title: Unsupervised Keyphrase Extraction via Interpretable Neural Networks

abstract:
  Keyphrase extraction aims at automatically extracting a list of "important"
phrases which represent the key concepts in a document. Prior approaches for
unsupervised keyphrase extraction resort to heuristic notions of phrase
importance via embedding similarities or graph centrality, requiring extensive
domain expertise to develop them. Our work proposes an alternative operational
definition: phrases that are most useful for predicting the topic of a text are
important keyphrases. To this end, we propose INSPECT -- a self-explaining
neural framework for identifying influential keyphrases by measuring the
predictive impact of input phrases on the downstream task of topic
classification. We show that this novel approach not only alleviates the need
for ad-hoc heuristics but also achieves state-of-the-art results in
unsupervised keyphrase extraction across four diverse datasets in two domains:
scientific publications and news articles. Ultimately, our study suggests a new
usage of interpretable neural networks as an intrinsic component in NLP
systems, and not only as a tool for explaining model predictions to humans.


title: Star formation in two irradiated globules around Cygnus OB2

abstract:
  We investigate the young stellar populations associated with DR 18 and ECX
6-21, which are two isolated globules irradiated by the O-type stars of the
Cygnus OB2 association. Both are HII regions containing obvious tracers of
recent and ongoing star formation. We also study smaller isolated molecular
structures in their surroundings. Both globules contain their own embedded
populations, with a higher fraction of the less-evolved classes. Masses and
temperatures are estimated under the assumption of a common age of 1 Myr, which
has been found to appropriately represent the general Cygnus OB2 YSO population
but is most probably an overestimate for both globules, especially ECX 6-21.
The early-B star responsible for the erosion of DR 18 is found to be part of a
small aggregate of intermediate-mass stars still embedded in the cloud, which
probably contains a second site of recent star formation, also with
intermediate-mass stars. We confirm the two main star forming sites embedded in
ECX 6-21 described in previous works, with the southern site being more evolved
than the northern site. We also discuss the small globule ECX 6-21-W ($=
G79.8+1.2$), and propose that its non thermal radio spectrum is due to
synchrotron emission from an embedded jet, whose existence is suggested by our
observations. The extreme youth of some of the YSOs suggests that star
formation in both globules started after they became externally irradiated. The
populations of both globules are not found to be particularly rich, but they
contain stars with estimated masses similar or above that of the Sun in numbers
that hint at some differences with respect to the star formation process taking
place in more quiescent regions where low-mass stars dominate, which deeper
observations may confirm.


title: Limits on the Detection of Planet Nine in the Dark Energy Survey

abstract:
  Studies of the clustering of the most distant Kuiper belt objects in the
outer solar system have hinted at the possible existence of a planet beyond
Neptune referred to as Planet Nine (P9). Recent efforts have constrained the
parameter space of the orbital elements of P9, allowing for the creation of a
synthetic catalog of hypothetical P9s. By examining the potential recovery of
such a catalog within numerous sky surveys, it is possible to further constrain
the parameter space for P9, providing direction for a more targeted search. We
examine the ability of the full six years of the Dark Energy Survey (DES) to
recover a synthetic Planet Nine population presented in Brown and Batygin
(2021a) [arXiv:2108.09868]. We find that out of 100,000 simulated objects,
11,709 cross the wide DES survey footprint of which 10,187 (87.0%) are
recovered. This rules out an additional 5% of the parameter space after
accounting for Planets Nine that would have been detected by both the Zwicky
Transient Facility and DES.


title: Can Synthetic Translations Improve Bitext Quality?

abstract:
  Synthetic translations have been used for a wide range of NLP tasks primarily
as a means of data augmentation. This work explores, instead, how synthetic
translations can be used to revise potentially imperfect reference translations
in mined bitext. We find that synthetic samples can improve bitext quality
without any additional bilingual supervision when they replace the originals
based on a semantic equivalence classifier that helps mitigate NMT noise. The
improved quality of the revised bitext is confirmed intrinsically via human
evaluation and extrinsically through bilingual induction and MT tasks.


title: Efficient Long Sequence Encoding via Synchronization

abstract:
  Pre-trained Transformer models have achieved successes in a wide range of NLP
tasks, but are inefficient when dealing with long input sequences. Existing
studies try to overcome this challenge via segmenting the long sequence
followed by hierarchical encoding or post-hoc aggregation. We propose a
synchronization mechanism for hierarchical encoding. Our approach first
identifies anchor tokens across segments and groups them by their roles in the
original input sequence. Then inside Transformer layer, anchor embeddings are
synchronized within their group via a self-attention module. Our approach is a
general framework with sufficient flexibility -- when adapted to a new task, it
is easy to be enhanced with the task-specific anchor definitions. Experiments
on two representative tasks with different types of long input texts,
NarrativeQA summary setting and wild multi-hop reasoning from HotpotQA,
demonstrate that our approach is able to improve the global information
exchange among segments while maintaining efficiency.


title: Software and Computing for Small HEP Experiments

abstract:
  This white paper briefly summarized key conclusions of the recent US
Community Study on the Future of Particle Physics (Snowmass 2021) workshop on
Software and Computing for Small High Energy Physics Experiments.


title: Strategy for Understanding the Higgs Physics: The Cool Copper Collider

abstract:
  A program to build a lepton-collider Higgs factory, to precisely measure the
couplings of the Higgs boson to other particles, followed by a higher energy
run to establish the Higgs self-coupling and expand the new physics reach, is
widely recognized as a primary focus of modern particle physics. We propose a
strategy that focuses on a new technology and preliminary estimates suggest
that can lead to a compact, affordable machine. New technology investigations
will provide much needed enthusiasm for our field, resulting in trained
workforce. This cost-effective, compact design, with technologies useful for a
broad range of other accelerator applications, could be realized as a project
in the US. Its technology innovations, both in the accelerator and the
detector, will offer unique and exciting opportunities to young scientists.
Moreover, cost effective compact designs, broadly applicable to other fields of
research, are more likely to obtain financial support from our funding
agencies.


title:
Elasticity of 2D ferroelectrics across their paraelectric phase
  transformation

abstract:
  The mechanical behavior of two-dimensional (2D) materials across 2D phase
changes is unknown, and the finite temperature ($T$) elasticity of paradigmatic
SnSe monolayers -- ferroelectric 2D materials turning paraelectric as their
unit cell (u.c.) turns from a rectangle onto a square -- is described here in a
progressive manner. To begin with, their zero$-T$ {\em elastic energy
landscape} gives way to (Boltzmann-like) averages from which the elastic
behavior is determined. These estimates are complemented with results from the
strain-fluctuation method, which employs the energy landscape or {\em ab
initio} molecular dynamics (MD) data. Both approaches capture the coalescence
of elastic moduli $\langle C_{11}(T)\rangle=\langle C_{22}(T)\rangle$ due to
the structural transformation. The broad evolution and sudden changes of
elastic parameters $\langle C_{11}(T)\rangle$, $\langle C_{22}(T)\rangle$, and
$\langle C_{12}(T)\rangle$ of these atomically-thin phase-change membranes
establishes a heretofore overlooked connection among 2D materials and soft
matter.


title:
InfoDCL: A Distantly Supervised Contrastive Learning Framework for
  Social Meaning

abstract:
  Existing supervised contrastive learning frameworks suffer from two major
drawbacks: (i) they depend on labeled data, which is limited for the majority
of tasks in real-world, and (ii) they incorporate inter-class relationships
based on instance-level information, while ignoring corpus-level information,
for weighting negative samples. To mitigate these challenges, we propose an
effective distantly supervised contrastive learning framework (InfoDCL) that
makes use of naturally occurring surrogate labels in the context of contrastive
learning and employs pointwise mutual information to leverage corpus-level
information. Our framework outperforms an extensive set of existing contrastive
learning methods (self-supervised, supervised, and weakly supervised) on a wide
range of social meaning tasks (in-domain and out-of-domain), in both the
general and few-shot settings. Our method is also language-agnostic, as we
demonstrate on three languages in addition to English.


title:
THz-induced high-order harmonic generation and nonlinear charge
  transport in graphene

abstract:
  We theoretically study the THz-induced high-order harmonic generation (HHG)
and nonlinear electric transport in graphene based on the quantum master
equation with the relaxation time approximation. To obtain microscopic insight
into the phenomena, we compare the results of the fully dynamical calculations
with those under a quasi-static approximation, where the electronic system is
approximated as a nonequilibrium steady state. As a result, we find that the
THz-induced electron dynamics in graphene can be accurately modeled with the
nonequilibrium steady-state at each instance. The population distribution
analysis further clarifies that the THz-induced HHG in graphene originates from
the reduction of effective conductivity due to a large displacement of
electrons in the Brillouin zone. By comparing the present nonequilibrium
picture with a thermodynamic picture, we explore the role of the nonequilibrium
nature of electron dynamics on the extremely nonlinear optical and transport
phenomena in graphene.


title: Floer lasagna modules from link Floer homology

abstract:
  In this paper, we introduce the notion of Floer lasagna modules, which is
inspired by the construction of skein lasagna module in [MWW19] by Morrison,
Walker and Wedrich. Here we use link Floer homology instead of
Khovanov-Rozansky homology. We give a description of the Floer lasagna module
for 4-manifolds obtained by adding 2-handles to the 4-ball and we compute some
examples.


title:
The REDTOP experiment: Rare $\eta/\eta^{\prime}$ Decays To Probe New
  Physics

abstract:
  The $\eta$ and $\eta^{\prime}$ mesons are nearly unique in the particle
universe since they are almost Goldstone bosons and the dynamics of their
decays are strongly constrained. The integrated $\eta$-meson samples collected
in earlier experiments amount to $\sim10^{9}$ events. A new experiment, REDTOP
(Rare Eta Decays To Probe New Physics), is being proposed, with the intent of
collecting a data sample of order 10$^{14}$ $\eta$ (10$^{12}$ $\eta^{\prime}$)
for studying very rare decays. Such statistics are sufficient for investigating
several symmetry violations, and for searching for particles and fields beyond
the Standard Model. In this work we present several studies evaluating REDTOP
sensitivity to processes that couple the Standard Model to New Physics through
all four of the so-called \emph{portals}: the Vector, the Scalar, the Axion and
the Heavy Lepton portal. The sensitivity of the experiment is also adequate for
probing several conservation laws, in particular $CP$, $T$ and Lepton
Universality, and for the determination of the $\eta$ form factors, which is
crucial for the interpretation of the recent measurement of muon $g-2$.


title: Nonperturbative renormalization of the lattice Sommerfield vector model

abstract:
  The lattice Sommerfield model, describing a massive vector gauge field
coupled to a light fermion in 2d, is an ideal candidate to verify perturbative
conclusions. In contrast with continuum exact solutions, we prove that there is
no infinite field renormalization, implying the reduction of the degree of the
ultraviolet divergence, and that anomalies are non renormalized. Such features
are the counterpart of analogue properties at the basis of the Standard model
perturbative renormalizability. The results are non-perturbative, in the sense
that the averages of gauge invariant observables are expressed in terms of
convergent expansions uniformly in the lattice and volume.


title:
Generalized but not Robust? Comparing the Effects of Data Modification
  Methods on Out-of-Domain Generalization and Adversarial Robustness

abstract:
  Data modification, either via additional training datasets, data
augmentation, debiasing, and dataset filtering, has been proposed as an
effective solution for generalizing to out-of-domain (OOD) inputs, in both
natural language processing and computer vision literature. However, the effect
of data modification on adversarial robustness remains unclear. In this work,
we conduct a comprehensive study of common data modification strategies and
evaluate not only their in-domain and OOD performance, but also their
adversarial robustness (AR). We also present results on a two-dimensional
synthetic dataset to visualize the effect of each method on the training
distribution. This work serves as an empirical study towards understanding the
relationship between generalizing to unseen domains and defending against
adversarial perturbations. Our findings suggest that more data (either via
additional datasets or data augmentation) benefits both OOD accuracy and AR.
However, data filtering (previously shown to improve OOD accuracy on natural
language inference) hurts OOD accuracy on other tasks such as question
answering and image classification. We provide insights from our experiments to
inform future work in this direction.


title:
White Paper on Leading-Edge technology And Feasibility-directed (LEAF)
  Program aimed at readiness demonstration for Energy Frontier Circular
  Colliders by the next decade

abstract:
  In this White Paper for the Snowmass 2021 Process, we propose the
establishment of a magnet Leading-Edge technology And Feasibility-directed
Program (LEAF Program) to achieve readiness for a future collider decision on
the timescale of the next decade.
  The LEAF Program would rely on, and be synergetic with, generic R&D efforts
presently covered - in the US - by the Magnet Development Program (MDP), the
Conductor Procurement and R&D (CPRD) Program and other activities in the Office
of HEP supported by Early Career Awards (ECA) or Lab Directed R&D (LDRD) funds.
Where possible, ties to synergetic efforts in other Offices of DOE or NSF are
highlighted and suggested as wider Collaborative efforts on the National scale.
International efforts are also mentioned as potential partners in the LEAF
Program.
  We envision the LEAF Program to concentrate on demonstrating the feasibility
of magnets for muon colliders as well as next generation high energy hadron
colliders, pursuing, where necessary and warranted by the nature of the
application, the transition from R&D models to long models/prototypes. The LEAF
Program will naturally drive accelerator-quality and experiment-interface
design considerations. LEAF will also concentrate, where necessary, on cost
reduction and/or industrialization steps.


title: Joint Time-Vertex Fractional Fourier Transform

abstract:
  Graphs signal processing successfully captures high-dimensional data on
non-Euclidean domains by using graph signals defined on graph vertices.
However, data sources on each vertex can also continually provide time-series
signals such that graph signals on each vertex are now time-series signals.
Joint time-vertex Fourier transform (JFT) and the associated framework of
time-vertex signal processing enable us to study such signals defined on joint
time-vertex domains by providing spectral analysis. Just as the fractional
Fourier transform (FRT) generalizes the ordinary Fourier transform (FT), we
propose the joint time-vertex fractional Fourier transform (JFRT) as a
generalization to the JFT. JFRT provides an additional fractional analysis tool
for joint time-vertex processing by extending both temporal and vertex domain
Fourier analysis to fractional orders. We theoretically show that the proposed
JFRT generalizes the JFT and satisfies the properties of index additivity,
reversibility, reduction to identity, and unitarity (for certain graph
topologies). We provide theoretical derivations for JFRT-based denoising as
well as computational cost analysis. Results of numerical experiments are also
presented to demonstrate the benefits of JFRT.


title:
Wave-SAN: Wavelet based Style Augmentation Network for Cross-Domain
  Few-Shot Learning

abstract:
  Previous few-shot learning (FSL) works mostly are limited to natural images
of general concepts and categories. These works assume very high visual
similarity between the source and target classes. In contrast, the recently
proposed cross-domain few-shot learning (CD-FSL) aims at transferring knowledge
from general nature images of many labeled examples to novel domain-specific
target categories of only a few labeled examples. The key challenge of CD-FSL
lies in the huge data shift between source and target domains, which is
typically in the form of totally different visual styles. This makes it very
nontrivial to directly extend the classical FSL methods to address the CD-FSL
task. To this end, this paper studies the problem of CD-FSL by spanning the
style distributions of the source dataset. Particularly, wavelet transform is
introduced to enable the decomposition of visual representations into
low-frequency components such as shape and style and high-frequency components
e.g., texture. To make our model robust to visual styles, the source images are
augmented by swapping the styles of their low-frequency components with each
other. We propose a novel Style Augmentation (StyleAug) module to implement
this idea. Furthermore, we present a Self-Supervised Learning (SSL) module to
ensure the predictions of style-augmented images are semantically similar to
the unchanged ones. This avoids the potential semantic drift problem in
exchanging the styles. Extensive experiments on two CD-FSL benchmarks show the
effectiveness of our method. Our codes and models will be released.


title:
Seamlessly Integrating Factual Information and Social Content with
  Persuasive Dialogue

abstract:
  Effective human-chatbot conversations need to achieve both coherence and
efficiency. Complex conversation settings such as persuasion involve
communicating changes in attitude or behavior, so users' perspectives need to
be carefully considered and addressed, even when not directly related to the
topic. In this work, we contribute a novel modular dialogue system framework
that seamlessly integrates factual information and social content into
persuasive dialogue. Our framework is generalizable to any dialogue tasks that
have mixed social and task contents. We conducted a study that compared user
evaluations of our framework versus a baseline end-to-end generation model. We
found our model was evaluated to be more favorable in all dimensions including
competence and friendliness compared to the baseline model which does not
explicitly handle social content or factual questions.


title: Neural Radiance Projection

abstract:
  The proposed method, Neural Radiance Projection (NeRP), addresses the three
most fundamental shortages of training such a convolutional neural network on
X-ray image segmentation: dealing with missing/limited human-annotated
datasets; ambiguity on the per-pixel label; and the imbalance across positive-
and negative- classes distribution. By harnessing a generative adversarial
network, we can synthesize a massive amount of physics-based X-ray images,
so-called Variationally Reconstructed Radiographs (VRRs), alongside their
segmentation from more accurate labeled 3D Computed Tomography data. As a
result, VRRs present more faithfully than other projection methods in terms of
photo-realistic metrics. Adding outputs from NeRP also surpasses the vanilla
UNet models trained on the same pairs of X-ray images.


title:
Breast Cancer Molecular Subtypes Prediction on Pathological Images with
  Discriminative Patch Selecting and Multi-Instance Learning

abstract:
  Molecular subtypes of breast cancer are important references to personalized
clinical treatment. For cost and labor savings, only one of the patient's
paraffin blocks is usually selected for subsequent immunohistochemistry (IHC)
to obtain molecular subtypes. Inevitable sampling error is risky due to tumor
heterogeneity and could result in a delay in treatment. Molecular subtype
prediction from conventional H&E pathological whole slide images (WSI) using AI
method is useful and critical to assist pathologists pre-screen proper paraffin
block for IHC. It's a challenging task since only WSI level labels of molecular
subtypes can be obtained from IHC. Gigapixel WSIs are divided into a huge
number of patches to be computationally feasible for deep learning. While with
coarse slide-level labels, patch-based methods may suffer from abundant noise
patches, such as folds, overstained regions, or non-tumor tissues. A weakly
supervised learning framework based on discriminative patch selecting and
multi-instance learning was proposed for breast cancer molecular subtype
prediction from H&E WSIs. Firstly, co-teaching strategy was adopted to learn
molecular subtype representations and filter out noise patches. Then, a
balanced sampling strategy was used to handle the imbalance in subtypes in the
dataset. In addition, a noise patch filtering algorithm that used local outlier
factor based on cluster centers was proposed to further select discriminative
patches. Finally, a loss function integrating patch with slide constraint
information was used to finetune MIL framework on obtained discriminative
patches and further improve the performance of molecular subtyping. The
experimental results confirmed the effectiveness of the proposed method and our
models outperformed even senior pathologists, with potential to assist
pathologists to pre-screen paraffin blocks for IHC in clinic.


title:
Effect of the COVID-19 vaccine on preventive behaviors: Evidence from
  Japan

abstract:
  Vaccination against the coronavirus disease 2019 (COVID-19) is a key measure
to reduce the probability of getting infected with the disease. Accordingly,
this might significantly change an individuals perception and decision-making
in daily life. For instance, it is predicted that with widespread vaccination,
individuals will exhibit less rigid preventive behaviors, such as staying at
home, frequently washing hands, and wearing a mask. We observed the same
individuals on a monthly basis for 18 months, from March 2020 (the early stage
of the COVID-19 pandemic) to September 2021, in Japan to independently
construct large sample panel data (N=54,007). Using the data, we compare the
individuals preventive behaviors before and after they got vaccinated;
additionally, we compare their behaviors with those individuals who did not get
vaccinated. Furthermore, we compare the effect of vaccination on the
individuals less than or equal to 40 years of age with those greater than 40
years old. The major findings determined after controlling for individual
characteristics using the fixed effects model and various factors are as
follows. First, as opposed to the prediction, based on the whole sample, the
vaccinated people were observed to stay at home and did not change their habits
of frequently washing hands and wearing a mask. Second, using the sub-sample of
individuals aged equal to or below 40, we find that the vaccinated people are
more likely to go out. Third, the results obtained using a sample comprising
people aged over 40 are similar to those obtained using the whole sample.
Preventive behaviors are affecting oneself and generating externalities on
others during this pandemic. Informal social norms motivate people to increase
or maintain preventive behaviors even after being vaccinated in societies where
such behaviors are not enforced.


title:
Lambda-Nucleon and Sigma-Nucleon potentials from space-time correlation
  function on the lattice

abstract:
  The hyperon-nucleon interaction with the strangeness $S=-1$ region is
complicated and difficult to investigate because its flavor sector involves all
the irreducible representation except the flavor singlet and has the worst
signal-to-noise ratio among the strangeness regions. In order to overcome such
difficulties the content of this report is twofold: (i) We present an
implementation of extended effective baryon block algorithm. This is a
straightforward extension of the original which was reported in LATTICE 2013.
(ii) We perform single channel analysis for the $\Lambda N$ system at nearly
physical quark masses corresponding to $(m_\pi,m_K)\approx(146,525)$~MeV and
large volume $(La)^4=(96a)^4\approx$ (8.1 fm)$^4$. Scattering phase shifts for
$\Lambda N$ system are presented.


title:
What's in the Black Box? The False Negative Mechanisms Inside Object
  Detectors

abstract:
  In object detection, false negatives arise when a detector fails to detect a
target object. To understand why object detectors produce false negatives, we
identify five 'false negative mechanisms', where each mechanism describes how a
specific component inside the detector architecture failed. Focusing on
two-stage and one-stage anchor-box object detector architectures, we introduce
a framework for quantifying these false negative mechanisms. Using this
framework, we investigate why Faster R-CNN and RetinaNet fail to detect objects
in benchmark vision datasets and robotics datasets. We show that a detector's
false negative mechanisms differ significantly between computer vision
benchmark datasets and robotics deployment scenarios. This has implications for
the translation of object detectors developed for benchmark datasets to
robotics applications.


title:
Gender differences of the effect of vaccination on perceptions of
  COVID-19 and mental health in Japan

abstract:
  Vaccination has been promoted to mitigate the spread of the coronavirus
disease 2019 (COVID-19). Vaccination is expected to reduce the probability of
and alleviate the seriousness of COVID-19 infection. Accordingly, this might
significantly change an individuals subjective well-being and mental health.
However, it is unknown how vaccinated people perceive the effectiveness of
COVID-19 and how their subjective well-being and mental health change after
vaccination. We thus observed the same individuals on a monthly basis from
March 2020 to September 2021 in all parts of Japan. Then, large sample panel
data (N=54,007) were independently constructed. Using the data, we compared the
individuals perceptions of COVID-19, subjective well-being, and mental health
before and after vaccination. Furthermore, we compared the effect of
vaccination on the perceptions of COVID-19 and mental health for females and
males. We used the fixed-effects model to control for individual time-invariant
characteristics. The major findings were as follows: First, the vaccinated
people perceived the probability of getting infected and the seriousness of
COVID-19 to be lower than before vaccination. This was observed not only when
we used the whole sample, but also when we used sub-samples. Second, using the
whole sample, subjective well-being and mental health improved. The same
results were also observed using the sub-sample of females, whereas the
improvements were not observed using a sub-sample of males.


title: Can you even tell left from right? Presenting a new challenge for VQA

abstract:
  Visual Question Answering (VQA) needs a means of evaluating the strengths and
weaknesses of models. One aspect of such an evaluation is the evaluation of
compositional generalisation, or the ability of a model to answer well on
scenes whose scene-setups are different from the training set. Therefore, for
this purpose, we need datasets whose train and test sets differ significantly
in composition. In this work, we present several quantitative measures of
compositional separation and find that popular datasets for VQA are not good
evaluators. To solve this, we present Uncommon Objects in Unseen Configurations
(UOUC), a synthetic dataset for VQA. UOUC is at once fairly complex while also
being well-separated, compositionally. The object-class of UOUC consists of 380
clasess taken from 528 characters from the Dungeons and Dragons game. The train
set of UOUC consists of 200,000 scenes; whereas the test set consists of 30,000
scenes. In order to study compositional generalisation, simple reasoning and
memorisation, each scene of UOUC is annotated with up to 10 novel questions.
These deal with spatial relationships, hypothetical changes to scenes,
counting, comparison, memorisation and memory-based reasoning. In total, UOUC
presents over 2 million questions. UOUC also finds itself as a strong challenge
to well-performing models for VQA. Our evaluation of recent models for VQA
shows poor compositional generalisation, and comparatively lower ability
towards simple reasoning. These results suggest that UOUC could lead to
advances in research by being a strong benchmark for VQA.


title: One Agent To Rule Them All: Towards Multi-agent Conversational AI

abstract:
  The increasing volume of commercially available conversational agents (CAs)
on the market has resulted in users being burdened with learning and adopting
multiple agents to accomplish their tasks. Though prior work has explored
supporting a multitude of domains within the design of a single agent, the
interaction experience suffers due to the large action space of desired
capabilities. To address these problems, we introduce a new task BBAI:
Black-Box Agent Integration, focusing on combining the capabilities of multiple
black-box CAs at scale. We explore two techniques: question agent pairing and
question response pairing aimed at resolving this task. Leveraging these
techniques, we design One For All (OFA), a scalable system that provides a
unified interface to interact with multiple CAs. Additionally, we introduce
MARS: Multi-Agent Response Selection, a new encoder model for question response
pairing that jointly encodes user question and agent response pairs. We
demonstrate that OFA is able to automatically and accurately integrate an
ensemble of commercially available CAs spanning disparate domains.
Specifically, using the MARS encoder we achieve the highest accuracy on our
BBAI task, outperforming strong baselines.


title:
Narrow Zero Mode in Organic Massless Dirac Electron System
  $\alpha$-(BEDT-TTF)$_2$I$_3$

abstract:
  We investigated the interlayer magnetoresistance in an organic massless Dirac
electron system $\alpha$-(BEDT-TTF)$_2$I$_3$ under pressure. We experimentally
demonstrate that the width of the zero mode owing to carrier scattering is much
narrower than that of the other Landau levels.


title: SATS: Self-Attention Transfer for Continual Semantic Segmentation

abstract:
  Continually learning to segment more and more types of image regions is a
desired capability for many intelligent systems. However, such continual
semantic segmentation suffers from the same catastrophic forgetting issue as in
continual classification learning. While multiple knowledge distillation
strategies originally for continual classification have been well adapted to
continual semantic segmentation, they only consider transferring old knowledge
based on the outputs from one or more layers of deep fully convolutional
networks. Different from existing solutions, this study proposes to transfer a
new type of information relevant to knowledge, i.e. the relationships between
elements (Eg. pixels or small local regions) within each image which can
capture both within-class and between-class knowledge. The relationship
information can be effectively obtained from the self-attention maps in a
Transformer-style segmentation model. Considering that pixels belonging to the
same class in each image often share similar visual properties, a
class-specific region pooling is applied to provide more efficient relationship
information for knowledge transfer. Extensive evaluations on multiple public
benchmarks support that the proposed self-attention transfer method can further
effectively alleviate the catastrophic forgetting issue, and its flexible
combination with one or more widely adopted strategies significantly
outperforms state-of-the-art solu


title: Measuring the tau polarization at ILC

abstract:
  Measurement of the tau lepton polarization in \eett\ is an important
electro-weak measurement at ILC and other future electron-positron colliders.
In this paper we discuss several methods to extract polarimeter information for
\eett\ events at the nominal centre-of-mass energy, and develop a new method,
based on charged particle impact parameter measurement, which can accurately
reconstruct tau momenta even in events with significant Initial State
Radiation.
  In future work we will extend the study to estimate the precision with which
the tau polarization can be measured at ILC-250, both for high-mass tau pairs
and for those which radiatively return to the $Z^0$ peak. This will complement
our past study which showed that the tau polarization can be measured to better
than 1\% at the ILC-500.


title: Progressive End-to-End Object Detection in Crowded Scenes

abstract:
  In this paper, we propose a new query-based detection framework for crowd
detection. Previous query-based detectors suffer from two drawbacks: first,
multiple predictions will be inferred for a single object, typically in crowded
scenes; second, the performance saturates as the depth of the decoding stage
increases. Benefiting from the nature of the one-to-one label assignment rule,
we propose a progressive predicting method to address the above issues.
Specifically, we first select accepted queries prone to generate true positive
predictions, then refine the rest noisy queries according to the previously
accepted predictions. Experiments show that our method can significantly boost
the performance of query-based detectors in crowded scenes. Equipped with our
approach, Sparse RCNN achieves 92.0\% $\text{AP}$, 41.4\% $\text{MR}^{-2}$ and
83.2\% $\text{JI}$ on the challenging CrowdHuman \cite{shao2018crowdhuman}
dataset, outperforming the box-based method MIP \cite{chu2020detection} that
specifies in handling crowded scenarios. Moreover, the proposed method, robust
to crowdedness, can still obtain consistent improvements on moderately and
slightly crowded datasets like CityPersons \cite{zhang2017citypersons} and COCO
\cite{lin2014microsoft}. Code will be made publicly available at
https://github.com/megvii-model/Iter-E2EDET.


title:
Towards Adversarial Control Loops in Sensor Attacks: A Case Study to
  Control the Kinematics and Actuation of Embedded Systems

abstract:
  Recent works investigated attacks on sensors by influencing analog sensor
components with acoustic, light, and electromagnetic signals. Such attacks can
have extensive security, reliability, and safety implications since many types
of the targeted sensors are also widely used in critical process control,
robotics, automation, and industrial control systems. While existing works
advanced our understanding of the physical-level risks that are hidden from a
digital-domain perspective, gaps exist in how the attack can be guided to
achieve system-level control in real-time, continuous processes. This paper
proposes an adversarial control loop-based approach for real-time attacks on
control systems relying on sensors. We study how to utilize the system feedback
extracted from physical-domain signals to guide the attacks. In the attack
process, injection signals are adjusted in real time based on the extracted
feedback to exert targeted influence on a victim control system that is
continuously affected by the injected perturbations and applying changes to the
physical environment. In our case study, we investigate how an external
adversarial control system can be constructed over sensor-actuator systems and
demonstrate the attacks with program-controlled processes to manipulate the
victim system without accessing its internal statuses.


title: Safe Neurosymbolic Learning with Differentiable Symbolic Execution

abstract:
  We study the problem of learning worst-case-safe parameters for programs that
use neural networks as well as symbolic, human-written code. Such neurosymbolic
programs arise in many safety-critical domains. However, because they can use
nondifferentiable operations, it is hard to learn their parameters using
existing gradient-based approaches to safe learning. Our approach to this
problem, Differentiable Symbolic Execution (DSE), samples control flow paths in
a program, symbolically constructs worst-case "safety losses" along these
paths, and backpropagates the gradients of these losses through program
operations using a generalization of the REINFORCE estimator. We evaluate the
method on a mix of synthetic tasks and real-world benchmarks. Our experiments
show that DSE significantly outperforms the state-of-the-art DiffAI method on
these tasks.


title: On the Advances and Challenges of Adaptive Online Testing

abstract:
  In recent years, the interest in developing adaptive solutions for online
testing has grown significantly in the industry. While the advances related to
this relative new technology have been developed in multiple domains, it lacks
in the literature a systematic and complete treatment of the procedure that
involves exploration, inference, and analysis. This short paper aims to develop
a comprehensive understanding of adaptive online testing, including various
building blocks and analytical results. We also address the latest
developments, research directions, and challenges that have been less mentioned
in the literature.


title: Distributed-Memory Sparse Kernels for Machine Learning

abstract:
  Sampled Dense Times Dense Matrix Multiplication (SDDMM) and Sparse Times
Dense Matrix Multiplication (SpMM) appear in diverse settings, such as
collaborative filtering, document clustering, and graph embedding. Frequently,
the SDDMM output becomes the input sparse matrix for a subsequent SpMM
operation. Existing work has focused on shared memory parallelization of these
primitives. While there has been extensive analysis of communication-minimizing
distributed 1.5D algorithms for SpMM, no such analysis exists for SDDMM or the
back-to-back sequence of SDDMM and SpMM, termed FusedMM. We show that
distributed memory 1.5D and 2.5D algorithms for SpMM can be converted to
algorithms for SDDMM with identical communication costs and input / output data
layouts. Further, we give two communication-eliding strategies to reduce costs
further for FusedMM kernels: either reusing the replication of an input dense
matrix for the SDDMM and SpMM in sequence, or fusing the local SDDMM and SpMM
kernels.
  We benchmark FusedMM algorithms on Cori, a Cray XC40 at LBNL, using
Erdos-Renyi random matrices and large real-world sparse matrices. On 256 nodes
with 68 cores each, 1.5D FusedMM algorithms using either communication eliding
approach can save at least 30% of time spent exclusively in communication
compared to executing a distributed-memory SpMM and SDDMM kernel in sequence.
On real-world matrices with hundreds of millions of edges, all of our
algorithms exhibit at least a 10x speedup over the SpMM algorithm in PETSc. On
these matrices, our communication-eliding techniques exhibit runtimes up to 1.6
times faster than an unoptimized sequence of SDDMM and SpMM. We embed and test
the scaling of our algorithms in real-world applications, including
collaborative filtering via alternating-least-squares and inference for
attention-based graph neural networks.


title: Return probability of quantum and correlated random walks

abstract:
  The analysis of the return probability is one of the most essential and
fundamental topics in the study of classical random walks. In this paper, we
study the return probability of quantum and correlated random walks in the
one-dimensional integer lattice by the path counting method. We show that the
return probability of both quantum and correlated random walks can be expressed
in terms of the Legendre polynomial. Moreover, the generating function of the
return probability can be written in terms of elliptic integrals of the first
and second kinds for the quantum walk.


title:
Preparing for low surface brightness science with the Vera C. Rubin
  Observatory: characterisation of tidal features from mock images

abstract:
  Tidal features in the outskirts of galaxies yield unique information about
their past interactions and are a key prediction of the hierarchical structure
formation paradigm. The Vera C. Rubin Observatory is poised to deliver deep
observations for potentially of millions of objects with visible tidal
features, but the inference of galaxy interaction histories from such features
is not straightforward. Utilising automated techniques and human visual
classification in conjunction with realistic mock images produced using the
NEWHORIZON cosmological simulation, we investigate the nature, frequency and
visibility of tidal features and debris across a range of environments and
stellar masses. In our simulated sample, around 80 per cent of the flux in the
tidal features around Milky Way or greater mass galaxies is detected at the
10-year depth of the Legacy Survey of Space and Time (30-31 mag / sq. arcsec),
falling to 60 per cent assuming a shallower final depth of 29.5 mag / sq.
arcsec. The fraction of total flux found in tidal features increases towards
higher masses, rising to 10 per cent for the most massive objects in our sample
(M*~10^{11.5} Msun). When observed at sufficient depth, such objects frequently
exhibit many distinct tidal features with complex shapes. The interpretation
and characterisation of such features varies significantly with image depth and
object orientation, introducing significant biases in their classification.
Assuming the data reduction pipeline is properly optimised, we expect the Rubin
Observatory to be capable of recovering much of the flux found in the outskirts
of Milky Way mass galaxies, even at intermediate redshifts (z<0.2).


title:
An Introduction to Multi-Agent Reinforcement Learning and Review of its
  Application to Autonomous Mobility

abstract:
  Many scenarios in mobility and traffic involve multiple different agents that
need to cooperate to find a joint solution. Recent advances in behavioral
planning use Reinforcement Learning to find effective and performant behavior
strategies. However, as autonomous vehicles and vehicle-to-X communications
become more mature, solutions that only utilize single, independent agents
leave potential performance gains on the road. Multi-Agent Reinforcement
Learning (MARL) is a research field that aims to find optimal solutions for
multiple agents that interact with each other. This work aims to give an
overview of the field to researchers in autonomous mobility. We first explain
MARL and introduce important concepts. Then, we discuss the central paradigms
that underlie MARL algorithms, and give an overview of state-of-the-art methods
and ideas in each paradigm. With this background, we survey applications of
MARL in autonomous mobility scenarios and give an overview of existing
scenarios and implementations.


title: Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning

abstract:
  We present an effective unpaired learning based image dehazing network from
an unpaired set of clear and hazy images. This paper provides a new perspective
to treat image dehazing as a two-class separated factor disentanglement task,
i.e, the task-relevant factor of clear image reconstruction and the
task-irrelevant factor of haze-relevant distribution. To achieve the
disentanglement of these two-class factors in deep feature space, contrastive
learning is introduced into a CycleGAN framework to learn disentangled
representations by guiding the generated images to be associated with latent
factors. With such formulation, the proposed contrastive disentangled dehazing
method (CDD-GAN) first develops negative generators to cooperate with the
encoder network to update alternately, so as to produce a queue of challenging
negative adversaries. Then these negative adversaries are trained end-to-end
together with the backbone representation network to enhance the discriminative
information and promote factor disentanglement performance by maximizing the
adversarial contrastive loss. During the training, we further show that hard
negative examples can suppress the task-irrelevant factors and unpaired clear
exemples can enhance the task-relevant factors, in order to better facilitate
haze removal and help image restoration. Extensive experiments on both
synthetic and real-world datasets demonstrate that our method performs
favorably against existing state-of-the-art unpaired dehazing approaches.


title:
Incorporating Heterophily into Graph Neural Networks for Graph
  Classification

abstract:
  Graph neural networks (GNNs) often assume strong homophily in graphs, seldom
considering heterophily which means connected nodes tend to have different
class labels and dissimilar features. In real-world scenarios, graphs may have
nodes that exhibit both homophily and heterophily. Failing to generalize to
this setting makes many GNNs underperform in graph classification. In this
paper, we address this limitation by identifying two useful designs and develop
a novel GNN architecture called IHGNN (Incorporating Heterophily into Graph
Neural Networks). These designs include integration and separation of the ego-
and neighbor-embeddings of nodes; and concatenation of all the node embeddings
as the final graph-level readout function. In the first design, integration is
combined with separation by an injective function which is the composition of
the MLP and the concatenation function. The second design enables the
graph-level readout function to differentiate between different node
embeddings. As the functions used in both the designs are injective, IHGNN,
while being simple, has an expressiveness as powerful as the 1-WL. We
empirically validate IHGNN on various graph datasets and demonstrate that it
achieves state-of-the-art performance on the graph classification task.


title:
Energy-efficient Dense DNN Acceleration with Signed Bit-slice
  Architecture

abstract:
  As the number of deep neural networks (DNNs) to be executed on a mobile
system-on-chip (SoC) increases, the mobile SoC suffers from the real-time DNN
acceleration within its limited hardware resources and power budget. Although
the previous mobile neural processing units (NPUs) take advantage of low-bit
computing and exploitation of the sparsity, it is incapable of accelerating
high-precision and dense DNNs. This paper proposes energy-efficient signed
bit-slice architecture which accelerates both high-precision and dense DNNs by
exploiting a large number of zero values of signed bit-slices. Proposed signed
bit-slice representation (SBR) changes signed $1111_{2}$ bit-slice to
$0000_{2}$ by borrowing a $1$ value from its lower order of bit-slice. As a
result, it generates a large number of zero bit-slices even in dense DNNs.
Moreover, it balances the positive and negative values of 2's complement data,
allowing bit-slice based output speculation which pre-computes high order of
bit-slices and skips the remaining dense low order of bit-slices. The signed
bit-slice architecture compresses and skips the zero input signed bit-slices,
and the zero skipping unit also supports the output skipping by masking the
speculated inputs as zero. Additionally, the heterogeneous network-on-chip
(NoC) benefits the exploitation of data reusability and reduction of
transmission bandwidth. The paper introduces a specialized instruction set
architecture (ISA) and a hierarchical instruction decoder for the control of
the signed bit-slice architecture. Finally, the signed bit-slice architecture
outperforms the previous bit-slice accelerator, Bit-fusion, over $\times3.65$
higher area-efficiency, $\times3.88$ higher energy-efficiency, and $\times5.35$
higher throughput.


title:
Optimal error estimates of a Crank--Nicolson finite element projection
  method for magnetohydrodynamic equations

abstract:
  In this paper, we propose and analyze a fully discrete finite element
projection method for the magnetohydrodynamic (MHD) equations. A modified
Crank--Nicolson method and the Galerkin finite element method are used to
discretize the model in time and space, respectively, and appropriate
semi-implicit treatments are applied to the fluid convection term and two
coupling terms. These semi-implicit approximations result in a linear system
with variable coefficients for which the unique solvability can be proved
theoretically. In addition, we use a second-order decoupling projection method
of the Van Kan type \cite{vankan1986} in the Stokes solver, which computes the
intermediate velocity field based on the gradient of the pressure from the
previous time level, and enforces the incompressibility constraint via the
Helmholtz decomposition of the intermediate velocity field. The energy
stability of the scheme is theoretically proved, in which the decoupled Stokes
solver needs to be analyzed in details. Error estimates are proved in the
discrete $L^\infty(0,T;L^2)$ norm for the proposed decoupled finite element
projection scheme. Numerical examples are provided to illustrate the
theoretical results.


title: DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting

abstract:
  Periodic time series (PTS) forecasting plays a crucial role in a variety of
industries to foster critical tasks, such as early warning, pre-planning,
resource scheduling, etc. However, the complicated dependencies of the PTS
signal on its inherent periodicity as well as the sophisticated composition of
various periods hinder the performance of PTS forecasting. In this paper, we
introduce a deep expansion learning framework, DEPTS, for PTS forecasting.
DEPTS starts with a decoupled formulation by introducing the periodic state as
a hidden variable, which stimulates us to make two dedicated modules to tackle
the aforementioned two challenges. First, we develop an expansion module on top
of residual learning to perform a layer-by-layer expansion of those complicated
dependencies. Second, we introduce a periodicity module with a parameterized
periodic function that holds sufficient capacity to capture diversified
periods. Moreover, our two customized modules also have certain interpretable
capabilities, such as attributing the forecasts to either local momenta or
global periodicity and characterizing certain core periodic properties, e.g.,
amplitudes and frequencies. Extensive experiments on both synthetic data and
real-world data demonstrate the effectiveness of DEPTS on handling PTS. In most
cases, DEPTS achieves significant improvements over the best baseline.
Specifically, the error reduction can even reach up to 20% for a few cases.
Finally, all codes are publicly available.


title: Rich CNN-Transformer Feature Aggregation Networks for Super-Resolution

abstract:
  Recent vision transformers along with self-attention have achieved promising
results on various computer vision tasks. In particular, a pure
transformer-based image restoration architecture surpasses the existing
CNN-based methods using multi-task pre-training with a large number of
trainable parameters. In this paper, we introduce an effective hybrid
architecture for super-resolution (SR) tasks, which leverages local features
from CNNs and long-range dependencies captured by transformers to further
improve the SR results. Specifically, our architecture comprises of transformer
and convolution branches, and we substantially elevate the performance by
mutually fusing two branches to complement each representation. Furthermore, we
propose a cross-scale token attention module, which allows the transformer to
efficiently exploit the informative relationships among tokens across different
scales. Our proposed method achieves state-of-the-art SR results on numerous
benchmark datasets.


title: The group inverse of the sum in a Banach algebra

abstract:
  In this paper, we present new necessary and sufficient conditions under which
the sum of two group invertible elements in a Banach algebra has group inverse.
We then apply these results to block operator matrices over Banach spaces. The
group inverses of certain operator block matrices are thereby obtained.
Additionally, this paper extends the results obtained in \cite{L}.


title:
FB-MSTCN: A Full-Band Single-Channel Speech Enhancement Method Based on
  Multi-Scale Temporal Convolutional Network

abstract:
  In recent years, deep learning-based approaches have significantly improved
the performance of single-channel speech enhancement. However, due to the
limitation of training data and computational complexity, real-time enhancement
of full-band (48 kHz) speech signals is still very challenging. Because of the
low energy of spectral information in the high-frequency part, it is more
difficult to directly model and enhance the full-band spectrum using neural
networks. To solve this problem, this paper proposes a two-stage real-time
speech enhancement model with extraction-interpolation mechanism for a
full-band signal. The 48 kHz full-band time-domain signal is divided into three
sub-channels by extracting, and a two-stage processing scheme of `masking +
compensation' is proposed to enhance the signal in the complex domain. After
the two-stage enhancement, the enhanced full-band speech signal is restored by
interval interpolation. In the subjective listening and word accuracy test, our
proposed model achieves superior performance and outperforms the baseline model
overall by 0.59 MOS and 4.0% WAcc for the non-personalized speech denoising
task.


title:
Offshore wind: Evidence for two-dimensional turbulence and role of sea
  horizon

abstract:
  We analyse offshore wind speeds with a high time resolution of one second
over a long period of 20 months for different heights above the sea level.
Power spectra $S(f)$ show a scaling behaviour consistent with three-dimensional
turbulence at high frequencies, followed by the so-called spectral gap region
at lower frequencies, where $fS(f)$ varies weakly. For frequencies lower than a
crossover frequency $f_{\rm\scriptscriptstyle 2D}$, a rapid rise of $fS(f)$
occurs, which reflects the scaling $fS(f)\sim f^{-2}$ and $fS(f)\sim f^{-2/3}$
of the enstrophy and inverse energy cascade of two-dimensional turbulence.
Contrary to earlier observations but in agreement with theoretical predictions,
the regime of the enstrophy cascade appears at higher frequencies compared to
that of the inverse energy cascade. Strikingly, an analysis of the third moment
(structure function) $D_3(\tau)$ of wind speed differences for a given time lag
$\tau$ provides strong further evidence of a transition to two-dimensional
turbulence. This is reflected in a rapid change from negative to positive
values of $D_3(\tau)$ at lags close to $1/f_{\rm\scriptscriptstyle 2D}$. We
argue that the physical meaning of $f_{\rm\scriptscriptstyle 2D}$ is connected
with the distance of the sea horizon from the measurement point.


title: On Comparable Box Dimension

abstract:
  Two boxes in $\mathbb{R}^d$ are comparable if one of them is a subset of a
translation of the other one. The comparable box dimension of a graph $G$ is
the minimum integer $d$ such that $G$ can be represented as a touching graph of
comparable axis-aligned boxes in $\mathbb{R}^d$. We show that proper
minor-closed classes have bounded comparable box dimensions and explore further
properties of this notion.


title:
Compressing Sentence Representation for Semantic Retrieval via
  Homomorphic Projective Distillation

abstract:
  How to learn highly compact yet effective sentence representation?
Pre-trained language models have been effective in many NLP tasks. However,
these models are often huge and produce large sentence embeddings. Moreover,
there is a big performance gap between large and small models. In this paper,
we propose Homomorphic Projective Distillation (HPD) to learn compressed
sentence embeddings. Our method augments a small Transformer encoder model with
learnable projection layers to produce compact representations while mimicking
a large pre-trained language model to retain the sentence representation
quality. We evaluate our method with different model sizes on both semantic
textual similarity (STS) and semantic retrieval (SR) tasks. Experiments show
that our method achieves 2.7-4.5 points performance gain on STS tasks compared
with previous best representations of the same size. In SR tasks, our method
improves retrieval speed (8.2$\times$) and memory usage (8.0$\times$) compared
with state-of-the-art large models.


title:
InsCon:Instance Consistency Feature Representation via Self-Supervised
  Learning

abstract:
  Feature representation via self-supervised learning has reached remarkable
success in image-level contrastive learning, which brings impressive
performances on image classification tasks. While image-level feature
representation mainly focuses on contrastive learning in single instance, it
ignores the objective differences between pretext and downstream prediction
tasks such as object detection and instance segmentation. In order to fully
unleash the power of feature representation on downstream prediction tasks, we
propose a new end-to-end self-supervised framework called InsCon, which is
devoted to capturing multi-instance information and extracting cell-instance
features for object recognition and localization. On the one hand, InsCon
builds a targeted learning paradigm that applies multi-instance images as
input, aligning the learned feature between corresponding instance views, which
makes it more appropriate for multi-instance recognition tasks. On the other
hand, InsCon introduces the pull and push of cell-instance, which utilizes cell
consistency to enhance fine-grained feature representation for precise boundary
localization. As a result, InsCon learns multi-instance consistency on semantic
feature representation and cell-instance consistency on spatial feature
representation. Experiments demonstrate the method we proposed surpasses MoCo
v2 by 1.1% AP^{bb} on COCO object detection and 1.0% AP^{mk} on COCO instance
segmentation using Mask R-CNN R50-FPN network structure with 90k iterations,
2.1% APbb on PASCAL VOC objection detection using Faster R-CNN R50-C4 network
structure with 24k iterations.


title:
Some Aspects of Rotation and Magnetic Field Morphology in the Infrared
  Dark Cloud G34.43+00.24

abstract:
  The infrared dark clouds (IRDCs) are molecular clouds with relatively greater
values in their magnetic field strengths. For example, the IRDC G34.43+00.24
(G34) has magnetic field strength of the order of a few hundred micro-Gauss. In
this study, we investigate if the dynamic motions of charged particles in an
IRDC such as G34 can produce this magnetic field strength inside it. The
observations show that the line-of-sight velocity of G34 has global gradient.
We assume that the measured global velocity gradient can correspond to the
cloud rotation. We attribute a large-scale current density to this rotating
cloud by considering a constant value for the incompleteness of charge
neutrality and the velocity differences between the positive and negative
particles with very low ionization fractions. We use the numerical package
FISHPACK to obtain the magnetic field strength and its morphology on the
plane-of-sky within G34. The results show that the magnetic field strengths are
of the order of several hundred micro-Gauss, and its morphology in the
plane-of-sky is somewhat consistent with the observational results. We also
obtain the relationship between magnetic field strength and density in G34. The
results show that with increasing density, the magnetic field strength
increases approximately as a power-law function. The amount of power is
approximately equal to 0.45, which is suitable for molecular clouds with strong
magnetic fields. Therefore, we can conclude that the dynamical motion of IRDCs,
and especially their rotations, can amplify the magnetic field strengths within
them.


title:
Asymptotically flat vacuum solution for a rotating black hole in a
  modified gravity theory

abstract:
  The theory of f(R)-gravity is one of the theories of modified Einstein
gravity. The vacuum solution, on the other hand, of the field equation is the
solution for black hole geometry. We establish here an asymptotically flat
rotating black hole solution in an f(R)-gravity. This essentially leads to the
modified solution to the Kerr black hole. This solution exhibits the change in
fundamental properties of the black hole and its geometry. It particularly
shows that radii of marginally stable and bound orbits and black hole event
horizon increase compared to those in Einstein gravity, depending on the
modified gravity parameter. It further argues for faster spinning black holes
with spin (Kerr) parameter greater than unity, without any naked singularity.
This supports the weak cosmic censorship hypothesis.


title:
Supervised Contrastive Learning with Structure Inference for Graph
  Classification

abstract:
  Advanced graph neural networks have shown great potentials in graph
classification tasks recently. Different from node classification where node
embeddings aggregated from local neighbors can be directly used to learn node
labels, graph classification requires a hierarchical accumulation of different
levels of topological information to generate discriminative graph embeddings.
Still, how to fully explore graph structures and formulate an effective graph
classification pipeline remains rudimentary. In this paper, we propose a novel
graph neural network based on supervised contrastive learning with structure
inference for graph classification. First, we propose a data-driven graph
augmentation strategy that can discover additional connections to enhance the
existing edge set. Concretely, we resort to a structure inference stage based
on diffusion cascades to recover possible connections with high node
similarities. Second, to improve the contrastive power of graph neural
networks, we propose to use a supervised contrastive loss for graph
classification. With the integration of label information, the one-vs-many
contrastive learning can be extended to a many-vs-many setting, so that the
graph-level embeddings with higher topological similarities will be pulled
closer. The supervised contrastive loss and structure inference can be
naturally incorporated within the hierarchical graph neural networks where the
topological patterns can be fully explored to produce discriminative graph
embeddings. Experiment results show the effectiveness of the proposed method
compared with recent state-of-the-art methods.


title: Revisiting the role of chaos in quantum tomography

abstract:
  Does chaos in the dynamics enable information gain in quantum tomography or
impedes it? We address this question by considering continuous measurement
tomography in which the measurement record is obtained as a sequence of
expectation values of a Hermitian observable evolving under the repeated
application of the Floquet map of the quantum kicked top. For a given dynamics
and Hermitian observables, we observe completely opposite behavior in the
tomography of well-localized spin coherent states compared to random states. As
the chaos in the dynamics increases, the reconstruction fidelity of spin
coherent states decreases. This contrasts with the previous results connecting
information gain in tomography of random states with the degree of chaos in the
dynamics that drives the system. The rate of information gain and hence the
fidelity obtained in tomography depends not only on the degree of chaos in the
dynamics and to what extent it causes the initial observable to spread in
various directions of the operator space but, more importantly, how well these
directions are aligned with the density matrix to be estimated. Our study also
gives an operational interpretation for operator spreading in terms of fidelity
gain in an actual quantum information tomography protocol.


title: An Automated Pipeline for Ultra-Violet Imaging Telescope (UVIT)

abstract:
  We describe a versatile pipeline for processing the data collected by the
Ultra-Violet Imaging Telescope (UVIT) on board Indian Multi-wavelength
astronomical satellite AstroSat.The UVIT instrument carries out simultaneous
astronomical imaging through selected filters / gratings in Far-Ultra-Violet
(FUV), Near-Ultra-Violet & visible (VIS) bands of the targeted circular sky
field (~ 0.5 deg dia). This pipeline converts the data (Level-1) emanating from
UVIT in their raw primitive format supplemented by inputs from the spacecraft
sub-systems into UV sky images (& slitless grating spectra) and associated
products readily usable by astronomers (Level-2). The primary products include
maps of Intensity (rate of photon arrival), error on Intensity and effective
Exposure. The pipeline is open source, extensively user configurable with many
selectable parameters and its execution is fully automated. The key ingredients
of the pipeline includes - extraction of drift in pointing of the spacecraft,
and disturbances in pointing due to internal movements; application of various
corrections to measured position in the detector for each photon - e.g.
differential pointing with respect to a reference frame for shift and add
operation, systematic effects and artifacts in the optics of the telescopes and
detectors, exposure tracking on the sky, alignment of sky products from
multi-episode exposures to generate a consolidated set and astrometry. Detailed
logs of operations and intermediate products for every processing stage are
accessible via user selectable options. While large number of selectable
parameters are available for the user, a well characterized standard default
set is used for executing this pipeline at the Payload Operation Centre (POC)
for UVIT and selected products are archived and disseminated by the Indian
Space Research Organization (ISRO) through its ISSDC portal.


title: Implicit field supervision for robust non-rigid shape matching

abstract:
  Establishing a correspondence between two non-rigidly deforming shapes is one
of the most fundamental problems in visual computing. Existing methods often
show weak resilience when presented with challenges innate to real-world data
such as noise, outliers, self-occlusion etc. On the other hand, auto-decoders
have demonstrated strong expressive power in learning geometrically meaningful
latent embeddings. However, their use in shape analysis and especially in
non-rigid shape correspondence has been limited. In this paper, we introduce an
approach based on auto-decoder framework, that learns a continuous shape-wise
deformation field over a fixed template. By supervising the deformation field
for points on-surface and regularising for points off-surface through a novel
Signed Distance Regularisation (SDR), we learn an alignment between the
template and shape volumes. Unlike classical correspondence techniques, our
method is remarkably robust in the presence of strong artefacts and can be
generalised to arbitrary shape categories. Trained on clean water-tight meshes,
without any data-augmentation, we demonstrate compelling performance on
compromised data and real-world scans.


title:
The scaling limit of the weakly self-avoiding walk on a high-dimensional
  torus

abstract:
  We prove that the scaling limit of the weakly self-avoiding walk on a
$d$-dimensional discrete torus is Brownian motion on the continuum torus if the
length of the rescaled walk is $o(V^{1/2})$ where $V$ is the volume (number of
points) of the torus and if $d>4$. We also prove that the diffusion constant of
the resulting torus Brownian motion is the same as the diffusion constant of
the scaling limit of the usual weakly self-avoiding walk on $\mathbb{Z}^d$.
This provides further manifestation of the fact that the weakly self-avoiding
walk model on the torus does not feel that it is on the torus up until it
reaches about $V^{1/2}$ steps which we believe is sharp.


title: P\'olya's conjecture for the disk: a computer-assisted proof

abstract:
  The celebrated P\'{o}lya's conjecture (1954) in spectral geometry states that
the eigenvalue counting functions of the Dirichlet and Neumann Laplacian on a
bounded Euclidean domain can be estimated from above and below, respectively,
by the leading term of Weyl's asymptotics. P\'{o}lya's conjecture is known to
be true for domains which tile the space, and, in addition, for some special
domains in higher dimensions. In this paper, we prove P\'{o}lya's conjecture
for the disk, making it the first non-tiling planar domain for which the
conjecture is verified. Along the way, we develop the known links between the
spectral problems in the disk and certain lattice counting problems. The key
novel ingredient is the observation, made in recent work of the last named
author, that the corresponding eigenvalue and lattice counting functions are
related not only asymptotically, but in fact satisfy certain uniform bounds.
Our proofs are purely analytic for the values of the spectral parameter above
some large (but explicitly given) number. Below that number we give a rigorous
computer-assisted proof which converges in a finite number of steps and uses
only integer arithmetic.


title:
Distribution-Aware Single-Stage Models for Multi-Person 3D Pose
  Estimation

abstract:
  In this paper, we present a novel Distribution-Aware Single-stage (DAS) model
for tackling the challenging multi-person 3D pose estimation problem. Different
from existing top-down and bottom-up methods, the proposed DAS model
simultaneously localizes person positions and their corresponding body joints
in the 3D camera space in a one-pass manner. This leads to a simplified
pipeline with enhanced efficiency. In addition, DAS learns the true
distribution of body joints for the regression of their positions, rather than
making a simple Laplacian or Gaussian assumption as previous works. This
provides valuable priors for model prediction and thus boosts the
regression-based scheme to achieve competitive performance with volumetric-base
ones. Moreover, DAS exploits a recursive update strategy for progressively
approaching to regression target, alleviating the optimization difficulty and
further lifting the regression performance. DAS is implemented with a fully
Convolutional Neural Network and end-to-end learnable. Comprehensive
experiments on benchmarks CMU Panoptic and MuPoTS-3D demonstrate the superior
efficiency of the proposed DAS model, specifically 1.5x speedup over previous
best model, and its stat-of-the-art accuracy for multi-person 3D pose
estimation.


title:
Electron-Hole Binding Governs Carrier Transport in Halide Perovskite
  Nanocrystal Thin Films

abstract:
  Two-dimensional halide perovskite nanoplatelets (NPLs) have exceptional
light-emitting properties, including wide spectral tunability, ultrafast
radiative decays, high quantum yields (QY), and oriented emission. To realize
efficient devices, it is imperative to understand how exciton transport
progresses in NPL thin films. Due to the high binding energies of electron-hole
pairs, excitons are generally considered the dominant species responsible for
carrier transfer. We employ spatially and temporally resolved optical
microscopy to map exciton diffusion in perovskite nanocrystal (NC) thin films
between 15 {\deg}C and 50 {\deg}C. At room temperature (RT), we find the
diffusion length to be inversely correlated to the thickness of the
nanocrystals (NCs). With increasing temperatures, exciton diffusion declines
for all NC films, but at different rates. This leads to specific temperature
turnover points, at which thinner NPLs exhibit higher diffusion lengths. We
attribute this anomalous diffusion behavior to the coexistence of excitons and
free electron hole-pairs inside the individual NCs within our temperature
range. The organic ligand shell surrounding the NCs prevents charge transfer.
Accordingly, any time an electron-hole pair spends in the unbound state reduces
the FRET-mediated inter-NC transfer rates and consequently the overall
diffusion. These results clarify how exciton diffusion progresses in strongly
confined halide perovskite NC films, emphasizing critical considerations for
optoelectronic devices.


title: Higher integrability and stability of $(p,q)$-quasiminimizers

abstract:
  Using purely variational methods, we prove local and global higher
integrability results for upper gradients of quasiminimizers of a
$(p,q)$-Dirichlet integral with fixed boundary data, assuming it belongs to a
slightly better Newtonian space. We also obtain a stability property with
respect to the varying exponents $p$ and $q$. The setting is a doubling metric
measure space supporting a Poincar\'e inequality.


title:
Snowmass2021 Cosmic Frontier: Modeling, statistics, simulations, and
  computing needs for direct dark matter detection

abstract:
  This paper summarizes the modeling, statistics, simulation, and computing
needs of direct dark matter detection experiments in the next decade.


title:
$t$-adic symmetric multiple zeta values for indices in which $1$ and $3$
  appear alternately

abstract:
  We consider the symmetric multiple zeta values in $\mathcal{S}_m$ without
modulo $\pi^2$ reduction for indices in which $1$ and $3$ appear alternately.
We investigate those values that can be expressed as a polynomial of the
Riemann zeta values, and give a conjecturally complete list of explicit
formulas for such values.


title:
Effect of AGN on the morphological properties of their host galaxies in
  the local Universe

abstract:
  The morphological classification of active galaxies may be affected by the
presence of active galactic nuclei (AGN). In this paper, we provide the most
detailed analysis on how different AGN contributions, from 5% to 75%, to the
total optical light may affect six commonly used morphological parameters and
the final classification of AGN host galaxies at z$\sim$0. We used a local
sample of >2000 visually classified non-active galaxies, to quantify how the
contribution of a bright nuclear point source of different intensity could
affect morphological parameters such as: asymmetry, Abraham concentration
index, Gini, M20 moment of light, smoothness, and Concelice-Bershady
concentration index. We found that most of the morphological parameters are
affected by AGN contributions above 25%, with late-type galaxies being more
affected than early-types. We found that Gini, Abraham concentration index and
asymmetry are the most stable parameters even for AGN contributions above 25%,
in comparison to Concelice-Bershady concentration index and M20 moment of
light. Smoothness parameter shall be avoided when classifying AGN host
galaxies, or at least it shall be used simultaneously in combination with
several other parameters.


title:
Benchmarking and Interpreting End-to-end Learning of MIMO and Multi-User
  Communication

abstract:
  End-to-end autoencoder (AE) learning has the potential of exceeding the
performance of human-engineered transceivers and encoding schemes, without a
priori knowledge of communication-theoretic principles. In this work, we aim to
understand to what extent and for which scenarios this claim holds true when
comparing with fair benchmarks. Our particular focus is on memoryless
multiple-input multiple-output (MIMO) and multi-user (MU) systems. Four case
studies are considered: two point-to-point (closed-loop and open-loop MIMO) and
two MU scenarios (MIMO broadcast and interference channels). For the
point-to-point scenarios, we explain some of the performance gains observed in
prior work through the selection of improved baseline schemes that include
geometric shaping as well as bit and power allocation. For the MIMO broadcast
channel, we demonstrate the feasibility of a novel AE method with centralized
learning and decentralized execution. Interestingly, the learned scheme
performs close to nonlinear vector-perturbation precoding and significantly
outperforms conventional zero-forcing. Lastly, we highlight potential pitfalls
when interpreting learned communication schemes. In particular, we show that
the AE for the considered interference channel learns to avoid interference,
albeit in a rotated reference frame. After de-rotating the learned signal
constellation of each user, the resulting scheme corresponds to conventional
time sharing with geometric shaping.


title: DP color functions versus chromatic polynomials (II)

abstract:
  For any connected graph $G$, let $P(G,m)$ and $P_{DP}(G,m)$ denote the
chromatic polynomial and DP color function of $G$, respectively. It is known
that $P_{DP}(G,m)\le P(G,m)$ holds for every positive integer $m$. Let
$DP_\approx$ (resp. $DP_<$) be the set of graphs $G$ for which there exists an
integer $M$ such that $P_{DP}(G,m)=P(G,m)$ (resp. $P_{DP}(G,m)<P(G,m)$) holds
for all integers $m \ge M$. Determining the sets $DP_\approx$ and $DP_<$ is a
key problem on the study of the DP color function. For any edge set $E_0$ of
$G$, let $\ell_G(E_0)$ be the length of a shortest cycle $C$ in $G$ such that
$|E(C)\cap E_0|$ is odd whenever such a cycle exists, and $\ell_G(E_0)=\infty$
otherwise. Write $\ell_G(E_0)$ as $\ell_G(e)$ if $E_0=\{e\}$.
  In this paper, we prove that if $G$ has a spanning tree $T$ such that
$\ell_G(e)$ is odd for each $e\in E(G)\setminus E(T)$, the edges in
$E(G)\setminus E(T)$ can be labeled as $e_1,e_2,\cdots, e_q$ with
$\ell_G(e_i)\le \ell_G(e_{i+1})$ for all $1\le i\le q-1$ and each edge $e_i$ is
contained in a cycle $C_i$ of length $\ell_G(e_i)$ with $E(C_i)\subseteq
E(T)\cup \{e_j: 1\le j\le i\}$, then $G$ is a graph in $DP_{\approx}$. As a
direct application of this conclusion, all plane near-triangulations and
complete multipartite graphs with at least three partite sets belong to
$DP_{\approx}$. We also show that if $E^*$ is an edge set of $G$ such that
$\ell_{G}(E^*)$ is even and $E^*$ satisfies certain conditions, then $G$
belongs to $DP_<$. In particular, if $\ell_G(E^*)=4$, where $E^*$ is a set of
edges between two disjoint vertex subsets of $G$, then $G$ belongs to $DP_<$.
Both results extend known ones in [DP color functions versus chromatic
polynomials, $Advances\ in\ Applied\ Mathematics$ 134 (2022), article 102301].


title:
APRNet: Attention-based Pixel-wise Rendering Network for Photo-Realistic
  Text Image Generation

abstract:
  Style-guided text image generation tries to synthesize text image by
imitating reference image's appearance while keeping text content unaltered.
The text image appearance includes many aspects. In this paper, we focus on
transferring style image's background and foreground color patterns to the
content image to generate photo-realistic text image. To achieve this goal, we
propose 1) a content-style cross attention based pixel sampling approach to
roughly mimicking the style text image's background; 2) a pixel-wise style
modulation technique to transfer varying color patterns of the style image to
the content image spatial-adaptively; 3) a cross attention based multi-scale
style fusion approach to solving text foreground misalignment issue between
style and content images; 4) an image patch shuffling strategy to create style,
content and ground truth image tuples for training. Experimental results on
Chinese handwriting text image synthesis with SCUT-HCCDoc and CASIA-OLHWDB
datasets demonstrate that the proposed method can improve the quality of
synthetic text images and make them more photo-realistic.


title:
ActFormer: A GAN Transformer Framework towards General
  Action-Conditioned 3D Human Motion Generation

abstract:
  We present a GAN Transformer framework for general action-conditioned 3D
human motion generation, including not only single-person actions but also
multi-person interactive actions. Our approach consists of a powerful
Action-conditioned motion transFormer (ActFormer) under a GAN training scheme,
equipped with a Gaussian Process latent prior. Such a design combines the
strong spatio-temporal representation capacity of Transformer, superiority in
generative modeling of GAN, and inherent temporal correlations from latent
prior. Furthermore, ActFormer can be naturally extended to multi-person motions
by alternately modeling temporal correlations and human interactions with
Transformer encoders. We validate our approach by comparison with other methods
on larger-scale benchmarks, including NTU RGB+D 120 and BABEL. We also
introduce a new synthetic dataset of complex multi-person combat behaviors to
facilitate research on multi-person motion generation. Our method demonstrates
adaptability to various human motion representations and achieves leading
performance over SOTA methods on both single-person and multi-person motion
generation tasks, indicating a hopeful step towards a universal human motion
generator.


title:
Magnification Prior: A Self-Supervised Method for Learning
  Representations on Breast Cancer Histopathological Images

abstract:
  This work presents a novel self-supervised pre-training method to learn
efficient representations without labels on histopathology medical images
utilizing magnification factors. Other state-of-theart works mainly focus on
fully supervised learning approaches that rely heavily on human annotations.
However, the scarcity of labeled and unlabeled data is a long-standing
challenge in histopathology. Currently, representation learning without labels
remains unexplored for the histopathology domain. The proposed method,
Magnification Prior Contrastive Similarity (MPCS), enables self-supervised
learning of representations without labels on small-scale breast cancer dataset
BreakHis by exploiting magnification factor, inductive transfer, and reducing
human prior. The proposed method matches fully supervised learning
state-of-the-art performance in malignancy classification when only 20% of
labels are used in fine-tuning and outperform previous works in fully
supervised learning settings. It formulates a hypothesis and provides empirical
evidence to support that reducing human-prior leads to efficient representation
learning in self-supervision. The implementation of this work is available
online on GitHub -
https://github.com/prakashchhipa/Magnification-Prior-Self-Supervised-Method


title: Strong cosmic censorship conjecture for a charged BTZ black hole

abstract:
  The strong cosmic censorship conjecture, whose validation asserts the
deterministic nature of general relativity, has been studied for charged BTZ
black holes in three dimensional general relativity, as well as for Nth order
pure Lovelock gravity in d=2N+1 spacetime dimensions. Through both analytical
and numerical routes, we have computed the ratio of the imaginary part of the
quasi-normal mode frequencies with the surface gravity at the Cauchy horizon.
The lowest of which corresponds to the key parameter associated with violation
of strong cosmic censorship conjecture. Our results demonstrate that this
parameter is always less than the critical value $(1/2)$, thereby respecting
the strong cosmic censorship conjecture. This is in complete contrast to the
four or, higher dimensional black holes, as well as for rotating BTZ black
hole, where the violation of strong cosmic censorship conjecture exists.
Implications and possible connection with the stability of the photon orbits
have been discussed.


title:
Adaptive Environment Modeling Based Reinforcement Learning for Collision
  Avoidance in Complex Scenes

abstract:
  The major challenges of collision avoidance for robot navigation in crowded
scenes lie in accurate environment modeling, fast perceptions, and trustworthy
motion planning policies. This paper presents a novel adaptive environment
model based collision avoidance reinforcement learning (i.e., AEMCARL)
framework for an unmanned robot to achieve collision-free motions in
challenging navigation scenarios. The novelty of this work is threefold: (1)
developing a hierarchical network of gated-recurrent-unit (GRU) for environment
modeling; (2) developing an adaptive perception mechanism with an attention
module; (3) developing an adaptive reward function for the reinforcement
learning (RL) framework to jointly train the environment model, perception
function and motion planning policy. The proposed method is tested with the
Gym-Gazebo simulator and a group of robots (Husky and Turtlebot) under various
crowded scenes. Both simulation and experimental results have demonstrated the
superior performance of the proposed method over baseline methods.


title: The number of nonunimodular roots of a reciprocal polynomial

abstract:
  We introduce a sequence P_d of monic reciprocal polynomials with integer
coefficients having the central coefficients fixed as well as the peripheral
coefficients. We prove that the ratio between number of nonunimodular roots of
P_d and its degree d has a limit L when d tends to infinity. We show that if
the coefficients of a polynomial can be arbitrarily large in modulus then L can
be arbitrarily close to 0. It seems reasonable to believe that if the
coefficients are bounded then the analogue of Lehmer's Conjecture is true:
either L = 0 or there exists a gap so that L could not be arbitrarily close to
0. We present an algorithm for calculation the limit ratio and a numerical
method for its approximation. We estimated the limit ratio for a family of
polynomials. We calculated the limit ratio of polynomials correlated to many
bivariate polynomials having small Mahler measure.


title: Maximizing Modular plus Non-monotone Submodular Functions

abstract:
  The research problem in this work is the relaxation of maximizing
non-negative submodular plus modular with the entire real number domain as its
value range over a family of down-closed sets. We seek a feasible point
$\mathbf{x}^*$ in the polytope of the given constraint such that
$\mathbf{x}^*\in\arg\max_{\mathbf{x}\in\mathcal{P}\subseteq[0,1]^n}F(\mathbf{x})+L(\mathbf{x})$,
where $F$, $L$ denote the extensions of the underlying submodular function $f$
and modular function $\ell$. We provide an approximation algorithm named
\textsc{Measured Continuous Greedy with Adaptive Weights}, which yields a
guarantee $F(\mathbf{x})+L(\mathbf{x})\geq
\left(1/e-\mathcal{O}(\epsilon)\right)\cdot
f(OPT)+\left(\frac{\beta-e}{e(\beta-1)}-\mathcal{O}(\epsilon)\right)\cdot\ell(OPT)$
under the assumption that the ratio of non-negative part within $\ell(OPT)$ to
the absolute value of its negative part is demonstrated by a parameter
$\beta\in[0, \infty]$, where $OPT$ is the optimal integral solution for the
discrete problem. It is obvious that the factor of $\ell(OPT)$ is $1$ when
$\beta=0$, which means the negative part is completely dominant at this time;
otherwise the factor is closed to $1/e$ whe $\beta\rightarrow\infty$. Our work
first breaks the restriction on the specific value range of the modular
function without assuming non-positivity or non-negativity as previous results
and quantifies the relative variation of the approximation guarantee for
optimal solutions with arbitrary structure. Moreover, we also give an analysis
for the inapproximability of the problem we consider. We show a hardness result
that there exists no polynomial algorithm whose output $S$ satisfies
$f(S)+\ell(S)\geq0.478\cdot f(OPT)+\ell(OPT)$.


title: Multi-Use Trust in Crowdsourced IoT Services

abstract:
  We introduce the concept of adaptive trust in crowdsourced IoT services. It
is a customized fine-grained trust tailored for specific IoT consumers. Usage
patterns of IoT consumers are exploited to provide an accurate trust value for
service providers. A novel adaptive trust management framework is proposed to
assess the dynamic trust of IoT services. The framework leverages a novel
detection algorithm to obtain trust indicators that are likely to influence the
trust level of a specific IoT service type. Detected trust indicators are then
used to build service-to-indicator model to evaluate a service's trust at each
indicator. Similarly, a usage-to-indicator model is built to obtain the
importance of each trust indicator for a particular usage scenario. The
per-indicator trust and the importance of each trust indicator are utilized to
obtain an overall value of a given service for a specific consumer. We conduct
a set of experiments on a real dataset to show the effectiveness of the
proposed framework.


title:
LDP: Learnable Dynamic Precision for Efficient Deep Neural Network
  Training and Inference

abstract:
  Low precision deep neural network (DNN) training is one of the most effective
techniques for boosting DNNs' training efficiency, as it trims down the
training cost from the finest bit level. While existing works mostly fix the
model precision during the whole training process, a few pioneering works have
shown that dynamic precision schedules help DNNs converge to a better accuracy
while leading to a lower training cost than their static precision training
counterparts. However, existing dynamic low precision training methods rely on
manually designed precision schedules to achieve advantageous efficiency and
accuracy trade-offs, limiting their more comprehensive practical applications
and achievable performance. To this end, we propose LDP, a Learnable Dynamic
Precision DNN training framework that can automatically learn a temporally and
spatially dynamic precision schedule during training towards optimal accuracy
and efficiency trade-offs. It is worth noting that LDP-trained DNNs are by
nature efficient during inference. Furthermore, we visualize the resulting
temporal and spatial precision schedule and distribution of LDP trained DNNs on
different tasks to better understand the corresponding DNNs' characteristics at
different training stages and DNN layers both during and after training,
drawing insights for promoting further innovations. Extensive experiments and
ablation studies (seven networks, five datasets, and three tasks) show that the
proposed LDP consistently outperforms state-of-the-art (SOTA) low precision DNN
training techniques in terms of training efficiency and achieved accuracy
trade-offs. For example, in addition to having the advantage of being
automated, our LDP achieves a 0.31\% higher accuracy with a 39.1\% lower
computational cost when training ResNet-20 on CIFAR-10 as compared with the
best SOTA method.


title:
Tensor description of vector field property and its application in
  classical continuum mechanics

abstract:
  In classical continuum mechanics the stress tensors are introduced to
describe the stress on an arbitrary micro surface at a point like the Kirchhoff
tress tensor and Cauchy stress tensor. Inspired by this idea, three
second-order tensors, spherical tensor, deviatoric tensor and spin tensor, are
introduced to describe the characteristics of any vector field. The study shows
that the spherical tensor and spin tensor describe the divergence and curl of
the vector field at a point respectively and the direction of the divergence
field or curl field at one point can be determined only when the spherical
tensor or spin tensor is combined with the deviatoric tensor. On this basis,
the study analyzed the shear wave in elastomer and shear flow in fluid. It is
concluded that the spin tensor describing local rigid body rotation in elastic
theory and fluid dynamics contributes stress, which is different from the
traditional understanding and shear stress reciprocity is unnecessary in
classical continuum mechanics.


title:
On fullness of von Neumann algebras associated with non-singular Borel
  equivalence relations

abstract:
  It is shown by Houdayer-Isono that a group measure space von Neumann algebra
is a full factor if the group is countable discrete and bi-exact, and the
action is strongly ergodic, essentially free and non-singular. Recently,
bi-exactness for locally compact groups was introduced by Brothier-Deprez-Vaes.
In this paper, we will show that Houdayer-Isono type result holds for bi-exact
locally compact groups.


title: Zero Trust Architecture for 6G Security

abstract:
  The upcoming sixth generation (6G) network is envisioned to be more open and
heterogeneous than earlier generations. This challenges conventional security
architectures, which typically rely on the construction of a security perimeter
at network boundaries. In this article, we propose a software-defined zero
trust architecture (ZTA) for 6G networks, which is promising for establishing
an elastic and scalable security regime. This architecture achieves secure
access control through adaptive collaborations among the involved control
domains, and can effectively prevent malicious access behaviors such as
distributed denial of service (DDoS) attacks, malware spread, and zero-day
exploits. We also introduce key design aspects of this architecture and show
the simulation results of a case study, which shows the effectiveness and
robustness of ZTA for 6G. Furthermore, we discuss open issues to further
promote this new architecture.


title:
Modern Lower Bound Techniques in Database Theory and Constraint
  Satisfaction

abstract:
  Conditional lower bounds based on $P\neq NP$, the Exponential-Time Hypothesis
(ETH), or similar complexity assumptions can provide very useful information
about what type of algorithms are likely to be possible. Ideally, such lower
bounds would be able to demonstrate that the best known algorithms are
essentially optimal and cannot be improved further. In this tutorial, we
overview different types of lower bounds, and see how they can be applied to
problems in database theory and constraint satisfaction.


title: Bio-inspired Multi-robot Autonomy

abstract:
  Increasingly, high value industrial markets are driving trends for improved
functionality and resilience from resident autonomous systems. This led to an
increase in multi-robot fleets that aim to leverage the complementary
attributes of the diverse platforms. In this paper we introduce a novel
bio-inspired Symbiotic System of Systems Approach (SSOSA) for designing the
operational governance of a multi-robot fleet consisting of ground-based
quadruped and wheeled platforms. SSOSA couples the MR-fleet to the resident
infrastructure monitoring systems into one collaborative digital commons. The
hyper visibility of the integrated distributed systems, achieved through a
latency bidirectional communication network, supports collaboration,
coordination and corroboration (3C) across the integrated systems. In our
experiment, we demonstrate how an operator can activate a pre-determined
autonomous mission and utilize SSOSA to overcome intrinsic and external risks
to the autonomous missions. We demonstrate how resilience can be enhanced by
local collaboration between SPOT and Husky wherein we detect a replacement
battery, and utilize the manipulator arm of SPOT to support a Clearpath Husky
A200 wheeled robotic platform. This allows for increased resilience of an
autonomous mission as robots can collaborate to ensure the battery state of the
Husky robot. Overall, these initial results demonstrate the value of a SSOSA
approach in addressing a key operational barrier to scalable autonomy, the
resilience.


title: Whom actually do multipole moments belong to?

abstract:
  Using an integral definition to calculate the relativistic multipole moments
(RMM), and the ensuing generalized relativistic Gauss theorem, we prove that
the evaluation of that volume integral in Erez-Rosen coordinates, leads to a
specific link between the RMM and the source of the exterior space--time,
provided we have a global static axisymmetric metric in that coordinate system
for any Weyl exterior field. This result allows to establish a relationship
between the RMM and certain volume integral expressions involving the material
content of the source from its energy-momentum tensor as well as the interior
metric. In particular the relativistic quadrupole moment for the Erez-Rosen
space-time is obtained.


title: Revitalize Region Feature for Democratizing Video-Language Pre-training

abstract:
  Recent dominant methods for video-language pre-training (VLP) learn
transferable representations from the raw pixels in an end-to-end manner to
achieve advanced performance on downstream video-language tasks. Despite the
impressive results, VLP research becomes extremely expensive with the need for
massive data and a long training time, preventing further explorations. In this
work, we revitalize region features of sparsely sampled video clips to
significantly reduce both spatial and temporal visual redundancy towards
democratizing VLP research at the same time achieving state-of-the-art results.
Specifically, to fully explore the potential of region features, we introduce a
novel bidirectional region-word alignment regularization that properly
optimizes the fine-grained relations between regions and certain words in
sentences, eliminating the domain/modality disconnections between pre-extracted
region features and text. Extensive results of downstream text-to-video
retrieval and video question answering tasks on seven datasets demonstrate the
superiority of our method on both effectiveness and efficiency, e.g., our
method achieves competing results with 80\% fewer data and 85\% less
pre-training time compared to the most efficient VLP method so far. The code
will be available at \url{https://github.com/showlab/DemoVLP}.