title:
Self-Supervised Representation Learning as Multimodal Variational
  Inference

abstract:
  This paper proposes a probabilistic extension of SimSiam, a recent
self-supervised learning (SSL) method. SimSiam trains a model by maximizing the
similarity between image representations of different augmented views of the
same image. Although uncertainty-aware machine learning has been getting
general like deep variational inference, SimSiam and other SSL are
insufficiently uncertainty-aware, which could lead to limitations on its
potential. The proposed extension is to make SimSiam uncertainty-aware based on
variational inference. Our main contributions are twofold: Firstly, we clarify
the theoretical relationship between non-contrastive SSL and multimodal
variational inference. Secondly, we introduce a novel SSL called variational
inference SimSiam (VI-SimSiam), which incorporates the uncertainty by involving
spherical posterior distributions. Our experiment shows that VI-SimSiam
outperforms SimSiam in classification tasks in ImageNette and ImageWoof by
successfully estimating the representation uncertainty.


title:
An Efficient Data-Driven Multiscale Stochastic Reduced Order Modeling
  Framework for Complex Systems

abstract:
  Suitable reduced order models (ROMs) are computationally efficient tools in
characterizing key dynamical and statistical features of nature. In this paper,
a systematic multiscale stochastic ROM framework is developed for complex
systems with strong chaotic or turbulent behavior. The new ROMs are
fundamentally different from the traditional Galerkin ROM (G-ROM) or those
deterministic ROMs that aim at minimizing the path-wise errors and applying
mainly to laminar systems. Here, the new ROM focuses on recovering the
large-scale dynamics to the maximum extent while it also exploits cheap but
effective conditional linear functions as the closure terms to capture the
statistical features of the medium-scale variables and its feedback to the
large scales. In addition, physics constraints are incorporated into the new
ROM. One unique feature of the resulting ROM is that it facilitates an
efficient and accurate scheme for nonlinear data assimilation, the solution of
which is provided by closed analytic formulae. Such an analytic solvable data
assimilation solution significantly accelerates the computational efficiency
and allows the new ROM to avoid many potential numerical and sampling issues in
recovering the unobserved states from partial observations. The overall model
calibration is efficient and systematic via explicit mathematical formulae. The
new ROM framework is applied to complex nonlinear systems, in which the
intrinsic turbulent behavior is either triggered by external random forcing or
deterministic nonlinearity. It is shown that the new ROM significantly
outperforms the G-ROM in both scenarios in terms of reproducing the dynamical
and statistical features as well as recovering unobserved states via the
associated efficient data assimilation scheme.


title: Bayesian outcome selection modelling

abstract:
  Psychiatric and social epidemiology often involves assessing the effects of
environmental exposure on outcomes that are difficult to measure directly. To
address this problem, it is common to measure outcomes using a comprehensive
battery of different tests thought to be related to a common, underlying
construct of interest. In the application that motivates our work, for example,
researchers wanted to assess the impact of in utero alcohol exposure on child
cognition and neuropsychological development, which were evaluated using a
range of different tests. Statistical analysis of the resulting multiple
outcomes data can be challenging, not only because of the need to account for
the correlation between outcomes measured on the same individual, but because
it is often unclear, a priori, which outcomes are impacted by the exposure
under study. While researchers will generally have some hypotheses about which
outcomes are important, a framework is needed to help identify outcomes that
are sensitive to the exposure and to quantify the associated treatment or
exposure effects of interest. We propose such a framework using a modification
of stochastic search variable selection (SSVS), a popular Bayesian variable
selection model and use it to quantify an overall effect of the exposure on the
affected outcomes. We investigate the performance of the method via simulation
and illustrate its application to data from a study involving the effects of
prenatal alcohol exposure on child cognition.


title:
Regional medical inter-institutional cooperation in medical provider
  network constructed using patient claims data from Japan

abstract:
  The aging world population requires a sustainable and high-quality healthcare
system. To examine the efficiency of medical cooperation, medical provider and
physician networks were constructed using patient claims data. Previous studies
have shown that these networks contain information on medical cooperation.
However, the usage patterns of multiple medical providers in a series of
medical services have not been considered. In addition, these studies used only
general network features to represent medical cooperation, but their expressive
ability was low. To overcome these limitations, we analyzed the medical
provider network to examine its overall contribution to the quality of
healthcare provided by cooperation between medical providers in a series of
medical services. This study focused on: i) the method of feature extraction
from the network, ii) incorporation of the usage pattern of medical providers,
and iii) expressive ability of the statistical model. Femoral neck fractures
were selected as the target disease. To build the medical provider networks, we
analyzed the patient claims data from a single prefecture in Japan between
January 1, 2014 and December 31, 2019. We considered four types of models: a
model using node strength and linear regression to a model using feature
representation by node2vec and regression tree ensemble, which is a machine
learning method. The results showed that a stronger medical provider reduces
the duration of hospital stay. The overall contribution of the medical
cooperation to the duration of hospital stay extracted from the medical
provider network using node2vec is approximately 20%, which is approximately 20
times higher than the model using strength.


title:
Multi-Modal Learning for AU Detection Based on Multi-Head Fused
  Transformers

abstract:
  Multi-modal learning has been intensified in recent years, especially for
applications in facial analysis and action unit detection whilst there still
exist two main challenges in terms of 1) relevant feature learning for
representation and 2) efficient fusion for multi-modalities. Recently, there
are a number of works have shown the effectiveness in utilizing the attention
mechanism for AU detection, however, most of them are binding the region of
interest (ROI) with features but rarely apply attention between features of
each AU. On the other hand, the transformer, which utilizes a more efficient
self-attention mechanism, has been widely used in natural language processing
and computer vision tasks but is not fully explored in AU detection tasks. In
this paper, we propose a novel end-to-end Multi-Head Fused Transformer (MFT)
method for AU detection, which learns AU encoding features representation from
different modalities by transformer encoder and fuses modalities by another
fusion transformer module. Multi-head fusion attention is designed in the
fusion transformer module for the effective fusion of multiple modalities. Our
approach is evaluated on two public multi-modal AU databases, BP4D, and BP4D+,
and the results are superior to the state-of-the-art algorithms and baseline
models. We further analyze the performance of AU detection from different
modalities.


title:
Associating Objects with Scalable Transformers for Video Object
  Segmentation

abstract:
  This paper investigates how to realize better and more efficient embedding
learning to tackle the semi-supervised video object segmentation under
challenging multi-object scenarios. The state-of-the-art methods learn to
decode features with a single positive object and thus have to match and
segment each target separately under multi-object scenarios, consuming multiple
times computation resources. To solve the problem, we propose an Associating
Objects with Transformers (AOT) approach to match and decode multiple objects
jointly and collaboratively. In detail, AOT employs an identification mechanism
to associate multiple targets into the same high-dimensional embedding space.
Thus, we can simultaneously process multiple objects' matching and segmentation
decoding as efficiently as processing a single object. To sufficiently model
multi-object association, a Long Short-Term Transformer (LSTT) is devised to
construct hierarchical matching and propagation. Based on AOT, we further
propose a more flexible and robust framework, Associating Objects with Scalable
Transformers (AOST), in which a scalable version of LSTT is designed to enable
run-time adaptation of accuracy-efficiency trade-offs. Besides, AOST introduces
a better layer-wise manner to couple identification and vision embeddings. We
conduct extensive experiments on multi-object and single-object benchmarks to
examine AOT series frameworks. Compared to the state-of-the-art competitors,
our methods can maintain times of run-time efficiency with superior
performance. Notably, we achieve new state-of-the-art performance on three
popular benchmarks, i.e., YouTube-VOS (86.5%), DAVIS 2017 Val/Test
(87.0%/84.7%), and DAVIS 2016 (93.0%). Project page:
https://github.com/z-x-yang/AOT.


title: Demo of the Linguistic Field Data Management and Analysis System -- LiFE

abstract:
  In the proposed demo, we will present a new software - Linguistic Field Data
Management and Analysis System - LiFE (https://github.com/kmi-linguistics/life)
- an open-source, web-based linguistic data management and analysis application
that allows for systematic storage, management, sharing and usage of linguistic
data collected from the field. The application allows users to store lexical
items, sentences, paragraphs, audio-visual content with rich glossing /
annotation; generate interactive and print dictionaries; and also train and use
natural language processing tools and models for various purposes using this
data. Since its a web-based application, it also allows for seamless
collaboration among multiple persons and sharing the data, models, etc with
each other.
  The system uses the Python-based Flask framework and MongoDB in the backend
and HTML, CSS and Javascript at the frontend. The interface allows creation of
multiple projects that could be shared with the other users. At the backend,
the application stores the data in RDF format so as to allow its release as
Linked Data over the web using semantic web technologies - as of now it makes
use of the OntoLex-Lemon for storing the lexical data and Ligt for storing the
interlinear glossed text and then internally linking it to the other linked
lexicons and databases such as DBpedia and WordNet. Furthermore it provides
support for training the NLP systems using scikit-learn and HuggingFace
Transformers libraries as well as make use of any model trained using these
libraries - while the user interface itself provides limited options for tuning
the system, an externally-trained model could be easily incorporated within the
application; similarly the dataset itself could be easily exported into a
standard machine-readable format like JSON or CSV that could be consumed by
other programs and pipelines.


title:
Root-aligned SMILES: A Tight Representation for Chemical Reaction
  Prediction

abstract:
  Chemical reaction prediction, involving forward synthesis and retrosynthesis
prediction, is a fundamental problem in organic synthesis. A popular
computational paradigm formulates synthesis prediction as a
sequence-to-sequence translation problem, where the typical SMILES is adopted
for molecule representations. However, the general-purpose SMILES neglects the
characteristics of chemical reactions, where the molecular graph topology is
largely unaltered from reactants to products, resulting in the suboptimal
performance of SMILES if straightforwardly applied. In this article, we propose
the root-aligned SMILES (R-SMILES), which specifies a tightly aligned
one-to-one mapping between the product and the reactant SMILES for more
efficient synthesis prediction. Due to the strict one-to-one mapping and
reduced edit distance, the computational model is largely relieved from
learning the complex syntax and dedicated to learning the chemical knowledge
for reactions. We compare the proposed R-SMILES with various state-of-the-art
baselines and show that it significantly outperforms them all, demonstrating
the superiority of the proposed method.


title: Polarized Synchrotron Foreground Assessment for CMB Experiments

abstract:
  Polarized Galactic synchrotron emission is an undesirable foreground for
cosmic microwave background (CMB) experiments observing at frequencies $< 150$
GHz. We perform a combined analysis of observational data at 1.4, 2.3, 23, 30
and 33 GHz to quantify the spatial variation of the polarized synchrotron
spectral index, $\beta^{pol}$, on $\sim3.5^\circ$ scales. We compare results
from different data combinations to address limitations and inconsistencies
present in these public data, and form a composite map of $\beta^{pol}$. Data
quality masking leaves 44% sky coverage (75% for $|b|> 45^\circ$). Generally
$-3.2 < \beta^{pol} \lesssim -3$ in the inner Galactic plane and spurs, but the
Fan Region in the outer Galaxy has a flatter index. We find a clear spectral
index steepening with increasing latitude south of the Galactic plane with
$\Delta \beta^{pol}=0.4$, and a smaller steepening of $0.25$ in the north. Near
the south Galactic pole the polarized synchrotron spectral index is
$\beta^{pol} \approx -3.4$. Longitudinal spectral index variations of $\Delta
\beta^{pol} \sim 0.1$ about the latitudinal mean are also detected. Within the
BICEP2/Keck survey footprint, we find consistency with a constant value,
$\beta^{pol} = -3.25 \pm 0.04$ (statistical) $\pm 0.02$ (systematic). We
compute a map of the frequency at which synchrotron and thermal dust emission
contribute equally to the total polarized foreground. The limitations and
inconsistencies among datasets encountered in this work make clear the value of
additional independent surveys at multiple frequencies, especially between
$10-20$ GHz, provided these surveys have sufficient sensitivity and control of
instrumental systematic errors.


title:
Mirror symmetry of height-periodic gradient Gibbs measures of a SOS
  model on Cayley trees

abstract:
  For the solid-on-solid (SOS) model with spin values from the set of all
integers on a Cayley tree we give gradient Gibbs measures (GGMs). Such a
measure corresponds to a boundary law (which is an infinite-dimensional
vector-valued function defined on vertices of the Cayley tree) satisfying an
infinite system of functional equations. We give several concrete GGMs of
boundary laws which are independent from vertices of the Cayley tree and (as an
infinite-dimensional vector) have periodic, (non-)mirror symmetric coordinates.


title:
Manipulating UAV Imagery for Satellite Model Training, Calibration and
  Testing

abstract:
  Modern livestock farming is increasingly data driven and frequently relies on
efficient remote sensing to gather data over wide areas. High resolution
satellite imagery is one such data source, which is becoming more accessible
for farmers as coverage increases and cost falls. Such images can be used to
detect and track animals, monitor pasture changes, and understand land use.
Many of the data driven models being applied to these tasks require ground
truthing at resolutions higher than satellites can provide. Simultaneously,
there is a lack of available aerial imagery focused on farmland changes that
occur over days or weeks, such as herd movement. With this goal in mind, we
present a new multi-temporal dataset of high resolution UAV imagery which is
artificially degraded to match satellite data quality. An empirical blurring
metric is used to calibrate the degradation process against actual satellite
imagery of the area. UAV surveys were flown repeatedly over several weeks, for
specific farm locations. This 5cm/pixel data is sufficiently high resolution to
accurately ground truth cattle locations, and other factors such as grass
cover. From 33 wide area UAV surveys, 1869 patches were extracted and
artificially degraded using an accurate satellite optical model to simulate
satellite data. Geographic patches from multiple time periods are aligned and
presented as sets, providing a multi-temporal dataset that can be used for
detecting changes on farms. The geo-referenced images and 27,853 manually
annotated cattle labels are made publicly available.


title:
Optimal Scheduling of Distributed Energy Resources Considering Volt-VAr
  Controller of PV Smart Inverters

abstract:
  This paper proposes an operational scheduling model of distributed energy
resources (DERs) and PV smart inverters with Volt-VAr controller using an
accurate AC optimal power flow (ACOPF) in an unbalanced distribution network. A
mathematical mixed-integer model of local Volt-VAr droop controller of the
distributed mixed-phase PV smart inverters is proposed based on the IEEE
1547-2018 standard and is incorporated in the unbalanced ACOPF, which enables
effective utilization of the Volt-VAr controllers to not only alleviate voltage
issues locally but also at the feeder level. The proposed model is tested on
two actual snapshots of a distribution feeder in Arizona. Also, the proposed
operational scheduling method considering the Volt-VAr droop controller of PV
smart inverters is compared with a recent work in scheduling of the PV smart
inverters. The results illustrate that the PV smart inverters dispatches
obtained by the proposed model can be practically implemented by local
controller of inverters.


title: Leveraging Textures in Zero-shot Understanding of Fine-Grained Domains

abstract:
  Textures can be used to describe the appearance of objects in a wide range of
fine-grained domains. Textures are localized and one can often refer to their
properties in a manner that is independent of the object identity. Moreover,
there is a rich vocabulary to describe textures corresponding to properties
such as their color, pattern, structure, periodicity, stochasticity, and
others. Motivated by this, we study the effectiveness of large-scale language
and vision models (e.g., CLIP) at recognizing texture attributes in natural
images. We first conduct a systematic study of CLIP on texture datasets where
we find that it has good coverage for a wide range of texture terms. CLIP can
also handle compositional phrases that consist of color and pattern terms
(e.g., red dots or yellow stripes). We then show how these attributes allow for
zero-shot fine-grained categorization on existing datasets.


title:
Implications of the correlation between bulge-to-total baryonic mass
  ratio and the number of satellites for SAGA galaxies

abstract:
  We searched for correlations between the number of satellites and fundamental
galactic properties for the Milky Way-like host galaxies in order to better
understand their diverse satellite populations. We specifically aim to
understand why galaxies that are very similar in stellar mass content, star
formation rate, and local environment have very different numbers of
satellites. Satellites of Galactic Analogs (SAGA) spectroscopic survey has
completed spectroscopic observations of 36 Milky Way-like galaxies within their
virial radii down to the luminosity of Leo I dwarf galaxy. All the available
galactic properties of SAGA galaxies from the literature along with measured
stellar masses were correlated with the number of satellites and no significant
correlation was found. However, when we considered the "expected" number of
satellites based on the correlation between the baryonic bulge-to-total ratio
and the number of satellites confirmed for several nearby galaxies then strong
correlations emerge between this number and (1) the mass of the bulge, and (2)
the total specific angular momentum. The first correlation is positive,
implying that galaxies with more massive bulges have more satellites, as
already confirmed. The second correlation with the angular momentum is
negative, meaning that, the smaller the angular momentum, the greater the
number of expected satellites. This would imply that either satellites cannot
form if galaxy angular momentum is too high, or that satellites form
inside-out, so that angular momentum is being transferred to the outer parts of
the galaxies. However, deeper spectroscopic observations are needed to confirm
these findings, because they rely on the expected rather than detected number
of satellites. There was a luminosity limit to the SAGA survey equivalent to
the luminosity of Leo I dwarf satellite of the Milky Way galaxy.


title:
Double-transmon coupler: Fast two-qubit gate with no residual coupling
  for highly detuned superconducting qubits

abstract:
  Although two-qubit entangling gates are necessary for universal quantum
computing, they are notoriously difficult to implement with high fidelity.
Recently, tunable couplers have become a key component for realizing
high-fidelity two-qubit gates in superconducting quantum computers. However, it
is still difficult to achieve tunable coupling free of unwanted residual
coupling for highly detuned qubits, which are desirable for mitigating
qubit-frequency crowding or errors due to crosstalk between qubits. We thus
propose a design for this kind of tunable coupler, which we call a
double-transmon coupler, because this is composed of two transmon qubits
coupled through a common loop with an additional Josephson junction.
Controlling the magnetic flux in the loop, we can achieve not only fast
high-fidelity two-qubit gates, but also no residual coupling during idle time,
where computational qubits are highly detuned fixed-frequency transmons. The
proposed coupler is expected to offer an alternative approach to
higher-performance superconducting quantum computers.


title:
Improved characterization of Lagrangian coherent structures through
  time-scale analysis

abstract:
  The computation of Lagrangian coherent structures (LCS) has established
itself as a prominent means to reveal significant geometric structures in
time-dependent vector fields. Their characterization, however, requires the
selection of a suitable time parameter for the construction of the flow map
that may not be known in advance. We present in this paper a continuous
time-scale framework for LCS extraction and visualization. Specifically, we
treat the time axis as a continuum from which a best temporal scale is
automatically determined at each spatial location for the extraction of LCS.
Beyond its effectiveness with vector fields we show that this method can be
successfully applied to improve the characterization of salient structures in
tensor fields and discrete maps. We present applications of our method to
problems spanning fluid dynamics, medical imaging, and orbital mechanics. The
results show that our approach can reveal important structural features that
are missed by existing LCS extraction methods.


title:
DepthGAN: GAN-based Depth Generation of Indoor Scenes from Semantic
  Layouts

abstract:
  Limited by the computational efficiency and accuracy, generating complex 3D
scenes remains a challenging problem for existing generation networks. In this
work, we propose DepthGAN, a novel method of generating depth maps with only
semantic layouts as input. First, we introduce a well-designed cascade of
transformer blocks as our generator to capture the structural correlations in
depth maps, which makes a balance between global feature aggregation and local
attention. Meanwhile, we propose a cross-attention fusion module to guide edge
preservation efficiently in depth generation, which exploits additional
appearance supervision information. Finally, we conduct extensive experiments
on the perspective views of the Structured3d panorama dataset and demonstrate
that our DepthGAN achieves superior performance both on quantitative results
and visual effects in the depth generation task.Furthermore, 3D indoor scenes
can be reconstructed by our generated depth maps with reasonable structure and
spatial coherency.


title:
Observing individual thermal motions of ions, and molecules in water
  with light

abstract:
  We observe thermal motions of ions, and molecules in water through light
extinction, at the individual particle level. The motions appear as time
dependent intensity variations, characterized through their averaged spectra.
Theoretical spectrum derived from random motions of one particle describes
these measured spectra. This theory is used to extract diffusion constants of
liquid mixtures and solutions, that correspond to binary diffusion, and thermal
diffusion, which are consistent with previous macroscopic measurements. We also
estimate the sizes of the particles.


title:
One more time on the helicity decomposition of spin and orbital optical
  currents

abstract:
  The helicity representation of the linear momentum density of a light wave is
well understood for monochromatic optical fields in both paraxial and
non-paraxial regimes of propagation. In this note we generalize such
representation to nonmonochromatic optical fields. We find that, differently
from the monochromatic case, the linear momentum density, aka the Poynting
vector divided by $c^2$, does not separate into the sum of right-handed and
left-handed terms, even when the so-called electric-magnetic democracy in
enforced by averaging the electric and magnetic contributions. However, for
quasimonochromatic light, such a separation is approximately restored after
time-averaging. This paper is dedicated to Sir Michael Berry on the occasion of
his $80$th birthday.


title: Bach Flow of Simply Connected Nilmanifolds

abstract:
  The Bach flow is a fourth order geometric flow defined on four manifolds. For
a compact manifold, it is a conformally modified gradient flow for the
$L^2$-norm of the Weyl curvature. In this paper we study the Bach flow on
four-dimensional simply connected nilmanifolds whose Lie algebra is
indecomposable. We show that the Bach flow beginning at an arbitrary left
invariant metric exists for all positive times and after rescaling converges in
the pointed Cheeger-Gromov sense to an expanding Bach soliton which is
non-gradient. Combining our results with previous results of Helliwell gives a
complete description of the Bach flow on simply connected nilmanifolds.


title: On the Frobenius Coin Problem in Three Variables

abstract:
  The Frobenius coin problem in three variables, for three positive relatively
prime integers $a_1< a_2< a_3$ asks to find the largest number not
representable as $a_1x_1+a_2x_2+a_3x_3$ with non-negative integer coefficients
$x_1$, $x_2$ and $x_3$. In this article, we present a new algorithm to solve
this problem that is faster and in our belief simpler than all existing
algorithms and runs in $\mbox{O}(\log a_1)$ steps.


title:
Hierarchical Graph Representation Learning for the Prediction of
  Drug-Target Binding Affinity

abstract:
  The identification of drug-target binding affinity (DTA) has attracted
increasing attention in the drug discovery process due to the more specific
interpretation than binary interaction prediction. Recently, numerous deep
learning-based computational methods have been proposed to predict the binding
affinities between drugs and targets benefiting from their satisfactory
performance. However, the previous works mainly focus on encoding biological
features and chemical structures of drugs and targets, with a lack of
exploiting the essential topological information from the drug-target affinity
network. In this paper, we propose a novel hierarchical graph representation
learning model for the drug-target binding affinity prediction, namely
HGRL-DTA. The main contribution of our model is to establish a hierarchical
graph learning architecture to incorporate the intrinsic properties of
drug/target molecules and the topological affinities of drug-target pairs. In
this architecture, we adopt a message broadcasting mechanism to integrate the
hierarchical representations learned from the global-level affinity graph and
the local-level molecular graph. Besides, we design a similarity-based
embedding map to solve the cold start problem of inferring representations for
unseen drugs and targets. Comprehensive experimental results under different
scenarios indicate that HGRL-DTA significantly outperforms the state-of-the-art
models and shows better model generalization among all the scenarios.


title: Spin precession in the gravity wave analogue black hole spacetime

abstract:
  It was predicted that the spin precession frequency of a stationary gyroscope
shows various anomalies in the strong gravity regime if its orbit shrinks, and
eventually its precession frequency becomes arbitrarily high very close to the
horizon of a rotating black hole. Considering the gravity waves of a flowing
fluid with vortex in a shallow basin, that acts as a rotating analogue black
hole, one can observe the predicted strong gravity effect on the spin
precession in the laboratory. Attaching a thread with the buoyant particles and
anchored it to the bottom of the fluid container with a short length of
miniature chain, one can construct a simple local test gyroscope to measure the
spin precession frequency in the vicinity of the gravity wave analogue black
hole. The thread acts as the axis of the gyroscope. By regulating the orbital
frequency of the test gyroscope, one can also be able to measure the strong
gravity Lense-Thirring effect and geodetic/de-Sitter effect with this
experimental set-up, as the special cases. For example, to measure the
Lense-Thirring effect, the length of the miniature chain can be set to zero, so
that the gyroscope becomes static. One can also measure the geodetic precession
with this system by orbiting the test gyroscope in the so-called Keplerian
frequency around the non-rotating analogue black hole that can be constructed
by making the rotation of the fluid/vortex negligible compared to its radial
velocity.

categories: gr-qc astro-ph.HE cond-mat.other physics.flu-dyn


title: On K-stability of Calabi-Yau fibrations

abstract:
  We study K-stability of algebraic fiber spaces. In this paper, we prove that
Calabi-Yau fibrations over curves are uniformly K-stable in an adiabatic sense
if and only if the base curves are log-twisted K-stable, where log-twisted
K-stability is a variant of K-stability. Moreover, we prove that there are cscK
metrics on rational elliptic surfaces that have only reduced fibers.


title:
Locally Adaptive Transfer Learning Algorithms for Large-Scale Multiple
  Testing

abstract:
  Transfer learning has enjoyed increasing popularity in a range of big data
applications. In the context of large-scale multiple testing, the goal is to
extract and transfer knowledge learned from related source domains to improve
the accuracy of simultaneously testing of a large number of hypotheses in the
target domain. This article develops a locally adaptive transfer learning
algorithm (LATLA) for transfer learning for multiple testing. In contrast with
existing covariate-assisted multiple testing methods that require the auxiliary
covariates to be collected alongside the primary data on the same testing
units, LATLA provides a principled and generic transfer learning framework that
is capable of incorporating multiple samples of auxiliary data from related
source domains, possibly in different dimensions/structures and from diverse
populations. Both the theoretical and numerical results show that LATLA
controls the false discovery rate and outperforms existing methods in power.
LATLA is illustrated through an application to genome-wide association studies
for the identification of disease-associated SNPs by cross-utilizing the
auxiliary data from a related linkage analysis.


title:
Co-positivity of tensors and Stability conditions of two Higgs
  potentials

abstract:
  We give how to calculate the vacuum stability conditions of scalar potential
for two Higgs doublet model with explicit CP conservation. Moreover, the
analytical sufficient and necessary conditions are obtained. The argument
methods are first used to study the tensor with two parameters.


title:
Identity and Access Management Framework for Multi-tenant Resources in
  Hybrid Cloud Computing

abstract:
  While more organizations have been trying to move their infrastructure to the
cloud in recent years, there have been significant challenges in how identities
and access are managed in a hybrid cloud setting. This paper showcases a novel
identity and access management framework for shared resources in a multi-tenant
hybrid cloud environment. The paper demonstrates a method to implement the
"mirror" identities of on-premise identities in the cloud. Following the best
security practices, the framework ensures that only rightful users can use
their mirror identities in the cloud. Furthermore, the paper also proposes a
technique in scaling the framework to accommodate large-scale enterprises. The
framework exhibited in the paper provides a comprehensive and scalable solution
for enterprises to implement identity and access control in their hybrid cloud
infrastructure. Although the paper focuses on implementing the framework in
Google Cloud Platform, it can be easily applied to any major public cloud
platform.


title: Luminosity Selection for Gamma Ray Burst

abstract:
  There exists an inevitable scatter in intrinsic luminosity of Gamma Ray
Bursts(GRBs). If there is relativistic beaming in the source, viewing angle
variation necessarily introduces variation in the intrinsic luminosity function
(ILF). Scatter in the ILF can cause a selection bias where distant sources that
are detected have a larger median luminosity than those detected close by.
Median luminosity divides any given population into equal halves. When the
functional form of a distribution is unknown, it can be a more robust
diagnostic than any that use trial functional forms. In this work we employ a
statistical test based on median luminosity and apply it to test a class of
models for GRBs. We assume that the GRB jet has a finite opening angle and that
the orientation of the GRB jet is random relative to the observer.We calculate
$L_{median}$ as a function of redshift by simulating GRBs empirically,
theoretically and use the luminosity vs redshift {\it Swift} data in order to
compare the theoretical results with the observed ones.
  The method accounts for the fact that at some redshifts there may be some
GRBs that go undetected. We find that $L_{median}$ is extremely insensitive to
the on-axis (i.e. maximal) luminosity of the jet.


title:
Quantum sensing and imaging of spin-orbit-torque-driven spin dynamics in
  noncollinear antiferromagnet Mn3Sn

abstract:
  Novel noncollinear antiferromagnets with spontaneous time-reversal symmetry
breaking, nontrivial band topology, and unconventional transport properties
have received immense research interest over the past decade due to their rich
physics and enormous promise in technological applications. One of the central
focuses in this emerging field is exploring the relationship between the
microscopic magnetic structure and exotic material properties. Here, the
nanoscale imaging of both spin-orbit-torque-induced deterministic magnetic
switching and chiral spin rotation in noncollinear antiferromagnet Mn3Sn films
using nitrogen-vacancy (NV) centers is reported. Direct evidence of the
off-resonance dipole-dipole coupling between the spin dynamics in Mn3Sn and
proximate NV centers is also demonstrated with NV relaxometry measurements.
These results demonstrate the unique capabilities of NV centers in accessing
the local information of the magnetic order and dynamics in these emergent
quantum materials and suggest new opportunities for investigating the interplay
between topology and magnetism in a broad range of topological magnets.


title: Wide-Band Spectral Variability of Peaked Spectrum Sources

abstract:
  Characterising spectral variability of radio sources is a technique that
offers the ability to determine the astrophysics of the intervening media,
source structure, emission and absorption processes. We present broadband
(0.072--10 GHz) spectral variability of 15 peaked-spectrum (PS) sources with
the Australia Telescope Compact Array (ATCA) and the Murchison Widefield Array
(MWA). These 15 PS sources were observed quasi-contemporaneously with ATCA and
the MWA four to six times during 2020 with approximately a monthly cadence.
Variability was not detected at 1--10GHz frequencies but 13 of the 15 targets
show significant variability with the MWA at megahertz frequencies. We conclude
the majority of variability seen at megahertz frequencies is due to refractive
interstellar scintillation of a compact component ~25 mas across. We also
identify four PS sources that show a change in their spectral shape at
megahertz frequencies. Three of these sources are consistent with a variable
optical depth from an inhomogeneous free-free absorbing cloud around the
source. One PS source with a variable spectral shape at megahertz frequencies
is consistent with an ejection travelling along the jet. We present spectral
variability as a method for determining the physical origins of observed
variability and for providing further evidence to support absorption models for
PS sources where spectral modelling alone is insufficient.


title:
Discovery of charge density wave in a correlated kagome lattice
  antiferromagnet

abstract:
  A hallmark of strongly correlated quantum materials is the rich phase diagram
resulting from competing and intertwined phases with nearly degenerate ground
state energies. A well-known example is the copper oxides, where a charge
density wave (CDW) is ordered well above and strongly coupled to the magnetic
order to form spin-charge separated stripes that compete with
superconductivity. Recently, such rich phase diagrams have also been revealed
in correlated topological materials. In two-dimensional kagome lattice metals
consisting of corner-sharing triangles, the geometry of the lattice can produce
flat bands with localized electrons, non-trivial topology, chiral magnetic
order, superconductivity and CDW order. While CDW has been found in weakly
electron correlated nonmagnetic AV3Sb5 (A = K, Rb, Cs), it has not yet been
observed in correlated magnetic ordered kagome lattice metals. Here we report
the discovery of CDW within the antiferromagnetic (AFM) ordered phase of kagome
lattice FeGe. The CDW in FeGe occurs at wavevectors identical to that of
AV3Sb5, enhances the AFM ordered moment, and induces an emergent anomalous Hall
effect. Our findings suggest that CDW in FeGe arises from the combination of
electron correlations-driven AFM order and van Hove singularities-driven
instability possibly associated with a chiral flux phase, in stark contrast to
strongly correlated copper oxides and nickelates, where the CDW precedes or
accompanies the magnetic order.


title: The role of antisymmetric functions in nonlocal equations

abstract:
  We prove a Hopf-type lemma for antisymmetric super-solutions to the Dirichlet
problem for the fractional Laplacian with zero-th order terms.
  As an application, we use such a Hopf-type lemma in combination with the
method of moving planes to prove symmetry for the semilinear fractional
parallel surface problem. That is, we prove that non-negative solutions to
semilinear Dirichlet problems for the fractional Laplacian in a bounded open
set $\Omega \subset \mathbb R^n$ must be radially symmetric if one of their
level surfaces is parallel to the boundary of $\Omega$; in turn, $\Omega$ must
be a ball.
  Furthermore, we discuss maximum principles and the Harnack inequality for
antisymmetric functions in the fractional setting and provide counter-examples
to these theorems when only `local' assumptions are imposed on the solutions.


title:
A new class of composite GBII regression models with varying threshold
  for modelling heavy-tailed data

abstract:
  The four-parameter generalized beta distribution of the second kind (GBII)
has been proposed for modelling insurance losses with heavy-tailed features.
The aim of this paper is to present a parametric composite GBII regression
modelling by splicing two GBII distributions using mode matching method. It is
designed for simultaneous modeling of small and large claims and capturing the
policyholder heterogeneity by introducing the covariates into the location
parameter. In such cases, the threshold that splits two GBII distributions
varies across individuals policyholders based on their risk features. The
proposed regression modelling also contains a wide range of insurance loss
distributions as the head and the tail respectively and provides the
close-formed expressions for parameter estimation and model prediction. A
simulation study is conducted to show the accuracy of the proposed estimation
method and the flexibility of the regressions. Some illustrations of the
applicability of the new class of distributions and regressions are provided
with a Danish fire losses data set and a Chinese medical insurance claims data
set, comparing with the results of competing models from the literature.


title:
Safety of Sampled-Data Systems with Control Barrier Functions via
  Approximate Discrete Time Models

abstract:
  Control Barrier Functions (CBFs) have been demonstrated to be a powerful tool
for safety-critical controller design for nonlinear systems. Existing design
paradigms do not address the gap between theory (controller design with
continuous time models) and practice (the discrete time sampled implementation
of the resulting controllers); this can lead to poor performance and violations
of safety for hardware instantiations. We propose an approach to close this gap
by synthesizing sampled-data counterparts to these CBF-based controllers using
approximate discrete time models and Sampled-Data Control Barrier Functions
(SD-CBFs). Using properties of a system's continuous time model, we establish a
relationship between SD-CBFs and a notion of practical safety for sampled-data
systems. Furthermore, we construct convex optimization-based controllers that
formally endow nonlinear systems with safety guarantees in practice. We
demonstrate the efficacy of these controllers in simulation.


title:
Ray3D: ray-based 3D human pose estimation for monocular absolute 3D
  localization

abstract:
  In this paper, we propose a novel monocular ray-based 3D (Ray3D) absolute
human pose estimation with calibrated camera. Accurate and generalizable
absolute 3D human pose estimation from monocular 2D pose input is an ill-posed
problem. To address this challenge, we convert the input from pixel space to 3D
normalized rays. This conversion makes our approach robust to camera intrinsic
parameter changes. To deal with the in-the-wild camera extrinsic parameter
variations, Ray3D explicitly takes the camera extrinsic parameters as an input
and jointly models the distribution between the 3D pose rays and camera
extrinsic parameters. This novel network design is the key to the outstanding
generalizability of Ray3D approach. To have a comprehensive understanding of
how the camera intrinsic and extrinsic parameter variations affect the accuracy
of absolute 3D key-point localization, we conduct in-depth systematic
experiments on three single person 3D benchmarks as well as one synthetic
benchmark. These experiments demonstrate that our method significantly
outperforms existing state-of-the-art models. Our code and the synthetic
dataset are available at https://github.com/YxZhxn/Ray3D .


title: BigBird: Big Data Storage and Analytics at Scale in Hybrid Cloud

abstract:
  Implementing big data storage at scale is a complex and arduous task that
requires an advanced infrastructure. With the rise of public cloud computing,
various big data management services can be readily leveraged. As a critical
part of Twitter's "Project Partly Cloudy", the cold storage data and analytics
systems are being moved to the public cloud. This paper showcases our approach
in designing a scalable big data storage and analytics management framework
using BigQuery in Google Cloud Platform while ensuring security, privacy, and
data protection. The paper also discusses the limitations on the public cloud
resources and how they can be effectively overcome when designing a big data
storage and analytics solution at scale. Although the paper discusses the
framework implementation in Google Cloud Platform, it can easily be applied to
all major cloud providers.


title: Federated Class-Incremental Learning

abstract:
  Federated learning (FL) has attracted growing attention via data-private
collaborative training on decentralized clients. However, most existing methods
unrealistically assume object classes of the overall framework are fixed over
time. It makes the global model suffer from significant catastrophic forgetting
on old classes in real-world scenarios, where local clients often collect new
classes continuously and have very limited storage memory to store old classes.
Moreover, new clients with unseen new classes may participate in the FL
training, further aggravating the catastrophic forgetting of the global model.
To address these challenges, we develop a novel Global-Local Forgetting
Compensation (GLFC) model, to learn a global class incremental model for
alleviating the catastrophic forgetting from both local and global
perspectives. Specifically, to address local forgetting caused by class
imbalance at the local clients, we design a class-aware gradient compensation
loss and a class-semantic relation distillation loss to balance the forgetting
of old classes and distill consistent inter-class relations across tasks. To
tackle the global forgetting brought by the non-i.i.d class imbalance across
clients, we propose a proxy server that selects the best old global model to
assist the local relation distillation. Moreover, a prototype gradient-based
communication mechanism is developed to protect privacy. Our model outperforms
state-of-the-art methods by 4.4%-15.1% in terms of average accuracy on
representative benchmark datasets.


title: Remember Intentions: Retrospective-Memory-based Trajectory Prediction

abstract:
  To realize trajectory prediction, most previous methods adopt the
parameter-based approach, which encodes all the seen past-future instance pairs
into model parameters. However, in this way, the model parameters come from all
seen instances, which means a huge amount of irrelevant seen instances might
also involve in predicting the current situation, disturbing the performance.
To provide a more explicit link between the current situation and the seen
instances, we imitate the mechanism of retrospective memory in neuropsychology
and propose MemoNet, an instance-based approach that predicts the movement
intentions of agents by looking for similar scenarios in the training data. In
MemoNet, we design a pair of memory banks to explicitly store representative
instances in the training set, acting as prefrontal cortex in the neural
system, and a trainable memory addresser to adaptively search a current
situation with similar instances in the memory bank, acting like basal ganglia.
During prediction, MemoNet recalls previous memory by using the memory
addresser to index related instances in the memory bank. We further propose a
two-step trajectory prediction system, where the first step is to leverage
MemoNet to predict the destination and the second step is to fulfill the whole
trajectory according to the predicted destinations. Experiments show that the
proposed MemoNet improves the FDE by 20.3%/10.2%/28.3% from the previous best
method on SDD/ETH-UCY/NBA datasets. Experiments also show that our MemoNet has
the ability to trace back to specific instances during prediction, promoting
more interpretability.


title: The Brascamp-Lieb inequality and its influence on Fourier analysis

abstract:
  Brascamp-Lieb inequalities have been important in analysis, mathematical
physics and neighboring areas. Recently, these inequalities have had a deep
influence on Fourier analysis and, in particular, on Fourier restriction
theory. In this article we motivate and explain this connection. A lot of our
examples are taken from a rapidly developing subarea called "decoupling". It is
the author's hope that this article will be accessible to graduate students in
fields broadly related to analysis.


title:
Modeling speech recognition and synthesis simultaneously: Encoding and
  decoding lexical and sublexical semantic information into speech with no
  direct access to speech data

abstract:
  Human speakers encode information into raw speech which is then decoded by
the listeners. This complex relationship between encoding (production) and
decoding (perception) is often modeled separately. Here, we test how encoding
and decoding of lexical semantic information can emerge automatically from raw
speech in unsupervised generative deep convolutional networks that combine the
production and perception principles of speech. We introduce, to our knowledge,
the most challenging objective in unsupervised lexical learning: a network that
must learn unique representations for lexical items with no direct access to
training data. We train several models (ciwGAN and fiwGAN arXiv:2006.02951) and
test how the networks classify acoustic lexical items in unobserved test data.
Strong evidence in favor of lexical learning and a causal relationship between
latent codes and meaningful sublexical units emerge. The architecture that
combines the production and perception principles is thus able to learn to
decode unique information from raw acoustic data without accessing real
training data directly. We propose a technique to explore lexical (holistic)
and sublexical (featural) learned representations in the classifier network.
The results bear implications for unsupervised speech technology, as well as
for unsupervised semantic modeling as language models increasingly bypass text
and operate from raw acoustics.


title:
Strong comparison principle for a p-Laplace equation involving
  singularity and its applications

abstract:
  In this paper we prove a strong comparison principle for radially decreasing
solutions $u,v\in C_{0}^{1,\alpha}(\Bar{B_R})$ of the singular equations
$-\Delta_p u-\frac{1}{u^\delta}=f(x)$ and $-\Delta_p v-\frac{1}{v^\delta}=g(x)$
in $B_R$. Here we assume that $ 1<p<2 , \; \delta\in (0,1)$ and $f,g$ are
continuous, radial functions such that $0 \leq f \leq g$ but $f\not \equiv g$
in $B_R.$ For the case $p>2$ a counterexample is provided where the strong
comparison principle is violated. As an application of strong comparison
principle, we prove a three solution theorem for p-Laplace equation and
illustrate with an example.


title: On the arithmetic of polynomial semidomains

abstract:
  A subset $S$ of an integral domain $R$ is called a semidomain provided that
the pairs $(S,+)$ and $(S, \cdot)$ are semigroups with identities. The study of
factorizations in integral domains was initiated by D. D. Anderson, D. F.
Anderson, and M. Zafrullah in 1990, and this area has been systematically
investigated since then. In this paper, we study the divisibility and
arithmetic of factorizations in the more general context of semidomains. We are
specially concerned with the ascent of the most standard divisibility and
factorization properties from a semidomain to its semidomain of (Laurent)
polynomials. As in the case of integral domains, here we prove that the
properties of satisfying ACCP, having bounded factorizations, and having finite
factorizations ascend in the class of semidomains. We also consider the ascent
of the property of being atomic, and we show that the property of having unique
factorization ascends only when a semidomain is an integral domain. Throughout
the paper we provide several examples aiming to shed some light upon the
arithmetic of factorizations of semidomains.


title:
How Interest-Driven Content Creation Shapes Opportunities for Informal
  Learning in Scratch: A Case Study on Novices' Use of Data Structures

abstract:
  Through a mixed-method analysis of data from Scratch, we examine how novices
learn to program with simple data structures by using community-produced
learning resources. First, we present a qualitative study that describes how
community-produced learning resources create archetypes that shape exploration
and may disadvantage some with less common interests. In a second quantitative
study, we find broad support for this dynamic in several hypothesis tests. Our
findings identify a social feedback loop that we argue could limit sources of
inspiration, pose barriers to broadening participation, and confine learners'
understanding of general concepts. We conclude by suggesting several approaches
that may mitigate these dynamics.


title: WuDaoMM: A large-scale Multi-Modal Dataset for Pre-training models

abstract:
  Compared with the domain-specific model, the vision-language pre-training
models (VLPMs) have shown superior performance on downstream tasks with fast
fine-tuning process. For example, ERNIE-ViL, Oscar and UNIMO trained VLPMs with
a uniform transformers stack architecture and large amounts of image-text
paired data, achieving remarkable results on downstream tasks such as
image-text reference(IR and TR), vision question answering (VQA) and image
captioning (IC) etc. During the training phase, VLPMs are always fed with a
combination of multiple public datasets to meet the demand of large-scare
training data. However, due to the unevenness of data distribution including
size, task type and quality, using the mixture of multiple datasets for model
training can be problematic. In this work, we introduce a large-scale
multi-modal corpora named WuDaoMM, totally containing more than 650M image-text
pairs. Specifically, about 600 million pairs of data are collected from
multiple webpages in which image and caption present weak correlation, and the
other 50 million strong-related image-text pairs are collected from some
high-quality graphic websites. We also release a base version of WuDaoMM with 5
million strong-correlated image-text pairs, which is sufficient to support the
common cross-modal model pre-training. Besides, we trained both an
understanding and a generation vision-language (VL) model to test the dataset
effectiveness. The results show that WuDaoMM can be applied as an efficient
dataset for VLPMs, especially for the model in text-to-image generation task.
The data is released at https://data.wudaoai.cn


title: Mixed Differential Privacy in Computer Vision

abstract:
  We introduce AdaMix, an adaptive differentially private algorithm for
training deep neural network classifiers using both private and public image
data. While pre-training language models on large public datasets has enabled
strong differential privacy (DP) guarantees with minor loss of accuracy, a
similar practice yields punishing trade-offs in vision tasks. A few-shot or
even zero-shot learning baseline that ignores private data can outperform
fine-tuning on a large private dataset. AdaMix incorporates few-shot training,
or cross-modal zero-shot learning, on public data prior to private fine-tuning,
to improve the trade-off. AdaMix reduces the error increase from the
non-private upper bound from the 167-311\% of the baseline, on average across 6
datasets, to 68-92\% depending on the desired privacy level selected by the
user. AdaMix tackles the trade-off arising in visual classification, whereby
the most privacy sensitive data, corresponding to isolated points in
representation space, are also critical for high classification accuracy. In
addition, AdaMix comes with strong theoretical privacy guarantees and
convergence analysis.


title:
Widely-tunable optical parametric oscillator in lithium niobate
  nanophotonics

abstract:
  Widely-tunable coherent sources in nanophotonics are desirable for a
multitude of applications ranging from communications to sensing. The
mid-infrared spectral region (wavelengths beyond 2 $\mu$m) is particularly
important for applications relying on molecular spectroscopy. Among tunable
sources, optical parametric oscillators typically offer some of the broadest
tuning ranges; however, their implementations in nanophotonics have been
limited to modest tuning ranges and only at visible and near-infrared
wavelengths. Here, we surpass these limits by demonstrating octave-spanning
tunable optical parametric oscillators in dispersion-engineered
periodically-poled lithium niobate nanophotonics. With a pump wavelength near 1
$\mu$m, we generate output wavelengths tunable from 1.53 $\mu$m to 3.25 $\mu$m,
including common telecommunication bands and into the mid-infrared. Our results
enable opportunities for numerous integrated photonic applications requiring
compact tunable sources.


title:
Practical Stereo Matching via Cascaded Recurrent Network with Adaptive
  Correlation

abstract:
  With the advent of convolutional neural networks, stereo matching algorithms
have recently gained tremendous progress. However, it remains a great challenge
to accurately extract disparities from real-world image pairs taken by
consumer-level devices like smartphones, due to practical complicating factors
such as thin structures, non-ideal rectification, camera module inconsistencies
and various hard-case scenes. In this paper, we propose a set of innovative
designs to tackle the problem of practical stereo matching: 1) to better
recover fine depth details, we design a hierarchical network with recurrent
refinement to update disparities in a coarse-to-fine manner, as well as a
stacked cascaded architecture for inference; 2) we propose an adaptive group
correlation layer to mitigate the impact of erroneous rectification; 3) we
introduce a new synthetic dataset with special attention to difficult cases for
better generalizing to real-world scenes. Our results not only rank 1st on both
Middlebury and ETH3D benchmarks, outperforming existing state-of-the-art
methods by a notable margin, but also exhibit high-quality details for
real-life photos, which clearly demonstrates the efficacy of our contributions.


title: A Virtual Point Light Generation Method in Close-Range Area

abstract:
  This paper proposes a new hybrid algorithm for sampling virtual point light
(VPL). The indirect lighting calculation of the scene is used to distribute the
VPL reasonably. In the process of generating VPL, we divide the scene into two
parts according to the camera position and orientation. The close-range part:
the part that the camera pays attention to. The distant-range part: the part
that the camera does not pay attention to or rarely pays attention to. For the
close-range part, we use a patch-based vPL sampling method to distribute the
VPL as evenly as possible on the patch in the near-field area; for the
distant-range part, we use sparse instant radiosity (IR) for sampling. It turns
out that, in contrast to conventional multiple instant radiance Compared with
the VPL generation algorithm, the method proposed in this paper can greatly
improve the quality of the final result graph when the number of VPLs is the
same; Under the same rendering quality, the rendering speed can be greatly
improved.


title:
On the $L^2$ Rate of Convergence in the Limit from the Hartree to the
  Vlasov$\unicode{x2013}$Poisson Equation

abstract:
  Using a new stability estimate for the difference of the square roots of two
solutions of the Vlasov$\unicode{x2013}$Poisson equation, we obtain the
convergence in the $L^2$ norm of the Wigner transform of a solution of the
Hartree equation with Coulomb potential to a solution of the
Vlasov$\unicode{x2013}$Poisson equation, with a rate of convergence
proportional to $\hbar$. This improves the $\hbar^{3/4-\varepsilon}$ rate of
convergence in $L^2$ obtained in [L.~Lafleche, C.~Saffirio: Analysis & PDE, to
appear]. Another reason of interest of this paper is the new method,
reminiscent of the ones used to prove the mean-field limit from the many-body
Schr\"odinger equation towards the Hartree$\unicode{x2013}$Fock equation for
mixed states.


title:
Approaches for Improving the Performance of Fake News Detection in
  Bangla: Imbalance Handling and Model Stacking

abstract:
  Imbalanced datasets can lead to biasedness into the detection of fake news.
In this work, we present several strategies for resolving the imbalance issue
for fake news detection in Bangla with a comparative assessment of proposed
methodologies. Additionally, we propose a technique for improving performance
even when the dataset is imbalanced. We applied our proposed approaches to
BanFakeNews, a dataset developed for the purpose of detecting fake news in
Bangla comprising of 50K instances but is significantly skewed, with 97% of
majority instances. We obtained a 93.1% F1-score using data manipulation
manipulation techniques such as SMOTE, and a 79.1% F1-score using without data
manipulation approaches such as Stacked Generalization. Without implementing
these techniques, the F1-score would have been 67.6% for baseline models. We
see this work as an important step towards paving the way of fake news
detection in Bangla. By implementing these strategies the obstacles of
imbalanced dataset can be removed and improvement in the performance can be
achieved.


title:
Overview and Applications of GPGPU Based Parallel Ant Colony
  Optimization

abstract:
  Ant Colony Optimization algorithm is a magnificent heuristics technique based
on the behavior of ants. Parallel computing is a means to achieve the desired
results in commensurable execution time. Parallelization of Ant Colony
Optimization is utilized to solve large and complex problems. This paper
discusses a review of different parallelization approaches for Ant Colony
Optimization and its various applications. Parallel Ant Colony Optimization has
proved to be a successful approach for highly constrained problems such as
routing, scheduling, timetabling, etc. Parallelization of Ant Colony
Optimization reduces the execution time, increases the size of the problem,
etc.


title: Derived Zeta Functions for Curves over Finite Fields

abstract:
  For each $(m+1)$-tuple ${\bf n}_m=(n_0,n_1,\ldots,n_m)$ of positive integers,
the ${\bf n}_m$-derived zeta function $\widehat\zeta_{X,\mathbb F_q}^{\,({\bf
n}_m)}(s)$ is defined for a curve $X$ over $\mathbb F_q$. This derived zeta
function satisfies standard zeta properties. In particular, similar to the
Artin Zeta function of $X/\mathbb F_q$, this ${\bf n}_m$-derived Zeta function
of $X$ over $\mathbb F_q$ is a ratio of a degree $2g$ polynomial $P_{X,\mathbb
F_q}^{({\bf n}_m)}$ in $T_{{\bf n}_m}=q^{-s\prod_{k=0}^mn_k}$ by $(1-T_{{\bf
n}_m})(1-q_{{\bf n}_m}T_{{\bf n}_m})T_{{\bf n}_m}^{g-1}$ with $q_{{\bf
n}_m}=q^{\prod_{k=0}^mn_k}$. Indeed, we have $$\begin{aligned} &\widehat
\zeta_{X,\mathbb F_q}^{\,({\bf n}_{m})}(s)=\widehat Z_{X,\mathbb F_q}^{\,({\bf
n}_{m})}(T_{{\bf n}_{m}})\\ =& \left(\sum_{\ell=0}^{g-2}\alpha_{X,\mathbb
F_q}^{({\bf n}_{m})}(\ell)\Big(T_{{\bf n}_{m}}^{\ell-(g-1)}+q_{{\bf
n}_{m}}^{(g-1)-\ell}T_{{\bf n}_{m}}^{(g-1)-\ell}\Big) +\alpha_{X,\mathbb
F_q}^{({\bf n}_{m})}(g-1))\Big)\right)+\frac{(q_{{\bf n}_{m}}-1)T_{{\bf
n}_{m}}\beta_{X,\mathbb F_q}^{({\bf n}_{m})}}{(1-T_{{\bf n}_{m}})(1-q_{{\bf
n}_{m}}T_{{\bf n}_{m}})}\\ \end{aligned}$$ for some ${\bf n}_m$-derived alpha
and beta invariants of $X/\mathbb F_q$. Furthermore, when $X$ restrict to an
elliptic curve, or when ${\bf n}_m=(2,2,\ldots 2)$, established is the ${\bf
n}_m$-derived Riemann hypothesis claiming that all zeros of $\widehat
\zeta_{X,\mathbb F_q}^{\,({\bf n}_{m})}(s)$ lie on the central line
$\Re(s)=\frac{1}{2}$. In addition, formulated is the Positivity Conjecture
claiming that the above ${\bf n}_m$-derived alpha and beta invariants are all
strict positivity.


title:
A Note on Target Q-learning For Solving Finite MDPs with A Generative
  Oracle

abstract:
  Q-learning with function approximation could diverge in the off-policy
setting and the target network is a powerful technique to address this issue.
In this manuscript, we examine the sample complexity of the associated target
Q-learning algorithm in the tabular case with a generative oracle. We point out
a misleading claim in [Lee and He, 2020] and establish a tight analysis. In
particular, we demonstrate that the sample complexity of the target Q-learning
algorithm in [Lee and He, 2020] is $\widetilde{\mathcal O}(|\mathcal
S|^2|\mathcal A|^2 (1-\gamma)^{-5}\varepsilon^{-2})$. Furthermore, we show that
this sample complexity is improved to $\widetilde{\mathcal O}(|\mathcal
S||\mathcal A| (1-\gamma)^{-5}\varepsilon^{-2})$ if we can sequentially update
all state-action pairs and $\widetilde{\mathcal O}(|\mathcal S||\mathcal A|
(1-\gamma)^{-4}\varepsilon^{-2})$ if $\gamma$ is further in $(1/2, 1)$.
Compared with the vanilla Q-learning, our results conclude that the
introduction of a periodically-frozen target Q-function does not sacrifice the
sample complexity.


title:
SSD-KD: A Self-supervised Diverse Knowledge Distillation Method for
  Lightweight Skin Lesion Classification Using Dermoscopic Images

abstract:
  Skin cancer is one of the most common types of malignancy, affecting a large
population and causing a heavy economic burden worldwide. Over the last few
years, computer-aided diagnosis has been rapidly developed and make great
progress in healthcare and medical practices due to the advances in artificial
intelligence. However, most studies in skin cancer detection keep pursuing high
prediction accuracies without considering the limitation of computing resources
on portable devices. In this case, knowledge distillation (KD) has been proven
as an efficient tool to help improve the adaptability of lightweight models
under limited resources, meanwhile keeping a high-level representation
capability. To bridge the gap, this study specifically proposes a novel method,
termed SSD-KD, that unifies diverse knowledge into a generic KD framework for
skin diseases classification. Our method models an intra-instance relational
feature representation and integrates it with existing KD research. A dual
relational knowledge distillation architecture is self-supervisedly trained
while the weighted softened outputs are also exploited to enable the student
model to capture richer knowledge from the teacher model. To demonstrate the
effectiveness of our method, we conduct experiments on ISIC 2019, a large-scale
open-accessed benchmark of skin diseases dermoscopic images. Experiments show
that our distilled lightweight model can achieve an accuracy as high as 85% for
the classification tasks of 8 different skin diseases with minimal parameters
and computing requirements. Ablation studies confirm the effectiveness of our
intra- and inter-instance relational knowledge integration strategy. Compared
with state-of-the-art knowledge distillation techniques, the proposed method
demonstrates improved performances for multi-diseases classification on the
large-scale dermoscopy database.


title:
Making Recommender Systems Forget: Learning and Unlearning for Erasable
  Recommendation

abstract:
  Privacy laws and regulations enforce data-driven systems, e.g., recommender
systems, to erase the data that concern individuals. As machine learning models
potentially memorize the training data, data erasure should also unlearn the
data lineage in models, which raises increasing interest in the problem of
Machine Unlearning (MU). However, existing MU methods cannot be directly
applied into recommendation. The basic idea of most recommender systems is
collaborative filtering, but existing MU methods ignore the collaborative
information across users and items. In this paper, we propose a general
erasable recommendation framework, namely LASER, which consists of Group module
and SeqTrain module. Firstly, Group module partitions users into balanced
groups based on their similarity of collaborative embedding learned via
hypergraph. Then SeqTrain module trains the model sequentially on all groups
with curriculum learning. Both theoretical analysis and experiments on two
real-world datasets demonstrate that LASER can not only achieve efficient
unlearning, but also outperform the state-of-the-art unlearning framework in
terms of model utility.


title: Exploring High-Order Structure for Robust Graph Structure Learning

abstract:
  Recent studies show that Graph Neural Networks (GNNs) are vulnerable to
adversarial attack, i.e., an imperceptible structure perturbation can fool GNNs
to make wrong predictions. Some researches explore specific properties of clean
graphs such as the feature smoothness to defense the attack, but the analysis
of it has not been well-studied. In this paper, we analyze the adversarial
attack on graphs from the perspective of feature smoothness which further
contributes to an efficient new adversarial defensive algorithm for GNNs. We
discover that the effect of the high-order graph structure is a smoother filter
for processing graph structures. Intuitively, the high-order graph structure
denotes the path number between nodes, where larger number indicates closer
connection, so it naturally contributes to defense the adversarial
perturbation. Further, we propose a novel algorithm that incorporates the
high-order structural information into the graph structure learning. We perform
experiments on three popular benchmark datasets, Cora, Citeseer and Polblogs.
Extensive experiments demonstrate the effectiveness of our method for defending
against graph adversarial attacks.


title:
FrameHopper: Selective Processing of Video Frames in Detection-driven
  Real-Time Video Analytics

abstract:
  Detection-driven real-time video analytics require continuous detection of
objects contained in the video frames using deep learning models like YOLOV3,
EfficientDet. However, running these detectors on each and every frame in
resource-constrained edge devices is computationally intensive. By taking the
temporal correlation between consecutive video frames into account, we note
that detection outputs tend to be overlapping in successive frames. Elimination
of similar consecutive frames will lead to a negligible drop in performance
while offering significant performance benefits by reducing overall computation
and communication costs. The key technical questions are, therefore, (a) how to
identify which frames to be processed by the object detector, and (b) how many
successive frames can be skipped (called skip-length) once a frame is selected
to be processed. The overall goal of the process is to keep the error due to
skipping frames as small as possible. We introduce a novel error vs processing
rate optimization problem with respect to the object detection task that
balances between the error rate and the fraction of frames filtering.
Subsequently, we propose an off-line Reinforcement Learning (RL)-based
algorithm to determine these skip-lengths as a state-action policy of the RL
agent from a recorded video and then deploy the agent online for live video
streams. To this end, we develop FrameHopper, an edge-cloud collaborative video
analytics framework, that runs a lightweight trained RL agent on the camera and
passes filtered frames to the server where the object detection model runs for
a set of applications. We have tested our approach on a number of live videos
captured from real-life scenarios and show that FrameHopper processes only a
handful of frames but produces detection results closer to the oracle solution
and outperforms recent state-of-the-art solutions in most cases.


title:
Physical link of the polar field build-up with the Waldmeier effect
  broadens the scope of early solar cycle prediction: Cycle 25 is likely to be
  stronger than Cycle 24

abstract:
  Prediction of the solar cycle is challenging but essential because it drives
space weather. Several predictions with varying amplitudes of the ongoing
Cycle~25 have been made. We show that an aspect of the Waldmeier effect (WE2),
i.e., a strong positive correlation between the rise rate and the amplitude of
the cycle, has a physical link with the build-up of the previous cycle's polar
field after its reversal. We find that the rise rate of the polar field is
highly correlated with the rise rate and the amplitude of the next solar cycle.
Thus, the prediction of the amplitude of the solar cycle can be made just a few
years after the reversal of the previous cycle's polar field, thereby extending
the scope of the solar cycle prediction to much earlier than the usual time.
Our prediction of Cycle 25 based on the rise rate of the previous polar field
is $137\pm 23$, which is quite close to the prediction $138\pm 26$ based on the
WE2 computed from the available 2 years sunspot data of the ongoing cycle.


title: Radiation from a Classical Harmonic Oscillator

abstract:
  This article presents the calculation of gravitational and electromagnetic
radiation emitted from a classical simple harmonic oscillator (SHO). Here we
show only the selected formulae and apply them to a toy problem without
rigorous derivation. First, we calculate the explicit expressions for the
gravitational waves polarizations and then obtain the power radiated away in
gravitational waves using Brans-Dicke theory (BD). We also calculate the
electromagnetic dipole and quadrupole power emitted by the SHO and compare them
with their gravitational counterparts.


title:
TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with
  Transformers

abstract:
  LiDAR and camera are two important sensors for 3D object detection in
autonomous driving. Despite the increasing popularity of sensor fusion in this
field, the robustness against inferior image conditions, e.g., bad illumination
and sensor misalignment, is under-explored. Existing fusion methods are easily
affected by such conditions, mainly due to a hard association of LiDAR points
and image pixels, established by calibration matrices. We propose TransFusion,
a robust solution to LiDAR-camera fusion with a soft-association mechanism to
handle inferior image conditions. Specifically, our TransFusion consists of
convolutional backbones and a detection head based on a transformer decoder.
The first layer of the decoder predicts initial bounding boxes from a LiDAR
point cloud using a sparse set of object queries, and its second decoder layer
adaptively fuses the object queries with useful image features, leveraging both
spatial and contextual relationships. The attention mechanism of the
transformer enables our model to adaptively determine where and what
information should be taken from the image, leading to a robust and effective
fusion strategy. We additionally design an image-guided query initialization
strategy to deal with objects that are difficult to detect in point clouds.
TransFusion achieves state-of-the-art performance on large-scale datasets. We
provide extensive experiments to demonstrate its robustness against degenerated
image quality and calibration errors. We also extend the proposed method to the
3D tracking task and achieve the 1st place in the leaderboard of nuScenes
tracking, showing its effectiveness and generalization capability.


title:
Repulsive gravity effects in horizon formation. Horizon remnants in
  naked singularities

abstract:
  Repulsive gravity is a well known characteristic of naked singularities. In
this work, we explore light surfaces and find new effects of repulsive gravity.
We compare Kerr naked singularities with the corresponding black hole
counterparts and find certain structures that are identified as horizon
remnants. We argue that these features might be significant for the
comprehension of processes that lead to the formation or eventually destruction
of black hole Killing horizons. These features can be detected by observing
photon orbits, particularly close to the rotation axis, which can be used to
distinguish naked singularities from black hole.


title:
Chebotarev-Sato-Tate distribution for abelian surfaces potentially of
  $\rm{GL}_2$-type

abstract:
  In this paper, we state a hybrid Chebotarev-Sato-Tate conjecture for abelian
varieties and we prove it in several particular cases using current potential
automorphy theorems.


title: Residual-Guided Non-Intrusive Speech Quality Assessment

abstract:
  This paper proposes an approach to improve Non-Intrusive speech quality
assessment(NI-SQA) based on the residuals between impaired speech and enhanced
speech. The difficulty in our task is particularly lack of information, for
which the corresponding reference speech is absent. We generate an enhanced
speech on the impaired speech to compensate for the absence of the reference
audio, then pair the information of residuals with the impaired speech.
Compared to feeding the impaired speech directly into the model, residuals
could bring some extra helpful information from the contrast in enhancement.
The human ear is sensitive to certain noises but different to deep learning
model. Causing the Mean Opinion Score(MOS) the model predicted is not enough to
fit our subjective sensitive well and causes deviation. These residuals have a
close relationship to reference speech and then improve the ability of the deep
learning models to predict MOS. During the training phase, experimental results
demonstrate that paired with residuals can quickly obtain better evaluation
indicators under the same conditions. Furthermore, our final results improved
31.3 percent and 14.1 percent, respectively, in PLCC and RMSE.


title:
Joint Noise Reduction and Listening Enhancement for Full-End Speech
  Enhancement

abstract:
  Speech enhancement (SE) methods mainly focus on recovering clean speech from
noisy input. In real-world speech communication, however, noises often exist in
not only speaker but also listener environments. Although SE methods can
suppress the noise contained in the speaker's voice, they cannot deal with the
noise that is physically present in the listener side. To address such a
complicated but common scenario, we investigate a deep learning-based joint
framework integrating noise reduction (NR) with listening enhancement (LE), in
which the NR module first suppresses noise and the LE module then modifies the
denoised speech, i.e., the output of the NR module, to further improve speech
intelligibility. The enhanced speech can thus be less noisy and more
intelligible for listeners. Experimental results show that our proposed method
achieves promising results and significantly outperforms the disjoint
processing methods in terms of various speech evaluation metrics.


title: Unified thermodynamic-kinetic uncertainty relation

abstract:
  Understanding current fluctuations is of fundamental importance and paves the
way for the development of practical applications. According to the
thermodynamic and kinetic uncertainty relations, the precision of currents can
be constrained solely by total entropy production or dynamical activity. In
this study, we derive a tighter bound on the precision of currents in terms of
both thermodynamic and kinetic quantities, demonstrating that these quantities
jointly constrain current fluctuations. The thermodynamic and kinetic
uncertainty relations become particular cases of our result in asymptotic
limits. Intriguingly, the unified thermodynamic-kinetic uncertainty relation
leads to a tighter classical speed limit, refining the time constraint on the
system's state transformation. The proposed framework can be extended to apply
to state observables and systems with unidirectional transitions, thereby
providing a constraint on the precision of the first-passage time.


title:
Phenomenological modelling of the Crab Nebula's broad-band energy
  spectrum and its apparent extension

abstract:
  The Crab Nebula emits bright non-thermal radiation from radio to the most
energetic photons. The underlying physical model of a relativistic wind from
the pulsar terminating in a hydrodynamic standing shock remains unchanged since
the early 1970s. In this model, an increase of the toroidal magnetic field
downstream from the shock is expected. We introduce a detailed radiative model
to calculate non-thermal synchrotron and inverse Compton as well as thermal
dust emission self-consistently to compare quantitatively with observational
data. Special care is given to the radial dependence of electron and seed field
density. The radiative model is used to estimate the parameters of electrons
and dust in the nebula. A combined fit based upon a $\chi^2$ minimisation
reproduces successfully the complete data set used. For the best-fitting model,
the energy density of the magnetic field dominates over the particle energy
density up to a distance of $\approx 1.3~r_s$ ($r_s$: distance of the
termination shock from the pulsar). The very high energy (VHE: $E>100$ GeV)
gamma-ray spectra set the strongest constraints on the radial dependence of the
magnetic field: $B(r)=(264\pm9)~\mu\mathrm{G} (r/r_s)^{-0.51\pm0.03}$. The
reconstructed magnetic field and its radial dependence indicates a ratio of
Poynting to kinetic energy flux $\sigma\approx 0.1$ at the termination shock,
$\approx 30$ times larger than estimated up to now. Consequently, the
confinement of the nebula would require additional mechanisms to slow down the
flow through e.g. excitation of small-scale turbulence with possible
dissipation of magnetic field.


abstract:
  We study the geometry of $\mathcal{Q}$-conic arrangements in the complex
projective plane. These are arrangements consisting of smooth conics and they
admit certain quasi-homogeneous singularities. We show, to our surprise, that
such $\mathcal{Q}$-conic arrangements are never free. Moreover, we provide
combinatorial constraints of the weak combinatorics of such arrangements.


title:
On the behaviour of the interquark potential in the vicinity of the
  deconfinement transition

abstract:
  In the vicinity of the deconfinement transition the behaviour of the
interquark potential can be precisely predicted using the Effective String
Theory (EST). If the transition is continuous we can combine EST results with a
conformal perturbation analysis and reach the degree of precision needed to
detect the corrections beyond the Nambu-Goto approximation in the EST. We
discuss in detail this issue in the case of the deconfinement transition of the
SU(2) gauge theory in $(2+1)$ dimensions (which belongs to the same
universality class of the 2d Ising model) by means of an extensive set of high
precision simulations. We show that the Polyakov loops correlator of the SU(2)
model is precisely described by the spin-spin correlator of the 2d Ising model
not only at the critical point, but also down to temperatures of the order of
$0.8 T_c$. Thanks to the exact integrability of the Ising model we can extend
the comparison in the whole range of Polyakov loop separations, even beyond the
conformal perturbation regime. We use these results to quantify the first EST
correction beyond Nambu-Goto and show that it is compatible with the bounds
imposed by a bootstrap analysis of EST. This correction encodes important
physical information and may shed light on the nature of the flux tube and of
its EST description.


title:
Four-hundred Very Metal-poor Stars studied with LAMOST and Subaru. I.
  Survey Design, Follow-up Program, and Binary Frequency

abstract:
  The chemical abundances of very metal-poor stars provide important
constraints on the nucleosynthesis of the first generation of stars and early
chemical evolution of the Galaxy. We have obtained high-resolution spectra with
the Subaru Telescope for candidates of very metal-poor stars selected with a
large survey of Galactic stars carried out with LAMOST. In this series of
papers, we report on the elemental abundances of about 400 very metal-poor
stars and discuss the kinematics of the sample obtained by combining the radial
velocities measured in this study and recent astrometry obtained with Gaia.
This paper provides an overview of our survey and follow-up program, and
reports radial velocities for the whole sample. We identify seven double-lined
spectroscopic binaries from our high-resolution spectra, for which radial
velocities of the components are reported. We discuss the frequency of such
relatively short-period binaries at very low metallicity.


title: Rebalanced Siamese Contrastive Mining for Long-Tailed Recognition

abstract:
  Deep neural networks perform poorly on heavily class-imbalanced datasets.
Given the promising performance of contrastive learning, we propose
$\mathbf{Re}$balanced $\mathbf{S}$iamese $\mathbf{Co}$ntrastive
$\mathbf{m}$ining ( $\mathbf{ResCom}$) to tackle imbalanced recognition. Based
on the mathematical analysis and simulation results, we claim that supervised
contrastive learning suffers a dual class-imbalance problem at both the
original batch and Siamese batch levels, which is more serious than long-tailed
classification learning. In this paper, at the original batch level, we
introduce a class-balanced supervised contrastive loss to assign adaptive
weights for different classes. At the Siamese batch level, we present a
class-balanced queue, which maintains the same number of keys for all classes.
Furthermore, we note that the contrastive loss gradient with respect to the
contrastive logits can be decoupled into the positives and negatives, and easy
positives and easy negatives will make the contrastive gradient vanish. We
propose supervised hard positive and negative pairs mining to pick up
informative pairs for contrastive computation and improve representation
learning. Finally, to approximately maximize the mutual information between the
two views, we propose Siamese Balanced Softmax and joint it with the
contrastive loss for one-stage training. ResCom outperforms the previous
methods by large margins on multiple long-tailed recognition benchmarks. Our
code will be made publicly available at:
https://github.com/dvlab-research/ResCom.


title:
Enhancement of Ce and Cr Codopant Solubility and Chemical Homogeneity in
  TiO2 Nanoparticles through Sol Gel versus Pechini Syntheses

abstract:
  Ce/Cr codoped TiO2 nanoparticles were synthesized using sol gel and Pechini
methods with heat treatment at 400 C for 4 h. A conventional sol gel process
produced well-crystallized anatase, while Pechini synthesis yielded less
ordered mixed-phase anatase and rutile; this suggests that the latter method
enhances Ce solubility and increases chemical homogeneity but destabilizes the
TiO2 lattice. Greater structural disruption from the decomposition of the
Pechini precursor formed more open agglomerated morphologies, while the lower
levels of structural disruption from pyrolysis of the dried sol gel precursor
resulted in denser agglomerates of lower surface areas. Codoping and associated
destabilization of the lattice reduced the binding energies in both powders.
Cr4+ formation in sol gel powders and Cr6+ formation in Pechini powders suggest
that these valence changes derive from synergistic electron exchange from
intervalence and/or multivalence charge transfer. Since Ce is too large to
allow either substitutional or interstitial solid solubility, the concept of
integrated solubility is introduced, in which the Ti site and an adjacent
interstice are occupied by the large Ce ion. The photocatalytic performance
data show that codoping was detrimental owing to the effects of reduced
crystallinity from lattice destabilization and surface area. Two regimes of
mechanistic behavior are seen, which are attributed to the unsaturated solid
solutions at lower codopant levels and supersaturated solid solutions at higher
levels. The present work demonstrates that the Pechini method offers a
processing technique that is superior to sol gel because the former facilitates
solid solubility and consequent chemical homogeneity.


title: Policing and Gatekeeping in STEM

abstract:
  The purpose of this white paper is to lay out the impacts of policing and
gatekeeping in STEM, illustrated with lived experiences of scientists of color
who are achieving despite the daunting challenges they face.


title: Unsupervised Deraining: Where Contrastive Learning Meets Self-similarity

abstract:
  Image deraining is a typical low-level image restoration task, which aims at
decomposing the rainy image into two distinguishable layers: the clean image
layer and the rain layer. Most of the existing learning-based deraining methods
are supervisedly trained on synthetic rainy-clean pairs. The domain gap between
the synthetic and real rains makes them less generalized to different real
rainy scenes. Moreover, the existing methods mainly utilize the property of the
two layers independently, while few of them have considered the mutually
exclusive relationship between the two layers. In this work, we propose a novel
non-local contrastive learning (NLCL) method for unsupervised image deraining.
Consequently, we not only utilize the intrinsic self-similarity property within
samples but also the mutually exclusive property between the two layers, so as
to better differ the rain layer from the clean image. Specifically, the
non-local self-similarity image layer patches as the positives are pulled
together and similar rain layer patches as the negatives are pushed away. Thus
the similar positive/negative samples that are close in the original space
benefit us to enrich more discriminative representation. Apart from the
self-similarity sampling strategy, we analyze how to choose an appropriate
feature encoder in NLCL. Extensive experiments on different real rainy datasets
demonstrate that the proposed method obtains state-of-the-art performance in
real deraining.


title:
Continuous Optimization for Control of Hybrid Systems with Hysteresis
  via Time-Freezing

abstract:
  This article regards numerical optimal control of a class of hybrid systems
with hysteresis using solely techniques from nonlinear optimization, without
any integer variables. Hysteresis is a rate independent memory effect which
often results in severe nonsmoothness in the dynamics. These systems are not
simply Piecewise Smooth Systems (PSS); they are a more complicated form of
hybrid systems. We introduce a time-freezing reformulation which transforms
these systems into a PSS. From the theoretical side, this reformulation opens
the door to study systems with hysteresis via the rich tools developed for
Filippov systems. From the practical side, it enables the use of the recently
developed Finite Elements with Switch Detection [Nurkanovic et al., 2022],
which makes high accuracy numerical optimal control of hybrid systems with
hysteresis possible.


title:
Frequency perturbation theory of bound states in the continuum in a
  periodic waveguide

abstract:
  In a lossless periodic structure, a bound state in the continuum (BIC) is
characterized by a real frequency and a real Bloch wavevector for which there
exist waves propagating to or from infinity in the surrounding media. For
applications, it is important to analyze the high-$Q$ resonances that either
exist naturally for wavevectors near that of the BIC or appear when the
structure is perturbed. Existing theories provide quantitative results for the
complex frequency (and the $Q$-factor) of resonant modes that appear/exist due
to structural perturbations or wavevector variations. When a periodic structure
is regarded as a periodic waveguide, eigenmodes are often analyzed for a given
real frequency. In this paper, we consider periodic waveguides with a BIC, and
study the eigenmodes for given real frequencies near the frequency of the BIC.
It turns out that such eigenmodes near the BIC always have a complex Bloch
wavenumber, but they may or may not be leaky modes that radiate out power
laterally to infinity. These eigenmodes can also be complex modes that decay
exponentially in the lateral direction. Our study is relevant for applications
of BICs in optical waveguides, and it is also helpful for analyzing photonic
devices operating near the frequency of a BIC.


title:
Gradient Vector Fields of Discrete Morse Functions are Minimum Spanning
  Forests

abstract:
  In this paper, we prove that discrete Morse functions are equivalent to
simplicial stacks under reasonable constraints. We also show that, as in
Discrete Morse Theory, we can see the GVF of a simplicial stack (seen as a
discrete Morse function) as the only relevant information we should consider.
Last, but not least, we prove that the Minimum Spanning Forest on the dual
graph of a simplicial stack (or a discrete Morse function) is equal to the GVF
of the initial function. In other words, the GVF of a discrete Morse function
is related to a classic combinatorial minimization problem. This paper is the
sequel of a sequence of papers showing that strong relations exist between
different domains: Topology, Discrete Morse Theory, Topological Data Analysis
and Mathematical Morphology.


title: Power Dynamics in Physics

abstract:
  The purpose of this white paper is to describe how unfair power dynamics
related to various aspects of identity -- race, gender identity, gender
expression, sexual orientation, and ability status -- operate in physics
settings and offer concrete steps that one can take to make our discipline more
equitable and just.


title:
New penalized criteria for smooth non-negative tensor factorization with
  missing entries

abstract:
  Tensor factorization models are widely used in many applied fields such as
chemometrics, psychometrics, computer vision or communication networks. Real
life data collection is often subject to errors, resulting in missing data.
Here we focus in understanding how this issue should be dealt with for
nonnegative tensor factorization. We investigate several criteria used for
non-negative tensor factorization in the case where some entries are missing.
In particular we show how smoothness penalties can compensate the presence of
missing values in order to ensure the existence of an optimum. This lead us to
propose new criteria with efficient numerical optimization algorithms.
Numerical experiments are conducted to support our claims.


title:
Heat kernel and gradient estimates for kinetic SDEs with low regularity
  coefficients

abstract:
  We establish heat kernel and gradient estimates for the density of kinetic
degenerate Kolmogorov stochastic differentia equations. Our results are
established under somehow minimal assumptions that guarantee the SDE is weakly
well posed.


title:
NOS-NOC: A Software Package for Numerical Optimal Control of Nonsmooth
  Systems

abstract:
  This letter introduces the open source software package for Nonsmooth
Numerical Optimal Control (NOS-NOC). It is a modular tool based on CasADi
[Andersson et al., 2019], IPOPT [W\"achter and Biegler, 2006] and MATLAB, for
numerically solving Optimal Control Problems (OCP) with piecewise smooth
systems (PSS). It relies on the recently introduced Finite Elements with Switch
Detection [Nurkanovi\'c et al., 2022] which enables high accuracy optimal
control and simulation of PSS. The time-freezing reformulation [Nurkanovi\'c et
al., 2021], which transforms several classes of systems with state jumps into
PSS is supported as well. This enables the treatment of a broad class of
nonsmooth systems in a unified way. The algorithms and reformulations yield
mathematical programs with complementarity constraints (MPCC). They can be
solved with techniques of continuous optimization in a homotopy procedure,
without the use of integer variables. The goal of the package is to automate
all reformulations and to make nonsmooth optimal control problems practically
solvable, without deep expert knowledge.


title: Adaptative clustering by minimization of the mixing entropy criterion

abstract:
  We present a clustering method and provide a theoretical analysis and an
explanation to a phenomenon encountered in the applied statistical literature
since the 1990's. This phenomenon is the natural adaptability of the order when
using a clustering method derived from the famous EM algorithm. We define a new
statistic, the relative entropic order, that represents the number of clumps in
the target distribution. We prove in particular that the empirical version of
this relative entropic order is consistent. Our approach is easy to implement
and has a high potential of applications. Perspectives of this works are
algorithmic and theoretical, with possible natural extensions to various cases
such as dependent or multidimensional data.


title: Informal Socialization in Physics Training

abstract:
  This paper addresses issues related to the process of informal socialization
into physics, particularly for senior graduate students and postdoctoral
scholars. Many physicists' careers are built on the relationships they have and
develop during these critical years.


title: Comparing the expressiveness of the $\pi$-calculus and CCS

abstract:
  This paper shows that the $\pi$-calculus with implicit matching is no more
expressive than CCS$\gamma$, a variant of CCS in which the result of a
synchronisation of two actions is itself an action subject to relabelling or
restriction, rather than the silent action $\tau$. This is done by exhibiting a
compositional translation from the $\pi$-calculus with implicit matching to
CCS$\gamma$ that is valid up to strong barbed bisimilarity. The full
$\pi$-calculus can be similarly expressed in CCS$\gamma$ enriched with the
triggering operation of Meije. I also show that these results cannot be
recreated with CCS in the role of CCS$\gamma$, not even up to reduction
equivalence, and not even for the asynchronous $\pi$-calculus without
restriction or replication. Finally I observe that CCS cannot be encoded in the
$\pi$-calculus.