title:
Flat bubble regime and laminar plasma flow in a plasma wake field
  accelerator

abstract:
  A simple 2D model of the bubble formation in a plasma wakefield accelerator
is developed and investigated. It is shown that in the case of a flat driver
the bubble may consist of two parts that correspond to two different types of
the plasma flow: a laminar flow where plasma electron streams do not cross and
a two-stream (turbulent) flow. The laminar flow turns out to be robust to the
symmetry breaking. Building-of of the developed model we demonstrate that in
the case of the laminar flow and non-relativistic plasma electrons the
transverse wake field is absent inside the bubble even in the case of a
transversely nonuniform plasma.


title: Quantum Lefschetz property for genus two stable quasimap invariants

abstract:
  By the reduced component in a moduli space of stable quasimaps to
n-dimensional projective space we mean the closure of the locus in which the
domain curves are smooth. As in the moduli space of stable maps, we prove the
reduced component is smooth in genus 2, degree greater or equal to 3.
  Then we prove the virtual fundamental cycle of the moduli space of stable
quasimaps to a complete intersection X in the projective space of genus 2,
degree greater or equal to 3 is explicitly expressed in terms of the
fundamental cycle of the reduced component of the projective space and virtual
cycles of lower genus moduli spaces of X.


title:
Enhancing CTR Prediction with Context-Aware Feature Representation
  Learning

abstract:
  CTR prediction has been widely used in the real world. Many methods model
feature interaction to improve their performance. However, most methods only
learn a fixed representation for each feature without considering the varying
importance of each feature under different contexts, resulting in inferior
performance. Recently, several methods tried to learn vector-level weights for
feature representations to address the fixed representation issue. However,
they only produce linear transformations to refine the fixed feature
representations, which are still not flexible enough to capture the varying
importance of each feature under different contexts. In this paper, we propose
a novel module named Feature Refinement Network (FRNet), which learns
context-aware feature representations at bit-level for each feature in
different contexts. FRNet consists of two key components: 1) Information
Extraction Unit (IEU), which captures contextual information and cross-feature
relationships to guide context-aware feature refinement; and 2) Complementary
Selection Gate (CSGate), which adaptively integrates the original and
complementary feature representations learned in IEU with bit-level weights.
Notably, FRNet is orthogonal to existing CTR methods and thus can be applied in
many existing methods to boost their performance. Comprehensive experiments are
conducted to verify the effectiveness, efficiency, and compatibility of FRNet.


title:
Edge-enhanced Feature Distillation Network for Efficient
  Super-Resolution

abstract:
  With the recently massive development in convolution neural networks,
numerous lightweight CNN-based image super-resolution methods have been
proposed for practical deployments on edge devices. However, most existing
methods focus on one specific aspect: network or loss design, which leads to
the difficulty of minimizing the model size. To address the issue, we conclude
block devising, architecture searching, and loss design to obtain a more
efficient SR structure. In this paper, we proposed an edge-enhanced feature
distillation network, named EFDN, to preserve the high-frequency information
under constrained resources. In detail, we build an edge-enhanced convolution
block based on the existing reparameterization methods. Meanwhile, we propose
edge-enhanced gradient loss to calibrate the reparameterized path training.
Experimental results show that our edge-enhanced strategies preserve the edge
and significantly improve the final restoration quality. Code is available at
https://github.com/icandle/EFDN.


title:
Low c-differentially uniform functions via an extension of Dillon's
  switching method

abstract:
  In this paper we generalize Dillon's switching method to characterize the
exact $c$-differential uniformity of functions constructed via this method.
More precisely, we modify some PcN/APcN and other functions with known
$c$-differential uniformity in a controllable number of coordinates to render
more such functions. We present several applications of the method in
constructing PcN and APcN functions with respect to all $c\neq 1$. As a
byproduct, we generalize some result of [Y. Wu, N. Li, X. Zeng, {\em New PcN
and APcN functions over finite fields}, Designs Codes Crypt. 89 (2021),
2637--2651]. Computational results rendering functions with low differential
uniformity, as well as, other good cryptographic properties are sprinkled
throughout the paper.


title:
Fourier and Fourier-Stieltjes algebra of Fell bundles over discrete
  groups

abstract:
  For a Fell bundle $\mathcal{B}=\left\{B_{s}\right\}_{s \in G}$ over a
discrete group $G$, we use representations theory of $\mathcal{B}$ to construct
the Fourier and Fourier-Stieltjes spaces $A(\mathcal{B})$ and $B(\mathcal{B})$
of $\mathcal B$. When $\mathcal B$ is saturated we show $B(\mathcal{B})$ is
canonically isomorphic to the dual space of the cross sectional $C^{*}$-algebra
$C^{*}(\mathcal{B})$ of $\mathcal{B}$. When there is a compatible family of
co-multiplications on the fibers we show that $B(\mathcal{B})$ and
$A(\mathcal{B})$ are Banach algebras. This holds in particular if either the
fiber $B_e$ at identity is a Hopf $C^*$-algebra or $\mathcal{B}$ is the Fell
bundle of a $C^*$-dynamical system. When $A(\mathcal{B})$ is a Banach algebra
with bounded approximate identity, we show that $B(\mathcal{B})$ is the
multiplier algebra of $A(\mathcal{B})$. We prove a Leptin type theorem by
showing that amenability of $G$ implies the existence of bounded approximate
identity for $A(\mathcal{B})$ for bundles coming from a $C^*$-dynamical system
$(A,G,\gamma)$. The converse is left as an open problem.


title:
Measurement-induced nonbilocal correlation based on Wigner-Yanase skew
  information

abstract:
  Measurement-induced nonlocality (MIN) was proposed for measure the maximum
global effect caused by locally invariant measurements. Similarly, the
Measurement-induced nonbilocal correlation is a generalization of MIN can be
used to measure the maximal global influence caused by the local measurement in
the bilocal scenario. In this paper, we propose a new nonbilocal correlation
measure based on the Wigner-Yanase skew information. The relationship between
the MIN based on Wigner-Yanase skew information and our measure is discussed.
We present an analytical expression of our measure for pure input states and
also provide upper bounds for input general mixed states.


title:
Incorporating Semi-Supervised and Positive-Unlabeled Learning for
  Boosting Full Reference Image Quality Assessment

abstract:
  Full-reference (FR) image quality assessment (IQA) evaluates the visual
quality of a distorted image by measuring its perceptual difference with
pristine-quality reference, and has been widely used in low-level vision tasks.
Pairwise labeled data with mean opinion score (MOS) are required in training
FR-IQA model, but is time-consuming and cumbersome to collect. In contrast,
unlabeled data can be easily collected from an image degradation or restoration
process, making it encouraging to exploit unlabeled training data to boost
FR-IQA performance. Moreover, due to the distribution inconsistency between
labeled and unlabeled data, outliers may occur in unlabeled data, further
increasing the training difficulty. In this paper, we suggest to incorporate
semi-supervised and positive-unlabeled (PU) learning for exploiting unlabeled
data while mitigating the adverse effect of outliers. Particularly, by treating
all labeled data as positive samples, PU learning is leveraged to identify
negative samples (i.e., outliers) from unlabeled data. Semi-supervised learning
(SSL) is further deployed to exploit positive unlabeled data by dynamically
generating pseudo-MOS. We adopt a dual-branch network including reference and
distortion branches. Furthermore, spatial attention is introduced in the
reference branch to concentrate more on the informative regions, and sliced
Wasserstein distance is used for robust difference map computation to address
the misalignment issues caused by images recovered by GAN models. Extensive
experiments show that our method performs favorably against state-of-the-arts
on the benchmark datasets PIPAL, KADID-10k, TID2013, LIVE and CSIQ.


title:
Optimum telescope focal ratios for microlens-to-fiber coupled integral
  field spectrographs

abstract:
  We describe the optimum telescope focal ratio for a two-element, three
surface, telecentric image-transfer microlens-to-fiber coupled integral field
unit within the constraints imposed by micro-optics fabrication and optical
aberrations. We create a generalized analytical description of the micro-optics
optical parameters from first principals. We find that the optical performance,
including all aberrations, of a design constrained by an analytic model
considering only spherical aberration and diffraction matches within +/-4% of a
design optimized by ray-tracing software such as Zemax. The analytical model
does not require any compromise on the available clear aperture; about 90%
mechanical aperture of a hexagonal microlens is available for light collection.
The optimum telescope f-ratio for a 200{\mu}m core fiber fed at f/3.5 is
between f/7 and f/12. We find the optimum telescope focal ratio changes as a
function of fiber core diameter and fiber input beam speed. A telescope focal
ratio of f/8 would support the largest range of fiber diameters (100 to
500{\mu}m) and fiber injection speeds (between f/3 and f/5). The optimization
of telescope and lenslet-coupled fibers is relevant for the design of
high-efficiency dedicated survey telescopes, and for retro-fitting existing
facilities via introducing focal macro-optics to match the instrument input
requirements.


title:
Single-Channel Speech Dereverberation using Subband Network with A
  Reverberation Time Shortening Target

abstract:
  This work proposes a subband network for single-channel speech
dereverberation, and also a new learning target based on reverberation time
shortening (RTS). In the time-frequency domain, we propose to use a subband
network to perform dereverberation for different frequency bands independently.
The time-domain convolution can be well decomposed to subband convolutions,
thence it is reasonable to train the subband network to perform subband
deconvolution. The learning target for dereverberation is usually set as the
direct-path speech or optionally with some early reflections. This type of
target suddenly truncates the reverberation, and thus it may not be suitable
for network training, and leads to a large prediction error. In this work, we
propose a RTS learning target to suppress reverberation and meanwhile maintain
the exponential decaying property of reverberation, which will ease the network
training, and thus reduce the prediction error and signal distortions.
Experiments show that the subband network can achieve outstanding
dereverberation performance, and the proposed target has a smaller prediction
error than the target of direct-path speech and early reflections.


title:
Modeling Missing Annotations for Incremental Learning in Object
  Detection

abstract:
  Despite the recent advances in the field of object detection, common
architectures are still ill-suited to incrementally detect new categories over
time. They are vulnerable to catastrophic forgetting: they forget what has been
already learned while updating their parameters in absence of the original
training data. Previous works extended standard classification methods in the
object detection task, mainly adopting the knowledge distillation framework.
However, we argue that object detection introduces an additional problem, which
has been overlooked. While objects belonging to new classes are learned thanks
to their annotations, if no supervision is provided for other objects that may
still be present in the input, the model learns to associate them to background
regions. We propose to handle these missing annotations by revisiting the
standard knowledge distillation framework. Our approach outperforms current
state-of-the-art methods in every setting of the Pascal-VOC dataset. We further
propose an extension to instance segmentation, outperforming the other
baselines. Code can be found here: https://github.com/fcdl94/MMA


title:
The wave resolvent for compactly supported perturbations of static
  spacetimes

abstract:
  In this note, we consider the wave operator $\square_g$ in the case of
globally hyperbolic, compactly supported perturbations of static spacetimes. We
give an elementary proof of the essential self-adjointness of $\square_g$ and
of uniform microlocal estimates for the resolvent in this setting. This
provides a model for studying Lorentzian spectral zeta functions which is
particularly simple, yet sufficiently general for deriving Einstein equations
from a spectral action.


title: Binary Multi Channel Morphological Neural Network

abstract:
  Neural networks and particularly Deep learning have been comparatively little
studied from the theoretical point of view. Conversely, Mathematical Morphology
is a discipline with solid theoretical foundations. We combine these domains to
propose a new type of neural architecture that is theoretically more
explainable. We introduce a Binary Morphological Neural Network (BiMoNN) built
upon the convolutional neural network. We design it for learning morphological
networks with binary inputs and outputs. We demonstrate an equivalence between
BiMoNNs and morphological operators that we can use to binarize entire
networks. These can learn classical morphological operators and show promising
results on a medical imaging application.


title:
Bodyless Block Propagation: TPS Fully Scalable Blockchain with
  Pre-Validation

abstract:
  The fundamental tradeoff between transaction per second (TPS) and security in
blockchain systems persists despite numerous prior attempts to boost TPS. To
increase TPS without compromising security, we propose a bodyless block
propagation (BBP) scheme for which the block body is not validated and
transmitted during the block propagation process. Rather, the nodes in the
blockchain network anticipate the transactions and their ordering in the next
upcoming block so that these transactions can be pre-executed and pre-validated
before the birth of the block. It is critical, however, all nodes have a
consensus on the transaction content of the next block.
  This paper puts forth a transaction selection, ordering, and synchronization
algorithm to drive the nodes to reach such a consensus. Yet, the coinbase
address of the miner of the next block cannot be anticipated, and therefore
transactions that depend on the coinbase address cannot be pre-executed and
pre-validated. This paper further puts forth an algorithm to deal with such
unresolvable transactions for an overall consistent and TPS-efficient scheme.
With our scheme, most transactions do not need to be validated and transmitted
during block propagation, ridding the dependence of propagation time on the
number of transactions in the block, and making the system fully TPS scalable.
Experimental results show that our protocol can reduce propagation time by 4x
with respect to the current Ethereum blockchain, and its TPS performance is
limited by the node hardware performance rather than block propagation.


title:
GroupNet: Multiscale Hypergraph Neural Networks for Trajectory
  Prediction with Relational Reasoning

abstract:
  Demystifying the interactions among multiple agents from their past
trajectories is fundamental to precise and interpretable trajectory prediction.
However, previous works only consider pair-wise interactions with limited
relational reasoning. To promote more comprehensive interaction modeling for
relational reasoning, we propose GroupNet, a multiscale hypergraph neural
network, which is novel in terms of both interaction capturing and
representation learning. From the aspect of interaction capturing, we propose a
trainable multiscale hypergraph to capture both pair-wise and group-wise
interactions at multiple group sizes. From the aspect of interaction
representation learning, we propose a three-element format that can be learnt
end-to-end and explicitly reason some relational factors including the
interaction strength and category. We apply GroupNet into both CVAE-based
prediction system and previous state-of-the-art prediction systems for
predicting socially plausible trajectories with relational reasoning. To
validate the ability of relational reasoning, we experiment with synthetic
physics simulations to reflect the ability to capture group behaviors, reason
interaction strength and interaction category. To validate the effectiveness of
prediction, we conduct extensive experiments on three real-world trajectory
prediction datasets, including NBA, SDD and ETH-UCY; and we show that with
GroupNet, the CVAE-based prediction system outperforms state-of-the-art
methods. We also show that adding GroupNet will further improve the performance
of previous state-of-the-art prediction systems.


title:
EXIT: Extrapolation and Interpolation-based Neural Controlled
  Differential Equations for Time-series Classification and Forecasting

abstract:
  Deep learning inspired by differential equations is a recent research trend
and has marked the state of the art performance for many machine learning
tasks. Among them, time-series modeling with neural controlled differential
equations (NCDEs) is considered as a breakthrough. In many cases, NCDE-based
models not only provide better accuracy than recurrent neural networks (RNNs)
but also make it possible to process irregular time-series. In this work, we
enhance NCDEs by redesigning their core part, i.e., generating a continuous
path from a discrete time-series input. NCDEs typically use interpolation
algorithms to convert discrete time-series samples to continuous paths.
However, we propose to i) generate another latent continuous path using an
encoder-decoder architecture, which corresponds to the interpolation process of
NCDEs, i.e., our neural network-based interpolation vs. the existing explicit
interpolation, and ii) exploit the generative characteristic of the decoder,
i.e., extrapolation beyond the time domain of original data if needed.
Therefore, our NCDE design can use both the interpolated and the extrapolated
information for downstream machine learning tasks. In our experiments with 5
real-world datasets and 12 baselines, our extrapolation and interpolation-based
NCDEs outperform existing baselines by non-trivial margins.


title: Strategies for Asymptotic Normalization

abstract:
  We present a technique to study normalizing strategies when termination is
asymptotic, that is, it appears as a limit, as opposite to reaching a normal
form in a finite number of steps. Asymptotic termination occurs in several
settings, such as effectful, and in particular probabilistic computation --
where the limits are distributions over the possible outputs -- or infinitary
lambda-calculi -- where the limits are infinitary normal forms such as Boehm
trees.
  As a concrete application, we obtain a result which is of independent
interest: a normalization theorem for Call-by-Value (and -- in a uniform way --
for Call-by-Name) probabilistic lambda-calculus.


title: $Q\widetilde{Q}$-systems for twisted quantum affine algebras

abstract:
  We establish the $Q \widetilde{Q}$-systems for the twisted quantum affine
algebras that were conjectured in arXiv:1606.05301. We develop the
representation theory of Borel subalgebra of twisted quantum affine algebras
and we construct their prefundamental representations. We also propose a
general conjecture on the relations between twisted and non-twisted types. We
prove this conjecture for some particular classes of representations, including
prefundamental representations.


title:
Target-oriented least-squares reverse-time migration using Marchenko
  double-focusing: reducing the artifacts caused by overburden multiples

abstract:
  Geophysicists have widely used Least-squares reverse-time migration (LSRTM)
to obtain high-resolution images of the subsurface. However, LSRTM needs an
accurate velocity model similar to other migration methods. Otherwise, it
suffers from depth estimation errors and out of focus images. Moreover, LSRTM
is computationally expensive and it can suffer from multiple reflections.
Recently, a target-oriented approach to LSRTM has been proposed, which focuses
the wavefield above the target of interest. Remarkably, this approach can be
helpful for imaging below complex overburdens and subsalt domains. Moreover,
this approach can significantly reduce the computational burden of the problem
by limiting the computational domain to a smaller area. Nevertheless,
target-oriented LSRTM still needs an accurate velocity model of the overburden
to focus the wavefield accurately and predict internal multiple reflections
correctly. In recent years, Marchenko redatuming has emerged as a novel
data-driven method that can predict Green's functions at any arbitrary depth,
including all orders of multiples. The only requirement for this method is a
smooth background velocity model of the overburden. Moreover, with Marchenko
double-focusing, one can make virtual sources and receivers at a boundary above
the target and bypass the overburden. This paper proposes a new algorithm for
target-oriented LSRTM, which fits the double-focused data with modeled data at
a boundary above the target of interest. Consequently, our target-oriented
LSRTM algorithm correctly accounts for all orders of overburden-related
multiples, resulting in a significant reduction of the artifacts caused by
overburden internal multiple reflections in the target image compared to
conventional LSRTM.


title:
Plots.jl -- a user extendable plotting API for the julia programming
  language

abstract:
  There are plenty of excellent plotting libraries in the world. Each one
excels for a different use case: one is good for printed 2D publication
figures, the other is good at interactive 3D graphics, another one has
excellent L A TEX integration and one is good for creating dashboards on the
web. The aim of Plots.jl is to enable the user to use the same syntax for many
different plotting libraries, such that it is possible to change the used
library without needing to touch the code that creates the content and having
to learn yet another application programming interface (API) with different
concepts. This is achieved by the separation of plot specification from the
backend implementation. Together with a user extendable recipe system that
allows users and package authors to create new plotting types from existing
ones yields a high reuse potential. Plots.jl is publicly available at
https://github.com/JuliaPlots/ Plots.jl.


title: IndicXNLI: Evaluating Multilingual Inference for Indian Languages

abstract:
  While Indic NLP has made rapid advances recently in terms of the availability
of corpora and pre-trained models, benchmark datasets on standard NLU tasks are
limited. To this end, we introduce IndicXNLI, an NLI dataset for 11 Indic
languages. It has been created by high-quality machine translation of the
original English XNLI dataset and our analysis attests to the quality of
IndicXNLI. By finetuning different pre-trained LMs on this IndicXNLI, we
analyze various cross-lingual transfer techniques with respect to the impact of
the choice of language models, languages, multi-linguality, mix-language input,
etc. These experiments provide us with useful insights into the behaviour of
pre-trained models for a diverse set of languages.


title: Sign Problem in Quantum Monte Carlo Simulation

abstract:
  Sign problem in quantum Monte Carlo (QMC) simulation appears to be an
extremely hard yet interesting problem. In this article, we present a
pedagogical overview on the origin of the sign problem in various quantum Monte
Carlo simulation techniques, ranging from the world-line and stochastic series
expansion Monte Carlo for boson and spin systems to the determinant and
momentum-space quantum Monte Carlo for interacting fermions. We point out the
basis dependency of the sign problem and summarize the progresses to cure, ease
and even make use of the sign problem over the years, such as symmetry analysis
of the underlying Hamiltonian, basis optimization in writting down the
partition functions and many others. Moreover, we state that although
traditional lore saying that in case of sign problem, the average sign in QMC
simulation approaches zero exponentially fast with the space-time volume of the
configurational space, there are recent breakthroughs showing this is not
always the case and based on the properties of the partition function for
finite size systems, one could distinguish when the average sign has the usual
exponential scaling and when it is bestowed with an algebraic scaling at the
low temperature limit. Fermionic QMC simulations with such algebraic sign
problems have been successfully carried out for extended Hubbard-type and
quantum Moir\'e lattice models.


title:
Spin correlations in the frustrated ferro-antiferromagnet SrZnVO(PO4)2
  near saturation

abstract:
  Single crystal elastic and inelastic neutron scattering experiments are
performed on the frustrated ferro-antiferromagnet SrZnVO(PO4)2 in high magnetic
fields. The fully polarized state, the presaturation phase and the
columnar-antiferromagnetic phase just bellow the presaturation phase were
investigated. The observed renormalization of spin wave bandwidths,
re-distribution of intensities between different branches and non-linearities
in the magnetization curve are all indicative of strong deviations from
classical spin wave theory. The previously observed presaturation transition is
attributed to a staggered pattern of Dzyaloshinskii-Moriya interactions.


title: Coupled interactions at the ionic graphene/water interface

abstract:
  We compute ionic free energy adsorption profiles at aqueous graphene
interface by developing a self-consistent approach. To do so, we design a
microscopic model for water and put the liquid on an equal footing with the
graphene described by its electronic band structure. By evaluating
progressively the electronic/dipolar coupled electrostatic interactions, we
show that the coupling level including mutual graphene/water screening permits
to recover remarkably the precision of extensive quantum simulations. We
further derive the potential of mean force evolution of several alkali cations
to gain insight on the ionic size effects.


title:
Sensor Data Fusion in Top-View Grid Maps using Evidential Reasoning with
  Advanced Conflict Resolution

abstract:
  We present a new method to combine evidential top-view grid maps estimated
based on heterogeneous sensor sources. Dempster's combination rule that is
usually applied in this context provides undesired results with highly
conflicting inputs. Therefore, we use more advanced evidential reasoning
techniques and improve the conflict resolution by modeling the reliability of
the evidence sources. We propose a data-driven reliability estimation to
optimize the fusion quality using the Kitti-360 dataset. We apply the proposed
method to the fusion of LiDAR and stereo camera data and evaluate the results
qualitatively and quantitatively. The results demonstrate that our proposed
method robustly combines measurements from heterogeneous sensors and
successfully resolves sensor conflicts.


title:
LORD: Lower-Dimensional Embedding of Log-Signature in Neural Rough
  Differential Equations

abstract:
  The problem of processing very long time-series data (e.g., a length of more
than 10,000) is a long-standing research problem in machine learning. Recently,
one breakthrough, called neural rough differential equations (NRDEs), has been
proposed and has shown that it is able to process such data. Their main concept
is to use the log-signature transform, which is known to be more efficient than
the Fourier transform for irregular long time-series, to convert a very long
time-series sample into a relatively shorter series of feature vectors.
However, the log-signature transform causes non-trivial spatial overheads. To
this end, we present the method of LOweR-Dimensional embedding of log-signature
(LORD), where we define an NRDE-based autoencoder to implant the higher-depth
log-signature knowledge into the lower-depth log-signature. We show that the
encoder successfully combines the higher-depth and the lower-depth
log-signature knowledge, which greatly stabilizes the training process and
increases the model accuracy. In our experiments with benchmark datasets, the
improvement ratio by our method is up to 75\% in terms of various
classification and forecasting evaluation metrics.


title: The Interplay between Quantum Contextuality and Wigner Negativity

abstract:
  The use of quantum information in technology promises to supersede the
so-called classical devices used nowadays. Understanding what features are
inherently non-classical is crucial for reaching better-than-classical
performance. This thesis focuses on two nonclassical behaviours: quantum
contextuality and Wigner negativity. The former is a notion superseding
nonlocality that can be exhibited by quantum systems. To date, it has mostly
been studied in discrete-variable scenarios. In those scenarios, contextuality
has been shown to be necessary and sufficient for advantages in some cases. On
the other hand, negativity of the Wigner function is another unsettling
non-classical feature of quantum states that originates from phase-space
formulation in continuous-variable quantum optics. Continuous-variable
scenarios offer promising candidates for implementing quantum computations.
Wigner negativity is known to be a necessary resource for quantum speedup with
continuous variables. However contextuality has been little understood and
studied in continuous-variable scenarios.
  We first set out a robust framework for properly treating contextuality in
continuous variables. We also quantify contextuality in such scenarios by using
tools from infinite-dimensional optimisation theory. Building upon this, we
show that Wigner negativity is equivalent to contextuality in continuous
variables with respect to Pauli measurements thus establishing a
continuous-variable analogue of a celebrated result by Howard et al. We then
introduce experimentally-friendly witnesses for Wigner negativity of single
mode and multimode quantum states, based on fidelities with Fock states, using
again tools from infinite-dimensional optimisation theory. We further extend
the range of previously known discrete-variable results linking contextuality
and advantage into a new territory of information retrieval.


title:
Structured and Unstructured Teams for Research Software Development at
  the Netherlands eScience Center

abstract:
  This paper presents the types of teams that are currently in place at the
Netherlands eScience Center. We categorize our teams into four different types:
Project Teams, Collectives, Agile Teams and Scrum Teams. We provide a brief
description of each team type and share stories from teams themselves to
reflect on the strengths and shortcomings of each model. From our observation,
we conclude that the type of team that is most suitable for each project
depends on the specific needs of that project. More importantly, different
types of teams are suitable for the different types of people working at the
Netherlands eScience Center.


title: Efficient charged particle propagation methods

abstract:
  In astrophysics, the search for sources of the highest-energy cosmic rays
continues. For further progress, not only ever better observatories but also
ever more realistic numerical simulations are needed. We compare different
approaches for numerical test simulations of UHECRs in the IGMF and show that
all methods provide correct statistical propagation characteristics of the
particles in means of their diffusive behaviour. Through convergence tests, we
show that the necessary requirements for the methods differ and ultimately
reveal significant differences in the required simulation time.


title: The semiclassical energy density of kinks and solitons

abstract:
  We compute semiclassical corrections to the energy density of kinks in
$\phi^4$ theory and of solitons in the sine-Gordon model in $1+1$ dimensions,
using local and covariant renormalization techniques from quantum field theory
in curved spacetimes. For the semiclassical correction to the energy, we
recover the known results. Our analysis highlights a subtlety in the definition
of a conserved stress tensor for scalar field theories in $1+1$ dimension.


title: Decentralized non-convex optimization via bi-level SQP and ADMM

abstract:
  Non-convex optimization problems arise in many problems of practical
relevance-for example in distributed nonlinear MPC or distributed optimal power
flow. Only few existing decentralized optimization methods have local
convergence guarantees for general nonconvex problems. We present novel
convergence results for non-convex problems for a bi-level SQP method that
solves the inner quadratic problems via ADMM. A decentralized stopping
criterion borrowed from inexact Newton methods allows the early termination of
ADMM as an inner algorithm to improve computational efficiency. The method
shows competitive numerical performance to existing methods for an optimal
power flow problem.


title:
Where Was COVID-19 First Discovered? Designing a Question-Answering
  System for Pandemic Situations

abstract:
  The COVID-19 pandemic is accompanied by a massive "infodemic" that makes it
hard to identify concise and credible information for COVID-19-related
questions, like incubation time, infection rates, or the effectiveness of
vaccines. As a novel solution, our paper is concerned with designing a
question-answering system based on modern technologies from natural language
processing to overcome information overload and misinformation in pandemic
situations. To carry out our research, we followed a design science research
approach and applied Ingwersen's cognitive model of information retrieval
interaction to inform our design process from a socio-technical lens. On this
basis, we derived prescriptive design knowledge in terms of design requirements
and design principles, which we translated into the construction of a
prototypical instantiation. Our implementation is based on the comprehensive
CORD-19 dataset, and we demonstrate our artifact's usefulness by evaluating its
answer quality based on a sample of COVID-19 questions labeled by biomedical
experts.


title: Programmable heralded linear optical generation of two-qubit states

abstract:
  We have investigated the heralded generation of two-qubit dual-rail-encoded
states by programmable linear optics. Two types of schemes generating the
states from four single photons, which is the minimal possible to accomplish
the task, have been considered. The schemes have different detection patterns
heralding successful generation events, namely, one-mode heralding, in which
the two auxiliary photons are detected in one mode, and two-mode heralding, in
which single photons are detected in each of the two modes simultaneously. We
have shown that the dependence of the schemes' success probabilities on the
target state's degree of entanglement are essentially different. In particular,
one-mode heralding yields better efficiency for highly-entangled states, if the
programmable interferometers can explore the full space of the unitary transfer
matrices,. It is reversed in case of weakly-entangled states where two-mode
heralding is better. We have found a minimal decomposition of the scheme with
two-mode heralding that is programmed by one variable phase shift. We infer
that the linear optical schemes designed specifically for generation of
two-qubit states are more efficient than schemes implementing gate-based
circuits with known two-qubit linear optical gates. Our results yield
substantial reduction of physical resources needed to generate two-qubit
dual-rail-encoded photonic states.


title:
Large deviations for marked sparse random graphs with applications to
  interacting diffusions

abstract:
  We consider the empirical neighborhood distribution of marked sparse
Erd\"os-R\'enyi random graphs, obtained by decorating edges and vertices of a
sparse Erd\"os-R\'enyi random graph with i.i.d.\ random elements taking values
on Polish spaces. We prove that the empirical neighborhood distribution of this
model satisfies a large deviation principle in the framework of local weak
convergence. We rely on the concept of BC-entropy introduced by Delgosha and
Anantharam~(2019) which is inspired on the previous work by Bordenave and
Caputo~(2015). Our main technical contribution is an approximation result that
allows one to pass from graph with marks in discrete spaces to marks in general
Polish spaces. As an application of the results developed here, we prove a
large deviation principle for interacting diffusions driven by gradient
evolution and defined on top of sparse Erd\"os-R\'enyi random graphs. In
particular, our results apply for the stochastic Kuramoto model. We obtain
analogous results for the sparse uniform random graph with given number of
edges.


title:
ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented
  Visual Models

abstract:
  Learning visual representations from natural language supervision has
recently shown great promise in a number of pioneering works. In general, these
language-augmented visual models demonstrate strong transferability to a
variety of datasets/tasks. However, it remains a challenge to evaluate the
transferablity of these foundation models due to the lack of easy-to-use
toolkits for fair benchmarking. To tackle this, we build ELEVATER (Evaluation
of Language-augmented Visual Task-level Transfer), the first benchmark to
compare and evaluate pre-trained language-augmented visual models. Several
highlights include: (i) Datasets. As downstream evaluation suites, it consists
of 20 image classification datasets and 35 object detection datasets, each of
which is augmented with external knowledge. (ii) Toolkit. An automatic
hyper-parameter tuning toolkit is developed to ensure the fairness in model
adaption. To leverage the full power of language-augmented visual models, novel
language-aware initialization methods are proposed to significantly improve the
adaption performance. (iii) Metrics. A variety of evaluation metrics are used,
including sample-efficiency (zero-shot and few-shot) and parameter-efficiency
(linear probing and full model fine-tuning). We will release our toolkit and
evaluation platforms for the research community.


title:
A qualitative investigation of optical flow algorithms for video
  denoising

abstract:
  A good optical flow estimation is crucial in many video analysis and
restoration algorithms employed in application fields like media industry,
industrial inspection and automotive. In this work, we investigate how well
optical flow algorithms perform qualitatively when integrated into a state of
the art video denoising algorithm. Both classic optical flow algorithms (e.g.
TV-L1) as well as recent deep learning based algorithm (like RAFT or BMBC) will
be taken into account. For the qualitative investigation, we will employ
realistic content with challenging characteristic (noisy content, large motion
etc.) instead of the standard images used in most publications.


title: Antiferromagnetic skyrmion crystal in Janus monolayer CrSi2N2As2

abstract:
  Antiferromagnetic skyrmion crystal (AF-SkX), a regular array of
antiferromagnetic skyrmions, is a fundamental phenomenon in the field of
condensed-matter physics. So far, only very few proposals have been made to
realize the AF-SkX, and most based on three-dimensional (3D) materials. Herein,
using first-principles calculations and Monte-Carlo simulations, we report the
identification of AF-SkX in two-dimensional lattice of Janus monolayer
CrSi2N2As2. Arising from the broken inversion symmetry and strong spin-orbit
coupling, large Dzyaloshinskii-Moriya interaction is obtained in Janus
monolayer CrSi2N2As2. This, combined with the geometric frustration of its
triangular lattice, gives rise to the skyrmion physics and long-sought AF-SkX
in the presence of external magnetic field. More intriguingly, this system
presents two different antiferromagnetic skyrmion phases, and such phenomenon
is distinct from those reported in 3D systems. Furthermore, by contacting with
Sc2CO2, the creation and annihilation of AF-SkX in Janus monolayer CrSi2N2As2
can be achieved through ferroelectricity. These findings greatly enrich the
research on antiferromagnetic skyrmions.


title: On the unirationality of quadric bundles

abstract:
  We prove that a general $n$-fold quadric bundle
$\mathcal{Q}^{n-1}\rightarrow\mathbb{P}^{1}$, over a number field, with
anti-canonical divisor of positive volume and discriminant of odd degree
$\delta_{\mathcal{Q}^{n-1}}$ is unirational. Furthermore, the same holds for
quadric bundles over an arbitrary infinite field provided that
$\mathcal{Q}^{n-1}$ has a point, is otherwise general and $n\leq 5$. We also
give similar results for quadric bundles over higher dimensional projective
spaces defined over an arbitrary field. For instance, we prove the
unirationality of a general $n$-fold quadric bundle
$\mathcal{Q}^{h}\rightarrow\mathbb{P}^{n-h}$ with discriminant of odd degree
$\delta_{\mathcal{Q}^{h}}\leq 3h+4$, and of any smooth $4$-fold quadric bundle
$\mathcal{Q}^{2}\rightarrow\mathbb{P}^{2}$, over an algebraically closed field,
with $\delta_{\mathcal{Q}^{2}}\leq 12$.


title: Noncommutative tensor triangulated categories and coherent frames

abstract:
  We develop a point-free approach for constructing the
Nakano-Vashaw-Yakimov-Balmer spectrum of a noncommutative tensor triangulated
category under some mild assumptions. In particular, we provide a conceptual
way of classifying radical thick tensor ideals of a noncommutative tensor
triangulated category using frame theoretic methods, recovering the universal
support data in the process. We further show that there is a homeomorphism
between the spectral space of radical thick tensor ideals of a noncommutative
tensor triangulated category and the collection of open subsets of its spectrum
in the Hochster dual topology.


title: Freeze-in and freeze-out of sterile neutrino dark matter

abstract:
  A sterile neutrino with a keV-scale mass is a compelling dark matter
candidate. We propose a new production mechanism involving the decay and
annihilation of a complex scalar singlet with a Higgs portal coupling which
develops a vacuum expectation value. The interactions of the resulting pseudo
Nambu-Goldstone boson may thermalise the dark sector. We determine the region
of parameter space where dark sector thermalisation is reached and discuss the
most relevant cosmological observables. The scenario can be considered as the
combination of a freeze-in of the dark sector followed by relativistic
freeze-out.


title:
Velocity-like maximum polarization: irreversibility and quantum
  measurements

abstract:
  The polarization emerging in the subsequent scattering processes can never
exceed $1$ which corresponds to the fully polarized pure state. This property
is shown to be provided by the addition rule similar to that for relativistic
velocities never exceeding the speed of light. The cases of spin $1/2$ and $1$
are considered. The photon linear polarization in Thomson scattering is
monotonically increasing. This directness is shown to be a consequence of spin
measurement procedure and may be the particular example of ithe anticipated
relation between quantum measurement and time irreversibility. The emergent
polarization may be considered as a case of opposing time's arrows
corresponding to microscopic (spin) and macroscopic (momentum) degrees of
freedom, respectively.


title:
Two-Stream Graph Convolutional Network for Intra-oral Scanner Image
  Segmentation

abstract:
  Precise segmentation of teeth from intra-oral scanner images is an essential
task in computer-aided orthodontic surgical planning. The state-of-the-art deep
learning-based methods often simply concatenate the raw geometric attributes
(i.e., coordinates and normal vectors) of mesh cells to train a single-stream
network for automatic intra-oral scanner image segmentation. However, since
different raw attributes reveal completely different geometric information, the
naive concatenation of different raw attributes at the (low-level) input stage
may bring unnecessary confusion in describing and differentiating between mesh
cells, thus hampering the learning of high-level geometric representations for
the segmentation task. To address this issue, we design a two-stream graph
convolutional network (i.e., TSGCN), which can effectively handle inter-view
confusion between different raw attributes to more effectively fuse their
complementary information and learn discriminative multi-view geometric
representations. Specifically, our TSGCN adopts two input-specific
graph-learning streams to extract complementary high-level geometric
representations from coordinates and normal vectors, respectively. Then, these
single-view representations are further fused by a self-attention module to
adaptively balance the contributions of different views in learning more
discriminative multi-view representations for accurate and fully automatic
tooth segmentation. We have evaluated our TSGCN on a real-patient dataset of
dental (mesh) models acquired by 3D intraoral scanners. Experimental results
show that our TSGCN significantly outperforms state-of-the-art methods in 3D
tooth (surface) segmentation. Github:
https://github.com/ZhangLingMing1/TSGCNet.


title: Semantics meets attractiveness: Choice by salience

abstract:
  We describe a context-sensitive model of choice, in which the selection
process is shaped not only by the attractiveness of items but also by their
semantics ('salience'). All items are ranked according to a relation of
salience, and a linear order is associated to each item. The selection of a
single element from a menu is justified by one of the linear orders indexed by
the most salient items in the menu. The general model provides a structured
explanation for any observed behavior, and allows us to to model the
'moodiness' of a decision maker, which is typical of choices requiring as many
distinct rationales as items. Asymptotically all choices are moody. We single
out a model of linear salience, in which the salience order is transitive and
complete, and characterize it by a behavioral property, called WARP(S). Choices
rationalizable by linear salience can only exhibit non-conflicting violations
of WARP. We also provide numerical estimates, which show the high selectivity
of this testable model of bounded rationality.


title:
Effect of strain and sulfur vacancies on the luminescence and valley
  polarization properties of CVD grown monolayer MoS$_2$ films

abstract:
  Using temperature dependent photoluminescence (PL), polarization resolved PL
and Raman spectroscopy, we investigate the effect of in situ vacuum annealing
as well as the relaxation of strain on the luminescence and the valley
polarization properties of large area strictly monolayer (1L)-MoS$_2$, grown on
sapphire and SiO$_2$/Si substrates by a microcavity based chemical vapor
deposition (CVD) technique. The study shows that the strain as well as the
physisorption of air molecules at the sulfur vacancy ($V_S$) sites play key
roles in governing the optical quality of CVD grown 1L-MoS$_2$. Removal of air
molecules from the $V_S$ sites enhances the relative strength of the
A-exciton/trion transition as compared to the broad luminescence (BL) band
arising from those defects at low temperatures. It has also been found that
such removal helps in improving the valley polarization property of the film.
Relaxation of biaxial tensile strain, which has been achieved by post growth
transferring of 1L-MoS$_2$ film from the sapphire to a SiO$_2$/Si substrate by
a polystyrene assisted transfer process, is also found to be helpful to get
back the high polarization character ($\sim$80%) of the valleys. The study
further shows that the transfer process not only facilitates the removal of
physisorbed air molecules from the $V_S$ sites but also puts in place a long
lasting capping layer on MoS$_2$ that shields the film from reacting with air
and hence enhances the relative yield of A-exciton/trion transition by
suppressing the BL transition. The study thus creates an opportunity to use CVD
grown large area 1L-MoS$_2$ for the development of optoelectronic as well as
valleytronic devices for practical applications for the future.


title:
Quantum phase transition of the two-dimensional Rydberg atom array in an
  optical cavity

abstract:
  We study the two-dimensional Rydberg atom array in an optical cavity with
help of the meanfield theory and the large-scale quantum Monte Carlo
simulations. The strong dipole-dipole interactions between Rydberg atoms can
make the system exhibit the crystal structure, and the coupling between
two-level atom and cavity photon mode can result in the formation of the
polariton. The interplay between them provides a rich quantum phase diagram
including the Mott, solid-1/2, superradiant and superradiant solid phases. As
the two-order co-existed phase, the superradiant solid phase breaks both
translational and U(1) symmetries. Based on both numerical and analytic
results, we found the region of superradiant solid is much larger than one
dimensional case, so that it can be more easily observed in the experiment.
Finally, we discuss how the energy gap of the Rydberg atom can affect the type
of the quantum phase transition and the number of triple points.


title: Generalized Supervised Meta-blocking (technical report)

abstract:
  Entity Resolution constitutes a core data integration task that relies on
Blocking in order to tame its quadratic time complexity. Schema-agnostic
blocking achieves very high recall, requires no domain knowledge and applies to
data of any structuredness and schema heterogeneity. This comes at the cost of
many irrelevant candidate pairs (i.e., comparisons), which can be significantly
reduced through Meta-blocking techniques, i.e., techniques that leverage the
co-occurrence patterns of entities inside the blocks: first, a weighting scheme
assigns a score to every pair of candidate entities in proportion to the
likelihood that they are matching and then, a pruning algorithm discards the
pairs with the lowest scores. Supervised Meta-blocking goes beyond this
approach by combining multiple scores per comparison into a feature vector that
is fed to a binary classifier. By using probabilistic classifiers, Generalized
Supervised Meta-blocking associates every pair of candidates with a score that
can be used by any pruning algorithm. For higher effectiveness, new weighting
schemes are examined as features. Through an extensive experimental analysis,
we identify the best pruning algorithms, their optimal sets of features as well
as the minimum possible size of the training set. The resulting approaches
achieve excellent performance across several established benchmark datasets.


title: What powers the wind from the black hole accretion disc in GRO J1655-40?

abstract:
  Black hole accretion discs can produce powerful outflowing plasma (disc
winds), seen as blue-shifted absorption lines in stellar and supermassive
systems. These winds in Quasars have an essential role in controlling galaxy
formation across cosmic time, but there is no consensus on how these are
physically launched. A single unique observation of a stellar-mass black hole
GRO J1655-40 showed the high wind density estimated from an absorption line
from the metastable level of Fe xxii and ruled out X-ray heating
(thermal-radiative wind) for the low observed luminosity. This left magnetic
driving as the only viable mechanism, motivating unified models of magnetic
winds in both binaries and Quasars. Here we reanalyse these data using a
photoionisation code that includes the contribution of radiative cascades and
collisions in populating the metastable level. The effect of radiative cascades
reduces the inferred wind density by orders of magnitude. The derived column is
also optically thick, so the source is intrinsically more luminous than
observed. We show that a thermal-radiative wind model calculated from a
radiation hydrodynamic simulation matches well with the data. We revisit the
previous magnetic wind solution and show that this is also optically thick.
Hence, it requires a larger source luminosity, and it struggles to reproduce
the overall ion population at the required density (both new and old). These
results remove the requirement for a magnetic wind in these data and remove the
basis of the self-similar unified magnetic wind models extrapolated to Quasar
outflows.


title: An Energy-Based Prior for Generative Saliency

abstract:
  We propose a novel energy-based prior for generative saliency prediction,
where the latent variables follow an informative energy-based prior. Both the
saliency generator and the energy-based prior are jointly trained via Markov
chain Monte Carlo-based maximum likelihood estimation, in which the sampling
from the intractable posterior and prior distributions of the latent variables
are performed by Langevin dynamics. With the generative saliency model, we can
obtain a pixel-wise uncertainty map from an image, indicating model confidence
in the saliency prediction. Different from existing generative models, which
define the prior distribution of the latent variable as a simple isotropic
Gaussian distribution, our model uses an energy-based informative prior which
can be more expressive in capturing the latent space of the data. With the
informative energy-based prior, we extend the Gaussian distribution assumption
of generative models to achieve a more representative distribution of the
latent space, leading to more reliable uncertainty estimation. We apply the
proposed frameworks to both RGB and RGB-D salient object detection tasks with
both transformer and convolutional neural network backbones. Experimental
results show that our generative saliency model with an energy-based prior can
achieve not only accurate saliency predictions but also reliable uncertainty
maps that are consistent with human perception.


title: Rainbow clique subdivisions

abstract:
  We show that for any integer $t \ge 2$, every properly edge colored
$n$-vertex graph with average degree at least $(\log n)^{2+o(1)}$ contains a
rainbow subdivision of a complete graph of size $t$. Note that this bound is
within a log factor of the lower bound. This also implies a result on the
rainbow Tur\'{a}n number of cycles.


title:
VCoach: A Customizable Visualization and Analysis System for Video-based
  Running Coaching

abstract:
  Videos are accessible media for analyzing sports postures and providing
feedback to athletes. Existing video-based coaching systems often present
feedback on the correctness of poses by augmenting videos with visual markers
either manually by a coach or automatically by computing key parameters from
poses. However, previewing and augmenting videos limit the analysis and
visualization of human poses due to the fixed viewpoints, which confine the
observation of captured human movements and cause ambiguity in the augmented
feedback. Besides, existing sport-specific systems with embedded bespoke pose
attributes can hardly generalize to new attributes; directly overlaying two
poses might not clearly visualize the key differences that viewers would like
to pursue. To address these issues, we analyze and visualize human pose data
with customizable viewpoints and attributes in the context of common
biomechanics of running poses, such as joint angles and step distances. Based
on existing literature and a formative study, we have designed and implemented
a system, VCoach, to provide feedback on running poses for amateurs. VCoach
provides automatic low-level comparisons of the running poses between a novice
and an expert, and visualizes the pose differences as part-based 3D animations
on a human model. Meanwhile, it retains the users' controllability and
customizability in high-level functionalities, such as navigating the viewpoint
for previewing feedback and defining their own pose attributes through our
interface. We conduct a user study to verify our design components and conduct
expert interviews to evaluate the usefulness of the system.


title: Understanding Toxicity Triggers on Reddit in the Context of Singapore

abstract:
  While the contagious nature of online toxicity sparked increasing interest in
its early detection and prevention, most of the literature focuses on the
Western world. In this work, we demonstrate that 1) it is possible to detect
toxicity triggers in an Asian online community, and 2) toxicity triggers can be
strikingly different between Western and Eastern contexts.


title:
Multi-level Cross-view Contrastive Learning for Knowledge-aware
  Recommender System

abstract:
  Knowledge graph (KG) plays an increasingly important role in recommender
systems. Recently, graph neural networks (GNNs) based model has gradually
become the theme of knowledge-aware recommendation (KGR). However, there is a
natural deficiency for GNN-based KGR models, that is, the sparse supervised
signal problem, which may make their actual performance drop to some extent.
Inspired by the recent success of contrastive learning in mining supervised
signals from data itself, in this paper, we focus on exploring the contrastive
learning in KG-aware recommendation and propose a novel multi-level cross-view
contrastive learning mechanism, named MCCLK. Different from traditional
contrastive learning methods which generate two graph views by uniform data
augmentation schemes such as corruption or dropping, we comprehensively
consider three different graph views for KG-aware recommendation, including
global-level structural view, local-level collaborative and semantic views.
Specifically, we consider the user-item graph as a collaborative view, the
item-entity graph as a semantic view, and the user-item-entity graph as a
structural view. MCCLK hence performs contrastive learning across three views
on both local and global levels, mining comprehensive graph feature and
structure information in a self-supervised manner. Besides, in semantic view, a
k-Nearest-Neighbor (kNN) item-item semantic graph construction module is
proposed, to capture the important item-item semantic relation which is usually
ignored by previous work. Extensive experiments conducted on three benchmark
datasets show the superior performance of our proposed method over the
state-of-the-arts. The implementations are available at:
https://github.com/CCIIPLab/MCCLK.


title:
SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic
  Segmentation

abstract:
  Domain adaptive semantic segmentation attempts to make satisfactory dense
predictions on an unlabeled target domain by utilizing the model trained on a
labeled source domain. One solution is self-training, which retrains models
with target pseudo labels. Many methods tend to alleviate noisy pseudo labels,
however, they ignore intrinsic connections among cross-domain pixels with
similar semantic concepts. Thus, they would struggle to deal with the semantic
variations across domains, leading to less discrimination and poor
generalization. In this work, we propose Semantic-Guided Pixel Contrast
(SePiCo), a novel one-stage adaptation framework that highlights the semantic
concepts of individual pixel to promote learning of class-discriminative and
class-balanced pixel embedding space across domains. Specifically, to explore
proper semantic concepts, we first investigate a centroid-aware pixel contrast
that employs the category centroids of the entire source domain or a single
source image to guide the learning of discriminative features. Considering the
possible lack of category diversity in semantic concepts, we then blaze a trail
of distributional perspective to involve a sufficient quantity of instances,
namely distribution-aware pixel contrast, in which we approximate the true
distribution of each semantic category from the statistics of labeled source
data. Moreover, such an optimization objective can derive a closed-form upper
bound by implicitly involving an infinite number of (dis)similar pairs.
Extensive experiments show that SePiCo not only helps stabilize training but
also yields discriminative features, making significant progress in both
daytime and nighttime scenarios. Most notably, SePiCo establishes excellent
results on tasks of GTAV/SYNTHIA-to-Cityscapes and Cityscapes-to-Dark Zurich,
improving by 12.8, 8.8, and 9.2 mIoUs compared to the previous best method,
respectively.


title: Making Progress Based on False Discoveries

abstract:
  We consider the question of adaptive data analysis within the framework of
convex optimization. We ask how many samples are needed in order to compute
$\epsilon$-accurate estimates of $O(1/\epsilon^2)$ gradients queried by
gradient descent, and we provide two intermediate answers to this question.
  First, we show that for a general analyst (not necessarily gradient descent)
$\Omega(1/\epsilon^3)$ samples are required. This rules out the possibility of
a foolproof mechanism. Our construction builds upon a new lower bound (that may
be of interest of its own right) for an analyst that may ask several non
adaptive questions in a batch of fixed and known $T$ rounds of adaptivity and
requires a fraction of true discoveries. We show that for such an analyst
$\Omega (\sqrt{T}/\epsilon^2)$ samples are necessary.
  Second, we show that, under certain assumptions on the oracle, in an
interaction with gradient descent $\tilde \Omega(1/\epsilon^{2.5})$ samples are
necessary. Our assumptions are that the oracle has only \emph{first order
access} and is \emph{post-hoc generalizing}. First order access means that it
can only compute the gradients of the sampled function at points queried by the
algorithm. Our assumption of \emph{post-hoc generalization} follows from
existing lower bounds for statistical queries. More generally then, we provide
a generic reduction from the standard setting of statistical queries to the
problem of estimating gradients queried by gradient descent.
  These results are in contrast with classical bounds that show that with
$O(1/\epsilon^2)$ samples one can optimize the population risk to accuracy of
$O(\epsilon)$ but, as it turns out, with spurious gradients.


title:
RNNCTPs: A Neural Symbolic Reasoning Method Using Dynamic Knowledge
  Partitioning Technology

abstract:
  Although traditional symbolic reasoning methods are highly interpretable,
their application in knowledge graph link prediction is limited due to their
low computational efficiency. In this paper, we propose a new neural symbolic
reasoning method: RNNCTPs, which improves computational efficiency by
re-filtering the knowledge selection of Conditional Theorem Provers (CTPs), and
is less sensitive to the embedding size parameter. RNNCTPs are divided into
relation selectors and predictors. The relation selectors are trained
efficiently and interpretably, so that the whole model can dynamically generate
knowledge for the inference of the predictor. In all four datasets, the method
shows competitive performance against traditional methods on the link
prediction task, and can have higher applicability to the selection of datasets
relative to CTPs.


title: SmartSales: Sales Script Extraction and Analysis from Sales Chatlog

abstract:
  In modern sales applications, automatic script extraction and management
greatly decrease the need for human labor to collect the winning sales scripts,
which largely boost the success rate for sales and can be shared across the
sales teams. In this work, we present the SmartSales system to serve both the
sales representatives and managers to attain the sales insights from the
large-scale sales chatlog. SmartSales consists of three modules: 1) Customer
frequently asked questions (FAQ) extraction aims to enrich the FAQ knowledge
base by harvesting high quality customer question-answer pairs from the
chatlog. 2) Customer objection response assists the salespeople to figure out
the typical customer objections and corresponding winning sales scripts, as
well as search for proper sales responses for a certain customer objection. 3)
Sales manager dashboard helps sales managers to monitor whether a specific
sales representative or team follows the sales standard operating procedures
(SOP). The proposed prototype system is empowered by the state-of-the-art
conversational intelligence techniques and has been running on the Tencent
Cloud to serve the sales teams from several different areas.


title: The number of relative equilibria in the PCR4PB

abstract:
  The aim of this paper is to present a new, analytical, method for computing
the exact number of relative equilibria in the planar, circular, restricted
4-body problem of celestial mechanics. The new approach allows for a very
efficient computer-aided proof, and opens a potential pathway to proving harder
instances of the n-body problem.


title:
UID2021: An Underwater Image Dataset for Evaluation of No-reference
  Quality Assessment Metrics

abstract:
  Achieving subjective and objective quality assessment of underwater images is
of high significance in underwater visual perception and image/video
processing. However, the development of underwater image quality assessment
(UIQA) is limited for the lack of comprehensive human subjective user study
with publicly available dataset and reliable objective UIQA metric. To address
this issue, we establish a large-scale underwater image dataset, dubbed
UID2021, for evaluating no-reference UIQA metrics. The constructed dataset
contains 60 multiply degraded underwater images collected from various sources,
covering six common underwater scenes (i.e. bluish scene, bluish-green scene,
greenish scene, hazy scene, low-light scene, and turbid scene), and their
corresponding 900 quality improved versions generated by employing fifteen
state-of-the-art underwater image enhancement and restoration algorithms. Mean
opinion scores (MOS) for UID2021 are also obtained by using the pair comparison
sorting method with 52 observers. Both in-air NR-IQA and underwater-specific
algorithms are tested on our constructed dataset to fairly compare the
performance and analyze their strengths and weaknesses. Our proposed UID2021
dataset enables ones to evaluate NR UIQA algorithms comprehensively and paves
the way for further research on UIQA. Our UID2021 will be a free download and
utilized for research purposes at: https://github.com/Hou-Guojia/UID2021.


title: Molecular-ion quantum technologies

abstract:
  Quantum-logic techniques for state preparation, manipulation, and
non-destructive interrogation are increasingly being adopted for experiments on
single molecular ions confined in traps. The ability to control molecular ions
on the quantum level via a co-trapped atomic ion offers intriguing
possibilities for new experiments in the realms of precision spectroscopy,
quantum information processing, cold chemistry, and quantum technologies with
molecules. The present article gives an overview of the basic experimental
methods, recent developments and prospects in this field.


title:
Coulomb-Nuclear Interference: Theory and Practice for pp-Scattering at
  13 TeV

abstract:
  We provide a detailed reconsideration of the theoretical basis for the
treatment of Coulomb-nuclear interference (CNI) and a corresponding thorough
analysis of the procedure of extraction of the basic parameters $\rho^{~pp},
\sigma_{tot}^{~pp}$ and $B^{~pp}$ from the TOTEM data at $\sqrt{s} = 13$ TeV. A
more substantiated account of CNI, as well as an in-depth statistical analysis
of the TOTEM data at low transferred momenta, give results that differ from
those published by the TOTEM collaboration.


title:
Radio Galaxy Zoo: Using semi-supervised learning to leverage large
  unlabelled data-sets for radio galaxy classification under data-set shift

abstract:
  In this work we examine the classification accuracy and robustness of a
state-of-the-art semi-supervised learning (SSL) algorithm applied to the
morphological classification of radio galaxies. We test if SSL with fewer
labels can achieve test accuracies comparable to the supervised
state-of-the-art and whether this holds when incorporating previously unseen
data. We find that for the radio galaxy classification problem considered, SSL
provides additional regularisation and outperforms the baseline test accuracy.
However, in contrast to model performance metrics reported on computer science
benchmarking data-sets, we find that improvement is limited to a narrow range
of label volumes, with performance falling off rapidly at low label volumes.
Additionally, we show that SSL does not improve model calibration, regardless
of whether classification is improved. Moreover, we find that when different
underlying catalogues drawn from the same radio survey are used to provide the
labelled and unlabelled data-sets required for SSL, a significant drop in
classification performance is observered, highlighting the difficulty of
applying SSL techniques under dataset shift. We show that a class-imbalanced
unlabelled data pool negatively affects performance through prior probability
shift, which we suggest may explain this performance drop, and that using the
Frechet Distance between labelled and unlabelled data-sets as a measure of
data-set shift can provide a prediction of model performance, but that for
typical radio galaxy data-sets with labelled sample volumes of O(1000), the
sample variance associated with this technique is high and the technique is in
general not sufficiently robust to replace a train-test cycle.


title:
An Efficient Domain-Incremental Learning Approach to Drive in All
  Weather Conditions

abstract:
  Although deep neural networks enable impressive visual perception performance
for autonomous driving, their robustness to varying weather conditions still
requires attention. When adapting these models for changed environments, such
as different weather conditions, they are prone to forgetting previously
learned information. This catastrophic forgetting is typically addressed via
incremental learning approaches which usually re-train the model by either
keeping a memory bank of training samples or keeping a copy of the entire model
or model parameters for each scenario. While these approaches show impressive
results, they can be prone to scalability issues and their applicability for
autonomous driving in all weather conditions has not been shown. In this paper
we propose DISC -- Domain Incremental through Statistical Correction -- a
simple online zero-forgetting approach which can incrementally learn new tasks
(i.e weather conditions) without requiring re-training or expensive memory
banks. The only information we store for each task are the statistical
parameters as we categorize each domain by the change in first and second order
statistics. Thus, as each task arrives, we simply 'plug and play' the
statistical vectors for the corresponding task into the model and it
immediately starts to perform well on that task. We show the efficacy of our
approach by testing it for object detection in a challenging domain-incremental
autonomous driving scenario where we encounter different adverse weather
conditions, such as heavy rain, fog, and snow.


title:
Exact Diagonalisation of Photon Bose-Einstein Condensates with
  Thermo-Optic Interaction

abstract:
  Although photon Bose-Einstein condensates have already been used for studying
many interesting effects, the precise role of the photon-photon interaction is
not fully clarified up to now. In view of this, it is advantageous that these
systems allow measuring both the intensity of the light leaking out of the
cavity and its spectrum at the same time. Therefore, the photon-photon
interaction strength can be determined once via analysing the condensate
broadening and once via examining the interaction-induced modifications of the
cavity modes. As the former method depends crucially on the concrete shape of
the trapping potential and the spatial resolution of the used camera,
interferometric methods promise more precise measurements. To this end, the
present paper works out the impact of the photon-photon interaction upon the
cavity modes. A quantum mechanical description of the photon-photon
interaction, including the thermal cloud, builds the theoretical backbone of
the method. An exact diagonalisation approach introduced here exposes how the
effective photon-photon interaction modifies both the spectrum and the width of
the photon gas. A comparison with a variational approach based on the
Gross-Pitaevskii equation quantifies the contribution of the thermal cloud in
the respective applications.


title: Counterexamples to the extendibility of positive unital norm-one maps

abstract:
  Arveson's extension theorem guarantees that every completely positive map
defined on an operator system can be extended to a completely positive map
defined on the whole C*-algebra containing it. An analogous statement where
complete positivity is replaced by positivity is known to be false. A natural
question is whether extendibility could still hold for positive maps satisfying
stronger conditions, such as being unital and norm 1. Here we provide three
counterexamples showing that positive norm-one unital maps defined on an
operator subsystem of a matrix algebra cannot be extended to a positive map on
the full matrix algebra. The first counterexample is an unextendible positive
unital map with unit norm, the second counterexample is an unextendible
positive unital isometry on a real operator space, and the third counterexample
is an unextendible positive unital isometry on a complex operator space.


title: Retrieving Inverse Seesaw parameter space for Dirac Phase Leptogenesis

abstract:
  This work addresses the viability of \textit {Dirac phase leptogenesis}, in a
scenario where the light Majorana neutrinos acquire masses by the inverse
seesaw (ISS) mechanism. We show that, a successful leptogenesis in the ISS,
driven (only) by the Dirac CP phase can be achieved with the involvement of an
unorthodox form of the rotational matrix $R = e^{i{\bf A}} \,\,\,(e^{{\bf A}})$
in the Casas-Ibarra parametrisation. This particular structure of $R$ turns out
to be an artefact in explaining the observed baryon asymmetry of the Universe
in a pure ISS scenario. We detail here the confined regions of the $R$ matrix
parameter space, essential for a successful leptogenesis. The $R$-matrix
parameter space assists in rescuing the ISS parameter space needed for
successful leptogenesis. This finding is otherwise unprecedented in the ISS set
up. Making use of the resulted $R$ matrix parameter space we have calculated
the branching ratio for the LFV decay $\mu \rightarrow e\gamma$. This accounts
for an indirect probe of the $R$-matrix parameter space. The branching ratio
obtained from the leptogenesis parameter space surpasses the existing bound on
the branching ratio that resulted in a scenario of combined effect of linear
and inverse seesaw. We also report here that, for $R = e^{i{\bf A}}$ choice
leptogenesis demands the Dirac CP phase ($\delta$) to oscillate around $\pi/2$,
although for the later choice the constraint on $\delta$ is much relaxed.


title: Conditions for LOCC transformations between sets of quantum states

abstract:
  We study the problem of transforming a set of pure bipartite states into
another using deterministic LOCC (local operations and classical
communication). Necessary conditions for the existence of such a transformation
are obtained using LOCC constraints on state transformation, entanglement, and
distinguishability. These conditions are shown to be independent but not
sufficient. We discuss their satisfiability and classify all possible
input-output pairs of sets accordingly. We also prove that strict inclusions
hold between LOCC, separable, and positive partial transpose operations for set
transformation problems.


title:
A Convolutional-Attentional Neural Framework for Structure-Aware
  Performance-Score Synchronization

abstract:
  Performance-score synchronization is an integral task in signal processing,
which entails generating an accurate mapping between an audio recording of a
performance and the corresponding musical score. Traditional synchronization
methods compute alignment using knowledge-driven and stochastic approaches, and
are typically unable to generalize well to different domains and modalities. We
present a novel data-driven method for structure-aware performance-score
synchronization. We propose a convolutional-attentional architecture trained
with a custom loss based on time-series divergence. We conduct experiments for
the audio-to-MIDI and audio-to-image alignment tasks pertained to different
score modalities. We validate the effectiveness of our method via ablation
studies and comparisons with state-of-the-art alignment approaches. We
demonstrate that our approach outperforms previous synchronization methods for
a variety of test settings across score modalities and acoustic conditions. Our
method is also robust to structural differences between the performance and
score sequences, which is a common limitation of standard alignment approaches.


title:
Development of A Hermite Weighted Compact Nonlinear Scheme based on the
  Two-Stage Fourth-Order Temporal Accurate Framework

abstract:
  Improved five-point low dissipation nonlinear schemes are proposed in this
paper within the framework of weighted compact nonlinear schemes (WCNSs)
\cite{Deng2000}. Particularly we follow the work of Li and Du \cite{Li2016} on
the two-stage fourth-order temporal accurate discretization scheme, which is
developed based on the Lax-Wendroff method.


title:
Semi-supervised 3D shape segmentation with multilevel consistency and
  part substitution

abstract:
  The lack of fine-grained 3D shape segmentation data is the main obstacle to
developing learning-based 3D segmentation techniques. We propose an effective
semi-supervised method for learning 3D segmentations from a few labeled 3D
shapes and a large amount of unlabeled 3D data. For the unlabeled data, we
present a novel multilevel consistency loss to enforce consistency of network
predictions between perturbed copies of a 3D shape at multiple levels:
point-level, part-level, and hierarchical level. For the labeled data, we
develop a simple yet effective part substitution scheme to augment the labeled
3D shapes with more structural variations to enhance training. Our method has
been extensively validated on the task of 3D object semantic segmentation on
PartNet and ShapeNetPart, and indoor scene semantic segmentation on ScanNet. It
exhibits superior performance to existing semi-supervised and unsupervised
pre-training 3D approaches. Our code and trained models are publicly available
at https://github.com/isunchy/semi_supervised_3d_segmentation.


title:
Ariel stellar characterisation: I -- homogeneous stellar parameters of
  187 FGK planet host stars Description and validation of the method

abstract:
  In 2020 the European Space Agency selected Ariel as the next mission to join
the space fleet of observatories to study planets outside our Solar System.
Ariel will be devoted to the characterisation of a thousand planetary
atmospheres, for understanding what exoplanets are made of, how they formed and
how they evolve. To achieve the last two goals all planets need to be studied
within the context of their own host stars, which in turn have to be analysed
with the same technique, in a uniform way. We present the spectro-photometric
method we have developed to infer the atmospheric parameters of the known host
stars in the Tier 1 of the Ariel Reference Sample. Our method is based on an
iterative approach, which combines spectral analysis, the determination of the
surface gravity from {\em Gaia} data, and the determination of stellar masses
from isochrone fitting. We validated our approach with the analysis of a
control sample, composed by members of three open clusters with well-known ages
and metallicities. We measured effective temperature, Teff, surface gravity,
logg, and the metallicity, [Fe/H], of 187 F-G-K stars within the Ariel
Reference Sample. We presented the general properties of the sample, including
their kinematics which allows us to separate them between thin and thick disc
populations. A homogeneous determination of the parameters of the host stars is
fundamental in the study of the stars themselves and their planetary systems.
Our analysis systematically improves agreement with theoretical models and
decreases uncertainties in the mass estimate (from 0.21+/-0.30 to 0.10+/-0.02
M_sun), providing useful data for the Ariel consortium and the astronomical
community at large.


title:
Architecture of planetary systems predicted from protoplanetary disks
  observed with ALMA II: evolution outcomes and dynamical stability

abstract:
  Recent ALMA observations on disk substructures suggest the presence of
embedded protoplanets in a large number disks. The primordial configurations of
these planetary systems can be deduced from the morphology of the disk
substructure and serve as initial conditions for numerical investigation of
their future evolution. Starting from the initial configurations of 12
multi-planetary systems deduced from ALMA disks, we carried out two-stage
N-body simulation to investigate the evolution of the planetary systems at the
disk stage as well as the long term orbital stability after the disk dispersal.
At the disk stage, our simulation includes both the orbital migration and
pebble/gas accretion effects. We found a variety of planetary systems are
produced and can be categorised into distant giant planets, Jupiter-like
planets, Neptune-like planets and distant small planets. We found the disk
stage evolution as well as the final configurations are sensitive to both the
initial mass assignments and viscosity. After the disk stage, we implement only
mutual gravity between star and planets and introduce stochastic perturbative
forces. All systems are integrated for up to 10 Gyr to test their orbital
stability. Most planetary systems are found to be stable for at least 10 Gyr
with perturbative force in a reasonable range. Our result implies that a strong
perturbation source such as stellar flybys is required to drive the planetary
system unstable. We discuss the implications of our results on both the disk
and planet observation, which may be confirmed by the next generation
telescopes such as JWST and ngVLA.


title:
Drift-implicit Euler scheme for sandwiched processes driven by H\"older
  noises

abstract:
  In this paper, we analyze the drift-implicit (or backward) Euler numerical
scheme for a class of stochastic differential equations with unbounded drift
driven by an arbitrary $\lambda$-H\"older continuous process,
$\lambda\in(0,1)$. We prove that, under some mild moment assumptions on the
H\"older constant of the noise, the $L^r(\Omega;L^\infty([0,T]))$-rate of
convergence is equal to $\lambda$. To exemplify, we consider numerical schemes
for the generalized Cox--Ingersoll-Ross and Tsallis--Stariolo--Borland models.
The results are illustrated by simulations.


title:
Detect-and-describe: Joint learning framework for detection and
  description of objects

abstract:
  Traditional object detection answers two questions; "what" (what the object
is?) and "where" (where the object is?). "what" part of the object detection
can be fine-grained further i.e. "what type", "what shape" and "what material"
etc. This results in the shifting of the object detection tasks to the object
description paradigm. Describing an object provides additional detail that
enables us to understand the characteristics and attributes of the object
("plastic boat" not just boat, "glass bottle" not just bottle). This additional
information can implicitly be used to gain insight into unseen objects (e.g.
unknown object is "metallic", "has wheels"), which is not possible in
traditional object detection. In this paper, we present a new approach to
simultaneously detect objects and infer their attributes, we call it Detect and
Describe (DaD) framework. DaD is a deep learning-based approach that extends
object detection to object attribute prediction as well. We train our model on
aPascal train set and evaluate our approach on aPascal test set. We achieve
97.0% in Area Under the Receiver Operating Characteristic Curve (AUC) for
object attributes prediction on aPascal test set. We also show qualitative
results for object attribute prediction on unseen objects, which demonstrate
the effectiveness of our approach for describing unknown objects.


title: A new method for directly computing reduced density matrices

abstract:
  We demonstrate the power of a first principle-based and practicable method
that allows for the perturbative computation of reduced density matrix elements
of an open quantum system without making use of any master equations. The
approach is based on techniques from non-equilibrium quantum field theory like
thermo field dynamics, the Schwinger-Keldsyh formalism, and the Feynman-Vernon
influence functional. It does not require the Markov approximation and is
essentially a Lehmann-Szymanzik-Zimmermann-like reduction. In order to
illustrate this method, we consider a real scalar field as an open quantum
system interacting with an environment comprising another real scalar field. We
give a general formula that allows for the perturbative computation of density
matrix elements for any number of particles in a momentum basis. Finally, we
consider a simple toy model and use this formula to obtain expressions for some
of the system's reduced density matrix elements.


title:
Parity of conjugate self-dual representations of inner forms of
  $\mathrm{GL}_n$ over $p$-adic fields

abstract:
  We prove a general formula that relates the parity of the Langlands parameter
of a conjugate self-dual discrete series representation of $\mathrm{GL}_n$ to
the parity of its Jacquet-Langlands image. It gives a generalization of a
partial result by Mieda concerning the case of invariant $1/n$ and
supercuspidal representations. It also gives a variation of the result on the
self-dual case by Prasad and Ramakrishnan.


title: Probing for the Usage of Grammatical Number

abstract:
  A central quest of probing is to uncover how pre-trained models encode a
linguistic property within their representations. An encoding, however, might
be spurious-i.e., the model might not rely on it when making predictions. In
this paper, we try to find encodings that the model actually uses, introducing
a usage-based probing setup. We first choose a behavioral task which cannot be
solved without using the linguistic property. Then, we attempt to remove the
property by intervening on the model's representations. We contend that, if an
encoding is used by the model, its removal should harm the performance on the
chosen behavioral task. As a case study, we focus on how BERT encodes
grammatical number, and on how it uses this encoding to solve the number
agreement task. Experimentally, we find that BERT relies on a linear encoding
of grammatical number to produce the correct behavioral output. We also find
that BERT uses a separate encoding of grammatical number for nouns and verbs.
Finally, we identify in which layers information about grammatical number is
transferred from a noun to its head verb.


title: Impact of Tokenization on Language Models: An Analysis for Turkish

abstract:
  Tokenization is an important text preprocessing step to prepare input tokens
for deep language models. WordPiece and BPE are de facto methods employed by
important models, such as BERT and GPT. However, the impact of tokenization can
be different for morphologically rich languages, such as Turkic languages,
where many words can be generated by adding prefixes and suffixes. We compare
five tokenizers at different granularity levels, i.e. their outputs vary from
smallest pieces of characters to the surface form of words, including a
Morphological-level tokenizer. We train these tokenizers and pretrain
medium-sized language models using RoBERTa pretraining procedure on the Turkish
split of the OSCAR corpus. We then fine-tune our models on six downstream
tasks. Our experiments, supported by statistical tests, reveal that
Morphological-level tokenizer has challenging performance with de facto
tokenizers. Furthermore, we find that increasing the vocabulary size improves
the performance of Morphological and Word-level tokenizers more than that of de
facto tokenizers. The ratio of the number of vocabulary parameters to the total
number of model parameters can be empirically chosen as 20% for de facto
tokenizers and 40% for other tokenizers to obtain a reasonable trade-off
between model size and performance.


title: C(X) determines X -- an inherent theory

abstract:
  One of the fundamental problem in rings of continuous function is to extract
those spaces for which C(X) determines X, that is to investigate X and Y such
that C(X) isomorphic with C(Y ) implies X homeomorphic with Y . The development
started back from Tychono? who first pointed out inevitability of Tychono?
space in this category of problem. Later S.Banach and M. Stone proved
independently with slight variance, that if X is compact Hausdor? space, C(X)
also determine X. Their works were maximally extended by E. Hewitt by
introducing realcompact spaces and later Melvin Henriksen and Biswajit Mitra
solved the problem for locally compact and nearly realcompact spaces. In this
paper we tried to develop an inherent theory of this problem to cover up all
the works in the literature introducing a notion so called P-compact spaces.


title: FluTAS: A GPU-accelerated finite difference code for multiphase flows

abstract:
  We present the Fluid Transport Accelerated Solver, FluTAS, a scalable GPU
code for multiphase flows with thermal effects. The code solves the
incompressible Navier-Stokes equation for two-fluid systems, with a direct
FFT-based Poisson solver for the pressure equation. The interface between the
two fluids is represented with the Volume of Fluid (VoF) method, which is mass
conserving and well suited for complex flows thanks to its capacity of handling
topological changes. The energy equation is explicitly solved and coupled with
the momentum equation through the Boussinesq approximation. The code is
conceived in a modular fashion so that different numerical methods can be used
independently, the existing routines can be modified, and new ones can be
included in a straightforward and sustainable manner. FluTAS is written in
modern Fortran and parallelized using hybrid MPI/OpenMP in the CPU-only version
and accelerated with OpenACC directives in the GPU implementation. We present
different benchmarks to validate the code, and two large-scale simulations of
fundamental interest in turbulent multiphase flows: isothermal emulsions in HIT
and two-layer Rayleigh-B\'enard convection. FluTAS is distributed through a MIT
license and arises from a collaborative effort of several scientists, aiming to
become a flexible tool to study complex multiphase flows.


title:
Structural properties of local integrals of motion across the many-body
  localization transition via a fast and efficient method for their
  construction

abstract:
  Many-body localization (MBL) is a novel prototype of ergodicity breaking due
to the emergence of local integrals of motion (LIOMs) in a disordered
interacting quantum system. To better understand the role played by the
existence of such macroscopically LIOMs, we explore and study some of their
structural properties across the MBL transition. We first, consider a
one-dimensional XXZ spin chain in a disordered magnetic field and introduce and
implement a non-perturbative, fast, and accurate method of constructing LIOMs.
In contrast to already existing methods, our scheme allows obtaining LIOMs not
only in the deep MBL phase but rather, near the transition point too. Then, we
take the matrix representation of LIOM operators as an adjacency matrix of a
directed graph whose elements describe the connectivity of ordered eigenbasis
in the Hilbert-space. Our cluster size analysis for this graph shows that the
MBL transition coincides with a percolation transition in the Hilbert-space. By
performing finite-size scaling, we compare the critical disorder and
correlation exponent $\nu$ both in the presence and absence of interaction.
Finally, we also discuss how the distribution of diagonal elements of LIOM
operators in a typical cluster signals the transition.


title: System Analysis for Responsible Design of Modern AI/ML Systems

abstract:
  The irresponsible use of ML algorithms in practical settings has received a
lot of deserved attention in the recent years. We posit that the traditional
system analysis perspective is needed when designing and implementing ML
algorithms and systems. Such perspective can provide a formal way for
evaluating and enabling responsible ML practices. In this paper, we review
components of the System Analysis methodology and highlight how they connect
and enable responsible practices of ML design.


title: 4-Dimensional Symmetry Breaking of Light in Kerr Ring Resonators

abstract:
  Symmetry breaking of light states is of interest for the understanding of
nonlinear optics, photonic circuits, telecom applications and optical pulse
generation. Here we demonstrate 4-fold symmetry breaking of the resonances of
ring resonators with Kerr nonlinearity. This symmetry breaking naturally occurs
in a resonator with bidirectionally propagating light with orthogonal
polarization components. The four circulating field components are shown to
exhibit multiple and, nested and isolated, spontaneous symmetry breaking
bifurcations, and are also capable of complex oscillatory dynamics - such as
four-field self-switching, and unusual temporal cavity soliton-like dynamics on
time scales of multiple round-trip times, with extended delays between
subsequent generations.


title: Rumor Detection with Self-supervised Learning on Texts and Social Graph

abstract:
  Rumor detection has become an emerging and active research field in recent
years. At the core is to model the rumor characteristics inherent in rich
information, such as propagation patterns in social network and semantic
patterns in post content, and differentiate them from the truth. However,
existing works on rumor detection fall short in modeling heterogeneous
information, either using one single information source only (e.g. social
network, or post content) or ignoring the relations among multiple sources
(e.g. fusing social and content features via simple concatenation). Therefore,
they possibly have drawbacks in comprehensively understanding the rumors, and
detecting them accurately. In this work, we explore contrastive self-supervised
learning on heterogeneous information sources, so as to reveal their relations
and characterize rumors better. Technically, we supplement the main supervised
task of detection with an auxiliary self-supervised task, which enriches post
representations via post self-discrimination. Specifically, given two
heterogeneous views of a post (i.e. representations encoding social patterns
and semantic patterns), the discrimination is done by maximizing the mutual
information between different views of the same post compared to that of other
posts. We devise cluster-wise and instance-wise approaches to generate the
views and conduct the discrimination, considering different relations of
information sources. We term this framework as Self-supervised Rumor Detection
(SRD). Extensive experiments on three real-world datasets validate the
effectiveness of SRD for automatic rumor detection on social media.


title:
Unsupervised Learning of Efficient Geometry-Aware Neural Articulated
  Representations

abstract:
  We propose an unsupervised method for 3D geometry-aware representation
learning of articulated objects. Though photorealistic images of articulated
objects can be rendered with explicit pose control through existing 3D neural
representations, these methods require ground truth 3D pose and foreground
masks for training, which are expensive to obtain. We obviate this need by
learning the representations with GAN training. From random poses and latent
vectors, the generator is trained to produce realistic images of articulated
objects by adversarial training. To avoid a large computational cost for GAN
training, we propose an efficient neural representation for articulated objects
based on tri-planes and then present a GAN-based framework for its unsupervised
training. Experiments demonstrate the efficiency of our method and show that
GAN-based training enables learning of controllable 3D representations without
supervision.


title: Spectroscopy of Light Baryons: $\Delta$ Resonances

abstract:
  The spectroscopy of light, strange baryons have been an important aspect with
still unknown resonances and intrinsic baryonic properties. The present
document is focused on the $\Delta$ baryons unlike earlier work, here all the
four isospin states have been separately obtained treating u and d quarks with
different constituent masses. The theoretical framework for calculating the
resonance masses is hypercentral Constituent Quark Model (hCQM). The confining
potential is taken as of linear form alongwith the addition of spin-dependent
as well as first order correction term. The results obtained have been compared
with many varied approaches as well as experimental available states. In
addition to mass spectra, Regge trajectories for (n,$M^{2}$) and (J,$M^{2}$)
have been plotted. The magnetic moment as well as transition magnetic moment
have been calculated using effective quark mass. The radiative decay width has
been studied for $\Delta^{+}$ and $\Delta^{0}$ channels.