type-based retrieval methods
require mention boundaries as input, but open-domain tasks run on unstructured
text, (2) type-based methods should not compromise overall performance, and (3)
type-based methods should be robust to noisy and missing types. In this work,
we introduce TABi, a method to jointly train bi-encoders on knowledge graph
types and unstructured text for entity retrieval for open-domain tasks. TABi
leverages a type-enforced contrastive loss to encourage entities and queries of
similar types to be close in the embedding space. TABi improves retrieval of
rare entities on the Ambiguous Entity Retrieval (AmbER) sets, while maintaining
strong overall retrieval performance on open-domain tasks in the KILT benchmark
compared to state-of-the-art retrievers. TABi is also robust to incomplete type
systems, improving rare entity retrieval over baselines with only 5% type
coverage of the training dataset. We make our code publicly available at
https://github.com/HazyResearch/tabi.


title: Linear level repulsions near exceptional points of non-Hermitian systems

abstract:
  The nearest-neighbor level-spacing distributions are a fundamental quantity
of disordered systems and universal. It is well-known that extended and
localized states of random Hermitian systems follow the Wigner-Dyson and the
Poison distributions, respectively, while the Ginibre distributions describe
random non-Hermitian systems with complex eigenvalues. However, the level
distribution of systems of neither Hermitian nor non-Hermitian with full
complex eigenvalues is still unknown. Here we show a new class of universal
level distributions in the vicinity of exceptional points of non-Hermitian
Hamiltonians. Two universal distribution functions, $P_{\text{SP}}(s)$ for the
symmetry-preserved phase and $P_{\text{SB}}(s)$ for the symmetry-broken phase,
are needed to describe the nearest-neighbor level-spacing distributions near
exceptional points. Surprisingly, both $P_{\text{SP}}(s)$ and
$P_{\text{SB}}(s)$ are proportional to $s$ for small $s$, or a linear level
repulsion, in contrast to the cubic level repulsions of the Ginibre ensembles.
For non-Hermitian disordered tight-binding Hamiltonians, $P_{\text{SP}}(s)$ and
$P_{\text{SB}}(s)$ can be well described by
$P_{\text{SP(SB)}}(s)=\tilde{c}_1s\exp[-\tilde{c}_2s^{\tilde{\alpha}}]$ in the
thermodynamic limit (of infinite systems) with a constant $\tilde{\alpha}$ that
depends on the localization nature of states at exceptional points.


title: Usage of specific attention improves change point detection

abstract:
  The change point is a moment of an abrupt alteration in the data
distribution. Current methods for change point detection are based on recurrent
neural methods suitable for sequential data. However, recent works show that
transformers based on attention mechanisms perform better than standard
recurrent models for many tasks. The most benefit is noticeable in the case of
longer sequences. In this paper, we investigate different attentions for the
change point detection task and proposed specific form of attention related to
the task at hand. We show that using a special form of attention outperforms
state-of-the-art results.


title:
HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric
  Regularization

abstract:
  In large-scale recommender systems, the user-item networks are generally
scale-free or expand exponentially. The latent features (also known as
embeddings) used to describe the user and item are determined by how well the
embedding space fits the data distribution. Hyperbolic space offers a spacious
room to learn embeddings with its negative curvature and metric properties,
which can well fit data with tree-like structures. Recently, several hyperbolic
approaches have been proposed to learn high-quality representations for the
users and items. However, most of them concentrate on developing the hyperbolic
similitude by designing appropriate projection operations, whereas many
advantageous and exciting geometric properties of hyperbolic space have not
been explicitly explored. For example, one of the most notable properties of
hyperbolic space is that its capacity space increases exponentially with the
radius, which indicates the area far away from the hyperbolic origin is much
more embeddable. Regarding the geometric properties of hyperbolic space, we
bring up a \textit{Hyperbolic Regularization powered Collaborative Filtering}
(HRCF) and design a geometric-aware hyperbolic regularizer. Specifically, the
proposal boosts optimization procedure via the root alignment and origin-aware
penalty, which is simple yet impressively effective. Through theoretical
analysis, we further show that our proposal is able to tackle the
over-smoothing problem caused by hyperbolic aggregation and also brings the
models a better discriminative ability. We conduct extensive empirical
analysis, comparing our proposal against a large set of baselines on several
public benchmarks. The empirical results show that our approach achieves highly
competitive performance and surpasses both the leading Euclidean and hyperbolic
baselines by considerable margins. Further analysis verifies ...


title:
Cosmic microwave background spectral distortions from Rayleigh
  scattering at second order

abstract:
  Cosmic microwave background (CMB) spectral distortion from Rayleigh
scattering is calculated for the first time in rigorous second-order
cosmological perturbation theory. The new spectral distortion is sensitive to
acoustic dissipation at $10^{-2}<k{\rm Mpc}/h<1$, which slightly extends the
scale constrained by the CMB anisotropies. The spectral shape is different from
either temperature perturbations or any other traditional spectral distortions
from Compton scattering, such as $y$ and $\mu$. The new spectral distortion is
not formed in the late Universe, unlike the thermal Sunyaev-Zel'dovich effect
degenerated with the primordial $y$ distortions since photons must be hot for
Rayleigh scattering. Therefore, ideal measurements can distinguish the signal
from the other effects and extract new information during recombination.
Assuming cosmological parameters consistent with the recent CMB anisotropy
measurements, we find the new spectral distortion is $6.5\times 10^{-3}$Jy/str,
which is one order of magnitude smaller than the currently proposed target
sensitivity range of voyage 2050.


title: Self-Supporting Wormholes in Four Dimensions with Scalar Field

abstract:
  In this paper, we investigated the space-time obtained by quotients of the
$AdS_4$ space-time. Further quotient with specific $\mathbb{Z}_2$ is
considered. Taking up to the first-order perturbation in metric, we estimated
the backreaction of the matter field on space-time geometry. We can calculate
the stress-energy tensor's expected value by pulling it back onto the covering
space. The average null energy becomes negative when the suitable boundary
condition is chosen, resulting in a traversable wormhole.


title: Real-world Deep Local Motion Deblurring

abstract:
  Most existing deblurring methods focus on removing global blur caused by
camera shake, while they cannot well handle local blur caused by object
movements. To fill the vacancy of local deblurring in real scenes, we establish
the first real local motion blur dataset (ReLoBlur), which is captured by a
synchronized beam-splitting photographing system and corrected by a
post-progressing pipeline. Based on ReLoBlur, we propose a Local Blur-Aware
Gated network (LBAG) and several local blur-aware techniques to bridge the gap
between global and local deblurring: 1) a blur detection approach based on
background subtraction to localize blurred regions; 2) a gate mechanism to
guide our network to focus on blurred regions; and 3) a blur-aware patch
cropping strategy to address data imbalance problem. Extensive experiments
prove the reliability of ReLoBlur dataset, and demonstrate that LBAG achieves
better performance than state-of-the-art global deblurring methods without our
proposed local blur-aware techniques.


title: A Taxonomy of Error Sources in HPC I/O Machine Learning Models

abstract:
  I/O efficiency is crucial to productivity in scientific computing, but the
increasing complexity of the system and the applications makes it difficult for
practitioners to understand and optimize I/O behavior at scale. Data-driven
machine learning-based I/O throughput models offer a solution: they can be used
to identify bottlenecks, automate I/O tuning, or optimize job scheduling with
minimal human intervention. Unfortunately, current state-of-the-art I/O models
are not robust enough for production use and underperform after being deployed.
  We analyze multiple years of application, scheduler, and storage system logs
on two leadership-class HPC platforms to understand why I/O models underperform
in practice. We propose a taxonomy consisting of five categories of I/O
modeling errors: poor application and system modeling, inadequate dataset
coverage, I/O contention, and I/O noise. We develop litmus tests to quantify
each category, allowing researchers to narrow down failure modes, enhance I/O
throughput models, and improve future generations of HPC logging and analysis
tools.